diff --git a/notebooks/btcusdt_15m_evaluation.ipynb b/notebooks/btcusdt_15m_evaluation.ipynb index dc206ef..2a83d04 100644 --- a/notebooks/btcusdt_15m_evaluation.ipynb +++ b/notebooks/btcusdt_15m_evaluation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -52,7 +52,7 @@ "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" + "Done. 0:0:0.8\n" ] } ], @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": 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plot_sweep_results(pd.DataFrame([result for result, _ in rsi_sweep_results[0]]), parameters=RSI_PARAMS.keys())" ] }, + { + "cell_type": "code", + "execution_count": 7, + "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 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"100%|██████████| 1176/1176 [00:39<00:00, 29.69it/s]\n", + "100%|██████████| 1176/1176 [00:41<00:00, 28.17it/s]\n", + "100%|██████████| 1176/1176 [00:40<00:00, 29.34it/s]\n", + "100%|██████████| 1176/1176 [00:40<00:00, 29.28it/s]\n", + "100%|██████████| 1176/1176 [00:40<00:00, 29.11it/s]\n", + "100%|██████████| 1176/1176 [00:40<00:00, 29.39it/s]\n" + ] + } + ], + "source": [ + "MODEL_RMSE_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", + "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", + " \n", + "\n", + "rmse_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rmse_model_sweep_results]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -186,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -231,12 +320,12 @@ "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" + "100%|██████████| 1125/1125 [00:25<00:00, 44.03it/s]\n", + "100%|██████████| 1125/1125 [00:20<00:00, 53.68it/s]\n", + "100%|██████████| 1125/1125 [00:20<00:00, 54.38it/s]\n", + "100%|██████████| 1125/1125 [00:20<00:00, 54.70it/s]\n", + "100%|██████████| 1125/1125 [00:20<00:00, 54.23it/s]\n", + "100%|██████████| 1125/1125 [00:21<00:00, 53.31it/s]\n" ] } ], @@ -323,19 +412,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "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" + "100%|██████████| 1176/1176 [00:22<00:00, 53.42it/s]\n", + "100%|██████████| 1176/1176 [00:19<00:00, 59.87it/s]\n", + "100%|██████████| 1176/1176 [00:17<00:00, 66.89it/s]\n", + "100%|██████████| 1176/1176 [00:17<00:00, 65.52it/s]\n", + "100%|██████████| 1176/1176 [00:17<00:00, 67.44it/s]\n", + "100%|██████████| 1176/1176 [00:17<00:00, 67.15it/s]\n" ] } ], @@ -372,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -381,11 +470,12 @@ " '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/15min-best-strategies.pkl', 'wb') as outp:\n", + "with open('cache/15min-best-strategies-v2.pkl', 'wb') as outp:\n", " pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)" ] }, @@ -398,11 +488,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "with open('cache/15min-best-strategies.pkl', 'rb') as inpt:\n", + "with open('cache/15min-best-strategies-v2.pkl', 'rb') as inpt:\n", " best_strategies = pickle.load(inpt)" ] }, @@ -516,6 +606,56 @@ "print(latextable.draw_latex(table_rsi_params))" ] }, + { + "cell_type": "code", + "execution_count": 4, + "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 & 0.002 & - & -0.001 & - \\\\\n", + "\t\t\tW2-15min & - & - & -0.002 & 0.002 \\\\\n", + "\t\t\tW3-15min & - & - & -0.001 & 0.002 \\\\\n", + "\t\t\tW4-15min & 0.001 & - & -0.002 & - \\\\\n", + "\t\t\tW5-15min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW6-15min & - & - & -0.002 & 0.001 \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], + "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": 18, @@ -627,19 +767,32 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "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", + "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", @@ -683,7 +836,7 @@ " 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", + "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", @@ -706,6 +859,7 @@ " ('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", @@ -713,10 +867,10 @@ " table_eval_windows.add_row([\n", " strategy_name,\n", " result['value'],\n", - " result['arc'],\n", - " result['asd'],\n", + " f\"{result['arc']*100:.2f}\\%\",\n", + " f\"{result['asd']*100:.2f}\\%\",\n", " result['ir'],\n", - " result['md'],\n", + " f\"{result['md']*100:.2f}\\%\",\n", " result['mod_ir'],\n", " result['n_trades'],\n", " f\"{result['long_pos']*100:.2f}\\%\",\n", @@ -728,61 +882,154 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.933 & -13.15\\% & 66.69\\% & -0.197 & 51.81\\% & -0.050 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.357 & -87.59\\% & 66.96\\% & -1.308 & 73.46\\% & -1.559 & 852 & 52.49\\% & 47.51\\% \\\\\n", + "\t\t\tRSI Strategy & 0.621 & -61.93\\% & 66.94\\% & -0.925 & 48.44\\% & -1.183 & 882 & 58.61\\% & 41.39\\% \\\\\n", + "\t\t\tRMSE Informer & 1.498 & 127.03\\% & 52.56\\% & 2.417 & 22.20\\% & 13.827 & 3 & 0.00\\% & 61.05\\% \\\\\n", + "\t\t\tQuantile Informer & 0.715 & -49.34\\% & 66.74\\% & -0.739 & 40.17\\% & -0.908 & 182 & 52.75\\% & 47.25\\% \\\\\n", + "\t\t\tGMADL Informer & 2.173 & 382.27\\% & 66.83\\% & 5.720 & 29.76\\% & 73.474 & 146 & 36.88\\% & 63.12\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.548 & -70.48\\% & 72.12\\% & -0.977 & 63.18\\% & -1.090 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.714 & -49.48\\% & 72.07\\% & -0.687 & 50.67\\% & -0.670 & 118 & 57.61\\% & 42.39\\% \\\\\n", + "\t\t\tRSI Strategy & 1.141 & 30.61\\% & 72.05\\% & 0.425 & 49.51\\% & 0.263 & 58 & 19.64\\% & 80.36\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 0.903 & -18.73\\% & 15.66\\% & -1.196 & 14.91\\% & -1.503 & 202 & 4.74\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 0.885 & -21.88\\% & 72.10\\% & -0.303 & 43.48\\% & -0.153 & 54 & 35.85\\% & 64.15\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.016 & 3.26\\% & 50.58\\% & 0.065 & 37.76\\% & 0.006 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.060 & 12.62\\% & 50.57\\% & 0.250 & 38.35\\% & 0.082 & 55 & 66.38\\% & 32.66\\% \\\\\n", + "\t\t\tRSI Strategy & 0.898 & -19.62\\% & 50.65\\% & -0.387 & 25.20\\% & -0.302 & 174 & 23.30\\% & 76.70\\% \\\\\n", + "\t\t\tRMSE Informer & 0.982 & -3.55\\% & 50.69\\% & -0.070 & 34.60\\% & -0.007 & 2 & 0.00\\% & 100.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.081 & 17.17\\% & 40.53\\% & 0.424 & 31.82\\% & 0.229 & 103 & 0.00\\% & 53.36\\% \\\\\n", + "\t\t\tGMADL Informer & 1.158 & 34.60\\% & 31.80\\% & 1.088 & 18.06\\% & 2.085 & 32 & 0.00\\% & 59.07\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.231 & 52.34\\% & 44.11\\% & 1.186 & 22.01\\% & 2.822 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.020 & 4.03\\% & 44.11\\% & 0.091 & 25.36\\% & 0.015 & 74 & 34.59\\% & 65.41\\% \\\\\n", + "\t\t\tRSI Strategy & 0.794 & -37.41\\% & 44.15\\% & -0.848 & 43.03\\% & -0.737 & 86 & 39.98\\% & 60.02\\% \\\\\n", + "\t\t\tRMSE Informer & 1.232 & 52.63\\% & 42.30\\% & 1.244 & 22.01\\% & 2.975 & 3 & 91.43\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.122 & 26.21\\% & 21.18\\% & 1.238 & 9.32\\% & 3.481 & 106 & 24.72\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 0.718 & -48.86\\% & 44.08\\% & -1.108 & 41.58\\% & -1.302 & 10 & 60.34\\% & 39.66\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.440 & 109.38\\% & 44.60\\% & 2.452 & 20.31\\% & 13.204 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.218 & 49.24\\% & 33.63\\% & 1.464 & 13.88\\% & 5.195 & 118 & 50.76\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1.440 & 109.38\\% & 44.60\\% & 2.452 & 20.31\\% & 13.204 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 0.832 & -31.06\\% & 13.66\\% & -2.274 & 19.29\\% & -3.662 & 4 & 0.00\\% & 4.56\\% \\\\\n", + "\t\t\tQuantile Informer & 1.206 & 46.21\\% & 37.95\\% & 1.218 & 19.49\\% & 2.887 & 147 & 78.97\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.398 & 97.31\\% & 44.61\\% & 2.181 & 22.22\\% & 9.554 & 26 & 99.64\\% & 0.36\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.558 & 145.73\\% & 51.90\\% & 2.808 & 26.76\\% & 15.290 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.402 & 98.29\\% & 34.32\\% & 2.864 & 20.22\\% & 13.925 & 93 & 48.96\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1.104 & 22.18\\% & 49.12\\% & 0.452 & 26.76\\% & 0.374 & 3 & 82.57\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.000 & 0.04\\% & 18.06\\% & 0.002 & 9.32\\% & 0.000 & 81 & 9.73\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.463 & 116.41\\% & 45.31\\% & 2.569 & 21.45\\% & 13.942 & 94 & 63.52\\% & 0.00\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "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", + "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_quantile_model, result_gmadl_model)\n", - "# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\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", " " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.440 & 13.10\\% & 56.03\\% & 0.234 & 77.23\\% & 0.040 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.468 & -22.64\\% & 52.43\\% & -0.432 & 83.18\\% & -0.118 & 1311 & 51.80\\% & 31.33\\% \\\\\n", + "\t\t\tRSI Strategy & 0.800 & -7.28\\% & 55.66\\% & -0.131 & 66.67\\% & -0.014 & 1206 & 54.02\\% & 43.08\\% \\\\\n", + "\t\t\tRMSE Informer & 1.509 & 14.93\\% & 34.90\\% & 0.428 & 45.54\\% & 0.140 & 16 & 15.24\\% & 27.60\\% \\\\\n", + "\t\t\tQuantile Informer & 0.945 & -1.91\\% & 37.77\\% & -0.051 & 48.30\\% & -0.002 & 824 & 28.48\\% & 16.77\\% \\\\\n", + "\t\t\tGMADL Informer & 3.296 & 49.65\\% & 52.70\\% & 0.942 & 47.39\\% & 0.987 & 362 & 49.37\\% & 37.72\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "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", + "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", + "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", - "# 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", + "# v_lines=[data_window[1]['close_time'].iloc[-1] for data_window in data_windows][:-1]\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()" + "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" ] }, { diff --git a/notebooks/btcusdt_30m_evaluation.ipynb b/notebooks/btcusdt_30m_evaluation.ipynb index 3579f8e..7a5849e 100644 --- a/notebooks/btcusdt_30m_evaluation.ipynb +++ b/notebooks/btcusdt_30m_evaluation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,16 +42,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-30m-long:latest, 74.51MB. 6 files... \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 6 of 6 files downloaded. \n", - "Done. 0:0:0.4\n" + "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-30m:latest, 124.19MB. 12 files... \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n", + "Done. 0:0:0.5\n" ] } ], @@ -66,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -89,19 +90,19 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", 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[00:20<00:00, 57.39it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.46it/s]\n", + "100%|██████████| 1176/1176 [00:22<00:00, 52.43it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.39it/s]\n" + ] + } + ], + "source": [ + "MODEL_RMSE_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", + "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", + " \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": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# plot_sweep_results(pd.DataFrame([result for result, _ in rmse_model_sweep_results[0]]), parameters=['enter_long', 'exit_long', 'enter_short', 'exit_short'], round=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -239,19 +341,19 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1125/1125 [00:28<00:00, 40.16it/s]\n", - "100%|██████████| 1125/1125 [00:27<00:00, 41.37it/s]\n", - "100%|██████████| 1125/1125 [00:27<00:00, 41.47it/s]\n", - "100%|██████████| 1125/1125 [00:27<00:00, 40.47it/s]\n", - "100%|██████████| 1125/1125 [00:25<00:00, 44.84it/s]\n", - "100%|██████████| 1125/1125 [00:25<00:00, 44.11it/s]\n" + "100%|██████████| 1125/1125 [00:23<00:00, 47.74it/s]\n", + "100%|██████████| 1125/1125 [00:23<00:00, 48.10it/s]\n", + "100%|██████████| 1125/1125 [00:23<00:00, 47.79it/s]\n", + "100%|██████████| 1125/1125 [00:23<00:00, 47.27it/s]\n", + "100%|██████████| 1125/1125 [00:23<00:00, 47.82it/s]\n", + "100%|██████████| 1125/1125 [00:23<00:00, 47.74it/s]\n" ] } ], @@ -293,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -333,7 +435,9 @@ "source": [ "# Model with gmadl loss\n", "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/tmqx4epx'\n", - "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7p7tdxbn'\n", + "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7p7tdxbn' (old)\n", + "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7x42xn5j'\n", + "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/z776cpvj'\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')" @@ -341,19 +445,19 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1176/1176 [00:22<00:00, 51.45it/s]\n", - "100%|██████████| 1176/1176 [00:24<00:00, 48.45it/s]\n", - "100%|██████████| 1176/1176 [00:21<00:00, 53.53it/s]\n", - "100%|██████████| 1176/1176 [00:24<00:00, 48.36it/s]\n", - "100%|██████████| 1176/1176 [00:22<00:00, 51.90it/s]\n", - "100%|██████████| 1176/1176 [00:21<00:00, 54.04it/s]\n" + "100%|██████████| 1176/1176 [00:20<00:00, 58.39it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.42it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.34it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.48it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 58.43it/s]\n", + "100%|██████████| 1176/1176 [00:20<00:00, 57.45it/s]\n" ] } ], @@ -401,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -410,11 +514,12 @@ " '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/30min-best-strategies-long.pkl', 'wb') as outp:\n", + "with open('cache/30min-best-strategies-v6.pkl', 'wb') as outp:\n", " pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)" ] }, @@ -427,12 +532,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "with open('cache/30min-best-strategies.pkl', 'rb') as inpt:\n", - " best_strategies = pickle.load(inpt)" + "with open('cache/30min-best-strategies-v6.pkl', 'rb') as inpt:\n", + " best_strategies = pickle.load(inpt)\n", + " buyandhold_best_strategies = best_strategies['buy_and_hold']\n", + " macd_best_strategies = best_strategies['macd_strategies']\n", + " rsi_best_strategies = best_strategies['rsi_strategies']\n", + " rmse_model_best_strategies = best_strategies['rmse_model']\n", + " quantile_model_best_strategies = best_strategies['quantile_model']\n", + " gmadl_model_best_strategies = best_strategies['gmadl_model']" ] }, { @@ -599,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -611,12 +722,62 @@ "\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\tW1-30min & 0.007 & - & -0.001 & - \\\\\n", - "\t\t\tW2-30min & 0.003 & - & -0.007 & - \\\\\n", - "\t\t\tW3-30min & - & - & -0.001 & 0.005 \\\\\n", - "\t\t\tW4-30min & 0.002 & - & -0.006 & - \\\\\n", - "\t\t\tW5-30min & 0.005 & -0.001 & - & - \\\\\n", - "\t\t\tW6-30min & 0.003 & - & -0.004 & - \\\\\n", + "\t\t\tW1-30min & 0.002 & - & -0.003 & - \\\\\n", + "\t\t\tW2-30min & - & - & -0.002 & 0.001 \\\\\n", + "\t\t\tW3-30min & - & - & -0.001 & 0.002 \\\\\n", + "\t\t\tW4-30min & 0.001 & - & -0.002 & - \\\\\n", + "\t\t\tW5-30min & 0.001 & - & -0.002 & - \\\\\n", + "\t\t\tW6-30min & 0.001 & - & -0.002 & - \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], + "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": 5, + "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\tW1-30min & - & - & -0.007 & 0.005 \\\\\n", + "\t\t\tW2-30min & - & -0.006 & -0.007 & 0.004 \\\\\n", + "\t\t\tW3-30min & - & - & -0.004 & 0.007 \\\\\n", + "\t\t\tW4-30min & 0.003 & -0.007 & - & - \\\\\n", + "\t\t\tW5-30min & 0.006 & - & -0.004 & - \\\\\n", + "\t\t\tW6-30min & 0.001 & - & -0.005 & - \\\\\n", "\t\t\\end{tabular}\n", "\t\\end{center}\n", "\\end{table}\n" @@ -656,19 +817,32 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "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", + "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(width=1.5, dash='dash', color='rgb(140,140,140)')))\n", " fig.update_layout(\n", " title={\n", " 'text': f\"W{idx}-{INTERVAL}\",\n", @@ -712,7 +886,7 @@ " 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", + "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", @@ -735,6 +909,7 @@ " ('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", @@ -742,14 +917,14 @@ " table_eval_windows.add_row([\n", " strategy_name,\n", " result['value'],\n", - " f\"{result['arc']*100:.1f}\\%\",\n", - " f\"{result['asd']*100:.1f}\\%\",\n", + " f\"{result['arc']*100:.2f}\\%\",\n", + " f\"{result['asd']*100:.2f}\\%\",\n", " result['ir'],\n", - " f\"{result['md']*100:.1f}\\%\",\n", + " f\"{result['md']*100:.2f}\\%\",\n", " result['mod_ir'],\n", " result['n_trades'],\n", - " f\"{result['long_pos']*100:.1f}\\%\",\n", - " f\"{result['short_pos']*100:.1f}\\%\",\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" @@ -757,20 +932,112 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.924 & -14.84\\% & 66.12\\% & -0.225 & 51.75\\% & -0.064 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.269 & 62.19\\% & 65.90\\% & 0.944 & 31.81\\% & 1.845 & 15 & 50.36\\% & 47.71\\% \\\\\n", + "\t\t\tRSI Strategy & 1.843 & 245.59\\% & 66.51\\% & 3.693 & 35.02\\% & 25.897 & 26 & 42.40\\% & 57.60\\% \\\\\n", + "\t\t\tRMSE Informer & 0.924 & -14.84\\% & 66.12\\% & -0.225 & 51.75\\% & -0.064 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 0.743 & -45.20\\% & 60.57\\% & -0.746 & 29.11\\% & -1.159 & 443 & 30.43\\% & 53.98\\% \\\\\n", + "\t\t\tGMADL Informer & 1.030 & 6.23\\% & 19.98\\% & 0.312 & 10.01\\% & 0.194 & 39 & 0.00\\% & 8.72\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.548 & -70.51\\% & 72.38\\% & -0.974 & 63.18\\% & -1.087 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.303 & 70.93\\% & 71.82\\% & 0.988 & 36.06\\% & 1.943 & 19 & 57.32\\% & 40.76\\% \\\\\n", + "\t\t\tRSI Strategy & 1.703 & 194.24\\% & 72.36\\% & 2.684 & 34.89\\% & 14.945 & 106 & 39.96\\% & 60.04\\% \\\\\n", + "\t\t\tRMSE Informer & 1.387 & 94.09\\% & 41.55\\% & 2.264 & 16.34\\% & 13.037 & 8 & 0.00\\% & 34.41\\% \\\\\n", + "\t\t\tQuantile Informer & 1.403 & 98.74\\% & 27.62\\% & 3.575 & 9.44\\% & 37.393 & 255 & 0.00\\% & 18.79\\% \\\\\n", + "\t\t\tGMADL Informer & 1.050 & 10.44\\% & 23.40\\% & 0.446 & 14.45\\% & 0.322 & 90 & 0.00\\% & 5.98\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.018 & 3.75\\% & 51.25\\% & 0.073 & 37.47\\% & 0.007 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.673 & -55.16\\% & 51.01\\% & -1.081 & 59.24\\% & -1.007 & 95 & 81.60\\% & 16.48\\% \\\\\n", + "\t\t\tRSI Strategy & 0.988 & -2.50\\% & 38.04\\% & -0.066 & 30.42\\% & -0.005 & 238 & 2.34\\% & 50.54\\% \\\\\n", + "\t\t\tRMSE Informer & 0.980 & -4.00\\% & 51.36\\% & -0.078 & 34.51\\% & -0.009 & 2 & 0.00\\% & 100.00\\% \\\\\n", + "\t\t\tQuantile Informer & 0.884 & -22.19\\% & 23.41\\% & -0.948 & 18.39\\% & -1.144 & 171 & 0.00\\% & 18.83\\% \\\\\n", + "\t\t\tGMADL Informer & 0.737 & -46.11\\% & 42.58\\% & -1.083 & 42.71\\% & -1.169 & 302 & 0.00\\% & 81.21\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.229 & 51.90\\% & 45.01\\% & 1.153 & 21.74\\% & 2.753 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.031 & 6.45\\% & 45.08\\% & 0.143 & 30.15\\% & 0.031 & 94 & 35.18\\% & 64.82\\% \\\\\n", + "\t\t\tRSI Strategy & 1.010 & 2.03\\% & 1.87\\% & 1.084 & 0.30\\% & 7.410 & 4 & 0.02\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1.414 & 101.80\\% & 45.03\\% & 2.260 & 21.74\\% & 10.584 & 10 & 92.95\\% & 7.05\\% \\\\\n", + "\t\t\tQuantile Informer & 0.629 & -60.99\\% & 28.76\\% & -2.121 & 39.23\\% & -3.297 & 448 & 36.32\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.054 & 11.31\\% & 28.25\\% & 0.400 & 24.18\\% & 0.187 & 345 & 34.69\\% & 0.00\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.439 & 109.30\\% & 41.49\\% & 2.634 & 20.18\\% & 14.265 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.393 & 95.71\\% & 31.47\\% & 3.041 & 15.00\\% & 19.407 & 90 & 48.66\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1.439 & 109.30\\% & 41.49\\% & 2.634 & 20.18\\% & 14.265 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1.439 & 109.30\\% & 41.49\\% & 2.634 & 20.18\\% & 14.265 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.277 & 64.08\\% & 34.28\\% & 1.869 & 16.61\\% & 7.212 & 311 & 76.90\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.672 & 183.71\\% & 41.56\\% & 4.420 & 20.18\\% & 40.230 & 22 & 78.48\\% & 21.52\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.559 & 145.95\\% & 52.85\\% & 2.762 & 26.69\\% & 15.103 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.227 & 51.44\\% & 36.38\\% & 1.414 & 18.55\\% & 3.921 & 11 & 40.69\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1.059 & 12.32\\% & 52.86\\% & 0.233 & 41.44\\% & 0.069 & 10 & 93.44\\% & 6.56\\% \\\\\n", + "\t\t\tQuantile Informer & 0.855 & -27.14\\% & 34.50\\% & -0.787 & 35.23\\% & -0.606 & 150 & 37.79\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.617 & 165.10\\% & 52.85\\% & 3.124 & 24.64\\% & 20.929 & 10 & 99.88\\% & 0.12\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "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", + "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_quantile_model, result_gmadl_model)\n", - "# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\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" ] }, @@ -783,20 +1050,51 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.440 & 13.12\\% & 55.95\\% & 0.235 & 77.20\\% & 0.040 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.952 & 25.37\\% & 52.36\\% & 0.485 & 59.24\\% & 0.207 & 327 & 52.30\\% & 28.30\\% \\\\\n", + "\t\t\tRSI Strategy & 4.542 & 66.77\\% & 46.25\\% & 1.444 & 39.91\\% & 2.415 & 377 & 30.79\\% & 28.03\\% \\\\\n", + "\t\t\tRMSE Informer & 2.727 & 40.37\\% & 50.47\\% & 0.800 & 51.75\\% & 0.624 & 34 & 64.40\\% & 24.67\\% \\\\\n", + "\t\t\tQuantile Informer & 0.629 & -14.51\\% & 36.91\\% & -0.393 & 55.09\\% & -0.104 & 1783 & 30.24\\% & 15.27\\% \\\\\n", + "\t\t\tGMADL Informer & 2.263 & 31.79\\% & 36.70\\% & 0.866 & 53.35\\% & 0.516 & 811 & 35.51\\% & 19.59\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], + "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", + "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", + "\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=1200, height=500, notitle=True)\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, 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" - ] + "source": [] } ], "metadata": { diff --git a/notebooks/btcusdt_5m_evaluation.ipynb b/notebooks/btcusdt_5m_evaluation.ipynb index 02ec02f..0ad3b02 100644 --- a/notebooks/btcusdt_5m_evaluation.ipynb +++ b/notebooks/btcusdt_5m_evaluation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,16 +41,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m-short:latest, 1341.36MB. 24 files... \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 24 of 24 files downloaded. \n", - "Done. 0:0:1.1\n" + "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-5m:latest, 745.12MB. 12 files... \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n", + "Done. 0:0:1.4\n" ] } ], @@ -65,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -88,19 +89,19 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 3840/3840 [00:25<00:00, 148.93it/s]\n", - "100%|██████████| 3840/3840 [00:25<00:00, 149.05it/s]\n", - "100%|██████████| 3840/3840 [00:27<00:00, 138.86it/s]\n", - "100%|██████████| 3840/3840 [00:28<00:00, 133.50it/s]\n", - "100%|██████████| 3840/3840 [00:25<00:00, 152.79it/s]\n", - "100%|██████████| 3840/3840 [00:22<00:00, 168.94it/s]\n" + "100%|██████████| 3840/3840 [00:18<00:00, 206.53it/s]\n", + "100%|██████████| 3840/3840 [00:18<00:00, 205.12it/s]\n", + "100%|██████████| 3840/3840 [00:18<00:00, 205.54it/s]\n", + "100%|██████████| 3840/3840 [00:18<00:00, 204.08it/s]\n", + "100%|██████████| 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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", + " \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": 10, "metadata": {}, "outputs": [ { @@ -219,19 +309,19 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1125/1125 [02:49<00:00, 6.65it/s]\n", - "100%|██████████| 1125/1125 [02:51<00:00, 6.55it/s]\n", - "100%|██████████| 1125/1125 [02:56<00:00, 6.36it/s]\n", - "100%|██████████| 1125/1125 [02:50<00:00, 6.61it/s]\n", - "100%|██████████| 1125/1125 [02:47<00:00, 6.72it/s]\n", - "100%|██████████| 1125/1125 [02:48<00:00, 6.66it/s]\n" + "100%|██████████| 1125/1125 [02:35<00:00, 7.23it/s]\n", + "100%|██████████| 1125/1125 [02:28<00:00, 7.56it/s]\n", + "100%|██████████| 1125/1125 [02:35<00:00, 7.22it/s]\n", + "100%|██████████| 1125/1125 [02:47<00:00, 6.71it/s]\n", + "100%|██████████| 1125/1125 [02:39<00:00, 7.06it/s]\n", + "100%|██████████| 1125/1125 [02:35<00:00, 7.25it/s]\n" ] } ], @@ -279,25 +369,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "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", 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[00:43<00:00, 27.34it/s]\n", - "100%|██████████| 1176/1176 [00:43<00:00, 27.33it/s]\n" + "100%|██████████| 1176/1176 [00:58<00:00, 20.16it/s]\n", + "100%|██████████| 1176/1176 [00:55<00:00, 21.38it/s]\n", + "100%|██████████| 1176/1176 [00:54<00:00, 21.43it/s]\n", + "100%|██████████| 1176/1176 [00:54<00:00, 21.38it/s]\n", + "100%|██████████| 1176/1176 [00:55<00:00, 21.29it/s]\n", + "100%|██████████| 1176/1176 [00:55<00:00, 21.03it/s]\n" ] } ], @@ -386,13 +458,14 @@ "# 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", + " '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-long.pkl', 'wb') as outp:\n", + "with open('cache/5min-best-strategies-v2.pkl', 'wb') as outp:\n", " pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)" ] }, @@ -405,11 +478,11 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "with open('cache/5min-best-strategies.pkl', 'rb') as inpt:\n", + "with open('cache/5min-best-strategies-v2.pkl', 'rb') as inpt:\n", " best_strategies = pickle.load(inpt)" ] }, @@ -523,6 +596,56 @@ "print(latextable.draw_latex(table_rsi_params))" ] }, + { + "cell_type": "code", + "execution_count": 5, + "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\tW1-5min & 0.002 & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW2-5min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW3-5min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW4-5min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW5-5min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\tW6-5min & - & - & -0.001 & 0.001 \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], + "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": 56, @@ -634,19 +757,32 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "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", + "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", @@ -690,7 +826,7 @@ " 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", + "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", @@ -713,6 +849,7 @@ " ('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", @@ -720,10 +857,10 @@ " table_eval_windows.add_row([\n", " strategy_name,\n", " result['value'],\n", - " result['arc'],\n", - " result['asd'],\n", + " f\"{result['arc']*100:.2f}\\%\",\n", + " f\"{result['asd']*100:.2f}\\%\",\n", " result['ir'],\n", - " result['md'],\n", + " f\"{result['md']*100:.2f}\\%\",\n", " result['mod_ir'],\n", " result['n_trades'],\n", " f\"{result['long_pos']*100:.2f}\\%\",\n", @@ -735,38 +872,153 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.930 & -13.76\\% & 70.24\\% & -0.196 & 51.87\\% & -0.052 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.375 & -86.35\\% & 70.71\\% & -1.221 & 71.90\\% & -1.467 & 1542 & 52.05\\% & 47.95\\% \\\\\n", + "\t\t\tRSI Strategy & 1.428 & 106.05\\% & 70.55\\% & 1.503 & 26.24\\% & 6.074 & 114 & 26.31\\% & 73.69\\% \\\\\n", + "\t\t\tRMSE Informer & 0.965 & -6.93\\% & 3.11\\% & -2.227 & 3.54\\% & -4.361 & 12 & 0.02\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.117 & 25.15\\% & 43.69\\% & 0.576 & 16.27\\% & 0.889 & 365 & 0.00\\% & 45.14\\% \\\\\n", + "\t\t\tGMADL Informer & 1.502 & 128.10\\% & 70.54\\% & 1.816 & 30.70\\% & 7.576 & 82 & 41.54\\% & 58.46\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 0.549 & -70.37\\% & 74.61\\% & -0.943 & 63.35\\% & -1.048 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.498 & -75.63\\% & 74.63\\% & -1.013 & 62.97\\% & -1.217 & 446 & 50.72\\% & 49.28\\% \\\\\n", + "\t\t\tRSI Strategy & 1.368 & 88.76\\% & 74.58\\% & 1.190 & 27.34\\% & 3.864 & 78 & 61.40\\% & 38.60\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 0.941 & -11.54\\% & 74.69\\% & -0.154 & 44.84\\% & -0.040 & 990 & 50.50\\% & 49.50\\% \\\\\n", + "\t\t\tGMADL Informer & 1.635 & 170.94\\% & 74.67\\% & 2.289 & 30.47\\% & 12.844 & 522 & 20.20\\% & 79.80\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.016 & 3.23\\% & 51.77\\% & 0.062 & 37.98\\% & 0.005 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.081 & 17.10\\% & 51.79\\% & 0.330 & 20.87\\% & 0.270 & 154 & 58.24\\% & 41.76\\% \\\\\n", + "\t\t\tRSI Strategy & 1.127 & 27.41\\% & 51.92\\% & 0.528 & 22.30\\% & 0.649 & 398 & 11.48\\% & 88.52\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.057 & 11.93\\% & 51.90\\% & 0.230 & 30.10\\% & 0.091 & 894 & 27.62\\% & 72.38\\% \\\\\n", + "\t\t\tGMADL Informer & 1.198 & 44.15\\% & 19.18\\% & 2.302 & 7.68\\% & 13.227 & 16 & 0.00\\% & 17.86\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.231 & 52.35\\% & 45.25\\% & 1.157 & 22.29\\% & 2.718 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.612 & 163.18\\% & 45.34\\% & 3.599 & 21.23\\% & 27.660 & 190 & 44.69\\% & 55.31\\% \\\\\n", + "\t\t\tRSI Strategy & 0.866 & -25.30\\% & 19.03\\% & -1.330 & 15.65\\% & -2.150 & 205 & 23.63\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 0.825 & -32.24\\% & 27.88\\% & -1.156 & 22.72\\% & -1.641 & 290 & 44.45\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.673 & 183.80\\% & 45.26\\% & 4.061 & 24.78\\% & 30.125 & 62 & 65.14\\% & 34.86\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.439 & 109.23\\% & 44.72\\% & 2.443 & 21.07\\% & 12.664 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.346 & 82.57\\% & 32.85\\% & 2.514 & 15.44\\% & 13.447 & 94 & 49.06\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1.169 & 37.33\\% & 13.86\\% & 2.692 & 4.73\\% & 21.237 & 42 & 5.67\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 0.667 & -56.06\\% & 36.94\\% & -1.518 & 42.77\\% & -1.989 & 3 & 0.00\\% & 57.45\\% \\\\\n", + "\t\t\tQuantile Informer & 0.984 & -3.12\\% & 33.97\\% & -0.092 & 20.63\\% & -0.014 & 578 & 59.26\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.582 & 153.60\\% & 45.11\\% & 3.405 & 14.37\\% & 36.394 & 130 & 44.70\\% & 55.30\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n", + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.560 & 146.27\\% & 52.72\\% & 2.775 & 26.96\\% & 15.054 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 1.179 & 39.63\\% & 34.65\\% & 1.144 & 28.87\\% & 1.570 & 111 & 47.58\\% & 0.00\\% \\\\\n", + "\t\t\tRSI Strategy & 1.507 & 129.58\\% & 38.15\\% & 3.396 & 17.97\\% & 24.493 & 7 & 41.29\\% & 0.00\\% \\\\\n", + "\t\t\tRMSE Informer & 1 & 0.00\\% & 0.00\\% & 0 & 0.00\\% & 0 & 0 & 0.00\\% & 0.00\\% \\\\\n", + "\t\t\tQuantile Informer & 1.056 & 11.76\\% & 40.73\\% & 0.289 & 30.60\\% & 0.111 & 280 & 59.60\\% & 0.00\\% \\\\\n", + "\t\t\tGMADL Informer & 1.253 & 57.92\\% & 52.71\\% & 1.099 & 32.66\\% & 1.949 & 34 & 97.21\\% & 2.79\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "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", + "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_quantile_model, result_gmadl_model)\n", - " # results_plot(i+1, result_buyandhold, None, None, None, result_gmadl_model)\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": 2, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.441 & 13.14\\% & 57.74\\% & 0.228 & 77.31\\% & 0.039 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy & 0.516 & -20.04\\% & 54.14\\% & -0.370 & 85.77\\% & -0.087 & 2535 & 50.39\\% & 32.38\\% \\\\\n", + "\t\t\tRSI Strategy & 3.341 & 50.34\\% & 50.41\\% & 0.999 & 29.99\\% & 1.676 & 846 & 28.29\\% & 33.47\\% \\\\\n", + "\t\t\tRMSE Informer & 0.643 & -13.88\\% & 15.13\\% & -0.917 & 44.61\\% & -0.285 & 16 & 0.00\\% & 9.58\\% \\\\\n", + "\t\t\tQuantile Informer & 0.956 & -1.52\\% & 47.91\\% & -0.032 & 53.96\\% & -0.001 & 3395 & 40.24\\% & 27.84\\% \\\\\n", + "\t\t\tGMADL Informer & 9.747 & 115.88\\% & 54.44\\% & 2.129 & 32.66\\% & 7.552 & 846 & 44.80\\% & 41.51\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "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", - "# 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", + "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", - "# results_table(buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat)\n", - "# results_plot(0, buy_and_hold_concat, None, None, None, gmadl_model_concat, width=1200, notitle=True)\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" ] }, diff --git a/notebooks/btcusdt_dataset.ipynb b/notebooks/btcusdt_dataset.ipynb index 9461bcf..147ef61 100644 --- a/notebooks/btcusdt_dataset.ipynb +++ b/notebooks/btcusdt_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -117,162711 +117,32 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:================================================================================\n", - "INFO:root:Loading dataset '../data/raw_data/btc-usdt-5m.csv' with interval 0 days 00:05:00\n", - "INFO:root:Inserted 614 new intervals.\n", - "INFO:root:Fixed 8 intervals.\n", - "INFO:root:There is the same interval between all open times: 0 days 00:05:00\n", - "INFO:root:There is the same interval between all close times: 0 days 00:05:00\n", - "INFO:root:First data point: 2019-07-21 00:04:59.999000\n", - "INFO:root:Last data point: 2024-07-27 09:44:59.999000\n", - "INFO:root:Number of data points: 528021\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "type": "scatter", - "x": [ - "2019-07-21T00:04:59.999000", - "2019-07-21T00:54:59.999000", - "2019-07-21T01:44:59.999000", - "2019-07-21T02:34:59.999000", - "2019-07-21T03:24:59.999000", - "2019-07-21T04:14:59.999000", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_time
02019-07-21 00:00:0010740.2710745.8810650.2110669.59396.4043762019-07-21 00:04:59.999
12019-07-21 00:05:0010671.6510733.6310669.6010709.24358.0221622019-07-21 00:09:59.999
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" - ], - "text/plain": [ - " open_time open_price high_price low_price close_price \\\n", - "0 2019-07-21 00:00:00 10740.27 10745.88 10650.21 10669.59 \n", - "1 2019-07-21 00:05:00 10671.65 10733.63 10669.60 10709.24 \n", - "2 2019-07-21 00:10:00 10711.64 10765.85 10703.05 10751.59 \n", - "3 2019-07-21 00:15:00 10751.59 10785.37 10743.26 10782.84 \n", - "4 2019-07-21 00:20:00 10783.15 10787.42 10754.37 10757.64 \n", - "\n", - " volume close_time \n", - "0 396.404376 2019-07-21 00:04:59.999 \n", - "1 358.022162 2019-07-21 00:09:59.999 \n", - "2 207.683629 2019-07-21 00:14:59.999 \n", - "3 194.634797 2019-07-21 00:19:59.999 \n", - "4 178.566163 2019-07-21 00:24:59.999 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "btc_usdt_5_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-5m.csv', INTERVAL_5_MIN)\n", - "btc_usdt_5_min_data.head()" + "# btc_usdt_5_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-5m.csv', INTERVAL_5_MIN)\n", + "# btc_usdt_5_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_time
02019-07-21 00:00:0010740.2710765.8510650.2110751.59962.1101672019-07-21 00:14:59.999
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" - ], - "text/plain": [ - " open_time open_price high_price low_price close_price \\\n", - "0 2019-07-21 00:00:00 10740.27 10765.85 10650.21 10751.59 \n", - "1 2019-07-21 00:15:00 10751.59 10787.42 10740.00 10757.31 \n", - "2 2019-07-21 00:30:00 10758.84 10817.90 10752.49 10800.00 \n", - "3 2019-07-21 00:45:00 10800.00 10809.03 10752.06 10776.63 \n", - "4 2019-07-21 01:00:00 10776.59 10809.01 10751.85 10799.67 \n", - "\n", - " volume close_time \n", - "0 962.110167 2019-07-21 00:14:59.999 \n", - "1 491.528754 2019-07-21 00:29:59.999 \n", - "2 428.768955 2019-07-21 00:44:59.999 \n", - "3 451.601255 2019-07-21 00:59:59.999 \n", - "4 217.555507 2019-07-21 01:14:59.999 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "btc_usdt_15_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-15m.csv', INTERVAL_15_MIN)\n", - "btc_usdt_15_min_data.head()" + "# btc_usdt_15_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-15m.csv', INTERVAL_15_MIN)\n", + "# btc_usdt_15_min_data.head()" ] }, { "cell_type": "code", - 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" - ], - "text/plain": [ - " open_time open_price high_price low_price close_price \\\n", - "0 2019-07-21 00:00:00 10740.27 10787.42 10650.21 10757.31 \n", - "1 2019-07-21 00:30:00 10758.84 10817.90 10752.06 10776.63 \n", - "2 2019-07-21 01:00:00 10776.59 10810.63 10751.85 10770.95 \n", - "3 2019-07-21 01:30:00 10770.95 10796.55 10746.07 10793.86 \n", - "4 2019-07-21 02:00:00 10793.81 10794.83 10637.50 10680.63 \n", - "\n", - " volume close_time \n", - "0 1453.638921 2019-07-21 00:29:59.999 \n", - "1 880.370210 2019-07-21 00:59:59.999 \n", - "2 422.890156 2019-07-21 01:29:59.999 \n", - "3 347.777487 2019-07-21 01:59:59.999 \n", - "4 1524.774686 2019-07-21 02:29:59.999 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "btc_usdt_30_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-30m.csv', INTERVAL_30_MIN)\n", - "btc_usdt_30_min_data.head()" + "# btc_usdt_30_min_data = load_btc_usdt_dataset('../data/raw_data/btc-usdt-30m.csv', INTERVAL_30_MIN)\n", + "# btc_usdt_30_min_data.head()" ] }, { @@ -162861,223397 +182,38 @@ }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_price
02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:04:59.99911.74
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" - ], - "text/plain": [ - " open_time open_price ... close_time vix_close_price\n", - "0 2017-08-17 04:00:00 4261.48 ... 2017-08-17 04:04:59.999 11.74\n", - "1 2017-08-17 04:05:00 4261.48 ... 2017-08-17 04:09:59.999 11.74\n", - "2 2017-08-17 04:10:00 4261.48 ... 2017-08-17 04:14:59.999 11.74\n", - "3 2017-08-17 04:15:00 4261.48 ... 2017-08-17 04:19:59.999 11.74\n", - "4 2017-08-17 04:20:00 4264.88 ... 2017-08-17 04:24:59.999 11.74\n", - "\n", - "[5 rows x 8 columns]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "vix_5_min_data = preprocess_vix_data(btc_usdt_5_min_data, vix_data)\n", - "btc_usdt_5_min_data[VIX_COL_NAME] = vix_5_min_data['close']\n", - "go.Figure(go.Scatter(y=btc_usdt_5_min_data['vix_close_price'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_5_min_data.head()" + "# vix_5_min_data = preprocess_vix_data(btc_usdt_5_min_data, vix_data)\n", + "# btc_usdt_5_min_data[VIX_COL_NAME] = vix_5_min_data['close']\n", + "# go.Figure(go.Scatter(y=btc_usdt_5_min_data['vix_close_price'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_5_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_price
02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:14:59.99911.74
12017-08-17 04:15:004261.484270.414261.324261.459.1198652017-08-17 04:29:59.99911.74
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" - ], - "text/plain": [ - " open_time open_price ... close_time vix_close_price\n", - "0 2017-08-17 04:00:00 4261.48 ... 2017-08-17 04:14:59.999 11.74\n", - "1 2017-08-17 04:15:00 4261.48 ... 2017-08-17 04:29:59.999 11.74\n", - "2 2017-08-17 04:30:00 4280.00 ... 2017-08-17 04:44:59.999 11.74\n", - "3 2017-08-17 04:45:00 4310.07 ... 2017-08-17 04:59:59.999 11.74\n", - "4 2017-08-17 05:00:00 4308.83 ... 2017-08-17 05:14:59.999 11.74\n", - "\n", - "[5 rows x 8 columns]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "vix_15_min_data = preprocess_vix_data(btc_usdt_15_min_data, vix_data)\n", - "btc_usdt_15_min_data[VIX_COL_NAME] = vix_15_min_data['close']\n", - "go.Figure(go.Scatter(y=btc_usdt_15_min_data['vix_close_price'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_15_min_data.head()" + "# vix_15_min_data = preprocess_vix_data(btc_usdt_15_min_data, vix_data)\n", + "# btc_usdt_15_min_data[VIX_COL_NAME] = vix_15_min_data['close']\n", + "# go.Figure(go.Scatter(y=btc_usdt_15_min_data['vix_close_price'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_15_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_price
02017-08-17 04:00:004261.484280.564261.324261.4511.3089262017-08-17 04:29:59.99911.74
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" - ], - "text/plain": [ - " open_time open_price ... close_time vix_close_price\n", - "0 2017-08-17 04:00:00 4261.48 ... 2017-08-17 04:29:59.999 11.74\n", - "1 2017-08-17 04:30:00 4280.00 ... 2017-08-17 04:59:59.999 11.74\n", - "2 2017-08-17 05:00:00 4308.83 ... 2017-08-17 05:29:59.999 11.74\n", - "3 2017-08-17 05:30:00 4320.00 ... 2017-08-17 05:59:59.999 11.74\n", - "4 2017-08-17 06:00:00 4330.29 ... 2017-08-17 06:29:59.999 11.74\n", - "\n", - "[5 rows x 8 columns]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "vix_30_min_data = preprocess_vix_data(btc_usdt_30_min_data, vix_data)\n", - "btc_usdt_30_min_data[VIX_COL_NAME] = vix_30_min_data['close']\n", - "go.Figure(go.Scatter(y=btc_usdt_30_min_data['vix_close_price'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_30_min_data.head()" + "# vix_30_min_data = preprocess_vix_data(btc_usdt_30_min_data, vix_data)\n", + "# btc_usdt_30_min_data[VIX_COL_NAME] = vix_30_min_data['close']\n", + "# go.Figure(go.Scatter(y=btc_usdt_30_min_data['vix_close_price'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_30_min_data.head()" ] }, { @@ -386291,223415 +253,38 @@ }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_priceeffective_rates
02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:04:59.99911.741.16
12017-08-17 04:05:004261.484261.484261.484261.480.0000002017-08-17 04:09:59.99911.741.16
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32017-08-17 04:15:004261.484264.884261.484261.480.4846662017-08-17 04:19:59.99911.741.16
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\n", - "
" - ], - "text/plain": [ - " open_time open_price ... vix_close_price effective_rates\n", - "0 2017-08-17 04:00:00 4261.48 ... 11.74 1.16\n", - "1 2017-08-17 04:05:00 4261.48 ... 11.74 1.16\n", - "2 2017-08-17 04:10:00 4261.48 ... 11.74 1.16\n", - "3 2017-08-17 04:15:00 4261.48 ... 11.74 1.16\n", - "4 2017-08-17 04:20:00 4264.88 ... 11.74 1.16\n", - "\n", - "[5 rows x 9 columns]" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fed_5_min_data = preprocess_fed_data(btc_usdt_5_min_data, fed_data)\n", - "btc_usdt_5_min_data[FED_COL_NAME] = fed_5_min_data['fedfunds']\n", - "go.Figure(go.Scatter(y=btc_usdt_5_min_data['effective_rates'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_5_min_data.head()" + "# fed_5_min_data = preprocess_fed_data(btc_usdt_5_min_data, fed_data)\n", + "# btc_usdt_5_min_data[FED_COL_NAME] = fed_5_min_data['fedfunds']\n", + "# go.Figure(go.Scatter(y=btc_usdt_5_min_data['effective_rates'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_5_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_priceeffective_rates
02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:14:59.99911.741.16
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" - ], - "text/plain": [ - " open_time open_price ... vix_close_price effective_rates\n", - "0 2017-08-17 04:00:00 4261.48 ... 11.74 1.16\n", - "1 2017-08-17 04:15:00 4261.48 ... 11.74 1.16\n", - "2 2017-08-17 04:30:00 4280.00 ... 11.74 1.16\n", - "3 2017-08-17 04:45:00 4310.07 ... 11.74 1.16\n", - "4 2017-08-17 05:00:00 4308.83 ... 11.74 1.16\n", - "\n", - "[5 rows x 9 columns]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fed_15_min_data = preprocess_fed_data(btc_usdt_15_min_data, fed_data)\n", - "btc_usdt_15_min_data[FED_COL_NAME] = fed_15_min_data['fedfunds']\n", - "go.Figure(go.Scatter(y=btc_usdt_15_min_data['effective_rates'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_15_min_data.head()" + "# fed_15_min_data = preprocess_fed_data(btc_usdt_15_min_data, fed_data)\n", + "# btc_usdt_15_min_data[FED_COL_NAME] = fed_15_min_data['fedfunds']\n", + "# go.Figure(go.Scatter(y=btc_usdt_15_min_data['effective_rates'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_15_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_priceeffective_rates
02017-08-17 04:00:004261.484280.564261.324261.4511.3089262017-08-17 04:29:59.99911.741.16
12017-08-17 04:30:004280.004313.624267.994308.8335.8720832017-08-17 04:59:59.99911.741.16
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" - ], - "text/plain": [ - " open_time open_price ... vix_close_price effective_rates\n", - "0 2017-08-17 04:00:00 4261.48 ... 11.74 1.16\n", - "1 2017-08-17 04:30:00 4280.00 ... 11.74 1.16\n", - "2 2017-08-17 05:00:00 4308.83 ... 11.74 1.16\n", - "3 2017-08-17 05:30:00 4320.00 ... 11.74 1.16\n", - "4 2017-08-17 06:00:00 4330.29 ... 11.74 1.16\n", - "\n", - "[5 rows x 9 columns]" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fed_30_min_data = preprocess_fed_data(btc_usdt_30_min_data, fed_data)\n", - "btc_usdt_30_min_data[FED_COL_NAME] = fed_30_min_data['fedfunds']\n", - "go.Figure(go.Scatter(y=btc_usdt_30_min_data['effective_rates'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_30_min_data.head()" + "# fed_30_min_data = preprocess_fed_data(btc_usdt_30_min_data, fed_data)\n", + "# btc_usdt_30_min_data[FED_COL_NAME] = fed_30_min_data['fedfunds']\n", + "# go.Figure(go.Scatter(y=btc_usdt_30_min_data['effective_rates'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_30_min_data.head()" ] }, { @@ -609740,223433 +325,38 @@ }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_priceeffective_ratesfear_greed_index
02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:04:59.99911.741.1630
12017-08-17 04:05:004261.484261.484261.484261.480.0000002017-08-17 04:09:59.99911.741.1630
22017-08-17 04:10:004261.484261.484261.484261.480.0000002017-08-17 04:14:59.99911.741.1630
32017-08-17 04:15:004261.484264.884261.484261.480.4846662017-08-17 04:19:59.99911.741.1630
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" - ], - "text/plain": [ - " open_time open_price ... effective_rates fear_greed_index\n", - "0 2017-08-17 04:00:00 4261.48 ... 1.16 30\n", - "1 2017-08-17 04:05:00 4261.48 ... 1.16 30\n", - "2 2017-08-17 04:10:00 4261.48 ... 1.16 30\n", - "3 2017-08-17 04:15:00 4261.48 ... 1.16 30\n", - "4 2017-08-17 04:20:00 4264.88 ... 1.16 30\n", - "\n", - "[5 rows x 10 columns]" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "feargreed_5_min_data = preprocess_feargreed_data(btc_usdt_5_min_data, feargreed_data)\n", - "btc_usdt_5_min_data[FEAR_GREED_COL_NAME] = feargreed_5_min_data['value']\n", - "go.Figure(go.Scatter(y=btc_usdt_5_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_5_min_data.head()" + "# feargreed_5_min_data = preprocess_feargreed_data(btc_usdt_5_min_data, feargreed_data)\n", + "# btc_usdt_5_min_data[FEAR_GREED_COL_NAME] = feargreed_5_min_data['value']\n", + "# go.Figure(go.Scatter(y=btc_usdt_5_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_5_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_5_min_data.head()" ] }, { "cell_type": "code", - 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02017-08-17 04:00:004261.484280.564261.484261.482.1890612017-08-17 04:14:59.99911.741.1630
12017-08-17 04:15:004261.484270.414261.324261.459.1198652017-08-17 04:29:59.99911.741.1630
22017-08-17 04:30:004280.004310.074267.994310.0721.9235522017-08-17 04:44:59.99911.741.1630
32017-08-17 04:45:004310.074313.624291.374308.8313.9485312017-08-17 04:59:59.99911.741.1630
42017-08-17 05:00:004308.834328.694304.314304.315.1011532017-08-17 05:14:59.99911.741.1630
\n", - "
" - ], - "text/plain": [ - " open_time open_price ... effective_rates fear_greed_index\n", - "0 2017-08-17 04:00:00 4261.48 ... 1.16 30\n", - "1 2017-08-17 04:15:00 4261.48 ... 1.16 30\n", - "2 2017-08-17 04:30:00 4280.00 ... 1.16 30\n", - "3 2017-08-17 04:45:00 4310.07 ... 1.16 30\n", - "4 2017-08-17 05:00:00 4308.83 ... 1.16 30\n", - "\n", - "[5 rows x 10 columns]" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "feargreed_15_min_data = preprocess_feargreed_data(btc_usdt_15_min_data, feargreed_data)\n", - "btc_usdt_15_min_data[FEAR_GREED_COL_NAME] = feargreed_15_min_data['value']\n", - "go.Figure(go.Scatter(y=btc_usdt_15_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_15_min_data.head()" + "# feargreed_15_min_data = preprocess_feargreed_data(btc_usdt_15_min_data, feargreed_data)\n", + "# btc_usdt_15_min_data[FEAR_GREED_COL_NAME] = feargreed_15_min_data['value']\n", + "# go.Figure(go.Scatter(y=btc_usdt_15_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_15_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_15_min_data.head()" ] }, { "cell_type": "code", - 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open_timeopen_pricehigh_pricelow_priceclose_pricevolumeclose_timevix_close_priceeffective_ratesfear_greed_index
02017-08-17 04:00:004261.484280.564261.324261.4511.3089262017-08-17 04:29:59.99911.741.1630
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" - ], - "text/plain": [ - " open_time open_price ... effective_rates fear_greed_index\n", - "0 2017-08-17 04:00:00 4261.48 ... 1.16 30\n", - "1 2017-08-17 04:30:00 4280.00 ... 1.16 30\n", - "2 2017-08-17 05:00:00 4308.83 ... 1.16 30\n", - "3 2017-08-17 05:30:00 4320.00 ... 1.16 30\n", - "4 2017-08-17 06:00:00 4330.29 ... 1.16 30\n", - "\n", - "[5 rows x 10 columns]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "feargreed_30_min_data = preprocess_feargreed_data(btc_usdt_30_min_data, feargreed_data)\n", - "btc_usdt_30_min_data[FEAR_GREED_COL_NAME] = feargreed_30_min_data['value']\n", - "go.Figure(go.Scatter(y=btc_usdt_30_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", - "btc_usdt_30_min_data.head()" + "# feargreed_30_min_data = preprocess_feargreed_data(btc_usdt_30_min_data, feargreed_data)\n", + "# btc_usdt_30_min_data[FEAR_GREED_COL_NAME] = feargreed_30_min_data['value']\n", + "# go.Figure(go.Scatter(y=btc_usdt_30_min_data['fear_greed_index'].iloc[::10], x=btc_usdt_30_min_data['close_time'].iloc[::10])).show()\n", + "# btc_usdt_30_min_data.head()" ] }, { @@ -833294,7 +484,7 @@ }, { "cell_type": "code", - 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"plot_moving_windows(btc_usdt_30_min_data, windows_30m_data)" + "# plot_moving_windows(btc_usdt_30_min_data, windows_30m_data)" ] }, { diff --git a/notebooks/btcusdt_dataset_analysis.ipynb b/notebooks/btcusdt_dataset_analysis.ipynb index a7e8e3d..e48f357 100644 --- a/notebooks/btcusdt_dataset_analysis.ipynb +++ b/notebooks/btcusdt_dataset_analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -99,18212 +99,48 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/anaconda3/envs/plotly/lib/python3.11/site-packages/_plotly_utils/basevalidators.py:107: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. 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" marker=dict(color='slateblue')\n", - " ))\n", + "# fig = go.Figure(\n", + "# go.Scatter(\n", + "# y=btc_usdt_30_min_data['close_price'].iloc[::10],\n", + "# x=btc_usdt_30_min_data['close_time'].iloc[::10], \n", + "# # marker=dict(color='rgb(80, 80, 80)')\n", + "# marker=dict(color='slateblue')\n", + "# ))\n", "\n", - "fig.update_layout(\n", - " yaxis_title=\"BTC/USDT\",\n", - " xaxis_title=\"Date\",\n", - " font=dict(\n", - " # family=\"Courier New, monospace\",\n", - " size=14,\n", - " ),\n", - " autosize=False,\n", - " width=1200,\n", - " height=400,\n", - " margin=dict(l=20, r=20, t=20, b=20),\n", - " plot_bgcolor='white'\n", - " )\n", + "# fig.update_layout(\n", + "# yaxis_title=\"BTC/USDT\",\n", + "# xaxis_title=\"Date\",\n", + "# font=dict(\n", + "# # family=\"Courier New, monospace\",\n", + "# size=14,\n", + "# ),\n", + "# autosize=False,\n", + "# width=1200,\n", + "# height=400,\n", + "# margin=dict(l=20, r=20, t=20, b=20),\n", + "# plot_bgcolor='white'\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.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", "\n", - "fig.show()" + "# fig.show()" ] }, { @@ -73032,3837 +54868,19 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 2, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "In sample size: 34560\n", - "Out of sample size: 8640\n" + "ename": "NameError", + "evalue": "name 'btc_usdt_30_min_data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 128\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;66;03m# fig.update_yaxes(title_text=\"Date\", row=NUM_WINDOWS+1, col=0)\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# # fig['layout'][f'xaxis{NUM_WINDOWS}_title']['title']='Label x-axis 1'\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;66;03m# # fig['layout']['xaxis2']['title']='Label x-axis 2'\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# # fig['layout']['yaxis']['title']='Label y-axis 1'\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;66;03m# # fig['layout']['yaxis2']['title']='Label y-axis 2'\u001b[39;00m\n\u001b[1;32m 125\u001b[0m fig\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m 127\u001b[0m rolling_windows_30m \u001b[38;5;241m=\u001b[39m split_dataset_moving_window(\n\u001b[0;32m--> 128\u001b[0m \u001b[43mbtc_usdt_30_min_data\u001b[49m,\n\u001b[1;32m 129\u001b[0m NUM_WINDOWS,\n\u001b[1;32m 130\u001b[0m in_sample_size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m24\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m30\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m24\u001b[39m \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m60\u001b[39m \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m30\u001b[39m)), \u001b[38;5;66;03m# 24 months\u001b[39;00m\n\u001b[1;32m 131\u001b[0m out_of_sample_size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m30\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m24\u001b[39m \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m60\u001b[39m \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m30\u001b[39m)) \u001b[38;5;66;03m# 6 months\u001b[39;00m\n\u001b[1;32m 132\u001b[0m )\n\u001b[1;32m 133\u001b[0m \u001b[38;5;66;03m# plot_moving_windows(btc_usdt_30_min_data, rolling_windows_30m)\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'btc_usdt_30_min_data' is not defined" ] - 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} - ], + "outputs": [], "source": [ "def w_distances(windows):\n", " w_dists = np.empty((NUM_WINDOWS, NUM_WINDOWS))\n", @@ -546766,7 +55187,7 @@ " )\n", " fig.show()\n", "\n", - "plot_window_returns_distributions(rolling_windows_5m, rolling_windows_15m, rolling_windows_30m)" + "# plot_window_returns_distributions(rolling_windows_5m, rolling_windows_15m, rolling_windows_30m)" ] }, { @@ -546955,7 +55376,7 @@ ], "metadata": { "kernelspec": { - "display_name": "wnemsc", + "display_name": "plotly", "language": "python", "name": "python3" }, diff --git a/notebooks/gmadl.ipynb b/notebooks/gmadl.ipynb index 97e9e40..491399a 100644 --- a/notebooks/gmadl.ipynb +++ b/notebooks/gmadl.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,16 +39,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def mae(y_pred, target):\n", " return np.abs(y_pred - target)\n", "\n", + "def rmse(y_pred, target):\n", + " return np.sqrt(np.power(y_pred - target, 2))\n", + "\n", "def madl(y_pred, target):\n", " return -1 * np.sign(y_pred * target) * np.abs(target)\n", "\n", + "\n", "def gmadl(y_pred, target, a=2000, b=2):\n", " return -1 * (1 /(1 + np.exp(-a * y_pred * target)) - 0.5) * np.abs(target)**b" ] @@ -78823,21759 +78827,28 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "showscale": false, - "type": "heatmap", - "x": [ - -0.1, - -0.09797979797979799, - -0.09595959595959597, - -0.09393939393939395, - -0.09191919191919193, - -0.08989898989898991, - -0.08787878787878789, - -0.08585858585858586, - -0.08383838383838385, - -0.08181818181818182, - -0.07979797979797981, - -0.07777777777777778, - -0.07575757575757576, - -0.07373737373737374, - -0.07171717171717172, - 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"linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "width": 1000, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 0.45 - ], - "title": { - "text": "y_predicted" - } - }, - "xaxis2": { - "anchor": "y2", - "domain": [ - 0.55, - 1 - ], - "title": { - "text": "y_predicted" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "y_actual" - } - }, - "yaxis2": { - "anchor": "x2", - "domain": [ - 0, - 1 - ], - "title": { - "text": "y_actual" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from plotly.subplots import make_subplots\n", + "# from plotly.subplots import make_subplots\n", "\n", - "fig = make_subplots(rows=1, cols=2, subplot_titles=['MAE', 'GMADL'])\n", - "x = np.linspace(-0.1, 0.1, 100)\n", - "y = np.linspace(-0.1, 0.1, 100)\n", - "xx, yy = np.meshgrid(x, y)\n", - "z1 = mae(xx, yy)\n", - "z2 = gmadl(xx, yy)\n", + "# fig = make_subplots(rows=1, cols=2, subplot_titles=['RMSE', 'GMADL'])\n", + "# x = np.linspace(-0.1, 0.1, 100)\n", + "# y = np.linspace(-0.1, 0.1, 100)\n", + "# xx, yy = np.meshgrid(x, y)\n", + "# # z1 = mae(xx, yy)\n", + "# z1 = rmse(xx, yy)\n", + "# z2 = gmadl(xx, yy)\n", "\n", - "fig.add_trace(go.Heatmap(z=z1, x=x, y=y, showscale=False), row=1, col=1)\n", - "fig.add_trace(go.Heatmap(z=z2, x=x, y=y, showscale=False), row=1, col=2)\n", - "fig.update_layout(width=800, height=400, margin=dict(l=20, r=20, t=20, b=20))\n", - "fig['layout']['xaxis']['title']='y_predicted'\n", - "fig['layout']['xaxis2']['title']='y_predicted'\n", - "fig['layout']['yaxis']['title']='y_actual'\n", - "fig['layout']['yaxis2']['title']='y_actual'\n", - "fig.show()\n", + "# fig.add_trace(go.Heatmap(z=z1, x=x, y=y, showscale=False), row=1, col=1)\n", + "# fig.add_trace(go.Heatmap(z=z2, x=x, y=y, showscale=False), row=1, col=2)\n", + "# fig.update_layout(width=800, height=400, margin=dict(l=20, r=20, t=20, b=20))\n", + "# fig['layout']['xaxis']['title']='y_predicted'\n", + "# fig['layout']['xaxis2']['title']='y_predicted'\n", + "# fig['layout']['yaxis']['title']='y_actual'\n", + "# fig['layout']['yaxis2']['title']='y_actual'\n", + "# fig.show()\n", "# def plot_heatmap(loss_fn, title=None, min_val=-0.1, max_val=0.1, n_val=100):\n", "# x = np.linspace(min_val, max_val, n_val)\n", "# y = np.linspace(min_val, max_val, n_val)\n", diff --git a/notebooks/sensitivity_analysis.ipynb b/notebooks/sensitivity_analysis.ipynb index bb6e0ee..9173746 100644 --- a/notebooks/sensitivity_analysis.ipynb +++ b/notebooks/sensitivity_analysis.ipynb @@ -52,7 +52,7 @@ "Done. 0:0:1.1\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. 0:0:0.6\n", + "Done. 0:0:0.5\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-30m:latest, 124.19MB. 12 files... \n", "\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n", "Done. 0:0:0.4\n" @@ -88,13 +88,13 @@ "metadata": {}, "outputs": [], "source": [ - "with open('cache/5min-best-strategies.pkl', 'rb') as inpt:\n", + "with open('cache/5min-best-strategies-v2.pkl', 'rb') as inpt:\n", " best_strategies_5min = pickle.load(inpt)\n", "\n", - "with open('cache/15min-best-strategies.pkl', 'rb') as inpt:\n", + "with open('cache/15min-best-strategies-v2.pkl', 'rb') as inpt:\n", " best_strategies_15min = pickle.load(inpt)\n", "\n", - "with open('cache/30min-best-strategies.pkl', 'rb') as inpt:\n", + "with open('cache/30min-best-strategies-v6.pkl', 'rb') as inpt:\n", " best_strategies_30min = pickle.load(inpt)" ] }, @@ -107,19 +107,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "def results_plot(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat, width=850, height=500, notitle=False):\n", + "def results_plot(\n", + " buy_and_hold_concat,\n", + " macd_30min_concat,\n", + " rsi_30min_concat,\n", + " rsi_5_min_concat,\n", + " rmse_30min_model_concat,\n", + " rmse_15min_model_concat,\n", + " gmadl_30min_model_concat,\n", + " gmadl_15min_model_concat,\n", + " gmadl_5min_model_concat, width=850, height=500, notitle=False):\n", "\n", " fig = go.Figure([\n", " go.Scatter(y=buy_and_hold_concat['portfolio_value'], x=buy_and_hold_concat['time'], name=\"Buy and Hold\"),\n", - " go.Scatter(y=gmadl_5min_model_concat['portfolio_value'], x=gmadl_5min_model_concat['time'], name=\"GMADL Informer Strategy (5min)\"),\n", - " go.Scatter(y=rsi_5_min_concat['portfolio_value'], x=rsi_5_min_concat['time'], name=\"RSI Strategy (5min)\"),\n", - " go.Scatter(y=gmadl_15min_model_concat['portfolio_value'], x=gmadl_15min_model_concat['time'], name='GMADL Informer Strategy (15min)'),\n", + " go.Scatter(y=macd_30min_concat['portfolio_value'], x=macd_30min_concat['time'], name='MACD Strategy (30min)'),\n", " go.Scatter(y=rsi_30min_concat['portfolio_value'], x=rsi_30min_concat['time'], name='RSI Strategy (30min)'),\n", - " go.Scatter(y=macd_30min_concat['portfolio_value'], x=macd_30min_concat['time'], name='MACD Strategy (30min)')\n", + " go.Scatter(y=rsi_5_min_concat['portfolio_value'], x=rsi_5_min_concat['time'], name=\"RSI Strategy (5min)\"),\n", + " go.Scatter(y=rmse_30min_model_concat['portfolio_value'], x=rmse_30min_model_concat['time'], name='RMSE Informer Strategy (30min)'),\n", + " go.Scatter(y=rmse_15min_model_concat['portfolio_value'], x=rmse_15min_model_concat['time'], name='RMSE Informer Strategy (15min)'),\n", + " go.Scatter(y=gmadl_30min_model_concat['portfolio_value'], x=gmadl_30min_model_concat['time'], name='GMADL Informer Strategy (30min)'),\n", + " go.Scatter(y=gmadl_15min_model_concat['portfolio_value'], x=gmadl_15min_model_concat['time'], name='GMADL Informer Strategy (15min)'),\n", + " go.Scatter(y=gmadl_5min_model_concat['portfolio_value'], x=gmadl_5min_model_concat['time'], name=\"GMADL Informer Strategy (5min)\")\n", " ])\n", " fig.update_layout(\n", " # title={\n", @@ -137,7 +149,7 @@ " autosize=False,\n", " width=width,\n", " height=height,\n", - " margin=dict(l=20, r=20, t=20, b=20),\n", + " margin=dict(l=20, r=20, t=50, b=20),\n", " plot_bgcolor='white',\n", " legend=dict(\n", " orientation=\"h\",\n", @@ -164,7 +176,17 @@ " # fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n", " fig.show()\n", " \n", - "def results_table(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat):\n", + "def results_table(\n", + " buy_and_hold_concat,\n", + " macd_30min_concat,\n", + " rsi_30min_concat,\n", + " rsi_5_min_concat,\n", + " rmse_30min_model_concat,\n", + " rmse_15min_model_concat,\n", + " gmadl_30min_model_concat,\n", + " gmadl_15min_model_concat,\n", + " gmadl_5min_model_concat):\n", + "\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", @@ -185,11 +207,14 @@ "\n", " strategy_name_result = [\n", " ('Buy and Hold', buy_and_hold_concat),\n", - " ('GMADL Informer (5min)', gmadl_5min_model_concat),\n", - " ('RSI Strategy (5min)', rsi_5_min_concat),\n", - " ('GMADL Informer (15min)', gmadl_15min_model_concat),\n", + " ('MACD Strategy (30min)', macd_30min_concat),\n", " ('RSI Strategy (30min)', rsi_30min_concat),\n", - " ('MACD Strategy (30min)', macd_30min_concat)\n", + " ('RSI Strategy (5min)', rsi_5_min_concat),\n", + " ('RMSE Informer (30min)', rmse_30min_model_concat),\n", + " ('RMSE Informer (15min)', rmse_15min_model_concat),\n", + " ('GMADL Informer (30min)', gmadl_30min_model_concat),\n", + " ('GMADL Informer (15min)', gmadl_15min_model_concat),\n", + " ('GMADL Informer (5min)', gmadl_5min_model_concat),\n", " ]\n", " for strategy_name, result in strategy_name_result:\n", " table_eval_windows.add_row([\n", @@ -209,23 +234,70 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\t\\begin{center}\n", + "\t\t\\begin{tabular}{lccccccccc}\n", + "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n", + "\t\t\t\\hline\n", + "\t\t\tBuy and Hold & 1.441 & 0.131 & 0.577 & 0.228 & 0.773 & 0.039 & 2 & 100.00\\% & 0.00\\% \\\\\n", + "\t\t\tMACD Strategy (30min) & 1.952 & 0.254 & 0.524 & 0.485 & 0.592 & 0.207 & 327 & 52.30\\% & 28.30\\% \\\\\n", + "\t\t\tRSI Strategy (30min) & 4.542 & 0.668 & 0.462 & 1.444 & 0.399 & 2.415 & 377 & 30.79\\% & 28.03\\% \\\\\n", + "\t\t\tRSI Strategy (5min) & 3.341 & 0.503 & 0.504 & 0.999 & 0.300 & 1.676 & 846 & 28.29\\% & 33.47\\% \\\\\n", + "\t\t\tRMSE Informer (30min) & 2.727 & 0.404 & 0.505 & 0.800 & 0.518 & 0.624 & 34 & 64.40\\% & 24.67\\% \\\\\n", + "\t\t\tRMSE Informer (15min) & 1.509 & 0.149 & 0.349 & 0.428 & 0.455 & 0.140 & 16 & 15.24\\% & 27.60\\% \\\\\n", + "\t\t\tGMADL Informer (30min) & 2.263 & 0.318 & 0.367 & 0.866 & 0.533 & 0.516 & 811 & 35.51\\% & 19.59\\% \\\\\n", + "\t\t\tGMADL Informer (15min) & 3.296 & 0.496 & 0.527 & 0.942 & 0.474 & 0.987 & 362 & 49.37\\% & 37.72\\% \\\\\n", + "\t\t\tGMADL Informer (5min) & 9.747 & 1.159 & 0.544 & 2.129 & 0.327 & 7.552 & 846 & 44.80\\% & 41.51\\% \\\\\n", + "\t\t\\end{tabular}\n", + "\t\\end{center}\n", + "\\end{table}\n" + ] + } + ], "source": [ - "# test_data_5min = pd.concat([data_windows_5min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_5min])\n", - "# test_data_15min = pd.concat([data_windows_15min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_15min])\n", - "# test_data_30min = pd.concat([data_windows_30min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_30min])\n", + "test_data_5min = pd.concat([data_windows_5min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_5min])\n", + "test_data_15min = pd.concat([data_windows_15min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_15min])\n", + "test_data_30min = pd.concat([data_windows_30min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_30min])\n", "\n", - "# buy_and_hold_concat = evaluate_strategy(test_data_5min, BuyAndHoldStrategy(), padding=PADDING, interval='5min')\n", - "# gmadl_5min_model_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min')\n", - "# gmadl_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min')\n", - "# rsi_5_min_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min')\n", - "# rsi_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", - "# macd_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", + "buy_and_hold_concat = evaluate_strategy(test_data_5min, BuyAndHoldStrategy(), padding=PADDING, interval='5min')\n", + "macd_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", + "rsi_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", + "rsi_5_min_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min')\n", + "rmse_30min_model_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rmse_model']], padding=PADDING), padding=PADDING, interval='30min')\n", + "rmse_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['rmse_model']], padding=PADDING), padding=PADDING, interval='15min')\n", + "gmadl_30min_model_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['gmadl_model']], padding=PADDING), padding=PADDING, interval='30min')\n", + "gmadl_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min')\n", + "gmadl_5min_model_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min')\n", "\n", - "# results_table(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat)\n", - "# results_plot(buy_and_hold_concat, gmadl_5min_model_concat, gmadl_15min_model_concat, rsi_5_min_concat, rsi_30min_concat, macd_30min_concat, width=1200, notitle=True)" + "results_table(\n", + " buy_and_hold_concat,\n", + " macd_30min_concat,\n", + " rsi_30min_concat,\n", + " rsi_5_min_concat,\n", + " rmse_30min_model_concat,\n", + " rmse_15min_model_concat,\n", + " gmadl_30min_model_concat,\n", + " gmadl_15min_model_concat,\n", + " gmadl_5min_model_concat)\n", + "\n", + "# results_plot(\n", + "# buy_and_hold_concat,\n", + "# macd_30min_concat,\n", + "# rsi_30min_concat,\n", + "# rsi_5_min_concat,\n", + "# rmse_30min_model_concat,\n", + "# rmse_15min_model_concat,\n", + "# gmadl_30min_model_concat,\n", + "# gmadl_15min_model_concat,\n", + "# gmadl_5min_model_concat, \n", + "# width=1200, notitle=True)" ] }, { @@ -248,27 +320,33 @@ "buy_and_hold_5min = evaluate_strategy(test_data_5min, BuyAndHoldStrategy(), padding=PADDING, interval='5min')\n", "buy_and_hold_15min = evaluate_strategy(test_data_15min, BuyAndHoldStrategy(), padding=PADDING, interval='15min')\n", "buy_and_hold_30min = evaluate_strategy(test_data_30min, BuyAndHoldStrategy(), padding=PADDING, interval='30min')\n", - "gmadl_5min_model_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min')\n", - "gmadl_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min')\n", - "rsi_5_min_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min')\n", + "macd_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", "rsi_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n", - "macd_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min')" + "rsi_5_min_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min')\n", + "rmse_30min_model_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rmse_model']], padding=PADDING), padding=PADDING, interval='30min')\n", + "rmse_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['rmse_model']], padding=PADDING), padding=PADDING, interval='15min')\n", + "gmadl_30min_model_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['gmadl_model']], padding=PADDING), padding=PADDING, interval='30min')\n", + "gmadl_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min')\n", + "gmadl_5min_model_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min')" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "GMADL (5min) & 311040 & 2.820834 & 375.84 & 0.000000*** \\\\\n", - "GMADL (15 min) & 103680 & 0.558743 & 408.13 & 0.000000*** \\\\\n", - "RSI (5 min) & 311040 & 0.648741 & 662.77 & 0.000000*** \\\\\n", + "MACD (30 min) & 51840 & 0.326504 & 174.34 & 0.000000*** \\\\\n", "RSI (30 min) & 51840 & 1.065079 & 258.49 & 0.000000*** \\\\\n", - "MACD (30 min) & 51840 & 0.326504 & 174.34 & 0.000000*** \\\\\n" + "RSI (5 min) & 311040 & 0.648741 & 662.77 & 0.000000*** \\\\\n", + "RMSE (30 min) & 51840 & 0.796647 & 161.58 & 0.000000*** \\\\\n", + "RMSE (15 min) & 103680 & 0.541611 & 115.29 & 0.000000*** \\\\\n", + "GMADL (30 min) & 51840 & 0.320689 & 448.49 & 0.000000*** \\\\\n", + "GMADL (15 min) & 103680 & 0.558743 & 408.13 & 0.000000*** \\\\\n", + "GMADL (5min) & 311040 & 2.820834 & 375.84 & 0.000000*** \\\\\n" ] } ], @@ -285,18 +363,24 @@ "\n", " return tt, pval, sigma, N\n", "\n", - "gmadl_5min_ttest = ttest(buy_and_hold_5min, gmadl_5min_model_concat)\n", - "gmadl_15min_ttest = ttest(buy_and_hold_15min, gmadl_15min_model_concat)\n", - "rsi_5min_ttest = ttest(buy_and_hold_5min, rsi_5_min_concat)\n", - "rsi_30min_ttest = ttest(buy_and_hold_30min, rsi_30min_concat)\n", "macd_30min_ttest = ttest(buy_and_hold_30min, macd_30min_concat)\n", + "rsi_30min_ttest = ttest(buy_and_hold_30min, rsi_30min_concat)\n", + "rsi_5min_ttest = ttest(buy_and_hold_5min, rsi_5_min_concat)\n", + "rmse_30min_ttest = ttest(buy_and_hold_30min, rmse_30min_model_concat)\n", + "rmse_15min_ttest = ttest(buy_and_hold_15min, rmse_15min_model_concat)\n", + "gmadl_30min_ttest = ttest(buy_and_hold_30min, gmadl_30min_model_concat)\n", + "gmadl_15min_ttest = ttest(buy_and_hold_15min, gmadl_15min_model_concat)\n", + "gmadl_5min_ttest = ttest(buy_and_hold_5min, gmadl_5min_model_concat)\n", "\n", "for name, result in [\n", - " (\"GMADL (5min)\", gmadl_5min_ttest),\n", - " (\"GMADL (15 min)\", gmadl_15min_ttest),\n", - " (\"RSI (5 min)\", rsi_5min_ttest),\n", + " (\"MACD (30 min)\", macd_30min_ttest),\n", " (\"RSI (30 min)\", rsi_30min_ttest),\n", - " (\"MACD (30 min)\", macd_30min_ttest)\n", + " (\"RSI (5 min)\", rsi_5min_ttest),\n", + " (\"RMSE (30 min)\", rmse_30min_ttest),\n", + " (\"RMSE (15 min)\", rmse_15min_ttest),\n", + " (\"GMADL (30 min)\", gmadl_30min_ttest),\n", + " (\"GMADL (15 min)\", gmadl_15min_ttest),\n", + " (\"GMADL (5min)\", gmadl_5min_ttest),\n", "]:\n", " print(f\"{name} & {result[3]} & {result[2]:.6f} & {result[0]:.2f} & {result[1]:.6f}*** \\\\\\\\\")" ] @@ -310,52 +394,88 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "# def results_for_strats(data_windows, best_strategies, interval='5min', top_n=10):\n", - "# test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n", + "def results_for_strats(\n", + " data_windows_5min, data_windows_15min, data_windows_30min, \n", + " best_strategies_5min, best_strategies_15min, best_strategies_30min,\n", + " top_n=10):\n", + " test_data_5min = pd.concat([data_windows_5min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_5min])\n", + " test_data_15min = pd.concat([data_windows_15min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_15min])\n", + " test_data_30min = pd.concat([data_windows_30min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_30min])\n", "\n", - " \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[x] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n", - "# rsi_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n", - "# quantile_model_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['quantile_model']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n", - "# gmadl_model_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n", + " buy_and_hold_concat = evaluate_strategy(test_data_5min, BuyAndHoldStrategy(), padding=PADDING, interval='5min')\n", + " macd_30min_concat = [evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[x] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min') for x in range(top_n)]\n", + " macd_15min_concat = [evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[x] for s in best_strategies_15min['macd_strategies']], padding=PADDING), padding=PADDING, interval='15min') for x in range(top_n)]\n", + " macd_5min_concat = [evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[x] for s in best_strategies_5min['macd_strategies']], padding=PADDING), padding=PADDING, interval='5min') for x in range(top_n)]\n", + " rsi_30min_concat = [evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[x] for s in best_strategies_30min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='30min') for x in range(top_n)]\n", + " rsi_15min_concat = [evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[x] for s in best_strategies_15min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='15min') for x in range(top_n)]\n", + " rsi_5_min_concat = [evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[x] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min') for x in range(top_n)]\n", + " rmse_30min_model_concat = [evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[x] for s in best_strategies_30min['rmse_model']], padding=PADDING), padding=PADDING, interval='30min') for x in range(top_n)]\n", + " rmse_15min_model_concat = [evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[x] for s in best_strategies_15min['rmse_model']], padding=PADDING), padding=PADDING, interval='15min') for x in range(top_n)]\n", + " rmse_5min_model_concat = [evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[x] for s in best_strategies_5min['rmse_model']], padding=PADDING), padding=PADDING, interval='5min') for x in range(top_n)]\n", + " quantile_30min_model_concat = [evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[x] for s in best_strategies_30min['quantile_model']], padding=PADDING), padding=PADDING, interval='30min') for x in range(top_n)]\n", + " quantile_15min_model_concat = [evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[x] for s in best_strategies_15min['quantile_model']], padding=PADDING), padding=PADDING, interval='15min') for x in range(top_n)]\n", + " quantile_5min_model_concat = [evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[x] for s in best_strategies_5min['quantile_model']], padding=PADDING), padding=PADDING, interval='5min') for x in range(top_n)]\n", + " gmadl_30min_model_concat = [evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[x] for s in best_strategies_30min['gmadl_model']], padding=PADDING), padding=PADDING, interval='30min') for x in range(top_n)]\n", + " gmadl_15min_model_concat = [evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[x] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min') for x in range(top_n)]\n", + " gmadl_5min_model_concat = [evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[x] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min') for x in range(top_n)]\n", "\n", - "# z = list(reversed([\n", - "# list(reversed([round(buy_and_hold_concat['mod_ir'], 3)]*top_n)),\n", - "# list(reversed([round(x['mod_ir'], 3) for x in macd_concat])),\n", - "# list(reversed([round(x['mod_ir'], 3) for x in rsi_concat])),\n", - "# list(reversed([round(x['mod_ir'], 3) for x in quantile_model_concat])),\n", - "# list(reversed([round(x['mod_ir'], 3) for x in gmadl_model_concat])),\n", - "# ]))\n", - "# x = list(reversed(range(1, top_n+1)))\n", - "# y = list(reversed([\n", - "# \"Buy and Hold\",\n", - "# \"MACD Strategy\",\n", - "# \"RSI Strategy\",\n", - "# \"Quantile Informer\",\n", - "# \"Gmadl Informer\"\n", - "# ]))\n", - "# # 'Portland'\n", - "# fig = ff.create_annotated_heatmap(z, x=x, y=y, colorscale='thermal', zmid=buy_and_hold_concat['mod_ir'])\n", - "# fig.update_layout(\n", - "# margin=dict(l=20, r=20, b=20, t=20),\n", - "# width=1100,\n", - "# height=450,\n", - "# font=dict(\n", - "# # family=\"Courier New, monospace\",\n", - "# size=18, # Set the font size here\n", - "# # color=\"RebeccaPurple\"\n", - "# )\n", - "# )\n", - "# fig.show()\n", + " z = list(reversed([\n", + " list(reversed([round(buy_and_hold_concat['mod_ir'], 3)]*top_n)),\n", + " list(reversed([round(x['mod_ir'], 3) for x in macd_30min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in macd_15min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in macd_5min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rsi_30min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rsi_15min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rsi_5_min_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rmse_30min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rmse_15min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in rmse_5min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in quantile_30min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in quantile_15min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in quantile_5min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in gmadl_30min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in gmadl_15min_model_concat])),\n", + " list(reversed([round(x['mod_ir'], 3) for x in gmadl_5min_model_concat])),\n", + " ]))\n", + " x = list(reversed(range(1, top_n+1)))\n", + " y = list(reversed([\n", + " \"Buy and Hold\",\n", + " \"MACD Strategy (30 min)\",\n", + " \"MACD Strategy (15 min)\",\n", + " \"MACD Strategy (5 min)\",\n", + " \"RSI Strategy (30 min)\",\n", + " \"RSI Strategy (15 min)\",\n", + " \"RSI Strategy (5 min)\",\n", + " \"RMSE Informer (30 min)\",\n", + " \"RMSE Informer (15 min)\",\n", + " \"RMSE Informer (5 min)\",\n", + " \"Quantile Informer (30 min)\",\n", + " \"Quantile Informer (15 min)\",\n", + " \"Quantile Informer (5 min)\",\n", + " \"Gmadl Informer (30 min)\",\n", + " \"Gmadl Informer (15 min)\",\n", + " \"Gmadl Informer (5 min)\"\n", + " ]))\n", + " # 'Portland'\n", + " fig = ff.create_annotated_heatmap(z, x=x, y=y, colorscale='thermal', zmid=buy_and_hold_concat['mod_ir'])\n", + " fig.update_layout(\n", + " margin=dict(l=20, r=20, b=20, t=20),\n", + " width=1100,\n", + " height=650,\n", + " font=dict(\n", + " # family=\"Courier New, monospace\",\n", + " size=16, # Set the font size here\n", + " # color=\"RebeccaPurple\"\n", + " )\n", + " )\n", + " fig.show()\n", "\n", - "# results_for_strats(data_windows_5min, best_strategies_5min, interval='5min', top_n=10) \n", - "# results_for_strats(data_windows_15min, best_strategies_15min, interval='15min', top_n=10) \n", - "# results_for_strats(data_windows_30min, best_strategies_30min, interval='30min', top_n=10) \n" + "# results_for_strats(data_windows_5min, data_windows_15min, data_windows_30min,\n", + "# best_strategies_5min, best_strategies_15min, best_strategies_30min, top_n=10) " ] }, {