{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%autoreload 2 " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import pickle\n", "import plotly.graph_objs as go\n", "import latextable\n", "from texttable import Texttable\n", "from strategy.strategy import (\n", " BuyAndHoldStrategy,\n", " MACDStrategy,\n", " RSIStrategy,\n", " ModelQuantilePredictionsStrategy,\n", " ModelGmadlPredictionsStrategy,\n", " ConcatenatedStrategies\n", ")\n", "from strategy.util import (\n", " get_data_windows,\n", " get_sweep_window_predictions,\n", " get_predictions_dataframe\n", ")\n", "from strategy.evaluation import (\n", " parameter_sweep,\n", " evaluate_strategy\n", ")\n", "from strategy.plotting import (\n", " plot_sweep_results\n", ")\n", "\n", "PADDING=5000\n", "VALID_PART=0.2\n", "INTERVAL='5min'\n", "METRIC='mod_ir'\n", "TOP_N=10" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-5m-features-full:latest, 72.63MB. 1 files... \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n", "Done. 0:0:0.3\n" ] } ], "source": [ "data_windows = get_data_windows(\n", " 'wne-masters-thesis-testing',\n", " \"btc-5m-features-full:latest\",\n", " min_window=0, \n", " max_window=5\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def sweeps_on_all_windows(data_windows, strategy_class, params, **kwargs):\n", " result = []\n", " for in_sample, _ in data_windows:\n", " data_part = int((1 - VALID_PART) * len(in_sample))\n", " result.append(parameter_sweep(in_sample[data_part-PADDING:], strategy_class, params, padding=PADDING, interval=INTERVAL, **kwargs))\n", " return result" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running 3840 tasks in parallel with joblib (n_jobs=4)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "817c3009ddf94691a6b152c4daf25984", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3840 [00:00 p['fast_window_size']\n", ")\n", "macd_sweep_results = sweeps_on_all_windows(\n", " data_windows,\n", " MACDStrategy,\n", " MACD_PARAMS,\n", " params_filter=MACD_PARAMS_FILTER,\n", " sort_by=METRIC)\n", "macd_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in macd_sweep_results]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { 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"title": { "font": { "size": 28 }, "text": "Hyperparameters search results", "x": 0.5, "xanchor": "center", "y": 0.98, "yanchor": "top" }, "xaxis": { "dtick": 1, "gridwidth": 2, "showticklabels": false, "tick0": -0.5, "ticks": "", "zeroline": false }, "yaxis": { "autorange": "reversed", "domain": [ 0, 0.4 ], "dtick": 1, "gridwidth": 2, "showticklabels": false, "tick0": 0.5, "ticks": "", "zeroline": false } } }, "text/html": [ "
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"text": [ "Running 11088 tasks in parallel with joblib (n_jobs=7)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e9ce10f364e34bec8ae52d8b50000c54", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11088 [00:00 p['enter_short']))\n", " and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n", "\n", "RSI_PARAMS = {\n", " 'window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n", " 'enter_long': [None, 70, 75, 80, 85, 90, 95],\n", " 'exit_long': [None, 5, 10, 15, 20, 25, 30],\n", " 'enter_short': [None, 5, 10, 15, 20, 25, 30],\n", " 'exit_short': [None, 70, 75, 80, 85, 90, 95],\n", "}\n", "rsi_sweep_results = sweeps_on_all_windows(data_windows, RSIStrategy, RSI_PARAMS, params_filter=RSI_FILTER, sort_by=METRIC, num_workers=7)\n", "rsi_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rsi_sweep_results]" ] }, { "cell_type": "code", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_sweep_results(pd.DataFrame([result for result, _ in rsi_sweep_results[0]]), parameters=RSI_PARAMS.keys(), objective=METRIC)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n" ] } ], "source": [ "# Model with rmse loss\n", "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/9afp99kz'\n", "train_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n", "valid_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n", "test_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running 1176 tasks in parallel with joblib (n_jobs=7)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8482ca53c5934b9781af871d5836392f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1176 [00:00 p['enter_short']))\n", " and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n", "\n", "rmse_model_sweep_results = []\n", "for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_pred_windows, valid_pred_windows, test_pred_windows):\n", " data_part = int((1 - VALID_PART) * len(in_sample))\n", " params={\n", " 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n", " 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n", " 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n", " 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n", " 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n", " # 'enter_short': [None],\n", " # 'exit_short': [None],\n", " }\n", " \n", " rmse_model_sweep_results.append(parameter_sweep(\n", " in_sample[data_part-PADDING:],\n", " ModelGmadlPredictionsStrategy,\n", " params,\n", " params_filter=MODEL_RMSE_LOSS_FILTER,\n", " padding=PADDING,\n", " interval=INTERVAL,\n", " sort_by=METRIC,\n", " num_workers = 7))\n", " \n", "\n", "rmse_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rmse_model_sweep_results]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Model with quantile loss\n", "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/8m3hwwmx'\n", "train_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n", "valid_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n", "test_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_windows = get_data_windows(\n", " 'wne-masters-thesis-testing',\n", " \"btc-5m-features-full:latest\",\n", " min_window=0, \n", " max_window=5\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "MODEL_QUANTILE_LOSS_FILTER = lambda p: (\n", " ((p['quantile_enter_long'] is not None and (p['quantile_enter_short'] is not None or p['quantile_exit_long'] is not None))\n", " or (p['quantile_enter_short'] is not None and (p['quantile_exit_short'] is not None or p['quantile_enter_long'] is not None)))\n", " and (p['quantile_enter_short'] is None or p['quantile_exit_long'] is None or (p['quantile_exit_long'] < p['quantile_enter_short']))\n", " and (p['quantile_enter_long'] is None or p['quantile_exit_short'] is None or (p['quantile_exit_short'] < p['quantile_enter_long'])))\n", "\n", "quantile_model_sweep_results = []\n", "for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_pred_windows, valid_pred_windows, test_pred_windows):\n", " data_part = int((1 - VALID_PART) * len(in_sample))\n", " params={\n", " 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n", " 'quantiles': [[0.01, 0.02, 0.03, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.97, 0.98, 0.99]],\n", " 'quantile_enter_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n", " 'quantile_exit_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n", " 'quantile_enter_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n", " 'quantile_exit_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n", " 'exchange_fee': [0.001, 0.002, 0.003],\n", " 'future': [1]\n", " }\n", " \n", " quantile_model_sweep_results.append(parameter_sweep(\n", " in_sample[data_part-PADDING:],\n", " ModelQuantilePredictionsStrategy,\n", " params,\n", " params_filter=MODEL_QUANTILE_LOSS_FILTER,\n", " padding=PADDING,\n", " interval=INTERVAL,\n", " sort_by=METRIC))\n", "\n", "quantile_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in quantile_model_sweep_results]" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "# plot_sweep_results(pd.DataFrame([result for result, _ in quantile_model_sweep_results[0]]), parameters=['quantile_enter_long', 'quantile_exit_long', 'quantile_enter_short', 'quantile_exit_short', 'future'], objective=METRIC)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Model with gmadl loss\n", "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/0pro3i5i'\n", "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/v3epl3qk'\n", "# train_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n", "valid_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n", "test_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "MODEL_GMADL_LOSS_FILTER = lambda p: (\n", " ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n", " or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n", " and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n", " and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n", "\n", "gmadl_model_sweep_results = []\n", "for (in_sample, _), valid_preds, test_preds in zip(data_windows, valid_gmadl_pred_windows, test_gmadl_pred_windows):\n", " data_part = int((1 - VALID_PART) * len(in_sample))\n", " params={\n", " 'predictions': [get_predictions_dataframe(valid_preds, test_preds)],\n", " 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n", " 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n", " 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n", " 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n", " }\n", " \n", " gmadl_model_sweep_results.append(parameter_sweep(\n", " in_sample[data_part-PADDING:],\n", " ModelGmadlPredictionsStrategy,\n", " params,\n", " params_filter=MODEL_GMADL_LOSS_FILTER,\n", " padding=PADDING,\n", " interval=INTERVAL,\n", " sort_by=METRIC))\n", " \n", "\n", "gmadl_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in gmadl_model_sweep_results]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Persist best strategies, so that they don't have to be recomputed every time\n", "best_strategies = {\n", " 'buy_and_hold': buyandhold_best_strategies,\n", " 'macd_strategies': macd_best_strategies,\n", " 'rsi_strategies': rsi_best_strategies,\n", " 'rmse_model': rmse_model_best_strategies,\n", " 'quantile_model': quantile_model_best_strategies,\n", " 'gmadl_model': gmadl_model_best_strategies\n", "}\n", "\n", "with open('cache/5min-best-strategies-v2.pkl', 'wb') as outp:\n", " pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizations & Tables" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "with open('cache/5min-best-strategies-v2.pkl', 'rb') as inpt:\n", " best_strategies = pickle.load(inpt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Tables with parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Best hparams for MACD strategy\n", "table_macd_params = Texttable()\n", "table_macd_params.set_deco(Texttable.HEADER)\n", "table_macd_params.set_cols_align([\"l\", \"c\", \"c\", \"c\", \"c\"])\n", "table_macd_params.header([\n", " \"\\\\textbf{Window}\",\n", " \"\\\\textbf{Fast Window Size}\",\n", " \"\\\\textbf{Slow Window Size}\",\n", " \"\\\\textbf{Signal Window Size}\",\n", " \"\\\\textbf{Short sell}\"\n", "])\n", "\n", "for i, macd_strategy in enumerate(best_strategies['macd_strategies']):\n", " macd_strategy_info = macd_strategy[0].info()\n", " table_macd_params.add_row([\n", " f\"Window {i+1}\",\n", " macd_strategy_info['fast_window_size'],\n", " macd_strategy_info['slow_window_size'],\n", " macd_strategy_info['signal_window_size'],\n", " macd_strategy_info['short_sell']\n", " ])\n", "print(latextable.draw_latex(table_macd_params))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table_rsi_params = Texttable()\n", "table_rsi_params.set_deco(Texttable.HEADER)\n", "table_rsi_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n", "table_rsi_params.header([\n", " \"\\\\textbf{Window}\",\n", " \"\\\\textbf{\\\\textit{window}}\",\n", " \"\\\\textbf{\\\\textit{enter long}}\",\n", " \"\\\\textbf{\\\\textit{exit long}}\",\n", " \"\\\\textbf{\\\\textit{enter short}}\",\n", " \"\\\\textbf{\\\\textit{exit short}}\"\n", "])\n", "\n", "for i, rsi_strategy in enumerate(best_strategies['rsi_strategies']):\n", " rsi_strategy_info = rsi_strategy[0].info()\n", " table_rsi_params.add_row([\n", " f\"W{i+1}-5min\",\n", " rsi_strategy_info['window_size'] or '-',\n", " rsi_strategy_info['enter_long'] or '-',\n", " rsi_strategy_info['exit_long'] or '-',\n", " rsi_strategy_info['enter_short'] or '-',\n", " rsi_strategy_info['exit_short'] or '-'\n", " ])\n", "print(latextable.draw_latex(table_rsi_params))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table_rmse_params = Texttable()\n", "table_rmse_params.set_deco(Texttable.HEADER)\n", "table_rmse_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\"])\n", "table_rmse_params.header([\n", " \"\\\\textbf{Window}\",\n", " \"\\\\textbf{\\\\textit{enter long}}\",\n", " \"\\\\textbf{\\\\textit{exit Long}}\",\n", " \"\\\\textbf{\\\\textit{enter Short}}\",\n", " \"\\\\textbf{\\\\textit{exit Short}}\",\n", "])\n", "\n", "for i, rmse_strategy in enumerate(best_strategies['rmse_model']):\n", " rmse_strategy_info = rmse_strategy[0].info()\n", " table_rmse_params.add_row([\n", " f\"W{i+1}-{INTERVAL}\",\n", " rmse_strategy_info['enter_long'] or '-',\n", " rmse_strategy_info['exit_long'] or '-',\n", " rmse_strategy_info['enter_short'] or '-',\n", " rmse_strategy_info['exit_short'] or '-'\n", " ])\n", "print(latextable.draw_latex(table_rmse_params))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table_quantile_params = Texttable()\n", "table_quantile_params.set_deco(Texttable.HEADER)\n", "table_quantile_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n", "table_quantile_params.header([\n", " \"\\\\textbf{Window}\",\n", " \"\\\\textbf{\\\\textit{enter long}}\",\n", " \"\\\\textbf{\\\\textit{exit Long}}\",\n", " \"\\\\textbf{\\\\textit{enter Short}}\",\n", " \"\\\\textbf{\\\\textit{exit Short}}\",\n", " \"\\\\textbf{\\\\textit{threshold}}\"\n", "])\n", "\n", "for i, quantile_strategy in enumerate(best_strategies['quantile_model']):\n", " quantile_strategy_info = quantile_strategy[0].info()\n", " table_quantile_params.add_row([\n", " f\"W{i+1}-{INTERVAL}\",\n", " quantile_strategy_info['quantile_enter_long'] or '-',\n", " quantile_strategy_info['quantile_exit_long'] or '-',\n", " quantile_strategy_info['quantile_enter_short'] or '-',\n", " quantile_strategy_info['quantile_exit_short'] or '-',\n", " quantile_strategy_info['exchange_fee']\n", " ])\n", "print(latextable.draw_latex(table_quantile_params))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table_gmadl_params = Texttable()\n", "table_gmadl_params.set_deco(Texttable.HEADER)\n", "table_gmadl_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\"])\n", "table_gmadl_params.header([\n", " \"\\\\textbf{Window}\",\n", " \"\\\\textbf{\\\\textit{enter long}}\",\n", " \"\\\\textbf{\\\\textit{exit Long}}\",\n", " \"\\\\textbf{\\\\textit{enter Short}}\",\n", " \"\\\\textbf{\\\\textit{exit Short}}\",\n", "])\n", "\n", "for i, gmadl_strategy in enumerate(best_strategies['gmadl_model']):\n", " gmadl_strategy_info = gmadl_strategy[0].info()\n", " table_gmadl_params.add_row([\n", " f\"W{i+1}-{INTERVAL}\",\n", " gmadl_strategy_info['enter_long'] or '-',\n", " gmadl_strategy_info['exit_long'] or '-',\n", " gmadl_strategy_info['enter_short'] or '-',\n", " gmadl_strategy_info['exit_short'] or '-'\n", " ])\n", "print(latextable.draw_latex(table_gmadl_params))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Evaluation on the windows" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def results_plot(idx, result_buyandhold, result_macd, result_rsi, result_rmse_model, result_quantile_model, result_gmadl_model, width=850, height=500, notitle=False, v_lines=None):\n", "\n", " fig = go.Figure([\n", " go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\"),\n", " go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=\"MACD Strategy\"),\n", " go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=\"RSI Strategy\"),\n", " go.Scatter(y=result_rmse_model['portfolio_value'], x=result_rmse_model['time'], name='RMSE Informer Strategy'),\n", " go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Informer Strategy'),\n", " go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n", " ])\n", " \n", " if v_lines:\n", " for v_line in v_lines:\n", " fig.add_shape(\n", " go.layout.Shape(type=\"line\",\n", " yref=\"paper\",\n", " xref=\"x\",\n", " x0=v_line,\n", " x1=v_line,\n", " y0=0,\n", " y1=1,\n", " line=dict(dash='dash', color='rgb(140,140,140)')))\n", " fig.update_layout(\n", " title={\n", " 'text': f\"W{idx}-{INTERVAL}\",\n", " 'y':0.97,\n", " 'x':0.5,\n", " 'xanchor': 'center',\n", " 'yanchor': 'top'} if not notitle else None,\n", " yaxis_title=\"Portfolio Value\",\n", " xaxis_title=\"Date\",\n", " font=dict(\n", " # family=\"Courier New, monospace\",\n", " size=14,\n", " ),\n", " autosize=False,\n", " width=width,\n", " height=height,\n", " margin=dict(l=20, r=20, t=20 if notitle else 110, b=20),\n", " plot_bgcolor='white',\n", " legend=dict(\n", " orientation=\"h\",\n", " yanchor=\"bottom\",\n", " y=1.02,\n", " xanchor=\"left\",\n", " x=0.02\n", " )\n", " )\n", " fig.update_xaxes(\n", " mirror=True,\n", " ticks='outside',\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey'\n", " )\n", " fig.update_yaxes(\n", " mirror=True,\n", " ticks='outside',\n", " showline=True,\n", " linecolor='black',\n", " gridcolor='lightgrey'\n", " )\n", " fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n", " fig.show()\n", " \n", "def results_table(result_buyandhold, result_macd, result_rsi, result_rmse_model, result_quantile_model, result_gmadl_model):\n", " table_eval_windows = Texttable()\n", " table_eval_windows.set_deco(Texttable.HEADER)\n", " table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n", " table_eval_windows.set_precision(3)\n", "\n", " table_eval_windows.header([\n", " \"\\\\textbf{Strategy}\",\n", " \"\\\\textbf{VAL}\",\n", " \"\\\\textbf{ARC}\",\n", " \"\\\\textbf{ASD}\",\n", " \"\\\\textbf{IR*}\",\n", " \"\\\\textbf{MD}\",\n", " \"\\\\textbf{IR**}\",\n", " \"\\\\textbf{N}\",\n", " \"\\\\textbf{LONG}\",\n", " \"\\\\textbf{SHORT}\",\n", " ])\n", "\n", " strategy_name_result = [\n", " ('Buy and Hold', result_buyandhold),\n", " ('MACD Strategy', result_macd),\n", " ('RSI Strategy', result_rsi),\n", " ('RMSE Informer', result_rmse_model),\n", " ('Quantile Informer', result_quantile_model),\n", " ('GMADL Informer', result_gmadl_model)\n", " ]\n", " for strategy_name, result in strategy_name_result:\n", " table_eval_windows.add_row([\n", " strategy_name,\n", " result['value'],\n", " f\"{result['arc']*100:.2f}\\%\",\n", " f\"{result['asd']*100:.2f}\\%\",\n", " result['ir'],\n", " f\"{result['md']*100:.2f}\\%\",\n", " result['mod_ir'],\n", " result['n_trades'],\n", " f\"{result['long_pos']*100:.2f}\\%\",\n", " f\"{result['short_pos']*100:.2f}\\%\",\n", " ])\n", " print(latextable.draw_latex(table_eval_windows))\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for i, (in_sample, out_of_sample) in enumerate(data_windows):\n", " padded_window = pd.concat([in_sample.iloc[-PADDING:], out_of_sample])\n", " result_buyandhold = evaluate_strategy(padded_window, best_strategies['buy_and_hold'][i], padding=PADDING, interval=INTERVAL)\n", " result_macd = evaluate_strategy(padded_window, [s[0] for s in best_strategies['macd_strategies']][i], padding=PADDING, interval=INTERVAL)\n", " result_rsi = evaluate_strategy(padded_window, [s[0] for s in best_strategies['rsi_strategies']][i], padding=PADDING, interval=INTERVAL)\n", " result_rmse_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['rmse_model']][i], padding=PADDING, interval=INTERVAL)\n", " result_quantile_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['quantile_model']][i], padding=PADDING, interval=INTERVAL)\n", " result_gmadl_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['gmadl_model']][i], padding=PADDING, interval=INTERVAL)\n", "\n", " results_table(result_buyandhold, result_macd, result_rsi, result_rmse_model, result_quantile_model, result_gmadl_model)\n", " # results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_rmse_model, result_quantile_model, result_gmadl_model)\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n", "buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING)\n", "macd_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n", "rsi_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n", "rmse_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rmse_model']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n", "quantile_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['quantile_model']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n", "gmadl_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n", "\n", "v_lines=[data_window[1]['close_time'].iloc[-1] for data_window in data_windows][:-1]\n", "results_table(buy_and_hold_concat, macd_concat, rsi_concat, rmse_model_concat, quantile_model_concat, gmadl_model_concat)\n", "# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, rmse_model_concat, quantile_model_concat, gmadl_model_concat, width=1300, height=500, notitle=True)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# No transaction fees\n", "test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n", "buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING)\n", "macd_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL, exchange_fee=0)\n", "rsi_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL, exchange_fee=0)\n", "rmse_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rmse_model']], padding=PADDING), padding=PADDING, interval=INTERVAL, exchange_fee=0)\n", "quantile_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['quantile_model']], padding=PADDING), padding=PADDING, interval=INTERVAL, exchange_fee=0)\n", "gmadl_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval=INTERVAL, exchange_fee=0)\n", "\n", "v_lines=[data_window[1]['close_time'].iloc[-1] for data_window in data_windows][:-1]\n", "results_table(buy_and_hold_concat, macd_concat, rsi_concat, rmse_model_concat, quantile_model_concat, gmadl_model_concat)\n", "# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, rmse_model_concat, quantile_model_concat, gmadl_model_concat, width=1300, height=500, notitle=True)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "venv-py310", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }