2180 lines
111 KiB
Plaintext
2180 lines
111 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "79a7758178bafdd3",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T16:39:30.609224Z",
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"start_time": "2025-04-09T16:39:29.929606Z"
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},
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"# %load_ext autoreload\n",
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"# %autoreload 2\n",
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"\n",
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"import pandas as pd\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"pd.set_option('display.max_columns', None)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2c66084a979c42dd",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T16:39:30.914968Z",
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"start_time": "2025-04-09T16:39:30.858395Z"
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},
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"\n",
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"import talib\n",
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"\n",
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"\n",
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"def get_rolling_factor(df):\n",
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" old_columns = df.columns.tolist()[:]\n",
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"\n",
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" # 按股票和日期排序（如果尚未排序）\n",
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" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
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"\n",
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" grouped = df.groupby('ts_code', group_keys=False)\n",
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"\n",
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" window = 20\n",
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" df['_is_positive'] = (df['pct_chg'] > 0).astype(int)\n",
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" df['_is_negative'] = (df['pct_chg'] < 0).astype(int)\n",
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" df['cat_is_positive'] = (df['pct_chg'] > 0).astype(int)\n",
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"\n",
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" # 分离正负收益率 (用于计算各自的均值和平方均值)\n",
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" # 注意：这里我们保留原始收益率用于计算，而不是 clip 到 0\n",
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" df['_pos_returns'] = df['pct_chg'].where(df['pct_chg'] > 0, 0) # 非正设为0，便于求和\n",
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" df['_neg_returns'] = df['pct_chg'].where(df['pct_chg'] < 0, 0) # 非负设为0，便于求和\n",
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"\n",
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" # 计算收益率的平方 (用于计算 E[X^2])\n",
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" df['_pos_returns_sq'] = np.square(df['_pos_returns'])\n",
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" df['_neg_returns_sq'] = np.square(df['_neg_returns']) # 平方后负数变正\n",
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"\n",
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" # 4. 计算滚动统计量 (使用内置函数，速度较快)\n",
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" # 计算正收益日的统计量\n",
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" rolling_pos_count = grouped['_is_positive'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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" rolling_pos_sum = grouped['_pos_returns'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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" rolling_pos_sum_sq = grouped['_pos_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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"\n",
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" # 计算负收益日的统计量\n",
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" rolling_neg_count = grouped['_is_negative'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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" rolling_neg_sum = grouped['_neg_returns'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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" rolling_neg_sum_sq = grouped['_neg_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()\n",
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"\n",
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" # 5. 计算方差和标准差\n",
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" pos_mean_sq = rolling_pos_sum_sq / rolling_pos_count\n",
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" pos_mean = rolling_pos_sum / rolling_pos_count\n",
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" pos_var = pos_mean_sq - np.square(pos_mean)\n",
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" pos_var = pos_var.where(rolling_pos_count >= 2, np.nan).clip(lower=0)\n",
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" upside_vol = np.sqrt(pos_var)\n",
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"\n",
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" neg_mean_sq = rolling_neg_sum_sq / rolling_neg_count\n",
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" neg_mean = rolling_neg_sum / rolling_neg_count # 注意 neg_mean 是负数\n",
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" neg_var = neg_mean_sq - np.square(neg_mean)\n",
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" neg_var = neg_var.where(rolling_neg_count >= 2, np.nan).clip(lower=0)\n",
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" downside_vol = np.sqrt(neg_var)\n",
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"\n",
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" # rolling 操作后结果带有 MultiIndex，需要去除股票代码层级以便合并\n",
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" df['upside_vol'] = upside_vol.reset_index(level=0, drop=True)\n",
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" df['downside_vol'] = downside_vol.reset_index(level=0, drop=True)\n",
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"\n",
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" df['vol_ratio'] = df['upside_vol'] / df['downside_vol']\n",
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" df['vol_ratio'] = df['vol_ratio'].replace([np.inf, -np.inf], np.nan).fillna(0) # 或 fillna(np.nan)\n",
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"\n",
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" df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n",
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" df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n",
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"\n",
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" # 因子 1：短期成交量变化率\n",
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" df['volume_change_rate'] = (\n",
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" grouped['vol'].rolling(window=2).mean() /\n",
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" grouped['vol'].rolling(window=10).mean() - 1\n",
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" ).reset_index(level=0, drop=True) # 确保索引对齐\n",
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"\n",
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" # 因子 2：成交量突破信号\n",
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" max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n",
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" df['cat_volume_breakout'] = (df['vol'] > max_volume)\n",
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"\n",
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" # 因子 3：换手率均线偏离度\n",
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" mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n",
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" std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n",
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" df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n",
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"\n",
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" # 因子 4：换手率激增信号\n",
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" df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n",
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"\n",
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" # 因子 5：量比均值\n",
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" df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n",
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"\n",
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" # 因子 6：量比突破信号\n",
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" max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n",
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" df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n",
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"\n",
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" df['vol_spike'] = grouped.apply(\n",
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" lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n",
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" )\n",
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" df['vol_std_5'] = grouped['vol'].pct_change().rolling(window=5).std()\n",
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"\n",
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" # 计算 ATR\n",
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" df['atr_14'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n",
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" index=x.index)\n",
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" )\n",
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" df['atr_6'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n",
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" index=x.index)\n",
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" )\n",
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"\n",
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" # 计算 OBV 及其均线\n",
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" df['obv'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
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" )\n",
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" print(df.columns)\n",
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" df['maobv_6'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n",
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" )\n",
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"\n",
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" df['rsi_3'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n",
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" )\n",
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" # df['rsi_6'] = grouped.apply(\n",
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" # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n",
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" # )\n",
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" # df['rsi_9'] = grouped.apply(\n",
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" # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n",
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" # )\n",
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"\n",
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" # 计算 return_10 和 return_20\n",
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" df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
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" # df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n",
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" df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
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"\n",
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" # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n",
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"\n",
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" # 计算标准差指标\n",
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" df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n",
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" # df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n",
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" # df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n",
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" df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n",
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" df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n",
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"\n",
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" # 计算 EMA 指标\n",
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" df['_ema_5'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n",
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" )\n",
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" df['_ema_13'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n",
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" )\n",
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" df['_ema_20'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n",
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" )\n",
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" df['_ema_60'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n",
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" )\n",
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"\n",
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" # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
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" df['act_factor1'] = grouped['_ema_5'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n",
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" )\n",
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" df['act_factor2'] = grouped['_ema_13'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n",
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" )\n",
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" df['act_factor3'] = grouped['_ema_20'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n",
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" )\n",
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" df['act_factor4'] = grouped['_ema_60'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n",
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" )\n",
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"\n",
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" # 根据 trade_date 截面计算排名\n",
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" df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n",
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" df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n",
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" df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n",
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"\n",
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" df['log(circ_mv)'] = np.log(df['circ_mv'])\n",
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"\n",
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" window_high_volume = 5\n",
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" window_close_stddev = 20\n",
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" period_delta = 5\n",
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"\n",
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" # 计算每只股票的滚动协方差\n",
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" def calculate_rolling_cov(group):\n",
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" return group['high'].rolling(window_high_volume).cov(group['vol'])\n",
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"\n",
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" df['cov'] = grouped.apply(calculate_rolling_cov)\n",
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"\n",
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" # 计算每只股票的协方差差分\n",
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" def calculate_delta_cov(group):\n",
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" return group['cov'].diff(period_delta)\n",
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"\n",
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" df['delta_cov'] = grouped.apply(calculate_delta_cov)\n",
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"\n",
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" # 计算每只股票的滚动标准差\n",
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" def calculate_stddev_close(group):\n",
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" return group['close'].rolling(window_close_stddev).std()\n",
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"\n",
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" df['_stddev_close'] = grouped.apply(calculate_stddev_close)\n",
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" df['_rank_stddev'] = df.groupby('trade_date')['_stddev_close'].rank(pct=True)\n",
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" df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n",
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"\n",
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"\n",
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" df['alpha_003'] = np.where(df['high'] != df['low'],\n",
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" (df['close'] - df['open']) / (df['high'] - df['low']),\n",
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" 0)\n",
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"\n",
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" df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol']))\n",
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" df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n",
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"\n",
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" df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n",
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" df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n",
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"\n",
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" df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）\n",
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" df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）\n",
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" df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n",
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" drop=True)\n",
|
|||
|
|
" df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n",
|
|||
|
|
" drop=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 3. 最近连续涨跌停天数\n",
|
|||
|
|
" def calculate_consecutive_limits(series):\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" 计算连续涨停/跌停天数。\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n",
|
|||
|
|
" consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n",
|
|||
|
|
" return consecutive_up, consecutive_down\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 连续涨停天数\n",
|
|||
|
|
" df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n",
|
|||
|
|
" lambda x: calculate_consecutive_limits(x)[0]\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n",
|
|||
|
|
"\n",
|
|||
|
|
" def rolling_corr(group):\n",
|
|||
|
|
" roc_close = group['close'].pct_change()\n",
|
|||
|
|
" roc_weight = group['weight_avg'].pct_change()\n",
|
|||
|
|
" return roc_close.rolling(10).corr(roc_weight)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['price_cost_divergence'] = grouped.apply(rolling_corr)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 16. 筹码稳定性指数 (20日波动率)\n",
|
|||
|
|
" df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n",
|
|||
|
|
" df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 17. 成本区间突破标记\n",
|
|||
|
|
" df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 20. 筹码-流动性风险\n",
|
|||
|
|
" df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n",
|
|||
|
|
" 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # # 7. 市值波动率因子\n",
|
|||
|
|
" # df['turnover_std'] = df.groupby('ts_code')['turnover_rate'].transform(lambda x: x.rolling(window=20).std())\n",
|
|||
|
|
" # df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)'])\n",
|
|||
|
|
" #\n",
|
|||
|
|
" # # 8. 市值成长性因子\n",
|
|||
|
|
" # df['volume_growth'] = df.groupby('ts_code')['vol'].pct_change(periods=20)\n",
|
|||
|
|
" # df['mv_growth'] = df['volume_growth'] / df['log(circ_mv)']\n",
|
|||
|
|
" #\n",
|
|||
|
|
" # df[\"ar\"] = df.groupby('ts_code').apply(lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100).reset_index(level='ts_code', drop=True)\n",
|
|||
|
|
" # df[\"pre_close\"] = df.groupby('ts_code')[\"close\"].shift(1)\n",
|
|||
|
|
" # df[\"br_up\"] = (df[\"high\"] - df[\"pre_close\"]).clip(lower=0)\n",
|
|||
|
|
" # df[\"br_down\"] = (df[\"pre_close\"] - df[\"low\"]).clip(lower=0)\n",
|
|||
|
|
" # df[\"br\"] = df.groupby('ts_code').apply(lambda x: (x[\"br_up\"].rolling(3).sum()) / (x[\"br_down\"].rolling(3).sum()) * 100).reset_index(level='ts_code', drop=True)\n",
|
|||
|
|
" # df['arbr'] = df['ar'] - df['br']\n",
|
|||
|
|
" # df.drop(columns=[\"pre_close\", \"br_up\", \"br_down\", 'ar', 'br'], inplace=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 7. 市值波动率因子 (使用 grouped)\n",
|
|||
|
|
" df['turnover_std'] = grouped['turnover_rate'].transform(lambda x: x.rolling(window=20).std())\n",
|
|||
|
|
" df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 8. 市值成长性因子\n",
|
|||
|
|
" df['volume_growth'] = grouped['vol'].pct_change(periods=20)\n",
|
|||
|
|
" df['mv_growth'] = df['volume_growth'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # AR 指标\n",
|
|||
|
|
" df[\"ar\"] = grouped.apply(lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # BR 指标\n",
|
|||
|
|
" df[\"pre_close\"] = grouped[\"close\"].shift(1)\n",
|
|||
|
|
" df[\"br_up\"] = (df[\"high\"] - df[\"pre_close\"]).clip(lower=0)\n",
|
|||
|
|
" df[\"br_down\"] = (df[\"pre_close\"] - df[\"low\"]).clip(lower=0)\n",
|
|||
|
|
" df[\"br\"] = grouped.apply(lambda x: (x[\"br_up\"].rolling(3).sum()) / (x[\"br_down\"].rolling(3).sum()) * 100)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # ARBR\n",
|
|||
|
|
" df['arbr'] = df['ar'] - df['br']\n",
|
|||
|
|
" df.drop(columns=[\"pre_close\", \"br_up\", \"br_down\", 'ar', 'br'], inplace=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n",
|
|||
|
|
" df.drop(\n",
|
|||
|
|
" columns=['_is_positive', '_is_negative', '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq'],\n",
|
|||
|
|
" inplace=True, errors='ignore')\n",
|
|||
|
|
" new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df, new_columns\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def get_simple_factor(df):\n",
|
|||
|
|
" old_columns = df.columns.tolist()[:]\n",
|
|||
|
|
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" alpha = 0.5\n",
|
|||
|
|
" df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n",
|
|||
|
|
" df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']\n",
|
|||
|
|
" df['log_close'] = np.log(df['close'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n",
|
|||
|
|
" df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 计算比值指标\n",
|
|||
|
|
" df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n",
|
|||
|
|
" # df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 计算标准差差值\n",
|
|||
|
|
" df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # df['cat_af1'] = df['act_factor1'] > 0\n",
|
|||
|
|
" df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n",
|
|||
|
|
" df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n",
|
|||
|
|
" df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 计算 act_factor5 和 act_factor6\n",
|
|||
|
|
" df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n",
|
|||
|
|
" df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n",
|
|||
|
|
" df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n",
|
|||
|
|
" df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n",
|
|||
|
|
" df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
|
|||
|
|
" df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['log(circ_mv)'] = np.log(df['circ_mv'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['lock_factor'] = df['turnover_rate'] * (\n",
|
|||
|
|
" 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 12. 小盘股筹码集中度\n",
|
|||
|
|
" df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n",
|
|||
|
|
" (df['volume_ratio'] > 1.5) &\n",
|
|||
|
|
" (df['winner_rate'] > 0.7))\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['mv_turnover_ratio'] = df['turnover_rate'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['mv_adjusted_volume'] = df['vol'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['log(circ_mv)'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['nonlinear_mv_volume'] = df['vol'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['mv_volume_ratio'] = df['volume_ratio'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['log(circ_mv)']\n",
|
|||
|
|
"\n",
|
|||
|
|
" drop_columns = [col for col in df.columns if col.startswith('_')]\n",
|
|||
|
|
" df.drop(columns=drop_columns, inplace=True, errors='ignore')\n",
|
|||
|
|
"\n",
|
|||
|
|
" new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n",
|
|||
|
|
" return df, new_columns\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"id": "a79cafb06a7e0e43",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:40:19.471361Z",
|
|||
|
|
"start_time": "2025-04-09T16:39:30.917824Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
},
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"daily data\n",
|
|||
|
|
"daily basic\n",
|
|||
|
|
"inner merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"stk limit\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"money flow\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"cyq perf\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 4051406 entries, 0 to 4051405\n",
|
|||
|
|
"Data columns (total 31 columns):\n",
|
|||
|
|
" # Column Dtype \n",
|
|||
|
|
"--- ------ ----- \n",
|
|||
|
|
" 0 ts_code object \n",
|
|||
|
|
" 1 trade_date datetime64[ns]\n",
|
|||
|
|
" 2 open float64 \n",
|
|||
|
|
" 3 close float64 \n",
|
|||
|
|
" 4 high float64 \n",
|
|||
|
|
" 5 low float64 \n",
|
|||
|
|
" 6 vol float64 \n",
|
|||
|
|
" 7 pct_chg float64 \n",
|
|||
|
|
" 8 turnover_rate float64 \n",
|
|||
|
|
" 9 pe_ttm float64 \n",
|
|||
|
|
" 10 circ_mv float64 \n",
|
|||
|
|
" 11 volume_ratio float64 \n",
|
|||
|
|
" 12 is_st bool \n",
|
|||
|
|
" 13 up_limit float64 \n",
|
|||
|
|
" 14 down_limit float64 \n",
|
|||
|
|
" 15 buy_sm_vol float64 \n",
|
|||
|
|
" 16 sell_sm_vol float64 \n",
|
|||
|
|
" 17 buy_lg_vol float64 \n",
|
|||
|
|
" 18 sell_lg_vol float64 \n",
|
|||
|
|
" 19 buy_elg_vol float64 \n",
|
|||
|
|
" 20 sell_elg_vol float64 \n",
|
|||
|
|
" 21 net_mf_vol float64 \n",
|
|||
|
|
" 22 his_low float64 \n",
|
|||
|
|
" 23 his_high float64 \n",
|
|||
|
|
" 24 cost_5pct float64 \n",
|
|||
|
|
" 25 cost_15pct float64 \n",
|
|||
|
|
" 26 cost_50pct float64 \n",
|
|||
|
|
" 27 cost_85pct float64 \n",
|
|||
|
|
" 28 cost_95pct float64 \n",
|
|||
|
|
" 29 weight_avg float64 \n",
|
|||
|
|
" 30 winner_rate float64 \n",
|
|||
|
|
"dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n",
|
|||
|
|
"memory usage: 931.2+ MB\n",
|
|||
|
|
"None\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from code.utils.utils import read_and_merge_h5_data\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('daily data')\n",
|
|||
|
|
"df1 = read_and_merge_h5_data('../../data-copy/daily_data.h5', key='daily_data',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n",
|
|||
|
|
" df=None)\n",
|
|||
|
|
"df1 = df1[df1['trade_date'] >= '2022-01-01']\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('daily basic')\n",
|
|||
|
|
"df1 = read_and_merge_h5_data('../../data-copy/daily_basic.h5', key='daily_basic',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
|
|||
|
|
" 'is_st'], df=df1, join='inner')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('stk limit')\n",
|
|||
|
|
"df1 = read_and_merge_h5_data('../../data-copy/stk_limit.h5', key='stk_limit',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
|
|||
|
|
" df=df1)\n",
|
|||
|
|
"print('money flow')\n",
|
|||
|
|
"df1 = read_and_merge_h5_data('../../data-copy/money_flow.h5', key='money_flow',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
|
|||
|
|
" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
|
|||
|
|
" df=df1)\n",
|
|||
|
|
"print('cyq perf')\n",
|
|||
|
|
"df1 = read_and_merge_h5_data('../../data-copy/cyq_perf.h5', key='cyq_perf',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
|||
|
|
" 'cost_50pct',\n",
|
|||
|
|
" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
|
|||
|
|
" df=df1)\n",
|
|||
|
|
"print(df1.info())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"id": "cac01788dac10678",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:40:23.694912Z",
|
|||
|
|
"start_time": "2025-04-09T16:40:19.488481Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"industry\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print('industry')\n",
|
|||
|
|
"industry_df1 = read_and_merge_h5_data('../../data-copy/industry_data.h5', key='industry_data',\n",
|
|||
|
|
" columns=['ts_code', 'l2_code', 'in_date'],\n",
|
|||
|
|
" df=None, on=['ts_code'], join='left')\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def merge_with_industry_data(df, industry_df):\n",
|
|||
|
|
" # 确保日期字段是 datetime 类型\n",
|
|||
|
|
" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
|
|||
|
|
" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对 industry_df 按 ts_code 和 in_date 排序\n",
|
|||
|
|
" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对原始 df 按 ts_code 和 trade_date 排序\n",
|
|||
|
|
" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 使用 merge_asof 进行向后合并\n",
|
|||
|
|
" merged = pd.merge_asof(\n",
|
|||
|
|
" df_sorted,\n",
|
|||
|
|
" industry_df_sorted,\n",
|
|||
|
|
" by='ts_code', # 按 ts_code 分组\n",
|
|||
|
|
" left_on='trade_date',\n",
|
|||
|
|
" right_on='in_date',\n",
|
|||
|
|
" direction='backward'\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 获取每个 ts_code 的最早 in_date 记录\n",
|
|||
|
|
" min_in_date_per_ts = (industry_df_sorted\n",
|
|||
|
|
" .groupby('ts_code')\n",
|
|||
|
|
" .first()\n",
|
|||
|
|
" .reset_index()[['ts_code', 'l2_code']])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
|
|||
|
|
" merged['l2_code'] = merged['l2_code'].fillna(\n",
|
|||
|
|
" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 保留需要的列并重置索引\n",
|
|||
|
|
" result = merged.reset_index(drop=True)\n",
|
|||
|
|
" return result\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 使用示例\n",
|
|||
|
|
"df1 = merge_with_industry_data(df1, industry_df1)\n",
|
|||
|
|
"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"id": "5f7a8b42681606f6",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:40:30.145830Z",
|
|||
|
|
"start_time": "2025-04-09T16:40:23.712071Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from code.utils.factor import get_act_factor\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def read_industry_data(h5_filename):\n",
|
|||
|
|
" # 读取 H5 文件中所有的行业数据\n",
|
|||
|
|
" industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n",
|
|||
|
|
" 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n",
|
|||
|
|
" ]) # 假设 H5 文件的键是 'industry_data'\n",
|
|||
|
|
" industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
" industry_data = industry_data.reindex()\n",
|
|||
|
|
" industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n",
|
|||
|
|
"\n",
|
|||
|
|
" grouped = industry_data.groupby('ts_code', group_keys=False)\n",
|
|||
|
|
" industry_data['obv'] = grouped.apply(\n",
|
|||
|
|
" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
|
|||
|
|
" industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" industry_data = get_act_factor(industry_data, cat=False)\n",
|
|||
|
|
" industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n",
|
|||
|
|
" # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n",
|
|||
|
|
" #\n",
|
|||
|
|
" # for factor in factor_columns:\n",
|
|||
|
|
" # if factor in industry_data.columns:\n",
|
|||
|
|
" # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n",
|
|||
|
|
" # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n",
|
|||
|
|
" # lambda x: x - x.mean())\n",
|
|||
|
|
"\n",
|
|||
|
|
" industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n",
|
|||
|
|
" lambda x: x.rank(pct=True))\n",
|
|||
|
|
" industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n",
|
|||
|
|
" lambda x: x.rank(pct=True))\n",
|
|||
|
|
" industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" industry_data = industry_data.rename(\n",
|
|||
|
|
" columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
|
|||
|
|
"\n",
|
|||
|
|
" industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
|
|||
|
|
" return industry_data\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"industry_df1 = read_industry_data('../../data-copy/sw_daily.h5')\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"id": "85c3e3d0235ffffa",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:41:39.580305Z",
|
|||
|
|
"start_time": "2025-04-09T16:40:30.170820Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
|
|||
|
|
" 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
|
|||
|
|
" 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n",
|
|||
|
|
" 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n",
|
|||
|
|
" 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
|||
|
|
" 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n",
|
|||
|
|
" 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n",
|
|||
|
|
" '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n",
|
|||
|
|
" 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n",
|
|||
|
|
" 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n",
|
|||
|
|
" 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n",
|
|||
|
|
" 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n",
|
|||
|
|
" 'atr_6', 'obv'],\n",
|
|||
|
|
" dtype='object')\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 2425287 entries, 0 to 2425286\n",
|
|||
|
|
"Columns: 137 entries, ts_code to industry_return_20_percentile\n",
|
|||
|
|
"dtypes: bool(12), datetime64[ns](1), float64(119), int32(2), int64(1), object(2)\n",
|
|||
|
|
"memory usage: 2.3+ GB\n",
|
|||
|
|
"None\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"def filter_data(df):\n",
|
|||
|
|
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
|||
|
|
" df = df[~df['is_st']]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
|||
|
|
" if 'in_date' in df.columns:\n",
|
|||
|
|
" df = df.drop(columns=['in_date'])\n",
|
|||
|
|
" df = df.reset_index(drop=True)\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"df1 = filter_data(df1)\n",
|
|||
|
|
"df1, _ = get_rolling_factor(df1)\n",
|
|||
|
|
"df1, _ = get_simple_factor(df1)\n",
|
|||
|
|
"df1 = df1.rename(columns={'l2_code': 'cat_l2_code'})\n",
|
|||
|
|
"df1 = df1.merge(industry_df1, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(df1.info())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"id": "5dabff1e7bdd48c0",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:42:29.604069Z",
|
|||
|
|
"start_time": "2025-04-09T16:41:39.621703Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"daily data\n",
|
|||
|
|
"daily basic\n",
|
|||
|
|
"inner merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"stk limit\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"money flow\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"cyq perf\n",
|
|||
|
|
"left merge on ['ts_code', 'trade_date']\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 4062142 entries, 0 to 4062141\n",
|
|||
|
|
"Data columns (total 31 columns):\n",
|
|||
|
|
" # Column Dtype \n",
|
|||
|
|
"--- ------ ----- \n",
|
|||
|
|
" 0 ts_code object \n",
|
|||
|
|
" 1 trade_date datetime64[ns]\n",
|
|||
|
|
" 2 open float64 \n",
|
|||
|
|
" 3 close float64 \n",
|
|||
|
|
" 4 high float64 \n",
|
|||
|
|
" 5 low float64 \n",
|
|||
|
|
" 6 vol float64 \n",
|
|||
|
|
" 7 pct_chg float64 \n",
|
|||
|
|
" 8 turnover_rate float64 \n",
|
|||
|
|
" 9 pe_ttm float64 \n",
|
|||
|
|
" 10 circ_mv float64 \n",
|
|||
|
|
" 11 volume_ratio float64 \n",
|
|||
|
|
" 12 is_st bool \n",
|
|||
|
|
" 13 up_limit float64 \n",
|
|||
|
|
" 14 down_limit float64 \n",
|
|||
|
|
" 15 buy_sm_vol float64 \n",
|
|||
|
|
" 16 sell_sm_vol float64 \n",
|
|||
|
|
" 17 buy_lg_vol float64 \n",
|
|||
|
|
" 18 sell_lg_vol float64 \n",
|
|||
|
|
" 19 buy_elg_vol float64 \n",
|
|||
|
|
" 20 sell_elg_vol float64 \n",
|
|||
|
|
" 21 net_mf_vol float64 \n",
|
|||
|
|
" 22 his_low float64 \n",
|
|||
|
|
" 23 his_high float64 \n",
|
|||
|
|
" 24 cost_5pct float64 \n",
|
|||
|
|
" 25 cost_15pct float64 \n",
|
|||
|
|
" 26 cost_50pct float64 \n",
|
|||
|
|
" 27 cost_85pct float64 \n",
|
|||
|
|
" 28 cost_95pct float64 \n",
|
|||
|
|
" 29 weight_avg float64 \n",
|
|||
|
|
" 30 winner_rate float64 \n",
|
|||
|
|
"dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n",
|
|||
|
|
"memory usage: 933.6+ MB\n",
|
|||
|
|
"None\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from code.utils.utils import read_and_merge_h5_data\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('daily data')\n",
|
|||
|
|
"df2 = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n",
|
|||
|
|
" df=None)\n",
|
|||
|
|
"df2 = df2[df2['trade_date'] >= '2022-01-01']\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('daily basic')\n",
|
|||
|
|
"df2 = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
|
|||
|
|
" 'is_st'], df=df2, join='inner')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('stk limit')\n",
|
|||
|
|
"df2 = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
|
|||
|
|
" df=df2)\n",
|
|||
|
|
"print('money flow')\n",
|
|||
|
|
"df2 = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
|
|||
|
|
" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
|
|||
|
|
" df=df2)\n",
|
|||
|
|
"print('cyq perf')\n",
|
|||
|
|
"df2 = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n",
|
|||
|
|
" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
|||
|
|
" 'cost_50pct',\n",
|
|||
|
|
" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
|
|||
|
|
" df=df2)\n",
|
|||
|
|
"print(df2.info())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"id": "7da9e79ee7f2eeb2",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:42:33.590224Z",
|
|||
|
|
"start_time": "2025-04-09T16:42:29.605171Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"industry\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print('industry')\n",
|
|||
|
|
"industry_df2 = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n",
|
|||
|
|
" columns=['ts_code', 'l2_code', 'in_date'],\n",
|
|||
|
|
" df=None, on=['ts_code'], join='left')\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def merge_with_industry_data(df, industry_df):\n",
|
|||
|
|
" # 确保日期字段是 datetime 类型\n",
|
|||
|
|
" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
|
|||
|
|
" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对 industry_df 按 ts_code 和 in_date 排序\n",
|
|||
|
|
" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对原始 df 按 ts_code 和 trade_date 排序\n",
|
|||
|
|
" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 使用 merge_asof 进行向后合并\n",
|
|||
|
|
" merged = pd.merge_asof(\n",
|
|||
|
|
" df_sorted,\n",
|
|||
|
|
" industry_df_sorted,\n",
|
|||
|
|
" by='ts_code', # 按 ts_code 分组\n",
|
|||
|
|
" left_on='trade_date',\n",
|
|||
|
|
" right_on='in_date',\n",
|
|||
|
|
" direction='backward'\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 获取每个 ts_code 的最早 in_date 记录\n",
|
|||
|
|
" min_in_date_per_ts = (industry_df_sorted\n",
|
|||
|
|
" .groupby('ts_code')\n",
|
|||
|
|
" .first()\n",
|
|||
|
|
" .reset_index()[['ts_code', 'l2_code']])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
|
|||
|
|
" merged['l2_code'] = merged['l2_code'].fillna(\n",
|
|||
|
|
" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 保留需要的列并重置索引\n",
|
|||
|
|
" result = merged.reset_index(drop=True)\n",
|
|||
|
|
" return result\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 使用示例\n",
|
|||
|
|
"df2 = merge_with_industry_data(df2, industry_df2)\n",
|
|||
|
|
"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"id": "7f0830ced3ce1050",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:42:39.280494Z",
|
|||
|
|
"start_time": "2025-04-09T16:42:33.613600Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"industry_df2 = read_industry_data('../../data/sw_daily.h5')\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"id": "ee9d7511597a312b",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:43:50.865104Z",
|
|||
|
|
"start_time": "2025-04-09T16:42:39.340589Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
|
|||
|
|
" 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
|
|||
|
|
" 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n",
|
|||
|
|
" 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n",
|
|||
|
|
" 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
|||
|
|
" 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n",
|
|||
|
|
" 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n",
|
|||
|
|
" '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n",
|
|||
|
|
" 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n",
|
|||
|
|
" 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n",
|
|||
|
|
" 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n",
|
|||
|
|
" 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n",
|
|||
|
|
" 'atr_6', 'obv'],\n",
|
|||
|
|
" dtype='object')\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 2431461 entries, 0 to 2431460\n",
|
|||
|
|
"Columns: 137 entries, ts_code to industry_return_20_percentile\n",
|
|||
|
|
"dtypes: bool(12), datetime64[ns](1), float64(119), int32(2), int64(1), object(2)\n",
|
|||
|
|
"memory usage: 2.3+ GB\n",
|
|||
|
|
"None\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"def filter_data(df):\n",
|
|||
|
|
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
|||
|
|
" df = df[~df['is_st']]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
|||
|
|
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
|||
|
|
" if 'in_date' in df.columns:\n",
|
|||
|
|
" df = df.drop(columns=['in_date'])\n",
|
|||
|
|
" df = df.reset_index(drop=True)\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"df2 = filter_data(df2)\n",
|
|||
|
|
"df2, _ = get_rolling_factor(df2)\n",
|
|||
|
|
"df2, _ = get_simple_factor(df2)\n",
|
|||
|
|
"df2 = df2.rename(columns={'l2_code': 'cat_l2_code'})\n",
|
|||
|
|
"df2 = df2.merge(industry_df2, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(df2.info())"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"id": "4ae711775caefbe5",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:43:53.621695Z",
|
|||
|
|
"start_time": "2025-04-09T16:43:50.925481Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# print(df1[df1['trade_date'] == '2025-04-07'][['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']].tail())\n",
|
|||
|
|
"# print(df2[df2['trade_date'] == '2025-04-07'][['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']].tail())\n",
|
|||
|
|
"# print(df1[df1['trade_date'] == '2025-04-07'].equals(df2[df2['trade_date'] == '2025-04-07']))\n",
|
|||
|
|
"days = 2\n",
|
|||
|
|
"df1 = df1.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
|
|||
|
|
"df1['future_return'] = (df1.groupby('ts_code')['close'].shift(-days) - df1.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
|||
|
|
" df1.groupby('ts_code')['open'].shift(-1)\n",
|
|||
|
|
"df1['future_score'] = calculate_score(df1, days=2, lambda_param=0.3)\n",
|
|||
|
|
"df1['label'] = df1.groupby('trade_date', group_keys=False)['future_score'].transform(\n",
|
|||
|
|
" lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"df2 = df2.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
|
|||
|
|
"df2['future_return'] = (df2.groupby('ts_code')['close'].shift(-days) - df2.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
|||
|
|
" df2.groupby('ts_code')['open'].shift(-1)\n",
|
|||
|
|
"df2['future_score'] = calculate_score(df2, days=2, lambda_param=0.3)\n",
|
|||
|
|
"df2['label'] = df2.groupby('trade_date', group_keys=False)['future_score'].transform(\n",
|
|||
|
|
" lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"id": "350bf91df8c3dfc2",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:43:53.723327Z",
|
|||
|
|
"start_time": "2025-04-09T16:43:53.658090Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"日期: 2025-03-26\n",
|
|||
|
|
"------------------------------\n",
|
|||
|
|
"Slice 1 形状: (3086, 141)\n",
|
|||
|
|
"Slice 2 形状: (3086, 141)\n",
|
|||
|
|
"!!! 索引不同，尝试按 ts_code 对齐 !!!\n",
|
|||
|
|
"------------------------------\n",
|
|||
|
|
"使用 compare() 方法查找差异:\n",
|
|||
|
|
"!!! 发现差异 (compare结果):\n",
|
|||
|
|
"MultiIndex([('vol_std_5', 'self'),\n",
|
|||
|
|
" ('vol_std_5', 'other')],\n",
|
|||
|
|
" )\n",
|
|||
|
|
" vol_std_5 \n",
|
|||
|
|
" self other\n",
|
|||
|
|
"ts_code \n",
|
|||
|
|
"000004.SZ 1.076957 1.076957\n",
|
|||
|
|
"000006.SZ 1.228637 1.228637\n",
|
|||
|
|
"000007.SZ 0.533913 0.533913\n",
|
|||
|
|
"000008.SZ 0.368086 0.368086\n",
|
|||
|
|
"000009.SZ 0.393264 0.393264\n",
|
|||
|
|
"... ... ...\n",
|
|||
|
|
"605580.SH 1.164645 1.164645\n",
|
|||
|
|
"605588.SH 0.314876 0.314876\n",
|
|||
|
|
"605589.SH 0.562543 0.562543\n",
|
|||
|
|
"605598.SH 1.057029 1.057029\n",
|
|||
|
|
"605599.SH 0.193314 0.193314\n",
|
|||
|
|
"\n",
|
|||
|
|
"[3001 rows x 2 columns]\n",
|
|||
|
|
"\n",
|
|||
|
|
"存在差异的列: ['vol_std_5']\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# 假设 slice1 和 slice2 已经获取，并且索引和列已对齐\n",
|
|||
|
|
"# (如果索引或列不同，需要先用 .sort_index() 或 .sort_index(axis=1) 对齐)\n",
|
|||
|
|
"# 假设 pdf1 和 pdf2 已经是处理到最后一步的结果\n",
|
|||
|
|
"date_to_compare = '2025-03-26'\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 1. 获取两个 DataFrame 在该日期的切片\n",
|
|||
|
|
"slice1 = df1[df1['trade_date'] == date_to_compare]\n",
|
|||
|
|
"slice2 = df2[df2['trade_date'] == date_to_compare]\n",
|
|||
|
|
"\n",
|
|||
|
|
"def get_diff(slice1, slice2):\n",
|
|||
|
|
" print(f\"日期: {date_to_compare}\")\n",
|
|||
|
|
" print(\"-\" * 30)\n",
|
|||
|
|
" print(f\"Slice 1 形状: {slice1.shape}\")\n",
|
|||
|
|
" print(f\"Slice 2 形状: {slice2.shape}\")\n",
|
|||
|
|
" if slice1.shape != slice2.shape:\n",
|
|||
|
|
" print(\"!!! 形状不同 !!!\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" if not slice1.index.equals(slice2.index):\n",
|
|||
|
|
" print(\"!!! 索引不同，尝试按 ts_code 对齐 !!!\")\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" slice1 = slice1.set_index('ts_code').sort_index()\n",
|
|||
|
|
" slice2 = slice2.set_index('ts_code').sort_index()\n",
|
|||
|
|
" except KeyError:\n",
|
|||
|
|
" print(\"错误：无法按 ts_code 设置索引，请确保该列存在。\")\n",
|
|||
|
|
" # 或者尝试其他对齐方式，例如 reset_index\n",
|
|||
|
|
" # slice1 = slice1.reset_index(drop=True)\n",
|
|||
|
|
" # slice2 = slice2.reset_index(drop=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if not slice1.columns.equals(slice2.columns):\n",
|
|||
|
|
" print(\"!!! 列名或顺序不同，尝试按列名排序对齐 !!!\")\n",
|
|||
|
|
" slice1 = slice1.sort_index(axis=1)\n",
|
|||
|
|
" slice2 = slice2.sort_index(axis=1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 再次检查对齐情况\n",
|
|||
|
|
" if slice1.index.equals(slice2.index) and slice1.columns.equals(slice2.columns):\n",
|
|||
|
|
" print(\"-\" * 30)\n",
|
|||
|
|
" print(\"使用 compare() 方法查找差异:\")\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" # compare 会返回一个显示差异的 DataFrame\n",
|
|||
|
|
" # self 列显示 slice1 的值，other 列显示 slice2 的值\n",
|
|||
|
|
" diff_compare = slice1.compare(slice2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if diff_compare.empty:\n",
|
|||
|
|
" print(\"使用 compare() 未发现差异。\")\n",
|
|||
|
|
" # 如果 compare 为空但 equals 仍为 False, 可能是非常细微的浮点差异或类型差异\n",
|
|||
|
|
" # 可以再检查一下dtypes\n",
|
|||
|
|
" if not slice1.dtypes.equals(slice2.dtypes):\n",
|
|||
|
|
" print(\"!!! 发现数据类型 (dtypes) 不同 !!!\")\n",
|
|||
|
|
" print(slice1.dtypes[slice1.dtypes != slice2.dtypes])\n",
|
|||
|
|
" print(slice2.dtypes[slice1.dtypes != slice2.dtypes])\n",
|
|||
|
|
"\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" print(\"!!! 发现差异 (compare结果):\")\n",
|
|||
|
|
" # 默认情况下，compare 的列是 MultiIndex ('列名', 'self'/'other')\n",
|
|||
|
|
" # 为了更清晰地显示，可以调整一下格式\n",
|
|||
|
|
" # diff_compare.columns = ['_'.join(col) for col in diff_compare.columns]\n",
|
|||
|
|
" print(diff_compare.columns)\n",
|
|||
|
|
" print(diff_compare[diff_compare[('vol_std_5', 'self')] != diff_compare[('vol_std_5', 'other')]]) # 打印差异的头部\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 找出哪些列存在差异\n",
|
|||
|
|
" differing_columns = diff_compare.columns.get_level_values(0).unique().tolist()\n",
|
|||
|
|
" print(f\"\\n存在差异的列: {differing_columns}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" except Exception as e:\n",
|
|||
|
|
" print(f\"使用 compare() 时出错: {e}\")\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" print(\"-\" * 30)\n",
|
|||
|
|
" print(\"索引或列在对齐后仍然不匹配，无法使用 compare()。请检查对齐逻辑。\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"get_diff(slice1, slice2)\n",
|
|||
|
|
"# print(df1['trade_date'].unique().tolist()[-5:])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"id": "9df2781fc6c7ae44",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:43:55.223316Z",
|
|||
|
|
"start_time": "2025-04-09T16:43:53.868461Z"
|
|||
|
|
},
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"from scipy.stats import ks_2samp, wasserstein_distance\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def remove_shifted_features(train_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1, size=0.8,\n",
|
|||
|
|
" log=True):\n",
|
|||
|
|
" dropped_features = []\n",
|
|||
|
|
"\n",
|
|||
|
|
" all_dates = sorted(train_data['trade_date'].unique().tolist()) # 获取所有唯一的 trade_date\n",
|
|||
|
|
" split_date = all_dates[int(len(all_dates) * size)] # 划分点为倒数第 validation_days 天\n",
|
|||
|
|
" train_data_split = train_data[train_data['trade_date'] < split_date] # 训练集\n",
|
|||
|
|
" val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n",
|
|||
|
|
"\n",
|
|||
|
|
" # **统计数据漂移**\n",
|
|||
|
|
" numeric_columns = train_data_split.select_dtypes(include=['float64', 'int64']).columns\n",
|
|||
|
|
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
|||
|
|
" for feature in numeric_columns:\n",
|
|||
|
|
" ks_stat, p_value = ks_2samp(train_data_split[feature], val_data_split[feature])\n",
|
|||
|
|
" wasserstein_dist = wasserstein_distance(train_data_split[feature], val_data_split[feature])\n",
|
|||
|
|
"\n",
|
|||
|
|
" if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n",
|
|||
|
|
" dropped_features.append(feature)\n",
|
|||
|
|
" if log:\n",
|
|||
|
|
" print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # **应用阈值进行最终筛选**\n",
|
|||
|
|
" filtered_features = [f for f in feature_columns if f not in dropped_features]\n",
|
|||
|
|
"\n",
|
|||
|
|
" return filtered_features, dropped_features\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
|
|||
|
|
" log=True):\n",
|
|||
|
|
" if not (0 <= lower_percentile < upper_percentile <= 1):\n",
|
|||
|
|
" raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Calculate lower and upper bounds based on percentiles\n",
|
|||
|
|
" lower_bound = label.quantile(lower_percentile)\n",
|
|||
|
|
" upper_bound = label.quantile(upper_percentile)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Filter out values outside the bounds\n",
|
|||
|
|
" filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Print the number of removed outliers\n",
|
|||
|
|
" if log:\n",
|
|||
|
|
" print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
|
|||
|
|
" return filtered_label\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def calculate_risk_adjusted_target(df, days=5):\n",
|
|||
|
|
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
|
|||
|
|
" df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n",
|
|||
|
|
" df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n",
|
|||
|
|
"\n",
|
|||
|
|
" df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n",
|
|||
|
|
" level=0, drop=True)\n",
|
|||
|
|
" sharpe_ratio = df['future_return'] * df['future_volatility']\n",
|
|||
|
|
" sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return sharpe_ratio\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def calculate_score(df, days=5, lambda_param=1.0):\n",
|
|||
|
|
" def calculate_max_drawdown(prices):\n",
|
|||
|
|
" peak = prices.iloc[0] # 初始化峰值\n",
|
|||
|
|
" max_drawdown = 0 # 初始化最大回撤\n",
|
|||
|
|
"\n",
|
|||
|
|
" for price in prices:\n",
|
|||
|
|
" if price > peak:\n",
|
|||
|
|
" peak = price # 更新峰值\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" drawdown = (peak - price) / peak # 计算当前回撤\n",
|
|||
|
|
" max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n",
|
|||
|
|
"\n",
|
|||
|
|
" return max_drawdown\n",
|
|||
|
|
"\n",
|
|||
|
|
" def compute_stock_score(stock_df):\n",
|
|||
|
|
" stock_df = stock_df.sort_values(by=['trade_date'])\n",
|
|||
|
|
" future_return = stock_df['future_return']\n",
|
|||
|
|
" # 使用已有的 pct_chg 字段计算波动率\n",
|
|||
|
|
" volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n",
|
|||
|
|
" max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n",
|
|||
|
|
" score = future_return - lambda_param * max_drawdown\n",
|
|||
|
|
" return score\n",
|
|||
|
|
"\n",
|
|||
|
|
" # # 确保 DataFrame 按照股票代码和交易日期排序\n",
|
|||
|
|
" # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对每个股票分别计算 score\n",
|
|||
|
|
" df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df['score']\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n",
|
|||
|
|
" numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n",
|
|||
|
|
" if not numeric_features:\n",
|
|||
|
|
" raise ValueError(\"No numeric features found in the provided data.\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" corr_matrix = df[numeric_features].corr().abs()\n",
|
|||
|
|
" upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
|
|||
|
|
" to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
|
|||
|
|
" remaining_features = [col for col in feature_columns if col not in to_drop\n",
|
|||
|
|
" or 'act' in col or 'af' in col]\n",
|
|||
|
|
" return remaining_features\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def cross_sectional_standardization(df, features):\n",
|
|||
|
|
" df_sorted = df.sort_values(by='trade_date') # 按时间排序\n",
|
|||
|
|
" df_standardized = df_sorted.copy()\n",
|
|||
|
|
"\n",
|
|||
|
|
" for date in df_sorted['trade_date'].unique():\n",
|
|||
|
|
" # 获取当前时间点的数据\n",
|
|||
|
|
" current_data = df_standardized[df_standardized['trade_date'] == date]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 只对指定特征进行标准化\n",
|
|||
|
|
" scaler = StandardScaler()\n",
|
|||
|
|
" standardized_values = scaler.fit_transform(current_data[features])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 将标准化结果重新赋值回去\n",
|
|||
|
|
" df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df_standardized\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def neutralize_manual(df, features, industry_col, mkt_cap_col):\n",
|
|||
|
|
" \"\"\" 手动实现简单回归以提升速度 \"\"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" residuals = []\n",
|
|||
|
|
" for _, group in df.groupby(industry_col):\n",
|
|||
|
|
" if len(group) > 1:\n",
|
|||
|
|
" x = np.log(group[mkt_cap_col]) # 市值对数\n",
|
|||
|
|
" y = group[col] # 因子值\n",
|
|||
|
|
" beta = np.cov(y, x)[0, 1] / np.var(x) # 计算斜率\n",
|
|||
|
|
" alpha = np.mean(y) - beta * np.mean(x) # 计算截距\n",
|
|||
|
|
" resid = y - (alpha + beta * x) # 计算残差\n",
|
|||
|
|
" residuals.extend(resid)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" residuals.extend(group[col]) # 样本不足时保留原值\n",
|
|||
|
|
"\n",
|
|||
|
|
" df[col] = residuals\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"import gc\n",
|
|||
|
|
"\n",
|
|||
|
|
"gc.collect()\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def mad_filter(df, features, n=3):\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" median = df[col].median()\n",
|
|||
|
|
" mad = np.median(np.abs(df[col] - median))\n",
|
|||
|
|
" upper = median + n * mad\n",
|
|||
|
|
" lower = median - n * mad\n",
|
|||
|
|
" df[col] = np.clip(df[col], lower, upper) # 截断极值\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" # 按日期分组计算上下百分位数\n",
|
|||
|
|
" lower_bound = df.groupby('trade_date')[col].transform(\n",
|
|||
|
|
" lambda x: x.quantile(lower_percentile)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" upper_bound = df.groupby('trade_date')[col].transform(\n",
|
|||
|
|
" lambda x: x.quantile(upper_percentile)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" # 截断超出范围的值\n",
|
|||
|
|
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"from scipy.stats import iqr\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def iqr_filter(df, features):\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" df[col] = df.groupby('trade_date')[col].transform(\n",
|
|||
|
|
" lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n",
|
|||
|
|
" )\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
|
|||
|
|
" df = df.copy()\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" # 计算 rolling 统计量，需要按日期进行 groupby\n",
|
|||
|
|
" rolling_lower = df.groupby('trade_date')[col].transform(\n",
|
|||
|
|
" lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
|
|||
|
|
" rolling_upper = df.groupby('trade_date')[col].transform(\n",
|
|||
|
|
" lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 对数据进行裁剪\n",
|
|||
|
|
" df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"def time_series_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
|
|||
|
|
" df = df.copy()\n",
|
|||
|
|
" # 确保按股票和时间排序\n",
|
|||
|
|
" df = df.sort_values(['ts_code', 'trade_date'])\n",
|
|||
|
|
" grouped = df.groupby('ts_code')\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" # 对每个股票的时间序列计算滚动分位数\n",
|
|||
|
|
" rolling_lower = grouped[col].rolling(window=window, min_periods=window // 2).quantile(lower_quantile)\n",
|
|||
|
|
" rolling_upper = grouped[col].rolling(window=window, min_periods=window // 2).quantile(upper_quantile)\n",
|
|||
|
|
" # rolling结果带有多重索引，需要对齐\n",
|
|||
|
|
" rolling_lower = rolling_lower.reset_index(level=0, drop=True)\n",
|
|||
|
|
" rolling_upper = rolling_upper.reset_index(level=0, drop=True)\n",
|
|||
|
|
" # 应用 clip\n",
|
|||
|
|
" df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
|
|||
|
|
" return df\n",
|
|||
|
|
"\n",
|
|||
|
|
"def cross_sectional_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99):\n",
|
|||
|
|
" df = df.copy()\n",
|
|||
|
|
" grouped = df.groupby('trade_date')\n",
|
|||
|
|
" for col in features:\n",
|
|||
|
|
" # 计算每日截面的分位数边界\n",
|
|||
|
|
" lower_bound = grouped[col].transform(lambda x: x.quantile(lower_quantile))\n",
|
|||
|
|
" upper_bound = grouped[col].transform(lambda x: x.quantile(upper_quantile))\n",
|
|||
|
|
" # 应用 clip\n",
|
|||
|
|
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
|||
|
|
" return df"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 43,
|
|||
|
|
"id": "99f677aca6a286d0",
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"end_time": "2025-04-09T16:54:07.250024Z",
|
|||
|
|
"start_time": "2025-04-09T16:53:57.299050Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"[Timestamp('2025-04-01 00:00:00')] 19 [19. 0. 5. 2. 1. 6. 10. 18. 4. 12. 17. 16. 11. 8. 15. 7. 14. 9.\n",
|
|||
|
|
" 13.]\n",
|
|||
|
|
"[Timestamp('2025-04-02 00:00:00')] 19 [18. 19. 1. 0. 3. 7. 17. 10. 16. 5. 9. 15. 8. 6. 4. 13. 2. 11.\n",
|
|||
|
|
" 14.]\n",
|
|||
|
|
"[Timestamp('2025-04-03 00:00:00')] 0 [nan]\n",
|
|||
|
|
"[Timestamp('2025-04-07 00:00:00')] 0 [nan]\n",
|
|||
|
|
"2025-04-07 00:00:00\n",
|
|||
|
|
"[Timestamp('2025-04-01 00:00:00')] 19 [19. 0. 5. 2. 1. 6. 10. 18. 4. 12. 17. 16. 11. 8. 15. 7. 14. 9.\n",
|
|||
|
|
" 13.]\n",
|
|||
|
|
"[Timestamp('2025-04-02 00:00:00')] 19 [18. 19. 1. 0. 3. 7. 17. 10. 16. 5. 9. 15. 8. 6. 4. 13. 2. 11.\n",
|
|||
|
|
" 14.]\n",
|
|||
|
|
"[Timestamp('2025-04-03 00:00:00')] 19 [ 2. 15. 19. 0. 1. 5. 18. 17. 4. 6. 16. 8. 13. 14. 9. 7. 12. 11.\n",
|
|||
|
|
" 3.]\n",
|
|||
|
|
"[Timestamp('2025-04-07 00:00:00')] 19 [ 0. 18. 4. 17. 1. 19. 9. 13. 7. 5. 2. 16. 15. 6. 12. 11. 3. 14.\n",
|
|||
|
|
" 8.]\n",
|
|||
|
|
"[Timestamp('2025-04-08 00:00:00')] 0 [nan]\n",
|
|||
|
|
"[Timestamp('2025-04-09 00:00:00')] 0 [nan]\n",
|
|||
|
|
"2025-04-09 00:00:00\n",
|
|||
|
|
"日期: 2025-04-07\n",
|
|||
|
|
"------------------------------\n",
|
|||
|
|
"Slice 1 形状: (100, 159)\n",
|
|||
|
|
"Slice 2 形状: (110, 159)\n",
|
|||
|
|
"!!! 形状不同 !!!\n",
|
|||
|
|
"!!! 索引不同，尝试按 ts_code 对齐 !!!\n",
|
|||
|
|
"------------------------------\n",
|
|||
|
|
"索引或列在对齐后仍然不匹配，无法使用 compare()。请检查对齐逻辑。\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"def get_pdf(df, industry_df):\n",
|
|||
|
|
" origin_columns = df.columns.tolist()\n",
|
|||
|
|
" origin_columns = [col for col in origin_columns if\n",
|
|||
|
|
" col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n",
|
|||
|
|
" origin_columns = [col for col in origin_columns if 'cyq' not in col]\n",
|
|||
|
|
"\n",
|
|||
|
|
" days = 2\n",
|
|||
|
|
" # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
" # # df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
|
|||
|
|
" # df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
|||
|
|
" # df.groupby('ts_code')['open'].shift(-1)\n",
|
|||
|
|
" # df['future_score'] = calculate_score(df, days=2, lambda_param=0.3)\n",
|
|||
|
|
" # df['label'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n",
|
|||
|
|
" # lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n",
|
|||
|
|
" # )\n",
|
|||
|
|
" # df['label'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n",
|
|||
|
|
" # lambda x: pd.qcut(x.rank(method='first'), q=20, labels=False, duplicates='raise')\n",
|
|||
|
|
" # )\n",
|
|||
|
|
" # df['future_score'] = (\n",
|
|||
|
|
" # 0.7 * df['future_return']\n",
|
|||
|
|
" # * 0.3 * df['future_volatility']\n",
|
|||
|
|
" # )\n",
|
|||
|
|
"\n",
|
|||
|
|
" def select_pre_zt_stocks_dynamic(stock_df):\n",
|
|||
|
|
" def select_stocks(group):\n",
|
|||
|
|
" max_stocks = 150\n",
|
|||
|
|
" initial_data = group.nlargest(100, 'return_20')\n",
|
|||
|
|
" unique_labels = initial_data['label'].nunique()\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(group['trade_date'].unique().tolist(), initial_data['label'].nunique(), initial_data['label'].unique())\n",
|
|||
|
|
" if unique_labels >= 20 or unique_labels == 0: # 包含标签种类为0的情况\n",
|
|||
|
|
" return initial_data\n",
|
|||
|
|
"\n",
|
|||
|
|
" for i in range(110, max_stocks + 1, 10):\n",
|
|||
|
|
" data = group.nlargest(i, 'return_20')\n",
|
|||
|
|
" unique_labels = data['label'].nunique()\n",
|
|||
|
|
" if unique_labels >= 20:\n",
|
|||
|
|
" return data\n",
|
|||
|
|
"\n",
|
|||
|
|
" return group.nlargest(max_stocks, 'return_20') # 如果循环结束仍未找到足够标签，则返回最大数量的股票\n",
|
|||
|
|
"\n",
|
|||
|
|
" stock_df = stock_df.groupby('trade_date', group_keys=False).apply(select_stocks)\n",
|
|||
|
|
" return stock_df\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
" pdf = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-01-01') & (df['trade_date'] <= '2029-04-07')])\n",
|
|||
|
|
" print(pdf['trade_date'].max())\n",
|
|||
|
|
"\n",
|
|||
|
|
" pdf = pdf.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
|||
|
|
" pdf = pdf.replace([np.inf, -np.inf], np.nan)\n",
|
|||
|
|
"\n",
|
|||
|
|
" feature_columns = [col for col in pdf.columns if col in pdf.columns]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if col not in ['trade_date',\n",
|
|||
|
|
" 'ts_code',\n",
|
|||
|
|
" 'label']]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'future' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'label' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'score' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'gen' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'volatility' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
|
|||
|
|
" feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
|||
|
|
" # feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n",
|
|||
|
|
"\n",
|
|||
|
|
" numeric_columns = pdf.select_dtypes(include=['float64', 'int64']).columns\n",
|
|||
|
|
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
|||
|
|
"\n",
|
|||
|
|
" pdf = cross_sectional_quantile_filter(pdf, numeric_columns)\n",
|
|||
|
|
" # pdf = cross_sectional_standardization(pdf, numeric_columns)\n",
|
|||
|
|
"\n",
|
|||
|
|
" pdf = pdf.sort_values(by=['ts_code', 'trade_date'])\n",
|
|||
|
|
"\n",
|
|||
|
|
" filter_index = pdf['future_return'].between(pdf['future_return'].quantile(0.01), pdf['future_return'].quantile(0.99))\n",
|
|||
|
|
"\n",
|
|||
|
|
" feature_columns = remove_highly_correlated_features(pdf, feature_columns)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return pdf, feature_columns, filter_index\n",
|
|||
|
|
"\n",
|
|||
|
|
"pdf1, feature_columns1, filter_index1 = get_pdf(df1[df1['trade_date'] >= '2025-04-01'], industry_df1)\n",
|
|||
|
|
"pdf2, feature_columns2, filter_index2 = get_pdf(df2[df2['trade_date'] >= '2025-04-01'], industry_df2)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# date_to_compare = '2025-04-07'\n",
|
|||
|
|
"slice1 = pdf1[pdf1['trade_date'] == date_to_compare]\n",
|
|||
|
|
"slice2 = pdf2[pdf2['trade_date'] == date_to_compare]\n",
|
|||
|
|
"get_diff(slice1, slice2)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"id": "1b863e4115252d2d",
|
|||
|
|
"metadata": {
|
|||
|
|
"jupyter": {
|
|||
|
|
"source_hidden": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|||
|
|
"import lightgbm as lgb\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"def train_light_model(train_data_df, params, feature_columns, callbacks, evals,\n",
|
|||
|
|
" print_feature_importance=True, num_boost_round=100,\n",
|
|||
|
|
" validation_days=180, use_pca=False, split_date=None): # 新增参数：validation_days\n",
|
|||
|
|
" # 确保数据按时间排序\n",
|
|||
|
|
" train_data_df = train_data_df.sort_values(by='trade_date')\n",
|
|||
|
|
"\n",
|
|||
|
|
" numeric_columns = train_data_df.select_dtypes(include=['float64', 'int64']).columns\n",
|
|||
|
|
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
|||
|
|
" # X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n",
|
|||
|
|
" # X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n",
|
|||
|
|
" # train_data_df = cross_sectional_standardization(train_data_df, numeric_columns)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 去除标签为空的样本\n",
|
|||
|
|
" train_data_df = train_data_df.dropna(subset=['label'])\n",
|
|||
|
|
" # print('原始训练集大小: ', len(train_data_df))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 按时间顺序划分训练集和验证集\n",
|
|||
|
|
" if split_date is None:\n",
|
|||
|
|
" all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n",
|
|||
|
|
" if validation_days == 0:\n",
|
|||
|
|
" split_date = all_dates[-1]\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n",
|
|||
|
|
" if validation_days == 0:\n",
|
|||
|
|
" train_data_split = train_data_df\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n",
|
|||
|
|
" val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 打印划分结果\n",
|
|||
|
|
" print(f\"划分后的训练集大小: {len(train_data_split)}, 验证集大小: {len(val_data_split)}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 提取特征和标签\n",
|
|||
|
|
" X_train = train_data_split[feature_columns]\n",
|
|||
|
|
" y_train = train_data_split['label']\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 标准化数值特征\n",
|
|||
|
|
" scaler = StandardScaler()\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 计算每个 trade_date 内的样本数（LTR 需要 group 信息）\n",
|
|||
|
|
" train_groups = train_data_split.groupby('trade_date').size().tolist()\n",
|
|||
|
|
" val_groups = val_data_split.groupby('trade_date').size().tolist()\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 处理类别特征\n",
|
|||
|
|
" categorical_feature = [col for col in feature_columns if 'cat' in col]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 计算权重（基于时间）\n",
|
|||
|
|
" # trade_date = train_data_split['trade_date'] # 交易日期\n",
|
|||
|
|
" # weights = (trade_date - trade_date.min()).dt.days / (trade_date.max() - trade_date.min()).days + 1\n",
|
|||
|
|
" # weights = train_data_split.groupby('trade_date')['std_return_5'].transform(\n",
|
|||
|
|
" # lambda x: x / x.mean()\n",
|
|||
|
|
" # )\n",
|
|||
|
|
" ud = sorted(train_data_split[\"trade_date\"].unique().tolist())\n",
|
|||
|
|
" date_weights = {date: weight * weight for date, weight in zip(ud, np.linspace(1, 10, len(ud)))}\n",
|
|||
|
|
" params['weight'] = train_data_split[\"trade_date\"].map(date_weights).tolist()\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_dataset = lgb.Dataset(\n",
|
|||
|
|
" X_train, label=y_train, group=train_groups,\n",
|
|||
|
|
" categorical_feature=categorical_feature\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if validation_days > 0:\n",
|
|||
|
|
" X_val = val_data_split[feature_columns]\n",
|
|||
|
|
" y_val = val_data_split['label']\n",
|
|||
|
|
" val_groups = val_data_split.groupby('trade_date').size().tolist()\n",
|
|||
|
|
" val_dataset = lgb.Dataset(\n",
|
|||
|
|
" X_val, label=y_val, group=val_groups,\n",
|
|||
|
|
" categorical_feature=categorical_feature\n",
|
|||
|
|
" )\n",
|
|||
|
|
" # 训练模型\n",
|
|||
|
|
" model = lgb.train(\n",
|
|||
|
|
" params, train_dataset, num_boost_round=num_boost_round,\n",
|
|||
|
|
" valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
|
|||
|
|
" callbacks=callbacks\n",
|
|||
|
|
" )\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" model = lgb.train(\n",
|
|||
|
|
" params, train_dataset, num_boost_round=num_boost_round, callbacks=callbacks\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 打印特征重要性（如果需要）\n",
|
|||
|
|
" if print_feature_importance:\n",
|
|||
|
|
" lgb.plot_metric(evals)\n",
|
|||
|
|
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
" return model, scaler, None\n",
|
|||
|
|
"\n",
|
|||
|
|
"def rolling_train_predict(df, train_days, test_days, feature_columns_origin, days=5, use_pca=False, validation_days=60,\n",
|
|||
|
|
" filter_index=None, params=None):\n",
|
|||
|
|
" # 1. 按照交易日期排序\n",
|
|||
|
|
" unique_dates = df[df['trade_date'] >= '2020-01-01']['trade_date'].unique().tolist()\n",
|
|||
|
|
" unique_dates = sorted(unique_dates)\n",
|
|||
|
|
" n = len(unique_dates)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 2. 计算需要跳过的天数，使后续窗口对齐\n",
|
|||
|
|
" extra_days = (n - train_days) % test_days\n",
|
|||
|
|
" start_index = extra_days # 从此索引开始滚动\n",
|
|||
|
|
"\n",
|
|||
|
|
" predictions_list = []\n",
|
|||
|
|
"\n",
|
|||
|
|
" for start in range(start_index, n - train_days - test_days + 1, test_days):\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_dates = unique_dates[start: start + train_days]\n",
|
|||
|
|
" test_dates = unique_dates[start + train_days: start + train_days + test_days]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # 根据日期筛选数据\n",
|
|||
|
|
" # train_data = df[df['trade_date'].isin(train_dates)]\n",
|
|||
|
|
" train_data = df[filter_index & df['trade_date'].isin(train_dates)]\n",
|
|||
|
|
" test_data = df[df['trade_date'].isin(test_dates)]\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_data = train_data.sort_values('trade_date')\n",
|
|||
|
|
" test_data = test_data.sort_values('trade_date')\n",
|
|||
|
|
"\n",
|
|||
|
|
" feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_data = train_data.dropna(subset=feature_columns)\n",
|
|||
|
|
" train_data = train_data.dropna(subset=['label'])\n",
|
|||
|
|
" train_data = train_data.reset_index(drop=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # print(test_data.tail())\n",
|
|||
|
|
" test_data = test_data.dropna(subset=feature_columns)\n",
|
|||
|
|
" # test_data = test_data.dropna(subset=['label'])\n",
|
|||
|
|
" test_data = test_data.reset_index(drop=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # print(len(train_data))\n",
|
|||
|
|
" # print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
|||
|
|
" print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
|||
|
|
" # # print(len(test_data))\n",
|
|||
|
|
" # print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
|||
|
|
" print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" cat_columns = [col for col in df.columns if col.startswith('cat')]\n",
|
|||
|
|
" for col in cat_columns:\n",
|
|||
|
|
" train_data[col] = train_data[col].astype('category')\n",
|
|||
|
|
" test_data[col] = test_data[col].astype('category')\n",
|
|||
|
|
"\n",
|
|||
|
|
" label_gain = list(range(len(train_data['label'].unique())))\n",
|
|||
|
|
" label_gain = [(gain + 1) * (gain + 1) for gain in label_gain]\n",
|
|||
|
|
" params['label_gain'] = label_gain\n",
|
|||
|
|
"\n",
|
|||
|
|
" # ud = train_data[\"trade_date\"].unique()\n",
|
|||
|
|
" # date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))}\n",
|
|||
|
|
" # params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\n",
|
|||
|
|
"\n",
|
|||
|
|
" # print(f'feature_columns: {feature_columns}')\n",
|
|||
|
|
" # feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n",
|
|||
|
|
" # params['feature_contri'] = feature_contri\n",
|
|||
|
|
" evals = {}\n",
|
|||
|
|
" model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n",
|
|||
|
|
" params, feature_columns,\n",
|
|||
|
|
" [lgb.log_evaluation(period=100),\n",
|
|||
|
|
" lgb.callback.record_evaluation(evals),\n",
|
|||
|
|
" # lgb.early_stopping(100, first_metric_only=True)\n",
|
|||
|
|
" ], evals,\n",
|
|||
|
|
" num_boost_round=100, validation_days=validation_days,\n",
|
|||
|
|
" print_feature_importance=False, use_pca=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" score_df = test_data.copy()\n",
|
|||
|
|
" score_df['score'] = model.predict(score_df[feature_columns])\n",
|
|||
|
|
" score_df = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
|
|||
|
|
" score_df = score_df[['trade_date', 'score', 'ts_code']]\n",
|
|||
|
|
" predictions_list.append(score_df)\n",
|
|||
|
|
"\n",
|
|||
|
|
" final_predictions = pd.concat(predictions_list, ignore_index=True)\n",
|
|||
|
|
" return final_predictions"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"id": "ddb5b67a9852e2",
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"train_data最大日期: 2022-12-07\n",
|
|||
|
|
"test_data最大日期: 2022-12-08\n",
|
|||
|
|
"划分后的训练集大小: 525, 验证集大小: 106\n",
|
|||
|
|
"train_data最大日期: 2022-12-08\n",
|
|||
|
|
"test_data最大日期: 2022-12-09\n",
|
|||
|
|
"划分后的训练集大小: 531, 验证集大小: 109\n",
|
|||
|
|
"train_data最大日期: 2022-12-09\n",
|
|||
|
|
"test_data最大日期: 2022-12-12\n",
|
|||
|
|
"划分后的训练集大小: 516, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2022-12-12\n",
|
|||
|
|
"test_data最大日期: 2022-12-13\n",
|
|||
|
|
"划分后的训练集大小: 528, 验证集大小: 108\n",
|
|||
|
|
"train_data最大日期: 2022-12-13\n",
|
|||
|
|
"test_data最大日期: 2022-12-14\n",
|
|||
|
|
"划分后的训练集大小: 571, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2022-12-14\n",
|
|||
|
|
"test_data最大日期: 2022-12-15\n",
|
|||
|
|
"划分后的训练集大小: 565, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2022-12-15\n",
|
|||
|
|
"test_data最大日期: 2022-12-16\n",
|
|||
|
|
"划分后的训练集大小: 600, 验证集大小: 144\n",
|
|||
|
|
"train_data最大日期: 2022-12-16\n",
|
|||
|
|
"test_data最大日期: 2022-12-19\n",
|
|||
|
|
"划分后的训练集大小: 597, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2022-12-19\n",
|
|||
|
|
"test_data最大日期: 2022-12-20\n",
|
|||
|
|
"划分后的训练集大小: 633, 验证集大小: 144\n",
|
|||
|
|
"train_data最大日期: 2022-12-20\n",
|
|||
|
|
"test_data最大日期: 2022-12-21\n",
|
|||
|
|
"划分后的训练集大小: 627, 验证集大小: 142\n",
|
|||
|
|
"train_data最大日期: 2022-12-21\n",
|
|||
|
|
"test_data最大日期: 2022-12-22\n",
|
|||
|
|
"划分后的训练集大小: 624, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2022-12-22\n",
|
|||
|
|
"test_data最大日期: 2022-12-23\n",
|
|||
|
|
"划分后的训练集大小: 605, 验证集大小: 125\n",
|
|||
|
|
"train_data最大日期: 2022-12-23\n",
|
|||
|
|
"test_data最大日期: 2022-12-26\n",
|
|||
|
|
"划分后的训练集大小: 603, 验证集大小: 95\n",
|
|||
|
|
"train_data最大日期: 2022-12-26\n",
|
|||
|
|
"test_data最大日期: 2022-12-27\n",
|
|||
|
|
"划分后的训练集大小: 558, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2022-12-27\n",
|
|||
|
|
"test_data最大日期: 2022-12-28\n",
|
|||
|
|
"划分后的训练集大小: 510, 验证集大小: 94\n",
|
|||
|
|
"train_data最大日期: 2022-12-28\n",
|
|||
|
|
"test_data最大日期: 2022-12-29\n",
|
|||
|
|
"划分后的训练集大小: 508, 验证集大小: 95\n",
|
|||
|
|
"train_data最大日期: 2022-12-29\n",
|
|||
|
|
"test_data最大日期: 2022-12-30\n",
|
|||
|
|
"划分后的训练集大小: 528, 验证集大小: 145\n",
|
|||
|
|
"train_data最大日期: 2022-12-30\n",
|
|||
|
|
"test_data最大日期: 2023-01-03\n",
|
|||
|
|
"划分后的训练集大小: 570, 验证集大小: 137\n",
|
|||
|
|
"train_data最大日期: 2023-01-03\n",
|
|||
|
|
"test_data最大日期: 2023-01-04\n",
|
|||
|
|
"划分后的训练集大小: 618, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-01-04\n",
|
|||
|
|
"test_data最大日期: 2023-01-05\n",
|
|||
|
|
"划分后的训练集大小: 666, 验证集大小: 142\n",
|
|||
|
|
"train_data最大日期: 2023-01-05\n",
|
|||
|
|
"test_data最大日期: 2023-01-06\n",
|
|||
|
|
"划分后的训练集大小: 717, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-01-06\n",
|
|||
|
|
"test_data最大日期: 2023-01-09\n",
|
|||
|
|
"划分后的训练集大小: 670, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-01-09\n",
|
|||
|
|
"test_data最大日期: 2023-01-10\n",
|
|||
|
|
"划分后的训练集大小: 630, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2023-01-10\n",
|
|||
|
|
"test_data最大日期: 2023-01-11\n",
|
|||
|
|
"划分后的训练集大小: 589, 验证集大小: 106\n",
|
|||
|
|
"train_data最大日期: 2023-01-11\n",
|
|||
|
|
"test_data最大日期: 2023-01-12\n",
|
|||
|
|
"划分后的训练集大小: 543, 验证集大小: 96\n",
|
|||
|
|
"train_data最大日期: 2023-01-12\n",
|
|||
|
|
"test_data最大日期: 2023-01-13\n",
|
|||
|
|
"划分后的训练集大小: 544, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-01-13\n",
|
|||
|
|
"test_data最大日期: 2023-01-16\n",
|
|||
|
|
"划分后的训练集大小: 553, 验证集大小: 107\n",
|
|||
|
|
"train_data最大日期: 2023-01-16\n",
|
|||
|
|
"test_data最大日期: 2023-01-17\n",
|
|||
|
|
"划分后的训练集大小: 573, 验证集大小: 117\n",
|
|||
|
|
"train_data最大日期: 2023-01-17\n",
|
|||
|
|
"test_data最大日期: 2023-01-18\n",
|
|||
|
|
"划分后的训练集大小: 604, 验证集大小: 137\n",
|
|||
|
|
"train_data最大日期: 2023-01-18\n",
|
|||
|
|
"test_data最大日期: 2023-01-19\n",
|
|||
|
|
"划分后的训练集大小: 625, 验证集大小: 117\n",
|
|||
|
|
"train_data最大日期: 2023-01-19\n",
|
|||
|
|
"test_data最大日期: 2023-01-20\n",
|
|||
|
|
"划分后的训练集大小: 616, 验证集大小: 138\n",
|
|||
|
|
"train_data最大日期: 2023-01-20\n",
|
|||
|
|
"test_data最大日期: 2023-01-30\n",
|
|||
|
|
"划分后的训练集大小: 609, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-01-30\n",
|
|||
|
|
"test_data最大日期: 2023-01-31\n",
|
|||
|
|
"划分后的训练集大小: 621, 验证集大小: 129\n",
|
|||
|
|
"train_data最大日期: 2023-01-31\n",
|
|||
|
|
"test_data最大日期: 2023-02-01\n",
|
|||
|
|
"划分后的训练集大小: 584, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-02-01\n",
|
|||
|
|
"test_data最大日期: 2023-02-02\n",
|
|||
|
|
"划分后的训练集大小: 583, 验证集大小: 116\n",
|
|||
|
|
"train_data最大日期: 2023-02-02\n",
|
|||
|
|
"test_data最大日期: 2023-02-03\n",
|
|||
|
|
"划分后的训练集大小: 553, 验证集大小: 108\n",
|
|||
|
|
"train_data最大日期: 2023-02-03\n",
|
|||
|
|
"test_data最大日期: 2023-02-06\n",
|
|||
|
|
"划分后的训练集大小: 581, 验证集大小: 128\n",
|
|||
|
|
"train_data最大日期: 2023-02-06\n",
|
|||
|
|
"test_data最大日期: 2023-02-07\n",
|
|||
|
|
"划分后的训练集大小: 572, 验证集大小: 120\n",
|
|||
|
|
"train_data最大日期: 2023-02-07\n",
|
|||
|
|
"test_data最大日期: 2023-02-08\n",
|
|||
|
|
"划分后的训练集大小: 622, 验证集大小: 150\n",
|
|||
|
|
"train_data最大日期: 2023-02-08\n",
|
|||
|
|
"test_data最大日期: 2023-02-09\n",
|
|||
|
|
"划分后的训练集大小: 656, 验证集大小: 150\n",
|
|||
|
|
"train_data最大日期: 2023-02-09\n",
|
|||
|
|
"test_data最大日期: 2023-02-10\n",
|
|||
|
|
"划分后的训练集大小: 697, 验证集大小: 149\n",
|
|||
|
|
"train_data最大日期: 2023-02-10\n",
|
|||
|
|
"test_data最大日期: 2023-02-13\n",
|
|||
|
|
"划分后的训练集大小: 698, 验证集大小: 129\n",
|
|||
|
|
"train_data最大日期: 2023-02-13\n",
|
|||
|
|
"test_data最大日期: 2023-02-14\n",
|
|||
|
|
"划分后的训练集大小: 717, 验证集大小: 139\n",
|
|||
|
|
"train_data最大日期: 2023-02-14\n",
|
|||
|
|
"test_data最大日期: 2023-02-15\n",
|
|||
|
|
"划分后的训练集大小: 715, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-02-15\n",
|
|||
|
|
"test_data最大日期: 2023-02-16\n",
|
|||
|
|
"划分后的训练集大小: 714, 验证集大小: 149\n",
|
|||
|
|
"train_data最大日期: 2023-02-16\n",
|
|||
|
|
"test_data最大日期: 2023-02-17\n",
|
|||
|
|
"划分后的训练集大小: 713, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-02-17\n",
|
|||
|
|
"test_data最大日期: 2023-02-20\n",
|
|||
|
|
"划分后的训练集大小: 682, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-02-20\n",
|
|||
|
|
"test_data最大日期: 2023-02-21\n",
|
|||
|
|
"划分后的训练集大小: 681, 验证集大小: 138\n",
|
|||
|
|
"train_data最大日期: 2023-02-21\n",
|
|||
|
|
"test_data最大日期: 2023-02-22\n",
|
|||
|
|
"划分后的训练集大小: 632, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-02-22\n",
|
|||
|
|
"test_data最大日期: 2023-02-23\n",
|
|||
|
|
"划分后的训练集大小: 619, 验证集大小: 136\n",
|
|||
|
|
"train_data最大日期: 2023-02-23\n",
|
|||
|
|
"test_data最大日期: 2023-02-24\n",
|
|||
|
|
"划分后的训练集大小: 571, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-02-24\n",
|
|||
|
|
"test_data最大日期: 2023-02-27\n",
|
|||
|
|
"划分后的训练集大小: 621, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-02-27\n",
|
|||
|
|
"test_data最大日期: 2023-02-28\n",
|
|||
|
|
"划分后的训练集大小: 632, 验证集大小: 149\n",
|
|||
|
|
"train_data最大日期: 2023-02-28\n",
|
|||
|
|
"test_data最大日期: 2023-03-01\n",
|
|||
|
|
"划分后的训练集大小: 632, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-03-01\n",
|
|||
|
|
"test_data最大日期: 2023-03-02\n",
|
|||
|
|
"划分后的训练集大小: 596, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-03-02\n",
|
|||
|
|
"test_data最大日期: 2023-03-03\n",
|
|||
|
|
"划分后的训练集大小: 595, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-03-03\n",
|
|||
|
|
"test_data最大日期: 2023-03-06\n",
|
|||
|
|
"划分后的训练集大小: 596, 验证集大小: 149\n",
|
|||
|
|
"train_data最大日期: 2023-03-06\n",
|
|||
|
|
"test_data最大日期: 2023-03-07\n",
|
|||
|
|
"划分后的训练集大小: 547, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-03-07\n",
|
|||
|
|
"test_data最大日期: 2023-03-08\n",
|
|||
|
|
"划分后的训练集大小: 567, 验证集大小: 119\n",
|
|||
|
|
"train_data最大日期: 2023-03-08\n",
|
|||
|
|
"test_data最大日期: 2023-03-09\n",
|
|||
|
|
"划分后的训练集大小: 585, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-03-09\n",
|
|||
|
|
"test_data最大日期: 2023-03-10\n",
|
|||
|
|
"划分后的训练集大小: 634, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-03-10\n",
|
|||
|
|
"test_data最大日期: 2023-03-13\n",
|
|||
|
|
"划分后的训练集大小: 630, 验证集大小: 145\n",
|
|||
|
|
"train_data最大日期: 2023-03-13\n",
|
|||
|
|
"test_data最大日期: 2023-03-14\n",
|
|||
|
|
"划分后的训练集大小: 638, 验证集大小: 108\n",
|
|||
|
|
"train_data最大日期: 2023-03-14\n",
|
|||
|
|
"test_data最大日期: 2023-03-15\n",
|
|||
|
|
"划分后的训练集大小: 665, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-03-15\n",
|
|||
|
|
"test_data最大日期: 2023-03-16\n",
|
|||
|
|
"划分后的训练集大小: 677, 验证集大小: 130\n",
|
|||
|
|
"train_data最大日期: 2023-03-16\n",
|
|||
|
|
"test_data最大日期: 2023-03-17\n",
|
|||
|
|
"划分后的训练集大小: 678, 验证集大小: 149\n",
|
|||
|
|
"train_data最大日期: 2023-03-17\n",
|
|||
|
|
"test_data最大日期: 2023-03-20\n",
|
|||
|
|
"划分后的训练集大小: 642, 验证集大小: 109\n",
|
|||
|
|
"train_data最大日期: 2023-03-20\n",
|
|||
|
|
"test_data最大日期: 2023-03-21\n",
|
|||
|
|
"划分后的训练集大小: 663, 验证集大小: 129\n",
|
|||
|
|
"train_data最大日期: 2023-03-21\n",
|
|||
|
|
"test_data最大日期: 2023-03-22\n",
|
|||
|
|
"划分后的训练集大小: 615, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-03-22\n",
|
|||
|
|
"test_data最大日期: 2023-03-23\n",
|
|||
|
|
"划分后的训练集大小: 633, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-03-23\n",
|
|||
|
|
"test_data最大日期: 2023-03-24\n",
|
|||
|
|
"划分后的训练集大小: 627, 验证集大小: 143\n",
|
|||
|
|
"train_data最大日期: 2023-03-24\n",
|
|||
|
|
"test_data最大日期: 2023-03-27\n",
|
|||
|
|
"划分后的训练集大小: 646, 验证集大小: 128\n",
|
|||
|
|
"train_data最大日期: 2023-03-27\n",
|
|||
|
|
"test_data最大日期: 2023-03-28\n",
|
|||
|
|
"划分后的训练集大小: 615, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-03-28\n",
|
|||
|
|
"test_data最大日期: 2023-03-29\n",
|
|||
|
|
"划分后的训练集大小: 644, 验证集大小: 127\n",
|
|||
|
|
"train_data最大日期: 2023-03-29\n",
|
|||
|
|
"test_data最大日期: 2023-03-30\n",
|
|||
|
|
"划分后的训练集大小: 623, 验证集大小: 127\n",
|
|||
|
|
"train_data最大日期: 2023-03-30\n",
|
|||
|
|
"test_data最大日期: 2023-03-31\n",
|
|||
|
|
"划分后的训练集大小: 577, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2023-03-31\n",
|
|||
|
|
"test_data最大日期: 2023-04-03\n",
|
|||
|
|
"划分后的训练集大小: 595, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-04-03\n",
|
|||
|
|
"test_data最大日期: 2023-04-04\n",
|
|||
|
|
"划分后的训练集大小: 644, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-04-04\n",
|
|||
|
|
"test_data最大日期: 2023-04-06\n",
|
|||
|
|
"划分后的训练集大小: 632, 验证集大小: 115\n",
|
|||
|
|
"train_data最大日期: 2023-04-06\n",
|
|||
|
|
"test_data最大日期: 2023-04-07\n",
|
|||
|
|
"划分后的训练集大小: 651, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-04-07\n",
|
|||
|
|
"test_data最大日期: 2023-04-10\n",
|
|||
|
|
"划分后的训练集大小: 702, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-04-10\n",
|
|||
|
|
"test_data最大日期: 2023-04-11\n",
|
|||
|
|
"划分后的训练集大小: 701, 验证集大小: 145\n",
|
|||
|
|
"train_data最大日期: 2023-04-11\n",
|
|||
|
|
"test_data最大日期: 2023-04-12\n",
|
|||
|
|
"划分后的训练集大小: 672, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-04-12\n",
|
|||
|
|
"test_data最大日期: 2023-04-13\n",
|
|||
|
|
"划分后的训练集大小: 694, 验证集大小: 137\n",
|
|||
|
|
"train_data最大日期: 2023-04-13\n",
|
|||
|
|
"test_data最大日期: 2023-04-14\n",
|
|||
|
|
"划分后的训练集大小: 695, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-04-14\n",
|
|||
|
|
"test_data最大日期: 2023-04-17\n",
|
|||
|
|
"划分后的训练集大小: 684, 验证集大小: 137\n",
|
|||
|
|
"train_data最大日期: 2023-04-17\n",
|
|||
|
|
"test_data最大日期: 2023-04-18\n",
|
|||
|
|
"划分后的训练集大小: 638, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-04-18\n",
|
|||
|
|
"test_data最大日期: 2023-04-19\n",
|
|||
|
|
"划分后的训练集大小: 649, 验证集大小: 129\n",
|
|||
|
|
"train_data最大日期: 2023-04-19\n",
|
|||
|
|
"test_data最大日期: 2023-04-20\n",
|
|||
|
|
"划分后的训练集大小: 610, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-04-20\n",
|
|||
|
|
"test_data最大日期: 2023-04-21\n",
|
|||
|
|
"划分后的训练集大小: 611, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-04-21\n",
|
|||
|
|
"test_data最大日期: 2023-04-24\n",
|
|||
|
|
"划分后的训练集大小: 610, 验证集大小: 136\n",
|
|||
|
|
"train_data最大日期: 2023-04-24\n",
|
|||
|
|
"test_data最大日期: 2023-04-25\n",
|
|||
|
|
"划分后的训练集大小: 657, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-04-25\n",
|
|||
|
|
"test_data最大日期: 2023-04-26\n",
|
|||
|
|
"划分后的训练集大小: 675, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-04-26\n",
|
|||
|
|
"test_data最大日期: 2023-04-27\n",
|
|||
|
|
"划分后的训练集大小: 677, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-04-27\n",
|
|||
|
|
"test_data最大日期: 2023-04-28\n",
|
|||
|
|
"划分后的训练集大小: 653, 验证集大小: 124\n",
|
|||
|
|
"train_data最大日期: 2023-04-28\n",
|
|||
|
|
"test_data最大日期: 2023-05-04\n",
|
|||
|
|
"划分后的训练集大小: 664, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-05-04\n",
|
|||
|
|
"test_data最大日期: 2023-05-05\n",
|
|||
|
|
"划分后的训练集大小: 636, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-05-05\n",
|
|||
|
|
"test_data最大日期: 2023-05-08\n",
|
|||
|
|
"划分后的训练集大小: 637, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-05-08\n",
|
|||
|
|
"test_data最大日期: 2023-05-09\n",
|
|||
|
|
"划分后的训练集大小: 685, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-05-09\n",
|
|||
|
|
"test_data最大日期: 2023-05-10\n",
|
|||
|
|
"划分后的训练集大小: 658, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2023-05-10\n",
|
|||
|
|
"test_data最大日期: 2023-05-11\n",
|
|||
|
|
"划分后的训练集大小: 638, 验证集大小: 127\n",
|
|||
|
|
"train_data最大日期: 2023-05-11\n",
|
|||
|
|
"test_data最大日期: 2023-05-12\n",
|
|||
|
|
"划分后的训练集大小: 666, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-05-12\n",
|
|||
|
|
"test_data最大日期: 2023-05-15\n",
|
|||
|
|
"划分后的训练集大小: 664, 验证集大小: 146\n",
|
|||
|
|
"train_data最大日期: 2023-05-15\n",
|
|||
|
|
"test_data最大日期: 2023-05-16\n",
|
|||
|
|
"划分后的训练集大小: 621, 验证集大小: 105\n",
|
|||
|
|
"train_data最大日期: 2023-05-16\n",
|
|||
|
|
"test_data最大日期: 2023-05-17\n",
|
|||
|
|
"划分后的训练集大小: 623, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-05-17\n",
|
|||
|
|
"test_data最大日期: 2023-05-18\n",
|
|||
|
|
"划分后的训练集大小: 606, 验证集大小: 110\n",
|
|||
|
|
"train_data最大日期: 2023-05-18\n",
|
|||
|
|
"test_data最大日期: 2023-05-19\n",
|
|||
|
|
"划分后的训练集大小: 578, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-05-19\n",
|
|||
|
|
"test_data最大日期: 2023-05-22\n",
|
|||
|
|
"划分后的训练集大小: 540, 验证集大小: 108\n",
|
|||
|
|
"train_data最大日期: 2023-05-22\n",
|
|||
|
|
"test_data最大日期: 2023-05-23\n",
|
|||
|
|
"划分后的训练集大小: 532, 验证集大小: 97\n",
|
|||
|
|
"train_data最大日期: 2023-05-23\n",
|
|||
|
|
"test_data最大日期: 2023-05-24\n",
|
|||
|
|
"划分后的训练集大小: 559, 验证集大小: 126\n",
|
|||
|
|
"train_data最大日期: 2023-05-24\n",
|
|||
|
|
"test_data最大日期: 2023-05-25\n",
|
|||
|
|
"划分后的训练集大小: 548, 验证集大小: 99\n",
|
|||
|
|
"train_data最大日期: 2023-05-25\n",
|
|||
|
|
"test_data最大日期: 2023-05-26\n",
|
|||
|
|
"划分后的训练集大小: 526, 验证集大小: 96\n",
|
|||
|
|
"train_data最大日期: 2023-05-26\n",
|
|||
|
|
"test_data最大日期: 2023-05-29\n",
|
|||
|
|
"划分后的训练集大小: 516, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-05-29\n",
|
|||
|
|
"test_data最大日期: 2023-05-30\n",
|
|||
|
|
"划分后的训练集大小: 527, 验证集大小: 108\n",
|
|||
|
|
"train_data最大日期: 2023-05-30\n",
|
|||
|
|
"test_data最大日期: 2023-05-31\n",
|
|||
|
|
"划分后的训练集大小: 546, 验证集大小: 145\n",
|
|||
|
|
"train_data最大日期: 2023-05-31\n",
|
|||
|
|
"test_data最大日期: 2023-06-01\n",
|
|||
|
|
"划分后的训练集大小: 594, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-06-01\n",
|
|||
|
|
"test_data最大日期: 2023-06-02\n",
|
|||
|
|
"划分后的训练集大小: 616, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-06-02\n",
|
|||
|
|
"test_data最大日期: 2023-06-05\n",
|
|||
|
|
"划分后的训练集大小: 666, 验证集大小: 148\n",
|
|||
|
|
"train_data最大日期: 2023-06-05\n",
|
|||
|
|
"test_data最大日期: 2023-06-06\n",
|
|||
|
|
"划分后的训练集大小: 676, 验证集大小: 118\n",
|
|||
|
|
"train_data最大日期: 2023-06-06\n",
|
|||
|
|
"test_data最大日期: 2023-06-07\n",
|
|||
|
|
"划分后的训练集大小: 626, 验证集大小: 95\n",
|
|||
|
|
"train_data最大日期: 2023-06-07\n",
|
|||
|
|
"test_data最大日期: 2023-06-08\n",
|
|||
|
|
"划分后的训练集大小: 626, 验证集大小: 147\n",
|
|||
|
|
"train_data最大日期: 2023-06-08\n",
|
|||
|
|
"test_data最大日期: 2023-06-09\n",
|
|||
|
|
"划分后的训练集大小: 606, 验证集大小: 98\n",
|
|||
|
|
"train_data最大日期: 2023-06-09\n",
|
|||
|
|
"test_data最大日期: 2023-06-12\n",
|
|||
|
|
"划分后的训练集大小: 558, 验证集大小: 100\n",
|
|||
|
|
"train_data最大日期: 2023-06-12\n",
|
|||
|
|
"test_data最大日期: 2023-06-13\n",
|
|||
|
|
"划分后的训练集大小: 579, 验证集大小: 139\n",
|
|||
|
|
"train_data最大日期: 2023-06-13\n",
|
|||
|
|
"test_data最大日期: 2023-06-14\n",
|
|||
|
|
"划分后的训练集大小: 630, 验证集大小: 146\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "LightGBMError",
|
|||
|
|
"evalue": "Forced splits file includes feature index 0, but maximum feature index in dataset is -1",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
|||
|
|
"\u001B[1;31mLightGBMError\u001B[0m Traceback (most recent call last)",
|
|||
|
|
"Cell \u001B[1;32mIn[36], line 38\u001B[0m\n\u001B[0;32m 34\u001B[0m final_predictions\u001B[38;5;241m.\u001B[39mto_csv(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpredictions_test.tsv\u001B[39m\u001B[38;5;124m'\u001B[39m, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 36\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m final_predictions\n\u001B[1;32m---> 38\u001B[0m final_predictions1 \u001B[38;5;241m=\u001B[39m train(pdf1, feature_columns1, filter_index1)\n\u001B[0;32m 39\u001B[0m final_predictions2 \u001B[38;5;241m=\u001B[39m train(pdf2, feature_columns2, filter_index2)\n",
|
|||
|
|
"Cell \u001B[1;32mIn[36], line 31\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(pdf, feature_columns, filter_index)\u001B[0m\n\u001B[0;32m 4\u001B[0m light_params \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m 5\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel_gain\u001B[39m\u001B[38;5;124m'\u001B[39m: label_gain,\n\u001B[0;32m 6\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mobjective\u001B[39m\u001B[38;5;124m'\u001B[39m: \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlambdarank\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 26\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mseed\u001B[39m\u001B[38;5;124m'\u001B[39m: \u001B[38;5;241m7\u001B[39m\n\u001B[0;32m 27\u001B[0m }\n\u001B[0;32m 29\u001B[0m gc\u001B[38;5;241m.\u001B[39mcollect()\n\u001B[1;32m---> 31\u001B[0m final_predictions \u001B[38;5;241m=\u001B[39m rolling_train_predict(\n\u001B[0;32m 32\u001B[0m pdf[(pdf[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2022-12-01\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;241m&\u001B[39m (pdf[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2029-03-26\u001B[39m\u001B[38;5;124m'\u001B[39m)], \u001B[38;5;241m5\u001B[39m, \u001B[38;5;241m1\u001B[39m, feature_columns,\n\u001B[0;32m 33\u001B[0m days\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, validation_days\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, filter_index\u001B[38;5;241m=\u001B[39mfilter_index, params\u001B[38;5;241m=\u001B[39mlight_params)\n\u001B[0;32m 34\u001B[0m final_predictions\u001B[38;5;241m.\u001B[39mto_csv(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpredictions_test.tsv\u001B[39m\u001B[38;5;124m'\u001B[39m, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 36\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m final_predictions\n",
|
|||
|
|
"Cell \u001B[1;32mIn[33], line 154\u001B[0m, in \u001B[0;36mrolling_train_predict\u001B[1;34m(df, train_days, test_days, feature_columns_origin, days, use_pca, validation_days, filter_index, params)\u001B[0m\n\u001B[0;32m 146\u001B[0m \u001B[38;5;66;03m# ud = train_data[\"trade_date\"].unique()\u001B[39;00m\n\u001B[0;32m 147\u001B[0m \u001B[38;5;66;03m# date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))}\u001B[39;00m\n\u001B[0;32m 148\u001B[0m \u001B[38;5;66;03m# params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 151\u001B[0m \u001B[38;5;66;03m# feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\u001B[39;00m\n\u001B[0;32m 152\u001B[0m \u001B[38;5;66;03m# params['feature_contri'] = feature_contri\u001B[39;00m\n\u001B[0;32m 153\u001B[0m evals \u001B[38;5;241m=\u001B[39m {}\n\u001B[1;32m--> 154\u001B[0m model, _, _ \u001B[38;5;241m=\u001B[39m train_light_model(train_data\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m]),\n\u001B[0;32m 155\u001B[0m params, feature_columns,\n\u001B[0;32m 156\u001B[0m [lgb\u001B[38;5;241m.\u001B[39mlog_evaluation(period\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m),\n\u001B[0;32m 157\u001B[0m lgb\u001B[38;5;241m.\u001B[39mcallback\u001B[38;5;241m.\u001B[39mrecord_evaluation(evals),\n\u001B[0;32m 158\u001B[0m \u001B[38;5;66;03m# lgb.early_stopping(100, first_metric_only=True)\u001B[39;00m\n\u001B[0;32m 159\u001B[0m ], evals,\n\u001B[0;32m 160\u001B[0m num_boost_round\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m, validation_days\u001B[38;5;241m=\u001B[39mvalidation_days,\n\u001B[0;32m 161\u001B[0m print_feature_importance\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, use_pca\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 163\u001B[0m score_df \u001B[38;5;241m=\u001B[39m test_data\u001B[38;5;241m.\u001B[39mcopy()\n\u001B[0;32m 164\u001B[0m score_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mscore\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict(score_df[feature_columns])\n",
|
|||
|
|
"Cell \u001B[1;32mIn[33], line 81\u001B[0m, in \u001B[0;36mtrain_light_model\u001B[1;34m(train_data_df, params, feature_columns, callbacks, evals, print_feature_importance, num_boost_round, validation_days, use_pca, split_date)\u001B[0m\n\u001B[0;32m 75\u001B[0m model \u001B[38;5;241m=\u001B[39m lgb\u001B[38;5;241m.\u001B[39mtrain(\n\u001B[0;32m 76\u001B[0m params, train_dataset, num_boost_round\u001B[38;5;241m=\u001B[39mnum_boost_round,\n\u001B[0;32m 77\u001B[0m valid_sets\u001B[38;5;241m=\u001B[39m[train_dataset, val_dataset], valid_names\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mvalid\u001B[39m\u001B[38;5;124m'\u001B[39m],\n\u001B[0;32m 78\u001B[0m callbacks\u001B[38;5;241m=\u001B[39mcallbacks\n\u001B[0;32m 79\u001B[0m )\n\u001B[0;32m 80\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 81\u001B[0m model \u001B[38;5;241m=\u001B[39m lgb\u001B[38;5;241m.\u001B[39mtrain(\n\u001B[0;32m 82\u001B[0m params, train_dataset, num_boost_round\u001B[38;5;241m=\u001B[39mnum_boost_round, callbacks\u001B[38;5;241m=\u001B[39mcallbacks\n\u001B[0;32m 83\u001B[0m )\n\u001B[0;32m 85\u001B[0m \u001B[38;5;66;03m# 打印特征重要性（如果需要）\u001B[39;00m\n\u001B[0;32m 86\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m print_feature_importance:\n",
|
|||
|
|
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\engine.py:297\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, keep_training_booster, callbacks)\u001B[0m\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# construct booster\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 297\u001B[0m booster \u001B[38;5;241m=\u001B[39m Booster(params\u001B[38;5;241m=\u001B[39mparams, train_set\u001B[38;5;241m=\u001B[39mtrain_set)\n\u001B[0;32m 298\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_valid_contain_train:\n\u001B[0;32m 299\u001B[0m booster\u001B[38;5;241m.\u001B[39mset_train_data_name(train_data_name)\n",
|
|||
|
|
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:3660\u001B[0m, in \u001B[0;36mBooster.__init__\u001B[1;34m(self, params, train_set, model_file, model_str)\u001B[0m\n\u001B[0;32m 3658\u001B[0m params\u001B[38;5;241m.\u001B[39mupdate(train_set\u001B[38;5;241m.\u001B[39mget_params())\n\u001B[0;32m 3659\u001B[0m params_str \u001B[38;5;241m=\u001B[39m _param_dict_to_str(params)\n\u001B[1;32m-> 3660\u001B[0m _safe_call(\n\u001B[0;32m 3661\u001B[0m _LIB\u001B[38;5;241m.\u001B[39mLGBM_BoosterCreate(\n\u001B[0;32m 3662\u001B[0m train_set\u001B[38;5;241m.\u001B[39m_handle,\n\u001B[0;32m 3663\u001B[0m _c_str(params_str),\n\u001B[0;32m 3664\u001B[0m ctypes\u001B[38;5;241m.\u001B[39mbyref(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle),\n\u001B[0;32m 3665\u001B[0m )\n\u001B[0;32m 3666\u001B[0m )\n\u001B[0;32m 3667\u001B[0m \u001B[38;5;66;03m# save reference to data\u001B[39;00m\n\u001B[0;32m 3668\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtrain_set \u001B[38;5;241m=\u001B[39m train_set\n",
|
|||
|
|
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:313\u001B[0m, in \u001B[0;36m_safe_call\u001B[1;34m(ret)\u001B[0m\n\u001B[0;32m 305\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Check the return value from C API call.\u001B[39;00m\n\u001B[0;32m 306\u001B[0m \n\u001B[0;32m 307\u001B[0m \u001B[38;5;124;03mParameters\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 310\u001B[0m \u001B[38;5;124;03m The return value from C API calls.\u001B[39;00m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ret \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 313\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m LightGBMError(_LIB\u001B[38;5;241m.\u001B[39mLGBM_GetLastError()\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n",
|
|||
|
|
"\u001B[1;31mLightGBMError\u001B[0m: Forced splits file includes feature index 0, but maximum feature index in dataset is -1"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def train(pdf, feature_columns, filter_index):\n",
|
|||
|
|
" label_gain = list(range(len(pdf['label'].unique())))\n",
|
|||
|
|
" label_gain = [gain * gain for gain in label_gain]\n",
|
|||
|
|
" light_params = {\n",
|
|||
|
|
" 'label_gain': label_gain,\n",
|
|||
|
|
" 'objective': 'lambdarank',\n",
|
|||
|
|
" 'metric': 'ndcg',\n",
|
|||
|
|
" 'learning_rate': 0.03,\n",
|
|||
|
|
" 'num_leaves': 32,\n",
|
|||
|
|
" # 'min_data_in_leaf': 128,\n",
|
|||
|
|
" 'max_depth': 8,\n",
|
|||
|
|
" 'max_bin': 32,\n",
|
|||
|
|
" 'feature_fraction': 0.7,\n",
|
|||
|
|
" # 'bagging_fraction': 0.7,\n",
|
|||
|
|
" 'bagging_freq': 5,\n",
|
|||
|
|
" 'lambda_l1': 0.1,\n",
|
|||
|
|
" 'lambda_l2': 0.1,\n",
|
|||
|
|
" 'boosting': 'gbdt',\n",
|
|||
|
|
" 'verbosity': -1,\n",
|
|||
|
|
" 'extra_trees': True,\n",
|
|||
|
|
" 'max_position': 5,\n",
|
|||
|
|
" 'ndcg_at': 1,\n",
|
|||
|
|
" 'quant_train_renew_leaf': True,\n",
|
|||
|
|
" 'lambdarank_truncation_level': 3,\n",
|
|||
|
|
" # 'lambdarank_position_bias_regularization': 1,\n",
|
|||
|
|
" 'seed': 7\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" gc.collect()\n",
|
|||
|
|
"\n",
|
|||
|
|
" final_predictions = rolling_train_predict(\n",
|
|||
|
|
" pdf[(pdf['trade_date'] >= '2022-12-01') & (pdf['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n",
|
|||
|
|
" days=0, validation_days=0, filter_index=filter_index, params=light_params)\n",
|
|||
|
|
" final_predictions.to_csv('predictions_test.tsv', index=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return final_predictions\n",
|
|||
|
|
"\n",
|
|||
|
|
"final_predictions1 = train(pdf1, feature_columns1, filter_index1)\n",
|
|||
|
|
"final_predictions2 = train(pdf2, feature_columns2, filter_index2)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "e7e470a2-e1e5-42e5-a2ee-a5fc80455d95",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"slice1 = final_predictions1[final_predictions1['trade_date'] == date_to_compare]\n",
|
|||
|
|
"slice2 = final_predictions2[final_predictions2['trade_date'] == date_to_compare]\n",
|
|||
|
|
"get_diff(slice1, slice2)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3 (ipykernel)",
|
|||
|
|
"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": "3.11.11"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|