2025-05-26 21:34:36 +08:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "79a7758178bafdd3",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:46:06.987506Z",
"start_time": "2025-04-03T12:46:06.259551Z"
},
"jupyter": {
"source_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"e:\\PyProject\\NewStock\\main\\train\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import gc\n",
"import os\n",
"import sys\n",
"sys.path.append('../../')\n",
"print(os.getcwd())\n",
"import pandas as pd\n",
"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
"from main.utils.factor import read_industry_data\n",
"from main.utils.factor_processor import calculate_score\n",
"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
"\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a79cafb06a7e0e43",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:00.212859Z",
"start_time": "2025-04-03T12:46:06.998047Z"
},
"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: 8665405 entries, 0 to 8665404\n",
"Data columns (total 32 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 total_mv float64 \n",
" 12 volume_ratio float64 \n",
" 13 is_st bool \n",
" 14 up_limit float64 \n",
" 15 down_limit float64 \n",
" 16 buy_sm_vol float64 \n",
" 17 sell_sm_vol float64 \n",
" 18 buy_lg_vol float64 \n",
" 19 sell_lg_vol float64 \n",
" 20 buy_elg_vol float64 \n",
" 21 sell_elg_vol float64 \n",
" 22 net_mf_vol float64 \n",
" 23 his_low float64 \n",
" 24 his_high float64 \n",
" 25 cost_5pct float64 \n",
" 26 cost_15pct float64 \n",
" 27 cost_50pct float64 \n",
" 28 cost_85pct float64 \n",
" 29 cost_95pct float64 \n",
" 30 weight_avg float64 \n",
" 31 winner_rate float64 \n",
"dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n",
"memory usage: 2.0+ GB\n",
"None\n"
]
}
],
"source": [
"from main.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', 'total_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())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cac01788dac10678",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:10.527104Z",
"start_time": "2025-04-03T12:47:00.488715Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"industry\n"
]
}
],
"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（
" 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']])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c4e9e1d31da6dba6",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:10.719252Z",
"start_time": "2025-04-03T12:47:10.541247Z"
},
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"from main.factor.factor import *\n",
"\n",
"def calculate_indicators(df):\n",
" \"\"\"\n",
" 计算四个指标：
" \"\"\"\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： （ ）
" 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： （ ）
" 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： （ ）
" 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： （ ）
" 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",
" # df = sentiment_panic_greed_index(df)\n",
" # df = sentiment_market_breadth_proxy(df)\n",
" # df = sentiment_reversal_indicator(df)\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，
" df_final = df_all_indicators.pivot_table(\n",
" index='trade_date',\n",
" columns='ts_code',\n",
" values=['daily_return', \n",
" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
" # 'sentiment_panic_greed_index',\n",
" '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"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a735bc02ceb4d872",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:10.821169Z",
"start_time": "2025-04-03T12:47:10.751831Z"
}
},
"outputs": [],
"source": [
"import talib\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "53f86ddc0677a6d7",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:15.944254Z",
"start_time": "2025-04-03T12:47:10.826179Z"
},
"jupyter": {
"source_hidden": true
},
"scrolled": true
},
"outputs": [],
"source": [
"from main.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",
"\n",
" # cs_rank_intraday_range(industry_data)\n",
" # cs_rank_close_pos_in_range(industry_data)\n",
"\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"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dbe2fd8021b9417f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:15.969344Z",
"start_time": "2025-04-03T12:47:15.963327Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
]
}
],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "85c3e3d0235ffffa",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T12:47:16.089879Z",
"start_time": "2025-04-03T12:47:15.990101Z"
}
},
"outputs": [],
"source": [
"fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n",
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n",
" df=None)\n",
"cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n",
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
" df=None)\n",
"balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n",
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
" df=None)\n",
"top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n",
" columns=['ts_code', 'trade_date', 'reason'],\n",
" df=None)\n",
"\n",
"top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": null,
2025-05-26 21:34:36 +08:00
"id": "92d84ce15a562ec6",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T13:08:01.612695Z",
"start_time": "2025-04-03T12:47:16.121802Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"使用 'ann_date' 作为财务数据生效日期。\n",
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
"使用 'ann_date' 作为财务数据生效日期。\n",
2025-05-28 14:16:04 +08:00
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
2025-05-26 21:34:36 +08:00
"开始计算因子: AR, BR (原地修改)...\n",
"因子 AR, BR 计算成功。\n",
"因子 AR, BR 计算流程结束。\n",
"使用 'ann_date' 作为财务数据生效日期。\n",
"使用 'ann_date' 作为财务数据生效日期。\n",
"使用 'ann_date' 作为财务数据生效日期。\n",
"使用 'ann_date' 作为财务数据生效日期。\n",
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
"计算 BBI...\n",
"--- 计算日级别偏离度 (使用 pct_chg) ---\n",
"--- 计算日级别动量基准 (使用 pct_chg) ---\n",
"日级别动量基准计算完成 (使用 pct_chg)。\n",
"日级别偏离度计算完成 (使用 pct_chg)。\n",
"--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
"--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n",
"错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
"错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
" 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
" 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
" 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
" 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
" 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
" 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n",
" 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
" 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
" 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
" 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n",
" 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n",
" 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n",
" 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n",
" 'cost_support_15pct_change', 'cat_winner_price_zone',\n",
" 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n",
" '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n",
" '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n",
" 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
" 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
" 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
" 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
2025-05-28 14:16:04 +08:00
" dtype='object')\n",
"Calculating lg_flow_mom_corr_20_60...\n",
"Finished lg_flow_mom_corr_20_60.\n",
"Calculating lg_flow_accel...\n",
"Finished lg_flow_accel.\n",
"Calculating profit_pressure...\n",
"Finished profit_pressure.\n",
"Calculating underwater_resistance...\n",
"Finished underwater_resistance.\n",
"Calculating cost_conc_std_20...\n",
"Finished cost_conc_std_20.\n",
"Calculating profit_decay_20...\n",
"Finished profit_decay_20.\n",
"Calculating vol_amp_loss_20...\n",
"Finished vol_amp_loss_20.\n",
"Calculating vol_drop_profit_cnt_5...\n",
"Finished vol_drop_profit_cnt_5.\n",
"Calculating lg_flow_vol_interact_20...\n",
"Finished lg_flow_vol_interact_20.\n",
"Calculating cost_break_confirm_cnt_5...\n",
"Finished cost_break_confirm_cnt_5.\n",
"Calculating atr_norm_channel_pos_14...\n",
"Finished atr_norm_channel_pos_14.\n",
"Calculating turnover_diff_skew_20...\n",
"Finished turnover_diff_skew_20.\n",
"Calculating lg_sm_flow_diverge_20...\n",
"Finished lg_sm_flow_diverge_20.\n",
"Calculating pullback_strong_20_20...\n",
"Finished pullback_strong_20_20.\n",
"Calculating vol_wgt_hist_pos_20...\n",
"Finished vol_wgt_hist_pos_20.\n",
"Calculating vol_adj_roc_20...\n",
"Finished vol_adj_roc_20.\n",
"Calculating cs_rank_net_lg_flow_val...\n",
"Finished cs_rank_net_lg_flow_val.\n",
"Calculating cs_rank_flow_divergence...\n",
"Finished cs_rank_flow_divergence.\n",
"Calculating cs_rank_ind_adj_lg_flow...\n",
"Finished cs_rank_ind_adj_lg_flow.\n",
"Calculating cs_rank_elg_buy_ratio...\n",
"Finished cs_rank_elg_buy_ratio.\n",
"Calculating cs_rank_rel_profit_margin...\n",
"Finished cs_rank_rel_profit_margin.\n",
"Calculating cs_rank_cost_breadth...\n",
"Finished cs_rank_cost_breadth.\n",
"Calculating cs_rank_dist_to_upper_cost...\n",
"Finished cs_rank_dist_to_upper_cost.\n",
"Calculating cs_rank_winner_rate...\n",
"Finished cs_rank_winner_rate.\n",
"Calculating cs_rank_intraday_range...\n",
"Finished cs_rank_intraday_range.\n",
"Calculating cs_rank_close_pos_in_range...\n",
"Finished cs_rank_close_pos_in_range.\n",
"Calculating cs_rank_opening_gap...\n",
"Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
"Calculating cs_rank_pos_in_hist_range...\n",
"Finished cs_rank_pos_in_hist_range.\n",
"Calculating cs_rank_vol_x_profit_margin...\n",
"Finished cs_rank_vol_x_profit_margin.\n",
"Calculating cs_rank_lg_flow_price_concordance...\n",
"Finished cs_rank_lg_flow_price_concordance.\n",
"Calculating cs_rank_turnover_per_winner...\n",
"Finished cs_rank_turnover_per_winner.\n",
"Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
"Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
"Calculating cs_rank_volume_ratio...\n",
"Finished cs_rank_volume_ratio.\n",
"Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
"Finished cs_rank_elg_buy_sell_sm_ratio.\n",
"Calculating cs_rank_cost_dist_vol_ratio...\n",
"Finished cs_rank_cost_dist_vol_ratio.\n",
"Calculating cs_rank_size...\n",
"Finished cs_rank_size.\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 4539678 entries, 0 to 4539677\n",
"Columns: 178 entries, ts_code to cs_rank_size\n",
"dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n",
"memory usage: 5.7+ GB\n",
"None\n",
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
2025-05-26 21:34:36 +08:00
]
}
],
"source": [
"\n",
"import numpy as np\n",
"from main.factor.factor import *\n",
"\n",
"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'] >= '2019-01-01']\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",
"gc.collect()\n",
"\n",
"df = filter_data(df)\n",
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
"\n",
"# df = price_minus_deduction_price(df, n=120)\n",
"# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
"# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
"# df = cat_reason(df, top_list_df)\n",
"# df = cat_is_on_top_list(df, top_list_df)\n",
"\n",
2025-05-28 14:16:04 +08:00
"df = ts_turnover_rate_acceleration_5_20(df)\n",
"df = ts_vol_sustain_10_30(df)\n",
"df = cs_turnover_rate_relative_strength_20(df)\n",
"df = cs_amount_outlier_10(df)\n",
"df = ts_ff_to_total_turnover_ratio(df)\n",
"df = ts_price_volume_trend_coherence_5_20(df)\n",
"df = ts_turnover_rate_trend_strength_5(df)\n",
"df = ts_ff_turnover_rate_surge_10(df)\n",
"\n",
2025-05-26 21:34:36 +08:00
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
"calculate_arbr(df, N=26)\n",
"df['log_circ_mv'] = np.log(df['circ_mv'])\n",
"df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
"df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
"df = turnover_rate_n(df, n=5)\n",
"df = variance_n(df, n=20)\n",
"df = bbi_ratio_factor(df)\n",
"df = daily_deviation(df)\n",
"df = daily_industry_deviation(df)\n",
"df, _ = get_rolling_factor(df)\n",
"df, _ = get_simple_factor(df)\n",
"\n",
"df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
"df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
"\n",
"lg_flow_mom_corr(df, N=20, M=60)\n",
"lg_flow_accel(df)\n",
"profit_pressure(df)\n",
"underwater_resistance(df)\n",
"cost_conc_std(df, N=20)\n",
"profit_decay(df, N=20)\n",
"vol_amp_loss(df, N=20)\n",
"vol_drop_profit_cnt(df, N=20, M=5)\n",
"lg_flow_vol_interact(df, N=20)\n",
"cost_break_confirm_cnt(df, M=5)\n",
"atr_norm_channel_pos(df, N=14)\n",
"turnover_diff_skew(df, N=20)\n",
"lg_sm_flow_diverge(df, N=20)\n",
"pullback_strong(df, N=20, M=20)\n",
"vol_wgt_hist_pos(df, N=20)\n",
"vol_adj_roc(df, N=20)\n",
"\n",
"cs_rank_net_lg_flow_val(df)\n",
"cs_rank_flow_divergence(df)\n",
"cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
"cs_rank_elg_buy_ratio(df)\n",
"cs_rank_rel_profit_margin(df)\n",
"cs_rank_cost_breadth(df)\n",
"cs_rank_dist_to_upper_cost(df)\n",
"cs_rank_winner_rate(df)\n",
"cs_rank_intraday_range(df)\n",
"cs_rank_close_pos_in_range(df)\n",
"cs_rank_opening_gap(df) # Needs pre_close\n",
"cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
"cs_rank_vol_x_profit_margin(df)\n",
"cs_rank_lg_flow_price_concordance(df)\n",
"cs_rank_turnover_per_winner(df)\n",
"cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
"cs_rank_volume_ratio(df) # Needs volume_ratio\n",
"cs_rank_elg_buy_sell_sm_ratio(df)\n",
"cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
"cs_rank_size(df) # Needs circ_mv\n",
"\n",
"\n",
"# df = df.merge(index_data, on='trade_date', how='left')\n",
"\n",
"print(df.info())\n",
"print(df.columns.tolist())"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": 10,
2025-05-26 21:34:36 +08:00
"id": "b87b938028afa206",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T13:08:03.658725Z",
"start_time": "2025-04-03T13:08:02.469611Z"
}
},
"outputs": [],
"source": [
"from scipy.stats import ks_2samp, wasserstein_distance\n",
"\n",
"\n",
"def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\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 or 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"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": 11,
2025-05-26 21:34:36 +08:00
"id": "f4f16d63ad18d1bc",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T13:08:03.670700Z",
"start_time": "2025-04-03T13:08:03.665739Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import statsmodels.api as sm # 用于中性化回归\n",
"from tqdm import tqdm # 可选，用于显示进度条\n",
"\n",
"# --- 常量 ---\n",
"epsilon = 1e-10 # 防止除零\n",
"\n",
"# --- 1. 中位数去极值 (MAD) ---\n",
"\n",
"def cs_mad_filter(df: pd.DataFrame,\n",
" features: list,\n",
" k: float = 3.0,\n",
" scale_factor: float = 1.4826):\n",
" \"\"\"\n",
" 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n",
"\n",
" 方法: 对每日截面数据，计算 median 和 MAD，
" 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n",
" 替换为边界值 (Winsorization)。\n",
" scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n",
"\n",
" Args:\n",
" df (pd.DataFrame): 输入 DataFrame，
" features (list): 需要处理的特征列名列表。\n",
" k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n",
" scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n",
"\n",
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
" \"\"\"\n",
" print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n",
" if not all(col in df.columns for col in features):\n",
" missing = [col for col in features if col not in df.columns]\n",
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n",
" return\n",
"\n",
" grouped = df.groupby('trade_date')\n",
"\n",
" for col in tqdm(features, desc=\"MAD Filtering\"):\n",
" try:\n",
" # 计算截面中位数\n",
" median = grouped[col].transform('median')\n",
" # 计算截面 MAD (Median Absolute Deviation from Median)\n",
" mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n",
"\n",
" # 计算上下边界\n",
" lower_bound = median - k * scale_factor * mad\n",
" upper_bound = median + k * scale_factor * mad\n",
"\n",
" # 原地应用 clip\n",
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
"\n",
" except KeyError:\n",
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n",
" except Exception as e:\n",
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n",
"\n",
" print(\"截面 MAD 去极值处理完成。\")\n",
"\n",
"\n",
"# --- 2. 行业市值中性化 ---\n",
"\n",
"def cs_neutralize_industry_cap(df: pd.DataFrame,\n",
" features: list,\n",
" industry_col: str = 'cat_l2_code',\n",
" market_cap_col: str = 'circ_mv'):\n",
" \"\"\"\n",
" 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n",
" 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n",
" 将回归残差写回原特征列。\n",
"\n",
" Args:\n",
" df (pd.DataFrame): 输入 DataFrame，
" industry_col, market_cap_col。\n",
" features (list): 需要处理的特征列名列表。\n",
" industry_col (str): 行业分类列名。\n",
" market_cap_col (str): 流通市值列名。\n",
"\n",
" WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n",
" 计算量较大，可能耗时较长。\n",
" 需要安装 statsmodels 库 (pip install statsmodels)。\n",
" \"\"\"\n",
" print(\"开始截面行业市值中性化...\")\n",
" required_cols = features + ['trade_date', industry_col, market_cap_col]\n",
" if not all(col in df.columns for col in required_cols):\n",
" missing = [col for col in required_cols if col not in df.columns]\n",
" print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n",
" return\n",
"\n",
" # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n",
" log_cap_col = '_log_market_cap'\n",
" df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n",
" # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n",
" # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n",
" # df[industry_col] = df[industry_col].astype('category') # 转为类别，
"\n",
" dates = df['trade_date'].unique()\n",
" all_residuals = [] # 用于收集所有日期的残差\n",
"\n",
" for date in tqdm(dates, desc=\"Neutralizing\"):\n",
" daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n",
"\n",
" # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n",
" X = daily_data[[log_cap_col]]\n",
" X = sm.add_constant(X, prepend=True) # 添加常数项\n",
" # 创建行业哑变量 (drop_first=True 避免共线性)\n",
" industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n",
" industry_dummies = industry_dummies.astype(int)\n",
" X = pd.concat([X, industry_dummies], axis=1)\n",
"\n",
" daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n",
"\n",
" for col in features:\n",
" Y = daily_data[col]\n",
"\n",
" # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n",
" valid_mask = Y.notna() & X.notna().all(axis=1)\n",
" if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n",
" print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n",
" daily_residuals[col] = np.nan\n",
" continue\n",
"\n",
" Y_valid = Y[valid_mask]\n",
" X_valid = X[valid_mask]\n",
"\n",
" # 执行 OLS 回归\n",
" try:\n",
" model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n",
" results = model.fit()\n",
" # 将残差填回对应位置\n",
" daily_residuals.loc[valid_mask, col] = results.resid\n",
" daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n",
" except Exception as e:\n",
" print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n",
" daily_residuals[col] = np.nan\n",
" break\n",
"\n",
" all_residuals.append(daily_residuals)\n",
"\n",
" # 合并所有日期的残差结果\n",
" if all_residuals:\n",
" residuals_df = pd.concat(all_residuals)\n",
" # 将残差结果更新回原始 df (原地修改)\n",
" # 使用 update 比 merge 更适合基于索引的原地更新\n",
" # 确保 residuals_df 的索引与 df 中对应部分一致\n",
" df.update(residuals_df)\n",
" else:\n",
" print(\"没有有效的残差结果可以合并。\")\n",
"\n",
"\n",
" # 清理临时列\n",
" df.drop(columns=[log_cap_col], inplace=True)\n",
" print(\"截面行业市值中性化完成。\")\n",
"\n",
"\n",
"# --- 3. Z-Score 标准化 ---\n",
"\n",
"def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n",
" \"\"\"\n",
" 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n",
" 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n",
"\n",
" Args:\n",
" df (pd.DataFrame): 输入 DataFrame，
" features (list): 需要处理的特征列名列表。\n",
" epsilon (float): 防止除以零的小常数。\n",
"\n",
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
" \"\"\"\n",
" print(\"开始截面 Z-Score 标准化...\")\n",
" if not all(col in df.columns for col in features):\n",
" missing = [col for col in features if col not in df.columns]\n",
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n",
" return\n",
"\n",
" grouped = df.groupby('trade_date')\n",
"\n",
" for col in tqdm(features, desc=\"Standardizing\"):\n",
" try:\n",
" # 使用 transform 计算截面均值和标准差\n",
" mean = grouped[col].transform('mean')\n",
" std = grouped[col].transform('std')\n",
"\n",
" # 计算 Z-Score 并原地赋值\n",
" df[col] = (df[col] - mean) / (std + epsilon)\n",
"\n",
" except KeyError:\n",
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n",
" except Exception as e:\n",
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n",
"\n",
" print(\"截面 Z-Score 标准化完成。\")\n",
"\n",
"def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n",
" \"\"\"\n",
" 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n",
"\n",
" 参数:\n",
" df (pd.DataFrame): 包含多日数据的DataFrame，
" feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n",
"\n",
" 返回:\n",
" pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n",
" \"\"\"\n",
" processed_df = df.copy() # 在副本上操作，保留原始数据\n",
"\n",
" # 确保 trade_date 是 datetime 类型以便正确分组\n",
" processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n",
"\n",
" def _fill_daily_nan(group):\n",
" # group 是某一个交易日的 DataFrame\n",
"\n",
" # 遍历指定的特征列\n",
" for feature_col in feature_columns:\n",
" # 检查列是否存在于当前分组中\n",
" if feature_col in group.columns:\n",
" # 计算当日该特征的中位数\n",
" median_val = group[feature_col].median()\n",
"\n",
" # 使用当日中位数填充该特征列的 NaN 值\n",
" # inplace=True 会直接修改 group DataFrame\n",
" group[feature_col].fillna(median_val, inplace=True)\n",
" # else:\n",
" # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n",
"\n",
" return group\n",
"\n",
" # 按交易日期分组，并应用每日填充函数\n",
" # group_keys=False 避免将分组键添加到结果索引中\n",
" filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n",
"\n",
" return filled_df"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": 12,
2025-05-26 21:34:36 +08:00
"id": "40e6b68a91b30c79",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T13:08:04.694262Z",
"start_time": "2025-04-03T13:08:03.694904Z"
}
},
"outputs": [],
"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",
" # 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_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n",
" \"\"\"\n",
" 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n",
" 对特征在行业内部进行市值中性化。\n",
"\n",
" Args:\n",
" df: 输入的 DataFrame，
" features: 需要进行中性化的特征列名列表。\n",
" industry_col: 行业分类列的列名。\n",
" mkt_cap_col: 市值列的列名。\n",
"\n",
" Returns:\n",
" 中性化后的 DataFrame。\n",
" \"\"\"\n",
"\n",
" df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n",
" df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n",
" df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n",
"\n",
" if df_cleaned.empty:\n",
" print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n",
" return df # 返回原始或空df，
"\n",
" processed_df = df\n",
"\n",
" for col in features:\n",
" if col not in df_cleaned.columns:\n",
" print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n",
" # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（ ）
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
" continue\n",
"\n",
" # 跳过对控制变量本身进行中性化\n",
" if col == mkt_cap_col or col == industry_col:\n",
" print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n",
" # 在结果 df 中也保持原样\n",
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
" continue\n",
"\n",
" residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n",
"\n",
" # 在分组前处理特征列的 NaN，
" df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n",
"\n",
" if not df_subset_factor.empty:\n",
" for industry, group in df_subset_factor.groupby(industry_col):\n",
" x = group[mkt_cap_col] # 市值对数\n",
" y = group[col] # 因子值\n",
"\n",
" # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n",
" # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n",
" if len(group) > 1 and np.var(x) > 1e-9:\n",
" try:\n",
" beta = np.cov(y, x)[0, 1] / np.var(x)\n",
" alpha = np.mean(y) - beta * np.mean(x)\n",
"\n",
" # 计算残差\n",
" resid = y - (alpha + beta * x)\n",
"\n",
" # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n",
" residual_series.loc[resid.index] = resid\n",
"\n",
" except Exception as e:\n",
" # 捕获可能的计算异常，
" print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n",
" # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n",
" # 也可以选择保留原始值：
"\n",
" else:\n",
" residual_series.loc[group.index] = group[col] # 保留原始因子值\n",
" processed_df.loc[residual_series.index, col] = residual_series\n",
"\n",
"\n",
" else:\n",
" processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n",
"\n",
" return processed_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(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
" rolling_upper = df.groupby('trade_date')[col].transform(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 select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n",
" \"\"\"\n",
" 计算给定特征与目标列的 RankIC，
"\n",
" Args:\n",
" df: 包含特征列和目标列的 Pandas DataFrame。\n",
" feature_columns: 包含所有待评估特征列名的列表。\n",
" n: 希望选取的 RankIC 绝对值最高的特征数量。\n",
" target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n",
"\n",
" Returns:\n",
" 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n",
" \"\"\"\n",
" numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
" if target_column not in df.columns:\n",
" raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n",
"\n",
" rankic_scores = {}\n",
" for feature in numeric_columns:\n",
" if feature not in df.columns:\n",
" print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n",
" continue\n",
"\n",
" # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n",
" # dropna() 是为了处理缺失值，确保相关性计算不失败\n",
" valid_data = df[[feature, target_column]].dropna()\n",
" if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n",
" # 计算斯皮尔曼相关性\n",
" correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n",
" rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n",
" else:\n",
" rankic_scores[feature] = 0 # 数据不足，
"\n",
" # 将 RankIC 分数转换为 Series 便于排序\n",
" rankic_series = pd.Series(rankic_scores)\n",
"\n",
" # 按 RankIC 绝对值降序排序，选取前 n 个特征\n",
" # handle case where n might be larger than available features\n",
" n_actual = min(n, len(rankic_series))\n",
" top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n",
" top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n",
" return top_features\n",
"\n",
"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"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": 13,
2025-05-26 21:34:36 +08:00
"id": "47c12bb34062ae7a",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T14:57:50.841165Z",
"start_time": "2025-04-03T14:49:25.889057Z"
}
},
"outputs": [],
"source": [
"days = 5\n",
"validation_days = 120\n",
"\n",
"import gc\n",
"\n",
"gc.collect()\n",
"\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",
"\n",
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"# df['cat_up_limit'] = df['pct_chg'] > 5\n",
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"# df['label'] = (df.groupby('ts_code')['cat_up_limit']\n",
"# .rolling(window=5, min_periods=1).sum()\n",
"# .groupby('ts_code') # 再次按 ts_code 分组\n",
"# .shift(-5)\n",
"# .fillna(0) # 填充每个股票组最后的 NaN\n",
"# .astype(int)\n",
"# .reset_index(level=0, drop=True))\n",
"df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
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" lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
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")\n",
"filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
"\n",
"# for col in [col for col in df.columns]:\n",
"# train_data[col] = train_data[col].astype('str')\n",
"# test_data[col] = test_data[col].astype('str')"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"id": "29221dde",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"191\n"
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]
}
],
"source": [
"feature_columns = [col for col in df.head(10)\n",
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
" .merge(index_data, on='trade_date', how='left')\n",
" .columns\n",
" ]\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 'is_st' 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 'circ_mv' not in col]\n",
"feature_columns = [col for col in feature_columns if '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",
"feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n",
" 'cap_neutral_cost_metric', \n",
" 'hurst_net_mf_vol_60', \n",
" 'complex_factor_deap_1', \n",
" 'lg_buy_consolidation_20',\n",
" 'cs_rank_ind_cap_neutral_pe',\n",
" 'cs_rank_opening_gap',\n",
" 'cs_rank_ind_adj_lg_flow']]\n",
"feature_columns = [col for col in feature_columns if col not in ['cat_reason', 'cat_is_on_top_list']]\n",
"print(len(feature_columns))"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"id": "03ee5daf",
"metadata": {},
"outputs": [],
"source": [
"# df = fill_nan_with_daily_median(df, feature_columns)\n",
"for feature_col in [col for col in feature_columns if col in df.columns]:\n",
" # median_val = df[feature_col].median()\n",
" df[feature_col].fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"id": "b76ea08a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date log_circ_mv\n",
"0 000001.SZ 2019-01-02 16.574219\n",
"1 000001.SZ 2019-01-03 16.583965\n",
"2 000001.SZ 2019-01-04 16.633371\n",
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"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
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"去除极值\n",
"开始截面 MAD 去极值处理 (k=3.0)...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"MAD Filtering: 100%|██████████| 132/132 [00:28<00:00, 4.64it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"截面 MAD 去极值处理完成。\n",
"开始截面 MAD 去极值处理 (k=3.0)...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"MAD Filtering: 100%|██████████| 132/132 [00:24<00:00, 5.45it/s]\n"
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"截面 MAD 去极值处理完成。\n",
"开始截面 MAD 去极值处理 (k=3.0)...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"MAD Filtering: 0it [00:00, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"截面 MAD 去极值处理完成。\n",
"开始截面 MAD 去极值处理 (k=3.0)...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"MAD Filtering: 0it [00:00, ?it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"截面 MAD 去极值处理完成。\n",
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"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
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"df最小日期: 2019-01-02\n",
"df最大日期: 2025-05-23\n",
"2057539\n",
"train_data最小日期: 2020-01-02\n",
"train_data最大日期: 2022-12-30\n",
"1766694\n",
"test_data最小日期: 2023-01-03\n",
"test_data最大日期: 2025-05-23\n",
" ts_code trade_date log_circ_mv\n",
"0 000001.SZ 2019-01-02 16.574219\n",
"1 000001.SZ 2019-01-03 16.583965\n",
"2 000001.SZ 2019-01-04 16.633371\n"
]
}
],
"source": [
"split_date = '2023-01-01'\n",
"train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n",
"test_data = df[(df['trade_date'] >= split_date)]\n",
"\n",
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\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, [col for col in feature_columns if col in train_data.columns])\n",
"# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
"\n",
"# feature_columns = [\n",
"# 'undist_profit_ps', \n",
"# 'AR_BR',\n",
"# 'pe_ttm',\n",
"# 'alpha_22_improved', \n",
"# 'alpha_003', \n",
"# 'alpha_007', \n",
"# 'alpha_013', \n",
"# 'cat_up_limit', \n",
"# 'cat_down_limit', \n",
"# 'up_limit_count_10d', \n",
"# 'down_limit_count_10d', \n",
"# 'consecutive_up_limit', \n",
"# 'vol_break', \n",
"# 'weight_roc5', \n",
"# 'price_cost_divergence', \n",
"# 'smallcap_concentration', \n",
"# 'cost_stability', \n",
"# 'high_cost_break_days', \n",
"# 'liquidity_risk', \n",
"# 'turnover_std', \n",
"# 'mv_volatility', \n",
"# 'volume_growth', \n",
"# 'mv_growth', \n",
"# 'lg_flow_mom_corr_20_60', \n",
"# 'lg_flow_accel', \n",
"# 'profit_pressure', \n",
"# 'underwater_resistance', \n",
"# 'cost_conc_std_20', \n",
"# 'profit_decay_20', \n",
"# 'vol_amp_loss_20', \n",
"# 'vol_drop_profit_cnt_5', \n",
"# 'lg_flow_vol_interact_20', \n",
"# 'cost_break_confirm_cnt_5', \n",
"# 'atr_norm_channel_pos_14', \n",
"# 'turnover_diff_skew_20', \n",
"# 'lg_sm_flow_diverge_20', \n",
"# 'pullback_strong_20_20', \n",
"# 'vol_wgt_hist_pos_20', \n",
"# 'vol_adj_roc_20',\n",
"# 'cashflow_to_ev_factor',\n",
"# 'ocfps',\n",
"# 'book_to_price_ratio',\n",
"# 'turnover_rate_mean_5',\n",
"# 'variance_20',\n",
"# 'bbi_ratio_factor'\n",
"# ]\n",
"# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
"# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
"\n",
"numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
"numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
"# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
"print(feature_columns)\n",
"\n",
"# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n",
"# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n",
"\n",
"train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
"train_data = train_data.dropna(subset=['label'])\n",
"train_data = train_data.reset_index(drop=True)\n",
"# print(test_data.tail())\n",
"test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
"# test_data = test_data.dropna(subset=['label'])\n",
"test_data = test_data.reset_index(drop=True)\n",
"\n",
"transform_feature_columns = feature_columns\n",
"transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
"# transform_feature_columns.remove('undist_profit_ps')\n",
"print('去除极值')\n",
"cs_mad_filter(train_data, transform_feature_columns)\n",
"# print('中性化')\n",
"# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n",
"# print('标准化')\n",
"# cs_zscore_standardize(train_data, transform_feature_columns)\n",
"\n",
"cs_mad_filter(test_data, transform_feature_columns)\n",
"# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n",
"# cs_zscore_standardize(test_data, transform_feature_columns)\n",
"\n",
"mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
"cs_mad_filter(train_data, mad_filter_feature_columns)\n",
"cs_mad_filter(test_data, mad_filter_feature_columns)\n",
"\n",
"\n",
"print(f'feature_columns: {feature_columns}')\n",
"\n",
"\n",
"print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
"print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
"print(len(train_data))\n",
"print(f\"train_data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\"test_data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 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",
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n"
]
},
{
"cell_type": "code",
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"execution_count": 100,
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"id": "3ff2d1c5",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"import matplotlib.pyplot as plt # 保持 matplotlib 导入，
"from sklearn.decomposition import PCA\n",
"import pandas as pd\n",
"import numpy as np\n",
"import datetime # 用于日期计算\n",
"from catboost import CatBoostClassifier, CatBoostRanker, CatBoostRegressor\n",
"from catboost import Pool\n",
"import lightgbm as lgb\n",
"from lightgbm import LGBMRanker, LGBMRegressor\n",
"\n",
"def train_model(train_data_df, feature_columns,\n",
" print_info=True, # 调整参数名，更通用\n",
" validation_days=180, use_pca=False, split_date=None,\n",
" target_column='label', type='light'): # 增加目标列参数\n",
"\n",
" print('train data size: ', len(train_data_df))\n",
" print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n",
" # 确保数据按时间排序\n",
" train_data_df = train_data_df.sort_values(by='trade_date')\n",
"\n",
" # 识别数值型特征列\n",
" numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
"\n",
" # 去除标签为空的样本\n",
" initial_len = len(train_data_df)\n",
" train_data_df = train_data_df.dropna(subset=[target_column])\n",
"\n",
" if print_info:\n",
" print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n",
"\n",
" # 提取特征和标签，只取数值型特征用于线性回归\n",
" \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",
" train_data_split = train_data_split.sort_values('trade_date')\n",
" val_data_split = val_data_split.sort_values('trade_date')\n",
"\n",
" \n",
" X_train = train_data_split[feature_columns]\n",
" y_train = train_data_split[target_column]\n",
" \n",
" X_val = val_data_split[feature_columns]\n",
" y_val = val_data_split[target_column]\n",
"\n",
"\n",
" # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n",
" scaler = StandardScaler()\n",
" # X_train = scaler.fit_transform(X_train)\n",
"\n",
" # 训练线性回归模型\n",
" # model = LogisticRegression(random_state=42)\n",
" \n",
" # # 使用处理后的特征和样本权重进行训练\n",
" # model.fit(X_train, y_train)\n",
"\n",
"\n",
" if type == 'cat':\n",
" params = {\n",
" 'loss_function': 'QueryRMSE', # 适用于二分类\n",
" 'eval_metric': 'NDCG', # 评估指标\n",
" 'iterations': 1500,\n",
" 'learning_rate': 0.01,\n",
" 'depth': 10, # 控制模型复杂度\n",
" # 'l2_leaf_reg': 0.1, # L2 正则化\n",
" 'verbose': 5000,\n",
" 'early_stopping_rounds': 300,\n",
" 'one_hot_max_size': 50,\n",
" # 'class_weights': [0.6, 1.2],\n",
" 'task_type': 'GPU',\n",
" 'has_time': True,\n",
" 'random_seed': 7\n",
" }\n",
" cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n",
" group_train = train_data_split['trade_date'].factorize()[0]\n",
" group_val = val_data_split['trade_date'].factorize()[0]\n",
" train_pool = Pool(\n",
" data=X_train,\n",
" label=y_train,\n",
" group_id=group_train,\n",
" cat_features=cat_features\n",
" )\n",
" val_pool = Pool(\n",
" data=X_val,\n",
" label=y_val,\n",
" group_id=group_val,\n",
" cat_features=cat_features\n",
" )\n",
"\n",
"\n",
" model = CatBoostRanker(**params)\n",
" model.fit(train_pool,\n",
" eval_set=val_pool, \n",
" plot=True, \n",
" use_best_model=True\n",
" )\n",
" elif type == 'light':\n",
" label_gain = list(range(len(train_data_split['label'].unique())))\n",
" params = {\n",
" 'label_gain': [gain * gain for gain in label_gain],\n",
" 'objective': 'lambdarank',\n",
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" 'metric': 'lambdarank',\n",
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" 'learning_rate': 0.01,\n",
" 'num_leaves': 1024,\n",
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" 'min_data_in_leaf': 256,\n",
" # 'max_depth': 64,\n",
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" 'max_bin': 1024,\n",
" 'feature_fraction': 0.7,\n",
" 'bagging_fraction': 0.7,\n",
" 'bagging_freq': 5,\n",
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" 'lambda_l1': 10,\n",
" # 'lambda_l2': 1,\n",
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" 'boosting': 'gbdt',\n",
" 'verbosity': -1,\n",
" 'extra_trees': True,\n",
" # 'max_position': 5,\n",
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" 'ndcg_at': 5,\n",
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" 'quant_train_renew_leaf': True,\n",
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" 'lambdarank_truncation_level': 10,\n",
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" # 'lambdarank_position_bias_regularization': 1,\n",
" 'seed': 7\n",
" }\n",
" train_groups = train_data_split.groupby('trade_date').size().tolist()\n",
" val_groups = val_data_split.groupby('trade_date').size().tolist()\n",
"\n",
" categorical_feature = [col for col in feature_columns if 'cat' in col]\n",
" train_dataset = lgb.Dataset(\n",
" X_train, label=y_train, \n",
" group=train_groups,\n",
" categorical_feature=categorical_feature\n",
" )\n",
" val_dataset = lgb.Dataset(\n",
" X_val, label=y_val, \n",
" group=val_groups,\n",
" categorical_feature=categorical_feature\n",
" )\n",
"\n",
" evals = {}\n",
" callbacks = [lgb.log_evaluation(period=1000),\n",
" lgb.callback.record_evaluation(evals),\n",
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" lgb.early_stopping(500, first_metric_only=False)\n",
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" ]\n",
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" # 训练模型\n",
" model = lgb.train(\n",
" params, train_dataset, num_boost_round=1000,\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 True:\n",
" lgb.plot_metric(evals)\n",
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
" plt.show()\n",
"\n",
" # from flaml import AutoML\n",
" # from sklearn.datasets import fetch_california_housing\n",
"\n",
" # # Initialize an AutoML instance\n",
" # model = AutoML()\n",
" # # Specify automl goal and constraint\n",
" # automl_settings = {\n",
" # \"time_budget\": 600, # in seconds\n",
" # \"metric\": \"ndcg@1\",\n",
" # \"task\": \"rank\",\n",
" # \"estimator_list\": [\n",
" # \"catboost\",\n",
" # \"lgbm\",\n",
" # \"xgboost\"\n",
" # ], \n",
" # \"ensemble\": {\n",
" # \"final_estimator\": LGBMRanker(),\n",
" # \"passthrough\": False,\n",
" # },\n",
" # }\n",
" # model.fit(X_train=X_train, y_train=y_train, groups=train_groups,\n",
" # X_val=X_val, y_val=y_val,groups_val=val_groups,\n",
" # mlflow_logging=False, **automl_settings)\n",
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"\n",
"\n",
" return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象"
]
},
{
"cell_type": "code",
2025-05-28 14:16:04 +08:00
"execution_count": 101,
2025-05-26 21:34:36 +08:00
"id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T15:03:18.426481Z",
"start_time": "2025-04-03T15:02:19.926352Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2025-05-28 14:16:04 +08:00
"train data size: 1091832\n",
" ts_code trade_date log_circ_mv\n",
"0 600306.SH 2020-01-02 11.552040\n",
"1 603269.SH 2020-01-02 11.324801\n",
"2 002633.SZ 2020-01-02 11.759023\n",
"3 603991.SH 2020-01-02 11.181150\n",
"4 000691.SZ 2020-01-02 11.677910\n",
"... ... ... ...\n",
"1091827 603698.SH 2022-12-30 13.370853\n",
"1091828 600789.SH 2022-12-30 13.372063\n",
"1091829 605366.SH 2022-12-30 12.642936\n",
"1091830 603219.SH 2022-12-30 12.089671\n",
"1091831 000615.SZ 2022-12-30 13.375555\n",
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"\n",
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"[1091832 rows x 3 columns]\n",
"原始样本数: 1091832, 去除标签为空后样本数: 1091832\n",
"Training until validation scores don't improve for 500 rounds\n",
"Early stopping, best iteration is:\n",
"[3]\ttrain's ndcg@5: 0.406681\tvalid's ndcg@5: 0.383736\n"
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]
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},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
"\n",
"gc.collect()\n",
"\n",
"use_pca = False\n",
"type = 'light'\n",
"# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat 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_model(train_data\n",
" .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n",
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" .apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
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" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
" .merge(index_data, on='trade_date', how='left'), \n",
" feature_columns, type=type, target_column='label')\n"
]
},
{
"cell_type": "code",
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"execution_count": 102,
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"id": "5d1522a7538db91b",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-03T15:04:39.656944Z",
"start_time": "2025-04-03T15:04:39.298483Z"
}
},
"outputs": [],
"source": [
"score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))\n",
"# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n",
"# score_df = score_df[score_df['pe_ttm'] > 0]\n",
"score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
"score_df = score_df.merge(index_data, on='trade_date', how='left')\n",
"# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n",
"numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n",
"numeric_columns = [col for col in feature_columns if col in numeric_columns]\n",
"\n",
"if type == 'cat':\n",
" score_df['score'] = model.predict(score_df[feature_columns])\n",
"elif type == 'light':\n",
" score_df['score'] = model.predict(score_df[feature_columns])\n",
"score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
"\n",
"score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
" lambda x: \n",
" x[\n",
" # (x['score'] <= x['score'].quantile(0.99)) & \n",
" (x['score'] >= x['score'].quantile(0.90))\n",
" ] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
").reset_index(drop=True) # drop=True 避免添加旧索引列\n",
"# df_to_drop = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
"# score_df = score_df.drop(df_to_drop.index)\n",
"save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n",
"# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
"save_df = save_df.sort_values(['trade_date', 'score'])\n",
"save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
]
},
{
"cell_type": "code",
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"execution_count": 103,
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"id": "09b1799e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"191\n",
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n"
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]
}
],
"source": [
"print(len(feature_columns))\n",
"print(feature_columns)"
]
},
{
"cell_type": "code",
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"execution_count": 104,
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"id": "bceabd1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n",
"\n",
"NDCG 结果\n",
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"{'ndcg@1': 0.9795918367346939, 'ndcg@3': 0.7667326972309916, 'ndcg@5': 0.6789315367339909}\n"
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]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def calculate_ndcg(df: pd.DataFrame, score_col: str, label_col: str, group_id: str = 'trade_date', k_values: list = [1, 3, 5, 10]):\n",
" \"\"\"\n",
" 计算 DataFrame 中 score 列和 label 列的 NDCG 值。\n",
"\n",
" Args:\n",
" df (pd.DataFrame): 包含 score (排序学习预测分数) 和 label (相关性标签) 的 DataFrame。\n",
" 假设每个需要排序的组（例如，每天的股票）在 DataFrame 中是连续的。\n",
" score_col (str): 包含模型预测分数的列名。\n",
" label_col (str): 包含相关性标签的列名。标签值越高表示相关性越高。\n",
" k_values (list): 一个整数列表，表示计算 NDCG 的 top-k 值。\n",
" 例如，[1, 3, 5] 将计算 NDCG@1, NDCG@3 和 NDCG@5。\n",
"\n",
" Returns:\n",
" dict: 一个字典，包含每个 k 值对应的平均 NDCG 值。\n",
" 例如: {'ndcg@1': 0.85, 'ndcg@3': 0.78, 'ndcg@5': 0.72, 'ndcg@10': 0.65}\n",
" \"\"\"\n",
" ndcg_scores = {f'ndcg@{k}': [] for k in k_values}\n",
"\n",
" def dcg_at_k(r, k):\n",
" r = np.asfarray(r)[:k] if len(r) > 0 else np.zeros(k)\n",
" return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n",
"\n",
" def ndcg_at_k(r, k):\n",
" dcg_max = dcg_at_k(sorted(r, reverse=True), k)\n",
" if not dcg_max:\n",
" return 0.\n",
" return dcg_at_k(r, k) / dcg_max\n",
"\n",
" # 假设 DataFrame 已经按照需要排序的组（例如，'trade_date'）进行了分组，\n",
" # 并且每个组内的顺序不重要，我们只需要计算每个组的 NDCG。\n",
" # 如果需要按特定组计算 NDCG，
" if group_id not in df.columns:\n",
" print(\"警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\")\n",
" group_df = df.sort_values(by=score_col, ascending=False)\n",
" relevant_labels = group_df[label_col].values\n",
" for k in k_values:\n",
" ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n",
" else:\n",
" for _, group_df in df.groupby(group_id):\n",
" group_df_sorted = group_df.sort_values(by=score_col, ascending=False)\n",
" relevant_labels = group_df_sorted[label_col].values\n",
" for k in k_values:\n",
" ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n",
"\n",
" avg_ndcg = {k: np.mean(v) if v else np.nan for k, v in ndcg_scores.items()}\n",
" return avg_ndcg\n",
"\n",
"\n",
"ndcg_results_single_group = calculate_ndcg(score_df, score_col='score', label_col='label', k_values=[1, 3, 5], group_id=None)\n",
"print(\"\\nNDCG 结果\")\n",
"print(ndcg_results_single_group)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "new_trader",
"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
}
