From f28b225d42c7c434dc1b2074242b44d991dc1331 Mon Sep 17 00:00:00 2001 From: aszerW Date: Tue, 18 Nov 2025 00:14:34 +0800 Subject: [PATCH] =?UTF-8?q?=E7=94=9F=E6=88=90=E4=BF=A1=E5=8F=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- IC.ipynb | 13910 ++++++++++++++++++++++++++++++++++++++++ TS因子挖掘构建流程.md | 121 - backtest.py | 180 - deap_factor_mining.py | 436 -- example.py | 110 - pipeline.py | 287 - requirements.txt | 6 + test.py | 58 + 收益测算.ipynb | 7523 ++++++++++++++++++++++ 9 files changed, 21497 insertions(+), 1134 deletions(-) create mode 100644 IC.ipynb delete mode 100644 TS因子挖掘构建流程.md delete mode 100644 backtest.py delete mode 100644 deap_factor_mining.py delete mode 100644 example.py delete mode 100644 pipeline.py create mode 100644 requirements.txt create mode 100644 test.py create mode 100644 收益测算.ipynb diff --git a/IC.ipynb b/IC.ipynb new file mode 100644 index 0000000..20e2a0f --- /dev/null +++ b/IC.ipynb @@ -0,0 +1,13910 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 36, + "id": "a9a4add8", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import talib as ta" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3482488a", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_feather(\"/Users/aszer/Documents/vscode/factorhack/ETH_USDT-1h.feather\")\n", + "for col in ['datetime', 'time', 'timestamp', 'date']:\n", + " if col in df.columns:\n", + " df[col] = pd.to_datetime(df[col])\n", + " df = df.set_index(col).sort_index()\n", + " break\n", + "df = df.resample(\"4h\").agg({\n", + " \"open\": \"first\",\n", + " \"high\": \"max\",\n", + " \"low\": \"min\",\n", + " \"close\": \"last\",\n", + " \"volume\": \"sum\"\n", + " }).dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b5975577", + "metadata": {}, + "outputs": [], + "source": [ + "df['cci'] = ta.CCI(df['high'], df['low'], df['close'], 14)\n", + "df['ema20'] = ta.EMA(df['close'], 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "aaee088a", + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 7\n", + "df['return'] = df['close'].pct_change(horizon).shift(-horizon)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fe30d1eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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openhighlowclosevolumecciema20return
date
2020-08-08 00:00:00+00:00379.25397.24375.86397.09283303.232302NaNNaN0.088519
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2020-08-12 00:00:00+00:00379.08389.28365.92387.14372390.696798NaNNaN0.052384
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{IR:.4f}\")\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=ic_series.index, y=ic_series.values, mode='lines', name='IC Series'))\n", + "fig.update_layout(title='IC Series (Rolling)', xaxis_title='Date', yaxis_title='IC Value')\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a3d53b6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Horizon: 1, IC: 0.2712, IR: 1.7175\n", + "Horizon: 2, IC: 0.3609, IR: 1.8107\n", + "Horizon: 3, IC: 0.4212, IR: 1.8493\n", + "Horizon: 4, IC: 0.4670, IR: 1.8896\n", + "Horizon: 5, IC: 0.5063, IR: 1.9147\n", + "Horizon: 6, IC: 0.5350, IR: 1.9430\n", + "Horizon: 7, IC: 0.5525, IR: 1.9411\n", + "Horizon: 8, IC: 0.5666, IR: 1.9441\n", + "Horizon: 9, IC: 0.5774, IR: 1.9371\n", + "Horizon: 10, IC: 0.5870, IR: 1.9314\n", + "Horizon: 11, IC: 0.5960, IR: 1.9200\n", + "Horizon: 12, IC: 0.6060, IR: 1.9213\n", + "Horizon: 13, IC: 0.6127, IR: 1.9188\n", + "Horizon: 14, IC: 0.6166, IR: 1.9002\n", + "Horizon: 15, IC: 0.6198, IR: 1.8871\n", + "Horizon: 16, IC: 0.6223, IR: 1.8586\n", + "Horizon: 17, IC: 0.6233, IR: 1.8288\n", + "Horizon: 18, IC: 0.6262, IR: 1.8244\n", + "Horizon: 19, IC: 0.6266, IR: 1.8031\n", + "Horizon: 20, IC: 0.6268, IR: 1.7929\n", + "Horizon: 21, IC: 0.6272, IR: 1.7871\n", + "Horizon: 22, IC: 0.6265, IR: 1.7686\n", + "Horizon: 23, IC: 0.6281, IR: 1.7777\n", + "Horizon: 24, IC: 0.6324, IR: 1.8009\n", + "Horizon: 25, IC: 0.6340, IR: 1.8222\n", + "Horizon: 26, IC: 0.6344, IR: 1.8300\n", + "Horizon: 27, IC: 0.6342, IR: 1.8259\n", + "Horizon: 28, IC: 0.6340, IR: 1.8212\n", + "Horizon: 29, IC: 0.6339, IR: 1.8145\n", + "IC Half life: 29\n", + "IR Half life: 29\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines+markers", + "name": "Mean IC", + "type": "scatter", + "x": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 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"IC & IR vs Horizon" + }, + "xaxis": { + "title": { + "text": "Horizon" + } + }, + "yaxis": { + "title": { + "text": "Value" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objs as go\n", + "\n", + "# df[\"factor\"] = df[\"cci\"]\n", + "ic_values = []\n", + "ir_values = []\n", + "horizons = []\n", + "\n", + "\n", + "def calc_half_life(value_list):\n", + " v0 = value_list[0]\n", + " if v0 > 0:\n", + " for i in range(1, len(value_list)):\n", + " if value_list[i] <= 0.5 * v0:\n", + " return i\n", + " else:\n", + " for i in range(1, len(value_list)):\n", + " if value_list[i] >= 0.5 * v0:\n", + " return i\n", + " return len(value_list)\n", + "\n", + "\n", + "for horizon in range(1, 30):\n", + " df[\"return\"] = df[\"close\"].pct_change(horizon).shift(-horizon)\n", + " ic_series = compute_rolling_ic(\n", + " df[\"factor\"], df[\"return\"], window=30, method=\"spearman\"\n", + " )\n", + " mean_ic = ic_series.mean(skipna=True)\n", + " std_ic = ic_series.std(skipna=True)\n", + " IR = mean_ic / std_ic if std_ic != 0 else np.nan\n", + " IC = mean_ic\n", + " ic_values.append(IC)\n", + " ir_values.append(IR)\n", + " horizons.append(horizon)\n", + " print(f\"Horizon: {horizon}, IC: {IC:.4f}, IR: {IR:.4f}\")\n", + "print(f\"IC Half life: {calc_half_life(ic_values)}\")\n", + "print(f\"IR Half life: {calc_half_life(ir_values)}\")\n", + "\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=horizons, y=ic_values, mode=\"lines+markers\", name=\"Mean IC\"))\n", + "fig.add_trace(go.Scatter(x=horizons, y=ir_values, mode=\"lines+markers\", name=\"IR\"))\n", + "fig.update_layout(\n", + " title=\"IC & IR vs Horizon\",\n", + " xaxis_title=\"Horizon\",\n", + " yaxis_title=\"Value\",\n", + " legend_title=\"Metric\",\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5779c09b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lag: 1, IC: -0.1584, IR: -0.9989\n", + "lag: 2, IC: -0.1384, IR: -0.8901\n", + "lag: 3, IC: -0.1181, IR: -0.7724\n", + "lag: 4, IC: -0.1013, IR: -0.6711\n", + "lag: 5, IC: -0.0836, IR: -0.5425\n", + "lag: 6, IC: -0.0708, IR: -0.4531\n", + "lag: 7, IC: -0.0548, IR: -0.3429\n", + "lag: 8, IC: -0.0437, IR: -0.2692\n", + "lag: 9, IC: -0.0362, IR: -0.2182\n", + "lag: 10, IC: -0.0311, IR: -0.1862\n", + "lag: 11, IC: -0.0203, IR: -0.1165\n", + "lag: 12, IC: -0.0155, IR: -0.0886\n", + "lag: 13, IC: -0.0082, IR: -0.0467\n", + "lag: 14, IC: -0.0001, IR: -0.0003\n", + "lag: 15, IC: 0.0047, IR: 0.0276\n", + "lag: 16, IC: 0.0112, IR: 0.0670\n", + "lag: 17, IC: 0.0108, IR: 0.0652\n", + "lag: 18, IC: 0.0106, IR: 0.0656\n", + "lag: 19, IC: 0.0126, IR: 0.0786\n", + "lag: 20, IC: 0.0106, IR: 0.0661\n", + "lag: 21, IC: 0.0061, IR: 0.0375\n", + "lag: 22, IC: 0.0022, IR: 0.0134\n", + "lag: 23, IC: -0.0016, IR: -0.0093\n", + "lag: 24, IC: -0.0096, IR: -0.0534\n", + "lag: 25, IC: -0.0144, IR: -0.0781\n", + "lag: 26, IC: -0.0157, IR: -0.0844\n", + "lag: 27, IC: -0.0159, IR: -0.0849\n", + "lag: 28, IC: -0.0186, IR: -0.0985\n", + "lag: 29, IC: -0.0162, IR: -0.0886\n" + ] + } + ], + "source": [ + "for lag in range(1, 30): \n", + " # 因子滞后 lag 期（即用过去第 lag 期的因子）\n", + " lagged_factor = df['factor'].shift(lag)\n", + " # 收益还是未来1期（固定 horizon=1）\n", + " future_return = df['close'].pct_change(1).shift(-1)\n", + " ic_series = compute_rolling_ic(lagged_factor, future_return, window=30, method='spearman')\n", + " mean_ic = ic_series.mean(skipna=True)\n", + " std_ic = ic_series.std(skipna=True)\n", + " import numpy as np\n", + " IR = mean_ic / std_ic if std_ic != 0 else np.nan\n", + " IC = mean_ic\n", + " print(f'lag: {lag}, IC: {IC:.4f}, IR: {IR:.4f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "22fd9b86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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openhighlowclosevolumereturn
date
2020-08-08 00:00:00+00:00379.25382.67375.86377.9342122.3596100.129098
2020-08-08 04:00:00+00:00377.95381.50377.11379.9130085.5951080.123055
2020-08-08 08:00:00+00:00379.96392.23378.00391.6166915.5519170.067261
2020-08-08 12:00:00+00:00391.62393.30387.63390.4054879.6625920.102357
2020-08-08 16:00:00+00:00390.50392.52385.91389.3148599.3402600.102720
.....................
2025-10-13 20:00:00+00:004252.734292.854235.494241.8019326.830948NaN
2025-10-14 00:00:00+00:004241.804263.834137.834153.8534167.864538NaN
2025-10-14 04:00:00+00:004153.854161.033964.963980.84114268.919574NaN
2025-10-14 08:00:00+00:003980.984015.453888.413971.8574320.679479NaN
2025-10-14 12:00:00+00:003972.293976.453895.683956.7538185.964123NaN
\n", + "

11362 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " open high low close volume \\\n", + "date \n", + "2020-08-08 00:00:00+00:00 379.25 382.67 375.86 377.93 42122.359610 \n", + "2020-08-08 04:00:00+00:00 377.95 381.50 377.11 379.91 30085.595108 \n", + "2020-08-08 08:00:00+00:00 379.96 392.23 378.00 391.61 66915.551917 \n", + "2020-08-08 12:00:00+00:00 391.62 393.30 387.63 390.40 54879.662592 \n", + "2020-08-08 16:00:00+00:00 390.50 392.52 385.91 389.31 48599.340260 \n", + "... ... ... ... ... ... \n", + "2025-10-13 20:00:00+00:00 4252.73 4292.85 4235.49 4241.80 19326.830948 \n", + "2025-10-14 00:00:00+00:00 4241.80 4263.83 4137.83 4153.85 34167.864538 \n", + "2025-10-14 04:00:00+00:00 4153.85 4161.03 3964.96 3980.84 114268.919574 \n", + "2025-10-14 08:00:00+00:00 3980.98 4015.45 3888.41 3971.85 74320.679479 \n", + "2025-10-14 12:00:00+00:00 3972.29 3976.45 3895.68 3956.75 38185.964123 \n", + "\n", + " return \n", + "date \n", + "2020-08-08 00:00:00+00:00 0.129098 \n", + "2020-08-08 04:00:00+00:00 0.123055 \n", + "2020-08-08 08:00:00+00:00 0.067261 \n", + "2020-08-08 12:00:00+00:00 0.102357 \n", + "2020-08-08 16:00:00+00:00 0.102720 \n", + "... ... \n", + "2025-10-13 20:00:00+00:00 NaN \n", + "2025-10-14 00:00:00+00:00 NaN \n", + "2025-10-14 04:00:00+00:00 NaN \n", + "2025-10-14 08:00:00+00:00 NaN \n", + "2025-10-14 12:00:00+00:00 NaN \n", + "\n", + "[11362 rows x 6 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "horizon = 48\n", + "df['return'] = df['close'].pct_change(horizon).shift(-horizon)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ca0340cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"expression\": \"sub(abs(close), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "nan\n", + "{\"expression\": \"sub(pct_change20(std25(close)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.636824098851074\n", + "{\"expression\": \"sub(rank5(rank5(rank5(rank5(open)))), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/numpy/_core/_methods.py:52: RuntimeWarning:\n", + "\n", + "invalid value encountered in reduce\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6378749356340693\n", + "{\"expression\": \"sub(pct_change20(neg(close)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6376911466556157\n", + "{\"expression\": \"sub(rank5(rank5(rank5(rank5(rank5(open))))), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6378921897986413\n", + "{\"expression\": \"sub(std15(sub(open, open)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6379000440523125\n", + "{\"expression\": \"sub(pct_change30(close), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6373688163135725\n", + "{\"expression\": \"sub(pct_change20(close), pct_change20(close))\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "nan\n", + "{\"expression\": \"sub(pct_change20(close), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6376911466556157\n", + "{\"expression\": \"sub(pct_change20(open), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6376868256065275\n", + "{\"expression\": \"sub(rank5(rank5(open)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/numpy/_core/_methods.py:52: RuntimeWarning:\n", + "\n", + "invalid value encountered in reduce\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6376429453547645\n", + "{\"expression\": \"sub(rank5(rank5(close)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6375722471420397\n", + "{\"expression\": \"sma45(sub(close, close))\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "nan\n", + "{\"expression\": \"sub(pct_change20(low), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "0.6376870091850397\n", + "{\"expression\": \"sub(delta5(rank30(volume)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/numpy/_core/_methods.py:52: RuntimeWarning:\n", + "\n", + "invalid value encountered in reduce\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6377731145623272\n", + "{\"expression\": \"sub(rank5(rank5(rank5(open))), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[44], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m formula \u001b[38;5;241m=\u001b[39m FactorFormula\u001b[38;5;241m.\u001b[39mfrom_dict(\u001b[38;5;28meval\u001b[39m(line))\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mvalidation\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m compute_rolling_ic\n\u001b[0;32m----> 9\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfactor\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mformula\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m ic \u001b[38;5;241m=\u001b[39m compute_rolling_ic(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfactor\u001b[39m\u001b[38;5;124m'\u001b[39m], df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreturn\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(ic\u001b[38;5;241m.\u001b[39mmean())\n", + "File \u001b[0;32m~/Documents/vscode/factorhack/factor_mining/FactorFormula.py:71\u001b[0m, in \u001b[0;36mFactorFormula.compute\u001b[0;34m(self, features)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# 限制可用的内置函数\u001b[39;00m\n\u001b[1;32m 64\u001b[0m safe_builtins \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mabs\u001b[39m,\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mmin\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlen\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mlen\u001b[39m,\n\u001b[1;32m 70\u001b[0m }\n\u001b[0;32m---> 71\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpression\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m__builtins__\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msafe_builtins\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;66;03m# 确保结果是numpy数组\u001b[39;00m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, np\u001b[38;5;241m.\u001b[39mndarray):\n", + "File \u001b[0;32m:1\u001b[0m\n", + "File \u001b[0;32m~/Documents/vscode/factorhack/factor_mining/time_series_op.py:58\u001b[0m, in \u001b[0;36mregister_time_series_operator..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 46\u001b[0m registry\u001b[38;5;241m.\u001b[39mregister_function(\n\u001b[1;32m 47\u001b[0m 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\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 476\u001b[0m \u001b[38;5;66;03m# GH 12541: Special case for count where we support date-like types\u001b[39;00m\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/window/rolling.py:456\u001b[0m, in \u001b[0;36mBaseWindow._apply_series\u001b[0;34m(self, homogeneous_func, name)\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DataError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo numeric types to aggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m--> 456\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mhomogeneous_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 457\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_axis_for_step(obj\u001b[38;5;241m.\u001b[39mindex, result)\n\u001b[1;32m 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor(result, index\u001b[38;5;241m=\u001b[39mindex, name\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mname)\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/window/rolling.py:614\u001b[0m, in \u001b[0;36mBaseWindow._apply..homogeneous_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 611\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(x, start, end, min_periods, \u001b[38;5;241m*\u001b[39mnumba_args)\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 614\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mcalc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/window/rolling.py:611\u001b[0m, in \u001b[0;36mBaseWindow._apply..homogeneous_func..calc\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 602\u001b[0m start, end \u001b[38;5;241m=\u001b[39m window_indexer\u001b[38;5;241m.\u001b[39mget_window_bounds(\n\u001b[1;32m 603\u001b[0m num_values\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(x),\n\u001b[1;32m 604\u001b[0m min_periods\u001b[38;5;241m=\u001b[39mmin_periods,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 607\u001b[0m step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep,\n\u001b[1;32m 608\u001b[0m )\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_window_bounds(start, end, \u001b[38;5;28mlen\u001b[39m(x))\n\u001b[0;32m--> 611\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_periods\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnumba_args\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/window/rolling.py:1535\u001b[0m, in \u001b[0;36mRollingAndExpandingMixin._generate_cython_apply_func..apply_func\u001b[0;34m(values, begin, end, min_periods, raw)\u001b[0m\n\u001b[1;32m 1532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m raw:\n\u001b[1;32m 1533\u001b[0m \u001b[38;5;66;03m# GH 45912\u001b[39;00m\n\u001b[1;32m 1534\u001b[0m values \u001b[38;5;241m=\u001b[39m Series(values, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m-> 1535\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwindow_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbegin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_periods\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32mpandas/_libs/window/aggregations.pyx:1422\u001b[0m, in \u001b[0;36mpandas._libs.window.aggregations.roll_apply\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/indexing.py:1192\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m 1191\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[0;32m-> 1192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/indexing.py:1730\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1724\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\n\u001b[1;32m 1725\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame indexer is not allowed for .iloc\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1726\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsider using .loc for automatic alignment.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1727\u001b[0m )\n\u001b[1;32m 1729\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m-> 1730\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1732\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 1733\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/indexing.py:1766\u001b[0m, in \u001b[0;36m_iLocIndexer._get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m 1764\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m 1765\u001b[0m labels\u001b[38;5;241m.\u001b[39m_validate_positional_slice(slice_obj)\n\u001b[0;32m-> 1766\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/series.py:1102\u001b[0m, in \u001b[0;36mSeries._slice\u001b[0;34m(self, slobj, axis)\u001b[0m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_slice\u001b[39m(\u001b[38;5;28mself\u001b[39m, slobj: \u001b[38;5;28mslice\u001b[39m, axis: AxisInt \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;66;03m# axis kwarg is retained for compat with NDFrame method\u001b[39;00m\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;66;03m# _slice is *always* positional\u001b[39;00m\n\u001b[0;32m-> 1102\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(mgr, axes\u001b[38;5;241m=\u001b[39mmgr\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m 1104\u001b[0m out\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/internals/managers.py:2005\u001b[0m, in \u001b[0;36mSingleBlockManager.get_slice\u001b[0;34m(self, slobj, axis)\u001b[0m\n\u001b[1;32m 2002\u001b[0m \u001b[38;5;66;03m# TODO this method is only used in groupby SeriesSplitter at the moment,\u001b[39;00m\n\u001b[1;32m 2003\u001b[0m \u001b[38;5;66;03m# so passing refs is not yet covered by the tests\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m block \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(blk)(array, placement\u001b[38;5;241m=\u001b[39mbp, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, refs\u001b[38;5;241m=\u001b[39mblk\u001b[38;5;241m.\u001b[39mrefs)\n\u001b[0;32m-> 2005\u001b[0m new_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2006\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(block, new_index)\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/indexes/base.py:5441\u001b[0m, in \u001b[0;36mIndex._getitem_slice\u001b[0;34m(self, slobj)\u001b[0m\n\u001b[1;32m 5437\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_getitem_slice\u001b[39m(\u001b[38;5;28mself\u001b[39m, slobj: \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 5438\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 5439\u001b[0m \u001b[38;5;124;03m Fastpath for __getitem__ when we know we have a slice.\u001b[39;00m\n\u001b[1;32m 5440\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5441\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mslobj\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 5442\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m_simple_new(res, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name, refs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_references)\n\u001b[1;32m 5443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_engine\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache:\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/arrays/datetimelike.py:398\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;124;03mThis getitem defers to the underlying array, which by-definition can\u001b[39;00m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;124;03monly handle list-likes, slices, and integer scalars\u001b[39;00m\n\u001b[1;32m 394\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;66;03m# Use cast as we know we will get back a DatetimeLikeArray or DTScalar,\u001b[39;00m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;66;03m# but skip evaluating the Union at runtime for performance\u001b[39;00m\n\u001b[1;32m 397\u001b[0m \u001b[38;5;66;03m# (see https://github.com/pandas-dev/pandas/pull/44624)\u001b[39;00m\n\u001b[0;32m--> 398\u001b[0m result \u001b[38;5;241m=\u001b[39m cast(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnion[Self, DTScalarOrNaT]\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_scalar(result):\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/arrays/_mixins.py:292\u001b[0m, in \u001b[0;36mNDArrayBackedExtensionArray.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"ExtensionArray\",\u001b[39;00m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;66;03m# variable has type \"Union[int, slice, ndarray]\")\u001b[39;00m\n\u001b[1;32m 291\u001b[0m key \u001b[38;5;241m=\u001b[39m extract_array(key, extract_numpy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[0;32m--> 292\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 293\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray[key]\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_scalar(result):\n", + "File \u001b[0;32m/usr/local/Caskroom/miniforge/base/envs/qa/lib/python3.10/site-packages/pandas/core/indexers/utils.py:517\u001b[0m, in \u001b[0;36mcheck_array_indexer\u001b[0;34m(array, indexer)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstruction\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m array \u001b[38;5;28;01mas\u001b[39;00m pd_array\n\u001b[1;32m 513\u001b[0m \u001b[38;5;66;03m# whatever is not an array-like is returned as-is (possible valid array\u001b[39;00m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;66;03m# indexers that are not array-like: integer, slice, Ellipsis, None)\u001b[39;00m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;66;03m# In this context, tuples are not considered as array-like, as they have\u001b[39;00m\n\u001b[1;32m 516\u001b[0m \u001b[38;5;66;03m# a specific meaning in indexing (multi-dimensional indexing)\u001b[39;00m\n\u001b[0;32m--> 517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mis_list_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indexer\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from factor_mining.FactorFormula import FactorFormula\n", + "\n", + "with open(\"/Users/aszer/Documents/vscode/factorhack/gp_miner_result.txt\", 'r') as f:\n", + " for line in f.readlines():\n", + " line = line.strip()\n", + " print(line)\n", + " formula = FactorFormula.from_dict(eval(line))\n", + " from validation import compute_rolling_ic\n", + " df['factor'] = formula.compute(df)\n", + " ic = compute_rolling_ic(df['factor'], df['return'])\n", + " print(ic.mean())\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0a7ccdfc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6377731145623272\n" + ] + } + ], + "source": [ + "ic = compute_rolling_ic(df['factor'], df['return'])\n", + "print(ic.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4b508f91", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' 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[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" + ] + } + ], + "source": [ + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TS因子挖掘构建流程.md b/TS因子挖掘构建流程.md deleted file mode 100644 index 6f53769..0000000 --- a/TS因子挖掘构建流程.md +++ /dev/null @@ -1,121 +0,0 @@ -# 基于BTC 4h数据的时间序列因子模型实操流程（因子挖掘→检验→回测→信号生成） -结合《ssrn.3255748.pdf》中时间序列（TS）因子模型的核心逻辑，以及高维高频金融数据建模的前沿方法（如投影主成分分析P-PCA），以下为针对BTC 4h数据的完整实操流程，包含每一步的理论原理、操作细节及论文引用依据。 - - -## 一、数据准备与预处理（基础步骤，确保数据质量） -### 1. 数据来源与变量选择 -- **核心数据**：BTC的4h级原始数据，至少包含“开盘价、收盘价、最高价、最低价、成交量、成交额”，时间跨度建议≥5年（如2018年1月-2023年12月，共约11325个4h数据点），来源可选择CoinGecko、Binance API等合规平台。 -- **扩展变量**：基于原始数据计算技术指标变量（作为因子候选），参考《ssrn.3255748.pdf》中“资产特征驱动因子”的逻辑（），具体包括： - - 收益类：4h收益率（\(R_t = \ln(Close_t/Close_{t-1})\)）、滚动12期（48h）收益率标准差（波动率）、滚动6期（24h）收益率偏度（尾部风险）； - - 趋势类：EMA（指数移动平均，如4期/8期/16期）、MACD（异同移动平均线）、RSI（相对强弱指数，14期）； - - 量能类：成交量滚动6期均值、成交额/成交量比值（量价配合度）； - - 波动类：ATR（平均真实波幅，14期）、高低价差率（\((High_t-Low_t)/Close_{t-1}\)）。 - -### 2. 数据预处理（消除噪声与异常值） -- **异常值处理**：采用“3σ法则”识别异常收益率（如单日涨跌幅＞20%的4h数据），用前后相邻数据的线性插值替换，避免极端值对因子估计的干扰——这与中山大学研究中“抑制高频特异性波动”的思路一致（摘要1、5），该研究指出高频数据中的异常波动会扭曲因子估计，需通过预处理降低噪声。 -- **缺失值填补**：若存在数据缺失（如交易所维护导致的断更），采用“前向填充+滚动均值平滑”（如用前3期均值填补），确保时间序列的连续性。 -- **标准化**：对所有候选因子变量进行“Z-score标准化”（\(X_{std}=(X-\mu)/\sigma\)，其中\(\mu\)、\(\sigma\)为滚动30期（120h）的均值和标准差），避免量纲差异影响因子权重——参考《ssrn.3255748.pdf》中CS因子“标准化匹配TS因子标准差”的操作逻辑（）。 - - -## 二、因子挖掘：基于时间序列逻辑构建候选因子 -### 1. 因子构建原则（贴合TS因子“预设规则、可解释”的核心特性） -根据《ssrn.3255748.pdf》中TS因子的构建逻辑（），BTC 4h因子需满足“基于固定规则、反映特定风险/收益驱动逻辑”，避免CS因子“月度优化、非可投资”的缺陷（）。具体分为“基础因子”和“合成因子”两类： - -#### （1）基础因子：单一逻辑驱动的因子 -| 因子名称 | 因子逻辑 | 计算方式（4h频率） | 理论依据（论文关联） | -|----------|----------|----------------------|------------------------| -| 趋势因子（TREND） | 价格趋势方向，趋势向上则预期收益高 | \(TREND_t = I(Close_t > EMA_{16,t}) \times 1 + I(Close_t < EMA_{4,t}) \times (-1)\)，其中\(I(\cdot)\)为指示函数 | 类似《ssrn.3255748.pdf》中“特征驱动收益”逻辑（），用EMA交叉反映趋势特征 | -| 波动率因子（VOL） | 波动率越高，风险溢价越高 | \(VOL_t = \text{滚动12期收益率标准差}\) | 对应中山大学研究中“高频波动捕捉风险”的思路（摘要1、5），波动率是高频金融数据的核心风险特征 | -| 量价因子（VOLP） | 量价配合度，成交量放大且价格上涨则动量强 | \(VOLP_t = I(Volume_t > \text{滚动6期Volume均值}) \times R_t\) | 参考《ssrn.3255748.pdf》中“动量因子（UMD）”的“收益+量能”逻辑（），UMD通过前期收益反映动量，此处叠加成交量增强信号 | -| 反转因子（REV） | 短期反转效应，过度上涨/下跌后预期回调 | \(REV_t = -R_{t-1}\)（前1期4h收益率的相反数） | 符合时间序列因子“单一特征驱动”的特性（），捕捉BTC短期（4h级）的反转风险 | - -#### （2）合成因子：多变量降维得到的综合因子 -采用中山大学研究提出的**投影主成分分析（P-PCA）** 构建合成因子（摘要1、5），该方法相比传统PCA能更有效利用特征变量信息，抑制高频噪声，具体步骤： -1. **选择输入变量**：将上述基础因子（TREND、VOL、VOLP、REV）及3个核心技术指标（MACD差值、RSI、ATR）作为P-PCA的输入矩阵\(X_{T \times K}\)（T为时间维度，K=7为变量维度）。 -2. **投影步骤**：根据P-PCA理论（摘要1、5），先将输入变量投影到“可观测特征空间”（此处选择“滚动12期收益率”作为工具变量，反映BTC收益的长期动态），得到投影矩阵\(P\)； -3. **提取主成分**：对投影后的矩阵\(P \times X\)进行PCA，取前2个主成分（累计方差解释率需≥80%）作为合成因子： - - 合成因子1（PC1）：命名为“趋势-量能因子”，权重集中在TREND、VOLP，反映趋势与量能的协同效应； - - 合成因子2（PC2）：命名为“风险因子”，权重集中在VOL、ATR，反映4h级的风险暴露程度。 -4. **因子方向校准**：通过“因子与未来1期收益率的相关性”调整方向（如PC1与\(R_{t+1}\)正相关则保留原方向，负相关则取反），确保因子值越高，预期收益越高——贴合《ssrn.3255748.pdf》中“因子收益差为正”的TS因子设计逻辑（如HML=高BM收益-低BM收益）（）。 - - -## 三、因子检验：验证因子的有效性与稳健性 -### 1. 因子收益检验（核心：因子能否区分未来收益） -参考《ssrn.3255748.pdf》中“因子平均收益t统计量”的检验逻辑（），对每个候选因子进行“分组回测”，步骤如下： -- **分组规则**：每月（按4h频率约180个数据点）将BTC 4h数据按因子值分为3组（低因子组L、中因子组M、高因子组H）； -- **计算组收益**：每组的4h收益为该组内因子值对应的BTC收益率（因仅单标的，此处为“因子值分位数对应的收益”，如高因子组H为因子值前30%的4h数据的平均收益）； -- **检验指标**： - - 因子收益差：\(H-L\)收益（高因子组收益 - 低因子组收益），若显著为正，说明因子能区分收益； - - t统计量：用Newey-West调整的t统计量（滞后6期，对应24h）检验\(H-L\)收益的显著性（避免自相关导致的虚假显著），参考《ssrn.3255748.pdf》中“因子平均收益t统计量”的计算方式（）。 - -**示例结果要求**：如趋势因子（TREND）的\(H-L\)收益为0.35%/4h（年化约84%），t统计量=2.89（＞2，显著），说明该因子有效。 - -### 2. 因子跨度回归（检验因子的边际解释力） -根据《ssrn.3255748.pdf》中“因子跨度回归”的核心逻辑（），检验单个因子能否被其他因子替代，步骤如下： -- **回归模型**：以BTC未来1期4h收益率\(R_{t+1}\)为因变量，以候选因子及控制变量为自变量，构建时间序列回归： - \(R_{t+1} = \alpha + \beta_1 F_1_t + \beta_2 F_2_t + ... + \beta_k F_k_t + e_{t+1}\) - 其中\(F_1,F_2,...F_k\)为候选因子，\(\alpha\)为定价误差，\(\beta_i\)为因子载荷。 -- **检验标准**：若某因子的\(\beta_i\)显著不为0（t统计量＞2），且加入该因子后模型\(R^2\)提升≥5%，说明该因子具有“不可替代的边际解释力”，未被其他因子吸收——类似《ssrn.3255748.pdf》中TS因子“市场、规模因子边际信息显著”的结论（）。 - -**示例**：若加入合成因子PC1后，模型\(R^2\)从0.12提升至0.18，PC1的\(\beta=0.25\)（t=3.12），说明PC1具有独立解释力。 - -### 3. 稳健性检验（排除偶然因素） -- **样本外检验**：将数据分为“训练集（2018-2021年）”和“样本外集（2022-2023年）”，若因子在样本外的\(H-L\)收益t统计量仍＞1.8（接近显著），说明因子稳健——参考《ssrn.3255748.pdf》中“跨样本验证因子表现”的逻辑（）。 -- **频率敏感性检验**：将4h频率调整为2h或8h，若因子收益差的显著性变化≤20%，说明因子不受频率小幅变动影响——符合中山大学研究中“高频因子需跨频率稳健”的要求（摘要1、5）。 - - -## 四、因子组合：构建多因子模型（贴合TS模型“常数载荷、可投资”特性） -### 1. 因子权重确定（避免CS模型“月度优化”的复杂性） -根据《ssrn.3255748.pdf》中TS因子模型“预设因子权重、常数载荷”的逻辑（），采用“风险平价”或“回归系数加权”，避免动态优化导致的过拟合： -- **风险平价加权**：使每个因子的“风险贡献相等”（因子风险贡献=因子权重×因子波动率×因子与收益的相关性），公式为： - \(w_i = \frac{1/\sigma_i}{\sum_{j=1}^n 1/\sigma_j}\)，其中\(\sigma_i\)为因子\(F_i\)的滚动30期波动率； - 该方法确保单一因子不会过度主导组合，贴合《ssrn.3255748.pdf》中“多因子分散风险”的思路（如FF五因子模型的等权重逻辑）（）。 -- **回归系数加权**：用训练集的时间序列回归系数作为权重（如因子\(F_i\)的权重\(w_i = \beta_i / \sum_{j=1}^n |\beta_j|\)，\(\beta_i\)为\(R_{t+1}\)对\(F_i\)的回归系数），确保权重与因子解释力正相关。 - -### 2. 多因子综合得分（最终信号输入） -将筛选后的有效因子（如TREND、VOLP、PC1、PC2）按权重合并，得到4h级的“多因子综合得分”： -\(Score_t = \sum_{i=1}^n w_i \times F_{i,t}\) -其中\(F_{i,t}\)为标准化后的因子值，\(Score_t\)越高，代表未来1期（4h）BTC上涨概率越大——该得分对应《ssrn.3255748.pdf》中“因子组合预测收益”的逻辑（），通过多因子协同提升预测准确性。 - - -## 五、回测：验证因子模型的实战有效性 -### 1. 回测框架设计（贴合TS因子“可投资”的核心优势） -参考《ssrn.3255748.pdf》中“资产定价检验”的回测逻辑（），采用“等仓单边交易”（仅做多/做空BTC，无杠杆），避免CS因子“高杠杆”的非可投资性（），具体参数： -- **调仓频率**：4h调仓（与因子频率一致），每个4h周期根据\(Score_t\)生成交易信号； -- **手续费**：按0.1%/次（现货交易手续费，参考Binance等平台），滑点按0.05%/次（4h级BTC流动性充足，滑点较低）； -- **回测区间**：2019年1月-2023年12月（共约4680个4h数据点，包含牛熊周期，检验模型适应性）。 - -### 2. 交易信号规则（基于多因子得分的阈值策略） -根据《ssrn.3255748.pdf》中“因子值与收益正相关”的结论（），设定阈值生成买卖信号： -- **买入信号**：当\(Score_t > 0.8\sigma_{Score}\)（\(\sigma_{Score}\)为Score的滚动30期标准差），且前1期无持仓时，买入BTC（满仓）； -- **卖出信号**：当\(Score_t < -0.8\sigma_{Score}\)，且前1期有持仓时，卖出BTC（空仓）； -- **观望信号**：当\(Score_t\)在\([-0.8\sigma_{Score}, 0.8\sigma_{Score}]\)之间，维持原有持仓（避免频繁交易）。 - -### 3. 回测指标与评估（参考论文中的资产定价检验指标） -采用《ssrn.3255748.pdf》中“平均收益、夏普比率、最大回撤”等核心指标（），同时加入高频数据特有的“胜率、盈亏比”，具体如下： -| 回测指标 | 计算方式 | 合格标准（BTC 4h策略） | -|----------|----------|--------------------------| -| 年化收益率 | \((1+\text{累计收益})^{252×6/24} - 1\)（假设年交易252天，每天6个4h周期） | ＞30%（跑赢BTC现货年化收益） | -| 夏普比率 | 年化收益率 / 年化波动率 | ＞1.5（风险调整收益优秀） | -| 最大回撤 | 回测期间最大亏损幅度 | ＜50%（控制极端风险） | -| 胜率 | 盈利交易次数 / 总交易次数 | ＞55%（信号准确性高） | - -**示例结果**：若回测得到年化收益45%、夏普比率1.8、最大回撤42%、胜率58%，说明模型有效——类似《ssrn.3255748.pdf》中“TS因子模型解释力达标”的实证结论（）。 - - -## 六、信号优化与上线：动态适应市场变化 -### 1. 因子载荷动态调整（参考“时变载荷”的改进思路） -虽然《ssrn.3255748.pdf》中TS模型默认“常数载荷”，但中山大学研究指出“因子载荷时变能提升预测准确性”（摘要1、5），因此可引入“滚动窗口调整权重”： -- 每30天（180个4h周期）重新估计因子权重（如风险平价权重的波动率用最新30期数据），避免因子失效（如BTC在牛熊周期中，波动率因子的重要性会变化）； -- 若某因子连续60个4h周期（10天）的\(H-L\)收益t统计量＜1.0，暂时剔除该因子，待其恢复显著性后重新加入——贴合《ssrn.3255748.pdf》中“因子边际信息动态检验”的逻辑（）。 - -### 2. 实盘上线与监控 -- **信号输出**：每4h生成“Score_t”及对应买卖信号，通过API对接交易所（如Binance Spot API）实现自动交易； -- **风险监控**：实时监控“因子有效性指标”（如当前因子的\(H-L\)收益t统计量、模型\(R^2\)），若指标连续3天不达标（如t统计量＜1.2），暂停自动交易，人工排查原因（如市场结构变化导致因子失效）； -- **日志记录**：保存每4h的因子值、信号、交易结果，每月进行回测复盘，对比实盘与回测的差异，优化因子参数（如调整EMA周期、阈值系数）。 - - -## 七、关键论文引用与理论支撑总结 -1. 《ssrn.3255748.pdf》（Fama & French 2018）：核心支撑TS因子“预设规则、可投资、常数斜率回归检验”的逻辑，指导因子构建、检验、回测的整体框架（、、）； -2. 中山大学《高维、高频金融数据的因子建模》（摘要1、5）：提供P-PCA合成因子、高频噪声处理、时变载荷调整的方法，解决BTC 4h高频数据的因子估计问题； -3. 国家金融与发展实验室《收益率曲线三因子模型》（摘要2）：借鉴“因子解释度、动态调整”的思路，用于合成因子的方差解释率检验和权重动态优化。 \ No newline at end of file diff --git a/backtest.py b/backtest.py deleted file mode 100644 index 2c2940c..0000000 --- a/backtest.py +++ /dev/null @@ -1,180 +0,0 @@ -""" -回测模块 -""" -import numpy as np -import pandas as pd -from typing import Dict, Optional, Tuple - - -class BacktestEngine: - """回测引擎""" - - def __init__( - self, - commission: float = 0.001, # 手续费率 - slippage: float = 0.0005, # 滑点 - initial_capital: float = 10000.0 - ): - self.commission = commission - self.slippage = slippage - self.initial_capital = initial_capital - - def run( - self, - signals: pd.Series, - price: pd.Series, - score: Optional[pd.Series] = None - ) -> Dict: - """ - 运行回测 - - Parameters: - ----------- - signals : Series - 交易信号：1=买入，-1=卖出，0=持有 - price : Series - 价格序列 - score : Series, optional - 因子得分（用于记录） - - Returns: - -------- - dict: 回测结果 - """ - # 对齐数据 - aligned = pd.concat([signals, price], axis=1).dropna() - aligned.columns = ['signal', 'price'] - - if score is not None: - aligned = pd.concat([aligned, score], axis=1) - aligned.columns = ['signal', 'price', 'score'] - - # 向量化优化：先计算价格变化率 - price_pct = aligned['price'].pct_change().fillna(0) - - # 初始化 - capital = self.initial_capital - position = 0 # 持仓：0=空仓，1=满仓 - equity = np.zeros(len(aligned)) - equity[0] = capital - trades = [] - buy_price = None # 记录买入价格 - - # 检测信号变化点（向量化） - signal_changes = aligned['signal'].diff().fillna(0) != 0 - - # 遍历处理（优化：只在信号变化时处理） - for i in range(1, len(aligned)): - current_signal = aligned['signal'].iloc[i] - current_price = aligned['price'].iloc[i] - prev_signal = aligned['signal'].iloc[i-1] - - # 计算收益率（基于价格变化） - if position == 1: - period_return = price_pct.iloc[i] - else: - period_return = 0 - - # 交易逻辑（只在信号变化时处理） - if signal_changes.iloc[i]: - if current_signal == 1 and position == 0: # 买入 - # 扣除手续费和滑点 - cost = self.commission + self.slippage - capital *= (1 - cost) - position = 1 - buy_price = current_price - trades.append({ - 'date': aligned.index[i], - 'action': 'buy', - 'price': current_price, - 'capital': capital - }) - elif current_signal == -1 and position == 1: # 卖出 - # 扣除手续费和滑点 - cost = self.commission + self.slippage - capital *= (1 - cost) - position = 0 - buy_price = None - trades.append({ - 'date': aligned.index[i], - 'action': 'sell', - 'price': current_price, - 'capital': capital - }) - - # 更新权益 - if position == 1 and buy_price is not None: - equity[i] = capital * (current_price / buy_price) - else: - equity[i] = capital - - equity_series = pd.Series(equity, index=aligned.index) - returns_series = price_pct * (aligned['signal'].shift(1) == 1).astype(int) - - # 计算回测指标 - metrics = self._calculate_metrics(equity_series, returns_series, len(trades)) - - return { - 'equity': equity_series, - 'returns': returns_series, - 'trades': trades, - 'metrics': metrics, - 'final_capital': equity_series.iloc[-1] if len(equity_series) > 0 else self.initial_capital - } - - def _calculate_metrics( - self, - equity: pd.Series, - returns: pd.Series, - num_trades: int = 0 - ) -> Dict: - """计算回测指标""" - if len(equity) == 0 or len(returns) == 0: - return {} - - # 总收益率 - total_return = (equity.iloc[-1] / equity.iloc[0] - 1) if len(equity) > 0 else 0 - - # 年化收益率（假设每天6个4h周期，一年252个交易日） - periods_per_year = 252 * 6 - n_periods = len(returns) - if n_periods > 0: - annual_return = (1 + total_return) ** (periods_per_year / n_periods) - 1 - else: - annual_return = 0 - - # 年化波动率 - annual_vol = returns.std() * np.sqrt(periods_per_year) - - # 夏普比率 - sharpe = annual_return / (annual_vol + 1e-8) - - # 最大回撤 - cummax = equity.cummax() - drawdown = (equity - cummax) / cummax - max_drawdown = drawdown.min() - - # 胜率（基于实际交易） - # 只计算有持仓期间的收益率 - position_returns = returns[returns != 0] - winning_trades = (position_returns > 0).sum() - win_rate = winning_trades / len(position_returns) if len(position_returns) > 0 else 0 - - # 盈亏比 - positive_returns = position_returns[position_returns > 0] - negative_returns = position_returns[position_returns < 0] - avg_win = positive_returns.mean() if len(positive_returns) > 0 else 0 - avg_loss = abs(negative_returns.mean()) if len(negative_returns) > 0 else 0 - profit_loss_ratio = avg_win / (avg_loss + 1e-8) - - return { - 'total_return': total_return, - 'annual_return': annual_return, - 'annual_volatility': annual_vol, - 'sharpe_ratio': sharpe, - 'max_drawdown': max_drawdown, - 'win_rate': win_rate, - 'profit_loss_ratio': profit_loss_ratio, - 'total_trades': num_trades # 实际交易次数 - } - diff --git a/deap_factor_mining.py b/deap_factor_mining.py deleted file mode 100644 index 12db657..0000000 --- a/deap_factor_mining.py +++ /dev/null @@ -1,436 +0,0 @@ -import argparse -import math -import operator -import random -from dataclasses import dataclass -from typing import Callable, Dict, List, Optional, Tuple - -import numpy as np -import pandas as pd -from deap import algorithms, base, creator, gp, tools - - -# ------------------------------ -# Data & Config -# ------------------------------ - - -@dataclass -class EvolutionConfig: - population_size: int = 200 - generations: int = 30 - tournament_size: int = 5 - crossover_prob: float = 0.9 - mutation_prob: float = 0.05 - elitism: int = 5 - max_depth_init: int = 4 - max_depth: int = 8 - ic_window: int = 1000 - ret_horizon: int = 24 - ic_method: str = "spearman" # or "pearson" - complexity_penalty: float = 0.001 - seed: Optional[int] = 42 - - -# ------------------------------ -# Safe operators for GP -# ------------------------------ - - -def _safe_div(left: np.ndarray, right: np.ndarray) -> np.ndarray: - denom = np.where(np.abs(right) < 1e-12, np.nan, right) - return left / denom - - -def _safe_log(x: np.ndarray) -> np.ndarray: - return np.log(np.clip(np.abs(x), 1e-12, None)) - - -def _safe_sqrt(x: np.ndarray) -> np.ndarray: - return np.sqrt(np.clip(x, 0.0, None)) - - -def _safe_pow(x: np.ndarray, y: np.ndarray) -> np.ndarray: - # Limit exponent to avoid overflow - y_clip = np.clip(y, -3.0, 3.0) - with np.errstate(over="ignore", invalid="ignore"): - out = np.power(np.clip(x, -1e6, 1e6), y_clip) - out[~np.isfinite(out)] = np.nan - return out - - -def _rolling_mean(x: np.ndarray, window: int) -> np.ndarray: - s = pd.Series(x) - return s.rolling(window, min_periods=max(2, window // 2)).mean().to_numpy() - - -def _rolling_std(x: np.ndarray, window: int) -> np.ndarray: - s = pd.Series(x) - return s.rolling(window, min_periods=max(2, window // 2)).std().to_numpy() - - -def _ts_delta(x: np.ndarray, period: int) -> np.ndarray: - s = pd.Series(x) - return s.diff(period).to_numpy() - - -def _ts_rank(x: np.ndarray, window: int) -> np.ndarray: - s = pd.Series(x) - return s.rolling(window, min_periods=max(2, window // 2)).apply( - lambda a: pd.Series(a).rank(pct=True).iloc[-1], raw=False - ).to_numpy() - - -def _delay(x: np.ndarray, period: int) -> np.ndarray: - s = pd.Series(x) - return s.shift(period).to_numpy() - - -# ------------------------------ -# Primitive set -# ------------------------------ - - -def build_pset(feature_names: List[str]) -> gp.PrimitiveSetTyped: - # Each feature is a numpy array of floats; GP outputs numpy array - pset = gp.PrimitiveSetTyped("MAIN", [np.ndarray for _ in feature_names], np.ndarray) - - # Name the arguments for readability - for i, name in enumerate(feature_names): - pset.renameArguments(**{f"ARG{i}": name}) - - # Binary arithmetic - pset.addPrimitive(lambda x, y: x + y, [np.ndarray, np.ndarray], np.ndarray, name="add") - pset.addPrimitive(lambda x, y: x - y, [np.ndarray, np.ndarray], np.ndarray, name="sub") - pset.addPrimitive(lambda x, y: x * y, [np.ndarray, np.ndarray], np.ndarray, name="mul") - pset.addPrimitive(_safe_div, [np.ndarray, np.ndarray], np.ndarray, name="div") - - # Unary transforms - pset.addPrimitive(np.negative, [np.ndarray], np.ndarray, name="neg") - pset.addPrimitive(np.abs, [np.ndarray], np.ndarray, name="abs") - pset.addPrimitive(_safe_log, [np.ndarray], np.ndarray, name="log") - pset.addPrimitive(_safe_sqrt, [np.ndarray], np.ndarray, name="sqrt") - - # Power - pset.addPrimitive(_safe_pow, [np.ndarray, np.ndarray], np.ndarray, name="pow") - - # Rolling ops with fixed small set of windows via partials - for w in (3, 6, 12, 24, 48, 96): - pset.addPrimitive(lambda x, w=w: _rolling_mean(x, w), [np.ndarray], np.ndarray, name=f"sma{w}") - pset.addPrimitive(lambda x, w=w: _rolling_std(x, w), [np.ndarray], np.ndarray, name=f"std{w}") - pset.addPrimitive(lambda x, w=w: _ts_rank(x, w), [np.ndarray], np.ndarray, name=f"rank{w}") - pset.addPrimitive(lambda x, w=w: _ts_delta(x, w), [np.ndarray], np.ndarray, name=f"delta{w}") - pset.addPrimitive(lambda x, w=w: _delay(x, w), [np.ndarray], np.ndarray, name=f"delay{w}") - - # Ephemeral constants: scalar to array via broadcasting - # 随机加一个常数 不一定合理 - def _const() -> np.ndarray: - return np.array(random.uniform(-2.0, 2.0)) - - pset.addEphemeralConstant("const", _const, np.ndarray) - - return pset - - -# ------------------------------ -# Fitness and evaluation -# ------------------------------ - - -def compute_returns(price: pd.Series, horizon: int) -> pd.Series: - return price.pct_change(horizon).shift(-horizon) - - -def rank_ic(a: pd.Series, b: pd.Series, method: str = "spearman") -> float: - mask = a.notna() & b.notna() - if mask.sum() < 10: - return np.nan - x = a[mask] - y = b[mask] - if method == "spearman": - return x.rank(pct=True).corr(y.rank(pct=True)) - return x.corr(y) - - -def series_zscore(x: pd.Series) -> pd.Series: - return (x - x.mean()) / (x.std(ddof=0) + 1e-12) - - -def evaluate_individual( - individual, - toolbox: base.Toolbox, - features: List[pd.Series], - target: pd.Series, - config: EvolutionConfig, -) -> Tuple[float]: - func = toolbox.compile(expr=individual) - - # Build feature matrix aligned index - idx = target.index - inputs = [f.reindex(idx).to_numpy() for f in features] - - try: - raw = func(*inputs) - except Exception: - return (-1e6,) - - # Ensure array length - if not isinstance(raw, np.ndarray): - return (-1e6,) - if raw.shape[0] != len(idx): - return (-1e6,) - - # Convert to series and standardize per-window - factor = pd.Series(raw, index=idx) - factor = factor.replace([np.inf, -np.inf], np.nan) - factor = factor.ffill().bfill() - - # Rolling IC over window segments - window = config.ic_window - if len(factor) < window + 10: - return (-1e6,) - - ic_values: List[float] = [] - step = max(window // 5, 50) - for start in range(0, len(factor) - window, step): - end = start + window - sub_factor = factor.iloc[start:end] - sub_target = target.iloc[start:end] - ic = rank_ic(series_zscore(sub_factor), sub_target, method=config.ic_method) - if np.isfinite(ic): - ic_values.append(ic) - - if not ic_values: - return (-1e6,) - - mean_ic = float(np.nanmean(ic_values)) - - # Complexity penalty (size of tree) - complexity = len(individual) - fitness = mean_ic - config.complexity_penalty * complexity - if not np.isfinite(fitness): - fitness = -1e6 - return (fitness,) - - -# ------------------------------ -# Evolution runner -# ------------------------------ - - -def run_evolution( - df: pd.DataFrame, - price_col: str, - feature_cols: List[str], - config: EvolutionConfig, -) -> Tuple[tools.HallOfFame, base.Toolbox, gp.PrimitiveSetTyped, List[pd.Series]]: - if config.seed is not None: - random.seed(config.seed) - np.random.seed(config.seed) - - price = df[price_col].astype(float) - forward_ret = compute_returns(price, config.ret_horizon) - target = forward_ret - - features = [df[c].astype(float) for c in feature_cols] - - pset = build_pset(feature_cols) - - # Fitness: maximize IC (single objective) - if not hasattr(creator, "FitnessMax"): - creator.create("FitnessMax", base.Fitness, weights=(1.0,)) - if not hasattr(creator, "Individual"): - creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax) - - toolbox = base.Toolbox() - toolbox.register("expr", - gp.genHalfAndHalf, - pset=pset, - min_=1, - max_=config.max_depth_init) - toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) - toolbox.register("population", tools.initRepeat, list, toolbox.individual) - toolbox.register("compile", gp.compile, pset=pset) - - toolbox.register( - "evaluate", - evaluate_individual, - toolbox=toolbox, - features=features, - target=target, - config=config, - ) - - # Genetic operators - toolbox.register("select", tools.selTournament, tournsize=config.tournament_size) - toolbox.register("mate", gp.cxOnePoint) - toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) - toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) - - # bloat control - toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=config.max_depth)) - toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=config.max_depth)) - - pop = toolbox.population(n=config.population_size) - hof = tools.HallOfFame(maxsize=max(5, config.elitism)) - - stats_fit = tools.Statistics(lambda ind: ind.fitness.values[0]) - stats_size = tools.Statistics(len) - mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size) - mstats.register("avg", np.nanmean) - mstats.register("std", np.nanstd) - mstats.register("min", np.nanmin) - mstats.register("max", np.nanmax) - - pop, logbook = algorithms.eaSimple( - pop, - toolbox, - cxpb=config.crossover_prob, - mutpb=config.mutation_prob, - ngen=config.generations, - stats=mstats, - halloffame=hof, - verbose=True, - ) - - return hof, toolbox, pset, features - - -# ------------------------------ -# Factor compilation & backtest -# ------------------------------ - - -def compile_factor( - individual, - toolbox: base.Toolbox, - index: pd.Index, - features: List[pd.Series], -) -> pd.Series: - func = toolbox.compile(expr=individual) - inputs = [f.reindex(index).to_numpy() for f in features] - raw = func(*inputs) - s = pd.Series(raw, index=index) - s = s.replace([np.inf, -np.inf], np.nan).ffill().bfill() - return s - - -def simple_long_short_backtest( - factor: pd.Series, - price: pd.Series, - ret_horizon: int, - top_quantile: float = 0.2, - bottom_quantile: float = 0.2, -) -> pd.Series: - f = factor.align(price, join="right")[0] - future_ret = compute_returns(price, ret_horizon) - - ranks = f.rank(pct=True) - long_mask = ranks >= (1 - top_quantile) - short_mask = ranks <= bottom_quantile - ls_signal = long_mask.astype(float) - short_mask.astype(float) - ls_signal = ls_signal.shift(1) # trade on next bar - - pnl = ls_signal * future_ret - pnl = pnl.replace([np.inf, -np.inf], np.nan).fillna(0.0) - equity = (1.0 + pnl).cumprod() - return equity - - -# ------------------------------ -# CLI -# ------------------------------ - - -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser(description="DEAP-based factor mining (genetic programming)") - p.add_argument("--data", type=str, default="ETH_USDT-1h.feather", help="Input feather/csv file") - p.add_argument("--price_col", type=str, default="close", help="Price column name") - p.add_argument( - "--features", - type=str, - default="open,high,low,close,volume", - help="Comma-separated feature column names", - ) - p.add_argument("--ret_horizon", type=int, default=24) - p.add_argument("--population", type=int, default=200) - p.add_argument("--generations", type=int, default=30) - p.add_argument("--ic_window", type=int, default=1000) - p.add_argument("--seed", type=int, default=42) - p.add_argument("--ic_method", type=str, default="spearman", choices=["spearman", "pearson"]) - p.add_argument("--complexity_penalty", type=float, default=0.001) - p.add_argument("--save_best", type=str, default="best_factors.txt") - return p.parse_args() - - -def load_dataframe(path: str) -> pd.DataFrame: - if path.endswith(".feather"): - df = pd.read_feather(path) - elif path.endswith(".csv"): - df = pd.read_csv(path) - else: - raise ValueError("Unsupported file format. Use .feather or .csv") - - # Try to parse datetime index if present - for col in ["datetime", "time", "timestamp", "date"]: - if col in df.columns: - df[col] = pd.to_datetime(df[col]) - df = df.set_index(col).sort_index() - break - return df - - -def main(): - args = parse_args() - df = load_dataframe(args.data) - df = df.head(1000) - - feature_cols = [c.strip() for c in args.features.split(",") if c.strip()] - for c in [args.price_col] + feature_cols: - if c not in df.columns: - raise KeyError(f"Column '{c}' not found in data") - - config = EvolutionConfig( - population_size=args.population, - generations=args.generations, - ic_window=args.ic_window, - ret_horizon=args.ret_horizon, - ic_method=args.ic_method, - complexity_penalty=args.complexity_penalty, - seed=args.seed, - ) - - hof, toolbox, pset, features = run_evolution(df, args.price_col, feature_cols, config) - - price = df[args.price_col].astype(float) - best_expressions: List[str] = [] - for i, ind in enumerate(hof): - expr_str = str(ind) - best_expressions.append(expr_str) - - # Save best expressions - with open(args.save_best, "w", encoding="utf-8") as f: - for expr in best_expressions: - f.write(expr + "\n") - - # Compile the top-1 and run a simple long/short backtest for sanity - if len(hof) > 0: - best = hof[0] - factor_series = compile_factor(best, toolbox, df.index, features) - equity = simple_long_short_backtest(factor_series, price, config.ret_horizon) - print("Best expression:", str(best)) - print("Final equity (normalized):", float(equity.iloc[-1])) - # Also export factor and equity - out = pd.DataFrame({ - "factor": factor_series, - "equity": equity, - }) - out.to_csv("deap_factor_output.csv") - print("Saved best expressions to", args.save_best) - print("Saved factor/equity to deap_factor_output.csv") - - -if __name__ == "__main__": - main() - - diff --git a/example.py b/example.py deleted file mode 100644 index a57dc9b..0000000 --- a/example.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -使用示例：时间序列因子挖掘流程 -""" -from pipeline import FactorPipeline -from factors import FactorMiner, create_default_factors - -# 方式1：使用默认流程（最简单） -def example_simple(): - """简单示例""" - pipeline = FactorPipeline( - ret_horizon=1, # 未来1期收益率 - ic_window=30, # IC计算窗口 - commission=0.001, # 手续费0.1% - slippage=0.0005 # 滑点0.05% - ) - - # 运行完整流程 - results = pipeline.run_full_pipeline( - file_path="ETH_USDT-1h.feather", - min_ic=0.01, # 最小IC阈值 - min_tstat=1.5, # 最小t统计量 - weight_method='risk_parity', # 权重方法：risk_parity, regression, equal - buy_threshold=0.8, # 买入阈值（标准差倍数） - sell_threshold=-0.8 # 卖出阈值（标准差倍数） - ) - - return results - - -# 方式2：分步骤执行（更灵活） -def example_step_by_step(): - """分步骤示例""" - pipeline = FactorPipeline(ret_horizon=1, ic_window=30) - - # 步骤1：加载和预处理数据 - pipeline.load_and_preprocess("ETH_USDT-1h.feather") - - # 步骤2：因子挖掘（可以使用自定义因子） - custom_miner = create_default_factors() - # 可以在这里添加自定义因子 - # custom_miner.register_rule_factor('CUSTOM', your_custom_function) - pipeline.mine_factors(custom_miner) - - # 步骤3：因子检验 - pipeline.validate_factors(min_ic=0.01, min_tstat=1.5) - - # 步骤4：因子组合 - pipeline.combine_factors(weight_method='risk_parity') - - # 步骤5：生成信号 - signals = pipeline.generate_signals(buy_threshold=0.8, sell_threshold=-0.8) - - # 步骤6：回测 - backtest_results = pipeline.backtest(signals) - - return { - 'factors': pipeline.factors, - 'score': pipeline.score, - 'signals': signals, - 'backtest': backtest_results - } - - -# 方式3：自定义因子 -def example_custom_factors(): - """自定义因子示例""" - from factors import RuleFactor - import pandas as pd - import numpy as np - - # 定义自定义因子函数 - def my_custom_factor(data: pd.DataFrame) -> pd.Series: - """自定义因子：价格与均线的距离""" - return (data['close'] - data['ema8']) / data['ema8'] - - # 创建因子挖掘器 - miner = create_default_factors() - - # 注册自定义因子 - miner.register_rule_factor('CUSTOM_DISTANCE', my_custom_factor) - - # 使用自定义因子挖掘器 - pipeline = FactorPipeline() - pipeline.load_and_preprocess("ETH_USDT-1h.feather") - pipeline.mine_factors(custom_miner=miner) - pipeline.validate_factors() - pipeline.combine_factors() - pipeline.backtest() - - return pipeline - - -if __name__ == "__main__": - # 运行简单示例 - print("运行简单示例...") - results = example_simple() - - # 保存结果 - if results['factors'] is not None: - results['factors'].to_csv("factors_output.csv") - print("\n因子数据已保存到 factors_output.csv") - - if results['score'] is not None: - results['score'].to_csv("score_output.csv") - print("综合得分已保存到 score_output.csv") - - if results['backtest'] is not None and 'equity' in results['backtest']: - results['backtest']['equity'].to_csv("equity_curve.csv") - print("权益曲线已保存到 equity_curve.csv") - diff --git a/pipeline.py b/pipeline.py deleted file mode 100644 index d7fc180..0000000 --- a/pipeline.py +++ /dev/null @@ -1,287 +0,0 @@ -""" -主流程：时间序列因子挖掘、检验、回测、信号生成 -""" -import pandas as pd -from typing import Dict, List, Optional -import warnings -warnings.filterwarnings('ignore') - -from data import load_data, compute_technical_indicators, preprocess_data, compute_forward_returns -from factors import FactorMiner, create_default_factors -from validation import validate_factor, factor_span_regression -from combination import MultiFactorModel -from backtest import BacktestEngine -from signal import generate_signals - - -class FactorPipeline: - """因子挖掘流程""" - - def __init__( - self, - ret_horizon: int = 1, - ic_window: int = 30, - commission: float = 0.001, - slippage: float = 0.0005 - ): - """ - Parameters: - ----------- - ret_horizon : int - 未来收益率周期 - ic_window : int - IC计算窗口 - commission : float - 手续费率 - slippage : float - 滑点 - """ - self.ret_horizon = ret_horizon - self.ic_window = ic_window - self.commission = commission - self.slippage = slippage - - self.data: Optional[pd.DataFrame] = None - self.factors: Optional[pd.DataFrame] = None - self.forward_return: Optional[pd.Series] = None - self.factor_miner: Optional[FactorMiner] = None - self.validation_results: Dict = {} - self.model: Optional[MultiFactorModel] = None - self.score: Optional[pd.Series] = None - self.backtest_results: Optional[Dict] = None - - def load_and_preprocess(self, file_path: str) -> 'FactorPipeline': - """步骤1：加载和预处理数据""" - print("=" * 50) - print("步骤1：加载和预处理数据") - print("=" * 50) - - # 加载数据 - self.data = load_data(file_path) - print(f"加载数据: {len(self.data)} 条记录") - - # 计算技术指标 - self.data = compute_technical_indicators(self.data) - print("计算技术指标完成") - - # 预处理 - self.data = preprocess_data(self.data) - print("数据预处理完成") - - # 计算未来收益率 - self.forward_return = compute_forward_returns( - self.data['close'], - horizon=self.ret_horizon - ) - print(f"计算未来收益率完成（周期={self.ret_horizon}）") - - return self - - def mine_factors(self, custom_miner: Optional[FactorMiner] = None) -> 'FactorPipeline': - """步骤2：因子挖掘""" - print("\n" + "=" * 50) - print("步骤2：因子挖掘") - print("=" * 50) - - if self.data is None: - raise ValueError("请先加载数据") - - # 使用自定义或默认因子挖掘器 - if custom_miner is None: - self.factor_miner = create_default_factors() - else: - self.factor_miner = custom_miner - - # 计算所有因子 - self.factors = self.factor_miner.compute_all_factors(self.data) - print(f"计算因子完成: {list(self.factors.columns)}") - - return self - - def validate_factors(self, min_ic: float = 0.01, min_tstat: float = 1.5) -> 'FactorPipeline': - """步骤3：因子检验""" - print("\n" + "=" * 50) - print("步骤3：因子检验") - print("=" * 50) - - if self.factors is None or self.forward_return is None: - raise ValueError("请先完成因子挖掘") - - valid_factors = [] - self.validation_results = {} - - for factor_name in self.factors.columns: - factor = self.factors[factor_name] - - # 综合检验 - result = validate_factor(factor, self.forward_return, ic_window=self.ic_window) - self.validation_results[factor_name] = result - - # 筛选有效因子 - if (abs(result['mean_ic']) >= min_ic and - abs(result['mean_h_l_tstat']) >= min_tstat): - valid_factors.append(factor_name) - print(f"\n因子 {factor_name}:") - print(f" 平均IC: {result['mean_ic']:.4f}") - print(f" IC信息比率: {result['ic_ir']:.4f}") - print(f" H-L收益差: {result['mean_h_l_return']:.4f}") - print(f" H-L t统计量: {result['mean_h_l_tstat']:.4f}") - else: - print(f"\n因子 {factor_name} 未通过检验 (IC={result['mean_ic']:.4f}, t={result['mean_h_l_tstat']:.4f})") - - # 只保留有效因子 - if valid_factors: - self.factors = self.factors[valid_factors] - print(f"\n有效因子: {valid_factors}") - else: - print("\n警告：没有因子通过检验！") - - return self - - def combine_factors( - self, - weight_method: str = 'risk_parity', - window: Optional[int] = None - ) -> 'FactorPipeline': - """步骤4：因子组合""" - print("\n" + "=" * 50) - print("步骤4：因子组合") - print("=" * 50) - - if self.factors is None or len(self.factors.columns) == 0: - raise ValueError("没有有效因子可组合") - - # 创建多因子模型 - self.model = MultiFactorModel(weight_method=weight_method) - self.model.fit( - self.factors, - forward_return=self.forward_return, - window=window - ) - - # 计算综合得分 - self.score = self.model.predict(self.factors) - - # 显示权重 - weights = self.model.get_weights() - print("因子权重:") - for name, weight in weights.items(): - print(f" {name}: {weight:.4f}") - - print(f"\n综合得分统计:") - print(f" 均值: {self.score.mean():.4f}") - print(f" 标准差: {self.score.std():.4f}") - - return self - - def generate_signals( - self, - buy_threshold: float = 0.8, - sell_threshold: float = -0.8, - window: int = 30 - ) -> pd.Series: - """步骤5：生成交易信号""" - if self.score is None: - raise ValueError("请先完成因子组合") - - signals = generate_signals( - self.score, - buy_threshold=buy_threshold, - sell_threshold=sell_threshold, - window=window - ) - - return signals - - def backtest( - self, - signals: Optional[pd.Series] = None, - buy_threshold: float = 0.8, - sell_threshold: float = -0.8, - window: int = 30 - ) -> Dict: - """步骤6：回测""" - print("\n" + "=" * 50) - print("步骤6：回测") - print("=" * 50) - - if self.data is None: - raise ValueError("请先加载数据") - - if signals is None: - signals = self.generate_signals(buy_threshold, sell_threshold, window) - - # 创建回测引擎 - engine = BacktestEngine( - commission=self.commission, - slippage=self.slippage - ) - - # 运行回测 - self.backtest_results = engine.run( - signals, - self.data['close'], - score=self.score - ) - - # 显示结果 - metrics = self.backtest_results['metrics'] - print("\n回测结果:") - print(f" 总收益率: {metrics.get('total_return', 0)*100:.2f}%") - print(f" 年化收益率: {metrics.get('annual_return', 0)*100:.2f}%") - print(f" 年化波动率: {metrics.get('annual_volatility', 0)*100:.2f}%") - print(f" 夏普比率: {metrics.get('sharpe_ratio', 0):.2f}") - print(f" 最大回撤: {metrics.get('max_drawdown', 0)*100:.2f}%") - print(f" 胜率: {metrics.get('win_rate', 0)*100:.2f}%") - print(f" 盈亏比: {metrics.get('profit_loss_ratio', 0):.2f}") - print(f" 交易次数: {metrics.get('total_trades', 0)}") - - return self.backtest_results - - def run_full_pipeline( - self, - file_path: str, - custom_miner: Optional[FactorMiner] = None, - min_ic: float = 0.01, - min_tstat: float = 1.5, - weight_method: str = 'risk_parity', - buy_threshold: float = 0.8, - sell_threshold: float = -0.8 - ) -> Dict: - """运行完整流程""" - self.load_and_preprocess(file_path) \ - .mine_factors(custom_miner) \ - .validate_factors(min_ic, min_tstat) \ - .combine_factors(weight_method) \ - .backtest(buy_threshold=buy_threshold, sell_threshold=sell_threshold) - - return { - 'factors': self.factors, - 'score': self.score, - 'validation': self.validation_results, - 'backtest': self.backtest_results - } - - -if __name__ == "__main__": - # 示例使用 - pipeline = FactorPipeline(ret_horizon=1, ic_window=30) - - results = pipeline.run_full_pipeline( - file_path="ETH_USDT-1h.feather", - min_ic=0.01, - min_tstat=1.5, - weight_method='risk_parity', - buy_threshold=0.8, - sell_threshold=-0.8 - ) - - # 保存结果 - if results['factors'] is not None: - results['factors'].to_csv("factors.csv") - print("\n因子数据已保存到 factors.csv") - - if results['score'] is not None: - results['score'].to_csv("score.csv") - print("综合得分已保存到 score.csv") - diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..627e655 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +pandas>=1.3.0 +numpy>=1.20.0 +scipy>=1.7.0 +statsmodels>=0.13.0 +deap>=1.3.0 + diff --git a/test.py b/test.py new file mode 100644 index 0000000..e0af88b --- /dev/null +++ b/test.py @@ -0,0 +1,58 @@ +import warnings +import numpy as np +import pandas as pd +import json + +# 抑制numpy的警告（由于数据中包含NaN值，这是正常的） +warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy") +np.seterr(all="ignore") # 忽略numpy的浮点错误警告 + +from data import load_data +from factor_mining.gp_miner import GPMiner, GPConfig + +if __name__ == "__main__": + df = load_data("/Users/aszer/Documents/vscode/factorhack/ETH_USDT-1h.feather") + # 以4小时为周期重采样K线数据（假定有datetime索引，常见ohlcv列） + df = ( + df.resample("4h") + .agg( + { + "open": "first", + "high": "max", + "low": "min", + "close": "last", + "volume": "sum", + } + ) + .dropna() + ) + df = df[df.index < '2023-01-01'] + print("数据加载成功，前5行：") + print(df.head()) + print(f"\n数据形状: {df.shape}") + print(f"数据列: {df.columns.tolist()}") + gp_config = GPConfig( + ret_horizon=48, + ic_window=120, + ic_method="spearman", + seed=None, + population_size=200, + generations=30, + tournament_size=5, + crossover_prob=0.9, + mutation_prob=0.05, + elitism=5, + max_depth_init=1, + max_depth=30, + complexity_penalty=0.001, + ) + miner = GPMiner(config=gp_config) + res = miner.mine(df, ["open", "high", "low", "close", "volume"]) + with open("gp_miner_result.txt", "w") as out_file: + for formula, ic_tuple in res: + # ic_tuple 是元组，取第一个元素作为IC值 + ic = ic_tuple[0] if isinstance(ic_tuple, tuple) else ic_tuple + print(f"{formula.expression}, IC: {ic:.4f}") + # 将因子公式转换为字典并写入文件 + out_file.write(json.dumps(formula.to_dict(), ensure_ascii=False)) + out_file.write("\n") diff --git a/收益测算.ipynb b/收益测算.ipynb new file mode 100644 index 0000000..cc4f6ea --- /dev/null +++ b/收益测算.ipynb @@ -0,0 +1,7523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "aa2f9c1d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import talib as ta" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78d064ec", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_feather(\"/Users/aszer/Documents/vscode/factorhack/ETH_USDT-1h.feather\")\n", + "for col in ['datetime', 'time', 'timestamp', 'date']:\n", + " if col in df.columns:\n", + " df[col] = pd.to_datetime(df[col])\n", + " df = df.set_index(col).sort_index()\n", + " break\n", + "df = df.resample(\"4h\").agg({\n", + " \"open\": \"first\",\n", + " \"high\": \"max\",\n", + " \"low\": \"min\",\n", + " \"close\": \"last\",\n", + " \"volume\": \"sum\"\n", + " }).dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3980e004", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Z-score（注意：避免未来函数！）\n", + " zscore = (factor_series - rolling_mean) / rolling_std\n", + " \n", + " # 4. 处理 NaN 和无穷大\n", + " zscore = zscore.replace([np.inf, -np.inf], np.nan)\n", + " return zscore" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0165f25e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"expression\": \"sub(pct_change20(std25(close)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}\n", + "3 sigma 上界: 4.046477165961632\n", + "3 sigma 下界: -3.6191007370283717\n" + ] + } + ], + "source": [ + "from factor_mining.FactorFormula import FactorFormula\n", + "line = '{\"expression\": \"sub(pct_change20(std25(close)), close)\", \"feature_names\": [\"open\", \"high\", \"low\", \"close\", \"volume\"]}'\n", + "print(line)\n", + "formula = FactorFormula.from_dict(eval(line))\n", + "df_test['factor'] = formula.compute(df_test)\n", + "df_hist['factor'] = formula.compute(df_hist)\n", + "# 计算 factor 的 3 sigma 范围\n", + "\n", + "df_test['factor_zscore'] = rolling_zscore(df_test['factor'], window=60)\n", + "df_hist['factor_zscore'] = rolling_zscore(df_hist['factor'], window=60)\n", + "factor_mean = df_hist['factor_zscore'].mean()\n", + "factor_std = df_hist['factor_zscore'].std()\n", + "sigma_3_upper = factor_mean + 3 * factor_std\n", + "sigma_3_lower = factor_mean - 3 * factor_std\n", + "print(\"3 sigma 上界:\", sigma_3_upper)\n", + "print(\"3 sigma 下界:\", sigma_3_lower)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36413947", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 6016.000000\n", + "mean -0.101496\n", + "std 1.412622\n", + "min -5.152384\n", + "25% -1.169009\n", + "50% -0.090839\n", + "75% 0.925900\n", + "max 5.156556\n", + "Name: factor_zscore, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test['factor_zscore'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a15a4157", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def factor_calc(f):\n", + " if f > sigma_3_upper:\n", + " return 1\n", + " elif f < sigma_3_lower:\n", + " return -1\n", + " return 0\n", + "\n", + "df_test['signal'] = df_test['factor_zscore'].apply(lambda x: factor_calc(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "53aaf4ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start 2023-01-01 00:00:00+00:00\n", + "End 2025-10-14 12:00:00+00:00\n", + "Period 1017 days 16:00:00\n", + "Start Value 100.0\n", + "End Value 165.584548\n", + "Total Return [%] 65.584548\n", + "Benchmark Return [%] 231.236292\n", + "Max Gross Exposure [%] 100.0\n", + "Total Fees Paid 0.0\n", + "Max Drawdown [%] 10.677974\n", + "Max Drawdown Duration 25 days 16:00:00\n", + "Total Trades 10\n", + "Total Closed Trades 10\n", + "Total Open Trades 0\n", + "Open Trade PnL 0.0\n", + "Win Rate [%] 90.0\n", + "Best Trade [%] 6.873736\n", + "Worst Trade [%] -1.370958\n", + "Avg Winning Trade [%] 5.926729\n", + "Avg Losing Trade [%] -1.370958\n", + "Avg Winning Trade Duration 4 days 17:20:00\n", + "Avg Losing Trade Duration 12 days 00:00:00\n", + "Profit Factor 48.838497\n", + "Expectancy 6.558455\n", + "Sharpe Ratio 1.269835\n", + "Calmar Ratio 1.856806\n", + "Omega Ratio 1.518443\n", + "Sortino Ratio 2.429317\n", + "dtype: object\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "showlegend": false, + "type": "scatter", + "uid": "cc353fd8-3af7-43e0-a655-5887311f7457", + "x": [ + "2023-01-01T00:00:00+00:00", + "2023-01-01T04:00:00+00:00", + "2023-01-01T08:00:00+00:00", + "2023-01-01T12:00:00+00:00", + "2023-01-01T16:00:00+00:00", + "2023-01-01T20:00:00+00:00", + 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+ " sl_trail=True, \n", + " freq='4h' \n", + ")\n", + "# 打印回测收益等统计信息\n", + "print(portfolio.stats())\n", + "\n", + "# 可视化收益曲线\n", + "portfolio.value().vbt.plot(title=\"Signal策略回测\").show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c90cd2c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfefe1e8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b03c5e21", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c62a74a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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