5/5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55a2a234",
   "metadata": {},
   "source": [
    "# Algorithmic Trading with Bollinger Bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd1bbded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cvxpy as cvx\n",
    "import yfinance as yf\n",
    "import pandas_datareader as pdr\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91526efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub-Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>Saint Paul, Minnesota</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Building Products</td>\n",
       "      <td>Milwaukee, Wisconsin</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>91142</td>\n",
       "      <td>1916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1957-03-04</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Biotechnology</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol     Security             GICS Sector               GICS Sub-Industry  \\\n",
       "0    MMM           3M             Industrials        Industrial Conglomerates   \n",
       "1    AOS  A. O. Smith             Industrials               Building Products   \n",
       "2    ABT       Abbott             Health Care           Health Care Equipment   \n",
       "3   ABBV       AbbVie             Health Care                   Biotechnology   \n",
       "4    ACN    Accenture  Information Technology  IT Consulting & Other Services   \n",
       "\n",
       "     Headquarters Location  Date added      CIK      Founded  \n",
       "0    Saint Paul, Minnesota  1957-03-04    66740         1902  \n",
       "1     Milwaukee, Wisconsin  2017-07-26    91142         1916  \n",
       "2  North Chicago, Illinois  1957-03-04     1800         1888  \n",
       "3  North Chicago, Illinois  2012-12-31  1551152  2013 (1888)  \n",
       "4          Dublin, Ireland  2011-07-06  1467373         1989  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers_data = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]\n",
    "tickers_data.loc[:,'Symbol'] = tickers_data.loc[:,'Symbol'].apply(lambda x:str(x).replace('.', '-' ))\n",
    "tickers_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "149fa772",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '2016-01-01' #'2011-01-01'\n",
    "end = '2020-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8306e52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  503 of 503 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "7 Failed downloads:\n",
      "['VLTO', 'CEG', 'OTIS', 'ABNB', 'KVUE', 'GEHC', 'CARR']: Exception(\"%ticker%: Data doesn't exist for startDate = 1451624400, endDate = 1577854800\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>38.198929</td>\n",
       "      <td>40.689999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.189999</td>\n",
       "      <td>40.340000</td>\n",
       "      <td>41.060001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3287300.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAL</td>\n",
       "      <td>39.095463</td>\n",
       "      <td>40.910000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.349998</td>\n",
       "      <td>40.299999</td>\n",
       "      <td>41.290001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12037200.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>23.977476</td>\n",
       "      <td>26.337500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.342501</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>25.652500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270597600.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>40.778061</td>\n",
       "      <td>57.610001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.320000</td>\n",
       "      <td>56.400002</td>\n",
       "      <td>58.060001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10308400.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABT</td>\n",
       "      <td>36.956291</td>\n",
       "      <td>42.930000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.049999</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>43.939999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12227500.0</td>\n",
       "      <td>2016-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker  Adj Close      Close  Dividends       High        Low       Open  \\\n",
       "0      A  38.198929  40.689999        0.0  41.189999  40.340000  41.060001   \n",
       "1    AAL  39.095463  40.910000        0.0  41.349998  40.299999  41.290001   \n",
       "2   AAPL  23.977476  26.337500        0.0  26.342501  25.500000  25.652500   \n",
       "3   ABBV  40.778061  57.610001        0.0  58.320000  56.400002  58.060001   \n",
       "4    ABT  36.956291  42.930000        0.0  44.049999  42.500000  43.939999   \n",
       "\n",
       "   Stock Splits       Volume       Date  \n",
       "0           0.0    3287300.0 2016-01-04  \n",
       "1           0.0   12037200.0 2016-01-04  \n",
       "2           0.0  270597600.0 2016-01-04  \n",
       "3           0.0   10308400.0 2016-01-04  \n",
       "4           0.0   12227500.0 2016-01-04  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = yf.Tickers(list(tickers_data.loc[:,'Symbol']))\n",
    "\n",
    "data = tickers.history(start=start, end=end, auto_adjust=False, interval='1d')\n",
    "data = data.stack(level=1).rename_axis(['Date', 'Ticker']).reset_index(level=1)\n",
    "data['Date'] = data.index\n",
    "data = data.reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c768095c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>41.060001</td>\n",
       "      <td>41.290001</td>\n",
       "      <td>25.652500</td>\n",
       "      <td>58.060001</td>\n",
       "      <td>43.939999</td>\n",
       "      <td>22.940001</td>\n",
       "      <td>102.620003</td>\n",
       "      <td>91.769997</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>...</td>\n",
       "      <td>67.029999</td>\n",
       "      <td>35.610001</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>59.209999</td>\n",
       "      <td>35.919998</td>\n",
       "      <td>51.272465</td>\n",
       "      <td>98.174759</td>\n",
       "      <td>67.680000</td>\n",
       "      <td>26.559999</td>\n",
       "      <td>46.939999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>40.730000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>26.437500</td>\n",
       "      <td>57.990002</td>\n",
       "      <td>42.959999</td>\n",
       "      <td>22.893333</td>\n",
       "      <td>101.970001</td>\n",
       "      <td>92.010002</td>\n",
       "      <td>54.730000</td>\n",
       "      <td>35.820000</td>\n",
       "      <td>...</td>\n",
       "      <td>69.239998</td>\n",
       "      <td>35.680000</td>\n",
       "      <td>77.190002</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>36.060001</td>\n",
       "      <td>52.358017</td>\n",
       "      <td>99.029129</td>\n",
       "      <td>66.540001</td>\n",
       "      <td>26.770000</td>\n",
       "      <td>47.509998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>40.240002</td>\n",
       "      <td>40.049999</td>\n",
       "      <td>25.139999</td>\n",
       "      <td>56.369999</td>\n",
       "      <td>42.310001</td>\n",
       "      <td>22.809999</td>\n",
       "      <td>100.809998</td>\n",
       "      <td>91.099998</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>35.720001</td>\n",
       "      <td>...</td>\n",
       "      <td>67.730003</td>\n",
       "      <td>35.849998</td>\n",
       "      <td>76.639999</td>\n",
       "      <td>59.570000</td>\n",
       "      <td>36.430000</td>\n",
       "      <td>51.193386</td>\n",
       "      <td>99.524269</td>\n",
       "      <td>63.570000</td>\n",
       "      <td>25.910000</td>\n",
       "      <td>47.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>40.139999</td>\n",
       "      <td>40.970001</td>\n",
       "      <td>24.670000</td>\n",
       "      <td>56.520000</td>\n",
       "      <td>41.680000</td>\n",
       "      <td>22.683332</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>89.400002</td>\n",
       "      <td>50.450001</td>\n",
       "      <td>34.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.939999</td>\n",
       "      <td>36.099998</td>\n",
       "      <td>76.059998</td>\n",
       "      <td>58.299999</td>\n",
       "      <td>35.169998</td>\n",
       "      <td>50.186916</td>\n",
       "      <td>99.184464</td>\n",
       "      <td>60.669998</td>\n",
       "      <td>24.969999</td>\n",
       "      <td>46.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>39.220001</td>\n",
       "      <td>40.939999</td>\n",
       "      <td>24.637501</td>\n",
       "      <td>57.549999</td>\n",
       "      <td>41.860001</td>\n",
       "      <td>23.073334</td>\n",
       "      <td>99.480003</td>\n",
       "      <td>89.680000</td>\n",
       "      <td>50.740002</td>\n",
       "      <td>34.759998</td>\n",
       "      <td>...</td>\n",
       "      <td>61.509998</td>\n",
       "      <td>36.580002</td>\n",
       "      <td>76.349998</td>\n",
       "      <td>58.889999</td>\n",
       "      <td>34.869999</td>\n",
       "      <td>49.777138</td>\n",
       "      <td>99.233009</td>\n",
       "      <td>59.799999</td>\n",
       "      <td>25.180000</td>\n",
       "      <td>46.939999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A        AAL       AAPL       ABBV        ABT       ACGL  \\\n",
       "Date                                                                           \n",
       "2016-01-04  41.060001  41.290001  25.652500  58.060001  43.939999  22.940001   \n",
       "2016-01-05  40.730000  41.180000  26.437500  57.990002  42.959999  22.893333   \n",
       "2016-01-06  40.240002  40.049999  25.139999  56.369999  42.310001  22.809999   \n",
       "2016-01-07  40.139999  40.970001  24.670000  56.520000  41.680000  22.683332   \n",
       "2016-01-08  39.220001  40.939999  24.637501  57.549999  41.860001  23.073334   \n",
       "\n",
       "Ticker             ACN       ADBE        ADI        ADM  ...       WYNN  \\\n",
       "Date                                                     ...              \n",
       "2016-01-04  102.620003  91.769997  54.240002  36.110001  ...  67.029999   \n",
       "2016-01-05  101.970001  92.010002  54.730000  35.820000  ...  69.239998   \n",
       "2016-01-06  100.809998  91.099998  53.070000  35.720001  ...  67.730003   \n",
       "2016-01-07   99.750000  89.400002  50.450001  34.910000  ...  63.939999   \n",
       "2016-01-08   99.480003  89.680000  50.740002  34.759998  ...  61.509998   \n",
       "\n",
       "Ticker            XEL        XOM       XRAY        XYL        YUM        ZBH  \\\n",
       "Date                                                                           \n",
       "2016-01-04  35.610001  77.500000  59.209999  35.919998  51.272465  98.174759   \n",
       "2016-01-05  35.680000  77.190002  59.250000  36.060001  52.358017  99.029129   \n",
       "2016-01-06  35.849998  76.639999  59.570000  36.430000  51.193386  99.524269   \n",
       "2016-01-07  36.099998  76.059998  58.299999  35.169998  50.186916  99.184464   \n",
       "2016-01-08  36.580002  76.349998  58.889999  34.869999  49.777138  99.233009   \n",
       "\n",
       "Ticker           ZBRA       ZION        ZTS  \n",
       "Date                                         \n",
       "2016-01-04  67.680000  26.559999  46.939999  \n",
       "2016-01-05  66.540001  26.770000  47.509998  \n",
       "2016-01-06  63.570000  25.910000  47.270000  \n",
       "2016-01-07  60.669998  24.969999  46.990002  \n",
       "2016-01-08  59.799999  25.180000  46.939999  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_open = data.pivot(index='Date', columns='Ticker', values='Open')\n",
    "_open = _open.dropna(axis=1)\n",
    "_open.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc544702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>38.198929</td>\n",
       "      <td>39.095463</td>\n",
       "      <td>23.977476</td>\n",
       "      <td>40.778061</td>\n",
       "      <td>36.956291</td>\n",
       "      <td>22.950001</td>\n",
       "      <td>89.603111</td>\n",
       "      <td>91.970001</td>\n",
       "      <td>46.181301</td>\n",
       "      <td>28.631706</td>\n",
       "      <td>...</td>\n",
       "      <td>61.800697</td>\n",
       "      <td>28.352646</td>\n",
       "      <td>53.587521</td>\n",
       "      <td>54.965923</td>\n",
       "      <td>32.652332</td>\n",
       "      <td>44.780548</td>\n",
       "      <td>93.003998</td>\n",
       "      <td>66.489998</td>\n",
       "      <td>21.745005</td>\n",
       "      <td>44.720966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>38.067520</td>\n",
       "      <td>38.722759</td>\n",
       "      <td>23.376617</td>\n",
       "      <td>40.608196</td>\n",
       "      <td>36.947693</td>\n",
       "      <td>23.033333</td>\n",
       "      <td>90.069458</td>\n",
       "      <td>92.339996</td>\n",
       "      <td>45.842003</td>\n",
       "      <td>28.951262</td>\n",
       "      <td>...</td>\n",
       "      <td>62.968945</td>\n",
       "      <td>28.638550</td>\n",
       "      <td>54.044094</td>\n",
       "      <td>56.086536</td>\n",
       "      <td>32.643276</td>\n",
       "      <td>44.668919</td>\n",
       "      <td>94.940620</td>\n",
       "      <td>64.820000</td>\n",
       "      <td>21.508919</td>\n",
       "      <td>45.421062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>38.236492</td>\n",
       "      <td>39.401264</td>\n",
       "      <td>22.919146</td>\n",
       "      <td>40.615288</td>\n",
       "      <td>36.637791</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>89.893478</td>\n",
       "      <td>91.019997</td>\n",
       "      <td>43.890907</td>\n",
       "      <td>28.248249</td>\n",
       "      <td>...</td>\n",
       "      <td>59.706833</td>\n",
       "      <td>28.940346</td>\n",
       "      <td>53.594456</td>\n",
       "      <td>55.274090</td>\n",
       "      <td>32.236027</td>\n",
       "      <td>44.352646</td>\n",
       "      <td>95.351692</td>\n",
       "      <td>62.230000</td>\n",
       "      <td>20.882044</td>\n",
       "      <td>45.430527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>36.612392</td>\n",
       "      <td>38.655857</td>\n",
       "      <td>21.951859</td>\n",
       "      <td>40.494949</td>\n",
       "      <td>35.759720</td>\n",
       "      <td>23.046667</td>\n",
       "      <td>87.253708</td>\n",
       "      <td>89.110001</td>\n",
       "      <td>42.771152</td>\n",
       "      <td>27.577183</td>\n",
       "      <td>...</td>\n",
       "      <td>54.090210</td>\n",
       "      <td>29.051533</td>\n",
       "      <td>52.736595</td>\n",
       "      <td>54.788483</td>\n",
       "      <td>31.403431</td>\n",
       "      <td>42.839489</td>\n",
       "      <td>93.159279</td>\n",
       "      <td>59.410000</td>\n",
       "      <td>20.255171</td>\n",
       "      <td>44.049248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>36.227505</td>\n",
       "      <td>38.579418</td>\n",
       "      <td>22.067924</td>\n",
       "      <td>39.390724</td>\n",
       "      <td>35.010788</td>\n",
       "      <td>22.806667</td>\n",
       "      <td>86.408958</td>\n",
       "      <td>87.849998</td>\n",
       "      <td>42.397903</td>\n",
       "      <td>27.473330</td>\n",
       "      <td>...</td>\n",
       "      <td>51.888508</td>\n",
       "      <td>28.733862</td>\n",
       "      <td>51.671204</td>\n",
       "      <td>53.219639</td>\n",
       "      <td>31.104786</td>\n",
       "      <td>42.256557</td>\n",
       "      <td>92.766441</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>20.027224</td>\n",
       "      <td>43.405922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A        AAL       AAPL       ABBV        ABT       ACGL  \\\n",
       "Date                                                                           \n",
       "2016-01-04  38.198929  39.095463  23.977476  40.778061  36.956291  22.950001   \n",
       "2016-01-05  38.067520  38.722759  23.376617  40.608196  36.947693  23.033333   \n",
       "2016-01-06  38.236492  39.401264  22.919146  40.615288  36.637791  23.070000   \n",
       "2016-01-07  36.612392  38.655857  21.951859  40.494949  35.759720  23.046667   \n",
       "2016-01-08  36.227505  38.579418  22.067924  39.390724  35.010788  22.806667   \n",
       "\n",
       "Ticker            ACN       ADBE        ADI        ADM  ...       WYNN  \\\n",
       "Date                                                    ...              \n",
       "2016-01-04  89.603111  91.970001  46.181301  28.631706  ...  61.800697   \n",
       "2016-01-05  90.069458  92.339996  45.842003  28.951262  ...  62.968945   \n",
       "2016-01-06  89.893478  91.019997  43.890907  28.248249  ...  59.706833   \n",
       "2016-01-07  87.253708  89.110001  42.771152  27.577183  ...  54.090210   \n",
       "2016-01-08  86.408958  87.849998  42.397903  27.473330  ...  51.888508   \n",
       "\n",
       "Ticker            XEL        XOM       XRAY        XYL        YUM        ZBH  \\\n",
       "Date                                                                           \n",
       "2016-01-04  28.352646  53.587521  54.965923  32.652332  44.780548  93.003998   \n",
       "2016-01-05  28.638550  54.044094  56.086536  32.643276  44.668919  94.940620   \n",
       "2016-01-06  28.940346  53.594456  55.274090  32.236027  44.352646  95.351692   \n",
       "2016-01-07  29.051533  52.736595  54.788483  31.403431  42.839489  93.159279   \n",
       "2016-01-08  28.733862  51.671204  53.219639  31.104786  42.256557  92.766441   \n",
       "\n",
       "Ticker           ZBRA       ZION        ZTS  \n",
       "Date                                         \n",
       "2016-01-04  66.489998  21.745005  44.720966  \n",
       "2016-01-05  64.820000  21.508919  45.421062  \n",
       "2016-01-06  62.230000  20.882044  45.430527  \n",
       "2016-01-07  59.410000  20.255171  44.049248  \n",
       "2016-01-08  59.250000  20.027224  43.405922  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_adj_close = data.pivot(index='Date', columns='Ticker', values='Adj Close')\n",
    "_adj_close = _adj_close.dropna(axis=1)\n",
    "_adj_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aed1d61",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9010d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'address1': 'One Apple Park Way',\n",
       " 'city': 'Cupertino',\n",
       " 'state': 'CA',\n",
       " 'zip': '95014',\n",
       " 'country': 'United States',\n",
       " 'phone': '408 996 1010',\n",
       " 'website': 'https://www.apple.com',\n",
       " 'industry': 'Consumer Electronics',\n",
       " 'industryKey': 'consumer-electronics',\n",
       " 'industryDisp': 'Consumer Electronics',\n",
       " 'sector': 'Technology',\n",
       " 'sectorKey': 'technology',\n",
       " 'sectorDisp': 'Technology',\n",
       " 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',\n",
       " 'fullTimeEmployees': 164000,\n",
       " 'companyOfficers': [{'maxAge': 1,\n",
       "   'name': 'Mr. Timothy D. Cook',\n",
       "   'age': 61,\n",
       "   'title': 'CEO & Director',\n",
       "   'yearBorn': 1961,\n",
       "   'fiscalYear': 2022,\n",
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       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Luca  Maestri',\n",
       "   'age': 59,\n",
       "   'title': 'CFO & Senior VP',\n",
       "   'yearBorn': 1963,\n",
       "   'fiscalYear': 2022,\n",
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       "   'age': 58,\n",
       "   'title': 'Chief Operating Officer',\n",
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       "   'fiscalYear': 2022,\n",
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       "   'exercisedValue': 0,\n",
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       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Katherine L. Adams',\n",
       "   'age': 58,\n",
       "   'title': 'Senior VP, General Counsel & Secretary',\n",
       "   'yearBorn': 1964,\n",
       "   'fiscalYear': 2022,\n",
       "   'totalPay': 5015208,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': \"Ms. Deirdre  O'Brien\",\n",
       "   'age': 55,\n",
       "   'title': 'Senior Vice President of Retail',\n",
       "   'yearBorn': 1967,\n",
       "   'fiscalYear': 2022,\n",
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       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Chris  Kondo',\n",
       "   'title': 'Senior Director of Corporate Accounting',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. James  Wilson',\n",
       "   'title': 'Chief Technology Officer',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Mary  Demby',\n",
       "   'title': 'Chief Information Officer',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Nancy  Paxton',\n",
       "   'title': 'Senior Director of Investor Relations & Treasury',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Greg  Joswiak',\n",
       "   'title': 'Senior Vice President of Worldwide Marketing',\n",
       "   'fiscalYear': 2022,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0}],\n",
       " 'auditRisk': 4,\n",
       " 'boardRisk': 1,\n",
       " 'compensationRisk': 5,\n",
       " 'shareHolderRightsRisk': 1,\n",
       " 'overallRisk': 1,\n",
       " 'governanceEpochDate': 1698451200,\n",
       " 'compensationAsOfEpochDate': 1672444800,\n",
       " 'maxAge': 86400,\n",
       " 'priceHint': 2,\n",
       " 'previousClose': 166.89,\n",
       " 'open': 166.91,\n",
       " 'dayLow': 166.84,\n",
       " 'dayHigh': 168.96,\n",
       " 'regularMarketPreviousClose': 166.89,\n",
       " 'regularMarketOpen': 166.91,\n",
       " 'regularMarketDayLow': 166.84,\n",
       " 'regularMarketDayHigh': 168.96,\n",
       " 'dividendRate': 0.96,\n",
       " 'dividendYield': 0.0057,\n",
       " 'exDividendDate': 1691712000,\n",
       " 'payoutRatio': 0.156,\n",
       " 'fiveYearAvgDividendYield': 0.85,\n",
       " 'beta': 1.308,\n",
       " 'trailingPE': 28.224833,\n",
       " 'forwardPE': 27.17609,\n",
       " 'volume': 58499129,\n",
       " 'regularMarketVolume': 58499129,\n",
       " 'averageVolume': 58993159,\n",
       " 'averageVolume10days': 57437060,\n",
       " 'averageDailyVolume10Day': 57437060,\n",
       " 'bid': 168.02,\n",
       " 'ask': 168.03,\n",
       " 'bidSize': 1000,\n",
       " 'askSize': 800,\n",
       " 'marketCap': 2629985107968,\n",
       " 'fiftyTwoWeekLow': 124.17,\n",
       " 'fiftyTwoWeekHigh': 198.23,\n",
       " 'priceToSalesTrailing12Months': 6.850115,\n",
       " 'fiftyDayAverage': 176.904,\n",
       " 'twoHundredDayAverage': 170.53055,\n",
       " 'trailingAnnualDividendRate': 0.93,\n",
       " 'trailingAnnualDividendYield': 0.005572533,\n",
       " 'currency': 'USD',\n",
       " 'enterpriseValue': 2676788559872,\n",
       " 'profitMargins': 0.24681,\n",
       " 'floatShares': 15616878002,\n",
       " 'sharesOutstanding': 15634199552,\n",
       " 'sharesShort': 93026130,\n",
       " 'sharesShortPriorMonth': 107325921,\n",
       " 'sharesShortPreviousMonthDate': 1694736000,\n",
       " 'dateShortInterest': 1697155200,\n",
       " 'sharesPercentSharesOut': 0.006,\n",
       " 'heldPercentInsiders': 0.00071999995,\n",
       " 'heldPercentInstitutions': 0.61317,\n",
       " 'shortRatio': 1.6,\n",
       " 'shortPercentOfFloat': 0.006,\n",
       " 'impliedSharesOutstanding': 15634199552,\n",
       " 'bookValue': 3.852,\n",
       " 'priceToBook': 43.670822,\n",
       " 'lastFiscalYearEnd': 1663977600,\n",
       " 'nextFiscalYearEnd': 1695513600,\n",
       " 'mostRecentQuarter': 1688169600,\n",
       " 'earningsQuarterlyGrowth': 0.023,\n",
       " 'netIncomeToCommon': 94760001536,\n",
       " 'trailingEps': 5.96,\n",
       " 'forwardEps': 6.19,\n",
       " 'pegRatio': 4.33,\n",
       " 'lastSplitFactor': '4:1',\n",
       " 'lastSplitDate': 1598832000,\n",
       " 'enterpriseToRevenue': 6.972,\n",
       " 'enterpriseToEbitda': 21.594,\n",
       " '52WeekChange': 0.09703922,\n",
       " 'SandP52WeekChange': 0.06337583,\n",
       " 'lastDividendValue': 0.24,\n",
       " 'lastDividendDate': 1691712000,\n",
       " 'exchange': 'NMS',\n",
       " 'quoteType': 'EQUITY',\n",
       " 'symbol': 'AAPL',\n",
       " 'underlyingSymbol': 'AAPL',\n",
       " 'shortName': 'Apple Inc.',\n",
       " 'longName': 'Apple Inc.',\n",
       " 'firstTradeDateEpochUtc': 345479400,\n",
       " 'timeZoneFullName': 'America/New_York',\n",
       " 'timeZoneShortName': 'EDT',\n",
       " 'uuid': '8b10e4ae-9eeb-3684-921a-9ab27e4d87aa',\n",
       " 'messageBoardId': 'finmb_24937',\n",
       " 'gmtOffSetMilliseconds': -14400000,\n",
       " 'currentPrice': 168.22,\n",
       " 'targetHighPrice': 226.34,\n",
       " 'targetLowPrice': 140.52,\n",
       " 'targetMeanPrice': 187.73,\n",
       " 'targetMedianPrice': 188.62,\n",
       " 'recommendationMean': 2.1,\n",
       " 'recommendationKey': 'buy',\n",
       " 'numberOfAnalystOpinions': 37,\n",
       " 'totalCash': 62482001920,\n",
       " 'totalCashPerShare': 3.996,\n",
       " 'ebitda': 123957002240,\n",
       " 'totalDebt': 109280002048,\n",
       " 'quickRatio': 0.814,\n",
       " 'currentRatio': 0.982,\n",
       " 'totalRevenue': 383932989440,\n",
       " 'debtToEquity': 181.305,\n",
       " 'revenuePerShare': 24.22,\n",
       " 'returnOnAssets': 0.20896,\n",
       " 'returnOnEquity': 1.60093,\n",
       " 'grossProfits': 170782000000,\n",
       " 'freeCashflow': 90680500224,\n",
       " 'operatingCashflow': 113071996928,\n",
       " 'earningsGrowth': 0.05,\n",
       " 'revenueGrowth': -0.014,\n",
       " 'grossMargins': 0.43449003,\n",
       " 'ebitdaMargins': 0.32286,\n",
       " 'operatingMargins': 0.28116,\n",
       " 'financialCurrency': 'USD',\n",
       " 'trailingPegRatio': 2.1778}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers.tickers['AAPL'].info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdabe6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x142cda130>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('AAPL')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Price')\n",
    "ax.plot(_adj_close.index, _adj_close.loc[:,['AAPL']].values, marker='.', color='#0F00FF', markersize=1, linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c79bf9d",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "514234cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.003440</td>\n",
       "      <td>-0.009533</td>\n",
       "      <td>-0.025059</td>\n",
       "      <td>-0.004166</td>\n",
       "      <td>-0.000233</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.004023</td>\n",
       "      <td>-0.007347</td>\n",
       "      <td>0.011161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018903</td>\n",
       "      <td>0.010084</td>\n",
       "      <td>0.008520</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>-0.000277</td>\n",
       "      <td>-0.002493</td>\n",
       "      <td>0.020823</td>\n",
       "      <td>-0.025117</td>\n",
       "      <td>-0.010857</td>\n",
       "      <td>0.015655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.004439</td>\n",
       "      <td>0.017522</td>\n",
       "      <td>-0.019570</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>-0.008388</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>-0.001954</td>\n",
       "      <td>-0.014295</td>\n",
       "      <td>-0.042561</td>\n",
       "      <td>-0.024283</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051805</td>\n",
       "      <td>0.010538</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>-0.014486</td>\n",
       "      <td>-0.012476</td>\n",
       "      <td>-0.007080</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>-0.039957</td>\n",
       "      <td>-0.029145</td>\n",
       "      <td>0.000208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-0.042475</td>\n",
       "      <td>-0.018918</td>\n",
       "      <td>-0.042204</td>\n",
       "      <td>-0.002963</td>\n",
       "      <td>-0.023966</td>\n",
       "      <td>-0.001011</td>\n",
       "      <td>-0.029366</td>\n",
       "      <td>-0.020984</td>\n",
       "      <td>-0.025512</td>\n",
       "      <td>-0.023756</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.094070</td>\n",
       "      <td>0.003842</td>\n",
       "      <td>-0.016007</td>\n",
       "      <td>-0.008785</td>\n",
       "      <td>-0.025828</td>\n",
       "      <td>-0.034116</td>\n",
       "      <td>-0.022993</td>\n",
       "      <td>-0.045316</td>\n",
       "      <td>-0.030020</td>\n",
       "      <td>-0.030404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.010512</td>\n",
       "      <td>-0.001977</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>-0.027268</td>\n",
       "      <td>-0.020943</td>\n",
       "      <td>-0.010414</td>\n",
       "      <td>-0.009682</td>\n",
       "      <td>-0.014140</td>\n",
       "      <td>-0.008727</td>\n",
       "      <td>-0.003766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040704</td>\n",
       "      <td>-0.010935</td>\n",
       "      <td>-0.020202</td>\n",
       "      <td>-0.028635</td>\n",
       "      <td>-0.009510</td>\n",
       "      <td>-0.013607</td>\n",
       "      <td>-0.004217</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>-0.011254</td>\n",
       "      <td>-0.014605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-0.016844</td>\n",
       "      <td>0.017587</td>\n",
       "      <td>0.016192</td>\n",
       "      <td>-0.031806</td>\n",
       "      <td>0.001475</td>\n",
       "      <td>-0.002046</td>\n",
       "      <td>0.010489</td>\n",
       "      <td>0.017416</td>\n",
       "      <td>0.023809</td>\n",
       "      <td>-0.006688</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016453</td>\n",
       "      <td>0.008015</td>\n",
       "      <td>-0.013389</td>\n",
       "      <td>-0.019477</td>\n",
       "      <td>-0.008437</td>\n",
       "      <td>-0.001761</td>\n",
       "      <td>-0.017233</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>-0.019919</td>\n",
       "      <td>-0.028335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A       AAL      AAPL      ABBV       ABT      ACGL  \\\n",
       "Date                                                                     \n",
       "2016-01-05 -0.003440 -0.009533 -0.025059 -0.004166 -0.000233  0.003631   \n",
       "2016-01-06  0.004439  0.017522 -0.019570  0.000175 -0.008388  0.001592   \n",
       "2016-01-07 -0.042475 -0.018918 -0.042204 -0.002963 -0.023966 -0.001011   \n",
       "2016-01-08 -0.010512 -0.001977  0.005287 -0.027268 -0.020943 -0.010414   \n",
       "2016-01-11 -0.016844  0.017587  0.016192 -0.031806  0.001475 -0.002046   \n",
       "\n",
       "Ticker           ACN      ADBE       ADI       ADM  ...      WYNN       XEL  \\\n",
       "Date                                                ...                       \n",
       "2016-01-05  0.005205  0.004023 -0.007347  0.011161  ...  0.018903  0.010084   \n",
       "2016-01-06 -0.001954 -0.014295 -0.042561 -0.024283  ... -0.051805  0.010538   \n",
       "2016-01-07 -0.029366 -0.020984 -0.025512 -0.023756  ... -0.094070  0.003842   \n",
       "2016-01-08 -0.009682 -0.014140 -0.008727 -0.003766  ... -0.040704 -0.010935   \n",
       "2016-01-11  0.010489  0.017416  0.023809 -0.006688  ... -0.016453  0.008015   \n",
       "\n",
       "Ticker           XOM      XRAY       XYL       YUM       ZBH      ZBRA  \\\n",
       "Date                                                                     \n",
       "2016-01-05  0.008520  0.020387 -0.000277 -0.002493  0.020823 -0.025117   \n",
       "2016-01-06 -0.008320 -0.014486 -0.012476 -0.007080  0.004330 -0.039957   \n",
       "2016-01-07 -0.016007 -0.008785 -0.025828 -0.034116 -0.022993 -0.045316   \n",
       "2016-01-08 -0.020202 -0.028635 -0.009510 -0.013607 -0.004217 -0.002693   \n",
       "2016-01-11 -0.013389 -0.019477 -0.008437 -0.001761 -0.017233 -0.006582   \n",
       "\n",
       "Ticker          ZION       ZTS  \n",
       "Date                            \n",
       "2016-01-05 -0.010857  0.015655  \n",
       "2016-01-06 -0.029145  0.000208  \n",
       "2016-01-07 -0.030020 -0.030404  \n",
       "2016-01-08 -0.011254 -0.014605  \n",
       "2016-01-11 -0.019919 -0.028335  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = ((_adj_close - _adj_close.shift(1))/_adj_close.shift(1)).iloc[1:,:]\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef4149d",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70b659bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mu(signals, window_length):\n",
    "    return signals.rolling(window=window_length).mean().shift(1).iloc[window_length:,:]\n",
    "\n",
    "def sigma(signals, window_length):\n",
    "    return signals.rolling(window=window_length).std(ddof=0).shift(1).iloc[window_length:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e5712f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ACN</th>\n",
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       "      <th>ADI</th>\n",
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       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>35.699902</td>\n",
       "      <td>38.157017</td>\n",
       "      <td>22.379508</td>\n",
       "      <td>40.105414</td>\n",
       "      <td>34.915261</td>\n",
       "      <td>22.386333</td>\n",
       "      <td>89.157423</td>\n",
       "      <td>88.763999</td>\n",
       "      <td>43.608846</td>\n",
       "      <td>26.977233</td>\n",
       "      <td>...</td>\n",
       "      <td>54.526070</td>\n",
       "      <td>29.146835</td>\n",
       "      <td>52.666024</td>\n",
       "      <td>53.880799</td>\n",
       "      <td>31.124693</td>\n",
       "      <td>43.408564</td>\n",
       "      <td>92.009626</td>\n",
       "      <td>58.7535</td>\n",
       "      <td>18.779999</td>\n",
       "      <td>42.078078</td>\n",
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       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>35.529984</td>\n",
       "      <td>37.971622</td>\n",
       "      <td>22.255809</td>\n",
       "      <td>39.995936</td>\n",
       "      <td>34.708186</td>\n",
       "      <td>22.348667</td>\n",
       "      <td>89.244975</td>\n",
       "      <td>88.562999</td>\n",
       "      <td>43.477360</td>\n",
       "      <td>26.838228</td>\n",
       "      <td>...</td>\n",
       "      <td>54.221875</td>\n",
       "      <td>29.277082</td>\n",
       "      <td>52.566749</td>\n",
       "      <td>53.900410</td>\n",
       "      <td>31.069488</td>\n",
       "      <td>43.426900</td>\n",
       "      <td>91.837430</td>\n",
       "      <td>58.3255</td>\n",
       "      <td>18.569551</td>\n",
       "      <td>41.819873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>35.372268</td>\n",
       "      <td>37.827797</td>\n",
       "      <td>22.183432</td>\n",
       "      <td>39.998307</td>\n",
       "      <td>34.507605</td>\n",
       "      <td>22.306833</td>\n",
       "      <td>89.233538</td>\n",
       "      <td>88.345499</td>\n",
       "      <td>43.364536</td>\n",
       "      <td>26.726385</td>\n",
       "      <td>...</td>\n",
       "      <td>53.911838</td>\n",
       "      <td>29.403756</td>\n",
       "      <td>52.579202</td>\n",
       "      <td>53.848582</td>\n",
       "      <td>31.051388</td>\n",
       "      <td>43.454876</td>\n",
       "      <td>91.532775</td>\n",
       "      <td>58.0690</td>\n",
       "      <td>18.380676</td>\n",
       "      <td>41.507227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>35.216899</td>\n",
       "      <td>37.683495</td>\n",
       "      <td>22.142739</td>\n",
       "      <td>39.997462</td>\n",
       "      <td>34.322086</td>\n",
       "      <td>22.252667</td>\n",
       "      <td>89.258616</td>\n",
       "      <td>88.112499</td>\n",
       "      <td>43.361567</td>\n",
       "      <td>26.682047</td>\n",
       "      <td>...</td>\n",
       "      <td>53.792317</td>\n",
       "      <td>29.508192</td>\n",
       "      <td>52.660834</td>\n",
       "      <td>53.854653</td>\n",
       "      <td>31.117453</td>\n",
       "      <td>43.494295</td>\n",
       "      <td>91.220813</td>\n",
       "      <td>58.0080</td>\n",
       "      <td>18.234135</td>\n",
       "      <td>41.205012</td>\n",
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       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>35.077960</td>\n",
       "      <td>37.506700</td>\n",
       "      <td>22.120892</td>\n",
       "      <td>39.872456</td>\n",
       "      <td>34.154482</td>\n",
       "      <td>22.199333</td>\n",
       "      <td>89.250257</td>\n",
       "      <td>87.616999</td>\n",
       "      <td>43.336118</td>\n",
       "      <td>26.664073</td>\n",
       "      <td>...</td>\n",
       "      <td>53.869602</td>\n",
       "      <td>29.625336</td>\n",
       "      <td>52.794007</td>\n",
       "      <td>53.869595</td>\n",
       "      <td>31.206142</td>\n",
       "      <td>43.529778</td>\n",
       "      <td>90.902456</td>\n",
       "      <td>57.9065</td>\n",
       "      <td>18.101841</td>\n",
       "      <td>40.941994</td>\n",
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       "<p>5 rows × 483 columns</p>\n",
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      "text/plain": [
       "Ticker              A        AAL       AAPL       ABBV        ABT       ACGL  \\\n",
       "Date                                                                           \n",
       "2016-02-02  35.699902  38.157017  22.379508  40.105414  34.915261  22.386333   \n",
       "2016-02-03  35.529984  37.971622  22.255809  39.995936  34.708186  22.348667   \n",
       "2016-02-04  35.372268  37.827797  22.183432  39.998307  34.507605  22.306833   \n",
       "2016-02-05  35.216899  37.683495  22.142739  39.997462  34.322086  22.252667   \n",
       "2016-02-08  35.077960  37.506700  22.120892  39.872456  34.154482  22.199333   \n",
       "\n",
       "Ticker            ACN       ADBE        ADI        ADM  ...       WYNN  \\\n",
       "Date                                                    ...              \n",
       "2016-02-02  89.157423  88.763999  43.608846  26.977233  ...  54.526070   \n",
       "2016-02-03  89.244975  88.562999  43.477360  26.838228  ...  54.221875   \n",
       "2016-02-04  89.233538  88.345499  43.364536  26.726385  ...  53.911838   \n",
       "2016-02-05  89.258616  88.112499  43.361567  26.682047  ...  53.792317   \n",
       "2016-02-08  89.250257  87.616999  43.336118  26.664073  ...  53.869602   \n",
       "\n",
       "Ticker            XEL        XOM       XRAY        XYL        YUM        ZBH  \\\n",
       "Date                                                                           \n",
       "2016-02-02  29.146835  52.666024  53.880799  31.124693  43.408564  92.009626   \n",
       "2016-02-03  29.277082  52.566749  53.900410  31.069488  43.426900  91.837430   \n",
       "2016-02-04  29.403756  52.579202  53.848582  31.051388  43.454876  91.532775   \n",
       "2016-02-05  29.508192  52.660834  53.854653  31.117453  43.494295  91.220813   \n",
       "2016-02-08  29.625336  52.794007  53.869595  31.206142  43.529778  90.902456   \n",
       "\n",
       "Ticker         ZBRA       ZION        ZTS  \n",
       "Date                                       \n",
       "2016-02-02  58.7535  18.779999  42.078078  \n",
       "2016-02-03  58.3255  18.569551  41.819873  \n",
       "2016-02-04  58.0690  18.380676  41.507227  \n",
       "2016-02-05  58.0080  18.234135  41.205012  \n",
       "2016-02-08  57.9065  18.101841  40.941994  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window_length = 20\n",
    "\n",
    "mu_20d = mu(signals=_adj_close,\n",
    "            window_length=window_length)\n",
    "mu_20d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7756636a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>1.181406</td>\n",
       "      <td>0.918134</td>\n",
       "      <td>0.634470</td>\n",
       "      <td>1.373610</td>\n",
       "      <td>1.243830</td>\n",
       "      <td>0.451677</td>\n",
       "      <td>1.891084</td>\n",
       "      <td>1.948244</td>\n",
       "      <td>1.189843</td>\n",
       "      <td>1.239599</td>\n",
       "      <td>...</td>\n",
       "      <td>4.646436</td>\n",
       "      <td>0.547284</td>\n",
       "      <td>1.095661</td>\n",
       "      <td>1.297986</td>\n",
       "      <td>0.951007</td>\n",
       "      <td>1.042575</td>\n",
       "      <td>1.753042</td>\n",
       "      <td>2.919995</td>\n",
       "      <td>1.444401</td>\n",
       "      <td>1.583285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-03</th>\n",
       "      <td>1.046438</td>\n",
       "      <td>1.071475</td>\n",
       "      <td>0.545844</td>\n",
       "      <td>1.402586</td>\n",
       "      <td>1.231479</td>\n",
       "      <td>0.434173</td>\n",
       "      <td>1.949329</td>\n",
       "      <td>1.809547</td>\n",
       "      <td>1.033309</td>\n",
       "      <td>1.201570</td>\n",
       "      <td>...</td>\n",
       "      <td>4.349910</td>\n",
       "      <td>0.644175</td>\n",
       "      <td>1.097618</td>\n",
       "      <td>1.317056</td>\n",
       "      <td>0.890872</td>\n",
       "      <td>1.069422</td>\n",
       "      <td>1.814960</td>\n",
       "      <td>2.320436</td>\n",
       "      <td>1.296077</td>\n",
       "      <td>1.551999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>0.875913</td>\n",
       "      <td>1.151096</td>\n",
       "      <td>0.485009</td>\n",
       "      <td>1.403658</td>\n",
       "      <td>1.175821</td>\n",
       "      <td>0.405553</td>\n",
       "      <td>1.945124</td>\n",
       "      <td>1.590687</td>\n",
       "      <td>0.880912</td>\n",
       "      <td>1.099447</td>\n",
       "      <td>...</td>\n",
       "      <td>3.914617</td>\n",
       "      <td>0.747040</td>\n",
       "      <td>1.115575</td>\n",
       "      <td>1.248627</td>\n",
       "      <td>0.861920</td>\n",
       "      <td>1.108163</td>\n",
       "      <td>1.779536</td>\n",
       "      <td>1.817360</td>\n",
       "      <td>1.116812</td>\n",
       "      <td>1.419193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-05</th>\n",
       "      <td>0.579527</td>\n",
       "      <td>1.125412</td>\n",
       "      <td>0.454774</td>\n",
       "      <td>1.403292</td>\n",
       "      <td>1.116291</td>\n",
       "      <td>0.370868</td>\n",
       "      <td>1.956670</td>\n",
       "      <td>1.521664</td>\n",
       "      <td>0.879231</td>\n",
       "      <td>1.054126</td>\n",
       "      <td>...</td>\n",
       "      <td>3.769668</td>\n",
       "      <td>0.817622</td>\n",
       "      <td>1.239707</td>\n",
       "      <td>1.255816</td>\n",
       "      <td>0.991140</td>\n",
       "      <td>1.152532</td>\n",
       "      <td>1.622691</td>\n",
       "      <td>1.692884</td>\n",
       "      <td>0.960299</td>\n",
       "      <td>1.173923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>0.561117</td>\n",
       "      <td>1.231505</td>\n",
       "      <td>0.473529</td>\n",
       "      <td>1.463474</td>\n",
       "      <td>1.139267</td>\n",
       "      <td>0.326947</td>\n",
       "      <td>1.965555</td>\n",
       "      <td>2.447820</td>\n",
       "      <td>0.902997</td>\n",
       "      <td>1.041701</td>\n",
       "      <td>...</td>\n",
       "      <td>3.790768</td>\n",
       "      <td>0.906778</td>\n",
       "      <td>1.376234</td>\n",
       "      <td>1.268552</td>\n",
       "      <td>1.087445</td>\n",
       "      <td>1.142708</td>\n",
       "      <td>1.823333</td>\n",
       "      <td>1.666431</td>\n",
       "      <td>0.848507</td>\n",
       "      <td>1.093763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A       AAL      AAPL      ABBV       ABT      ACGL  \\\n",
       "Date                                                                     \n",
       "2016-02-02  1.181406  0.918134  0.634470  1.373610  1.243830  0.451677   \n",
       "2016-02-03  1.046438  1.071475  0.545844  1.402586  1.231479  0.434173   \n",
       "2016-02-04  0.875913  1.151096  0.485009  1.403658  1.175821  0.405553   \n",
       "2016-02-05  0.579527  1.125412  0.454774  1.403292  1.116291  0.370868   \n",
       "2016-02-08  0.561117  1.231505  0.473529  1.463474  1.139267  0.326947   \n",
       "\n",
       "Ticker           ACN      ADBE       ADI       ADM  ...      WYNN       XEL  \\\n",
       "Date                                                ...                       \n",
       "2016-02-02  1.891084  1.948244  1.189843  1.239599  ...  4.646436  0.547284   \n",
       "2016-02-03  1.949329  1.809547  1.033309  1.201570  ...  4.349910  0.644175   \n",
       "2016-02-04  1.945124  1.590687  0.880912  1.099447  ...  3.914617  0.747040   \n",
       "2016-02-05  1.956670  1.521664  0.879231  1.054126  ...  3.769668  0.817622   \n",
       "2016-02-08  1.965555  2.447820  0.902997  1.041701  ...  3.790768  0.906778   \n",
       "\n",
       "Ticker           XOM      XRAY       XYL       YUM       ZBH      ZBRA  \\\n",
       "Date                                                                     \n",
       "2016-02-02  1.095661  1.297986  0.951007  1.042575  1.753042  2.919995   \n",
       "2016-02-03  1.097618  1.317056  0.890872  1.069422  1.814960  2.320436   \n",
       "2016-02-04  1.115575  1.248627  0.861920  1.108163  1.779536  1.817360   \n",
       "2016-02-05  1.239707  1.255816  0.991140  1.152532  1.622691  1.692884   \n",
       "2016-02-08  1.376234  1.268552  1.087445  1.142708  1.823333  1.666431   \n",
       "\n",
       "Ticker          ZION       ZTS  \n",
       "Date                            \n",
       "2016-02-02  1.444401  1.583285  \n",
       "2016-02-03  1.296077  1.551999  \n",
       "2016-02-04  1.116812  1.419193  \n",
       "2016-02-05  0.960299  1.173923  \n",
       "2016-02-08  0.848507  1.093763  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window_length = 20 \n",
    "\n",
    "sigma_20d = sigma(signals=_adj_close,\n",
    "                  window_length=window_length)\n",
    "sigma_20d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "849f0a8d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Ticker</th>\n",
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       "      <th>AAL</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2016-02-04</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "Ticker        A  AAL  AAPL  ABBV  ABT  ACGL  ACN  ADBE  ADI  ADM  ...  WYNN  \\\n",
       "Date                                                              ...         \n",
       "2016-02-02  0.0  1.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-02-03  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-02-04  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-02-05  1.0  1.0   0.0   0.0  0.0   0.0  0.0   1.0  0.0  0.0  ...   0.0   \n",
       "2016-02-08  1.0  1.0   0.0   0.0  0.0   0.0  1.0   1.0  0.0  0.0  ...   0.0   \n",
       "\n",
       "Ticker      XEL  XOM  XRAY  XYL  YUM  ZBH  ZBRA  ZION  ZTS  \n",
       "Date                                                        \n",
       "2016-02-02  1.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-02-03  1.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-02-04  1.0  1.0   0.0  1.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-02-05  1.0  1.0   0.0  1.0  0.0  1.0   0.0   0.0  1.0  \n",
       "2016-02-08  0.0  1.0   0.0  0.0  0.0  1.0   1.0   0.0  1.0  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals = pd.DataFrame(np.sign(1.+np.sign(np.abs(_adj_close.loc[mu_20d.index,:].values-mu_20d.values) - 2*sigma_20d.values)),\n",
    "                       columns=mu_20d.columns, \n",
    "                       index=mu_20d.index)\n",
    "\n",
    "signals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af26017e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.87525354969574"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals.sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47f6121",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6db1c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trading_signals(signals, bottom_threshold, top_threshold):\n",
    "    filtered_signals = pd.DataFrame(np.zeros(shape=signals.shape))\n",
    "    filtered_signals = filtered_signals.set_index(signals.index)\n",
    "    filtered_signals.columns = signals.columns\n",
    "    for i in range(signals.shape[0]):\n",
    "        t = signals.iloc[i,:].sum()\n",
    "        if t<top_threshold and t>bottom_threshold:  \n",
    "            filtered_signals.iloc[i,:] = signals.iloc[i,:]  \n",
    "        else:\n",
    "            filtered_signals.iloc[i,:] = np.array(filtered_signals.shape[1]*[np.nan])\n",
    "    filtered_signals = filtered_signals.dropna(axis=0)\n",
    "    return filtered_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd73aec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-03-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker        A  AAL  AAPL  ABBV  ABT  ACGL  ACN  ADBE  ADI  ADM  ...  WYNN  \\\n",
       "Date                                                              ...         \n",
       "2016-02-24  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-02-29  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-08  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-09  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-23  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "\n",
       "Ticker      XEL  XOM  XRAY  XYL  YUM  ZBH  ZBRA  ZION  ZTS  \n",
       "Date                                                        \n",
       "2016-02-24  0.0  0.0   0.0  0.0  0.0  0.0   1.0   0.0  0.0  \n",
       "2016-02-29  0.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-03-08  1.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-03-09  1.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "2016-03-23  0.0  0.0   0.0  0.0  0.0  0.0   0.0   0.0  0.0  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_threshold = 10\n",
    "top_threshold = 30\n",
    "signals = trading_signals(signals=signals, \n",
    "                          bottom_threshold=bottom_threshold, \n",
    "                          top_threshold=top_threshold)\n",
    "signals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9d14e9",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5523434",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimal_portfolio(u, V, x0, risk_tolerance, turnover):\n",
    "    x = cvx.Variable(u.shape[0])\n",
    "    f = cvx.quad_form(x, V) - risk_tolerance*u@x + turnover*cvx.norm(x-x0, p=1) \n",
    "    prob = cvx.Problem(cvx.Minimize(f), \n",
    "                      [cvx.sum(x) == 1])\n",
    "    prob.solve(solver=cvx.ECOS)\n",
    "    return x.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6084c1c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trading_strategy(signals, returns, trading_window_length, risk_tolerance, turnover):\n",
    "    portfolio_positions = pd.DataFrame(np.zeros(shape=signals.shape))\n",
    "    portfolio_positions = portfolio_positions.set_index(signals.index)\n",
    "    portfolio_positions.columns = signals.columns\n",
    "    for i in range(signals.shape[0]):\n",
    "        date = signals.index[i]\n",
    "        tickers = signals.iloc[i,:].apply(lambda x:np.nan if x==0. else x).dropna().index            \n",
    "        r = returns.loc[:date,:].iloc[-trading_window_length:,:].loc[:,tickers].values\n",
    "        u = np.mean(r, axis=0)\n",
    "        V = np.cov(r.T)  \n",
    "        if i==0:\n",
    "            x0 = np.ones(tickers.shape[0])/tickers.shape[0]\n",
    "        else:\n",
    "            x0 = portfolio_positions.iloc[i-1,:].loc[tickers].values\n",
    "        x = optimal_portfolio(u, V, x0, risk_tolerance, turnover)\n",
    "        if x is not None:\n",
    "            portfolio_positions.loc[date,tickers] = x\n",
    "        else:\n",
    "            portfolio_positions.loc[date,:] = np.array(portfolio_positions.shape[1]*[np.nan])\n",
    "    portfolio_positions = portfolio_positions.dropna(axis=0)\n",
    "    return portfolio_positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "27133ce4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2016-03-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.730648e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "Ticker        A  AAL  AAPL  ABBV  ABT  ACGL  ACN  ADBE  ADI  ADM  ...  WYNN  \\\n",
       "Date                                                              ...         \n",
       "2016-02-24  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-02-29  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-08  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-09  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "2016-03-23  0.0  0.0   0.0   0.0  0.0   0.0  0.0   0.0  0.0  0.0  ...   0.0   \n",
       "\n",
       "Ticker               XEL  XOM  XRAY  XYL  YUM  ZBH      ZBRA  ZION  ZTS  \n",
       "Date                                                                     \n",
       "2016-02-24  0.000000e+00  0.0   0.0  0.0  0.0  0.0  0.035714   0.0  0.0  \n",
       "2016-02-29  0.000000e+00  0.0   0.0  0.0  0.0  0.0  0.000000   0.0  0.0  \n",
       "2016-03-08  4.266743e-09  0.0   0.0  0.0  0.0  0.0  0.000000   0.0  0.0  \n",
       "2016-03-09  4.730648e-09  0.0   0.0  0.0  0.0  0.0  0.000000   0.0  0.0  \n",
       "2016-03-23  0.000000e+00  0.0   0.0  0.0  0.0  0.0  0.000000   0.0  0.0  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trading_window_length = 30\n",
    "risk_tolerance = 1.\n",
    "turnover = 1.\n",
    "\n",
    "portfolio_positions = trading_strategy(signals=signals, \n",
    "                                       returns=returns, \n",
    "                                       trading_window_length=trading_window_length,\n",
    "                                       risk_tolerance=risk_tolerance,\n",
    "                                       turnover=turnover)\n",
    "portfolio_positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "836d0916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 53.99999997363772)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "portfolio_positions.shape[0], np.sum(portfolio_positions.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b23748",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f74dffd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-24</th>\n",
       "      <td>37.619999</td>\n",
       "      <td>40.759998</td>\n",
       "      <td>24.012501</td>\n",
       "      <td>55.169998</td>\n",
       "      <td>39.220001</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>99.959999</td>\n",
       "      <td>84.760002</td>\n",
       "      <td>53.060001</td>\n",
       "      <td>34.040001</td>\n",
       "      <td>...</td>\n",
       "      <td>78.919998</td>\n",
       "      <td>39.950001</td>\n",
       "      <td>81.559998</td>\n",
       "      <td>59.139999</td>\n",
       "      <td>37.570000</td>\n",
       "      <td>51.624729</td>\n",
       "      <td>92.262138</td>\n",
       "      <td>69.370003</td>\n",
       "      <td>20.790001</td>\n",
       "      <td>42.369999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>37.630001</td>\n",
       "      <td>41.439999</td>\n",
       "      <td>24.412500</td>\n",
       "      <td>54.779999</td>\n",
       "      <td>38.910000</td>\n",
       "      <td>22.763332</td>\n",
       "      <td>101.379997</td>\n",
       "      <td>85.919998</td>\n",
       "      <td>53.950001</td>\n",
       "      <td>35.130001</td>\n",
       "      <td>...</td>\n",
       "      <td>84.449997</td>\n",
       "      <td>39.669998</td>\n",
       "      <td>80.559998</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>37.709999</td>\n",
       "      <td>52.401150</td>\n",
       "      <td>94.174759</td>\n",
       "      <td>62.259998</td>\n",
       "      <td>21.459999</td>\n",
       "      <td>41.490002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-08</th>\n",
       "      <td>38.310001</td>\n",
       "      <td>40.740002</td>\n",
       "      <td>25.327499</td>\n",
       "      <td>55.509998</td>\n",
       "      <td>39.259998</td>\n",
       "      <td>23.026667</td>\n",
       "      <td>102.930000</td>\n",
       "      <td>84.879997</td>\n",
       "      <td>55.709999</td>\n",
       "      <td>36.279999</td>\n",
       "      <td>...</td>\n",
       "      <td>81.419998</td>\n",
       "      <td>40.639999</td>\n",
       "      <td>83.220001</td>\n",
       "      <td>61.790001</td>\n",
       "      <td>38.080002</td>\n",
       "      <td>55.427750</td>\n",
       "      <td>97.718445</td>\n",
       "      <td>63.779999</td>\n",
       "      <td>23.950001</td>\n",
       "      <td>40.549999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-09</th>\n",
       "      <td>38.169998</td>\n",
       "      <td>42.110001</td>\n",
       "      <td>25.352501</td>\n",
       "      <td>56.560001</td>\n",
       "      <td>39.410000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>103.639999</td>\n",
       "      <td>85.059998</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>...</td>\n",
       "      <td>82.739998</td>\n",
       "      <td>40.680000</td>\n",
       "      <td>82.419998</td>\n",
       "      <td>62.099998</td>\n",
       "      <td>38.009998</td>\n",
       "      <td>55.204887</td>\n",
       "      <td>98.592232</td>\n",
       "      <td>65.580002</td>\n",
       "      <td>23.840000</td>\n",
       "      <td>39.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-23</th>\n",
       "      <td>39.160000</td>\n",
       "      <td>41.880001</td>\n",
       "      <td>26.367500</td>\n",
       "      <td>56.139999</td>\n",
       "      <td>40.639999</td>\n",
       "      <td>23.459999</td>\n",
       "      <td>108.790001</td>\n",
       "      <td>91.500000</td>\n",
       "      <td>58.049999</td>\n",
       "      <td>36.299999</td>\n",
       "      <td>...</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>41.119999</td>\n",
       "      <td>82.940002</td>\n",
       "      <td>58.770000</td>\n",
       "      <td>39.040001</td>\n",
       "      <td>57.591660</td>\n",
       "      <td>100.563110</td>\n",
       "      <td>68.480003</td>\n",
       "      <td>24.670000</td>\n",
       "      <td>42.270000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker              A        AAL       AAPL       ABBV        ABT       ACGL  \\\n",
       "Date                                                                           \n",
       "2016-02-24  37.619999  40.759998  24.012501  55.169998  39.220001  23.070000   \n",
       "2016-02-29  37.630001  41.439999  24.412500  54.779999  38.910000  22.763332   \n",
       "2016-03-08  38.310001  40.740002  25.327499  55.509998  39.259998  23.026667   \n",
       "2016-03-09  38.169998  42.110001  25.352501  56.560001  39.410000  23.000000   \n",
       "2016-03-23  39.160000  41.880001  26.367500  56.139999  40.639999  23.459999   \n",
       "\n",
       "Ticker             ACN       ADBE        ADI        ADM  ...       WYNN  \\\n",
       "Date                                                     ...              \n",
       "2016-02-24   99.959999  84.760002  53.060001  34.040001  ...  78.919998   \n",
       "2016-02-29  101.379997  85.919998  53.950001  35.130001  ...  84.449997   \n",
       "2016-03-08  102.930000  84.879997  55.709999  36.279999  ...  81.419998   \n",
       "2016-03-09  103.639999  85.059998  55.459999  36.110001  ...  82.739998   \n",
       "2016-03-23  108.790001  91.500000  58.049999  36.299999  ...  90.500000   \n",
       "\n",
       "Ticker            XEL        XOM       XRAY        XYL        YUM         ZBH  \\\n",
       "Date                                                                            \n",
       "2016-02-24  39.950001  81.559998  59.139999  37.570000  51.624729   92.262138   \n",
       "2016-02-29  39.669998  80.559998  61.099998  37.709999  52.401150   94.174759   \n",
       "2016-03-08  40.639999  83.220001  61.790001  38.080002  55.427750   97.718445   \n",
       "2016-03-09  40.680000  82.419998  62.099998  38.009998  55.204887   98.592232   \n",
       "2016-03-23  41.119999  82.940002  58.770000  39.040001  57.591660  100.563110   \n",
       "\n",
       "Ticker           ZBRA       ZION        ZTS  \n",
       "Date                                         \n",
       "2016-02-24  69.370003  20.790001  42.369999  \n",
       "2016-02-29  62.259998  21.459999  41.490002  \n",
       "2016-03-08  63.779999  23.950001  40.549999  \n",
       "2016-03-09  65.580002  23.840000  39.919998  \n",
       "2016-03-23  68.480003  24.670000  42.270000  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trading_prices = _open.shift(-1).loc[portfolio_positions.index,:]\n",
    "trading_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bff300eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACGL</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZBRA</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.016683</td>\n",
       "      <td>0.016658</td>\n",
       "      <td>-0.007069</td>\n",
       "      <td>-0.007904</td>\n",
       "      <td>-0.013293</td>\n",
       "      <td>0.014206</td>\n",
       "      <td>0.013686</td>\n",
       "      <td>0.016773</td>\n",
       "      <td>0.032021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070071</td>\n",
       "      <td>-0.007009</td>\n",
       "      <td>-0.012261</td>\n",
       "      <td>0.033142</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.020730</td>\n",
       "      <td>-0.102494</td>\n",
       "      <td>0.032227</td>\n",
       "      <td>-0.020769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-08</th>\n",
       "      <td>0.018071</td>\n",
       "      <td>-0.016892</td>\n",
       "      <td>0.037481</td>\n",
       "      <td>0.013326</td>\n",
       "      <td>0.008995</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>0.015289</td>\n",
       "      <td>-0.012104</td>\n",
       "      <td>0.032623</td>\n",
       "      <td>0.032735</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035879</td>\n",
       "      <td>0.024452</td>\n",
       "      <td>0.033019</td>\n",
       "      <td>0.011293</td>\n",
       "      <td>0.009812</td>\n",
       "      <td>0.057758</td>\n",
       "      <td>0.037629</td>\n",
       "      <td>0.024414</td>\n",
       "      <td>0.116030</td>\n",
       "      <td>-0.022656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-09</th>\n",
       "      <td>-0.003654</td>\n",
       "      <td>0.033628</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.018916</td>\n",
       "      <td>0.003821</td>\n",
       "      <td>-0.001158</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>0.002121</td>\n",
       "      <td>-0.004488</td>\n",
       "      <td>-0.004686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016212</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>-0.009613</td>\n",
       "      <td>0.005017</td>\n",
       "      <td>-0.001838</td>\n",
       "      <td>-0.004021</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.028222</td>\n",
       "      <td>-0.004593</td>\n",
       "      <td>-0.015536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-23</th>\n",
       "      <td>0.025937</td>\n",
       "      <td>-0.005462</td>\n",
       "      <td>0.040035</td>\n",
       "      <td>-0.007426</td>\n",
       "      <td>0.031210</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.049691</td>\n",
       "      <td>0.075711</td>\n",
       "      <td>0.046700</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093788</td>\n",
       "      <td>0.010816</td>\n",
       "      <td>0.006309</td>\n",
       "      <td>-0.053623</td>\n",
       "      <td>0.027098</td>\n",
       "      <td>0.043235</td>\n",
       "      <td>0.019990</td>\n",
       "      <td>0.044221</td>\n",
       "      <td>0.034815</td>\n",
       "      <td>0.058868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-24</th>\n",
       "      <td>0.009704</td>\n",
       "      <td>-0.021012</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.003741</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>-0.011225</td>\n",
       "      <td>0.051843</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033481</td>\n",
       "      <td>0.005350</td>\n",
       "      <td>0.013142</td>\n",
       "      <td>0.020759</td>\n",
       "      <td>0.019979</td>\n",
       "      <td>-0.002996</td>\n",
       "      <td>0.016219</td>\n",
       "      <td>-0.001898</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>0.005205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 483 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker             A       AAL      AAPL      ABBV       ABT      ACGL  \\\n",
       "Date                                                                     \n",
       "2016-02-29  0.000266  0.016683  0.016658 -0.007069 -0.007904 -0.013293   \n",
       "2016-03-08  0.018071 -0.016892  0.037481  0.013326  0.008995  0.011568   \n",
       "2016-03-09 -0.003654  0.033628  0.000987  0.018916  0.003821 -0.001158   \n",
       "2016-03-23  0.025937 -0.005462  0.040035 -0.007426  0.031210  0.020000   \n",
       "2016-03-24  0.009704 -0.021012  0.005025  0.003741  0.003199 -0.011225   \n",
       "\n",
       "Ticker           ACN      ADBE       ADI       ADM  ...      WYNN       XEL  \\\n",
       "Date                                                ...                       \n",
       "2016-02-29  0.014206  0.013686  0.016773  0.032021  ...  0.070071 -0.007009   \n",
       "2016-03-08  0.015289 -0.012104  0.032623  0.032735  ... -0.035879  0.024452   \n",
       "2016-03-09  0.006898  0.002121 -0.004488 -0.004686  ...  0.016212  0.000984   \n",
       "2016-03-23  0.049691  0.075711  0.046700  0.005262  ...  0.093788  0.010816   \n",
       "2016-03-24  0.051843  0.012896  0.009991  0.008264  ...  0.033481  0.005350   \n",
       "\n",
       "Ticker           XOM      XRAY       XYL       YUM       ZBH      ZBRA  \\\n",
       "Date                                                                     \n",
       "2016-02-29 -0.012261  0.033142  0.003726  0.015040  0.020730 -0.102494   \n",
       "2016-03-08  0.033019  0.011293  0.009812  0.057758  0.037629  0.024414   \n",
       "2016-03-09 -0.009613  0.005017 -0.001838 -0.004021  0.008942  0.028222   \n",
       "2016-03-23  0.006309 -0.053623  0.027098  0.043235  0.019990  0.044221   \n",
       "2016-03-24  0.013142  0.020759  0.019979 -0.002996  0.016219 -0.001898   \n",
       "\n",
       "Ticker          ZION       ZTS  \n",
       "Date                            \n",
       "2016-02-29  0.032227 -0.020769  \n",
       "2016-03-08  0.116030 -0.022656  \n",
       "2016-03-09 -0.004593 -0.015536  \n",
       "2016-03-23  0.034815  0.058868  \n",
       "2016-03-24  0.016214  0.005205  \n",
       "\n",
       "[5 rows x 483 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trading_returns = ((trading_prices - trading_prices.shift(1))/trading_prices.shift(1)).iloc[1:,:]\n",
    "trading_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9fdec64b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-02-29    0.004629\n",
       "2016-03-08    0.033256\n",
       "2016-03-09   -0.000866\n",
       "2016-03-23    0.013723\n",
       "2016-03-24    0.008792\n",
       "2016-03-28    0.007554\n",
       "2016-07-19    0.548443\n",
       "2016-07-20   -0.030505\n",
       "2016-07-21    0.037238\n",
       "2016-07-22   -0.002729\n",
       "2016-11-28   -0.184090\n",
       "2016-11-29    0.001976\n",
       "2016-11-30   -0.029395\n",
       "2016-12-02   -0.004106\n",
       "2016-12-21   -0.018029\n",
       "2016-12-22   -0.011106\n",
       "2016-12-23    0.049861\n",
       "2016-12-27    0.061708\n",
       "2016-12-28   -0.115387\n",
       "2016-12-29   -0.010340\n",
       "2017-01-03    0.009054\n",
       "2017-12-28    0.255335\n",
       "2017-12-29   -0.002887\n",
       "2018-02-13   -0.125000\n",
       "2018-02-14    0.059042\n",
       "2018-02-15   -0.048882\n",
       "2018-02-16   -0.002758\n",
       "2018-02-20   -0.009108\n",
       "2018-02-21    0.012505\n",
       "2018-02-23    0.021417\n",
       "2018-02-26    0.002322\n",
       "2018-02-27    0.007814\n",
       "2018-02-28   -0.005525\n",
       "2018-03-15    0.331325\n",
       "2018-04-09   -0.056350\n",
       "2018-04-13   -0.010417\n",
       "2018-05-22    0.173626\n",
       "2018-05-23   -0.002985\n",
       "2018-10-17   -0.196272\n",
       "2018-11-21    0.026736\n",
       "2018-11-26    0.002972\n",
       "2018-11-27   -0.005884\n",
       "2019-01-02   -0.131193\n",
       "2019-01-03    0.011693\n",
       "2019-01-04    0.049722\n",
       "2019-01-14    0.012257\n",
       "2019-04-09   -0.170790\n",
       "2019-08-20    2.384843\n",
       "2019-08-26    0.013276\n",
       "2019-08-28    0.028378\n",
       "2019-09-19    0.031411\n",
       "2019-09-23    0.008227\n",
       "2019-09-24   -0.000766\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_returns = np.sum(trading_returns*portfolio_positions.shift(1).iloc[1:,:], axis=1)\n",
    "portfolio_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07564688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Portfolio Returns'}, ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('Portfolio Returns')\n",
    "sns.kdeplot(data=portfolio_returns, ax=ax, color='#0F00FF')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b9194",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef1b3be3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-02-29    1.004629\n",
       "2016-03-08    1.038039\n",
       "2016-03-09    1.037140\n",
       "2016-03-23    1.051372\n",
       "2016-03-24    1.060616\n",
       "2016-03-28    1.068628\n",
       "2016-07-19    1.654710\n",
       "2016-07-20    1.604232\n",
       "2016-07-21    1.663970\n",
       "2016-07-22    1.659429\n",
       "2016-11-28    1.353945\n",
       "2016-11-29    1.356620\n",
       "2016-11-30    1.316742\n",
       "2016-12-02    1.311335\n",
       "2016-12-21    1.287693\n",
       "2016-12-22    1.273391\n",
       "2016-12-23    1.336885\n",
       "2016-12-27    1.419382\n",
       "2016-12-28    1.255603\n",
       "2016-12-29    1.242619\n",
       "2017-01-03    1.253871\n",
       "2017-12-28    1.574028\n",
       "2017-12-29    1.569484\n",
       "2018-02-13    1.373299\n",
       "2018-02-14    1.454381\n",
       "2018-02-15    1.383288\n",
       "2018-02-16    1.379473\n",
       "2018-02-20    1.366908\n",
       "2018-02-21    1.384001\n",
       "2018-02-23    1.413642\n",
       "2018-02-26    1.416924\n",
       "2018-02-27    1.427997\n",
       "2018-02-28    1.420106\n",
       "2018-03-15    1.890624\n",
       "2018-04-09    1.784087\n",
       "2018-04-13    1.765503\n",
       "2018-05-22    2.072040\n",
       "2018-05-23    2.065854\n",
       "2018-10-17    1.660386\n",
       "2018-11-21    1.704778\n",
       "2018-11-26    1.709844\n",
       "2018-11-27    1.699783\n",
       "2019-01-02    1.476784\n",
       "2019-01-03    1.494052\n",
       "2019-01-04    1.568339\n",
       "2019-01-14    1.587563\n",
       "2019-04-09    1.316423\n",
       "2019-08-20    4.455886\n",
       "2019-08-26    4.515044\n",
       "2019-08-28    4.643173\n",
       "2019-09-19    4.789019\n",
       "2019-09-23    4.828420\n",
       "2019-09-24    4.824720\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discount_factor = np.cumprod(portfolio_returns+1)\n",
    "discount_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "484124bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = portfolio_positions.index[0]\n",
    "end = portfolio_positions.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb33f127",
   "metadata": {},
   "outputs": [],
   "source": [
    "market_portfolio_positions = _adj_close.loc[start:end,:].div(_adj_close.loc[start:end,:].sum(axis=1), axis=0)\n",
    "market_trading_prices = _open.shift(-1).loc[market_portfolio_positions.index,:]\n",
    "market_trading_returns = ((market_trading_prices - market_trading_prices.shift(1))/market_trading_prices.shift(1)).iloc[1:,:]\n",
    "market_portfolio_returns = np.sum(market_trading_returns*market_portfolio_positions.shift(1).iloc[1:,:], axis=1)\n",
    "market_discount_factor = np.cumprod(market_portfolio_returns+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dad64f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14a725e80>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('u$s1 Wealth Evolution')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Wealth')\n",
    "ax.plot(discount_factor.index, discount_factor.values, marker='.', color='#0F00FF', markersize=10, linestyle='--', label='Wealth')\n",
    "ax.plot(market_discount_factor.index, market_discount_factor.values, marker='.', color='#C7C3FF', markersize=1, linestyle='--', label='Default')\n",
    "ax.plot(discount_factor.index, np.ones(discount_factor.shape), marker='.', color='#FF00AE', markersize=1, linestyle='-', label='u$s 1 Initial Wealth')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "57e30b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_positions.to_csv('bollingerbands_trading.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab42e968",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c446eae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-24 00:00:00-05:00</th>\n",
       "      <td>1929.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-25 00:00:00-05:00</th>\n",
       "      <td>1951.699951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-26 00:00:00-05:00</th>\n",
       "      <td>1948.050049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29 00:00:00-05:00</th>\n",
       "      <td>1932.229980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01 00:00:00-05:00</th>\n",
       "      <td>1978.349976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Adj Close\n",
       "Date                                  \n",
       "2016-02-24 00:00:00-05:00  1929.800049\n",
       "2016-02-25 00:00:00-05:00  1951.699951\n",
       "2016-02-26 00:00:00-05:00  1948.050049\n",
       "2016-02-29 00:00:00-05:00  1932.229980\n",
       "2016-03-01 00:00:00-05:00  1978.349976"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = yf.Ticker(\"^GSPC\")\n",
    "\n",
    "sp500 = tickers.history(start=start, end=end, auto_adjust=False, interval='1d')\n",
    "sp500 = sp500.loc[:,['Adj Close']]\n",
    "sp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20b2f125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1465e6d60>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.grid(visible=True, linestyle='--')\n",
    "ax.set_title('Index Evolution')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Index')\n",
    "ax.plot(sp500.index, sp500.loc[:,'Adj Close'].values, marker='.', color='#32A899', markersize=1, linestyle='--', label='S&P500')\n",
    "ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8 [ds]",
   "language": "python",
   "name": "ds"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}