{ "cells": [ { "cell_type": "code", "id": "79a7758178bafdd3", "metadata": {}, "source": [ "# %load_ext autoreload\n", "# %autoreload 2\n", "\n", "import pandas as pd\n", "import warnings\n", "\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option('display.max_columns', None)\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "a79cafb06a7e0e43", "metadata": { "scrolled": true }, "source": [ "from code.utils.utils import read_and_merge_h5_data\n", "\n", "print('daily data')\n", "df = 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", "\n", "print('daily basic')\n", "df = 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=df, join='inner')\n", "\n", "print('stk limit')\n", "df = 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=df)\n", "print('money flow')\n", "df = 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=df)\n", "print('cyq perf')\n", "df = 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=df)\n", "print(df.info())" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "cac01788dac10678", "metadata": {}, "source": [ "print('industry')\n", "industry_df = 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", "df = merge_with_industry_data(df, industry_df)\n", "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "c4e9e1d31da6dba6", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "def calculate_indicators(df):\n", " \"\"\"\n", " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", " \"\"\"\n", " df = df.sort_values('trade_date')\n", " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", " delta = df['close'].diff()\n", " gain = delta.where(delta > 0, 0)\n", " loss = -delta.where(delta < 0, 0)\n", " avg_gain = gain.rolling(window=14).mean()\n", " avg_loss = loss.rolling(window=14).mean()\n", " rs = avg_gain / avg_loss\n", " df['RSI'] = 100 - (100 / (1 + rs))\n", "\n", " # 计算MACD\n", " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", " df['MACD'] = ema12 - ema26\n", " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", "\n", " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", "\n", " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", "\n", " # 6. 情绪因子3：波动率（Volatility）\n", " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", "\n", " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", "\n", " return df\n", "\n", "\n", "def generate_index_indicators(h5_filename):\n", " df = pd.read_hdf(h5_filename, key='index_data')\n", " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", " df = df.sort_values('trade_date')\n", "\n", " # 计算每个ts_code的相关指标\n", " df_indicators = []\n", " for ts_code in df['ts_code'].unique():\n", " df_index = df[df['ts_code'] == ts_code].copy()\n", " df_index = calculate_indicators(df_index)\n", " df_indicators.append(df_index)\n", "\n", " # 合并所有指数的结果\n", " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", "\n", " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", " df_final = df_all_indicators.pivot_table(\n", " index='trade_date',\n", " columns='ts_code',\n", " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", " 'amount_change_rate', 'amount_mean'],\n", " aggfunc='last'\n", " )\n", "\n", " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", " df_final = df_final.reset_index()\n", "\n", " return df_final\n", "\n", "\n", "# 使用函数\n", "h5_filename = '../../data/index_data.h5'\n", "index_data = generate_index_indicators(h5_filename)\n", "index_data = index_data.dropna()\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "a735bc02ceb4d872", "metadata": {}, "source": [ "import numpy as np\n", "import talib\n", "\n", "\n", "def get_rolling_factor(df):\n", " old_columns = df.columns.tolist()[:]\n", " # 按股票和日期排序\n", " df = df.sort_values(by=['ts_code', 'trade_date'])\n", " grouped = df.groupby('ts_code', group_keys=False)\n", "\n", " # df[\"gap_next_open\"] = (df[\"open\"].shift(-1) - df[\"close\"]) / df[\"close\"]\n", "\n", " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", "\n", " # 因子 1：短期成交量变化率\n", " df['volume_change_rate'] = (\n", " grouped['vol'].rolling(window=2).mean() /\n", " grouped['vol'].rolling(window=10).mean() - 1\n", " ).reset_index(level=0, drop=True) # 确保索引对齐\n", "\n", " # 因子 2：成交量突破信号\n", " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", "\n", " # 因子 3：换手率均线偏离度\n", " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", "\n", " # 因子 4：换手率激增信号\n", " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", "\n", " # 因子 5：量比均值\n", " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", "\n", " # 因子 6：量比突破信号\n", " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", "\n", " df['vol_spike'] = grouped.apply(\n", " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", " )\n", " df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n", "\n", " # 计算 ATR\n", " df['atr_14'] = grouped.apply(\n", " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", " index=x.index)\n", " )\n", " df['atr_6'] = grouped.apply(\n", " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", " index=x.index)\n", " )\n", "\n", " # 计算 OBV 及其均线\n", " df['obv'] = grouped.apply(\n", " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", " )\n", " df['maobv_6'] = grouped.apply(\n", " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", " )\n", "\n", " df['rsi_3'] = grouped.apply(\n", " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", " )\n", " df['rsi_6'] = grouped.apply(\n", " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", " )\n", " df['rsi_9'] = grouped.apply(\n", " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", " )\n", "\n", " # 计算 return_10 和 return_20\n", " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", "\n", " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", "\n", " # 计算标准差指标\n", " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", "\n", " # 计算 EMA 指标\n", " df['_ema_5'] = grouped['close'].apply(\n", " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", " )\n", " df['_ema_13'] = grouped['close'].apply(\n", " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", " )\n", " df['_ema_20'] = grouped['close'].apply(\n", " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", " )\n", " df['_ema_60'] = grouped['close'].apply(\n", " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", " )\n", "\n", " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", " df['act_factor1'] = grouped['_ema_5'].apply(\n", " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", " )\n", " df['act_factor2'] = grouped['_ema_13'].apply(\n", " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", " )\n", " df['act_factor3'] = grouped['_ema_20'].apply(\n", " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", " )\n", " df['act_factor4'] = grouped['_ema_60'].apply(\n", " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", " )\n", "\n", " # 根据 trade_date 截面计算排名\n", " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", "\n", " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", "\n", " def rolling_covariance(x, y, window):\n", " return x.rolling(window).cov(y)\n", "\n", " def delta(series, period):\n", " return series.diff(period)\n", "\n", " def rank(series):\n", " return series.rank(pct=True)\n", "\n", " def stddev(series, window):\n", " return series.rolling(window).std()\n", "\n", " window_high_volume = 5\n", " window_close_stddev = 20\n", " period_delta = 5\n", " df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)\n", " df['delta_cov'] = delta(df['cov'], period_delta)\n", " df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))\n", " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", "\n", " df['alpha_003'] = np.where(df['high'] != df['low'],\n", " (df['close'] - df['open']) / (df['high'] - df['low']),\n", " 0)\n", "\n", " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", "\n", " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", "\n", " 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", " 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", " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", " 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", " ).reset_index(level=0, drop=True)\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['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'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n", " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n", "\n", " # 8. 市值成长性因子\n", " df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n", " df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n", "\n", " df.drop(columns=['weight_std20'], 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['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['circ_mv']\n", "\n", " df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n", "\n", " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n", "\n", " df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n", "\n", " df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n", "\n", " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['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" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "53f86ddc0677a6d7", "metadata": { "jupyter": { "source_hidden": true }, "scrolled": true }, "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_df = read_industry_data('../../data/sw_daily.h5')\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "dbe2fd8021b9417f", "metadata": {}, "source": [ "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 col not in index_data.columns]\n", "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", "print(origin_columns)" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "92d84ce15a562ec6", "metadata": {}, "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", " df = df[df['trade_date'] >= '20180101']\n", " df = df.reset_index(drop=True)\n", " return df\n", "\n", "\n", "df = filter_data(df)\n", "# df = get_technical_factor(df)\n", "# df = get_act_factor(df)\n", "# df = get_money_flow_factor(df)\n", "# df = get_alpha_factor(df)\n", "# df = get_limit_factor(df)\n", "# df = get_cyp_perf_factor(df)\n", "# df = get_mv_factors(df)\n", "df, _ = get_rolling_factor(df)\n", "df, _ = get_simple_factor(df)\n", "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", "# df = df.merge(index_data, on='trade_date', how='left')\n", "\n", "print(df.info())" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "b87b938028afa206", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "from scipy.stats import ks_2samp, wasserstein_distance\n", "\n", "\n", "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.1, wasserstein_threshold=0.15,\n", " importance_threshold=0.05):\n", " dropped_features = []\n", "\n", " # **统计数据漂移**\n", " numeric_columns = train_data.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[feature], test_data[feature])\n", " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", "\n", " if p_value < ks_threshold and wasserstein_dist > wasserstein_threshold:\n", " dropped_features.append(feature)\n", "\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" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "f4f16d63ad18d1bc", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "def create_deviation_within_dates(df, feature_columns):\n", " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", " new_columns = {}\n", " ret_feature_columns = feature_columns[:]\n", "\n", " # 自动选择所有数值型特征\n", " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", "\n", " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", " num_features = [col for col in num_features if 'limit' not in col]\n", " num_features = [col for col in num_features if 'cyq' not in col]\n", "\n", " # 遍历所有数值型特征\n", " for feature in num_features:\n", " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", " continue\n", "\n", " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", " # deviation_col_name = f'deviation_mean_{feature}'\n", " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", " # ret_feature_columns.append(deviation_col_name)\n", "\n", " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", " deviation_col_name = f'deviation_mean_{feature}'\n", " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", " ret_feature_columns.append(deviation_col_name)\n", "\n", " # 将新计算的偏差特征与原始 DataFrame 合并\n", " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", "\n", " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", "\n", " return df, ret_feature_columns\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "40e6b68a91b30c79", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "import pandas as pd\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", " volatility = stock_df['close'].pct_change().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", "\n", " return score\n", "\n", " scores = df.groupby('ts_code').apply(lambda x: compute_stock_score(x))\n", " scores = scores.reset_index(level=0, drop=True)\n", "\n", " return scores\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", "import pandas as pd\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 gc\n", "\n", "gc.collect()" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "47c12bb34062ae7a", "metadata": {}, "source": [ "days = 2\n", "validation_days=120\n", "\n", "import gc\n", "\n", "gc.collect()\n", "\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_volatility'] = (\n", " df.groupby('ts_code')['future_return']\n", " .transform(lambda x: x.rolling(days).std())\n", ")\n", "\n", "df['future_score'] = (\n", " 0.7 * df['future_return'] +\n", " 0.15 * df['future_volatility']\n", ")\n", "\n", "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", "\n", "df['label'] = df.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", " lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", ")\n", "\n", "df['label2'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n", " lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", ")\n", "\n", "# df['1_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(days)\n", "# df['2_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(1 + days)\n", "# df['3_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(3 + days - 1)\n", "\n", "def symmetric_log_transform(values):\n", " return np.sign(values) * np.log1p(np.abs(values))\n", "\n", "\n", "train_data = df[filter_index & (df['trade_date'] <= '2024-06-01') & (df['trade_date'] >= '2021-01-01')]\n", "test_data = df[filter_index & (df['trade_date'] >= '2024-06-01')]\n", "\n", "\n", "def select_pre_zt_stocks_dynamic(stock_df):\n", " # 排序数据\n", " stock_df = stock_df.sort_values(by=['trade_date', 'ts_code'])\n", "\n", " # avg_vol_3 = stock_df.groupby('ts_code')['vol'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", " # avg_vol_5 = stock_df.groupby('ts_code')['vol'].rolling(window=5).mean().shift(3).reset_index(level=0, drop=True)\n", "\n", " # stock_df = stock_df[\n", " # (stock_df['cat_up_limit'] == 1) |\n", " # (stock_df['vol'] > vol_spike_multiplier * stock_df['vol_spike'])\n", " # ]\n", " # cd1 = stock_df[\"close\"] > stock_df[\"close\"].shift(1)\n", "\n", " # cd2 = stock_df[\"close\"] > stock_df[\"close\"].rolling(window=10).mean()\n", " #\n", " # cd3 = (avg_vol_3 > avg_vol_5 * 2)\n", " #\n", " # cd4 = stock_df['gap_next_open'] < 0\n", "\n", " # stock_df = stock_df[(cd2 & cd4) | cd3]\n", " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nlargest(1000, 'return_20')\n", " )\n", "\n", " return stock_df\n", "\n", "\n", "train_data = select_pre_zt_stocks_dynamic(train_data)\n", "test_data = select_pre_zt_stocks_dynamic(test_data)\n", "\n", "# train_data, _ = get_simple_factor(train_data)\n", "# test_data, _ = get_simple_factor(test_data)\n", "\n", "\n", "# train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", "# )\n", "# test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", "# )\n", "\n", "industry_df = industry_df.sort_values(by=['trade_date'])\n", "index_data = index_data.sort_values(by=['trade_date'])\n", "\n", "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", "\n", "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", "\n", "# feature_columns_new = feature_columns[:]\n", "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", "\n", "feature_columns = [col for col in train_data.columns if col in train_data.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 '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", "print(f'feature_columns size: {len(feature_columns)}')\n", "\n", "feature_columns, _ = remove_shifted_features(train_data[train_data['label'] == train_data['label'].max()],\n", " test_data[test_data['label'] == test_data['label'].max()],\n", " feature_columns)\n", "\n", "feature_columns = remove_highly_correlated_features(train_data[train_data['label'] == train_data['label'].max()],\n", " feature_columns)\n", "keep_columns = [col for col in train_data.columns if\n", " col in feature_columns or col in ['ts_code', 'trade_date', 'label', 'future_return',\n", " 'future_score', 'future_volatility']]\n", "# train_data = train_data[keep_columns]\n", "print(f'feature_columns: {feature_columns}')\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\"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\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", "\n", "cat_columns = [col for col in feature_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", "\n", "\n", "# feature_columns_new.remove('cat_l2_code')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "8f134d435f71e9e2", "metadata": { "jupyter": { "source_hidden": true } }, "source": [ "from sklearn.preprocessing import StandardScaler\n", "import lightgbm as lgb\n", "import matplotlib.pyplot as plt\n", "from sklearn.decomposition import PCA\n", "\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,\n", " label_column='label'): # 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_column])\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", " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\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_column]\n", "\n", " X_val = val_data_split[feature_columns]\n", " y_val = val_data_split[label_column]\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", " pca = None\n", " if use_pca:\n", " pca = PCA(n_components=0.95) # 或指定 n_components=固定值（如 10）\n", " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", " numeric_pca = pca.fit_transform(X_train[numeric_features])\n", " X_train = pd.concat([pd.DataFrame(numeric_pca, index=X_train.index), X_train[categorical_feature]], axis=1)\n", "\n", " numeric_pca = pca.transform(X_val[numeric_features])\n", " X_val = pd.concat([pd.DataFrame(numeric_pca, index=X_val.index), X_val[categorical_feature]], axis=1)\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", " print('feature_columns size: ', len(X_train.columns.tolist()))\n", "\n", " train_dataset = lgb.Dataset(\n", " X_train, label=y_train, group=train_groups,\n", " categorical_feature=categorical_feature\n", " )\n", "\n", " # weights = val_data_split.groupby('trade_date')['std_return_5'].transform(\n", " # lambda x: x / x.mean()\n", " # )\n", " val_dataset = lgb.Dataset(\n", " X_val, label=y_val, group=val_groups,\n", " categorical_feature=categorical_feature\n", " )\n", "\n", " # 训练模型\n", " # 显式创建 LGBMRanker 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", "\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, pca\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": {}, "source": [ "print('train data size: ', len(train_data))\n", "\n", "if 'gen_volatility' in feature_columns:\n", " feature_columns.remove('gen_volatility')\n", "\n", "label_gain = list(range(len(train_data['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': 'lambdarank',\n", " 'learning_rate': 0.03,\n", " 'num_leaves': 1024,\n", " 'min_data_in_leaf': 128,\n", " 'max_depth': 32,\n", " 'max_bin': 1024,\n", " 'feature_fraction': 0.7,\n", " 'bagging_fraction': 1,\n", " 'bagging_freq': 5,\n", " 'lambda_l1': 0.1,\n", " 'lambda_l2': 0.1,\n", " # 'boosting': 'dart',\n", " 'verbosity': -1,\n", " 'extra_trees': True,\n", " 'max_position': 5,\n", " 'ndcg_at': 1,\n", " 'seed': 7\n", "}\n", "evals = {}\n", "\n", "gc.collect()\n", "\n", "use_pca = False\n", "feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", "light_params['feature_contri'] = feature_contri\n", "print(f'feature_contri: {feature_contri}')\n", "model, scaler, pca = train_light_model(train_data.copy().dropna(subset=['label']),\n", " light_params, feature_columns,\n", " [lgb.log_evaluation(period=100),\n", " lgb.callback.record_evaluation(evals),\n", " lgb.early_stopping(50, first_metric_only=True)\n", " ], evals,\n", " num_boost_round=1000, validation_days=validation_days,\n", " print_feature_importance=True, use_pca=use_pca)\n", "\n", "print('train data size: ', len(train_data))" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "5d1522a7538db91b", "metadata": {}, "source": [ "days = 2\n", "\n", "import gc\n", "\n", "gc.collect()\n", "\n", "if 'gen_volatility' in feature_columns:\n", " feature_columns.remove('gen_volatility')\n", "\n", "train_data = train_data.sort_values(['trade_date', 'ts_code'])\n", "\n", "score_df = train_data\n", "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", "score_df['score'] = model.predict(score_df[feature_columns])\n", "\n", "train_data['gen_volatility'] = score_df['score']\n", "# train_data['gen_volatility'] = train_data.groupby('trade_date')['gen_volatility'].rank(pct=True)\n", "train_data['cat_gen_volatility'] = train_data.groupby('trade_date', group_keys=False)['gen_volatility'].transform(\n", " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", ")\n", "\n", "score_df = test_data\n", "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", "score_df['score'] = model.predict(score_df[feature_columns])\n", "\n", "test_data['gen_volatility'] = score_df['score']\n", "# test_data['gen_volatility'] = test_data.groupby('trade_date')['gen_volatility'].rank(pct=True)\n", "test_data['cat_gen_volatility'] = test_data.groupby('trade_date', group_keys=False)['gen_volatility'].transform(\n", " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", ")\n", "\n", "# test_data['future_score'] = (\n", "# 0.7 * test_data['future_return'] +\n", "# 0.3 * test_data['future_volatility']\n", "# )\n", "\n", "if 'gen_volatility' not in feature_columns:\n", " feature_columns.append('gen_volatility')\n", "\n", "print(feature_columns)" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "612d1bdc10933f64", "metadata": {}, "source": [ "# print(test_data[['gen_volatility', 'future_volatility_r']].head(15))\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "d86af99d15cb3bdd", "metadata": {}, "source": [ "print('train data size: ', len(train_data))\n", "\n", "label_gain = list(range(len(train_data['label'].unique())))\n", "light_params['label_gain'] = [gain * gain for gain in label_gain]\n", "# light_params['max_depth'] = 32\n", "# light_params = {\n", "# 'label_gain': label_gain,\n", "# 'objective': 'lambdarank',\n", "# 'metric': 'lambdarank',\n", "# 'learning_rate': 0.1,\n", "# 'num_leaves': 1024,\n", "# 'min_data_in_leaf': 128,\n", "# 'max_depth': 16,\n", "# 'max_bin': 1024,\n", "# 'feature_fraction': 0.7,\n", "# 'bagging_fraction': 1,\n", "# 'bagging_freq': 5,\n", "# 'lambda_l1': 1,\n", "# 'lambda_l2': 1,\n", "# # 'boosting': 'dart',\n", "# 'verbosity': -1,\n", "# 'extra_trees': True,\n", "# 'max_position': 5,\n", "# 'ndcg_at': 1,\n", "# 'seed': 7\n", "# }\n", "evals = {}\n", "\n", "gc.collect()\n", "\n", "use_pca = False\n", "# feature_contri = [2 if feat.startswith('gen_volatility') else 1 for feat in feature_columns]\n", "feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", "light_params['feature_contri'] = feature_contri\n", "print(f'feature_contri: {feature_contri}')\n", "model2, scaler, pca = train_light_model(train_data.dropna(subset=['label2']),\n", " light_params, feature_columns,\n", " [lgb.log_evaluation(period=100),\n", " lgb.callback.record_evaluation(evals),\n", " lgb.early_stopping(50, first_metric_only=True)\n", " ], evals,\n", " num_boost_round=1000, validation_days=validation_days,\n", " print_feature_importance=True, use_pca=use_pca,\n", " label_column='label2')\n", "\n", "print('train data size: ', len(train_data))" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "ef9d068e-67f7-412c-bbd8-cdee7492dbc9", "metadata": {}, "source": [ "# train_data = train_data.sort_values(by='trade_date')\n", "# all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date\n", "# split_date = all_dates[-120] # 划分点为倒数第 validation_days 天\n", "# print(split_date)\n", "# print(all_dates)\n", "# val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", "\n", "score_df = test_data\n", "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", "\n", "if use_pca and pca is not None:\n", " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", " numeric_pca = pca.transform(score_df[numeric_features])\n", " score_df = pd.concat([pd.DataFrame(numeric_pca), score_df[categorical_feature],\n", " score_df[['trade_date', 'ts_code', 'future_return', 'future_score', 'label']]], axis=1)\n", " score_df['score'] = model2.predict(score_df[[col for col in score_df.columns if\n", " col not in ['trade_date', 'ts_code', 'future_return', 'future_score',\n", " 'label']]])\n", "else:\n", " score_df['score'] = model2.predict(score_df[feature_columns])\n", "# train_data['score'] = catboost_model.predict(train_data[feature_columns])\n", "# 按 trade_date 分组，并对每组按 score 降序排序，取前 10\n", "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nlargest(1, 'score')\n", ")# score_df = score_df[score_df['score'] > 0]\n", "score_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n", "print(score_df['label2'].mean())\n", "print(score_df['future_return'].mean())" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "7ea31b06eec3cac9", "metadata": {}, "source": [ "print(score_df[score_df['trade_date'] >= '2024-08-01'][['trade_date', 'ts_code', 'future_return', 'future_score', 'label', 'label2']].head(15))\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "id": "bcc6a60c-a134-4eb2-9125-5a608ef88e27", "metadata": {}, "source": [ "test_data['future_volatility_r'] = test_data.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", ")\n", "td = test_data.dropna(subset=['cat_gen_volatility'])\n", "td = td.dropna(subset=['future_volatility'])\n", "\n", "gen_volatility = td['gen_volatility']\n", "future_volatility = td['future_volatility_r']\n", "\n", "from scipy.stats import spearmanr, kendalltau\n", "from sklearn.metrics import mean_squared_error, ndcg_score\n", "import numpy as np\n", "\n", "# (1) Spearman 相关系数\n", "spearman_corr, _ = spearmanr(gen_volatility, future_volatility)\n", "print(f\"Spearman Correlation: {spearman_corr}\")\n", "\n", "# (2) Kendall Tau 系数\n", "kendall_corr, _ = kendalltau(gen_volatility, future_volatility)\n", "print(f\"Kendall Tau Correlation: {kendall_corr}\")\n", "\n", "# (3) 均方误差 (MSE)\n", "mse = mean_squared_error(future_volatility, gen_volatility)\n", "print(f\"Mean Squared Error (MSE): {mse}\")\n", "\n", "# (4) Top-K 准确率\n", "def top_k_accuracy(y_true, y_pred, k=5):\n", " correct = 0\n", " for true, pred in zip(y_true, y_pred):\n", " if abs(true - pred) <= k:\n", " correct += 1\n", " return correct / len(y_true)\n", "\n", "top_5_acc = top_k_accuracy(future_volatility, gen_volatility, k=5)\n", "top_10_acc = top_k_accuracy(future_volatility, gen_volatility, k=20)\n", "# print(f\"Top-5 Accuracy: {top_5_acc}, Top-10 Accuracy: {top_10_acc}\")\n", "\n", "# (5) NDCG\n", "true_scores = 50 - future_volatility\n", "pred_scores = 50 - gen_volatility\n", "ndcg = ndcg_score([true_scores.values], [pred_scores.values])\n", "print(f\"NDCG Score: {ndcg}\")\n", "\n", "\n", "# print(gen_volatility, future_volatility)" ], "outputs": [], "execution_count": null } ], "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 }