V6_Goku.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abdulazeezb/1_SparkFundsInvestmentAnalysis/blob/master/V6_Goku.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking GPU Version"
      ],
      "metadata": {
        "id": "NAjscLW7Sk9V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "metadata": {
        "id": "t8nxH9ZvSaY6",
        "outputId": "182526a7-4a80-4f82-8059-2acee22cdc1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon May  1 12:15:48 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "metadata": {
        "id": "KEPzY7qCS2Lc",
        "outputId": "f3151c28-167c-4240-8245-518a75bdb5ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMMqmdiYMkvi"
      },
      "source": [
        "## Part 1: Getting Started - Install Python Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23TOba33L4qf",
        "outputId": "588c2ac5-2933-4a08-be76-f86976d7e2ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wrds\n",
            "  Downloading wrds-3.1.6-py3-none-any.whl (12 kB)\n",
            "Collecting sqlalchemy<2\n",
            "  Downloading SQLAlchemy-1.4.48-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
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            "\u001b[?25hCollecting psycopg2-binary\n",
            "  Downloading psycopg2_binary-2.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from wrds) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from wrds) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from wrds) (1.22.4)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<2->wrds) (2.0.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wrds) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wrds) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->wrds) (1.16.0)\n",
            "Installing collected packages: sqlalchemy, psycopg2-binary, wrds\n",
            "  Attempting uninstall: sqlalchemy\n",
            "    Found existing installation: SQLAlchemy 2.0.10\n",
            "    Uninstalling SQLAlchemy-2.0.10:\n",
            "      Successfully uninstalled SQLAlchemy-2.0.10\n",
            "Successfully installed psycopg2-binary-2.9.6 sqlalchemy-1.4.48 wrds-3.1.6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting swig\n",
            "  Downloading swig-4.1.1-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.8 MB)\n",
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            "\u001b[?25hInstalling collected packages: swig\n",
            "Successfully installed swig-4.1.1\n",
            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.1.0-1/Mambaforge-23.1.0-1-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:09\n",
            "🔁 Restarting kernel...\n",
            "Selecting previously unselected package libgl1-mesa-glx:amd64.\n",
            "(Reading database ... 122518 files and directories currently installed.)\n",
            "Preparing to unpack .../libgl1-mesa-glx_21.2.6-0ubuntu0.1~20.04.2_amd64.deb ...\n",
            "Unpacking libgl1-mesa-glx:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
            "Selecting previously unselected package swig4.0.\n",
            "Preparing to unpack .../swig4.0_4.0.1-5build1_amd64.deb ...\n",
            "Unpacking swig4.0 (4.0.1-5build1) ...\n",
            "Selecting previously unselected package swig.\n",
            "Preparing to unpack .../swig_4.0.1-5build1_all.deb ...\n",
            "Unpacking swig (4.0.1-5build1) ...\n",
            "Setting up libgl1-mesa-glx:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
            "Setting up swig4.0 (4.0.1-5build1) ...\n",
            "Setting up swig (4.0.1-5build1) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n"
          ]
        }
      ],
      "source": [
        "## install finrl library\n",
        "!pip install wrds\n",
        "!pip install swig\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()\n",
        "!apt-get update -y -qq && apt-get install -y -qq cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx swig"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git"
      ],
      "metadata": {
        "id": "haJsmd15TCdH",
        "outputId": "c572b46e-a989-431d-f68e-df9a90fc2cb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
            "  Cloning https://github.com/AI4Finance-Foundation/FinRL.git to /tmp/pip-req-build-h6jhwi36\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/FinRL.git /tmp/pip-req-build-h6jhwi36\n",
            "  Resolved https://github.com/AI4Finance-Foundation/FinRL.git to commit 58a8ec268c71941ecab9666afbf5db87562ba3b3\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl\n",
            "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /tmp/pip-install-wj363nwm/elegantrl_4855eabf3c6a4978bbfbaa0a644b8e46\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-wj363nwm/elegantrl_4855eabf3c6a4978bbfbaa0a644b8e46\n",
            "  Resolved https://github.com/AI4Finance-Foundation/ElegantRL.git to commit b974a806e6235f59055c954418e54640fa549331\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-wj363nwm/pyfolio_1a00986807c24dcaa3a34cd7d24439ca\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/quantopian/pyfolio.git /tmp/pip-install-wj363nwm/pyfolio_1a00986807c24dcaa3a34cd7d24439ca\n",
            "  Resolved https://github.com/quantopian/pyfolio.git to commit 4b901f6d73aa02ceb6d04b7d83502e5c6f2e81aa\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pandas>=1.1.5\n",
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            "\u001b[?25hBuilding wheels for collected packages: finrl, exchange_calendars, gym, elegantrl, gputil, pyfolio, empyrical, gpustat, thriftpy2, box2d-py, lit\n",
            "  Building wheel for finrl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.5-py3-none-any.whl size=4668722 sha256=b5d9990feddfce4dac1109677bd1616fcf78abb54bcf1b30c280638a7696b8d0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pl5j81lr/wheels/72/3b/1a/0fc805a8cc65ecd5bfe4f74a3c586b6075678b8ba53fd8f749\n",
            "  Building wheel for exchange_calendars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for exchange_calendars: filename=exchange_calendars-3.6.3-py3-none-any.whl size=182636 sha256=9c4788a9a73fe8f026fa94f48f3387c74732b2bf84529d1829c693d4b4094b33\n",
            "  Stored in directory: /root/.cache/pip/wheels/93/ca/8f/1e1c90cc79fb3ca9b5413ff58e3fccf3baf2182c994c6dfd37\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py bdist_wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for gym (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for gym\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[?25h  Running setup.py clean for gym\n",
            "  Building wheel for elegantrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for elegantrl: filename=elegantrl-0.3.6-py3-none-any.whl size=195067 sha256=0e465747a460c5f03c355315b3c9103c547757514189fdbf9d0aa03184316980\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pl5j81lr/wheels/c0/51/a5/b05f165548221bc570f7223babd33e2992fa873cdcebe2d229\n",
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gputil: filename=GPUtil-1.4.0-py3-none-any.whl size=7408 sha256=3129cb0062f9460ffe2ce8f2611b9f603ab38917f444e8e1a5e080e1f8cd8157\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/8a/bd/81082387151853ab8b6b3ef33426e98f5cbfebc3c397a9d4d0\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-py3-none-any.whl size=75775 sha256=60f2932870cb35d21b8877070af622a501367c15533396200f58a49f9efcd59e\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pl5j81lr/wheels/c4/1d/91/8ab5d1c88a11b06a63dcd6a69ea81547e2247123232949bb26\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-py3-none-any.whl size=39779 sha256=72316305ab0051cf1bc5476f9de95c32cd315f5c4625f6eb4455be5214a72b01\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/2e/f2/d6d2d9a1eb8fbbd9949bb5d4c00f753e3b74e5bd7ed10b1d36\n",
            "  Building wheel for gpustat (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-1.1-py3-none-any.whl size=26298 sha256=21959f61120d0d109a9df0adf64d48ba73b9eb46715f695e8a891b5369b34f75\n",
            "  Stored in directory: /root/.cache/pip/wheels/ee/d0/2c/1e02440645c2318ba03aea99993a44a9108dc8f74de0bd370b\n",
            "  Building wheel for thriftpy2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for thriftpy2: filename=thriftpy2-0.4.16-cp310-cp310-linux_x86_64.whl size=522576 sha256=32572ff4b8eb399560d77acb096f044d18b1589634ee5c3b964fe3194394d5e3\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/f5/1d/fe404692e1c8aaea45220c322d1d0f32c9fd40eb0e2bdd571e\n",
            "  Building wheel for box2d-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for box2d-py: filename=box2d_py-2.3.5-cp310-cp310-linux_x86_64.whl size=494649 sha256=2aa8cacf8f18351bb871be1de510643910c34aec28d5cdce2a9ccccda6950925\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/8f/6a/eaaadf056fba10a98d986f6dce954e6201ba3126926fc5ad9e\n",
            "  Building wheel for lit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-16.0.2-py3-none-any.whl size=88190 sha256=11cfa31c73a8363f9eeb62e3f0b39cf2f0c8710e07e6eceda80fd460afb7d230\n",
            "  Stored in directory: /root/.cache/pip/wheels/4d/31/6f/140862d5c69ddd665b3ed9485f4c93aa9c84cb34b359cba3ce\n",
            "Successfully built finrl exchange_calendars elegantrl gputil pyfolio empyrical gpustat thriftpy2 box2d-py lit\n",
            "Failed to build gym\n",
            "Installing collected packages: webencodings, wcwidth, pytz, pyglet, py-spy, pure-eval, ptyprocess, ply, pickleshare, opencensus-context, nvidia-ml-py, multitasking, msgpack, mpmath, lit, korean_lunar_calendar, gputil, executing, distlib, colorful, cmake, box2d-py, backcall, appdirs, zipp, websockets, websocket-client, tzdata, typing-extensions, traitlets, threadpoolctl, tabulate, sympy, soupsieve, smart-open, six, PyYAML, pyrsistent, pyparsing, pymysql, pyluach, pygments, pyasn1, psycopg2-binary, psutil, protobuf, prompt-toolkit, prometheus-client, platformdirs, pillow, pexpect, parso, packaging, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, numpy, networkx, multidict, MarkupSafe, lz4, lxml, kiwisolver, joblib, grpcio, greenlet, frozenlist, frozendict, fonttools, filelock, decorator, cycler, cloudpickle, click, charset-normalizer, cachetools, attrs, async-timeout, yarl, virtualenv, thriftpy2, tensorboardX, sqlalchemy, scipy, rsa, python-dateutil, pydantic, pycares, pyasn1-modules, nvidia-cusolver-cu11, nvidia-cudnn-cu11, matplotlib-inline, jsonschema, jinja2, jedi, importlib-metadata, html5lib, gym, googleapis-common-protos, deprecation, contourpy, blessed, beautifulsoup4, asttokens, aiosignal, stack-data, scikit-learn, ray, pandas, matplotlib, gpustat, google-auth, aiohttp, aiodns, yfinance, wrds, stockstats, seaborn, pandas-datareader, jqdatasdk, ipython, google-api-core, exchange_calendars, ccxt, alpaca_trade_api, aiohttp-cors, opencensus, empyrical, pyfolio, triton, torch, stable-baselines3, elegantrl, finrl\n",
            "  Attempting uninstall: charset-normalizer\n",
            "    Found existing installation: charset-normalizer 3.1.0\n",
            "    Uninstalling charset-normalizer-3.1.0:\n",
            "      Successfully uninstalled charset-normalizer-3.1.0\n",
            "  Running setup.py install for gym ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33m  DEPRECATION: gym was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. pip 23.1 will enforce this behaviour change. A possible replacement is to fix the wheel build issue reported above. Discussion can be found at https://github.com/pypa/pip/issues/8368\u001b[0m\u001b[33m\n",
            "\u001b[0mSuccessfully installed MarkupSafe-2.1.2 PyYAML-6.0 aiodns-3.0.0 aiohttp-3.8.1 aiohttp-cors-0.7.0 aiosignal-1.3.1 alpaca_trade_api-3.0.0 appdirs-1.4.4 asttokens-2.2.1 async-timeout-4.0.2 attrs-23.1.0 backcall-0.2.0 beautifulsoup4-4.12.2 blessed-1.20.0 box2d-py-2.3.5 cachetools-5.3.0 ccxt-3.0.85 charset-normalizer-2.1.1 click-8.1.3 cloudpickle-2.2.1 cmake-3.26.3 colorful-0.5.5 contourpy-1.0.7 cycler-0.11.0 decorator-5.1.1 deprecation-2.1.0 distlib-0.3.6 elegantrl-0.3.6 empyrical-0.5.5 exchange_calendars-3.6.3 executing-1.2.0 filelock-3.12.0 finrl-0.3.5 fonttools-4.39.3 frozendict-2.3.8 frozenlist-1.3.3 google-api-core-2.11.0 google-auth-2.17.3 googleapis-common-protos-1.59.0 gpustat-1.1 gputil-1.4.0 greenlet-2.0.2 grpcio-1.51.3 gym-0.21.0 html5lib-1.1 importlib-metadata-4.13.0 ipython-8.13.1 jedi-0.18.2 jinja2-3.1.2 joblib-1.2.0 jqdatasdk-1.8.11 jsonschema-4.17.3 kiwisolver-1.4.4 korean_lunar_calendar-0.3.1 lit-16.0.2 lxml-4.9.2 lz4-4.3.2 matplotlib-3.7.1 matplotlib-inline-0.1.6 mpmath-1.3.0 msgpack-1.0.3 multidict-6.0.4 multitasking-0.0.11 networkx-3.1 numpy-1.24.3 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-ml-py-11.525.112 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 opencensus-0.11.2 opencensus-context-0.1.3 packaging-23.1 pandas-2.0.1 pandas-datareader-0.10.0 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 pillow-9.5.0 platformdirs-3.5.0 ply-3.11 prometheus-client-0.16.0 prompt-toolkit-3.0.38 protobuf-3.20.3 psutil-5.9.5 psycopg2-binary-2.9.6 ptyprocess-0.7.0 pure-eval-0.2.2 py-spy-0.3.14 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycares-4.3.0 pydantic-1.10.7 pyfolio-0.9.2+75.g4b901f6 pyglet-2.0.5 pygments-2.15.1 pyluach-2.2.0 pymysql-1.0.3 pyparsing-3.0.9 pyrsistent-0.19.3 python-dateutil-2.8.2 pytz-2023.3 ray-2.4.0 rsa-4.9 scikit-learn-1.2.2 scipy-1.10.1 seaborn-0.12.2 six-1.16.0 smart-open-6.3.0 soupsieve-2.4.1 sqlalchemy-1.4.48 stable-baselines3-1.8.0 stack-data-0.6.2 stockstats-0.5.2 sympy-1.11.1 tabulate-0.9.0 tensorboardX-2.6 threadpoolctl-3.1.0 thriftpy2-0.4.16 torch-2.0.0 traitlets-5.9.0 triton-2.0.0 typing-extensions-4.5.0 tzdata-2023.3 virtualenv-20.21.0 wcwidth-0.2.6 webencodings-0.5.1 websocket-client-1.5.1 websockets-10.4 wrds-3.1.6 yarl-1.9.2 yfinance-0.2.18 zipp-3.15.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2 Check if the additional packages needed are present, if not install them"
      ],
      "metadata": {
        "id": "DMaIlOcVTJfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install trading_calendars\n",
        "# !pip install alpaca_trade_api\n",
        "# !pip install ccxt\n",
        "# !pip install jqdatasdk\n",
        "# !pip install wrds\n",
        "\n",
        "# !pip install lz4\n",
        "# !pip install ray[tune]\n",
        "# !pip install tensorboardX\n",
        "# !pip install gputil\n",
        "\n",
        "#%%capture\n",
        "if True:\n",
        "    # installing packages\n",
        "    !pip install pyfolio-reloaded  #original pyfolio no longer maintained\n",
        "    !pip install optuna\n",
        "    !pip install -U \"ray[rllib]\"\n",
        "    !pip install plotly\n",
        "    !pip install ipywidgets\n",
        "    !pip install -U kaleido   # enables saving plots to file\n",
        "    \n",
        "    "
      ],
      "metadata": {
        "id": "UfmXkH1rTFM7",
        "outputId": "8efc8841-de8b-4436-d2f7-6228db2b50cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyfolio-reloaded\n",
            "  Downloading pyfolio_reloaded-0.9.5-py3-none-any.whl (6.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (8.13.1)\n",
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            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (1.24.3)\n",
            "Collecting empyrical-reloaded>=0.5.8\n",
            "  Downloading empyrical_reloaded-0.5.9-py3-none-any.whl (32 kB)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (1.10.1)\n",
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            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.10/site-packages (from pyfolio-reloaded) (3.7.1)\n",
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            "Requirement already satisfied: pandas-datareader>=0.4 in /usr/local/lib/python3.10/site-packages (from empyrical-reloaded>=0.5.8->pyfolio-reloaded) (0.10.0)\n",
            "Collecting bottleneck>=1.3.0\n",
            "  Downloading Bottleneck-1.3.7-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (354 kB)\n",
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            "\u001b[?25hRequirement already satisfied: yfinance>=0.1.63 in /usr/local/lib/python3.10/site-packages (from empyrical-reloaded>=0.5.8->pyfolio-reloaded) (0.2.18)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /usr/local/lib/python3.10/site-packages (from ipython>=3.2.3->pyfolio-reloaded) (3.0.38)\n",
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            "Installing collected packages: widgetsnbextension, tornado, pyzmq, nest-asyncio, jupyterlab-widgets, jupyter-core, debugpy, comm, jupyter-client, ipykernel, ipywidgets\n",
            "Successfully installed comm-0.1.3 debugpy-1.6.7 ipykernel-6.22.0 ipywidgets-8.0.6 jupyter-client-8.2.0 jupyter-core-5.3.0 jupyterlab-widgets-3.0.7 nest-asyncio-1.5.6 pyzmq-25.0.2 tornado-6.3.1 widgetsnbextension-4.0.7\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kaleido\n",
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            "\u001b[?25hInstalling collected packages: kaleido\n",
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            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas==1.5.3\n",
        "!pip install gymnasium"
      ],
      "metadata": {
        "id": "9Zkmtvr-TFn_",
        "outputId": "432ff578-a0a2-4fda-a514-8ae351e33991",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas==1.5.3) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "metadata": {
        "id": "cMn1VNKCR0ld"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3 Import packages"
      ],
      "metadata": {
        "id": "BbOMJnGdTRYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "import optuna\n",
        "from pathlib import Path\n",
        "from google.colab import files\n",
        "%matplotlib inline\n",
        "from finrl import config\n",
        "from finrl import config_tickers\n",
        "from optuna.integration import PyTorchLightningPruningCallback\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv as StockTradingEnv_numpy\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from finrl.agents.rllib.models import DRLAgent as DRLAgent_rllib\n",
        "from finrl.meta.data_processor import DataProcessor\n",
        "import joblib\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "import ray\n",
        "from pprint import pprint\n",
        "import kaleido\n",
        "\n",
        "\n",
        "\n",
        "import itertools\n",
        "\n",
        "import torch\n",
        "if torch.cuda.is_available():\n",
        "  device = torch.device(\"cuda\")\n",
        "else:\n",
        "  device = torch.device(\"cpu\")\n",
        "print(f'Torch device: {device}')"
      ],
      "metadata": {
        "id": "tG5qIkVHTNv8",
        "outputId": "2b369bf4-096f-45ef-d2dd-8ff391f3540f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch device: cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/site-packages/pyfolio/pos.py:25: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DB11ygQLVLUD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fMaPm8MsUo0R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "metadata": {
        "id": "FAZpwTH3VVi-"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collecting data and preprocessing"
      ],
      "metadata": {
        "id": "cqJ8ngFWVcNG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config_tickers.DOW_30_TICKER = [\"PYPL\"]"
      ],
      "metadata": {
        "id": "4M4UwcP4Ap_o"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Custom ticker list dataframe download\n",
        "#TODO save df to avoid download\n",
        "path_pf = '/content/ticker_data.csv'\n",
        "if Path(path_pf).is_file():\n",
        "  print('Reading ticker data')\n",
        "  df = pd.read_csv(path_pf)\n",
        "  \n",
        "else:\n",
        "  print('Downloading ticker data')\n",
        "  ticker_list = config_tickers.DOW_30_TICKER\n",
        "  df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2023-04-30',\n",
        "                     ticker_list = ticker_list).fetch_data()\n",
        "  df.to_csv('ticker_data.csv')"
      ],
      "metadata": {
        "id": "7BJNlsAuVZ-o",
        "outputId": "97cf064a-ac1f-446f-c8de-350ef16d2c5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ticker data\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (1969, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_processed_full(processed):\n",
        "  list_ticker = processed[\"tic\"].unique().tolist()\n",
        "  list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "  combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "  processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "  processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "  processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "  processed_full = processed_full.fillna(0)\n",
        "  processed_full.sort_values(['date','tic'],ignore_index=True).head(5)\n",
        "\n",
        "  processed_full.to_csv('processed_full.csv')\n",
        "  return processed_full"
      ],
      "metadata": {
        "id": "RD6wwtGSVZVU"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#You can add technical indicators and turbulence factor to dataframe\n",
        "#Just set the use_technical_indicator=True, use_vix=True and use_turbulence=True\n",
        "def create_techind():\n",
        "  fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.INDICATORS,\n",
        "                    use_vix=True,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "  processed = fe.preprocess_data(df)\n",
        "  return processed"
      ],
      "metadata": {
        "id": "UNqEALmbVoJo"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load price and technical indicator data from file if available\n",
        "path_pf = '/content/processed_full.csv'\n",
        "if Path(path_pf).is_file():\n",
        "  print('Reading processed_full data')\n",
        "  processed_full = pd.read_csv(path_pf)\n",
        "\n",
        "else:\n",
        "  print('Creating processed_full file')\n",
        "  processed=create_techind()\n",
        "  processed_full=create_processed_full(processed)"
      ],
      "metadata": {
        "id": "Qr1Qd-lMVpzg",
        "outputId": "9183922f-3e70-4eee-909a-3a6eaf3e7ded",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating processed_full file\n",
            "Successfully added technical indicators\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (1968, 8)\n",
            "Successfully added vix\n",
            "Successfully added turbulence index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_col = \"date\"\n",
        "tic_col = \"tic\"\n",
        "\n",
        "init_train_trade_data = processed_full.sort_values([date_col, tic_col])\n",
        "\n",
        "init_train_trade_data = processed_full.fillna(0)\n",
        "\n",
        "init_train_data = data_split(\n",
        "    init_train_trade_data, '2020-01-01', '2021-05-01')\n",
        "init_trade_data = data_split(\n",
        "    init_train_trade_data, '2021-05-01','2021-10-01')\n",
        "\n",
        "print(f'Number of training samples: {len(init_train_data)}')\n",
        "print(f'Number of testing samples: {len(init_train_trade_data)}')"
      ],
      "metadata": {
        "id": "KFs4AjWIVr2s",
        "outputId": "44c54e8c-82f8-440f-b83b-6a04d2422f40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training samples: 335\n",
            "Number of testing samples: 1968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "init_trade_data.head()"
      ],
      "metadata": {
        "id": "GI2qgPGFTerU",
        "outputId": "e7326fc9-a11b-43d3-80bc-09a87ef8a773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date   tic        open        high         low       close   \n",
              "0  2021-05-03  PYPL  265.059998  265.799988  256.179993  259.089996  \\\n",
              "1  2021-05-04  PYPL  255.729996  256.799988  246.649994  250.160004   \n",
              "2  2021-05-05  PYPL  254.919998  254.919998  246.244995  247.399994   \n",
              "3  2021-05-06  PYPL  256.000000  256.308990  245.110001  252.020004   \n",
              "4  2021-05-07  PYPL  255.919998  258.640015  251.639999  253.360001   \n",
              "\n",
              "       volume  day      macd     boll_ub     boll_lb     rsi_30     cci_30   \n",
              "0   7522000.0  0.0  2.955487  277.396635  254.802364  51.077698  15.452974  \\\n",
              "1   9217300.0  1.0  1.551486  278.028200  253.867799  47.978254 -39.436852   \n",
              "2   9507300.0  2.0  0.213633  279.506075  251.569923  47.065121 -51.311288   \n",
              "3  17998700.0  3.0 -0.468429  280.136075  249.691923  48.754026 -46.518955   \n",
              "4  10170000.0  4.0 -0.890576  280.280883  248.206116  49.239951 -32.470775   \n",
              "\n",
              "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0   1.161617    257.776499    260.911084  18.309999    0.336045  \n",
              "1  14.697136    257.969000    260.589751  19.480000    1.991105  \n",
              "2  15.300334    258.089999    260.010251  19.150000    0.278252  \n",
              "3  12.386793    258.681666    259.473917  18.389999    0.336892  \n",
              "4   7.513054    259.318999    258.976918  16.690001    0.007169  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9620af9c-6072-4dae-87d4-ac28c3d08c8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>265.059998</td>\n",
              "      <td>265.799988</td>\n",
              "      <td>256.179993</td>\n",
              "      <td>259.089996</td>\n",
              "      <td>7522000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.955487</td>\n",
              "      <td>277.396635</td>\n",
              "      <td>254.802364</td>\n",
              "      <td>51.077698</td>\n",
              "      <td>15.452974</td>\n",
              "      <td>1.161617</td>\n",
              "      <td>257.776499</td>\n",
              "      <td>260.911084</td>\n",
              "      <td>18.309999</td>\n",
              "      <td>0.336045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>255.729996</td>\n",
              "      <td>256.799988</td>\n",
              "      <td>246.649994</td>\n",
              "      <td>250.160004</td>\n",
              "      <td>9217300.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.551486</td>\n",
              "      <td>278.028200</td>\n",
              "      <td>253.867799</td>\n",
              "      <td>47.978254</td>\n",
              "      <td>-39.436852</td>\n",
              "      <td>14.697136</td>\n",
              "      <td>257.969000</td>\n",
              "      <td>260.589751</td>\n",
              "      <td>19.480000</td>\n",
              "      <td>1.991105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-05-05</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>254.919998</td>\n",
              "      <td>254.919998</td>\n",
              "      <td>246.244995</td>\n",
              "      <td>247.399994</td>\n",
              "      <td>9507300.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.213633</td>\n",
              "      <td>279.506075</td>\n",
              "      <td>251.569923</td>\n",
              "      <td>47.065121</td>\n",
              "      <td>-51.311288</td>\n",
              "      <td>15.300334</td>\n",
              "      <td>258.089999</td>\n",
              "      <td>260.010251</td>\n",
              "      <td>19.150000</td>\n",
              "      <td>0.278252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>256.308990</td>\n",
              "      <td>245.110001</td>\n",
              "      <td>252.020004</td>\n",
              "      <td>17998700.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.468429</td>\n",
              "      <td>280.136075</td>\n",
              "      <td>249.691923</td>\n",
              "      <td>48.754026</td>\n",
              "      <td>-46.518955</td>\n",
              "      <td>12.386793</td>\n",
              "      <td>258.681666</td>\n",
              "      <td>259.473917</td>\n",
              "      <td>18.389999</td>\n",
              "      <td>0.336892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-05-07</td>\n",
              "      <td>PYPL</td>\n",
              "      <td>255.919998</td>\n",
              "      <td>258.640015</td>\n",
              "      <td>251.639999</td>\n",
              "      <td>253.360001</td>\n",
              "      <td>10170000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.890576</td>\n",
              "      <td>280.280883</td>\n",
              "      <td>248.206116</td>\n",
              "      <td>49.239951</td>\n",
              "      <td>-32.470775</td>\n",
              "      <td>7.513054</td>\n",
              "      <td>259.318999</td>\n",
              "      <td>258.976918</td>\n",
              "      <td>16.690001</td>\n",
              "      <td>0.007169</td>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9620af9c-6072-4dae-87d4-ac28c3d08c8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating Environment"
      ],
      "metadata": {
        "id": "L3VlkfBMW8e7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import annotations\n",
        "\n",
        "from typing import List\n",
        "\n",
        "import gym\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym import spaces\n",
        "from gym.utils import seeding\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\n",
        "matplotlib.use(\"Agg\")\n",
        "\n",
        "# from stable_baselines3.common.logger import Logger, KVWriter, CSVOutputFormat\n",
        "\n",
        "\n",
        "class GokuEnv(gym.Env):\n",
        "    \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
        "\n",
        "    metadata = {\"render.modes\": [\"human\"]}\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        df: pd.DataFrame,\n",
        "        stock_dim: int,\n",
        "        hmax: int,\n",
        "        initial_amount: int,\n",
        "        num_stock_shares: list[int],\n",
        "        buy_cost_pct: list[float],\n",
        "        sell_cost_pct: list[float],\n",
        "        reward_scaling: float,\n",
        "        state_space: int,\n",
        "        action_space: int,\n",
        "        tech_indicator_list: list[str],\n",
        "        turbulence_threshold=None,\n",
        "        risk_indicator_col=\"turbulence\",\n",
        "        make_plots: bool = False,\n",
        "        print_verbosity=10,\n",
        "        day=0,\n",
        "        initial=True,\n",
        "        previous_state=[],\n",
        "        model_name=\"\",\n",
        "        mode=\"\",\n",
        "        iteration=\"\",\n",
        "    ):\n",
        "        self.day = day\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.num_stock_shares = num_stock_shares\n",
        "        self.initial_amount = initial_amount  # get the initial cash\n",
        "        self.buy_cost_pct = buy_cost_pct\n",
        "        self.sell_cost_pct = sell_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "        self.action_space = spaces.Box(low=-1, high=1, shape=(self.action_space,))\n",
        "        self.observation_space = spaces.Box(\n",
        "            low=-np.inf, high=np.inf, shape=(self.state_space,)\n",
        "        )\n",
        "        self.data = self.df.loc[self.day, :]\n",
        "        self.terminal = False\n",
        "        self.make_plots = make_plots\n",
        "        self.print_verbosity = print_verbosity\n",
        "        self.turbulence_threshold = turbulence_threshold\n",
        "        self.risk_indicator_col = risk_indicator_col\n",
        "        self.initial = initial\n",
        "        self.previous_state = previous_state\n",
        "        self.model_name = model_name\n",
        "        self.mode = mode\n",
        "        self.iteration = iteration\n",
        "        # initalize state\n",
        "        self.state = self._initiate_state()\n",
        "\n",
        "        # initialize reward\n",
        "        self.reward = 0\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.episode = 0\n",
        "        # memorize all the total balance change\n",
        "        self.asset_memory = [\n",
        "            self.initial_amount\n",
        "            + np.sum(\n",
        "                np.array(self.num_stock_shares)\n",
        "                * np.array(self.state[1 : 1 + self.stock_dim])\n",
        "            )\n",
        "        ]  # the initial total asset is calculated by cash + sum (num_share_stock_i * price_stock_i)\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory = []\n",
        "        self.state_memory = (\n",
        "            []\n",
        "        )  # we need sometimes to preserve the state in the middle of trading process\n",
        "        self.date_memory = [self._get_date()]\n",
        "        #         self.logger = Logger('results',[CSVOutputFormat])\n",
        "        # self.reset()\n",
        "        self._seed()\n",
        "        self.total_price = np.array([0] * 29)\n",
        "        self.avg_price = np.array([0] * 29)\n",
        "        self.total_stockss = np.array([0] * 29)\n",
        "\n",
        "    def _sell_stock(self, index, action):\n",
        "        def _do_sell_normal():\n",
        "            if (\n",
        "                self.state[index + 2 * self.stock_dim + 1] != True\n",
        "            ):  # check if the stock is able to sell, for simlicity we just add it in techical index\n",
        "                # if self.state[index + 1] > 0: # if we use price<0 to denote a stock is unable to trade in that day, the total asset calculation may be wrong for the price is unreasonable\n",
        "                # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                # perform sell action based on the sign of the action\n",
        "                if self.state[index + self.stock_dim + 1] > 0:\n",
        "                    # Sell only if current asset is > 0\n",
        "                    sell_num_shares = min(\n",
        "                        abs(action), self.state[index + self.stock_dim + 1]\n",
        "                    )\n",
        "                    sell_amount = (\n",
        "                        self.state[index + 1]\n",
        "                        * sell_num_shares\n",
        "                        * (1 - self.sell_cost_pct[index])\n",
        "                    )\n",
        "                    # update balance\n",
        "                    self.state[0] += sell_amount\n",
        "\n",
        "                    self.state[index + self.stock_dim + 1] -= sell_num_shares\n",
        "                    self.cost += (\n",
        "                        self.state[index + 1]\n",
        "                        * sell_num_shares\n",
        "                        * self.sell_cost_pct[index]\n",
        "                    )\n",
        "                    self.trades += 1\n",
        "                    #if sell_num_shares >0:\n",
        "                    #  print(\"stocks Sold sell_num_shares\", sell_num_shares)\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = 0\n",
        "            return sell_num_shares\n",
        "\n",
        "        # perform sell action based on the sign of the action\n",
        "        if self.turbulence_threshold is not None:\n",
        "            if self.turbulence >= self.turbulence_threshold:\n",
        "                if self.state[index + 1] > 0:\n",
        "                    # Sell only if the price is > 0 (no missing data in this particular date)\n",
        "                    # if turbulence goes over threshold, just clear out all positions\n",
        "                    if self.state[index + self.stock_dim + 1] > 0:\n",
        "                        # Sell only if current asset is > 0\n",
        "                        sell_num_shares = self.state[index + self.stock_dim + 1]\n",
        "                        sell_amount = (\n",
        "                            self.state[index + 1]\n",
        "                            * sell_num_shares\n",
        "                            * (1 - self.sell_cost_pct[index])\n",
        "                        )\n",
        "                        # update balance\n",
        "                        self.state[0] += sell_amount\n",
        "                        self.state[index + self.stock_dim + 1] = 0\n",
        "                        self.cost += (\n",
        "                            self.state[index + 1]\n",
        "                            * sell_num_shares\n",
        "                            * self.sell_cost_pct[index]\n",
        "                        )\n",
        "                        self.trades += 1\n",
        "                    else:\n",
        "                        sell_num_shares = 0\n",
        "                else:\n",
        "                    sell_num_shares = 0\n",
        "            else:\n",
        "                sell_num_shares = _do_sell_normal()\n",
        "        else:\n",
        "            sell_num_shares = _do_sell_normal()\n",
        "        #print(\"stocks Sold sell_num_shares\", sell_num_shares)\n",
        "        return sell_num_shares\n",
        "\n",
        "    def _buy_stock(self, index, action):\n",
        "        def _do_buy():\n",
        "            if (self.state[index + 2 * self.stock_dim + 1] != True):  # check if the stock is able to buy\n",
        "                # if self.state[index + 1] >0:\n",
        "                # Buy only if the price is > 0 (no missing data in this particular date)\n",
        "                available_amount = self.state[0] // (self.state[index + 1] * (1 + self.buy_cost_pct[index]))\n",
        "                # when buying stocks, we should consider the cost of trading when calculating available_amount, or we may be have cash<0\n",
        "                # print('available_amount:{}'.format(available_amount))\n",
        "                # update balance\n",
        "                buy_num_shares = min(available_amount, action)\n",
        "                buy_amount = (self.state[index + 1] * buy_num_shares * (1 + self.buy_cost_pct[index]))\n",
        "                self.state[0] -= buy_amount\n",
        "                self.state[index + self.stock_dim + 1] += buy_num_shares\n",
        "                self.cost += (self.state[index + 1] * buy_num_shares * self.buy_cost_pct[index])\n",
        "                self.trades += 1\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "\n",
        "            return buy_num_shares\n",
        "\n",
        "        # perform buy action based on the sign of the action\n",
        "        if self.turbulence_threshold is None:\n",
        "            buy_num_shares = _do_buy()\n",
        "        else:\n",
        "            if self.turbulence < self.turbulence_threshold:\n",
        "                buy_num_shares = _do_buy()\n",
        "            else:\n",
        "                buy_num_shares = 0\n",
        "                pass\n",
        "\n",
        "        return buy_num_shares\n",
        "\n",
        "    def _make_plot(self):\n",
        "        plt.plot(self.asset_memory, \"r\")\n",
        "        plt.savefig(f\"results/account_value_trade_{self.episode}.png\")\n",
        "        plt.close()\n",
        "\n",
        "    def step(self, actions):\n",
        "        self.terminal = self.day >= len(self.df.index.unique()) - 1\n",
        "        if self.terminal:\n",
        "            # print(f\"Episode: {self.episode}\")\n",
        "            if self.make_plots:\n",
        "                self._make_plot()\n",
        "            end_total_asset = self.state[0] + sum(np.array(self.state[1 : (self.stock_dim + 1)]) * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]))\n",
        "            df_total_value = pd.DataFrame(self.asset_memory)\n",
        "            tot_reward = (self.state[0] + \n",
        "                          sum(np.array(self.state[1 : (self.stock_dim + 1)]) \n",
        "                          * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])) \n",
        "                          - self.asset_memory[0])  # initial_amount is only cash part of our initial asset\n",
        "            df_total_value.columns = [\"account_value\"]\n",
        "            df_total_value[\"date\"] = self.date_memory\n",
        "            df_total_value[\"daily_return\"] = df_total_value[\"account_value\"].pct_change(1)\n",
        "            if df_total_value[\"daily_return\"].std() != 0:\n",
        "                sharpe = ((252**0.5) \n",
        "                * df_total_value[\"daily_return\"].mean() \n",
        "                / df_total_value[\"daily_return\"].std())\n",
        "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
        "            df_rewards.columns = [\"account_rewards\"]\n",
        "            df_rewards[\"date\"] = self.date_memory[:-1]\n",
        "            \n",
        "            if self.episode % self.print_verbosity == 0:\n",
        "                print(f\"day: {self.day}, episode: {self.episode}\")\n",
        "                print(f\"begin_total_asset: {self.asset_memory[0]:0.2f}\")\n",
        "                print(f\"end_total_asset: {end_total_asset:0.2f}\")\n",
        "                print(f\"total_reward: {tot_reward:0.2f}\")\n",
        "                print(f\"total_cost: {self.cost:0.2f}\")\n",
        "                print(f\"total_trades: {self.trades}\")\n",
        "                if df_total_value[\"daily_return\"].std() != 0:\n",
        "                    print(f\"Sharpe: {sharpe:0.3f}\")\n",
        "                print(\"=================================\")\n",
        "\n",
        "            if (self.model_name != \"\") and (self.mode != \"\"):\n",
        "                df_actions = self.save_action_memory()\n",
        "                df_actions.to_csv(\"results/actions_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration))\n",
        "                df_total_value.to_csv(\"results/account_value_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration),index=False,)\n",
        "                df_rewards.to_csv(\"results/account_rewards_{}_{}_{}.csv\".format(self.mode, self.model_name, self.iteration),index=False,)\n",
        "                plt.plot(self.asset_memory, \"r\")\n",
        "                plt.savefig(\"results/account_value_{}_{}_{}.png\".format(self.mode, self.model_name, self.iteration))\n",
        "                plt.close()\n",
        "            return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "        else:\n",
        "            actions = actions * self.hmax  # actions initially is scaled between 0 to 1\n",
        "            actions = actions.astype(int)  # convert into integer because we can't by fraction of shares\n",
        "\n",
        "\n",
        "            if self.turbulence_threshold is not None:\n",
        "                if self.turbulence >= self.turbulence_threshold:\n",
        "                    actions = np.array([-self.hmax] * self.stock_dim)\n",
        "            \n",
        "            # Mandatory Selling Profits and Losses\n",
        "            for i in range(0,len(actions)):\n",
        "              # self.total_price\n",
        "              # self.total_stockss\n",
        "              # self.avg_price\n",
        "              print(\"Current Price\")\n",
        "              print(np.array(self.state[1 : (self.stock_dim + 1)])[i])\n",
        "              current_price  = np.array(self.state[1 : (self.stock_dim + 1)])[i]\n",
        "              avg_price = self.avg_price[i]\n",
        "              if avg_price > ( current_price * 0.3  + current_price)  and avg_price > 0:\n",
        "                actions[i] = self.total_stockss[i] * -1\n",
        "                #print(\"Updated actions\")\n",
        "                print(\"Current Price\", current_price , \" \", \" avg_price\", \" \", avg_price, \"Profit\", current_price -  avg_price)\n",
        "              elif avg_price < (current_price - current_price * 0.15)  and avg_price > 0:\n",
        "                actions[i] = self.total_stockss[i] * -1\n",
        "                print(\"Current Price\", current_price , \" \", \" avg_price\", \" \", avg_price, \"loss\", current_price -  avg_price )\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            begin_total_asset = self.state[0] + sum(np.array(self.state[1 : (self.stock_dim + 1)])* np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]))\n",
        "            # print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
        "            #print(\"*\" * 20 )\n",
        "            #print(np.array(self.state[1 : (self.stock_dim + 1)]))\n",
        "            \n",
        "            #print(np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]))\n",
        "            #print(\"*\" * 20 )\n",
        "            \n",
        "            #print(\"AVG PRices\")  \n",
        "            #print(self.avg_price)\n",
        "            #print(np.where(np.array(self.state[1 : (self.stock_dim + 1)]) > (self.avg_price + self.avg_price*.15))) \n",
        "            #print(self.avg_price)\n",
        "            #print(self.total_stockss)\n",
        "            #print(\"*\"*20)\n",
        "\n",
        "            # if sum(self.total_stockss) !=0:\n",
        "            #   for i in np.where(np.array(self.state[1 : (self.stock_dim + 1)]) > (self.avg_price + self.avg_price*.15)):\n",
        "            #     for a in i:\n",
        "            #       actions[a] = actions[a]*-1\n",
        "\n",
        "            # for a in actions:\n",
        "            #   print(a)\n",
        "\n",
        "            argsort_actions = np.argsort(actions)\n",
        "            sell_index = argsort_actions[: np.where(actions < 0)[0].shape[0]]\n",
        "            buy_index = argsort_actions[::-1][: np.where(actions > 0)[0].shape[0]]\n",
        "\n",
        "            for index in sell_index:\n",
        "              actions[index] = self._sell_stock(index, actions[index]) * (-1)\n",
        "\n",
        "            for index in buy_index:\n",
        "                actions[index] = self._buy_stock(index, actions[index])\n",
        "\n",
        "\n",
        "            self.actions_memory.append(actions)\n",
        "\n",
        "            # state: s -> s+1\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day, :]\n",
        "            if self.turbulence_threshold is not None:\n",
        "                if len(self.df.tic.unique()) == 1:\n",
        "                    self.turbulence = self.data[self.risk_indicator_col]\n",
        "                elif len(self.df.tic.unique()) > 1:\n",
        "                    self.turbulence = self.data[self.risk_indicator_col].values[0]\n",
        "\n",
        "\n",
        "\n",
        "            self.total_stockss = actions + self.total_stockss\n",
        "            print(\"Total Stocks\")\n",
        "            self.total_price = np.array(self.state[1 : (self.stock_dim + 1)])* np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]) + self.total_price\n",
        "\n",
        "            \n",
        "            self.avg_price = np.divide(self.total_price,\n",
        "                      self.total_stockss, out=np.zeros_like(self.total_price),\n",
        "                      where=self.total_stockss!=0)\n",
        "          \n",
        "            print(self.total_stockss)\n",
        "            print(\"Avg Price\")\n",
        "            print(self.avg_price)\n",
        "\n",
        "\n",
        "            self.state = self._update_state()\n",
        "\n",
        "            end_total_asset = self.state[0] + sum(\n",
        "                np.array(self.state[1 : (self.stock_dim + 1)])\n",
        "                * np.array(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "            )\n",
        "            self.asset_memory.append(end_total_asset)\n",
        "            self.date_memory.append(self._get_date())\n",
        "            self.reward = end_total_asset - begin_total_asset\n",
        "            self.rewards_memory.append(self.reward)\n",
        "            self.reward = self.reward * self.reward_scaling\n",
        "            self.state_memory.append(\n",
        "                self.state\n",
        "            )  # add current state in state_recorder for each step\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        # initiate state\n",
        "        self.state = self._initiate_state()\n",
        "\n",
        "        if self.initial:\n",
        "            self.asset_memory = [\n",
        "                self.initial_amount\n",
        "                + np.sum(\n",
        "                    np.array(self.num_stock_shares)\n",
        "                    * np.array(self.state[1 : 1 + self.stock_dim])\n",
        "                )\n",
        "            ]\n",
        "        else:\n",
        "            previous_total_asset = self.previous_state[0] + sum(\n",
        "                np.array(self.state[1 : (self.stock_dim + 1)])\n",
        "                * np.array(\n",
        "                    self.previous_state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)]\n",
        "                )\n",
        "            )\n",
        "            self.asset_memory = [previous_total_asset]\n",
        "\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day, :]\n",
        "        self.turbulence = 0\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.terminal = False\n",
        "        # self.iteration=self.iteration\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory = []\n",
        "        self.date_memory = [self._get_date()]\n",
        "\n",
        "        self.episode += 1\n",
        "\n",
        "        return self.state\n",
        "\n",
        "    def render(self, mode=\"human\", close=False):\n",
        "        return self.state\n",
        "\n",
        "    def _initiate_state(self):\n",
        "        if self.initial:\n",
        "            # For Initial State\n",
        "            if len(self.df.tic.unique()) > 1:\n",
        "                # for multiple stock\n",
        "                state = (\n",
        "                    [self.initial_amount]\n",
        "                    + self.data.close.values.tolist()\n",
        "                    + self.num_stock_shares\n",
        "                    + sum(\n",
        "                        (\n",
        "                            self.data[tech].values.tolist()\n",
        "                            for tech in self.tech_indicator_list\n",
        "                        ),\n",
        "                        [],\n",
        "                    )\n",
        "                )  # append initial stocks_share to initial state, instead of all zero\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = (\n",
        "                    [self.initial_amount]\n",
        "                    + [self.data.close]\n",
        "                    + [0] * self.stock_dim\n",
        "                    + sum(([self.data[tech]] for tech in self.tech_indicator_list), [])\n",
        "                )\n",
        "        else:\n",
        "            # Using Previous State\n",
        "            if len(self.df.tic.unique()) > 1:\n",
        "                # for multiple stock\n",
        "                state = (\n",
        "                    [self.previous_state[0]]\n",
        "                    + self.data.close.values.tolist()\n",
        "                    + self.previous_state[\n",
        "                        (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
        "                    ]\n",
        "                    + sum(\n",
        "                        (\n",
        "                            self.data[tech].values.tolist()\n",
        "                            for tech in self.tech_indicator_list\n",
        "                        ),\n",
        "                        [],\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                # for single stock\n",
        "                state = (\n",
        "                    [self.previous_state[0]]\n",
        "                    + [self.data.close]\n",
        "                    + self.previous_state[\n",
        "                        (self.stock_dim + 1) : (self.stock_dim * 2 + 1)\n",
        "                    ]\n",
        "                    + sum(([self.data[tech]] for tech in self.tech_indicator_list), [])\n",
        "                )\n",
        "        return state\n",
        "\n",
        "    def _update_state(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            # for multiple stock\n",
        "            state = (\n",
        "                [self.state[0]]\n",
        "                + self.data.close.values.tolist()\n",
        "                + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "                + sum((self.data[tech].values.tolist() for tech in self.tech_indicator_list),[],))\n",
        "\n",
        "        else:\n",
        "            # for single stock\n",
        "            state = ([self.state[0]] + [self.data.close] \n",
        "                     + list(self.state[(self.stock_dim + 1) : (self.stock_dim * 2 + 1)])\n",
        "                     + sum(([self.data[tech]] for tech in self.tech_indicator_list), []))\n",
        "\n",
        "        return state\n",
        "\n",
        "    def _get_date(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            date = self.data.date.unique()[0]\n",
        "        else:\n",
        "            date = self.data.date\n",
        "        return date\n",
        "\n",
        "\n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        asset_list = self.asset_memory\n",
        "        # print(len(date_list))\n",
        "        # print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({\"date\": date_list, \"account_value\": asset_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        if len(self.df.tic.unique()) > 1:\n",
        "            # date and close price length must match actions length\n",
        "            date_list = self.date_memory[:-1]\n",
        "            df_date = pd.DataFrame(date_list)\n",
        "            df_date.columns = [\"date\"]\n",
        "\n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame(action_list)\n",
        "            df_actions.columns = self.data.tic.values\n",
        "            df_actions.index = df_date.date\n",
        "            # df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        else:\n",
        "            date_list = self.date_memory[:-1]\n",
        "            action_list = self.actions_memory\n",
        "            df_actions = pd.DataFrame({\"date\": date_list, \"actions\": action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs\n"
      ],
      "metadata": {
        "id": "4p02b2cx_dj5"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_dimension = len(init_train_data.tic.unique())\n",
        "state_space = 1 + 2 * stock_dimension + len(config.INDICATORS) * stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n",
        "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
        "num_stock_shares = [0] * stock_dimension"
      ],
      "metadata": {
        "id": "rg7DTZbVV10n",
        "outputId": "975497f7-221e-402e-c99d-1c9a1035534c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Dimension: 1, State Space: 11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining the environment kwargs\n",
        "\n",
        "initial_amount = 1000000\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100,\n",
        "    \"initial_amount\": initial_amount,\n",
        "    \"num_stock_shares\": num_stock_shares,\n",
        "    \"buy_cost_pct\": buy_cost_list,\n",
        "    \"sell_cost_pct\": sell_cost_list,\n",
        "    \"state_space\": state_space,\n",
        "    \"stock_dim\": stock_dimension,\n",
        "    \"tech_indicator_list\": config.INDICATORS,\n",
        "    \"action_space\": stock_dimension,\n",
        "    \"reward_scaling\": 1e-4,\n",
        "}"
      ],
      "metadata": {
        "id": "zODPmc5hV42J"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instantiate the training gym compatible environment\n",
        "e_train_gym = GokuEnv(df = init_train_data, **env_kwargs)\n",
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "metadata": {
        "id": "7n2QYGP5V58p"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instantiate the trading environment\n",
        "e_trade_gym = GokuEnv(df = init_trade_data, turbulence_threshold = None, **env_kwargs)"
      ],
      "metadata": {
        "id": "xoBM74XEX1oI"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trade performance code\n",
        "The following code calculates trade performance metrics, which are then used as an objective for optimizing hyperparameter values.\n",
        "\n",
        "There are several available metrics. In this tutorial, the default choice is the ratio of average value of winning to losing trades."
      ],
      "metadata": {
        "id": "Dq6zJ14ZYsTK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Main method\n",
        "# Calculates Trade Performance for Objective\n",
        "# Called from objective method\n",
        "# Returns selected trade perf metric(s)\n",
        "# Requires actions and associated prices\n",
        "\n",
        "def calc_trade_perf_metric(df_actions, \n",
        "                           df_prices_trade,\n",
        "                           tp_metric,\n",
        "                           dbg=False):\n",
        "  \n",
        "    df_actions_p, df_prices_p, tics = prep_data(df_actions.copy(),\n",
        "                                                df_prices_trade.copy())\n",
        "    # actions predicted by trained model on trade data\n",
        "    df_actions_p.to_csv('df_actions.csv') \n",
        "\n",
        "    \n",
        "    # Confirms that actions, prices and tics are consistent\n",
        "    df_actions_s, df_prices_s, tics_prtfl = \\\n",
        "        sync_tickers(df_actions_p.copy(),df_prices_p.copy(),tics)\n",
        "    \n",
        "    # copy to ensure that tics from portfolio remains unchanged\n",
        "    tics = tics_prtfl.copy()\n",
        "    \n",
        "    # Analysis is performed on each portfolio ticker\n",
        "    perf_data= collect_performance_data(df_actions_s, df_prices_s, tics)\n",
        "    # profit/loss for each ticker\n",
        "    pnl_all = calc_pnl_all(perf_data, tics)\n",
        "    # values for trade performance metrics\n",
        "    perf_results = calc_trade_perf(pnl_all)\n",
        "    df = pd.DataFrame.from_dict(perf_results, orient='index')\n",
        "    \n",
        "    # calculate and return trade metric value as objective\n",
        "    m = calc_trade_metric(df,tp_metric)\n",
        "    print(f'Ratio Avg Win/Avg Loss: {m}')\n",
        "    k = str(len(tpm_hist)+1)\n",
        "    # save metric value\n",
        "    tpm_hist[k] = m\n",
        "    return m\n",
        "\n",
        "\n",
        "# Supporting methods\n",
        "def calc_trade_metric(df,metric='avgwl'):\n",
        "    '''# trades', '# wins', '# losses', 'wins total value', 'wins avg value',\n",
        "       'losses total value', 'losses avg value'''\n",
        "    # For this tutorial, the only metric available is the ratio of \n",
        "    #  average values of winning to losing trades. Others are in development.\n",
        "    \n",
        "    # some test cases produce no losing trades.\n",
        "    # The code below assigns a value as a multiple of the highest value during\n",
        "    # previous hp optimization runs. If the first run experiences no losses,\n",
        "    # a fixed value is assigned for the ratio\n",
        "    tpm_mult = 1.0\n",
        "    avgwl_no_losses = 25\n",
        "    if metric == 'avgwl':\n",
        "        if sum(df['# losses']) == 0:\n",
        "          try:\n",
        "            return max(tpm_hist.values())*tpm_mult\n",
        "          except ValueError:\n",
        "            return avgwl_no_losses\n",
        "        avg_w = sum(df['wins total value'])/sum(df['# wins'])\n",
        "        avg_l = sum(df['losses total value'])/sum(df['# losses'])\n",
        "        m = abs(avg_w/avg_l)\n",
        "\n",
        "    return m\n",
        "\n",
        "\n",
        "def prep_data(df_actions,\n",
        "              df_prices_trade):\n",
        "    \n",
        "    df=df_prices_trade[['date','close','tic']]\n",
        "    df['Date'] = pd.to_datetime(df['date'])\n",
        "    df = df.set_index('Date')\n",
        "    # set indices on both df to datetime\n",
        "    idx = pd.to_datetime(df_actions.index, infer_datetime_format=True)\n",
        "    df_actions.index=idx\n",
        "    tics = np.unique(df.tic)\n",
        "    n_tics = len(tics)\n",
        "    print(f'Number of tickers: {n_tics}')\n",
        "    print(f'Tickers: {tics}')\n",
        "    dategr = df.groupby('tic')\n",
        "    p_d={t:dategr.get_group(t).loc[:,'close'] for t in tics}\n",
        "    df_prices = pd.DataFrame.from_dict(p_d)\n",
        "    df_prices.index = df_prices.index.normalize()\n",
        "    return df_actions, df_prices, tics\n",
        "\n",
        "\n",
        "# prepares for integrating action and price files\n",
        "def link_prices_actions(df_a,\n",
        "                        df_p):\n",
        "    cols_a = [t + '_a' for t in df_a.columns]\n",
        "    df_a.columns = cols_a\n",
        "    cols_p = [t + '_p' for t in df_p.columns]\n",
        "    df_p.columns = cols_p\n",
        "    return df_a, df_p\n",
        "\n",
        "\n",
        "def sync_tickers(df_actions,df_tickers_p,tickers):\n",
        "    # Some DOW30 components may not be included in portfolio\n",
        "    # passed tickers includes all DOW30 components\n",
        "    # actions and ticker files may have different length indices\n",
        "    if len(df_actions) != len(df_tickers_p):\n",
        "      msng_dates = set(df_actions.index)^set(df_tickers_p.index)\n",
        "      try:\n",
        "        #assumption is prices has one additional timestamp (row)\n",
        "        df_tickers_p.drop(msng_dates,inplace=True)\n",
        "      except:\n",
        "        df_actions.drop(msng_dates,inplace=True)\n",
        "    df_actions, df_tickers_p = link_prices_actions(df_actions,df_tickers_p)\n",
        "    # identify any DOW components not in portfolio\n",
        "    t_not_in_a = [t for t in tickers if t + '_a' not in list(df_actions.columns)]\n",
        "  \n",
        "    # remove t_not_in_a from df_tickers_p\n",
        "    drop_cols = [t + '_p' for t in t_not_in_a]\n",
        "    df_tickers_p.drop(columns=drop_cols,inplace=True)\n",
        "    \n",
        "    # Tickers in portfolio\n",
        "    tickers_prtfl = [c.split('_')[0] for c in df_actions.columns]\n",
        "    return df_actions,df_tickers_p, tickers_prtfl\n",
        "\n",
        "def collect_performance_data(dfa,dfp,tics, dbg=False):\n",
        "    \n",
        "    perf_data = {}\n",
        "    # In current version, files columns include secondary identifier\n",
        "    for t in tics:\n",
        "        # actions: purchase/sale of DOW equities\n",
        "        acts = dfa['_'.join([t,'a'])].values\n",
        "        # ticker prices\n",
        "        prices = dfp['_'.join([t,'p'])].values\n",
        "        # market value of purchases/sales\n",
        "        tvals_init = np.multiply(acts,prices)\n",
        "        d={'actions':acts, 'prices':prices,'init_values':tvals_init}\n",
        "        perf_data[t]=d\n",
        "\n",
        "    return perf_data\n",
        "\n",
        "\n",
        "def calc_pnl_all(perf_dict, tics_all):\n",
        "    # calculate profit/loss for each ticker\n",
        "    print(f'Calculating profit/loss for each ticker')\n",
        "    pnl_all = {}\n",
        "    for tic in tics_all:\n",
        "        pnl_t = []\n",
        "        tic_data = perf_dict[tic]\n",
        "        init_values = tic_data['init_values']\n",
        "        acts = tic_data['actions']\n",
        "        prices = tic_data['prices']\n",
        "        cs = np.cumsum(acts)\n",
        "        args_s = [i + 1 for i in range(len(cs) - 1) if cs[i + 1] < cs[i]]\n",
        "        # tic actions with no sales\n",
        "        if not args_s:\n",
        "            pnl = complete_calc_buyonly(acts, prices, init_values)\n",
        "            pnl_all[tic] = pnl\n",
        "            continue\n",
        "        # copy acts: acts_rev will be revised based on closing/reducing init positions\n",
        "        pnl_all = execute_position_sales(tic,acts,prices,args_s,pnl_all)\n",
        "\n",
        "    return pnl_all\n",
        "\n",
        "\n",
        "def complete_calc_buyonly(actions, prices, init_values):\n",
        "    # calculate final pnl for each ticker assuming no sales\n",
        "    fnl_price = prices[-1]\n",
        "    final_values = np.multiply(fnl_price, actions)\n",
        "    pnl = np.subtract(final_values, init_values)\n",
        "    return pnl\n",
        "\n",
        "\n",
        "def execute_position_sales(tic,acts,prices,args_s,pnl_all):\n",
        "  # calculate final pnl for each ticker with sales\n",
        "    pnl_t = []\n",
        "    acts_rev = acts.copy()\n",
        "    # location of sales transactions\n",
        "    for s in args_s:  # s is scaler\n",
        "        # price_s = [prices[s]]\n",
        "        act_s = [acts_rev[s]]\n",
        "        args_b = [i for i in range(s) if acts_rev[i] > 0]\n",
        "        prcs_init_trades = prices[args_b]\n",
        "        acts_init_trades = acts_rev[args_b]\n",
        "  \n",
        "        # update actions for sales\n",
        "        # reduce/eliminate init values through trades\n",
        "        # always start with earliest purchase that has not been closed through sale\n",
        "        # selectors for purchase and sales trades\n",
        "        # find earliest remaining purchase\n",
        "        arg_sel = min(args_b)\n",
        "        # sel_s = len(acts_trades) - 1\n",
        "\n",
        "        # closing part/all of earliest init trade not yet closed\n",
        "        # sales actions are negative\n",
        "        # in this test case, abs_val of init and sales share counts are same\n",
        "        # zero-out sales actions\n",
        "        # market value of sale\n",
        "        # max number of shares to be closed: may be less than # originally purchased\n",
        "        acts_shares = min(abs(act_s.pop()), acts_rev[arg_sel])\n",
        "\n",
        "        # mv of shares when purchased\n",
        "        mv_p = abs(acts_shares * prices[arg_sel])\n",
        "        # mv of sold shares\n",
        "        mv_s = abs(acts_shares * prices[s])\n",
        "\n",
        "        # calc pnl\n",
        "        pnl = mv_s - mv_p\n",
        "        # reduce init share count\n",
        "        # close all/part of init purchase\n",
        "        acts_rev[arg_sel] -= acts_shares\n",
        "        acts_rev[s] += acts_shares\n",
        "        # calculate pnl for trade\n",
        "        # value of associated purchase\n",
        "        \n",
        "        # find earliest non-zero positive act in acts_revs\n",
        "        pnl_t.append(pnl)\n",
        "    \n",
        "    pnl_op = calc_pnl_for_open_positions(acts_rev, prices)\n",
        "    #pnl_op is list\n",
        "    # add pnl_op results (if any) to pnl_t (both lists)\n",
        "    pnl_t.extend(pnl_op)\n",
        "    #print(f'Total pnl for {tic}: {np.sum(pnl_t)}')\n",
        "    pnl_all[tic] = np.array(pnl_t)\n",
        "    return pnl_all\n",
        "\n",
        "\n",
        "def calc_pnl_for_open_positions(acts,prices):\n",
        "    # identify any positive share values after accounting for sales\n",
        "    pnl = []\n",
        "    fp = prices[-1] # last price\n",
        "    open_pos_arg = np.argwhere(acts>0)\n",
        "    if len(open_pos_arg)==0:return pnl # no open positions\n",
        "\n",
        "    mkt_vals_open = np.multiply(acts[open_pos_arg], prices[open_pos_arg])\n",
        "    # mkt val at end of testing period\n",
        "    # treat as trades for purposes of calculating pnl at end of testing period\n",
        "    mkt_vals_final = np.multiply(fp, acts[open_pos_arg])\n",
        "    pnl_a = np.subtract(mkt_vals_final, mkt_vals_open)\n",
        "    #convert to list\n",
        "    pnl = [i[0] for i in pnl_a.tolist()]\n",
        "    #print(f'Market value of open positions at end of testing {pnl}')\n",
        "    return pnl\n",
        "\n",
        "\n",
        "def calc_trade_perf(pnl_d):\n",
        "    # calculate trade performance metrics\n",
        "    perf_results = {}\n",
        "    for t,pnl in pnl_d.items():\n",
        "        wins = pnl[pnl>0]  # total val\n",
        "        losses = pnl[pnl<0]\n",
        "        n_wins = len(wins)\n",
        "        n_losses = len(losses)\n",
        "        n_trades = n_wins + n_losses\n",
        "        wins_val = np.sum(wins)\n",
        "        losses_val = np.sum(losses)\n",
        "        wins_avg = 0 if n_wins==0 else np.mean(wins)\n",
        "        #print(f'{t} n_wins: {n_wins} n_losses: {n_losses}')\n",
        "        losses_avg = 0 if n_losses==0 else np.mean(losses)\n",
        "        d = {'# trades':n_trades,'# wins':n_wins,'# losses':n_losses,\n",
        "             'wins total value':wins_val, 'wins avg value':wins_avg,\n",
        "             'losses total value':losses_val, 'losses avg value':losses_avg,}\n",
        "        perf_results[t] = d\n",
        "    return perf_results"
      ],
      "metadata": {
        "id": "QydaFexDX5BQ"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tuning hyperparameters using Optuna"
      ],
      "metadata": {
        "id": "c_AT1dsuZApj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_ddpg_params(trial:optuna.Trial):\n",
        "  # Size of the replay buffer\n",
        "  buffer_size = trial.suggest_categorical(\"buffer_size\", [int(1e4), int(1e5), int(1e6)])\n",
        "  learning_rate = trial.suggest_loguniform(\"learning_rate\", 1e-5, 1)\n",
        "  batch_size = trial.suggest_categorical(\"batch_size\", [32, 64, 128, 256, 512])\n",
        "  \n",
        "  return {\"buffer_size\": buffer_size,\n",
        "          \"learning_rate\":learning_rate,\n",
        "          \"batch_size\":batch_size}"
      ],
      "metadata": {
        "id": "fKGkKHV7Y9AA"
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Variables\n",
        "## Fixed\n",
        "tpm_hist = {}  # record tp metric values for trials\n",
        "tp_metric = 'avgwl'  # specified trade_param_metric: ratio avg value win/loss\n",
        "## Settable by User\n",
        "n_trials = 50  # number of HP optimization runs\n",
        "total_timesteps = 2000 # per HP optimization run\n",
        "## Logging callback params\n",
        "lc_threshold=1e-5\n",
        "lc_patience=15\n",
        "lc_trial_number=5"
      ],
      "metadata": {
        "id": "N6blMWpz-pR0"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OPTIONAL CODE FOR SAMPLING HYPERPARAMETERS\n",
        "\n",
        "Replace current call in function objective with\n",
        "\n",
        "hyperparameters = sample_ddpg_params_all(trial)"
      ],
      "metadata": {
        "id": "fHET-odKZShg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_ddpg_params_all(trial:optuna.Trial,\n",
        "                           # fixed values from previous study\n",
        "                           learning_rate=0.0103,\n",
        "                           batch_size=128,\n",
        "                           buffer_size=int(1e6)):\n",
        "\n",
        "    gamma = trial.suggest_categorical(\"gamma\", [0.94, 0.96, 0.98])\n",
        "    # Polyak coeff\n",
        "    tau = trial.suggest_categorical(\"tau\", [0.08, 0.1, 0.12])\n",
        "\n",
        "    train_freq = trial.suggest_categorical(\"train_freq\", [512,768,1024])\n",
        "    gradient_steps = train_freq\n",
        "    \n",
        "    noise_type = trial.suggest_categorical(\"noise_type\", [\"ornstein-uhlenbeck\", \"normal\", None])\n",
        "    noise_std = trial.suggest_categorical(\"noise_std\", [.1,.2,.3] )\n",
        "\n",
        "    # NOTE: Add \"verybig\" to net_arch when tuning HER (see TD3)\n",
        "    net_arch = trial.suggest_categorical(\"net_arch\", [\"small\", \"big\"])\n",
        "    # activation_fn = trial.suggest_categorical('activation_fn', [nn.Tanh, nn.ReLU, nn.ELU, nn.LeakyReLU])\n",
        "\n",
        "    net_arch = {\n",
        "        \"small\": [64, 64],\n",
        "        \"medium\": [256, 256],\n",
        "        \"big\": [512, 512],\n",
        "    }[net_arch]\n",
        "  \n",
        "    hyperparams = {\n",
        "        \"batch_size\": batch_size,\n",
        "        \"buffer_size\": buffer_size,\n",
        "        \"gamma\": gamma,\n",
        "        \"gradient_steps\": gradient_steps,\n",
        "        \"learning_rate\": learning_rate,\n",
        "        \"tau\": tau,\n",
        "        \"train_freq\": train_freq,\n",
        "        #\"noise_std\": noise_std,\n",
        "        #\"noise_type\": noise_type,\n",
        "        \n",
        "        \"policy_kwargs\": dict(net_arch=net_arch)\n",
        "    }\n",
        "    return hyperparams"
      ],
      "metadata": {
        "id": "htLdZHKTZKPJ"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_ddpg_params_all(trial:optuna.Trial,\n",
        "                           # fixed values from previous study\n",
        "                           learning_rate=0.0103,\n",
        "                           batch_size=128,\n",
        "                           buffer_size=int(1e6)):\n",
        "\n",
        "    gamma = trial.suggest_categorical(\"gamma\", [0.94, 0.96, 0.98])\n",
        "    # Polyak coeff\n",
        "    tau = trial.suggest_categorical(\"tau\", [0.08, 0.1, 0.12])\n",
        "\n",
        "    train_freq = trial.suggest_categorical(\"train_freq\", [512,768,1024])\n",
        "    gradient_steps = train_freq\n",
        "    \n",
        "    noise_type = trial.suggest_categorical(\"noise_type\", [\"ornstein-uhlenbeck\", \"normal\", None])\n",
        "    noise_std = trial.suggest_categorical(\"noise_std\", [.1,.2,.3] )\n",
        "\n",
        "    # NOTE: Add \"verybig\" to net_arch when tuning HER (see TD3)\n",
        "    net_arch = trial.suggest_categorical(\"net_arch\", [\"small\", \"big\"])\n",
        "    # activation_fn = trial.suggest_categorical('activation_fn', [nn.Tanh, nn.ReLU, nn.ELU, nn.LeakyReLU])\n",
        "\n",
        "    net_arch = {\n",
        "        \"small\": [64, 64],\n",
        "        \"medium\": [256, 256],\n",
        "        \"big\": [512, 512],\n",
        "    }[net_arch]\n",
        "  \n",
        "    hyperparams = {\n",
        "        \"batch_size\": batch_size,\n",
        "        \"buffer_size\": buffer_size,\n",
        "        \"gamma\": gamma,\n",
        "        \"gradient_steps\": gradient_steps,\n",
        "        \"learning_rate\": learning_rate,\n",
        "        \"tau\": tau,\n",
        "        \"train_freq\": train_freq,\n",
        "        #\"noise_std\": noise_std,\n",
        "        #\"noise_type\": noise_type,\n",
        "        \n",
        "        \"policy_kwargs\": dict(net_arch=net_arch)\n",
        "    }\n",
        "    return hyperparams"
      ],
      "metadata": {
        "id": "dJYdh-E4ZQKP"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Callbacks\n",
        "\n",
        "\n",
        "1. The callback will terminate if the improvement margin is below certain point\n",
        "2. It will terminate after certain number of trial_number are reached, not before that\n",
        "3. It will hold its patience to reach the threshold"
      ],
      "metadata": {
        "id": "mWEoJMPLZY4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LoggingCallback:\n",
        "    def __init__(self,threshold,trial_number,patience):\n",
        "      '''\n",
        "      threshold:int tolerance for increase in objective\n",
        "      trial_number: int Prune after minimum number of trials\n",
        "      patience: int patience for the threshold\n",
        "      '''\n",
        "      self.threshold = threshold\n",
        "      self.trial_number  = trial_number\n",
        "      self.patience = patience\n",
        "      print(f'Callback threshold {self.threshold}, \\\n",
        "            trial_number {self.trial_number}, \\\n",
        "            patience {self.patience}')\n",
        "      self.cb_list = [] #Trials list for which threshold is reached\n",
        "    def __call__(self,study:optuna.study, frozen_trial:optuna.Trial):\n",
        "      #Setting the best value in the current trial\n",
        "      study.set_user_attr(\"previous_best_value\", study.best_value)\n",
        "      \n",
        "      #Checking if the minimum number of trials have pass\n",
        "      if frozen_trial.number >self.trial_number:\n",
        "          previous_best_value = study.user_attrs.get(\"previous_best_value\",None)\n",
        "          #Checking if the previous and current objective values have the same sign\n",
        "          if previous_best_value * study.best_value >=0:\n",
        "              #Checking for the threshold condition\n",
        "              if abs(previous_best_value-study.best_value) < self.threshold: \n",
        "                  self.cb_list.append(frozen_trial.number)\n",
        "                  #If threshold is achieved for the patience amount of time\n",
        "                  if len(self.cb_list)>self.patience:\n",
        "                      print('The study stops now...')\n",
        "                      print('With number',frozen_trial.number ,'and value ',frozen_trial.value)\n",
        "                      print('The previous and current best values are {} and {} respectively'\n",
        "                              .format(previous_best_value, study.best_value))\n",
        "                      study.stop()"
      ],
      "metadata": {
        "id": "He7GTZZUZWP8"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output\n",
        "import sys   \n",
        "\n",
        "os.makedirs(\"models\",exist_ok=True)\n",
        "\n",
        "def objective(trial:optuna.Trial):\n",
        "  #Trial will suggest a set of hyperparamters from the specified range\n",
        "\n",
        "  # Optional to optimize larger set of parameters\n",
        "  # hyperparameters = sample_ddpg_params_all(trial)\n",
        "  \n",
        "  # Optimize buffer size, batch size, learning rate\n",
        "  hyperparameters = sample_ddpg_params_all(trial)\n",
        "  print(f'Hyperparameters from objective: {hyperparameters.keys()}')\n",
        "  policy_kwargs = None  # default\n",
        "  if 'policy_kwargs' in hyperparameters.keys():\n",
        "    policy_kwargs = hyperparameters['policy_kwargs']\n",
        "    del hyperparameters['policy_kwargs']\n",
        "    print(f'Policy keyword arguments {policy_kwargs}')\n",
        "  model_ddpg = agent.get_model(\"ddpg\",\n",
        "                               policy_kwargs = policy_kwargs,\n",
        "                               model_kwargs = hyperparameters )\n",
        "  \n",
        "  #You can increase it for better comparison\n",
        "  trained_ddpg = agent.train_model(model=model_ddpg,\n",
        "                                   tb_log_name=\"ddpg\",\n",
        "                                   total_timesteps=total_timesteps)\n",
        "  trained_ddpg.save('models/ddpg_{}.pth'.format(trial.number))\n",
        "  clear_output(wait=True)\n",
        "  \n",
        "  #For the given hyperparamters, determine the account value in the trading period\n",
        "  df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym)\n",
        " \n",
        "  # Calculate trade performance metric\n",
        "  # Currently ratio of average win and loss market values\n",
        "  tpm = calc_trade_perf_metric(df_actions,init_trade_data,tp_metric)\n",
        "  return tpm\n",
        "\n",
        "#Create a study object and specify the direction as 'maximize'\n",
        "#As you want to maximize sharpe\n",
        "#Pruner stops not promising iterations\n",
        "#Use a pruner, else you will get error related to divergence of model\n",
        "#You can also use Multivariate samplere\n",
        "#sampler = optuna.samplers.TPESampler(multivarite=True,seed=42)\n",
        "sampler = optuna.samplers.TPESampler()\n",
        "\n",
        "study = optuna.create_study(study_name=\"ddpg_study\",direction='maximize',\n",
        "                            sampler = sampler, pruner=optuna.pruners.HyperbandPruner())\n",
        "\n",
        "logging_callback = LoggingCallback(threshold=lc_threshold,\n",
        "                                   patience=lc_patience,\n",
        "                                   trial_number=lc_trial_number)\n",
        "#You can increase the n_trials for a better search space scanning\n",
        "study.optimize(objective, n_trials=n_trials,catch=(ValueError,),callbacks=[logging_callback])"
      ],
      "metadata": {
        "id": "b6EiwiiIZpRU",
        "outputId": "399f98ea-eb04-4155-8201-d5d49b2d0ae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-05-01 16:05:16,732]\u001b[0m A new study created in memory with name: ddpg_study\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Callback threshold 1e-05,             trial_number 5,             patience 15\n",
            "Hyperparameters from objective: dict_keys(['batch_size', 'buffer_size', 'gamma', 'gradient_steps', 'learning_rate', 'tau', 'train_freq', 'policy_kwargs'])\n",
            "Policy keyword arguments {'net_arch': [64, 64]}\n",
            "{'batch_size': 128, 'buffer_size': 1000000, 'gamma': 0.96, 'gradient_steps': 768, 'learning_rate': 0.0103, 'tau': 0.1, 'train_freq': 768}\n",
            "Using cuda device\n",
            "Current Price\n",
            "118.83999633789062\n",
            "Current Price 118.83999633789062    avg_price   17371.190326690674 Profit -17252.350330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "108.76000213623047\n",
            "Current Price 108.76000213623047    avg_price   17371.190326690674 Profit -17262.430324554443\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "110.16999816894531\n",
            "Current Price 110.16999816894531    avg_price   17371.190326690674 Profit -17261.02032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.66999816894531\n",
            "Current Price 109.66999816894531    avg_price   17371.190326690674 Profit -17261.52032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "111.81999969482422\n",
            "Current Price 111.81999969482422    avg_price   17371.190326690674 Profit -17259.37032699585\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "112.56999969482422\n",
            "Current Price 112.56999969482422    avg_price   17371.190326690674 Profit -17258.62032699585\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "112.93000030517578\n",
            "Current Price 112.93000030517578    avg_price   17371.190326690674 Profit -17258.260326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.27999877929688\n",
            "Current Price 115.27999877929688    avg_price   17371.190326690674 Profit -17255.910327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "114.63999938964844\n",
            "Current Price 114.63999938964844    avg_price   17371.190326690674 Profit -17256.550327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "114.8499984741211\n",
            "Current Price 114.8499984741211    avg_price   17371.190326690674 Profit -17256.340328216553\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.4000015258789\n",
            "Current Price 115.4000015258789    avg_price   17371.190326690674 Profit -17255.790325164795\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.04000091552734\n",
            "Current Price 116.04000091552734    avg_price   17371.190326690674 Profit -17255.150325775146\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.1500015258789\n",
            "Current Price 116.1500015258789    avg_price   17371.190326690674 Profit -17255.040325164795\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.5\n",
            "Current Price 115.5    avg_price   17371.190326690674 Profit -17255.690326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "117.88999938964844\n",
            "Current Price 117.88999938964844    avg_price   17371.190326690674 Profit -17253.300327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.9800033569336\n",
            "Current Price 116.9800033569336    avg_price   17371.190326690674 Profit -17254.21032333374\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.23999786376953\n",
            "Current Price 115.23999786376953    avg_price   17371.190326690674 Profit -17255.950328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.5199966430664\n",
            "Current Price 116.5199966430664    avg_price   17371.190326690674 Profit -17254.670330047607\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.66000366210938\n",
            "Current Price 116.66000366210938    avg_price   17371.190326690674 Profit -17254.530323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "117.12000274658203\n",
            "Current Price 117.12000274658203    avg_price   17371.190326690674 Profit -17254.07032394409\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "113.88999938964844\n",
            "Current Price 113.88999938964844    avg_price   17371.190326690674 Profit -17257.300327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.51000213623047\n",
            "Current Price 116.51000213623047    avg_price   17371.190326690674 Profit -17254.680324554443\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.08000183105469\n",
            "Current Price 120.08000183105469    avg_price   17371.190326690674 Profit -17251.11032485962\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "119.72000122070312\n",
            "Current Price 119.72000122070312    avg_price   17371.190326690674 Profit -17251.47032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "119.88999938964844\n",
            "Current Price 119.88999938964844    avg_price   17371.190326690674 Profit -17251.300327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "118.87999725341797\n",
            "Current Price 118.87999725341797    avg_price   17371.190326690674 Profit -17252.310329437256\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.05999755859375\n",
            "Current Price 120.05999755859375    avg_price   17371.190326690674 Profit -17251.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "118.83999633789062\n",
            "Current Price 118.83999633789062    avg_price   17371.190326690674 Profit -17252.350330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "118.55999755859375\n",
            "Current Price 118.55999755859375    avg_price   17371.190326690674 Profit -17252.63032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.51000213623047\n",
            "Current Price 120.51000213623047    avg_price   17371.190326690674 Profit -17250.680324554443\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "122.98999786376953\n",
            "Current Price 122.98999786376953    avg_price   17371.190326690674 Profit -17248.200328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "122.2699966430664\n",
            "Current Price 122.2699966430664    avg_price   17371.190326690674 Profit -17248.920330047607\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "123.91000366210938\n",
            "Current Price 123.91000366210938    avg_price   17371.190326690674 Profit -17247.280323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "122.43000030517578\n",
            "Current Price 122.43000030517578    avg_price   17371.190326690674 Profit -17248.760326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "119.48999786376953\n",
            "Current Price 119.48999786376953    avg_price   17371.190326690674 Profit -17251.700328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.44000244140625\n",
            "Current Price 116.44000244140625    avg_price   17371.190326690674 Profit -17254.750324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.66000366210938\n",
            "Current Price 109.66000366210938    avg_price   17371.190326690674 Profit -17261.530323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.11000061035156\n",
            "Current Price 109.11000061035156    avg_price   17371.190326690674 Profit -17262.080326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "107.83999633789062\n",
            "Current Price 107.83999633789062    avg_price   17371.190326690674 Profit -17263.350330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "107.98999786376953\n",
            "Current Price 107.98999786376953    avg_price   17371.190326690674 Profit -17263.200328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "112.86000061035156\n",
            "Current Price 112.86000061035156    avg_price   17371.190326690674 Profit -17258.330326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.76000213623047\n",
            "Current Price 109.76000213623047    avg_price   17371.190326690674 Profit -17261.430324554443\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.30000305175781\n",
            "Current Price 115.30000305175781    avg_price   17371.190326690674 Profit -17255.890323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "112.18000030517578\n",
            "Current Price 112.18000030517578    avg_price   17371.190326690674 Profit -17259.010326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "110.88999938964844\n",
            "Current Price 110.88999938964844    avg_price   17371.190326690674 Profit -17260.300327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "101.62999725341797\n",
            "Current Price 101.62999725341797    avg_price   17371.190326690674 Profit -17269.560329437256\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.73999786376953\n",
            "Current Price 109.73999786376953    avg_price   17371.190326690674 Profit -17261.450328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "103.9000015258789\n",
            "Current Price 103.9000015258789    avg_price   17371.190326690674 Profit -17267.290325164795\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "96.95999908447266\n",
            "Current Price 96.95999908447266    avg_price   17371.190326690674 Profit -17274.2303276062\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "110.1500015258789\n",
            "Current Price 110.1500015258789    avg_price   17371.190326690674 Profit -17261.040325164795\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "92.72000122070312\n",
            "Current Price 92.72000122070312    avg_price   17371.190326690674 Profit -17278.47032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "97.87000274658203\n",
            "Current Price 97.87000274658203    avg_price   17371.190326690674 Profit -17273.32032394409\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "91.45999908447266\n",
            "Current Price 91.45999908447266    avg_price   17371.190326690674 Profit -17279.7303276062\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "93.69000244140625\n",
            "Current Price 93.69000244140625    avg_price   17371.190326690674 Profit -17277.500324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "86.68000030517578\n",
            "Current Price 86.68000030517578    avg_price   17371.190326690674 Profit -17284.510326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "85.26000213623047\n",
            "Current Price 85.26000213623047    avg_price   17371.190326690674 Profit -17285.930324554443\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "97.29000091552734\n",
            "Current Price 97.29000091552734    avg_price   17371.190326690674 Profit -17273.900325775146\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "95.70999908447266\n",
            "Current Price 95.70999908447266    avg_price   17371.190326690674 Profit -17275.4803276062\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "100.62999725341797\n",
            "Current Price 100.62999725341797    avg_price   17371.190326690674 Profit -17270.560329437256\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "93.4800033569336\n",
            "Current Price 93.4800033569336    avg_price   17371.190326690674 Profit -17277.71032333374\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "97.02999877929688\n",
            "Current Price 97.02999877929688    avg_price   17371.190326690674 Profit -17274.160327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "95.73999786376953\n",
            "Current Price 95.73999786376953    avg_price   17371.190326690674 Profit -17275.450328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "91.37999725341797\n",
            "Current Price 91.37999725341797    avg_price   17371.190326690674 Profit -17279.810329437256\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "93.5199966430664\n",
            "Current Price 93.5199966430664    avg_price   17371.190326690674 Profit -17277.670330047607\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "92.38999938964844\n",
            "Current Price 92.38999938964844    avg_price   17371.190326690674 Profit -17278.800327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "101.66999816894531\n",
            "Current Price 101.66999816894531    avg_price   17371.190326690674 Profit -17269.52032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "100.27999877929688\n",
            "Current Price 100.27999877929688    avg_price   17371.190326690674 Profit -17270.910327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "105.05999755859375\n",
            "Current Price 105.05999755859375    avg_price   17371.190326690674 Profit -17266.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "105.83999633789062\n",
            "Current Price 105.83999633789062    avg_price   17371.190326690674 Profit -17265.350330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "105.9800033569336\n",
            "Current Price 105.9800033569336    avg_price   17371.190326690674 Profit -17265.21032333374\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "109.78500366210938\n",
            "Current Price 109.78500366210938    avg_price   17371.190326690674 Profit -17261.405323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "107.94999694824219\n",
            "Current Price 107.94999694824219    avg_price   17371.190326690674 Profit -17263.24032974243\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "108.30999755859375\n",
            "Current Price 108.30999755859375    avg_price   17371.190326690674 Profit -17262.88032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "111.8499984741211\n",
            "Current Price 111.8499984741211    avg_price   17371.190326690674 Profit -17259.340328216553\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "112.16999816894531\n",
            "Current Price 112.16999816894531    avg_price   17371.190326690674 Profit -17259.02032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "107.54000091552734\n",
            "Current Price 107.54000091552734    avg_price   17371.190326690674 Profit -17263.650325775146\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.19000244140625\n",
            "Current Price 115.19000244140625    avg_price   17371.190326690674 Profit -17256.000324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "115.23999786376953\n",
            "Current Price 115.23999786376953    avg_price   17371.190326690674 Profit -17255.950328826904\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.18000030517578\n",
            "Current Price 120.18000030517578    avg_price   17371.190326690674 Profit -17251.010326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.52999877929688\n",
            "Current Price 120.52999877929688    avg_price   17371.190326690674 Profit -17250.660327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "116.13999938964844\n",
            "Current Price 116.13999938964844    avg_price   17371.190326690674 Profit -17255.050327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "123.58000183105469\n",
            "Current Price 123.58000183105469    avg_price   17371.190326690674 Profit -17247.61032485962\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "123.0\n",
            "Current Price 123.0    avg_price   17371.190326690674 Profit -17248.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "120.61000061035156\n",
            "Current Price 120.61000061035156    avg_price   17371.190326690674 Profit -17250.580326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "123.66000366210938\n",
            "Current Price 123.66000366210938    avg_price   17371.190326690674 Profit -17247.530323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "125.43000030517578\n",
            "Current Price 125.43000030517578    avg_price   17371.190326690674 Profit -17245.760326385498\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "128.30999755859375\n",
            "Current Price 128.30999755859375    avg_price   17371.190326690674 Profit -17242.88032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "146.2899932861328\n",
            "Current Price 146.2899932861328    avg_price   17371.190326690674 Profit -17224.90033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "144.9600067138672\n",
            "Current Price 144.9600067138672    avg_price   17371.190326690674 Profit -17226.230319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "143.9600067138672\n",
            "Current Price 143.9600067138672    avg_price   17371.190326690674 Profit -17227.230319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "141.02999877929688\n",
            "Current Price 141.02999877929688    avg_price   17371.190326690674 Profit -17230.160327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "143.72999572753906\n",
            "Current Price 143.72999572753906    avg_price   17371.190326690674 Profit -17227.460330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "144.41000366210938\n",
            "Current Price 144.41000366210938    avg_price   17371.190326690674 Profit -17226.780323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "145.50999450683594\n",
            "Current Price 145.50999450683594    avg_price   17371.190326690674 Profit -17225.680332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "146.24000549316406\n",
            "Current Price 146.24000549316406    avg_price   17371.190326690674 Profit -17224.95032119751\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "145.38999938964844\n",
            "Current Price 145.38999938964844    avg_price   17371.190326690674 Profit -17225.800327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "150.94000244140625\n",
            "Current Price 150.94000244140625    avg_price   17371.190326690674 Profit -17220.250324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "148.22000122070312\n",
            "Current Price 148.22000122070312    avg_price   17371.190326690674 Profit -17222.97032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "150.86000061035156\n",
            "Current Price 150.86000061035156    avg_price   17371.190326690674 Profit -17220.330326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "147.0\n",
            "Current Price 147.0    avg_price   17371.190326690674 Profit -17224.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "145.9600067138672\n",
            "Current Price 145.9600067138672    avg_price   17371.190326690674 Profit -17225.230319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "148.7899932861328\n",
            "Current Price 148.7899932861328    avg_price   17371.190326690674 Profit -17222.40033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "155.00999450683594\n",
            "Current Price 155.00999450683594    avg_price   17371.190326690674 Profit -17216.180332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "154.52999877929688\n",
            "Current Price 154.52999877929688    avg_price   17371.190326690674 Profit -17216.660327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "157.49000549316406\n",
            "Current Price 157.49000549316406    avg_price   17371.190326690674 Profit -17213.70032119751\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "156.77999877929688\n",
            "Current Price 156.77999877929688    avg_price   17371.190326690674 Profit -17214.410327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "154.60000610351562\n",
            "Current Price 154.60000610351562    avg_price   17371.190326690674 Profit -17216.59032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "156.0\n",
            "Current Price 156.0    avg_price   17371.190326690674 Profit -17215.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "156.08999633789062\n",
            "Current Price 156.08999633789062    avg_price   17371.190326690674 Profit -17215.100330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "156.86000061035156\n",
            "Current Price 156.86000061035156    avg_price   17371.190326690674 Profit -17214.330326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "159.91000366210938\n",
            "Current Price 159.91000366210938    avg_price   17371.190326690674 Profit -17211.280323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "153.0399932861328\n",
            "Current Price 153.0399932861328    avg_price   17371.190326690674 Profit -17218.15033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "155.25999450683594\n",
            "Current Price 155.25999450683594    avg_price   17371.190326690674 Profit -17215.930332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "157.5500030517578\n",
            "Current Price 157.5500030517578    avg_price   17371.190326690674 Profit -17213.640323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "161.22000122070312\n",
            "Current Price 161.22000122070312    avg_price   17371.190326690674 Profit -17209.97032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "163.8300018310547\n",
            "Current Price 163.8300018310547    avg_price   17371.190326690674 Profit -17207.36032485962\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "168.0500030517578\n",
            "Current Price 168.0500030517578    avg_price   17371.190326690674 Profit -17203.140323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "164.36000061035156\n",
            "Current Price 164.36000061035156    avg_price   17371.190326690674 Profit -17206.830326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "170.25999450683594\n",
            "Current Price 170.25999450683594    avg_price   17371.190326690674 Profit -17200.930332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "172.7899932861328\n",
            "Current Price 172.7899932861328    avg_price   17371.190326690674 Profit -17198.40033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "168.0\n",
            "Current Price 168.0    avg_price   17371.190326690674 Profit -17203.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "172.5\n",
            "Current Price 172.5    avg_price   17371.190326690674 Profit -17198.690326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "170.8699951171875\n",
            "Current Price 170.8699951171875    avg_price   17371.190326690674 Profit -17200.320331573486\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "168.35000610351562\n",
            "Current Price 168.35000610351562    avg_price   17371.190326690674 Profit -17202.84032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "174.22999572753906\n",
            "Current Price 174.22999572753906    avg_price   17371.190326690674 Profit -17196.960330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "177.42999267578125\n",
            "Current Price 177.42999267578125    avg_price   17371.190326690674 Profit -17193.760334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "177.2100067138672\n",
            "Current Price 177.2100067138672    avg_price   17371.190326690674 Profit -17193.980319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "176.17999267578125\n",
            "Current Price 176.17999267578125    avg_price   17371.190326690674 Profit -17195.010334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "177.27999877929688\n",
            "Current Price 177.27999877929688    avg_price   17371.190326690674 Profit -17193.910327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "181.57000732421875\n",
            "Current Price 181.57000732421875    avg_price   17371.190326690674 Profit -17189.620319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "183.22999572753906\n",
            "Current Price 183.22999572753906    avg_price   17371.190326690674 Profit -17187.960330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "178.39999389648438\n",
            "Current Price 178.39999389648438    avg_price   17371.190326690674 Profit -17192.79033279419\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "171.50999450683594\n",
            "Current Price 171.50999450683594    avg_price   17371.190326690674 Profit -17199.680332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "172.3000030517578\n",
            "Current Price 172.3000030517578    avg_price   17371.190326690674 Profit -17198.890323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "172.05999755859375\n",
            "Current Price 172.05999755859375    avg_price   17371.190326690674 Profit -17199.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "169.80999755859375\n",
            "Current Price 169.80999755859375    avg_price   17371.190326690674 Profit -17201.38032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "173.9199981689453\n",
            "Current Price 173.9199981689453    avg_price   17371.190326690674 Profit -17197.27032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "178.82000732421875\n",
            "Current Price 178.82000732421875    avg_price   17371.190326690674 Profit -17192.370319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "175.5800018310547\n",
            "Current Price 175.5800018310547    avg_price   17371.190326690674 Profit -17195.61032485962\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "178.4199981689453\n",
            "Current Price 178.4199981689453    avg_price   17371.190326690674 Profit -17192.77032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "173.86000061035156\n",
            "Current Price 173.86000061035156    avg_price   17371.190326690674 Profit -17197.330326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "172.55999755859375\n",
            "Current Price 172.55999755859375    avg_price   17371.190326690674 Profit -17198.63032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "177.97999572753906\n",
            "Current Price 177.97999572753906    avg_price   17371.190326690674 Profit -17193.210330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "176.27000427246094\n",
            "Current Price 176.27000427246094    avg_price   17371.190326690674 Profit -17194.920322418213\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "184.60000610351562\n",
            "Current Price 184.60000610351562    avg_price   17371.190326690674 Profit -17186.59032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.50999450683594\n",
            "Current Price 192.50999450683594    avg_price   17371.190326690674 Profit -17178.680332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "196.07000732421875\n",
            "Current Price 196.07000732421875    avg_price   17371.190326690674 Profit -17175.120319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "197.07000732421875\n",
            "Current Price 197.07000732421875    avg_price   17371.190326690674 Profit -17174.120319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "197.3300018310547\n",
            "Current Price 197.3300018310547    avg_price   17371.190326690674 Profit -17173.86032485962\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "202.91000366210938\n",
            "Current Price 202.91000366210938    avg_price   17371.190326690674 Profit -17168.280323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.08999633789062\n",
            "Current Price 204.08999633789062    avg_price   17371.190326690674 Profit -17167.100330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "198.6300048828125\n",
            "Current Price 198.6300048828125    avg_price   17371.190326690674 Profit -17172.56032180786\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "193.32000732421875\n",
            "Current Price 193.32000732421875    avg_price   17371.190326690674 Profit -17177.870319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "189.02999877929688\n",
            "Current Price 189.02999877929688    avg_price   17371.190326690674 Profit -17182.160327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.32000732421875\n",
            "Current Price 191.32000732421875    avg_price   17371.190326690674 Profit -17179.870319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "193.07000732421875\n",
            "Current Price 193.07000732421875    avg_price   17371.190326690674 Profit -17178.120319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.4600067138672\n",
            "Current Price 191.4600067138672    avg_price   17371.190326690674 Profit -17179.730319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "196.2899932861328\n",
            "Current Price 196.2899932861328    avg_price   17371.190326690674 Profit -17174.90033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "194.57000732421875\n",
            "Current Price 194.57000732421875    avg_price   17371.190326690674 Profit -17176.620319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.44000244140625\n",
            "Current Price 192.44000244140625    avg_price   17371.190326690674 Profit -17178.750324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "198.17999267578125\n",
            "Current Price 198.17999267578125    avg_price   17371.190326690674 Profit -17173.010334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "196.7899932861328\n",
            "Current Price 196.7899932861328    avg_price   17371.190326690674 Profit -17174.40033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "198.8800048828125\n",
            "Current Price 198.8800048828125    avg_price   17371.190326690674 Profit -17172.31032180786\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "201.6699981689453\n",
            "Current Price 201.6699981689453    avg_price   17371.190326690674 Profit -17169.52032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "203.47999572753906\n",
            "Current Price 203.47999572753906    avg_price   17371.190326690674 Profit -17167.710330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.33999633789062\n",
            "Current Price 204.33999633789062    avg_price   17371.190326690674 Profit -17166.850330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.47999572753906\n",
            "Current Price 204.47999572753906    avg_price   17371.190326690674 Profit -17166.710330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.13999938964844\n",
            "Current Price 204.13999938964844    avg_price   17371.190326690674 Profit -17167.050327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "208.92999267578125\n",
            "Current Price 208.92999267578125    avg_price   17371.190326690674 Profit -17162.260334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "210.82000732421875\n",
            "Current Price 210.82000732421875    avg_price   17371.190326690674 Profit -17160.370319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.99000549316406\n",
            "Current Price 204.99000549316406    avg_price   17371.190326690674 Profit -17166.20032119751\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.83999633789062\n",
            "Current Price 191.83999633789062    avg_price   17371.190326690674 Profit -17179.350330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "185.9499969482422\n",
            "Current Price 185.9499969482422    avg_price   17371.190326690674 Profit -17185.24032974243\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "194.60000610351562\n",
            "Current Price 194.60000610351562    avg_price   17371.190326690674 Profit -17176.59032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "190.08999633789062\n",
            "Current Price 190.08999633789062    avg_price   17371.190326690674 Profit -17181.100330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "184.0\n",
            "Current Price 184.0    avg_price   17371.190326690674 Profit -17187.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "186.9600067138672\n",
            "Current Price 186.9600067138672    avg_price   17371.190326690674 Profit -17184.230319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "186.02000427246094\n",
            "Current Price 186.02000427246094    avg_price   17371.190326690674 Profit -17185.170322418213\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "180.91000366210938\n",
            "Current Price 180.91000366210938    avg_price   17371.190326690674 Profit -17190.280323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "175.7899932861328\n",
            "Current Price 175.7899932861328    avg_price   17371.190326690674 Profit -17195.40033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "176.07000732421875\n",
            "Current Price 176.07000732421875    avg_price   17371.190326690674 Profit -17195.120319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "183.2100067138672\n",
            "Current Price 183.2100067138672    avg_price   17371.190326690674 Profit -17187.980319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "187.77999877929688\n",
            "Current Price 187.77999877929688    avg_price   17371.190326690674 Profit -17183.410327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "181.61000061035156\n",
            "Current Price 181.61000061035156    avg_price   17371.190326690674 Profit -17189.580326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "182.19000244140625\n",
            "Current Price 182.19000244140625    avg_price   17371.190326690674 Profit -17189.000324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "187.25\n",
            "Current Price 187.25    avg_price   17371.190326690674 Profit -17183.940326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.02000427246094\n",
            "Current Price 192.02000427246094    avg_price   17371.190326690674 Profit -17179.170322418213\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "194.5\n",
            "Current Price 194.5    avg_price   17371.190326690674 Profit -17176.690326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "197.02999877929688\n",
            "Current Price 197.02999877929688    avg_price   17371.190326690674 Profit -17174.160327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "196.94000244140625\n",
            "Current Price 196.94000244140625    avg_price   17371.190326690674 Profit -17174.250324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.91000366210938\n",
            "Current Price 191.91000366210938    avg_price   17371.190326690674 Profit -17179.280323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "196.85000610351562\n",
            "Current Price 196.85000610351562    avg_price   17371.190326690674 Profit -17174.34032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.66000366210938\n",
            "Current Price 191.66000366210938    avg_price   17371.190326690674 Profit -17179.530323028564\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "194.61000061035156\n",
            "Current Price 194.61000061035156    avg_price   17371.190326690674 Profit -17176.580326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "193.05999755859375\n",
            "Current Price 193.05999755859375    avg_price   17371.190326690674 Profit -17178.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "197.27000427246094\n",
            "Current Price 197.27000427246094    avg_price   17371.190326690674 Profit -17173.920322418213\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "201.50999450683594\n",
            "Current Price 201.50999450683594    avg_price   17371.190326690674 Profit -17169.680332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "207.74000549316406\n",
            "Current Price 207.74000549316406    avg_price   17371.190326690674 Profit -17163.45032119751\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "203.60000610351562\n",
            "Current Price 203.60000610351562    avg_price   17371.190326690674 Profit -17167.59032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "203.13999938964844\n",
            "Current Price 203.13999938964844    avg_price   17371.190326690674 Profit -17168.050327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.4499969482422\n",
            "Current Price 204.4499969482422    avg_price   17371.190326690674 Profit -17166.74032974243\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "200.05999755859375\n",
            "Current Price 200.05999755859375    avg_price   17371.190326690674 Profit -17171.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "201.9600067138672\n",
            "Current Price 201.9600067138672    avg_price   17371.190326690674 Profit -17169.230319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "213.07000732421875\n",
            "Current Price 213.07000732421875    avg_price   17371.190326690674 Profit -17158.120319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "203.92999267578125\n",
            "Current Price 203.92999267578125    avg_price   17371.190326690674 Profit -17167.260334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "203.0399932861328\n",
            "Current Price 203.0399932861328    avg_price   17371.190326690674 Profit -17168.15033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "197.22000122070312\n",
            "Current Price 197.22000122070312    avg_price   17371.190326690674 Profit -17173.97032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "200.42999267578125\n",
            "Current Price 200.42999267578125    avg_price   17371.190326690674 Profit -17170.760334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.30999755859375\n",
            "Current Price 192.30999755859375    avg_price   17371.190326690674 Profit -17178.88032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "195.0399932861328\n",
            "Current Price 195.0399932861328    avg_price   17371.190326690674 Profit -17176.15033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "186.1300048828125\n",
            "Current Price 186.1300048828125    avg_price   17371.190326690674 Profit -17185.06032180786\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "187.75999450683594\n",
            "Current Price 187.75999450683594    avg_price   17371.190326690674 Profit -17183.430332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "179.80999755859375\n",
            "Current Price 179.80999755859375    avg_price   17371.190326690674 Profit -17191.38032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "194.2899932861328\n",
            "Current Price 194.2899932861328    avg_price   17371.190326690674 Profit -17176.90033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "204.55999755859375\n",
            "Current Price 204.55999755859375    avg_price   17371.190326690674 Profit -17166.63032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "202.72999572753906\n",
            "Current Price 202.72999572753906    avg_price   17371.190326690674 Profit -17168.460330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "184.72000122070312\n",
            "Current Price 184.72000122070312    avg_price   17371.190326690674 Profit -17186.47032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[33m[W 2023-05-01 16:05:18,165]\u001b[0m Trial 0 failed with parameters: {'gamma': 0.96, 'tau': 0.1, 'train_freq': 768, 'noise_type': 'ornstein-uhlenbeck', 'noise_std': 0.2, 'net_arch': 'small'} because of the following error: KeyboardInterrupt().\u001b[0m\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 200, in _run_trial\n",
            "    value_or_values = func(trial)\n",
            "  File \"<ipython-input-178-66f3543408ef>\", line 25, in objective\n",
            "    trained_ddpg = agent.train_model(model=model_ddpg,\n",
            "  File \"/usr/local/lib/python3.10/site-packages/finrl/agents/stablebaselines3/models.py\", line 103, in train_model\n",
            "    model = model.learn(\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/ddpg/ddpg.py\", line 123, in learn\n",
            "    return super().learn(\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/td3/td3.py\", line 216, in learn\n",
            "    return super().learn(\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/common/off_policy_algorithm.py\", line 311, in learn\n",
            "    rollout = self.collect_rollouts(\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/common/off_policy_algorithm.py\", line 543, in collect_rollouts\n",
            "    new_obs, rewards, dones, infos = env.step(actions)\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/base_vec_env.py\", line 163, in step\n",
            "    return self.step_wait()\n",
            "  File \"/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\", line 54, in step_wait\n",
            "    obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n",
            "  File \"<ipython-input-164-e53dd8899486>\", line 339, in step\n",
            "    self.total_stockss, out=np.zeros_like(self.total_price),\n",
            "  File \"<__array_function__ internals>\", line 177, in zeros_like\n",
            "KeyboardInterrupt\n",
            "\u001b[33m[W 2023-05-01 16:05:18,167]\u001b[0m Trial 0 failed with value None.\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Price\n",
            "183.36000061035156\n",
            "Current Price 183.36000061035156    avg_price   17371.190326690674 Profit -17187.830326080322\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.33999633789062\n",
            "Current Price 192.33999633789062    avg_price   17371.190326690674 Profit -17178.850330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "189.10000610351562\n",
            "Current Price 189.10000610351562    avg_price   17371.190326690674 Profit -17182.09032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "188.6199951171875\n",
            "Current Price 188.6199951171875    avg_price   17371.190326690674 Profit -17182.570331573486\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "191.94000244140625\n",
            "Current Price 191.94000244140625    avg_price   17371.190326690674 Profit -17179.250324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.3000030517578\n",
            "Current Price 192.3000030517578    avg_price   17371.190326690674 Profit -17178.890323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "190.1199951171875\n",
            "Current Price 190.1199951171875    avg_price   17371.190326690674 Profit -17181.070331573486\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "190.89999389648438\n",
            "Current Price 190.89999389648438    avg_price   17371.190326690674 Profit -17180.29033279419\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "192.6699981689453\n",
            "Current Price 192.6699981689453    avg_price   17371.190326690674 Profit -17178.52032852173\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "200.82000732421875\n",
            "Current Price 200.82000732421875    avg_price   17371.190326690674 Profit -17170.370319366455\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "206.0\n",
            "Current Price 206.0    avg_price   17371.190326690674 Profit -17165.190326690674\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "214.4600067138672\n",
            "Current Price 214.4600067138672    avg_price   17371.190326690674 Profit -17156.730319976807\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "211.38999938964844\n",
            "Current Price 211.38999938964844    avg_price   17371.190326690674 Profit -17159.800327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "214.1199951171875\n",
            "Current Price 214.1199951171875    avg_price   17371.190326690674 Profit -17157.070331573486\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "216.5399932861328\n",
            "Current Price 216.5399932861328    avg_price   17371.190326690674 Profit -17154.65033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "212.55999755859375\n",
            "Current Price 212.55999755859375    avg_price   17371.190326690674 Profit -17158.63032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "214.5399932861328\n",
            "Current Price 214.5399932861328    avg_price   17371.190326690674 Profit -17156.65033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "217.77000427246094\n",
            "Current Price 217.77000427246094    avg_price   17371.190326690674 Profit -17153.420322418213\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "217.92999267578125\n",
            "Current Price 217.92999267578125    avg_price   17371.190326690674 Profit -17153.260334014893\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "216.94000244140625\n",
            "Current Price 216.94000244140625    avg_price   17371.190326690674 Profit -17154.250324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "210.8000030517578\n",
            "Current Price 210.8000030517578    avg_price   17371.190326690674 Profit -17160.390323638916\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "215.33999633789062\n",
            "Current Price 215.33999633789062    avg_price   17371.190326690674 Profit -17155.850330352783\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "214.05999755859375\n",
            "Current Price 214.05999755859375    avg_price   17371.190326690674 Profit -17157.13032913208\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "220.7899932861328\n",
            "Current Price 220.7899932861328    avg_price   17371.190326690674 Profit -17150.40033340454\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "221.60000610351562\n",
            "Current Price 221.60000610351562    avg_price   17371.190326690674 Profit -17149.59032058716\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "230.1999969482422\n",
            "Current Price 230.1999969482422    avg_price   17371.190326690674 Profit -17140.99032974243\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "235.50999450683594\n",
            "Current Price 235.50999450683594    avg_price   17371.190326690674 Profit -17135.680332183838\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "236.4499969482422\n",
            "Current Price 236.4499969482422    avg_price   17371.190326690674 Profit -17134.74032974243\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "237.72000122070312\n",
            "Current Price 237.72000122070312    avg_price   17371.190326690674 Profit -17133.47032546997\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "243.49000549316406\n",
            "Current Price 243.49000549316406    avg_price   17371.190326690674 Profit -17127.70032119751\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "239.44000244140625\n",
            "Current Price 239.44000244140625    avg_price   17371.190326690674 Profit -17131.750324249268\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "238.63999938964844\n",
            "Current Price 238.63999938964844    avg_price   17371.190326690674 Profit -17132.550327301025\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "235.72999572753906\n",
            "Current Price 235.72999572753906    avg_price   17371.190326690674 Profit -17135.460330963135\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "231.02999877929688\n",
            "Current Price 231.02999877929688    avg_price   17371.190326690674 Profit -17140.160327911377\n",
            "Total Stocks\n",
            "[100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100\n",
            " 100 100 100 100 100 100 100 100 100 100 100]\n",
            "Avg Price\n",
            "[17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669 17371.19032669 17371.19032669 17371.19032669\n",
            " 17371.19032669]\n",
            "Current Price\n",
            "231.50999450683594\n",
            "Current Price 231.50999450683594    avg_price   17371.190326690674 Profit -17139.680332183838\n",
            "Total Stocks\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-178-66f3543408ef>\u001b[0m in \u001b[0;36m<cell line: 56>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     54\u001b[0m                                    trial_number=lc_trial_number)\n\u001b[1;32m     55\u001b[0m \u001b[0;31m#You can increase the n_trials for a better search space scanning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_trials\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlogging_callback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    423\u001b[0m         \"\"\"\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         _optimize(\n\u001b[0m\u001b[1;32m    426\u001b[0m             \u001b[0mstudy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             _optimize_sequential(\n\u001b[0m\u001b[1;32m     67\u001b[0m                 \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0;31m# The following line mitigates memory problems that can be occurred in some\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     ):\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfunc_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrozen_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mget_heartbeat_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m             \u001b[0mvalue_or_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m             \u001b[0;31m# TODO(mamu): Handle multi-objective cases.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-178-66f3543408ef>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m   \u001b[0;31m#You can increase it for better comparison\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m   trained_ddpg = agent.train_model(model=model_ddpg,\n\u001b[0m\u001b[1;32m     26\u001b[0m                                    \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ddpg\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                                    total_timesteps=total_timesteps)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/finrl/agents/stablebaselines3/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         model = model.learn(\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/ddpg/ddpg.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mprogress_bar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     ) -> SelfDDPG:\n\u001b[0;32m--> 123\u001b[0;31m         return super().learn(\n\u001b[0m\u001b[1;32m    124\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/td3/td3.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0mprogress_bar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     ) -> SelfTD3:\n\u001b[0;32m--> 216\u001b[0;31m         return super().learn(\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/off_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m             rollout = self.collect_rollouts(\n\u001b[0m\u001b[1;32m    312\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mtrain_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/off_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, train_freq, replay_buffer, action_noise, learning_starts, log_interval)\u001b[0m\n\u001b[1;32m    541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m             \u001b[0;31m# Rescale and perform action\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 543\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \"\"\"\n\u001b[1;32m    162\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mVecEnvStepReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0m\u001b[1;32m     55\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             )\n",
            "\u001b[0;32m<ipython-input-164-e53dd8899486>\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m             self.avg_price = np.divide(self.total_price,\n\u001b[0;32m--> 339\u001b[0;31m                       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_stockss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_price\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m                       where=self.total_stockss!=0)\n\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mzeros_like\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(study, \"final_ddpg_study__.pkl\")"
      ],
      "metadata": {
        "id": "1yIXC6W2ZqPP",
        "outputId": "7365a30a-64f0-4656-9030-cbada905c0fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['final_ddpg_study__.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the best hyperparamters\n",
        "print('Hyperparameters after tuning',study.best_params)\n",
        "print('Hyperparameters before tuning',config.DDPG_PARAMS)"
      ],
      "metadata": {
        "id": "gXzfenQTdqq_",
        "outputId": "ac510c77-8ba6-43fc-ae7a-396bb7496184",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperparameters after tuning {'gamma': 0.98, 'tau': 0.08, 'train_freq': 1024, 'noise_type': None, 'noise_std': 0.3, 'net_arch': 'big'}\n",
            "Hyperparameters before tuning {'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "study.best_trial"
      ],
      "metadata": {
        "id": "VQcuj-yidu0O",
        "outputId": "cfbbc138-4f22-4ecd-d187-c0a88c6167c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FrozenTrial(number=2, state=TrialState.COMPLETE, values=[0.9235269323597551], datetime_start=datetime.datetime(2023, 5, 1, 15, 32, 52, 652114), datetime_complete=datetime.datetime(2023, 5, 1, 15, 33, 11, 635757), params={'gamma': 0.98, 'tau': 0.08, 'train_freq': 1024, 'noise_type': None, 'noise_std': 0.3, 'net_arch': 'big'}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'gamma': CategoricalDistribution(choices=(0.94, 0.96, 0.98)), 'tau': CategoricalDistribution(choices=(0.08, 0.1, 0.12)), 'train_freq': CategoricalDistribution(choices=(512, 768, 1024)), 'noise_type': CategoricalDistribution(choices=('ornstein-uhlenbeck', 'normal', None)), 'noise_std': CategoricalDistribution(choices=(0.1, 0.2, 0.3)), 'net_arch': CategoricalDistribution(choices=('small', 'big'))}, trial_id=2, value=None)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from stable_baselines3 import DDPG\n",
        "tuned_model_ddpg = DDPG.load('models/ddpg_{}.pth'.format(study.best_trial.number),env=env_train)"
      ],
      "metadata": {
        "id": "ZjYe9lOIdw3i"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Trading period account value with tuned model\n",
        "df_account_value_tuned, df_actions_tuned = DRLAgent.DRL_prediction(\n",
        "    model=tuned_model_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "metadata": {
        "id": "3bnbx87vdzHZ",
        "outputId": "20164428-254c-4ca4-ac8a-aba8b32da06b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hit end!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_account_value_tuned[104:105][\"account_value\"]"
      ],
      "metadata": {
        "id": "G1Y1jjMLf0qF",
        "outputId": "dced00bf-cb09-401f-8c19-31a21926747e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104    1000000.0\n",
              "Name: account_value, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Backtesting with our pruned model\n",
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all_tuned = backtest_stats(account_value=df_account_value_tuned)\n",
        "perf_stats_all_tuned = pd.DataFrame(perf_stats_all_tuned)\n",
        "perf_stats_all_tuned.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_tuned_\"+now+'.csv')"
      ],
      "metadata": {
        "id": "FJrv144od3G_",
        "outputId": "af8d6ecb-c1c8-484b-ad93-13b6bc8dd544",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.0\n",
            "Cumulative returns     0.0\n",
            "Annual volatility      0.0\n",
            "Sharpe ratio           NaN\n",
            "Calmar ratio           NaN\n",
            "Stability              0.0\n",
            "Max drawdown           0.0\n",
            "Omega ratio            NaN\n",
            "Sortino ratio          NaN\n",
            "Skew                   NaN\n",
            "Kurtosis               NaN\n",
            "Tail ratio             NaN\n",
            "Daily value at risk    0.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/site-packages/empyrical/stats.py:1412: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  return np.abs(np.percentile(returns, 95)) / np.abs(np.percentile(returns, 5))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now train with not tuned hyperaparameters\n",
        "#Default config.ddpg_PARAMS\n",
        "non_tuned_model_ddpg = agent.get_model(\"ddpg\",model_kwargs = config.DDPG_PARAMS )\n",
        "trained_ddpg = agent.train_model(model=non_tuned_model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "metadata": {
        "id": "asWsWzemd5dk",
        "outputId": "5bfa4cdf-d304-46d8-adae-99c5dc6f4ab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cuda device\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    episodes        | 4           |\n",
            "|    fps             | 129         |\n",
            "|    time_elapsed    | 10          |\n",
            "|    total_timesteps | 1340        |\n",
            "| train/             |             |\n",
            "|    actor_loss      | -71         |\n",
            "|    critic_loss     | 2.97e+03    |\n",
            "|    learning_rate   | 0.001       |\n",
            "|    n_updates       | 1005        |\n",
            "|    reward          | -0.60931635 |\n",
            "------------------------------------\n",
            "day: 334, episode: 160\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 1143478.33\n",
            "total_reward: 143478.33\n",
            "total_cost: 999.00\n",
            "total_trades: 4008\n",
            "Sharpe: 0.458\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    episodes        | 8           |\n",
            "|    fps             | 119         |\n",
            "|    time_elapsed    | 22          |\n",
            "|    total_timesteps | 2680        |\n",
            "| train/             |             |\n",
            "|    actor_loss      | -15.4       |\n",
            "|    critic_loss     | 2.79e+03    |\n",
            "|    learning_rate   | 0.001       |\n",
            "|    n_updates       | 2345        |\n",
            "|    reward          | -0.60931635 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/              |             |\n",
            "|    episodes        | 12          |\n",
            "|    fps             | 115         |\n",
            "|    time_elapsed    | 34          |\n",
            "|    total_timesteps | 4020        |\n",
            "| train/             |             |\n",
            "|    actor_loss      | -7.04       |\n",
            "|    critic_loss     | 44.5        |\n",
            "|    learning_rate   | 0.001       |\n",
            "|    n_updates       | 3685        |\n",
            "|    reward          | -0.60931635 |\n",
            "------------------------------------\n",
            "day: 334, episode: 170\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 1143478.33\n",
            "total_reward: 143478.33\n",
            "total_cost: 999.00\n",
            "total_trades: 4008\n",
            "Sharpe: 0.458\n",
            "=================================\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-81-3b1b0264cc27>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#Default config.ddpg_PARAMS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mnon_tuned_model_ddpg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ddpg\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDDPG_PARAMS\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m trained_ddpg = agent.train_model(model=non_tuned_model_ddpg, \n\u001b[0m\u001b[1;32m      5\u001b[0m                              \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ddpg'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                              total_timesteps=50000)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/finrl/agents/stablebaselines3/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         model = model.learn(\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/ddpg/ddpg.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mprogress_bar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     ) -> SelfDDPG:\n\u001b[0;32m--> 123\u001b[0;31m         return super().learn(\n\u001b[0m\u001b[1;32m    124\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/td3/td3.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0mprogress_bar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     ) -> SelfTD3:\n\u001b[0;32m--> 216\u001b[0;31m         return super().learn(\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/common/off_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    328\u001b[0m                 \u001b[0;31m# Special case when the user passes `gradient_steps=0`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgradient_steps\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgradient_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_training_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/stable_baselines3/td3/td3.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, gradient_steps, batch_size)\u001b[0m\n\u001b[1;32m    192\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m                 \u001b[0mactor_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m                 \u001b[0mpolyak_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_target\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    278\u001b[0m                                                f\"but got {result}.\")\n\u001b[1;32m    279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36m_use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'differentiable'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprev_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    139\u001b[0m                 state_steps)\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             adam(\n\u001b[0m\u001b[1;32m    142\u001b[0m                 \u001b[0mparams_with_grad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_single_tensor_adam\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m     func(params,\n\u001b[0m\u001b[1;32m    282\u001b[0m          \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m          \u001b[0mexp_avgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36m_multi_tensor_adam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_mul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_exp_avgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_add_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_exp_avgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_grads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_mul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_exp_avg_sqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_ddpg, \n",
        "    environment = e_trade_gym)"
      ],
      "metadata": {
        "id": "mv1zgwOgd9FY",
        "outputId": "3fd9a94a-98d4-41c0-8870-74b8271b6494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-38368194789e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m df_account_value, df_actions = DRLAgent.DRL_prediction(\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrained_ddpg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     environment = e_trade_gym)\n",
            "\u001b[0;31mNameError\u001b[0m: name 'trained_ddpg' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Backtesting for not tuned hyperparamters\n",
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "# perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "metadata": {
        "id": "DuZL52qDeEz6",
        "outputId": "f4e9cd19-27cd-4dba-956e-353df0dabffa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.247826\n",
            "Cumulative returns     0.368416\n",
            "Annual volatility      0.167226\n",
            "Sharpe ratio           1.412093\n",
            "Calmar ratio           2.531845\n",
            "Stability              0.897149\n",
            "Max drawdown          -0.097883\n",
            "Omega ratio            1.280406\n",
            "Sortino ratio          2.010344\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.033938\n",
            "Daily value at risk   -0.020131\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#You can see with trial, our sharpe ratio is increasing\n",
        "#Certainly you can afford more number of trials for further optimization\n",
        "from optuna.visualization import plot_optimization_history\n",
        "plot_optimization_history(study)"
      ],
      "metadata": {
        "id": "5aSsEmpZeI5C",
        "outputId": "4e4acb29-dc3a-4b5b-fabe-736db19aea9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from optuna.visualization import plot_contour\n",
        "from optuna.visualization import plot_edf\n",
        "from optuna.visualization import plot_intermediate_values\n",
        "from optuna.visualization import plot_optimization_history\n",
        "from optuna.visualization import plot_parallel_coordinate\n",
        "from optuna.visualization import plot_param_importances\n",
        "from optuna.visualization import plot_slice"
      ],
      "metadata": {
        "id": "K9zcey_BeJMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hyperparamters importance\n",
        "#Ent_coef is the most important\n",
        "plot_param_importances(study)"
      ],
      "metadata": {
        "id": "J6WmB5DneOgh",
        "outputId": "9510c118-7601-45ac-adba-0226c3c76277",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    import warnings\n",
        "    warnings.filterwarnings(\"ignore\")\n",
        "    import pandas as pd\n",
        "    import numpy as np\n",
        "    import matplotlib\n",
        "    import matplotlib.pyplot as plt\n",
        "    # matplotlib.use('Agg')\n",
        "    import datetime\n",
        "\n",
        "\n",
        "    from finrl.config_tickers import DOW_30_TICKER\n",
        "    from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "    from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "    from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "    from finrl.agents.stablebaselines3.models import DRLAgent, DRLEnsembleAgent\n",
        "    from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "    from pprint import pprint\n",
        "\n",
        "    import sys\n",
        "    sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "    import itertools\n",
        "\n",
        "    import os\n",
        "    from finrl.main import check_and_make_directories\n",
        "    from finrl.config import (\n",
        "        DATA_SAVE_DIR,\n",
        "        TRAINED_MODEL_DIR,\n",
        "        TENSORBOARD_LOG_DIR,\n",
        "        RESULTS_DIR,\n",
        "        INDICATORS,\n",
        "        TRAIN_START_DATE,\n",
        "        TRAIN_END_DATE,\n",
        "        TEST_START_DATE,\n",
        "        TEST_END_DATE,\n",
        "        TRADE_START_DATE,\n",
        "        TRADE_END_DATE,\n",
        "    )\n",
        "\n",
        "    check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])\n",
        "    print(DOW_30_TICKER)\n",
        "    TRAIN_START_DATE = '2009-04-01'\n",
        "    TRAIN_END_DATE = '2021-01-01'\n",
        "    TEST_START_DATE = '2021-01-01'\n",
        "    TEST_END_DATE = '2022-06-01'\n",
        "\n",
        "    df = YahooDownloader(start_date=TRAIN_START_DATE,\n",
        "                         end_date=TEST_END_DATE,\n",
        "                         ticker_list=DOW_30_TICKER).fetch_data()\n",
        "\n",
        "    df.sort_values(['date', 'tic']).head()\n",
        "\n",
        "    fe = FeatureEngineer(use_technical_indicator=True,\n",
        "                         tech_indicator_list=INDICATORS,\n",
        "                         use_turbulence=True,\n",
        "                         user_defined_feature=False)\n",
        "\n",
        "    processed = fe.preprocess_data(df)\n",
        "    processed = processed.copy()\n",
        "    processed = processed.fillna(0)\n",
        "    processed = processed.replace(np.inf, 0)\n",
        "\n",
        "    stock_dimension = len(processed.tic.unique())\n",
        "    state_space = 1 + 2 * stock_dimension + len(INDICATORS) * stock_dimension\n",
        "    print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n",
        "\n",
        "    env_kwargs = {\n",
        "        \"hmax\": 100,\n",
        "        \"initial_amount\": 1000000,\n",
        "        \"buy_cost_pct\": 0.001,\n",
        "        \"sell_cost_pct\": 0.001,\n",
        "        \"state_space\": state_space,\n",
        "        \"stock_dim\": stock_dimension,\n",
        "        \"tech_indicator_list\": INDICATORS,\n",
        "        \"action_space\": stock_dimension,\n",
        "        \"reward_scaling\": 1e-4,\n",
        "        \"print_verbosity\": 5\n",
        "\n",
        "    }\n",
        "\n",
        "    rebalance_window = 63  # rebalance_window is the number of days to retrain the model\n",
        "    validation_window = 63  # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)\n",
        "\n",
        "    ensemble_agent = DRLEnsembleAgent(df=processed,\n",
        "                                      train_period=(TRAIN_START_DATE, TRAIN_END_DATE),\n",
        "                                      val_test_period=(TEST_START_DATE, TEST_END_DATE),\n",
        "                                      rebalance_window=rebalance_window,\n",
        "                                      validation_window=validation_window,\n",
        "                                      **env_kwargs)\n",
        "\n",
        "    A2C_model_kwargs = {\n",
        "        'n_steps': 5,\n",
        "        'ent_coef': 0.005,\n",
        "        'learning_rate': 0.0007\n",
        "    }\n",
        "\n",
        "    PPO_model_kwargs = {\n",
        "        \"ent_coef\": 0.01,\n",
        "        \"n_steps\": 2048,\n",
        "        \"learning_rate\": 0.00025,\n",
        "        \"batch_size\": 128\n",
        "    }\n",
        "\n",
        "    DDPG_model_kwargs = {\n",
        "        # \"action_noise\":\"ornstein_uhlenbeck\",\n",
        "        \"buffer_size\": 10_000,\n",
        "        \"learning_rate\": 0.0005,\n",
        "        \"batch_size\": 64\n",
        "    }\n",
        "\n",
        "    timesteps_dict = {'a2c': 10_000,\n",
        "                      'ppo': 10_000,\n",
        "                      'ddpg': 10_000\n",
        "                      }\n",
        "    df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,\n",
        "                                                      PPO_model_kwargs,\n",
        "                                                      DDPG_model_kwargs,\n",
        "                                                      timesteps_dict)\n",
        "\n",
        "    unique_trade_date = processed[(processed.date > TEST_START_DATE) & (processed.date <= TEST_END_DATE)].date.unique()\n",
        "\n",
        "    df_trade_date = pd.DataFrame({'datadate': unique_trade_date})\n",
        "\n",
        "    df_account_value = pd.DataFrame()\n",
        "    for i in range(rebalance_window + validation_window, len(unique_trade_date) + 1, rebalance_window):\n",
        "        temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format('ensemble', i))\n",
        "        df_account_value = df_account_value.append(temp, ignore_index=True)\n",
        "    sharpe = (252 ** 0.5) * df_account_value.account_value.pct_change(\n",
        "        1).mean() / df_account_value.account_value.pct_change(1).std()\n",
        "    print('Sharpe Ratio: ', sharpe)\n",
        "    df_account_value = df_account_value.join(df_trade_date[validation_window:].reset_index(drop=True))\n",
        "\n",
        "    df_account_value.account_value.plot()\n",
        "\n",
        "    print(\"==============Get Backtest Results===========\")\n",
        "    now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "    perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "    perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "\n",
        "    # baseline stats\n",
        "    print(\"==============Get Baseline Stats===========\")\n",
        "    baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\",\n",
        "        start=df_account_value.loc[0, 'date'],\n",
        "        end=df_account_value.loc[len(df_account_value) - 1, 'date'])\n",
        "\n",
        "    stats = backtest_stats(baseline_df, value_col_name='close')\n",
        "\n",
        "    print(\"==============Compare to DJIA===========\")\n",
        "\n",
        "    # S&P 500: ^GSPC\n",
        "    # Dow Jones Index: ^DJI\n",
        "    # NASDAQ 100: ^NDX\n",
        "    backtest_plot(df_account_value,\n",
        "                  baseline_ticker='^DJI',\n",
        "                  baseline_start=df_account_value.loc[0, 'date'],\n",
        "                  baseline_end=df_account_value.loc[len(df_account_value) - 1, 'date'])\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()"
      ],
      "metadata": {
        "id": "s_xcVpBSeOyE",
        "outputId": "52b3d82a-1f8d-4af4-a405-6b5b9778781d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (96942, 8)\n",
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n",
            "Stock Dimension: 29, State Space: 291\n",
            "============Start Ensemble Strategy============\n",
            "============================================\n",
            "turbulence_threshold:  203.40117769841157\n",
            "======Model training from:  2009-04-01 to  2021-01-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_126_1\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 100        |\n",
            "|    time_elapsed       | 4          |\n",
            "|    total_timesteps    | 500        |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0.00295    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 99         |\n",
            "|    policy_loss        | -71.2      |\n",
            "|    reward             | 0.78650117 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 12.2       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 101        |\n",
            "|    iterations         | 200        |\n",
            "|    time_elapsed       | 9          |\n",
            "|    total_timesteps    | 1000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | 0.00111    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 199        |\n",
            "|    policy_loss        | 6.59       |\n",
            "|    reward             | 0.66569686 |\n",
            "|    std                | 0.998      |\n",
            "|    value_loss         | 1.03       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 102       |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 14        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -123      |\n",
            "|    reward             | 2.9206326 |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 15.8      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 102        |\n",
            "|    iterations         | 400        |\n",
            "|    time_elapsed       | 19         |\n",
            "|    total_timesteps    | 2000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.1      |\n",
            "|    explained_variance | -1.19e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 399        |\n",
            "|    policy_loss        | -6.35      |\n",
            "|    reward             | -1.8242201 |\n",
            "|    std                | 0.999      |\n",
            "|    value_loss         | 3.27       |\n",
            "--------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                 |               |\n",
            "|    fps                | 102           |\n",
            "|    iterations         | 500           |\n",
            "|    time_elapsed       | 24            |\n",
            "|    total_timesteps    | 2500          |\n",
            "| train/                |               |\n",
            "|    entropy_loss       | -41.2         |\n",
            "|    explained_variance | -1.19e-07     |\n",
            "|    learning_rate      | 0.0007        |\n",
            "|    n_updates          | 499           |\n",
            "|    policy_loss        | 16            |\n",
            "|    reward             | -0.0061751665 |\n",
            "|    std                | 1             |\n",
            "|    value_loss         | 0.657         |\n",
            "-----------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 102       |\n",
            "|    iterations         | 600       |\n",
            "|    time_elapsed       | 29        |\n",
            "|    total_timesteps    | 3000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 599       |\n",
            "|    policy_loss        | -33.6     |\n",
            "|    reward             | 0.5337091 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 0.837     |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 102        |\n",
            "|    iterations         | 700        |\n",
            "|    time_elapsed       | 34         |\n",
            "|    total_timesteps    | 3500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -1.97      |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 699        |\n",
            "|    policy_loss        | -76.4      |\n",
            "|    reward             | -1.3246601 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 3.85       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 101        |\n",
            "|    iterations         | 800        |\n",
            "|    time_elapsed       | 39         |\n",
            "|    total_timesteps    | 4000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.0632     |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 799        |\n",
            "|    policy_loss        | 44.6       |\n",
            "|    reward             | -1.6959082 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.53       |\n",
            "--------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 44       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | 0.0211   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | -33.2    |\n",
            "|    reward             | 3.994608 |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.53     |\n",
            "------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 101         |\n",
            "|    iterations         | 1000        |\n",
            "|    time_elapsed       | 49          |\n",
            "|    total_timesteps    | 5000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.2       |\n",
            "|    explained_variance | 1.79e-07    |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 999         |\n",
            "|    policy_loss        | 36.3        |\n",
            "|    reward             | -0.20298508 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 1.68        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 101         |\n",
            "|    iterations         | 1100        |\n",
            "|    time_elapsed       | 54          |\n",
            "|    total_timesteps    | 5500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.1       |\n",
            "|    explained_variance | 0.0114      |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1099        |\n",
            "|    policy_loss        | 393         |\n",
            "|    reward             | -0.92543656 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 120         |\n",
            "---------------------------------------\n",
            "----------------------------------------\n",
            "| time/                 |              |\n",
            "|    fps                | 101          |\n",
            "|    iterations         | 1200         |\n",
            "|    time_elapsed       | 59           |\n",
            "|    total_timesteps    | 6000         |\n",
            "| train/                |              |\n",
            "|    entropy_loss       | -41.2        |\n",
            "|    explained_variance | 0.0236       |\n",
            "|    learning_rate      | 0.0007       |\n",
            "|    n_updates          | 1199         |\n",
            "|    policy_loss        | 151          |\n",
            "|    reward             | -0.099366836 |\n",
            "|    std                | 1            |\n",
            "|    value_loss         | 19.4         |\n",
            "----------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 101         |\n",
            "|    iterations         | 1300        |\n",
            "|    time_elapsed       | 64          |\n",
            "|    total_timesteps    | 6500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.2       |\n",
            "|    explained_variance | -0.000106   |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1299        |\n",
            "|    policy_loss        | -47.6       |\n",
            "|    reward             | -0.39146426 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 3.15        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 101         |\n",
            "|    iterations         | 1400        |\n",
            "|    time_elapsed       | 69          |\n",
            "|    total_timesteps    | 7000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.2       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1399        |\n",
            "|    policy_loss        | 31.8        |\n",
            "|    reward             | -0.25985184 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 1.84        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 101       |\n",
            "|    iterations         | 1500      |\n",
            "|    time_elapsed       | 74        |\n",
            "|    total_timesteps    | 7500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1499      |\n",
            "|    policy_loss        | -30.8     |\n",
            "|    reward             | 3.1766434 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 4.93      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 101        |\n",
            "|    iterations         | 1600       |\n",
            "|    time_elapsed       | 78         |\n",
            "|    total_timesteps    | 8000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.00673    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1599       |\n",
            "|    policy_loss        | 133        |\n",
            "|    reward             | -2.0720153 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 10.9       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 101       |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 83        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | -84.2     |\n",
            "|    reward             | 3.8356218 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 7         |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 101        |\n",
            "|    iterations         | 1800       |\n",
            "|    time_elapsed       | 88         |\n",
            "|    total_timesteps    | 9000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.063      |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1799       |\n",
            "|    policy_loss        | 42         |\n",
            "|    reward             | 0.97460705 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 2.23       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 101       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 93        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.0122    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | -84.7     |\n",
            "|    reward             | 2.2624621 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 8.34      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 101        |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 98         |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | 45.9       |\n",
            "|    reward             | -1.0189797 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.77       |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2021-01-04 to  2021-04-06\n",
            "A2C Sharpe Ratio:  0.26168708794170054\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_126_1\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    fps             | 111       |\n",
            "|    iterations      | 1         |\n",
            "|    time_elapsed    | 18        |\n",
            "|    total_timesteps | 2048      |\n",
            "| train/             |           |\n",
            "|    reward          | 1.8877938 |\n",
            "----------------------------------\n",
            "day: 2959, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3881161.61\n",
            "total_reward: 2881161.61\n",
            "total_cost: 376867.37\n",
            "total_trades: 82873\n",
            "Sharpe: 0.776\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 109         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 37          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016203282 |\n",
            "|    clip_fraction        | 0.192       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.0104     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4           |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0191     |\n",
            "|    reward               | 1.0358981   |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 11.3        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 109         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 56          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.012528071 |\n",
            "|    clip_fraction        | 0.126       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00217    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 57          |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0176     |\n",
            "|    reward               | 0.07821791  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 55.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 107         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 76          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.012139827 |\n",
            "|    clip_fraction        | 0.146       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00454    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 13.7        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.018      |\n",
            "|    reward               | -1.5925834  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 43.4        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 107         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 94          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019309906 |\n",
            "|    clip_fraction        | 0.17        |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | -0.0161     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.07        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0225     |\n",
            "|    reward               | 0.0615912   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 11.3        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2021-01-04 to  2021-04-06\n",
            "PPO Sharpe Ratio:  0.3016310407987196\n",
            "======DDPG Training========\n",
            "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_126_1\n",
            "day: 2959, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4424085.63\n",
            "total_reward: 3424085.63\n",
            "total_cost: 1037.34\n",
            "total_trades: 50241\n",
            "Sharpe: 0.797\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 78        |\n",
            "|    time_elapsed    | 151       |\n",
            "|    total_timesteps | 11840     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -60.3     |\n",
            "|    critic_loss     | 441       |\n",
            "|    learning_rate   | 0.0005    |\n",
            "|    n_updates       | 8880      |\n",
            "|    reward          | 3.0541244 |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2021-01-04 to  2021-04-06\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-04-06\n",
            "======Trading from:  2021-04-06 to  2021-07-06\n",
            "============================================\n",
            "turbulence_threshold:  203.40117769841157\n",
            "======Model training from:  2009-04-01 to  2021-04-06\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_189_1\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 99         |\n",
            "|    iterations         | 100        |\n",
            "|    time_elapsed       | 5          |\n",
            "|    total_timesteps    | 500        |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | -0.446     |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 99         |\n",
            "|    policy_loss        | -85.9      |\n",
            "|    reward             | -0.6058027 |\n",
            "|    std                | 0.996      |\n",
            "|    value_loss         | 9.16       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 9         |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | -19.4     |\n",
            "|    reward             | 1.8647146 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 0.253     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 14       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41      |\n",
            "|    explained_variance | 0.0122   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -179     |\n",
            "|    reward             | 3.291392 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 22.1     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 400       |\n",
            "|    time_elapsed       | 19        |\n",
            "|    total_timesteps    | 2000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0.0507    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 399       |\n",
            "|    policy_loss        | -11.1     |\n",
            "|    reward             | 0.3743614 |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 1.09      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 500        |\n",
            "|    time_elapsed       | 24         |\n",
            "|    total_timesteps    | 2500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 499        |\n",
            "|    policy_loss        | 169        |\n",
            "|    reward             | -0.3291204 |\n",
            "|    std                | 0.995      |\n",
            "|    value_loss         | 16         |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 600        |\n",
            "|    time_elapsed       | 29         |\n",
            "|    total_timesteps    | 3000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0.054      |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 599        |\n",
            "|    policy_loss        | 905        |\n",
            "|    reward             | -1.0711567 |\n",
            "|    std                | 0.994      |\n",
            "|    value_loss         | 521        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 700        |\n",
            "|    time_elapsed       | 34         |\n",
            "|    total_timesteps    | 3500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | -0.0433    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 699        |\n",
            "|    policy_loss        | -32.9      |\n",
            "|    reward             | -2.9666545 |\n",
            "|    std                | 0.995      |\n",
            "|    value_loss         | 0.946      |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 800        |\n",
            "|    time_elapsed       | 39         |\n",
            "|    total_timesteps    | 4000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | -1.19e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 799        |\n",
            "|    policy_loss        | -3.25      |\n",
            "|    reward             | -0.5585839 |\n",
            "|    std                | 0.994      |\n",
            "|    value_loss         | 1.05       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 44        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -0.66     |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 899       |\n",
            "|    policy_loss        | 306       |\n",
            "|    reward             | 2.3080251 |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 70.3      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 49        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | -108      |\n",
            "|    reward             | 1.1761196 |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 10.8      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 54        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | -117      |\n",
            "|    reward             | 3.2501192 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 10.7      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 59        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 362       |\n",
            "|    reward             | 6.932239  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 84.6      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 64         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | 44.8       |\n",
            "|    reward             | -1.5383602 |\n",
            "|    std                | 0.994      |\n",
            "|    value_loss         | 2.08       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 69        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | -143      |\n",
            "|    reward             | 0.7461217 |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 17        |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 1500       |\n",
            "|    time_elapsed       | 74         |\n",
            "|    total_timesteps    | 7500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1499       |\n",
            "|    policy_loss        | -145       |\n",
            "|    reward             | -1.6722031 |\n",
            "|    std                | 0.995      |\n",
            "|    value_loss         | 14.8       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 79        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 1.19e-07  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | 4.35      |\n",
            "|    reward             | 3.1992574 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 0.916     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 84        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | -0.00566  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | -1.16e+03 |\n",
            "|    reward             | 11.774156 |\n",
            "|    std                | 0.993     |\n",
            "|    value_loss         | 936       |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 100      |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 89       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -40.9    |\n",
            "|    explained_variance | -0.00464 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | -185     |\n",
            "|    reward             | 4.000574 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 41.6     |\n",
            "------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 100        |\n",
            "|    iterations         | 1900       |\n",
            "|    time_elapsed       | 94         |\n",
            "|    total_timesteps    | 9500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -40.9      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1899       |\n",
            "|    policy_loss        | 12.7       |\n",
            "|    reward             | -1.3208354 |\n",
            "|    std                | 0.991      |\n",
            "|    value_loss         | 0.306      |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 100       |\n",
            "|    iterations         | 2000      |\n",
            "|    time_elapsed       | 99        |\n",
            "|    total_timesteps    | 10000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -40.9     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1999      |\n",
            "|    policy_loss        | -10.6     |\n",
            "|    reward             | 0.7043803 |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 0.431     |\n",
            "-------------------------------------\n",
            "======A2C Validation from:  2021-04-06 to  2021-07-06\n",
            "A2C Sharpe Ratio:  0.23121777505990648\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_189_1\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    fps             | 104       |\n",
            "|    iterations      | 1         |\n",
            "|    time_elapsed    | 19        |\n",
            "|    total_timesteps | 2048      |\n",
            "| train/             |           |\n",
            "|    reward          | 1.1126131 |\n",
            "----------------------------------\n",
            "day: 3022, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3636477.12\n",
            "total_reward: 2636477.12\n",
            "total_cost: 386575.15\n",
            "total_trades: 84625\n",
            "Sharpe: 0.731\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 38          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015871149 |\n",
            "|    clip_fraction        | 0.234       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.0129     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 3.2         |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0282     |\n",
            "|    reward               | 0.88015693  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 10.1        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 58          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.019049045 |\n",
            "|    clip_fraction        | 0.166       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | 0.00262     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 32.1        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.023      |\n",
            "|    reward               | 0.49640504  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 53.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 77          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.015561214 |\n",
            "|    clip_fraction        | 0.161       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.0133     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 31.9        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.0201     |\n",
            "|    reward               | -0.7624933  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 45.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 105         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 96          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014756277 |\n",
            "|    clip_fraction        | 0.141       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00517    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 6.56        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0204     |\n",
            "|    reward               | 1.2296445   |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 14.8        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2021-04-06 to  2021-07-06\n",
            "PPO Sharpe Ratio:  0.010011405217654536\n",
            "======DDPG Training========\n",
            "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_189_1\n",
            "day: 3022, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5009064.34\n",
            "total_reward: 4009064.34\n",
            "total_cost: 1282.47\n",
            "total_trades: 51375\n",
            "Sharpe: 0.880\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 76       |\n",
            "|    time_elapsed    | 157      |\n",
            "|    total_timesteps | 12092    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -65.5    |\n",
            "|    critic_loss     | 3.14e+03 |\n",
            "|    learning_rate   | 0.0005   |\n",
            "|    n_updates       | 9069     |\n",
            "|    reward          | 8.301459 |\n",
            "---------------------------------\n",
            "======DDPG Validation from:  2021-04-06 to  2021-07-06\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-07-06\n",
            "======Trading from:  2021-07-06 to  2021-10-04\n",
            "============================================\n",
            "turbulence_threshold:  203.40117769841157\n",
            "======Model training from:  2009-04-01 to  2021-07-06\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_252_1\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 100         |\n",
            "|    time_elapsed       | 5           |\n",
            "|    total_timesteps    | 500         |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.1       |\n",
            "|    explained_variance | -0.0351     |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 99          |\n",
            "|    policy_loss        | -28.6       |\n",
            "|    reward             | -0.15436654 |\n",
            "|    std                | 0.998       |\n",
            "|    value_loss         | 5.15        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 97        |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 10        |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | 50.5      |\n",
            "|    reward             | 1.3676119 |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 2.12      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 15        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -179      |\n",
            "|    reward             | 5.1952586 |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 22.2      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 400        |\n",
            "|    time_elapsed       | 20         |\n",
            "|    total_timesteps    | 2000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 399        |\n",
            "|    policy_loss        | 79.1       |\n",
            "|    reward             | -0.3978011 |\n",
            "|    std                | 0.995      |\n",
            "|    value_loss         | 7.82       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 500        |\n",
            "|    time_elapsed       | 25         |\n",
            "|    total_timesteps    | 2500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -40.9      |\n",
            "|    explained_variance | 0.19       |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 499        |\n",
            "|    policy_loss        | -8.81      |\n",
            "|    reward             | 0.19138733 |\n",
            "|    std                | 0.993      |\n",
            "|    value_loss         | 6.38       |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 600        |\n",
            "|    time_elapsed       | 30         |\n",
            "|    total_timesteps    | 3000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | -0.00925   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 599        |\n",
            "|    policy_loss        | -498       |\n",
            "|    reward             | -11.956305 |\n",
            "|    std                | 0.996      |\n",
            "|    value_loss         | 222        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 700        |\n",
            "|    time_elapsed       | 35         |\n",
            "|    total_timesteps    | 3500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41        |\n",
            "|    explained_variance | -0.169     |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 699        |\n",
            "|    policy_loss        | -68.7      |\n",
            "|    reward             | 0.39965674 |\n",
            "|    std                | 0.996      |\n",
            "|    value_loss         | 3.86       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 800         |\n",
            "|    time_elapsed       | 40          |\n",
            "|    total_timesteps    | 4000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41         |\n",
            "|    explained_variance | -0.0865     |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 799         |\n",
            "|    policy_loss        | -164        |\n",
            "|    reward             | -0.33571205 |\n",
            "|    std                | 0.996       |\n",
            "|    value_loss         | 16.1        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 900         |\n",
            "|    time_elapsed       | 45          |\n",
            "|    total_timesteps    | 4500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41         |\n",
            "|    explained_variance | 0.159       |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 899         |\n",
            "|    policy_loss        | 46.5        |\n",
            "|    reward             | -0.19355251 |\n",
            "|    std                | 0.996       |\n",
            "|    value_loss         | 1.99        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 1000        |\n",
            "|    time_elapsed       | 50          |\n",
            "|    total_timesteps    | 5000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.1       |\n",
            "|    explained_variance | 0.0618      |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 999         |\n",
            "|    policy_loss        | -15.1       |\n",
            "|    reward             | -0.48996487 |\n",
            "|    std                | 0.997       |\n",
            "|    value_loss         | 0.677       |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1100      |\n",
            "|    time_elapsed       | 55        |\n",
            "|    total_timesteps    | 5500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41       |\n",
            "|    explained_variance | -0.192    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1099      |\n",
            "|    policy_loss        | -233      |\n",
            "|    reward             | 1.1524396 |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 35.2      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 60        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.1     |\n",
            "|    explained_variance | -0.214    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 14.9      |\n",
            "|    reward             | -2.838138 |\n",
            "|    std                | 0.998     |\n",
            "|    value_loss         | 3.95      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 65         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0.167      |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | 43.7       |\n",
            "|    reward             | -1.0078373 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.86       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 70        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.0727   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | -17.9     |\n",
            "|    reward             | 2.1590974 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.71      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1500       |\n",
            "|    time_elapsed       | 75         |\n",
            "|    total_timesteps    | 7500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1499       |\n",
            "|    policy_loss        | 42.5       |\n",
            "|    reward             | 0.47163484 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 1.17       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0.000262  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | -73.2     |\n",
            "|    reward             | 0.8027305 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 19.5      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1700      |\n",
            "|    time_elapsed       | 85        |\n",
            "|    total_timesteps    | 8500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1699      |\n",
            "|    policy_loss        | -184      |\n",
            "|    reward             | 0.3686392 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 29.3      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 90        |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 116       |\n",
            "|    reward             | 1.6162095 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 21.6      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 96        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.2     |\n",
            "|    explained_variance | -0.00951  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | -1.74     |\n",
            "|    reward             | 1.1433389 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 0.195     |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 101        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.0113    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | -38.9      |\n",
            "|    reward             | 0.97623837 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 0.806      |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2021-07-06 to  2021-10-04\n",
            "A2C Sharpe Ratio:  -0.0528373614471615\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_252_1\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    fps             | 101       |\n",
            "|    iterations      | 1         |\n",
            "|    time_elapsed    | 20        |\n",
            "|    total_timesteps | 2048      |\n",
            "| train/             |           |\n",
            "|    reward          | 1.2801732 |\n",
            "----------------------------------\n",
            "day: 3085, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4550937.53\n",
            "total_reward: 3550937.53\n",
            "total_cost: 408098.28\n",
            "total_trades: 86397\n",
            "Sharpe: 0.873\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 39          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.018978704 |\n",
            "|    clip_fraction        | 0.233       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00994    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.48        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0284     |\n",
            "|    reward               | 0.49718028  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 10.8        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 103         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 59          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.012346724 |\n",
            "|    clip_fraction        | 0.155       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.00278    |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 19.6        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0154     |\n",
            "|    reward               | -0.8982904  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 50.6        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 103         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 78          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.014507987 |\n",
            "|    clip_fraction        | 0.123       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.012      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 43.8        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.016      |\n",
            "|    reward               | -0.0212465  |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 74.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 104         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 98          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016703494 |\n",
            "|    clip_fraction        | 0.186       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | -0.0496     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.09        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0285     |\n",
            "|    reward               | 1.0109037   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 14.2        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2021-07-06 to  2021-10-04\n",
            "PPO Sharpe Ratio:  -0.05262682094979255\n",
            "======DDPG Training========\n",
            "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_252_1\n",
            "day: 3085, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4501430.61\n",
            "total_reward: 3501430.61\n",
            "total_cost: 1082.97\n",
            "total_trades: 61741\n",
            "Sharpe: 0.737\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 76        |\n",
            "|    time_elapsed    | 161       |\n",
            "|    total_timesteps | 12344     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -3.66     |\n",
            "|    critic_loss     | 176       |\n",
            "|    learning_rate   | 0.0005    |\n",
            "|    n_updates       | 9258      |\n",
            "|    reward          | 2.1645079 |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2021-07-06 to  2021-10-04\n",
            "======Best Model Retraining from:  2009-04-01 to  2021-10-04\n",
            "======Trading from:  2021-10-04 to  2022-01-03\n",
            "============================================\n",
            "turbulence_threshold:  203.40117769841157\n",
            "======Model training from:  2009-04-01 to  2021-10-04\n",
            "======A2C Training========\n",
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0007}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/a2c/a2c_315_1\n",
            "----------------------------------------\n",
            "| time/                 |              |\n",
            "|    fps                | 98           |\n",
            "|    iterations         | 100          |\n",
            "|    time_elapsed       | 5            |\n",
            "|    total_timesteps    | 500          |\n",
            "| train/                |              |\n",
            "|    entropy_loss       | -41.3        |\n",
            "|    explained_variance | 0            |\n",
            "|    learning_rate      | 0.0007       |\n",
            "|    n_updates          | 99           |\n",
            "|    policy_loss        | -51.2        |\n",
            "|    reward             | -0.096114956 |\n",
            "|    std                | 1.01         |\n",
            "|    value_loss         | 4.56         |\n",
            "----------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 10        |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 5.96e-08  |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | -36.9     |\n",
            "|    reward             | 2.9646184 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 0.756     |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 97        |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 15        |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | -236      |\n",
            "|    reward             | 1.1201329 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 36.4      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 400        |\n",
            "|    time_elapsed       | 20         |\n",
            "|    total_timesteps    | 2000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 399        |\n",
            "|    policy_loss        | -53.9      |\n",
            "|    reward             | 0.03644575 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 2.41       |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 25        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 34.1      |\n",
            "|    reward             | 0.3948357 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 1.08      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 600        |\n",
            "|    time_elapsed       | 30         |\n",
            "|    total_timesteps    | 3000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | -1.19e-07  |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 599        |\n",
            "|    policy_loss        | -618       |\n",
            "|    reward             | -12.502208 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 338        |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 700        |\n",
            "|    time_elapsed       | 35         |\n",
            "|    total_timesteps    | 3500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.2      |\n",
            "|    explained_variance | -0.0164    |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 699        |\n",
            "|    policy_loss        | -105       |\n",
            "|    reward             | 0.30092302 |\n",
            "|    std                | 1          |\n",
            "|    value_loss         | 10.8       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 800         |\n",
            "|    time_elapsed       | 40          |\n",
            "|    total_timesteps    | 4000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | 1.19e-07    |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 799         |\n",
            "|    policy_loss        | 74.6        |\n",
            "|    reward             | 0.009111182 |\n",
            "|    std                | 1           |\n",
            "|    value_loss         | 4.73        |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 900       |\n",
            "|    time_elapsed       | 45        |\n",
            "|    total_timesteps    | 4500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.3     |\n",
            "|    explained_variance | -0.0124   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 899       |\n",
            "|    policy_loss        | -131      |\n",
            "|    reward             | 2.0478728 |\n",
            "|    std                | 1         |\n",
            "|    value_loss         | 16.5      |\n",
            "-------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1000       |\n",
            "|    time_elapsed       | 50         |\n",
            "|    total_timesteps    | 5000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.3      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 999        |\n",
            "|    policy_loss        | -34.9      |\n",
            "|    reward             | 0.04952446 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 2.24       |\n",
            "--------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 1100        |\n",
            "|    time_elapsed       | 55          |\n",
            "|    total_timesteps    | 5500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.3       |\n",
            "|    explained_variance | -0.0188     |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1099        |\n",
            "|    policy_loss        | 344         |\n",
            "|    reward             | -0.11844914 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 64.7        |\n",
            "---------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 1200        |\n",
            "|    time_elapsed       | 60          |\n",
            "|    total_timesteps    | 6000        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.4       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1199        |\n",
            "|    policy_loss        | 58.9        |\n",
            "|    reward             | -0.28185675 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 6.99        |\n",
            "---------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1300       |\n",
            "|    time_elapsed       | 66         |\n",
            "|    total_timesteps    | 6500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0.549      |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1299       |\n",
            "|    policy_loss        | -11.7      |\n",
            "|    reward             | -0.7957832 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 0.135      |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1400       |\n",
            "|    time_elapsed       | 71         |\n",
            "|    total_timesteps    | 7000       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 1.19e-07   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1399       |\n",
            "|    policy_loss        | -29.7      |\n",
            "|    reward             | 0.16117185 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 0.859      |\n",
            "--------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 1500       |\n",
            "|    time_elapsed       | 76         |\n",
            "|    total_timesteps    | 7500       |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 1.79e-07   |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1499       |\n",
            "|    policy_loss        | 15.7       |\n",
            "|    reward             | 0.17504984 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 0.528      |\n",
            "--------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 81        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | 0         |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | 48.7      |\n",
            "|    reward             | 3.1540215 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 6.42      |\n",
            "-------------------------------------\n",
            "---------------------------------------\n",
            "| time/                 |             |\n",
            "|    fps                | 98          |\n",
            "|    iterations         | 1700        |\n",
            "|    time_elapsed       | 86          |\n",
            "|    total_timesteps    | 8500        |\n",
            "| train/                |             |\n",
            "|    entropy_loss       | -41.4       |\n",
            "|    explained_variance | 0           |\n",
            "|    learning_rate      | 0.0007      |\n",
            "|    n_updates          | 1699        |\n",
            "|    policy_loss        | 22.9        |\n",
            "|    reward             | -0.09754947 |\n",
            "|    std                | 1.01        |\n",
            "|    value_loss         | 0.677       |\n",
            "---------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 1800      |\n",
            "|    time_elapsed       | 91        |\n",
            "|    total_timesteps    | 9000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -0.171    |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 1799      |\n",
            "|    policy_loss        | 99        |\n",
            "|    reward             | -1.175583 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 9.98      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | -24.6    |\n",
            "|    reward             | 0.362812 |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.367    |\n",
            "------------------------------------\n",
            "--------------------------------------\n",
            "| time/                 |            |\n",
            "|    fps                | 98         |\n",
            "|    iterations         | 2000       |\n",
            "|    time_elapsed       | 101        |\n",
            "|    total_timesteps    | 10000      |\n",
            "| train/                |            |\n",
            "|    entropy_loss       | -41.4      |\n",
            "|    explained_variance | 0          |\n",
            "|    learning_rate      | 0.0007     |\n",
            "|    n_updates          | 1999       |\n",
            "|    policy_loss        | -146       |\n",
            "|    reward             | -1.7740997 |\n",
            "|    std                | 1.01       |\n",
            "|    value_loss         | 13.5       |\n",
            "--------------------------------------\n",
            "======A2C Validation from:  2021-10-04 to  2022-01-03\n",
            "A2C Sharpe Ratio:  0.3744625013790845\n",
            "======PPO Training========\n",
            "{'ent_coef': 0.01, 'n_steps': 2048, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ppo/ppo_315_1\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    fps             | 106       |\n",
            "|    iterations      | 1         |\n",
            "|    time_elapsed    | 19        |\n",
            "|    total_timesteps | 2048      |\n",
            "| train/             |           |\n",
            "|    reward          | 0.7561091 |\n",
            "----------------------------------\n",
            "day: 3148, episode: 5\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4259003.47\n",
            "total_reward: 3259003.47\n",
            "total_cost: 426068.70\n",
            "total_trades: 87901\n",
            "Sharpe: 0.790\n",
            "=================================\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 101         |\n",
            "|    iterations           | 2           |\n",
            "|    time_elapsed         | 40          |\n",
            "|    total_timesteps      | 4096        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.016951367 |\n",
            "|    clip_fraction        | 0.229       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.2       |\n",
            "|    explained_variance   | -0.000697   |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 5.25        |\n",
            "|    n_updates            | 10          |\n",
            "|    policy_gradient_loss | -0.0307     |\n",
            "|    reward               | 0.9373541   |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 9.91        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 3           |\n",
            "|    time_elapsed         | 60          |\n",
            "|    total_timesteps      | 6144        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.017283382 |\n",
            "|    clip_fraction        | 0.176       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.00633     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 33.4        |\n",
            "|    n_updates            | 20          |\n",
            "|    policy_gradient_loss | -0.0207     |\n",
            "|    reward               | 7.712371    |\n",
            "|    std                  | 1           |\n",
            "|    value_loss           | 56.7        |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 4           |\n",
            "|    time_elapsed         | 79          |\n",
            "|    total_timesteps      | 8192        |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.010827523 |\n",
            "|    clip_fraction        | 0.0962      |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.3       |\n",
            "|    explained_variance   | 0.00682     |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 73.4        |\n",
            "|    n_updates            | 30          |\n",
            "|    policy_gradient_loss | -0.018      |\n",
            "|    reward               | -0.83698237 |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 134         |\n",
            "-----------------------------------------\n",
            "-----------------------------------------\n",
            "| time/                   |             |\n",
            "|    fps                  | 102         |\n",
            "|    iterations           | 5           |\n",
            "|    time_elapsed         | 99          |\n",
            "|    total_timesteps      | 10240       |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 0.013603406 |\n",
            "|    clip_fraction        | 0.172       |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -41.4       |\n",
            "|    explained_variance   | 0.0243      |\n",
            "|    learning_rate        | 0.00025     |\n",
            "|    loss                 | 4.64        |\n",
            "|    n_updates            | 40          |\n",
            "|    policy_gradient_loss | -0.0243     |\n",
            "|    reward               | 0.5904208   |\n",
            "|    std                  | 1.01        |\n",
            "|    value_loss           | 12.7        |\n",
            "-----------------------------------------\n",
            "======PPO Validation from:  2021-10-04 to  2022-01-03\n",
            "PPO Sharpe Ratio:  0.0593551480877188\n",
            "======DDPG Training========\n",
            "{'buffer_size': 10000, 'learning_rate': 0.0005, 'batch_size': 64}\n",
            "Using cuda device\n",
            "Logging to tensorboard_log/ddpg/ddpg_315_1\n",
            "day: 3148, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 4617783.42\n",
            "total_reward: 3617783.42\n",
            "total_cost: 1110.92\n",
            "total_trades: 49019\n",
            "Sharpe: 0.745\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 75        |\n",
            "|    time_elapsed    | 167       |\n",
            "|    total_timesteps | 12596     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | 36.1      |\n",
            "|    critic_loss     | 656       |\n",
            "|    learning_rate   | 0.0005    |\n",
            "|    n_updates       | 9447      |\n",
            "|    reward          | 5.4972343 |\n",
            "----------------------------------\n",
            "======DDPG Validation from:  2021-10-04 to  2022-01-03\n",
            "======Best Model Retraining from:  2009-04-01 to  2022-01-03\n",
            "======Trading from:  2022-01-03 to  2022-04-04\n",
            "Ensemble Strategy took:  25.210217595100403  minutes\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-4153cda81a48>\u001b[0m in \u001b[0;36m<cell line: 163>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-30-4153cda81a48>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrebalance_window\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mvalidation_window\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munique_trade_date\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrebalance_window\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'results/account_value_trade_{}_{}.csv'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ensemble'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0mdf_account_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_account_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m     sharpe = (252 ** 0.5) * df_account_value.account_value.pct_change(\n\u001b[1;32m    131\u001b[0m         1).mean() / df_account_value.account_value.pct_change(1).std()\n",
            "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5987\u001b[0m         ):\n\u001b[1;32m   5988\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5989\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5991\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "current_price  = 110\n",
        "avg_price = 111\n",
        "print(( current_price * 0.3 ) + current_price)\n",
        "if avg_price > ( current_price * 0.3  + current_price)  and avg_price > 0:\n",
        "  #actions[i] = self.total_stockss[i] * -1\n",
        "  #print(\"Updated actions\")\n",
        "  print(\"Current Price\", current_price , \" \", \" avg_price\", \" \", avg_price, \"Profit\", current_price -  avg_price)\n",
        "elif avg_price < (current_price - current_price * 0.15)  and avg_price > 0:\n",
        "  #actions[i] = self.total_stockss[i] * -1\n",
        "  print(\"Current Price\", current_price , \" \", \" avg_price\", \" \", avg_price, \"loss\", current_price -  avg_price )"
      ],
      "metadata": {
        "id": "R6wjk6Qwg-ar",
        "outputId": "b5271ab6-dbf6-4851-96bd-679c8016b825",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "143.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg_price < ( current_price * 0.3  + current_price)"
      ],
      "metadata": {
        "id": "lZm_y4awCWO9",
        "outputId": "89222edb-bc50-4871-c67d-ee4300ed49db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "current_price - current_price * 0.15"
      ],
      "metadata": {
        "id": "wr3yRoNwCvV7",
        "outputId": "60d7ebbb-13fb-4b17-ece3-b008217d2987",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.5"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JtmhGYiRC07B"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Making the most of your colab subscription",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}