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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "# We are going to use just a subset to see the graph better\n", + "candles = candles.tail(800)\n", + "\n", + "# Create a candlestick chart\n", + "fig = go.Figure(data=[go.Candlestick(\n", + " x=candles['date'],\n", + " open=candles['open'],\n", + " high=candles['high'],\n", + " low=candles['low'],\n", + " close=candles['close'])\n", + "])\n", + "fig.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T14:49:10.265443Z", + "start_time": "2023-12-07T14:49:08.864495Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "close": [ + 2.4215, + 2.4244, + 2.4238, + 2.4302, + 2.4289, + 2.4367, + 2.4176, + 2.4239, + 2.4175, + 2.4117, + 2.4193, + 2.4204, + 2.4184, + 2.4073, + 2.4035, + 2.4144, + 2.4139, + 2.4521, + 2.4458, + 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+ "candles[\"percentage_distance\"] = abs(candles[\"close\"] - candles[\"SUPERT_20_3.0\"]) / candles[\"close\"]\n", + "\n", + "candles[\"signal\"] = 0\n", + "long_condition = (candles[\"SUPERTd_20_3.0\"] == 1) & (candles[\"percentage_distance\"] < percentage_threshold)\n", + "short_condition = (candles[\"SUPERTd_20_3.0\"] == -1) & (candles[\"percentage_distance\"] < percentage_threshold)\n", + "\n", + "candles.loc[long_condition, \"signal\"] = 1\n", + "candles.loc[short_condition, \"signal\"] = -1" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T14:49:10.271367Z", + "start_time": "2023-12-07T14:49:10.270936Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "close": [ + 2.4215, + 2.4244, + 2.4238, + 2.4302, + 2.4289, + 2.4367, + 2.4176, + 2.4239, + 2.4175, + 2.4117, + 2.4193, + 2.4204, + 2.4184, + 2.4073, + 2.4035, + 2.4144, + 2.4139, + 2.4521, 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from plotly.subplots import make_subplots\n", + "\n", + "fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.02, subplot_titles=('OHLC', 'Signal'),\n", + " row_heights=[0.7, 0.3])\n", + "\n", + "# Add candlestick\n", + "fig.add_trace(go.Candlestick(\n", + " x=candles['date'],\n", + " open=candles['open'],\n", + " high=candles['high'],\n", + " low=candles['low'],\n", + " close=candles['close']),\n", + " row=1, col=1)\n", + "\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_long['date'], y=super_trend_long['SUPERT_20_3.0'],\n", + " mode='markers',\n", + " name='SuperTrend Long',\n", + " line=dict(color=\"green\")),\n", + " row=1, col=1)\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_short['date'], y=super_trend_short['SUPERT_20_3.0'],\n", + " mode='markers',\n", + " name='SuperTrend Short',\n", + " line=dict(color=\"red\")),\n", + " row=1, col=1)\n", + "\n", + "# Add the signal line\n", + "fig.add_trace(go.Scatter(x=candles['date'], y=candles['signal'],\n", + " mode='lines',\n", + " name='SuperTrend',\n", + " line=dict(color=\"white\")),\n", + " row=2, col=1)\n", + "\n", + "# Update x-axis and grid properties\n", + "fig.update_xaxes(showline=True, linewidth=2, linecolor='grey', gridcolor='lightgrey')\n", + "fig.update_yaxes(showline=True, linewidth=2, linecolor='grey', gridcolor='lightgrey')\n", + "\n", + "# Update layout to adjust the size and title\n", + "fig.update_layout(height=800, title_text=\"OHLC Chart with SuperTrend and Signals\",\n", + " yaxis_title='Price',\n", + " xaxis_rangeslider_visible=False)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T14:50:42.903377Z", + "start_time": "2023-12-07T14:50:42.863110Z" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From e1f8decdfc276be4b7da57f4e621bd80b2db41c2 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:17:32 -0300 Subject: [PATCH 02/14] (feat) create single controller backtes notebook --- .../02_single_controller_backtest.ipynb | 490 ++++++++++++++++++ 1 file changed, 490 insertions(+) create mode 100644 quants_lab/research_notebooks/02_single_controller_backtest.ipynb diff --git a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb new file mode 100644 index 00000000..73333420 --- /dev/null +++ b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import datetime\n", + "from decimal import Decimal\n", + "\n", + "# Market configuration\n", + "exchange = \"binance_perpetual\"\n", + "trading_pair = \"WLD-USDT\"\n", + "interval = \"3m\"\n", + "\n", + "# Account configuration\n", + "initial_portfolio_usd = 1000\n", + "order_amount = Decimal(\"25\")\n", + "n_levels = 1\n", + "leverage = 20\n", + "trade_cost = 0.0006\n", + "\n", + "# Backtest period\n", + "start = \"2023-01-01\"\n", + "end = \"2024-01-02\"\n", + "\n", + "# Triple barrier configuration\n", + "stop_loss = Decimal(\"0.015\")\n", + "take_profit = Decimal(\"0.03\")\n", + "time_limit = 60 * 60 * 12 # 12 hours\n", + "trailing_stop_activation_price_delta = Decimal(\"0.008\")\n", + "trailing_stop_trailing_delta = Decimal(\"0.004\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from hummingbot.smart_components.utils.order_level_builder import OrderLevelBuilder\n", + "from hummingbot.smart_components.strategy_frameworks.data_types import (\n", + " TripleBarrierConf\n", + ")\n", + "\n", + "# Building the order levels\n", + "order_level_builder = OrderLevelBuilder(n_levels=n_levels)\n", + "order_levels = order_level_builder.build_order_levels(\n", + " amounts=order_amount,\n", + " spreads=Decimal(\"0\"),\n", + " # for directional strategies we don't need spreads since we are going to use market orders to enter\n", + " triple_barrier_confs=TripleBarrierConf(\n", + " stop_loss=stop_loss, take_profit=take_profit, time_limit=time_limit,\n", + " trailing_stop_activation_price_delta=trailing_stop_activation_price_delta,\n", + " trailing_stop_trailing_delta=trailing_stop_trailing_delta),\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's inpect the order levels\n", + "order_levels" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import sys\n", + "from hummingbot.data_feed.candles_feed.candles_factory import CandlesConfig\n", + "from quants_lab.controllers.supertrend import SuperTrend, SuperTrendConfig\n", + "\n", + "# Controller configuration\n", + "length = 100\n", + "multiplier = 3.0\n", + "percentage_threshold = 0.01\n", + "\n", + "# Creating the instance of the configuration and the controller\n", + "config = SuperTrendConfig(\n", + " exchange=exchange,\n", + " trading_pair=trading_pair,\n", + " order_levels=order_levels,\n", + " candles_config=[\n", + " CandlesConfig(connector=exchange, trading_pair=trading_pair, interval=interval, max_records=sys.maxsize),\n", + " ],\n", + " leverage=leverage,\n", + " length=length,\n", + " multiplier=multiplier,\n", + " percentage_threshold=percentage_threshold,\n", + ")\n", + "controller = SuperTrend(config=config)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from quants_lab.strategy.strategy_analysis import StrategyAnalysis\n", + "\n", + "from hummingbot.smart_components.strategy_frameworks.directional_trading.directional_trading_backtesting_engine import \\\n", + " DirectionalTradingBacktestingEngine\n", + "\n", + "# Creating the backtesting engine and loading the historical data\n", + "engine = DirectionalTradingBacktestingEngine(controller=controller)\n", + "engine.load_controller_data(\"../../data/candles\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's see what is inside the candles of the controller\n", + "engine.controller.candles" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "engine.controller.candles[0].candles_df" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's understand what is inside the processed data since this is what we are going to use when generating the signal ;)\n", + "engine.controller.get_processed_data()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's run the backtesting\n", + "\n", + "backtesting_results = engine.run_backtesting(initial_portfolio_usd=initial_portfolio_usd,\n", + " trade_cost=trade_cost,\n", + " start=start, end=end)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's see what is inside the backtesting results\n", + "backtesting_results.keys()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# now let's analyze each of the dataframes\n", + "\n", + "# 1. The processed data: this is the data that we are going to use to generate the signal\n", + "backtesting_results[\"processed_data\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# 2. The executors dataframe: this is the dataframe that contains the information of the orders that were executed\n", + "backtesting_results[\"executors_df\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# 3. The results dataframe: this is the dataframe that contains the information of the pnl of the strategy\n", + "backtesting_results[\"results\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Now let's analyze the results using the StrategyAnalysis class\n", + "strategy_analysis = StrategyAnalysis(\n", + " positions=backtesting_results[\"executors_df\"],\n", + " candles_df=backtesting_results[\"processed_data\"],\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# let's visualize the PNL over time of the strategy\n", + "strategy_analysis.pnl_over_time()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "strategy_analysis.create_base_figure(volume=False, positions=False, trade_pnl=True)\n", + "fig = strategy_analysis.figure()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Now let's see how we can add the SuperTrend to the plot\n", + "\n", + "import plotly.graph_objects as go\n", + "\n", + "super_trend_long = strategy_analysis.candles_df[strategy_analysis.candles_df[f\"SUPERTd_{length}_{multiplier}\"] == 1]\n", + "super_trend_short = strategy_analysis.candles_df[strategy_analysis.candles_df[f\"SUPERTd_{length}_{multiplier}\"] == -1]\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_long.index, y=super_trend_long[f'SUPERT_{length}_{multiplier}'],\n", + " mode='markers',\n", + " name='SuperTrend Long',\n", + " line=dict(color=\"green\")),\n", + " row=1, col=1)\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_short.index, y=super_trend_short[f'SUPERT_{length}_{multiplier}'],\n", + " mode='markers',\n", + " name='SuperTrend Short',\n", + " line=dict(color=\"red\")),\n", + " row=1, col=1)\n", + "\n", + "fig" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# To see the trades we will need to select a lower timeframe due the restrictions and speed of the plotly library\n", + "start_time = \"2023-11-03\"\n", + "end_time = \"2023-11-05\"\n", + "\n", + "processed_data_filtered = backtesting_results[\"processed_data\"][\n", + " (backtesting_results[\"processed_data\"][\"timestamp\"] >= start_time) &\n", + " (backtesting_results[\"processed_data\"][\"timestamp\"] <= end_time)\n", + "]\n", + "\n", + "executors_filtered = backtesting_results[\"executors_df\"][\n", + " (backtesting_results[\"executors_df\"][\"timestamp\"] >= start_time) &\n", + " (backtesting_results[\"executors_df\"][\"timestamp\"] <= end_time)\n", + "]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "executors_filtered" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "\n", + "strategy_analysis = StrategyAnalysis(\n", + " positions=executors_filtered,\n", + " candles_df=processed_data_filtered,\n", + ")\n", + "\n", + "strategy_analysis.create_base_figure(volume=False, positions=True, trade_pnl=True)\n", + "fig = strategy_analysis.figure()\n", + "super_trend_long = strategy_analysis.candles_df[strategy_analysis.candles_df[f\"SUPERTd_{length}_{multiplier}\"] == 1]\n", + "super_trend_short = strategy_analysis.candles_df[strategy_analysis.candles_df[f\"SUPERTd_{length}_{multiplier}\"] == -1]\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_long.index, y=super_trend_long[f'SUPERT_{length}_{multiplier}'],\n", + " mode='markers',\n", + " name='SuperTrend Long',\n", + " line=dict(color=\"green\")),\n", + " row=1, col=1)\n", + "# Add the SuperTrend line\n", + "fig.add_trace(go.Scatter(x=super_trend_short.index, y=super_trend_short[f'SUPERT_{length}_{multiplier}'],\n", + " mode='markers',\n", + " name='SuperTrend Short',\n", + " line=dict(color=\"red\")),\n", + " row=1, col=1)\n", + "fig" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Scatter of PNL per Trade\n", + "This bar chart illustrates the PNL for each individual trade. Positive PNLs are shown in green and negative PNLs in red, providing a clear view of profitable vs. unprofitable trades.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "\n", + "executors_df = backtesting_results[\"executors_df\"]\n", + "\n", + "fig = px.scatter(executors_df, x=\"timestamp\", y='net_pnl_quote', title='PNL per Trade',\n", + " color='profitable', color_continuous_scale=['red', 'green'])\n", + "fig.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Scatter Plot of Volume vs. PNL\n", + "This scatter plot explores the relationship between the trade volume and the PNL for each trade. It can reveal if larger volumes are associated with higher profits or losses.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig = px.scatter(executors_df, x='volume', y='net_pnl_quote', title='Trade Volume vs. PNL')\n", + "fig.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Histogram of PNL Distribution\n", + "The histogram displays the distribution of PNL values across all trades. It helps in understanding the frequency and range of profit and loss outcomes.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig = px.histogram(executors_df, x='net_pnl_quote', title='PNL Distribution')\n", + "fig.show()\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# Conclusion\n", + "We can see that the indicator has potential to bring good signals to trade and might be interesting to see how we can design a market maker that shifts the mid price based on this indicator.\n", + "A lot of the short signals are wrong but if we zoom in into the loss signals we can see that the losses are not that big and the wins are bigger and if we had implemented the trailing stop feature probably a lot of them are going to be profits." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# Next steps\n", + "- Filter only the loss signals and understand what you can do to prevent them\n", + "- Try different configuration values for the indicator\n", + "- Test in multiple markets, pick mature markets like BTC-USDT or ETH-USDT and also volatile markets like DOGE-USDT or SHIB-USDT" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 2e90750961ae27cf85469d165a20a1ecacff7bc1 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:17:42 -0300 Subject: [PATCH 03/14] (feat) create optimization notebook --- .../03_optimization_notebook.ipynb | 285 ++++++++++++++++++ 1 file changed, 285 insertions(+) create mode 100644 quants_lab/research_notebooks/03_optimization_notebook.ipynb diff --git a/quants_lab/research_notebooks/03_optimization_notebook.ipynb b/quants_lab/research_notebooks/03_optimization_notebook.ipynb new file mode 100644 index 00000000..e87dd25e --- /dev/null +++ b/quants_lab/research_notebooks/03_optimization_notebook.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import sys\n", + "import traceback\n", + "from decimal import Decimal\n", + "import pandas_ta as ta # noqa: F401\n", + "\n", + "from hummingbot.core.data_type.common import PositionMode, TradeType, OrderType\n", + "from hummingbot.data_feed.candles_feed.candles_factory import CandlesConfig\n", + "from hummingbot.smart_components.strategy_frameworks.data_types import TripleBarrierConf, OrderLevel\n", + "from hummingbot.smart_components.strategy_frameworks.directional_trading import DirectionalTradingBacktestingEngine\n", + "from hummingbot.smart_components.utils.config_encoder_decoder import ConfigEncoderDecoder\n", + "from optuna import TrialPruned\n", + "from hummingbot.smart_components.utils.order_level_builder import OrderLevelBuilder\n", + "\n", + "from quants_lab.controllers.supertrend import SuperTrend, SuperTrendConfig\n", + "\n", + "# To run an optimization with optuna we need to define the objective function that will be executed for each trial\n", + "\n", + "def objective(trial):\n", + " try:\n", + " # Market configuration\n", + " exchange = \"binance_perpetual\"\n", + " trading_pair = \"WLD-USDT\"\n", + " interval = \"3m\"\n", + "\n", + " # Account configuration\n", + " initial_portfolio_usd = 1000\n", + " order_amount = Decimal(\"25\")\n", + " n_levels = 1\n", + " leverage = 20\n", + " trade_cost = 0.0006\n", + "\n", + " # Backtest period\n", + " start = \"2023-01-01\"\n", + " end = \"2024-01-02\"\n", + "\n", + " # Triple barrier configuration\n", + " stop_loss = trial.suggest_float('stop_loss', 0.01, 0.02, step=0.01)\n", + " take_profit = trial.suggest_float('take_profit', 0.01, 0.04, step=0.01)\n", + " time_limit = 60 * 60 * 12 # 12 hours\n", + " trailing_stop_activation_price_delta = Decimal(\"0.008\")\n", + " trailing_stop_trailing_delta = Decimal(\"0.004\")\n", + "\n", + " length = trial.suggest_int('length', 20, 200, step=20)\n", + " multiplier = trial.suggest_float('multiplier', 2.0, 6.0, step=1.0)\n", + " percentage_threshold = trial.suggest_float('percentage_threshold', 0.01, 0.03, step=0.01)\n", + "\n", + " # Building the order levels\n", + " order_level_builder = OrderLevelBuilder(n_levels=n_levels)\n", + " order_levels = order_level_builder.build_order_levels(\n", + " amounts=order_amount,\n", + " spreads=Decimal(\"0\"),\n", + " triple_barrier_confs=TripleBarrierConf(\n", + " stop_loss=stop_loss, take_profit=take_profit, time_limit=time_limit,\n", + " trailing_stop_activation_price_delta=trailing_stop_activation_price_delta,\n", + " trailing_stop_trailing_delta=trailing_stop_trailing_delta),\n", + " )\n", + " config = SuperTrendConfig(\n", + " exchange=exchange,\n", + " trading_pair=trading_pair,\n", + " strategy_name='supertrend',\n", + " candles_config=[\n", + " CandlesConfig(connector=exchange, trading_pair=trading_pair,\n", + " interval=interval, max_records=sys.maxsize)\n", + " ],\n", + " order_levels=order_levels,\n", + " leverage=leverage,\n", + " position_mode=PositionMode.HEDGE,\n", + " length=length,\n", + " multiplier=multiplier,\n", + " percentage_threshold=percentage_threshold,\n", + "\n", + " )\n", + " controller = SuperTrend(config=config)\n", + " engine = DirectionalTradingBacktestingEngine(controller=controller)\n", + " engine.load_controller_data(\"../../data/candles\")\n", + " backtesting_results = engine.run_backtesting(\n", + " initial_portfolio_usd=initial_portfolio_usd,\n", + " trade_cost=trade_cost,\n", + " start=start, end=end)\n", + "\n", + " strategy_analysis = backtesting_results[\"results\"]\n", + " encoder_decoder = ConfigEncoderDecoder(TradeType, OrderType, PositionMode)\n", + "\n", + " trial.set_user_attr(\"net_pnl_quote\", strategy_analysis[\"net_pnl_quote\"])\n", + " trial.set_user_attr(\"net_pnl_pct\", strategy_analysis[\"net_pnl\"])\n", + " trial.set_user_attr(\"max_drawdown_usd\", strategy_analysis[\"max_drawdown_usd\"])\n", + " trial.set_user_attr(\"max_drawdown_pct\", strategy_analysis[\"max_drawdown_pct\"])\n", + " trial.set_user_attr(\"sharpe_ratio\", strategy_analysis[\"sharpe_ratio\"])\n", + " trial.set_user_attr(\"accuracy\", strategy_analysis[\"accuracy\"])\n", + " trial.set_user_attr(\"total_positions\", strategy_analysis[\"total_positions\"])\n", + " trial.set_user_attr(\"profit_factor\", strategy_analysis[\"profit_factor\"])\n", + " trial.set_user_attr(\"duration_in_hours\", strategy_analysis[\"duration_minutes\"] / 60)\n", + " trial.set_user_attr(\"avg_trading_time_in_hours\", strategy_analysis[\"avg_trading_time_minutes\"] / 60)\n", + " trial.set_user_attr(\"win_signals\", strategy_analysis[\"win_signals\"])\n", + " trial.set_user_attr(\"loss_signals\", strategy_analysis[\"loss_signals\"])\n", + " trial.set_user_attr(\"config\", encoder_decoder.encode(config.dict()))\n", + " return strategy_analysis[\"net_pnl\"]\n", + " except Exception as e:\n", + " traceback.print_exc()\n", + " raise TrialPruned()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T20:10:50.249860Z", + "start_time": "2023-12-07T20:10:48.906572Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-12-07 17:10:50,338] Using an existing study with name 'super_trend_optimization_1' instead of creating a new one.\n" + ] + } + ], + "source": [ + "import optuna\n", + "\n", + "# Now let's configure the parameters for the optimization\n", + "study_name = \"super_trend_optimization_1\"\n", + "storage= \"sqlite:///../../data/backtesting/backtesting_report.db\"\n", + "\n", + "study = optuna.create_study(direction=\"maximize\", study_name=study_name,\n", + " storage=storage,\n", + " load_if_exists=True # If the study already exists, we load it\n", + " )" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T20:10:50.345407Z", + "start_time": "2023-12-07T20:10:50.251540Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2023-12-07 17:10:56,238] Trial 23 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", + "[I 2023-12-07 17:11:00,180] Trial 24 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", + "[I 2023-12-07 17:11:05,299] Trial 25 finished with value: 0.2513856255976169 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", + "[I 2023-12-07 17:11:09,145] Trial 26 finished with value: 0.2543681375647173 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 140, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", + "[I 2023-12-07 17:11:12,064] Trial 27 finished with value: -0.029959249164181148 and parameters: {'stop_loss': 0.01, 'take_profit': 0.02, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", + "[I 2023-12-07 17:11:15,997] Trial 28 finished with value: 0.5421656349751423 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:21,124] Trial 29 finished with value: 0.15573798497951996 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:24,019] Trial 30 finished with value: -0.1108467390202585 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 40, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:26,857] Trial 31 finished with value: -0.06356581160968328 and parameters: {'stop_loss': 0.01, 'take_profit': 0.02, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:30,808] Trial 32 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:34,707] Trial 33 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:38,644] Trial 34 finished with value: 0.5421656349751423 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:45,073] Trial 35 finished with value: 0.38219663974909746 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 60, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 28 with value: 0.5421656349751423.\n", + "[I 2023-12-07 17:11:48,950] Trial 36 finished with value: 0.592806376208812 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:11:54,104] Trial 37 finished with value: 0.30028336678525713 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 80, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:11:56,984] Trial 38 finished with value: 0.17279403265558407 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:03,556] Trial 39 finished with value: -0.10925259585987201 and parameters: {'stop_loss': 0.01, 'take_profit': 0.01, 'length': 40, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:08,996] Trial 40 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:11,102] Trial 41 finished with value: -0.3598184663012044 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 20, 'multiplier': 5.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:16,575] Trial 42 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:22,015] Trial 43 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:26,600] Trial 44 finished with value: 0.37136937238312684 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:31,960] Trial 45 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:37,371] Trial 46 finished with value: 0.393770422017225 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:40,220] Trial 47 finished with value: -0.0634041192516708 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:44,761] Trial 48 finished with value: 0.3559925148282939 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:46,260] Trial 49 finished with value: -0.2708608988066281 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 6.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:51,691] Trial 50 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:12:56,145] Trial 51 finished with value: 0.40027329750088514 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:01,576] Trial 52 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:07,025] Trial 53 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:12,482] Trial 54 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:17,908] Trial 55 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:23,389] Trial 56 finished with value: 0.5659722220669056 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 40, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:28,832] Trial 57 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:34,195] Trial 58 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:38,710] Trial 59 finished with value: 0.29894352712212513 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 160, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:45,401] Trial 60 finished with value: 0.3661882820037683 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:49,988] Trial 61 finished with value: 0.3559925148282939 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:13:55,453] Trial 62 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:00,893] Trial 63 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:06,295] Trial 64 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:11,725] Trial 65 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:17,161] Trial 66 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:23,932] Trial 67 finished with value: 0.35517980524237147 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:29,330] Trial 68 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:33,877] Trial 69 finished with value: 0.15944412260328772 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:39,319] Trial 70 finished with value: 0.393770422017225 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:44,745] Trial 71 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:50,176] Trial 72 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:14:55,613] Trial 73 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:01,030] Trial 74 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:06,492] Trial 75 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:11,934] Trial 76 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:18,290] Trial 77 finished with value: 0.2936873783086804 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:22,988] Trial 78 finished with value: -0.18258751093632875 and parameters: {'stop_loss': 0.02, 'take_profit': 0.01, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:28,426] Trial 79 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:35,168] Trial 80 finished with value: 0.31306649255438035 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:41,525] Trial 81 finished with value: 0.28570398621287874 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 40, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:46,990] Trial 82 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:52,425] Trial 83 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:15:57,842] Trial 84 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:03,272] Trial 85 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:08,700] Trial 86 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:14,161] Trial 87 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:19,557] Trial 88 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:24,164] Trial 89 finished with value: 0.15944412260328772 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:30,525] Trial 90 finished with value: 0.33271963140347904 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:35,992] Trial 91 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:41,419] Trial 92 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:46,832] Trial 93 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:52,313] Trial 94 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:16:58,227] Trial 95 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:03,808] Trial 96 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:09,482] Trial 97 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:15,184] Trial 98 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:21,686] Trial 99 finished with value: 0.2936873783086804 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:27,349] Trial 100 finished with value: 0.30175973701819403 and parameters: {'stop_loss': 0.02, 'take_profit': 0.02, 'length': 180, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:31,189] Trial 101 finished with value: -0.45309169439992525 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 6.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", + "[I 2023-12-07 17:17:36,805] Trial 102 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n" + ] + } + ], + "source": [ + "# Not let's run the optimization!\n", + "\n", + "n_trials = 200\n", + "study.optimize(objective, n_trials=n_trials)" + ], + "metadata": { + "collapsed": false, + "is_executing": true, + "ExecuteTime": { + "start_time": "2023-12-07T20:10:52.276919Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6ddd74e4dd7f73d3be40a2ac075593e8136c7f21 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:17:45 -0300 Subject: [PATCH 04/14] (feat) create analyze notebook --- .../04_analyze_optimization_results.ipynb | 11681 ++++++++++++++++ 1 file changed, 11681 insertions(+) create mode 100644 quants_lab/research_notebooks/04_analyze_optimization_results.ipynb diff --git a/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb new file mode 100644 index 00000000..be1b76ce --- /dev/null +++ b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb @@ -0,0 +1,11681 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import pandas as pd\n", + "\n", + "from utils.optuna_database_manager import OptunaDBManager\n", + "\n", + "db_root_path = \"../../data/backtesting/\"\n", + "db_name = \"backtesting_report.db\"\n", + "\n", + "optuna_db_manager = OptunaDBManager(db_name=db_name,\n", + " db_root_path=db_root_path)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:42:11.517410Z", + "start_time": "2023-12-07T19:42:11.511217Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " study_id study_name\n0 1 super_trend_optimization_1", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
study_idstudy_name
01super_trend_optimization_1
\n
" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optuna_db_manager.studies" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:42:15.133179Z", + "start_time": "2023-12-07T19:42:15.126891Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [], + "source": [ + "study_name = \"super_trend_optimization_1\"\n", + "df = optuna_db_manager.merged_df[optuna_db_manager.merged_df[\"study_name\"] == study_name]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:50:22.294655Z", + "start_time": "2023-12-07T19:50:22.278335Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [ + { + "data": { + "text/plain": " trial_id number study_id state datetime_start \\\n0 1 0 1 COMPLETE 2023-12-07 16:34:08.257012 \n1 2 1 1 COMPLETE 2023-12-07 16:34:11.226266 \n2 3 2 1 COMPLETE 2023-12-07 16:34:17.914268 \n3 4 3 1 COMPLETE 2023-12-07 16:34:19.428529 \n4 5 4 1 COMPLETE 2023-12-07 16:34:27.069613 \n5 6 5 1 COMPLETE 2023-12-07 16:34:34.249291 \n6 7 6 1 COMPLETE 2023-12-07 16:34:38.900783 \n7 8 7 1 COMPLETE 2023-12-07 16:34:41.799046 \n8 9 8 1 COMPLETE 2023-12-07 16:34:47.391790 \n9 10 9 1 COMPLETE 2023-12-07 16:34:51.940085 \n10 11 10 1 COMPLETE 2023-12-07 16:34:58.029861 \n11 12 11 1 COMPLETE 2023-12-07 16:35:05.542421 \n12 13 12 1 COMPLETE 2023-12-07 16:35:12.873439 \n13 14 13 1 COMPLETE 2023-12-07 16:35:19.334082 \n14 15 14 1 COMPLETE 2023-12-07 16:35:25.994879 \n15 16 15 1 COMPLETE 2023-12-07 16:35:33.368754 \n16 17 16 1 COMPLETE 2023-12-07 16:35:40.634983 \n17 18 17 1 COMPLETE 2023-12-07 16:35:48.191608 \n18 19 18 1 COMPLETE 2023-12-07 16:35:52.183184 \n19 20 19 1 COMPLETE 2023-12-07 16:35:59.436981 \n\n datetime_complete study_name accuracy \\\n0 2023-12-07 16:34:11.216496 super_trend_optimization_1 0.224806 \n1 2023-12-07 16:34:17.906535 super_trend_optimization_1 0.314465 \n2 2023-12-07 16:34:19.421124 super_trend_optimization_1 0.351351 \n3 2023-12-07 16:34:27.061663 super_trend_optimization_1 0.499397 \n4 2023-12-07 16:34:34.241515 super_trend_optimization_1 0.352941 \n5 2023-12-07 16:34:38.893255 super_trend_optimization_1 0.510753 \n6 2023-12-07 16:34:41.791808 super_trend_optimization_1 0.505415 \n7 2023-12-07 16:34:47.384110 super_trend_optimization_1 0.364238 \n8 2023-12-07 16:34:51.932509 super_trend_optimization_1 0.375000 \n9 2023-12-07 16:34:58.022508 super_trend_optimization_1 0.300813 \n10 2023-12-07 16:35:05.534558 super_trend_optimization_1 0.344464 \n11 2023-12-07 16:35:12.866085 super_trend_optimization_1 0.282353 \n12 2023-12-07 16:35:19.326887 super_trend_optimization_1 0.436364 \n13 2023-12-07 16:35:25.986171 super_trend_optimization_1 0.326733 \n14 2023-12-07 16:35:33.361421 super_trend_optimization_1 0.366460 \n15 2023-12-07 16:35:40.626251 super_trend_optimization_1 0.279817 \n16 2023-12-07 16:35:48.184393 super_trend_optimization_1 0.363107 \n17 2023-12-07 16:35:52.175694 super_trend_optimization_1 0.314410 \n18 2023-12-07 16:35:59.429546 super_trend_optimization_1 0.351005 \n19 2023-12-07 16:36:06.399604 super_trend_optimization_1 0.257812 \n\n avg_trading_time_in_hours \\\n0 4.111413 \n1 2.170325 \n2 6.009647 \n3 0.773337 \n4 3.923623 \n5 2.671102 \n6 1.081769 \n7 4.141523 \n8 4.488008 \n9 5.096682 \n10 1.284204 \n11 1.785208 \n12 3.458712 \n13 2.271603 \n14 1.396986 \n15 1.735047 \n16 1.322296 \n17 2.462154 \n18 1.336770 \n19 2.653945 \n\n config ... total_positions \\\n0 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 129 \n1 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 318 \n2 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 74 \n3 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 829 \n4 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 187 \n5 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 186 \n6 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 277 \n7 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 151 \n8 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 128 \n9 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 123 \n10 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 569 \n11 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 425 \n12 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 220 \n13 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 303 \n14 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 483 \n15 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 436 \n16 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 515 \n17 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 229 \n18 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 547 \n19 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 256 \n\n win_signals trial_value_id objective_x value value_type \\\n0 29 1 0 0.008787 FINITE \n1 100 2 0 0.291448 FINITE \n2 26 3 0 -0.085482 FINITE \n3 414 4 0 -0.141714 FINITE \n4 66 5 0 -0.223673 FINITE \n5 95 6 0 -0.045817 FINITE \n6 140 7 0 -0.071774 FINITE \n7 55 8 0 0.012283 FINITE \n8 48 9 0 0.085992 FINITE \n9 37 10 0 -0.266434 FINITE \n10 196 11 0 -0.558586 FINITE \n11 120 12 0 -0.223067 FINITE \n12 96 13 0 0.192491 FINITE \n13 99 14 0 0.422687 FINITE \n14 177 15 0 0.022237 FINITE \n15 122 16 0 -0.329032 FINITE \n16 187 17 0 0.053164 FINITE \n17 72 18 0 0.254011 FINITE \n18 192 19 0 -0.358893 FINITE \n19 66 20 0 0.189835 FINITE \n\n study_direction_id direction objective_y \\\n0 1 MAXIMIZE 0 \n1 1 MAXIMIZE 0 \n2 1 MAXIMIZE 0 \n3 1 MAXIMIZE 0 \n4 1 MAXIMIZE 0 \n5 1 MAXIMIZE 0 \n6 1 MAXIMIZE 0 \n7 1 MAXIMIZE 0 \n8 1 MAXIMIZE 0 \n9 1 MAXIMIZE 0 \n10 1 MAXIMIZE 0 \n11 1 MAXIMIZE 0 \n12 1 MAXIMIZE 0 \n13 1 MAXIMIZE 0 \n14 1 MAXIMIZE 0 \n15 1 MAXIMIZE 0 \n16 1 MAXIMIZE 0 \n17 1 MAXIMIZE 0 \n18 1 MAXIMIZE 0 \n19 1 MAXIMIZE 0 \n\n hover_text \n0 Trial ID: 1
Study: super_trend_op... \n1 Trial ID: 2
Study: super_trend_op... \n2 Trial ID: 3
Study: super_trend_op... \n3 Trial ID: 4
Study: super_trend_op... \n4 Trial ID: 5
Study: super_trend_op... \n5 Trial ID: 6
Study: super_trend_op... \n6 Trial ID: 7
Study: super_trend_op... \n7 Trial ID: 8
Study: super_trend_op... \n8 Trial ID: 9
Study: super_trend_op... \n9 Trial ID: 10
Study: super_trend_o... \n10 Trial ID: 11
Study: super_trend_o... \n11 Trial ID: 12
Study: super_trend_o... \n12 Trial ID: 13
Study: super_trend_o... \n13 Trial ID: 14
Study: super_trend_o... \n14 Trial ID: 15
Study: super_trend_o... \n15 Trial ID: 16
Study: super_trend_o... \n16 Trial ID: 17
Study: super_trend_o... \n17 Trial ID: 18
Study: super_trend_o... \n18 Trial ID: 19
Study: super_trend_o... \n19 Trial ID: 20
Study: super_trend_o... \n\n[20 rows x 28 columns]", + "text/html": "
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trial_idnumberstudy_idstatedatetime_startdatetime_completestudy_nameaccuracyavg_trading_time_in_hoursconfig...total_positionswin_signalstrial_value_idobjective_xvaluevalue_typestudy_direction_iddirectionobjective_yhover_text
0101COMPLETE2023-12-07 16:34:08.2570122023-12-07 16:34:11.216496super_trend_optimization_10.2248064.111413\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12929100.008787FINITE1MAXIMIZE0<b>Trial ID: 1</b><br><b>Study: super_trend_op...
1211COMPLETE2023-12-07 16:34:11.2262662023-12-07 16:34:17.906535super_trend_optimization_10.3144652.170325\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......318100200.291448FINITE1MAXIMIZE0<b>Trial ID: 2</b><br><b>Study: super_trend_op...
2321COMPLETE2023-12-07 16:34:17.9142682023-12-07 16:34:19.421124super_trend_optimization_10.3513516.009647\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......742630-0.085482FINITE1MAXIMIZE0<b>Trial ID: 3</b><br><b>Study: super_trend_op...
3431COMPLETE2023-12-07 16:34:19.4285292023-12-07 16:34:27.061663super_trend_optimization_10.4993970.773337\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......82941440-0.141714FINITE1MAXIMIZE0<b>Trial ID: 4</b><br><b>Study: super_trend_op...
4541COMPLETE2023-12-07 16:34:27.0696132023-12-07 16:34:34.241515super_trend_optimization_10.3529413.923623\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......1876650-0.223673FINITE1MAXIMIZE0<b>Trial ID: 5</b><br><b>Study: super_trend_op...
5651COMPLETE2023-12-07 16:34:34.2492912023-12-07 16:34:38.893255super_trend_optimization_10.5107532.671102\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......1869560-0.045817FINITE1MAXIMIZE0<b>Trial ID: 6</b><br><b>Study: super_trend_op...
6761COMPLETE2023-12-07 16:34:38.9007832023-12-07 16:34:41.791808super_trend_optimization_10.5054151.081769\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......27714070-0.071774FINITE1MAXIMIZE0<b>Trial ID: 7</b><br><b>Study: super_trend_op...
7871COMPLETE2023-12-07 16:34:41.7990462023-12-07 16:34:47.384110super_trend_optimization_10.3642384.141523\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......15155800.012283FINITE1MAXIMIZE0<b>Trial ID: 8</b><br><b>Study: super_trend_op...
8981COMPLETE2023-12-07 16:34:47.3917902023-12-07 16:34:51.932509super_trend_optimization_10.3750004.488008\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12848900.085992FINITE1MAXIMIZE0<b>Trial ID: 9</b><br><b>Study: super_trend_op...
91091COMPLETE2023-12-07 16:34:51.9400852023-12-07 16:34:58.022508super_trend_optimization_10.3008135.096682\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12337100-0.266434FINITE1MAXIMIZE0<b>Trial ID: 10</b><br><b>Study: super_trend_o...
1011101COMPLETE2023-12-07 16:34:58.0298612023-12-07 16:35:05.534558super_trend_optimization_10.3444641.284204\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......569196110-0.558586FINITE1MAXIMIZE0<b>Trial ID: 11</b><br><b>Study: super_trend_o...
1112111COMPLETE2023-12-07 16:35:05.5424212023-12-07 16:35:12.866085super_trend_optimization_10.2823531.785208\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......425120120-0.223067FINITE1MAXIMIZE0<b>Trial ID: 12</b><br><b>Study: super_trend_o...
1213121COMPLETE2023-12-07 16:35:12.8734392023-12-07 16:35:19.326887super_trend_optimization_10.4363643.458712\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......220961300.192491FINITE1MAXIMIZE0<b>Trial ID: 13</b><br><b>Study: super_trend_o...
1314131COMPLETE2023-12-07 16:35:19.3340822023-12-07 16:35:25.986171super_trend_optimization_10.3267332.271603\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......303991400.422687FINITE1MAXIMIZE0<b>Trial ID: 14</b><br><b>Study: super_trend_o...
1415141COMPLETE2023-12-07 16:35:25.9948792023-12-07 16:35:33.361421super_trend_optimization_10.3664601.396986\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......4831771500.022237FINITE1MAXIMIZE0<b>Trial ID: 15</b><br><b>Study: super_trend_o...
1516151COMPLETE2023-12-07 16:35:33.3687542023-12-07 16:35:40.626251super_trend_optimization_10.2798171.735047\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......436122160-0.329032FINITE1MAXIMIZE0<b>Trial ID: 16</b><br><b>Study: super_trend_o...
1617161COMPLETE2023-12-07 16:35:40.6349832023-12-07 16:35:48.184393super_trend_optimization_10.3631071.322296\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......5151871700.053164FINITE1MAXIMIZE0<b>Trial ID: 17</b><br><b>Study: super_trend_o...
1718171COMPLETE2023-12-07 16:35:48.1916082023-12-07 16:35:52.175694super_trend_optimization_10.3144102.462154\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......229721800.254011FINITE1MAXIMIZE0<b>Trial ID: 18</b><br><b>Study: super_trend_o...
1819181COMPLETE2023-12-07 16:35:52.1831842023-12-07 16:35:59.429546super_trend_optimization_10.3510051.336770\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......547192190-0.358893FINITE1MAXIMIZE0<b>Trial ID: 19</b><br><b>Study: super_trend_o...
1920191COMPLETE2023-12-07 16:35:59.4369812023-12-07 16:36:06.399604super_trend_optimization_10.2578122.653945\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......256662000.189835FINITE1MAXIMIZE0<b>Trial ID: 20</b><br><b>Study: super_trend_o...
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" + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:50:22.449488Z", + "start_time": "2023-12-07T19:50:22.446896Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Bar Chart of Average Trading Time\n", + "This bar chart compares the average trading time across trials. It helps to quickly identify trials with unusually long or short trading times.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "trial_id=%{x}
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "fig = px.bar(df, x='trial_id', y='avg_trading_time_in_hours', title='Average Trading Time per Trial')\n", + "fig.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:51:22.536707Z", + "start_time": "2023-12-07T19:51:22.508892Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Scatter Plot of Accuracy vs. Average Trading Time\n", + "This scatter plot shows the relationship between the accuracy and the average trading time of each trial. It can help to identify if there is any correlation between these two metrics.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "hovertemplate": "avg_trading_time_in_hours=%{x}
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"title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } + }, + "legend": { + "tracegroupgap": 0 + }, + "title": { + "text": "Proportion of Win Signals per Trial" + } + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.pie(df, names='trial_id', values='win_signals', title='Proportion of Win Signals per Trial')\n", + "fig.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:52:32.851708Z", + "start_time": "2023-12-07T19:52:32.831722Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Box Plot for Trial Value\n", + "A box plot for trial values to identify the range, median, and any potential outliers in the trial values.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "value=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa" + }, + "name": "", + "notched": false, + "offsetgroup": "", + "orientation": "v", + "showlegend": false, + "x0": " ", + "xaxis": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.box(df, y='value', title='Box Plot of Trial PNL')\n", + "fig.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:53:12.660103Z", + "start_time": "2023-12-07T19:53:12.651243Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Heatmap for Correlation Analysis\n", + "This heatmap illustrates the correlation between various numerical variables such as accuracy, average trading time, total positions, win signals, and value.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "coloraxis": "coloraxis", + "name": "0", + "x": [ + "accuracy", + "avg_trading_time_in_hours", + "total_positions", + "win_signals", + "value" + ], + "y": [ + "accuracy", + "avg_trading_time_in_hours", + "total_positions", + "win_signals", + "value" + ], + "z": [ + [ + 1.0, + -0.25082646547605214, + 0.21565967932924882, + 0.47222047914081533, + -0.025966420595914527 + ], + [ + -0.25082646547605214, + 1.0, + -0.8347559243766088, + -0.7520347845858392, + 0.11948445589078598 + ], + [ + 0.21565967932924882, + -0.8347559243766088, + 1.0, + 0.942873210592411, + -0.342926368090527 + ], + [ + 0.47222047914081533, + -0.7520347845858392, + 0.942873210592411, + 1.0, + -0.2815070805718038 + ], + [ + -0.025966420595914527, + 0.11948445589078598, + -0.342926368090527, + -0.2815070805718038, + 1.0 + ] + ], + "type": "heatmap", + "xaxis": "x", + "yaxis": "y", + "hovertemplate": "Variable: %{x}
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}, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "# Selecting columns for scatter matrix\n", + "selected_columns = ['accuracy', 'avg_trading_time_in_hours', 'total_positions', 'win_signals', 'value']\n", + "\n", + "# Creating scatter matrix\n", + "fig_matrix = px.scatter_matrix(df[selected_columns], dimensions=selected_columns, title='Scatter Matrix of Variables', height=800)\n", + "fig_matrix.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-07T19:58:29.594262Z", + "start_time": "2023-12-07T19:58:29.573443Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 018aa5cbadacc8fbb0f32c5dfc1f8716032436bd Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:18:00 -0300 Subject: [PATCH 05/14] (feat) add db_path optional --- utils/optuna_database_manager.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/utils/optuna_database_manager.py b/utils/optuna_database_manager.py index abd23186..a1241533 100644 --- a/utils/optuna_database_manager.py +++ b/utils/optuna_database_manager.py @@ -1,5 +1,6 @@ import os import json +from typing import Optional import pandas as pd from sqlalchemy import create_engine, text @@ -9,9 +10,10 @@ class OptunaDBManager: - def __init__(self, db_name): + def __init__(self, db_name, db_root_path: Optional[str]): + db_root_path = db_root_path or "data/backtesting" self.db_name = db_name - self.db_path = f'sqlite:///{os.path.join("data/backtesting", db_name)}' + self.db_path = f'sqlite:///{os.path.join(db_root_path, db_name)}' self.engine = create_engine(self.db_path, connect_args={'check_same_thread': False}) self.session_maker = sessionmaker(bind=self.engine) From b42fb0f1302f204ea23fc723094e3f94f3bff05d Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:18:12 -0300 Subject: [PATCH 06/14] (feat) fix strategy analysis graph --- quants_lab/strategy/strategy_analysis.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/quants_lab/strategy/strategy_analysis.py b/quants_lab/strategy/strategy_analysis.py index be570805..c95c8d8e 100644 --- a/quants_lab/strategy/strategy_analysis.py +++ b/quants_lab/strategy/strategy_analysis.py @@ -17,10 +17,10 @@ def __init__(self, positions: pd.DataFrame, candles_df: Optional[pd.DataFrame] = self.base_figure = None def create_base_figure(self, candlestick=True, volume=True, positions=False, trade_pnl=False, extra_rows=0): - rows, heights = self.get_n_rows_and_heights(extra_rows + volume + trade_pnl, volume) + rows, heights = self.get_n_rows_and_heights(extra_rows + trade_pnl, volume) self.rows = rows specs = [[{"secondary_y": True}]] * rows - self.base_figure = make_subplots(rows=rows, cols=1, shared_xaxes=True, vertical_spacing=0.05, + self.base_figure = make_subplots(rows=rows, cols=1, shared_xaxes=True, vertical_spacing=0.01, row_heights=heights, specs=specs) if candlestick: self.add_candles_graph() @@ -29,7 +29,7 @@ def create_base_figure(self, candlestick=True, volume=True, positions=False, tra if positions: self.add_positions() if trade_pnl: - self.add_trade_pnl() + self.add_trade_pnl(row=rows) self.update_layout(volume) def add_positions(self): From 1602c91d67a5bb4dbb5c4e4392848a5ed3ce3f8a Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:46:14 -0300 Subject: [PATCH 07/14] (feat) clean outputs --- .../03_optimization_notebook.ipynb | 128 +----------------- 1 file changed, 6 insertions(+), 122 deletions(-) diff --git a/quants_lab/research_notebooks/03_optimization_notebook.ipynb b/quants_lab/research_notebooks/03_optimization_notebook.ipynb index e87dd25e..23448129 100644 --- a/quants_lab/research_notebooks/03_optimization_notebook.ipynb +++ b/quants_lab/research_notebooks/03_optimization_notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "outputs": [], "source": [ "import sys\n", @@ -107,25 +107,13 @@ " raise TrialPruned()\n" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T20:10:50.249860Z", - "start_time": "2023-12-07T20:10:48.906572Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2023-12-07 17:10:50,338] Using an existing study with name 'super_trend_optimization_1' instead of creating a new one.\n" - ] - } - ], + "execution_count": null, + "outputs": [], "source": [ "import optuna\n", "\n", @@ -139,123 +127,19 @@ " )" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T20:10:50.345407Z", - "start_time": "2023-12-07T20:10:50.251540Z" - } + "collapsed": false } }, { "cell_type": "code", "execution_count": null, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2023-12-07 17:10:56,238] Trial 23 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", - "[I 2023-12-07 17:11:00,180] Trial 24 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", - "[I 2023-12-07 17:11:05,299] Trial 25 finished with value: 0.2513856255976169 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", - "[I 2023-12-07 17:11:09,145] Trial 26 finished with value: 0.2543681375647173 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 140, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", - "[I 2023-12-07 17:11:12,064] Trial 27 finished with value: -0.029959249164181148 and parameters: {'stop_loss': 0.01, 'take_profit': 0.02, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 13 with value: 0.4226868845214273.\n", - "[I 2023-12-07 17:11:15,997] Trial 28 finished with value: 0.5421656349751423 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:21,124] Trial 29 finished with value: 0.15573798497951996 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:24,019] Trial 30 finished with value: -0.1108467390202585 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 40, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:26,857] Trial 31 finished with value: -0.06356581160968328 and parameters: {'stop_loss': 0.01, 'take_profit': 0.02, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:30,808] Trial 32 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:34,707] Trial 33 finished with value: 0.38811148004126206 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:38,644] Trial 34 finished with value: 0.5421656349751423 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:45,073] Trial 35 finished with value: 0.38219663974909746 and parameters: {'stop_loss': 0.01, 'take_profit': 0.03, 'length': 60, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 28 with value: 0.5421656349751423.\n", - "[I 2023-12-07 17:11:48,950] Trial 36 finished with value: 0.592806376208812 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:11:54,104] Trial 37 finished with value: 0.30028336678525713 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 80, 'multiplier': 2.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:11:56,984] Trial 38 finished with value: 0.17279403265558407 and parameters: {'stop_loss': 0.01, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:03,556] Trial 39 finished with value: -0.10925259585987201 and parameters: {'stop_loss': 0.01, 'take_profit': 0.01, 'length': 40, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:08,996] Trial 40 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:11,102] Trial 41 finished with value: -0.3598184663012044 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 20, 'multiplier': 5.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:16,575] Trial 42 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:22,015] Trial 43 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:26,600] Trial 44 finished with value: 0.37136937238312684 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:31,960] Trial 45 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:37,371] Trial 46 finished with value: 0.393770422017225 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:40,220] Trial 47 finished with value: -0.0634041192516708 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:44,761] Trial 48 finished with value: 0.3559925148282939 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:46,260] Trial 49 finished with value: -0.2708608988066281 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 6.0, 'percentage_threshold': 0.01}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:51,691] Trial 50 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:12:56,145] Trial 51 finished with value: 0.40027329750088514 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:01,576] Trial 52 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:07,025] Trial 53 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:12,482] Trial 54 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:17,908] Trial 55 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:23,389] Trial 56 finished with value: 0.5659722220669056 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 40, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:28,832] Trial 57 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:34,195] Trial 58 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:38,710] Trial 59 finished with value: 0.29894352712212513 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 160, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:45,401] Trial 60 finished with value: 0.3661882820037683 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:49,988] Trial 61 finished with value: 0.3559925148282939 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:13:55,453] Trial 62 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:00,893] Trial 63 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:06,295] Trial 64 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:11,725] Trial 65 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:17,161] Trial 66 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:23,932] Trial 67 finished with value: 0.35517980524237147 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:29,330] Trial 68 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:33,877] Trial 69 finished with value: 0.15944412260328772 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:39,319] Trial 70 finished with value: 0.393770422017225 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 120, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:44,745] Trial 71 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:50,176] Trial 72 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:14:55,613] Trial 73 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:01,030] Trial 74 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:06,492] Trial 75 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:11,934] Trial 76 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:18,290] Trial 77 finished with value: 0.2936873783086804 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:22,988] Trial 78 finished with value: -0.18258751093632875 and parameters: {'stop_loss': 0.02, 'take_profit': 0.01, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:28,426] Trial 79 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:35,168] Trial 80 finished with value: 0.31306649255438035 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.03}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:41,525] Trial 81 finished with value: 0.28570398621287874 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 40, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:46,990] Trial 82 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:52,425] Trial 83 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:15:57,842] Trial 84 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:03,272] Trial 85 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:08,700] Trial 86 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:14,161] Trial 87 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:19,557] Trial 88 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:24,164] Trial 89 finished with value: 0.15944412260328772 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 5.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:30,525] Trial 90 finished with value: 0.33271963140347904 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:35,992] Trial 91 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:41,419] Trial 92 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:46,832] Trial 93 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:52,313] Trial 94 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:16:58,227] Trial 95 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:03,808] Trial 96 finished with value: 0.4924856225984462 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:09,482] Trial 97 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:15,184] Trial 98 finished with value: 0.45377866296721386 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 60, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:21,686] Trial 99 finished with value: 0.2936873783086804 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 3.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:27,349] Trial 100 finished with value: 0.30175973701819403 and parameters: {'stop_loss': 0.02, 'take_profit': 0.02, 'length': 180, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:31,189] Trial 101 finished with value: -0.45309169439992525 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 100, 'multiplier': 6.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n", - "[I 2023-12-07 17:17:36,805] Trial 102 finished with value: 0.5672264928817745 and parameters: {'stop_loss': 0.02, 'take_profit': 0.04, 'length': 80, 'multiplier': 4.0, 'percentage_threshold': 0.02}. Best is trial 36 with value: 0.592806376208812.\n" - ] - } - ], + "outputs": [], "source": [ "# Not let's run the optimization!\n", "\n", "n_trials = 200\n", "study.optimize(objective, n_trials=n_trials)" ], - "metadata": { - "collapsed": false, - "is_executing": true, - "ExecuteTime": { - "start_time": "2023-12-07T20:10:52.276919Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], "metadata": { "collapsed": false } From 2c381c3a5c2c7ae89b5715ae8dec3c2c47f47f77 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Thu, 7 Dec 2023 17:46:24 -0300 Subject: [PATCH 08/14] (feat) add trial analysis --- .../04_analyze_optimization_results.ipynb | 11543 +--------------- 1 file changed, 147 insertions(+), 11396 deletions(-) diff --git a/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb index be1b76ce..fc5ef11a 100644 --- a/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb +++ b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "outputs": [], "source": [ "import os\n", @@ -18,77 +18,41 @@ " db_root_path=db_root_path)" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T19:42:11.517410Z", - "start_time": "2023-12-07T19:42:11.511217Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " study_id study_name\n0 1 super_trend_optimization_1", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
study_idstudy_name
01super_trend_optimization_1
\n
" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "optuna_db_manager.studies" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T19:42:15.133179Z", - "start_time": "2023-12-07T19:42:15.126891Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "outputs": [], "source": [ "study_name = \"super_trend_optimization_1\"\n", "df = optuna_db_manager.merged_df[optuna_db_manager.merged_df[\"study_name\"] == study_name]" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T19:50:22.294655Z", - "start_time": "2023-12-07T19:50:22.278335Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "data": { - "text/plain": " trial_id number study_id state datetime_start \\\n0 1 0 1 COMPLETE 2023-12-07 16:34:08.257012 \n1 2 1 1 COMPLETE 2023-12-07 16:34:11.226266 \n2 3 2 1 COMPLETE 2023-12-07 16:34:17.914268 \n3 4 3 1 COMPLETE 2023-12-07 16:34:19.428529 \n4 5 4 1 COMPLETE 2023-12-07 16:34:27.069613 \n5 6 5 1 COMPLETE 2023-12-07 16:34:34.249291 \n6 7 6 1 COMPLETE 2023-12-07 16:34:38.900783 \n7 8 7 1 COMPLETE 2023-12-07 16:34:41.799046 \n8 9 8 1 COMPLETE 2023-12-07 16:34:47.391790 \n9 10 9 1 COMPLETE 2023-12-07 16:34:51.940085 \n10 11 10 1 COMPLETE 2023-12-07 16:34:58.029861 \n11 12 11 1 COMPLETE 2023-12-07 16:35:05.542421 \n12 13 12 1 COMPLETE 2023-12-07 16:35:12.873439 \n13 14 13 1 COMPLETE 2023-12-07 16:35:19.334082 \n14 15 14 1 COMPLETE 2023-12-07 16:35:25.994879 \n15 16 15 1 COMPLETE 2023-12-07 16:35:33.368754 \n16 17 16 1 COMPLETE 2023-12-07 16:35:40.634983 \n17 18 17 1 COMPLETE 2023-12-07 16:35:48.191608 \n18 19 18 1 COMPLETE 2023-12-07 16:35:52.183184 \n19 20 19 1 COMPLETE 2023-12-07 16:35:59.436981 \n\n datetime_complete study_name accuracy \\\n0 2023-12-07 16:34:11.216496 super_trend_optimization_1 0.224806 \n1 2023-12-07 16:34:17.906535 super_trend_optimization_1 0.314465 \n2 2023-12-07 16:34:19.421124 super_trend_optimization_1 0.351351 \n3 2023-12-07 16:34:27.061663 super_trend_optimization_1 0.499397 \n4 2023-12-07 16:34:34.241515 super_trend_optimization_1 0.352941 \n5 2023-12-07 16:34:38.893255 super_trend_optimization_1 0.510753 \n6 2023-12-07 16:34:41.791808 super_trend_optimization_1 0.505415 \n7 2023-12-07 16:34:47.384110 super_trend_optimization_1 0.364238 \n8 2023-12-07 16:34:51.932509 super_trend_optimization_1 0.375000 \n9 2023-12-07 16:34:58.022508 super_trend_optimization_1 0.300813 \n10 2023-12-07 16:35:05.534558 super_trend_optimization_1 0.344464 \n11 2023-12-07 16:35:12.866085 super_trend_optimization_1 0.282353 \n12 2023-12-07 16:35:19.326887 super_trend_optimization_1 0.436364 \n13 2023-12-07 16:35:25.986171 super_trend_optimization_1 0.326733 \n14 2023-12-07 16:35:33.361421 super_trend_optimization_1 0.366460 \n15 2023-12-07 16:35:40.626251 super_trend_optimization_1 0.279817 \n16 2023-12-07 16:35:48.184393 super_trend_optimization_1 0.363107 \n17 2023-12-07 16:35:52.175694 super_trend_optimization_1 0.314410 \n18 2023-12-07 16:35:59.429546 super_trend_optimization_1 0.351005 \n19 2023-12-07 16:36:06.399604 super_trend_optimization_1 0.257812 \n\n avg_trading_time_in_hours \\\n0 4.111413 \n1 2.170325 \n2 6.009647 \n3 0.773337 \n4 3.923623 \n5 2.671102 \n6 1.081769 \n7 4.141523 \n8 4.488008 \n9 5.096682 \n10 1.284204 \n11 1.785208 \n12 3.458712 \n13 2.271603 \n14 1.396986 \n15 1.735047 \n16 1.322296 \n17 2.462154 \n18 1.336770 \n19 2.653945 \n\n config ... total_positions \\\n0 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 129 \n1 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 318 \n2 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 74 \n3 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 829 \n4 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 187 \n5 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 186 \n6 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 277 \n7 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 151 \n8 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 128 \n9 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 123 \n10 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 569 \n11 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 425 \n12 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 220 \n13 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 303 \n14 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 483 \n15 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 436 \n16 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 515 \n17 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 229 \n18 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 547 \n19 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 256 \n\n win_signals trial_value_id objective_x value value_type \\\n0 29 1 0 0.008787 FINITE \n1 100 2 0 0.291448 FINITE \n2 26 3 0 -0.085482 FINITE \n3 414 4 0 -0.141714 FINITE \n4 66 5 0 -0.223673 FINITE \n5 95 6 0 -0.045817 FINITE \n6 140 7 0 -0.071774 FINITE \n7 55 8 0 0.012283 FINITE \n8 48 9 0 0.085992 FINITE \n9 37 10 0 -0.266434 FINITE \n10 196 11 0 -0.558586 FINITE \n11 120 12 0 -0.223067 FINITE \n12 96 13 0 0.192491 FINITE \n13 99 14 0 0.422687 FINITE \n14 177 15 0 0.022237 FINITE \n15 122 16 0 -0.329032 FINITE \n16 187 17 0 0.053164 FINITE \n17 72 18 0 0.254011 FINITE \n18 192 19 0 -0.358893 FINITE \n19 66 20 0 0.189835 FINITE \n\n study_direction_id direction objective_y \\\n0 1 MAXIMIZE 0 \n1 1 MAXIMIZE 0 \n2 1 MAXIMIZE 0 \n3 1 MAXIMIZE 0 \n4 1 MAXIMIZE 0 \n5 1 MAXIMIZE 0 \n6 1 MAXIMIZE 0 \n7 1 MAXIMIZE 0 \n8 1 MAXIMIZE 0 \n9 1 MAXIMIZE 0 \n10 1 MAXIMIZE 0 \n11 1 MAXIMIZE 0 \n12 1 MAXIMIZE 0 \n13 1 MAXIMIZE 0 \n14 1 MAXIMIZE 0 \n15 1 MAXIMIZE 0 \n16 1 MAXIMIZE 0 \n17 1 MAXIMIZE 0 \n18 1 MAXIMIZE 0 \n19 1 MAXIMIZE 0 \n\n hover_text \n0 Trial ID: 1
Study: super_trend_op... \n1 Trial ID: 2
Study: super_trend_op... \n2 Trial ID: 3
Study: super_trend_op... \n3 Trial ID: 4
Study: super_trend_op... \n4 Trial ID: 5
Study: super_trend_op... \n5 Trial ID: 6
Study: super_trend_op... \n6 Trial ID: 7
Study: super_trend_op... \n7 Trial ID: 8
Study: super_trend_op... \n8 Trial ID: 9
Study: super_trend_op... \n9 Trial ID: 10
Study: super_trend_o... \n10 Trial ID: 11
Study: super_trend_o... \n11 Trial ID: 12
Study: super_trend_o... \n12 Trial ID: 13
Study: super_trend_o... \n13 Trial ID: 14
Study: super_trend_o... \n14 Trial ID: 15
Study: super_trend_o... \n15 Trial ID: 16
Study: super_trend_o... \n16 Trial ID: 17
Study: super_trend_o... \n17 Trial ID: 18
Study: super_trend_o... \n18 Trial ID: 19
Study: super_trend_o... \n19 Trial ID: 20
Study: super_trend_o... \n\n[20 rows x 28 columns]", - "text/html": "
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trial_idnumberstudy_idstatedatetime_startdatetime_completestudy_nameaccuracyavg_trading_time_in_hoursconfig...total_positionswin_signalstrial_value_idobjective_xvaluevalue_typestudy_direction_iddirectionobjective_yhover_text
0101COMPLETE2023-12-07 16:34:08.2570122023-12-07 16:34:11.216496super_trend_optimization_10.2248064.111413\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12929100.008787FINITE1MAXIMIZE0<b>Trial ID: 1</b><br><b>Study: super_trend_op...
1211COMPLETE2023-12-07 16:34:11.2262662023-12-07 16:34:17.906535super_trend_optimization_10.3144652.170325\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......318100200.291448FINITE1MAXIMIZE0<b>Trial ID: 2</b><br><b>Study: super_trend_op...
2321COMPLETE2023-12-07 16:34:17.9142682023-12-07 16:34:19.421124super_trend_optimization_10.3513516.009647\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......742630-0.085482FINITE1MAXIMIZE0<b>Trial ID: 3</b><br><b>Study: super_trend_op...
3431COMPLETE2023-12-07 16:34:19.4285292023-12-07 16:34:27.061663super_trend_optimization_10.4993970.773337\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......82941440-0.141714FINITE1MAXIMIZE0<b>Trial ID: 4</b><br><b>Study: super_trend_op...
4541COMPLETE2023-12-07 16:34:27.0696132023-12-07 16:34:34.241515super_trend_optimization_10.3529413.923623\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......1876650-0.223673FINITE1MAXIMIZE0<b>Trial ID: 5</b><br><b>Study: super_trend_op...
5651COMPLETE2023-12-07 16:34:34.2492912023-12-07 16:34:38.893255super_trend_optimization_10.5107532.671102\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......1869560-0.045817FINITE1MAXIMIZE0<b>Trial ID: 6</b><br><b>Study: super_trend_op...
6761COMPLETE2023-12-07 16:34:38.9007832023-12-07 16:34:41.791808super_trend_optimization_10.5054151.081769\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......27714070-0.071774FINITE1MAXIMIZE0<b>Trial ID: 7</b><br><b>Study: super_trend_op...
7871COMPLETE2023-12-07 16:34:41.7990462023-12-07 16:34:47.384110super_trend_optimization_10.3642384.141523\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......15155800.012283FINITE1MAXIMIZE0<b>Trial ID: 8</b><br><b>Study: super_trend_op...
8981COMPLETE2023-12-07 16:34:47.3917902023-12-07 16:34:51.932509super_trend_optimization_10.3750004.488008\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12848900.085992FINITE1MAXIMIZE0<b>Trial ID: 9</b><br><b>Study: super_trend_op...
91091COMPLETE2023-12-07 16:34:51.9400852023-12-07 16:34:58.022508super_trend_optimization_10.3008135.096682\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......12337100-0.266434FINITE1MAXIMIZE0<b>Trial ID: 10</b><br><b>Study: super_trend_o...
1011101COMPLETE2023-12-07 16:34:58.0298612023-12-07 16:35:05.534558super_trend_optimization_10.3444641.284204\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......569196110-0.558586FINITE1MAXIMIZE0<b>Trial ID: 11</b><br><b>Study: super_trend_o...
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20 rows × 28 columns

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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "import plotly.express as px\n", "\n", @@ -11640,11 +285,117 @@ "fig_matrix.show()\n" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T19:58:29.594262Z", - "start_time": "2023-12-07T19:58:29.573443Z" - } + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import json\n", + "from hummingbot.core.data_type.common import PositionMode\n", + "from hummingbot.connector.connector_base import TradeType, OrderType\n", + "from hummingbot.smart_components.utils.config_encoder_decoder import ConfigEncoderDecoder\n", + "\n", + "trial_to_analyze = 36\n", + "\n", + "trial = df[df[\"trial_id\"] == trial_to_analyze]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "trial" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Transform trial config in a dictionary\n", + "encoder_decoder = ConfigEncoderDecoder(TradeType, OrderType, PositionMode)\n", + "trial_config = encoder_decoder.decode(json.loads(trial[\"config\"].item()))\n", + "trial_config" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from quants_lab.controllers.supertrend import SuperTrend, SuperTrendConfig\n", + "\n", + "# In this case we are using the supertrend controller but we can also access to the controller by using the method load_controllers\n", + "config = SuperTrendConfig(**trial_config)\n", + "controller = SuperTrend(config)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from quants_lab.strategy.strategy_analysis import StrategyAnalysis\n", + "from hummingbot.smart_components.strategy_frameworks.directional_trading import DirectionalTradingBacktestingEngine\n", + "\n", + "# Backtest configuration\n", + "trade_cost = 0.0006\n", + "initial_portfolio_usd = 1000\n", + "start = \"2023-01-01\"\n", + "end = \"2024-01-02\"\n", + "\n", + "# Load the data\n", + "engine = DirectionalTradingBacktestingEngine(controller=controller)\n", + "engine.load_controller_data(\"../../data/candles\")\n", + "backtesting_results = engine.run_backtesting(initial_portfolio_usd=initial_portfolio_usd,\n", + " trade_cost=trade_cost,\n", + " start=start, end=end)\n", + "strategy_analysis = StrategyAnalysis(\n", + " positions=backtesting_results[\"executors_df\"],\n", + " candles_df=backtesting_results[\"processed_data\"],\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "strategy_analysis.create_base_figure(volume=False, positions=False, trade_pnl=True)\n", + "strategy_analysis.figure()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "strategy_analysis.pnl_over_time()" + ], + "metadata": { + "collapsed": false } }, { From 3b7ad004f14f46662b3a8362b34d180eb02c5fba Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 11:45:10 -0300 Subject: [PATCH 09/14] (feat) add path to run with jupyter --- .../02_single_controller_backtest.ipynb | 335 +++++++++++------- 1 file changed, 213 insertions(+), 122 deletions(-) diff --git a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb index 73333420..91a09d2e 100644 --- a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb +++ b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb @@ -2,7 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-12-08T14:37:55.756030Z", + "start_time": "2023-12-08T14:37:55.739563Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import datetime\n", @@ -30,14 +40,17 @@ "time_limit = 60 * 60 * 12 # 12 hours\n", "trailing_stop_activation_price_delta = Decimal(\"0.008\")\n", "trailing_stop_trailing_delta = Decimal(\"0.004\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "from hummingbot.smart_components.utils.order_level_builder import OrderLevelBuilder\n", @@ -56,26 +69,32 @@ " trailing_stop_activation_price_delta=trailing_stop_activation_price_delta,\n", " trailing_stop_trailing_delta=trailing_stop_trailing_delta),\n", ")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Let's inpect the order levels\n", "order_levels" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import sys\n", @@ -101,14 +120,17 @@ " percentage_threshold=percentage_threshold,\n", ")\n", "controller = SuperTrend(config=config)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "from quants_lab.strategy.strategy_analysis import StrategyAnalysis\n", @@ -119,49 +141,61 @@ "# Creating the backtesting engine and loading the historical data\n", "engine = DirectionalTradingBacktestingEngine(controller=controller)\n", "engine.load_controller_data(\"../../data/candles\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Let's see what is inside the candles of the controller\n", "engine.controller.candles" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "engine.controller.candles[0].candles_df" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Let's understand what is inside the processed data since this is what we are going to use when generating the signal ;)\n", "engine.controller.get_processed_data()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Let's run the backtesting\n", @@ -169,64 +203,79 @@ "backtesting_results = engine.run_backtesting(initial_portfolio_usd=initial_portfolio_usd,\n", " trade_cost=trade_cost,\n", " start=start, end=end)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Let's see what is inside the backtesting results\n", "backtesting_results.keys()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# now let's analyze each of the dataframes\n", "\n", "# 1. The processed data: this is the data that we are going to use to generate the signal\n", "backtesting_results[\"processed_data\"]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# 2. The executors dataframe: this is the dataframe that contains the information of the orders that were executed\n", "backtesting_results[\"executors_df\"]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# 3. The results dataframe: this is the dataframe that contains the information of the pnl of the strategy\n", "backtesting_results[\"results\"]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Now let's analyze the results using the StrategyAnalysis class\n", @@ -234,49 +283,61 @@ " positions=backtesting_results[\"executors_df\"],\n", " candles_df=backtesting_results[\"processed_data\"],\n", ")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# let's visualize the PNL over time of the strategy\n", "strategy_analysis.pnl_over_time()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "strategy_analysis.create_base_figure(volume=False, positions=False, trade_pnl=True)\n", "fig = strategy_analysis.figure()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "fig" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# Now let's see how we can add the SuperTrend to the plot\n", @@ -299,14 +360,17 @@ " row=1, col=1)\n", "\n", "fig" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# To see the trades we will need to select a lower timeframe due the restrictions and speed of the plotly library\n", @@ -322,25 +386,31 @@ " (backtesting_results[\"executors_df\"][\"timestamp\"] >= start_time) &\n", " (backtesting_results[\"executors_df\"][\"timestamp\"] <= end_time)\n", "]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "executors_filtered" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "\n", @@ -366,24 +436,30 @@ " line=dict(color=\"red\")),\n", " row=1, col=1)\n", "fig" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "### Scatter of PNL per Trade\n", "This bar chart illustrates the PNL for each individual trade. Positive PNLs are shown in green and negative PNLs in red, providing a clear view of profitable vs. unprofitable trades.\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import plotly.express as px\n", @@ -393,98 +469,113 @@ "fig = px.scatter(executors_df, x=\"timestamp\", y='net_pnl_quote', title='PNL per Trade',\n", " color='profitable', color_continuous_scale=['red', 'green'])\n", "fig.show()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "### Scatter Plot of Volume vs. PNL\n", "This scatter plot explores the relationship between the trade volume and the PNL for each trade. It can reveal if larger volumes are associated with higher profits or losses.\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "fig = px.scatter(executors_df, x='volume', y='net_pnl_quote', title='Trade Volume vs. PNL')\n", "fig.show()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "### Histogram of PNL Distribution\n", "The histogram displays the distribution of PNL values across all trades. It helps in understanding the frequency and range of profit and loss outcomes.\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "fig = px.histogram(executors_df, x='net_pnl_quote', title='PNL Distribution')\n", "fig.show()\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "# Conclusion\n", "We can see that the indicator has potential to bring good signals to trade and might be interesting to see how we can design a market maker that shifts the mid price based on this indicator.\n", "A lot of the short signals are wrong but if we zoom in into the loss signals we can see that the losses are not that big and the wins are bigger and if we had implemented the trailing stop feature probably a lot of them are going to be profits." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "# Next steps\n", "- Filter only the loss signals and understand what you can do to prevent them\n", "- Try different configuration values for the indicator\n", "- Test in multiple markets, pick mature markets like BTC-USDT or ETH-USDT and also volatile markets like DOGE-USDT or SHIB-USDT" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.10.13" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } From de9e7c4e8bbe986f44ba7a93159a816d64828a56 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 11:49:31 -0300 Subject: [PATCH 10/14] (feat) add path to run with jupyter --- .../02_single_controller_backtest.ipynb | 28 ++++++++++++++----- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb index 91a09d2e..3cfeab78 100644 --- a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb +++ b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb @@ -2,12 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 0, "metadata": { - "ExecuteTime": { - "end_time": "2023-12-08T14:37:55.756030Z", - "start_time": "2023-12-08T14:37:55.739563Z" - }, "collapsed": false, "jupyter": { "outputs_hidden": false @@ -15,7 +11,18 @@ }, "outputs": [], "source": [ - "import datetime\n", + "import os\n", + "import sys\n", + "\n", + "root_path = os.path.abspath(os.path.join(os.getcwd(), '../..'))\n", + "sys.path.append(root_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ "from decimal import Decimal\n", "\n", "# Market configuration\n", @@ -40,7 +47,14 @@ "time_limit = 60 * 60 * 12 # 12 hours\n", "trailing_stop_activation_price_delta = Decimal(\"0.008\")\n", "trailing_stop_trailing_delta = Decimal(\"0.004\")" - ] + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-08T14:37:55.756030Z", + "start_time": "2023-12-08T14:37:55.739563Z" + } + } }, { "cell_type": "code", From 0d078a535a0f534682d33fe40ea2f712f743e20d Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 11:49:51 -0300 Subject: [PATCH 11/14] (feat) add path to run with jupyter --- .../03_optimization_notebook.ipynb | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/quants_lab/research_notebooks/03_optimization_notebook.ipynb b/quants_lab/research_notebooks/03_optimization_notebook.ipynb index 23448129..3db92b90 100644 --- a/quants_lab/research_notebooks/03_optimization_notebook.ipynb +++ b/quants_lab/research_notebooks/03_optimization_notebook.ipynb @@ -5,7 +5,21 @@ "execution_count": null, "outputs": [], "source": [ + "import os\n", "import sys\n", + "\n", + "root_path = os.path.abspath(os.path.join(os.getcwd(), '../..'))\n", + "sys.path.append(root_path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ "import traceback\n", "from decimal import Decimal\n", "import pandas_ta as ta # noqa: F401\n", From 255b57aee9e04fc419e13f78eb3bb00cc883df02 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 11:50:03 -0300 Subject: [PATCH 12/14] (feat) add path to run with jupyter --- .../04_analyze_optimization_results.ipynb | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb index fc5ef11a..2a545c28 100644 --- a/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb +++ b/quants_lab/research_notebooks/04_analyze_optimization_results.ipynb @@ -6,8 +6,20 @@ "outputs": [], "source": [ "import os\n", + "import sys\n", "\n", - "import pandas as pd\n", + "root_path = os.path.abspath(os.path.join(os.getcwd(), '../..'))\n", + "sys.path.append(root_path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ "\n", "from utils.optuna_database_manager import OptunaDBManager\n", "\n", From 28c67e73f706a9d3af3455ce9a1e19d486a0d63d Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 12:06:26 -0300 Subject: [PATCH 13/14] (feat) clean outputs --- .../01_strategy_design_supertrend.ipynb | 19650 +--------------- .../02_single_controller_backtest.ipynb | 10 +- 2 files changed, 28 insertions(+), 19632 deletions(-) diff --git a/quants_lab/research_notebooks/01_strategy_design_supertrend.ipynb b/quants_lab/research_notebooks/01_strategy_design_supertrend.ipynb index 7abbf0e4..c5f7b906 100644 --- a/quants_lab/research_notebooks/01_strategy_design_supertrend.ipynb +++ b/quants_lab/research_notebooks/01_strategy_design_supertrend.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "super_trend_long = candles[candles[\"SUPERTd_20_3.0\"] == 1]\n", "super_trend_short = candles[candles[\"SUPERTd_20_3.0\"] == -1]\n", @@ -11520,16 +115,12 @@ " line=dict(color=\"red\")))" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T14:49:10.267118Z", - "start_time": "2023-12-07T14:49:10.266038Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "outputs": [], "source": [ "percentage_threshold = 0.01 # This is an example threshold value\n", @@ -11544,8200 +135,13 @@ "candles.loc[short_condition, \"signal\"] = -1" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T14:49:10.271367Z", - "start_time": "2023-12-07T14:49:10.270936Z" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "from plotly.subplots import make_subplots\n", "\n", @@ -19783,11 +187,7 @@ " xaxis_rangeslider_visible=False)\n" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-07T14:50:42.903377Z", - "start_time": "2023-12-07T14:50:42.863110Z" - } + "collapsed": false } } ], diff --git a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb index 3cfeab78..95a49ef3 100644 --- a/quants_lab/research_notebooks/02_single_controller_backtest.ipynb +++ b/quants_lab/research_notebooks/02_single_controller_backtest.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "outputs": [], "source": [ "from decimal import Decimal\n", @@ -49,11 +49,7 @@ "trailing_stop_trailing_delta = Decimal(\"0.004\")" ], "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-12-08T14:37:55.756030Z", - "start_time": "2023-12-08T14:37:55.739563Z" - } + "collapsed": false } }, { From 3910d7fac02bd949641033daee66cb7e343f2780 Mon Sep 17 00:00:00 2001 From: cardosofede Date: Fri, 8 Dec 2023 15:59:49 -0300 Subject: [PATCH 14/14] (feat) add supertrend controller --- quants_lab/controllers/supertrend.py | 52 ++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 quants_lab/controllers/supertrend.py diff --git a/quants_lab/controllers/supertrend.py b/quants_lab/controllers/supertrend.py new file mode 100644 index 00000000..6da96c44 --- /dev/null +++ b/quants_lab/controllers/supertrend.py @@ -0,0 +1,52 @@ +import time + +import pandas as pd +from pydantic import Field + +from hummingbot.smart_components.executors.position_executor.position_executor import PositionExecutor +from hummingbot.smart_components.strategy_frameworks.data_types import OrderLevel +from hummingbot.smart_components.strategy_frameworks.directional_trading.directional_trading_controller_base import ( + DirectionalTradingControllerBase, + DirectionalTradingControllerConfigBase, +) + + +class SuperTrendConfig(DirectionalTradingControllerConfigBase): + strategy_name: str = "supertrend" + length: int = Field(default=20, ge=5, le=200) + multiplier: float = Field(default=4.0, ge=2.0, le=7.0) + percentage_threshold: float = Field(default=0.01, ge=0.005, le=0.05) + + +class SuperTrend(DirectionalTradingControllerBase): + def __init__(self, config: SuperTrendConfig): + super().__init__(config) + self.config = config + + def early_stop_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool: + # If an executor has an active position, should we close it based on a condition. This feature is not available + # for the backtesting yet + return False + + def cooldown_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool: + # After finishing an order, the executor will be in cooldown for a certain amount of time. + # This prevents the executor from creating a new order immediately after finishing one and execute a lot + # of orders in a short period of time from the same side. + if executor.close_timestamp and executor.close_timestamp + order_level.cooldown_time > time.time(): + return True + return False + + def get_processed_data(self) -> pd.DataFrame: + df = self.candles[0].candles_df + df.ta.supertrend(length=self.config.length, multiplier=self.config.multiplier, append=True) + df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{self.config.length}_{self.config.multiplier}"]) / df["close"] + + # Generate long and short conditions + long_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == 1) & (df["percentage_distance"] < self.config.percentage_threshold) + short_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == -1) & (df["percentage_distance"] < self.config.percentage_threshold) + + # Choose side + df['signal'] = 0 + df.loc[long_condition, 'signal'] = 1 + df.loc[short_condition, 'signal'] = -1 + return df