{ "cells": [ { "cell_type": "code", "execution_count": 2, "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", "import plotly.graph_objs as go\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='15min'\n", "METRIC='mod_ir'\n", "TOP_N=10" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-15m:latest, 248.65MB. 12 files... \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n", "Done. 0:0:0.5\n" ] } ], "source": [ "data_windows = get_data_windows(\n", " 'wne-masters-thesis-testing',\n", " 'btc-usdt-15m:latest',\n", " min_window=0, \n", " max_window=5\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "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": 5, "metadata": {}, "outputs": [], "source": [ "buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 3840/3840 [00:06<00:00, 614.41it/s]\n", "100%|██████████| 3840/3840 [00:08<00:00, 479.05it/s]\n", "100%|██████████| 3840/3840 [00:06<00:00, 554.94it/s]\n", "100%|██████████| 3840/3840 [00:06<00:00, 575.21it/s]\n", "100%|██████████| 3840/3840 [00:06<00:00, 571.03it/s]\n", "100%|██████████| 3840/3840 [00:06<00:00, 576.86it/s]\n" ] } ], "source": [ "MACD_PARAMS = {\n", " 'fast_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n", " 'slow_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n", " 'signal_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n", " 'short_sell': [True, False]\n", "}\n", "MACD_PARAMS_FILTER = lambda p: (\n", " p['slow_window_size'] > 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": 41, "metadata": {}, "outputs": [], "source": [ "# plot_sweep_results(pd.DataFrame([result for result, _ in macd_sweep_results[0]]), parameters=MACD_PARAMS.keys())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 11088/11088 [00:19<00:00, 580.54it/s]\n", "100%|██████████| 11088/11088 [00:17<00:00, 627.38it/s]\n", "100%|██████████| 11088/11088 [00:17<00:00, 624.72it/s]\n", "100%|██████████| 11088/11088 [00:18<00:00, 586.62it/s]\n", "100%|██████████| 11088/11088 [00:17<00:00, 618.44it/s]\n", "100%|██████████| 11088/11088 [00:18<00:00, 597.40it/s]\n" ] } ], "source": [ "RSI_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", "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)\n", "rsi_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rsi_sweep_results]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "# plot_sweep_results(pd.DataFrame([result for result, _ in rsi_sweep_results[0]]), parameters=RSI_PARAMS.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 8, "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 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get_sweep_window_predictions(SWEEP_ID, 'test')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1125/1125 [00:21<00:00, 51.66it/s]\n", "100%|██████████| 1125/1125 [00:20<00:00, 53.98it/s]\n", "100%|██████████| 1125/1125 [00:22<00:00, 49.63it/s]\n", "100%|██████████| 1125/1125 [00:24<00:00, 45.57it/s]\n", "100%|██████████| 1125/1125 [00:23<00:00, 47.76it/s]\n", "100%|██████████| 1125/1125 [00:24<00:00, 46.33it/s]\n" ] } ], "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(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": 46, "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'])" ] }, { "cell_type": "code", "execution_count": 11, "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", 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"source": [ "# Model with quantile loss\n", "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/a6q8zv10'\n", "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7n3w718v'\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": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1176/1176 [00:19<00:00, 60.00it/s]\n", "100%|██████████| 1176/1176 [00:19<00:00, 61.63it/s]\n", "100%|██████████| 1176/1176 [00:16<00:00, 69.59it/s]\n", "100%|██████████| 1176/1176 [00:17<00:00, 65.37it/s]\n", "100%|██████████| 1176/1176 [00:17<00:00, 66.33it/s]\n", "100%|██████████| 1176/1176 [00:17<00:00, 67.86it/s]\n" ] } ], "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, _), train_preds, valid_preds, test_preds in zip(data_windows, train_gmadl_pred_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": 14, "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", " 'quantile_model': quantile_model_best_strategies,\n", " 'gmadl_model': gmadl_model_best_strategies\n", "}\n", "\n", "with open('cache/15min-best-strategies.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": 15, "metadata": {}, "outputs": [], "source": [ "with open('cache/15min-best-strategies.pkl', 'rb') as inpt:\n", " best_strategies = pickle.load(inpt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Tables with parameters" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{table}\n", "\t\\begin{center}\n", "\t\t\\begin{tabular}{lcccc}\n", "\t\t\t\\textbf{Window} & \\textbf{Fast Window Size} & \\textbf{Slow Window Size} & \\textbf{Signal Window Size} & \\textbf{Short sell} \\\\\n", "\t\t\t\\hline\n", "\t\t\tWindow 1 & 8 & 377 & 1597 & True \\\\\n", "\t\t\tWindow 2 & 144 & 987 & 2584 & True \\\\\n", "\t\t\tWindow 3 & 377 & 2584 & 2584 & True \\\\\n", "\t\t\tWindow 4 & 377 & 2584 & 610 & True \\\\\n", "\t\t\tWindow 5 & 233 & 610 & 233 & False \\\\\n", "\t\t\tWindow 6 & 144 & 377 & 2584 & False \\\\\n", "\t\t\\end{tabular}\n", "\t\\end{center}\n", "\\end{table}\n" ] } ], "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": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{table}\n", "\t\\begin{center}\n", "\t\t\\begin{tabular}{lccccc}\n", "\t\t\t\\textbf{Window} & \\textbf{\\textit{window}} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit long}} & \\textbf{\\textit{enter short}} & \\textbf{\\textit{exit short}} \\\\\n", "\t\t\t\\hline\n", "\t\t\tW1-15min & 5 & 80 & - & 15 & - \\\\\n", "\t\t\tW2-15min & 34 & 75 & - & 30 & - \\\\\n", "\t\t\tW3-15min & 5 & 95 & - & 10 & - \\\\\n", "\t\t\tW4-15min & 34 & 75 & - & 30 & - \\\\\n", "\t\t\tW5-15min & 13 & 70 & - & 5 & - \\\\\n", "\t\t\tW6-15min & 21 & 85 & 15 & - & - \\\\\n", "\t\t\\end{tabular}\n", "\t\\end{center}\n", "\\end{table}\n" ] } ], "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}-15min\",\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": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{table}\n", "\t\\begin{center}\n", "\t\t\\begin{tabular}{lccccc}\n", "\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} & \\textbf{\\textit{threshold}} \\\\\n", "\t\t\t\\hline\n", "\t\t\tW1-15min & 0.980 & - & 0.990 & - & 0.003 \\\\\n", "\t\t\tW2-15min & 0.990 & 0.950 & - & 0.900 & 0.003 \\\\\n", "\t\t\tW3-15min & - & - & 0.990 & 0.990 & 0.003 \\\\\n", "\t\t\tW4-15min & 0.990 & 0.970 & - & 0.980 & 0.003 \\\\\n", "\t\t\tW5-15min & 0.950 & 0.990 & - & - & 0.001 \\\\\n", "\t\t\tW6-15min & 0.990 & 0.950 & - & 0.950 & 0.002 \\\\\n", "\t\t\\end{tabular}\n", "\t\\end{center}\n", "\\end{table}\n" ] } ], "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": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{table}\n", "\t\\begin{center}\n", "\t\t\\begin{tabular}{lcccc}\n", "\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} \\\\\n", "\t\t\t\\hline\n", "\t\t\tW1-15min & 0.005 & - & -0.002 & - \\\\\n", "\t\t\tW2-15min & 0.006 & - & -0.002 & - \\\\\n", "\t\t\tW3-15min & - & - & -0.001 & 0.005 \\\\\n", "\t\t\tW4-15min & 0.007 & - & -0.005 & - \\\\\n", "\t\t\tW5-15min & 0.001 & - & -0.004 & - \\\\\n", "\t\t\tW6-15min & 0.002 & -0.002 & - & - \\\\\n", "\t\t\\end{tabular}\n", "\t\\end{center}\n", "\\end{table}\n" ] } ], "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": 29, "metadata": {}, "outputs": [], "source": [ "def results_plot(idx, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model, width=850, height=500, notitle=False):\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_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", " 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_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", " ('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", " result['arc'],\n", " result['asd'],\n", " result['ir'],\n", " result['md'],\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": 1, "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_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_quantile_model, result_gmadl_model)\n", "# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\n", " " ] }, { "cell_type": "code", "execution_count": 2, "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, interval=INTERVAL)\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", "# 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", "# results_table(buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat)\n", "# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat, width=1200, notitle=True)\n" ] }, { "cell_type": "code", "execution_count": 23, "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, buyandhold_best_strategies[i], padding=PADDING, interval='15min')\n", "# result_macd = evaluate_strategy(padded_window, macd_best_strategies[i], padding=PADDING, interval='15min')\n", "# result_rsi = evaluate_strategy(padded_window, rsi_best_strategies[i], padding=PADDING, interval='15min')\n", "# result_quantile_model = evaluate_strategy(padded_window, quantile_model_best_strategies[i], padding=PADDING, interval='15min')\n", "# result_gmadl_model = evaluate_strategy(padded_window, gmadl_model_best_strategies[i], padding=PADDING, interval='15min')\n", "\n", "# go.Figure([\n", "# go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=result_buyandhold['strategy_name']),\n", "# go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=result_macd['strategy_name']),\n", "# go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=result_rsi['strategy_name']),\n", "# go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Model'),\n", "# go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL model')\n", "# ]).show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "wnemsc", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 2 }