diff --git a/quants_lab/research_notebooks/01_analyze_optimization_results.ipynb b/quants_lab/research_notebooks/01_analyze_optimization_results.ipynb new file mode 100644 index 0000000..8ecdcc4 --- /dev/null +++ b/quants_lab/research_notebooks/01_analyze_optimization_results.ipynb @@ -0,0 +1,10911 @@ +{ + "cells": [ + { + "cell_type": "code", + "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": 1, + "outputs": [], + "source": [ + "\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": "2023-12-12T01:19:16.027765Z", + "start_time": "2023-12-12T01:19:15.541461Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": " study_id study_name\n0 1 super_trend_optimization_1\n1 2 super_trend_optimization_mt", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
study_idstudy_name
01super_trend_optimization_1
12super_trend_optimization_mt
\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optuna_db_manager.studies" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T01:19:16.035913Z", + "start_time": "2023-12-12T01:19:16.028683Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "study_name = \"super_trend_optimization_mt\"\n", + "df = optuna_db_manager.merged_df[optuna_db_manager.merged_df[\"study_name\"] == study_name]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T01:19:19.411856Z", + "start_time": "2023-12-12T01:19:19.363030Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": " trial_id number study_id state datetime_start \\\n227 230 0 2 COMPLETE 2023-12-11 22:02:43.231166 \n228 231 1 2 COMPLETE 2023-12-11 22:03:00.567468 \n229 232 2 2 COMPLETE 2023-12-11 22:03:17.064624 \n230 233 3 2 COMPLETE 2023-12-11 22:03:33.427127 \n231 234 4 2 COMPLETE 2023-12-11 22:03:49.957449 \n232 235 5 2 COMPLETE 2023-12-11 22:04:07.168221 \n233 236 6 2 COMPLETE 2023-12-11 22:04:24.076702 \n234 237 7 2 COMPLETE 2023-12-11 22:04:41.112936 \n235 238 8 2 COMPLETE 2023-12-11 22:04:57.612671 \n236 239 9 2 COMPLETE 2023-12-11 22:05:15.275493 \n237 240 10 2 COMPLETE 2023-12-11 22:05:33.219372 \n238 241 11 2 COMPLETE 2023-12-11 22:05:50.586509 \n239 242 12 2 COMPLETE 2023-12-11 22:06:07.817956 \n240 243 13 2 COMPLETE 2023-12-11 22:06:24.458958 \n241 244 14 2 COMPLETE 2023-12-11 22:06:41.104551 \n242 245 15 2 COMPLETE 2023-12-11 22:06:57.609660 \n243 246 16 2 COMPLETE 2023-12-11 22:07:14.212057 \n244 247 17 2 COMPLETE 2023-12-11 22:07:30.827380 \n245 248 18 2 COMPLETE 2023-12-11 22:07:47.529136 \n246 249 19 2 COMPLETE 2023-12-11 22:08:04.398085 \n\n datetime_complete study_name accuracy \\\n227 2023-12-11 22:03:00.557164 super_trend_optimization_mt 0.479042 \n228 2023-12-11 22:03:17.057086 super_trend_optimization_mt 0.417391 \n229 2023-12-11 22:03:33.419313 super_trend_optimization_mt 0.340796 \n230 2023-12-11 22:03:49.949266 super_trend_optimization_mt 0.283824 \n231 2023-12-11 22:04:07.160226 super_trend_optimization_mt 0.417303 \n232 2023-12-11 22:04:24.069134 super_trend_optimization_mt 0.623590 \n233 2023-12-11 22:04:41.105547 super_trend_optimization_mt 0.625000 \n234 2023-12-11 22:04:57.605407 super_trend_optimization_mt 0.355278 \n235 2023-12-11 22:05:15.267079 super_trend_optimization_mt 0.513237 \n236 2023-12-11 22:05:33.211438 super_trend_optimization_mt 0.482476 \n237 2023-12-11 22:05:50.577500 super_trend_optimization_mt 0.294807 \n238 2023-12-11 22:06:07.810065 super_trend_optimization_mt 0.294807 \n239 2023-12-11 22:06:24.450827 super_trend_optimization_mt 0.294807 \n240 2023-12-11 22:06:41.097271 super_trend_optimization_mt 0.294807 \n241 2023-12-11 22:06:57.602507 super_trend_optimization_mt 0.260870 \n242 2023-12-11 22:07:14.204659 super_trend_optimization_mt 0.288591 \n243 2023-12-11 22:07:30.819529 super_trend_optimization_mt 0.303597 \n244 2023-12-11 22:07:47.521386 super_trend_optimization_mt 0.293132 \n245 2023-12-11 22:08:04.390746 super_trend_optimization_mt 0.238185 \n246 2023-12-11 22:08:20.923331 super_trend_optimization_mt 0.295058 \n\n avg_trading_time_in_hours \\\n227 2.822156 \n228 4.070543 \n229 1.579353 \n230 2.090662 \n231 3.651018 \n232 1.696103 \n233 1.627661 \n234 1.569410 \n235 0.961312 \n236 0.928934 \n237 3.181910 \n238 3.181910 \n239 3.181910 \n240 3.181910 \n241 2.892609 \n242 3.179195 \n243 2.316331 \n244 3.186348 \n245 2.656522 \n246 2.328125 \n\n config ... total_positions \\\n227 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 501 \n228 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 460 \n229 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 804 \n230 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 680 \n231 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\\\"binance_perpetual\\\", \\\"tradi... ... 597 \n245 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 529 \n246 \"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi... ... 688 \n\n win_signals trial_value_id objective_x value value_type \\\n227 240 228 0 -0.350982 FINITE \n228 192 229 0 0.124883 FINITE \n229 274 230 0 -0.453629 FINITE \n230 193 231 0 -0.050734 FINITE \n231 164 232 0 0.161915 FINITE \n232 608 233 0 -0.185476 FINITE \n233 505 234 0 -0.316476 FINITE \n234 313 235 0 -0.103853 FINITE \n235 853 236 0 0.740488 FINITE \n236 647 237 0 -0.960720 FINITE \n237 176 238 0 1.199858 FINITE \n238 176 239 0 1.199858 FINITE \n239 176 240 0 1.199858 FINITE \n240 176 241 0 1.199858 FINITE \n241 150 242 0 0.020470 FINITE \n242 172 243 0 0.993143 FINITE \n243 211 244 0 0.378650 FINITE \n244 175 245 0 1.089455 FINITE \n245 126 246 0 -0.201113 FINITE \n246 203 247 0 0.192099 FINITE \n\n study_direction_id direction objective_y \\\n227 2 MAXIMIZE 0 \n228 2 MAXIMIZE 0 \n229 2 MAXIMIZE 0 \n230 2 MAXIMIZE 0 \n231 2 MAXIMIZE 0 \n232 2 MAXIMIZE 0 \n233 2 MAXIMIZE 0 \n234 2 MAXIMIZE 0 \n235 2 MAXIMIZE 0 \n236 2 MAXIMIZE 0 \n237 2 MAXIMIZE 0 \n238 2 MAXIMIZE 0 \n239 2 MAXIMIZE 0 \n240 2 MAXIMIZE 0 \n241 2 MAXIMIZE 0 \n242 2 MAXIMIZE 0 \n243 2 MAXIMIZE 0 \n244 2 MAXIMIZE 0 \n245 2 MAXIMIZE 0 \n246 2 MAXIMIZE 0 \n\n hover_text \n227 Trial ID: 230
Study: super_trend_... \n228 Trial ID: 231
Study: super_trend_... \n229 Trial ID: 232
Study: super_trend_... \n230 Trial ID: 233
Study: super_trend_... \n231 Trial ID: 234
Study: super_trend_... \n232 Trial ID: 235
Study: super_trend_... \n233 Trial ID: 236
Study: super_trend_... \n234 Trial ID: 237
Study: super_trend_... \n235 Trial ID: 238
Study: super_trend_... \n236 Trial ID: 239
Study: super_trend_... \n237 Trial ID: 240
Study: super_trend_... \n238 Trial ID: 241
Study: super_trend_... \n239 Trial ID: 242
Study: super_trend_... \n240 Trial ID: 243
Study: super_trend_... \n241 Trial ID: 244
Study: super_trend_... \n242 Trial ID: 245
Study: super_trend_... \n243 Trial ID: 246
Study: super_trend_... \n244 Trial ID: 247
Study: super_trend_... \n245 Trial ID: 248
Study: super_trend_... \n246 Trial ID: 249
Study: super_trend_... \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
22723002COMPLETE2023-12-11 22:02:43.2311662023-12-11 22:03:00.557164super_trend_optimization_mt0.4790422.822156\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......5012402280-0.350982FINITE2MAXIMIZE0<b>Trial ID: 230</b><br><b>Study: super_trend_...
22823112COMPLETE2023-12-11 22:03:00.5674682023-12-11 22:03:17.057086super_trend_optimization_mt0.4173914.070543\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......46019222900.124883FINITE2MAXIMIZE0<b>Trial ID: 231</b><br><b>Study: super_trend_...
22923222COMPLETE2023-12-11 22:03:17.0646242023-12-11 22:03:33.419313super_trend_optimization_mt0.3407961.579353\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......8042742300-0.453629FINITE2MAXIMIZE0<b>Trial ID: 232</b><br><b>Study: super_trend_...
23023332COMPLETE2023-12-11 22:03:33.4271272023-12-11 22:03:49.949266super_trend_optimization_mt0.2838242.090662\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......6801932310-0.050734FINITE2MAXIMIZE0<b>Trial ID: 233</b><br><b>Study: super_trend_...
23123442COMPLETE2023-12-11 22:03:49.9574492023-12-11 22:04:07.160226super_trend_optimization_mt0.4173033.651018\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......39316423200.161915FINITE2MAXIMIZE0<b>Trial ID: 234</b><br><b>Study: super_trend_...
23223552COMPLETE2023-12-11 22:04:07.1682212023-12-11 22:04:24.069134super_trend_optimization_mt0.6235901.696103\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......9756082330-0.185476FINITE2MAXIMIZE0<b>Trial ID: 235</b><br><b>Study: super_trend_...
23323662COMPLETE2023-12-11 22:04:24.0767022023-12-11 22:04:41.105547super_trend_optimization_mt0.6250001.627661\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......8085052340-0.316476FINITE2MAXIMIZE0<b>Trial ID: 236</b><br><b>Study: super_trend_...
23423772COMPLETE2023-12-11 22:04:41.1129362023-12-11 22:04:57.605407super_trend_optimization_mt0.3552781.569410\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......8813132350-0.103853FINITE2MAXIMIZE0<b>Trial ID: 237</b><br><b>Study: super_trend_...
23523882COMPLETE2023-12-11 22:04:57.6126712023-12-11 22:05:15.267079super_trend_optimization_mt0.5132370.961312\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......166285323600.740488FINITE2MAXIMIZE0<b>Trial ID: 238</b><br><b>Study: super_trend_...
23623992COMPLETE2023-12-11 22:05:15.2754932023-12-11 22:05:33.211438super_trend_optimization_mt0.4824760.928934\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......13416472370-0.960720FINITE2MAXIMIZE0<b>Trial ID: 239</b><br><b>Study: super_trend_...
237240102COMPLETE2023-12-11 22:05:33.2193722023-12-11 22:05:50.577500super_trend_optimization_mt0.2948073.181910\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59717623801.199858FINITE2MAXIMIZE0<b>Trial ID: 240</b><br><b>Study: super_trend_...
238241112COMPLETE2023-12-11 22:05:50.5865092023-12-11 22:06:07.810065super_trend_optimization_mt0.2948073.181910\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59717623901.199858FINITE2MAXIMIZE0<b>Trial ID: 241</b><br><b>Study: super_trend_...
239242122COMPLETE2023-12-11 22:06:07.8179562023-12-11 22:06:24.450827super_trend_optimization_mt0.2948073.181910\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59717624001.199858FINITE2MAXIMIZE0<b>Trial ID: 242</b><br><b>Study: super_trend_...
240243132COMPLETE2023-12-11 22:06:24.4589582023-12-11 22:06:41.097271super_trend_optimization_mt0.2948073.181910\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59717624101.199858FINITE2MAXIMIZE0<b>Trial ID: 243</b><br><b>Study: super_trend_...
241244142COMPLETE2023-12-11 22:06:41.1045512023-12-11 22:06:57.602507super_trend_optimization_mt0.2608702.892609\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......57515024200.020470FINITE2MAXIMIZE0<b>Trial ID: 244</b><br><b>Study: super_trend_...
242245152COMPLETE2023-12-11 22:06:57.6096602023-12-11 22:07:14.204659super_trend_optimization_mt0.2885913.179195\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59617224300.993143FINITE2MAXIMIZE0<b>Trial ID: 245</b><br><b>Study: super_trend_...
243246162COMPLETE2023-12-11 22:07:14.2120572023-12-11 22:07:30.819529super_trend_optimization_mt0.3035972.316331\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......69521124400.378650FINITE2MAXIMIZE0<b>Trial ID: 246</b><br><b>Study: super_trend_...
244247172COMPLETE2023-12-11 22:07:30.8273802023-12-11 22:07:47.521386super_trend_optimization_mt0.2931323.186348\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......59717524501.089455FINITE2MAXIMIZE0<b>Trial ID: 247</b><br><b>Study: super_trend_...
245248182COMPLETE2023-12-11 22:07:47.5291362023-12-11 22:08:04.390746super_trend_optimization_mt0.2381852.656522\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......5291262460-0.201113FINITE2MAXIMIZE0<b>Trial ID: 248</b><br><b>Study: super_trend_...
246249192COMPLETE2023-12-11 22:08:04.3980852023-12-11 22:08:20.923331super_trend_optimization_mt0.2950582.328125\"{\\\"exchange\\\": \\\"binance_perpetual\\\", \\\"tradi......68820324700.192099FINITE2MAXIMIZE0<b>Trial ID: 249</b><br><b>Study: super_trend_...
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" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T01:19:20.320562Z", + "start_time": "2023-12-12T01:19:20.307716Z" + } + } + }, + { + "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": 5, + "outputs": [ + { + "data": { + "text/html": " \n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "trial_id=%{x}
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.histogram(df, x='total_positions', title='Distribution of Total Positions')\n", + "fig.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T01:19:27.300286Z", + "start_time": "2023-12-12T01:19:27.273268Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Pie Chart of Win Signals\n", + "This pie chart shows the proportion of win signals in each trial, providing a visual representation of the success rate distribution across trials.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "domain": { + "x": [ + 0.0, + 1.0 + ], + "y": [ + 0.0, + 1.0 + ] + }, + "hovertemplate": "trial_id=%{label}
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"domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "value" + } + }, + "legend": { + "tracegroupgap": 0 + }, + "title": { + "text": "Win Signals vs PNL" + } + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter Plot for Accuracy vs PNL\n", + "fig_accuracy_pnl = px.scatter(df, x='accuracy', y='value', title='Accuracy vs PNL')\n", + "fig_accuracy_pnl.show()\n", + "\n", + "# Scatter Plot for Average Trading Time vs PNL\n", + "fig_tradingtime_pnl = px.scatter(df, x='avg_trading_time_in_hours', y='value', title='Average Trading Time vs PNL')\n", + "fig_tradingtime_pnl.show()\n", + "\n", + "# Scatter Plot for Total Positions vs PNL\n", + "fig_positions_pnl = px.scatter(df, x='total_positions', y='value', title='Total Positions vs PNL')\n", + "fig_positions_pnl.show()\n", + "\n", + "# Scatter Plot for Win Signals vs PNL\n", + "fig_winsignals_pnl = px.scatter(df, x='win_signals', y='value', title='Win Signals vs PNL')\n", + "fig_winsignals_pnl.show()\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-12-12T01:19:56.004126Z", + "start_time": "2023-12-12T01:19:55.945664Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Scatter Matrix of All Variables\n", + "A scatter matrix allows us to see both the distribution of single variables and the relationships between two variables. The diagonal shows histograms for each variable, and the scatter plots show correlations between them.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "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 + } + }, + { + "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 + } + }, + { + "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 +} diff --git a/quants_lab/research_notebooks/supertrend/04_analyze_optimization_results.ipynb b/quants_lab/research_notebooks/supertrend/04_analyze_optimization_results.ipynb deleted file mode 100644 index f82a704..0000000 --- a/quants_lab/research_notebooks/supertrend/04_analyze_optimization_results.ipynb +++ /dev/null @@ -1,444 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "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": [ - "\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 - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "optuna_db_manager.studies" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "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 - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df" - ], - "metadata": { - "collapsed": false - } - }, - { - "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": null, - "outputs": [], - "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 - } - }, - { - "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": null, - "outputs": [], - "source": [ - "fig = px.scatter(df, x='avg_trading_time_in_hours', y='accuracy', title='Accuracy vs. Average Trading Time')\n", - "fig.show()\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Histogram of Total Positions\n", - "The histogram represents the distribution of total positions across all trials. This visualization is useful for understanding the general spread and most common values of positions.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig = px.histogram(df, x='total_positions', title='Distribution of Total Positions')\n", - "fig.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Pie Chart of Win Signals\n", - "This pie chart shows the proportion of win signals in each trial, providing a visual representation of the success rate distribution across trials.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "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 - } - }, - { - "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": null, - "outputs": [], - "source": [ - "fig = px.box(df, y='value', title='Box Plot of Trial PNL')\n", - "fig.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "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": null, - "outputs": [], - "source": [ - "import plotly.express as px\n", - "\n", - "# Calculate the correlation matrix\n", - "correlation_matrix = df[['accuracy', 'avg_trading_time_in_hours', 'total_positions', 'win_signals', 'value']].corr()\n", - "\n", - "# Generate the heatmap\n", - "fig = px.imshow(correlation_matrix,\n", - " x=correlation_matrix.columns,\n", - " y=correlation_matrix.columns,\n", - " title='Correlation Heatmap',\n", - " labels=dict(x=\"Variable\", y=\"Variable\", color=\"Correlation\"),\n", - " color_continuous_scale='RdBu')\n", - "\n", - "fig.show()\n", - "\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Stacked Bar Chart for Win Signals vs Total Positions\n", - "A stacked bar chart displaying the ratio of win signals to total positions for each trial. This helps in visualizing the efficiency and effectiveness of each trial.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "df['loss_signals'] = df['total_positions'] - df['win_signals']\n", - "fig = px.bar(df, x='trial_id', y=['win_signals', 'loss_signals'], title='Win vs Loss Signals per Trial', labels={'value':'Number of Signals'}, hover_data=['total_positions'])\n", - "fig.show()\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Scatter Plots Against PNL\n", - "These scatter plots show how various metrics behave in relation to the PNL (Profit and Loss). This analysis can help in understanding which factors have a stronger relationship with financial outcomes.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Scatter Plot for Accuracy vs PNL\n", - "fig_accuracy_pnl = px.scatter(df, x='accuracy', y='value', title='Accuracy vs PNL')\n", - "fig_accuracy_pnl.show()\n", - "\n", - "# Scatter Plot for Average Trading Time vs PNL\n", - "fig_tradingtime_pnl = px.scatter(df, x='avg_trading_time_in_hours', y='value', title='Average Trading Time vs PNL')\n", - "fig_tradingtime_pnl.show()\n", - "\n", - "# Scatter Plot for Total Positions vs PNL\n", - "fig_positions_pnl = px.scatter(df, x='total_positions', y='value', title='Total Positions vs PNL')\n", - "fig_positions_pnl.show()\n", - "\n", - "# Scatter Plot for Win Signals vs PNL\n", - "fig_winsignals_pnl = px.scatter(df, x='win_signals', y='value', title='Win Signals vs PNL')\n", - "fig_winsignals_pnl.show()\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Scatter Matrix of All Variables\n", - "A scatter matrix allows us to see both the distribution of single variables and the relationships between two variables. The diagonal shows histograms for each variable, and the scatter plots show correlations between them.\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "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 - } - }, - { - "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 - } - }, - { - "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 -}