2553 lines
285 KiB
Plaintext
2553 lines
285 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "79a7758178bafdd3",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-03T12:46:06.987506Z",
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"start_time": "2025-04-03T12:46:06.259551Z"
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},
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"e:\\PyProject\\NewStock\\main\\train\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import gc\n",
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"import os\n",
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"import sys\n",
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"sys.path.append('../../')\n",
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"print(os.getcwd())\n",
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"import pandas as pd\n",
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"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
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"from main.utils.factor import read_industry_data\n",
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"from main.utils.factor_processor import calculate_score\n",
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"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a79cafb06a7e0e43",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:47:00.212859Z",
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"start_time": "2025-04-03T12:46:06.998047Z"
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},
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"daily data\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"daily basic\n",
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"inner merge on ['ts_code', 'trade_date']\n",
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"stk limit\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"money flow\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"cyq perf\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 8665405 entries, 0 to 8665404\n",
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"Data columns (total 32 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 ts_code object \n",
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" 1 trade_date datetime64[ns]\n",
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" 2 open float64 \n",
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" 3 close float64 \n",
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" 4 high float64 \n",
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" 5 low float64 \n",
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" 6 vol float64 \n",
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" 7 pct_chg float64 \n",
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" 8 turnover_rate float64 \n",
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" 9 pe_ttm float64 \n",
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" 10 circ_mv float64 \n",
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" 11 total_mv float64 \n",
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" 12 volume_ratio float64 \n",
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" 13 is_st bool \n",
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" 14 up_limit float64 \n",
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" 15 down_limit float64 \n",
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" 16 buy_sm_vol float64 \n",
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" 17 sell_sm_vol float64 \n",
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" 18 buy_lg_vol float64 \n",
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" 19 sell_lg_vol float64 \n",
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" 20 buy_elg_vol float64 \n",
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" 21 sell_elg_vol float64 \n",
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" 22 net_mf_vol float64 \n",
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" 23 his_low float64 \n",
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" 24 his_high float64 \n",
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" 25 cost_5pct float64 \n",
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" 26 cost_15pct float64 \n",
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" 27 cost_50pct float64 \n",
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" 28 cost_85pct float64 \n",
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" 29 cost_95pct float64 \n",
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" 30 weight_avg float64 \n",
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" 31 winner_rate float64 \n",
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"dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n",
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"memory usage: 2.0+ GB\n",
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"None\n"
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]
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}
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||
],
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"source": [
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"from main.utils.utils import read_and_merge_h5_data\n",
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"\n",
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"print('daily data')\n",
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"df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
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" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n",
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" df=None)\n",
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"\n",
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"print('daily basic')\n",
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"df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n",
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" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
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" 'is_st'], df=df, join='inner')\n",
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"\n",
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"print('stk limit')\n",
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"df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n",
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" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
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" df=df)\n",
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"print('money flow')\n",
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"df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n",
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" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
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" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
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" df=df)\n",
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"print('cyq perf')\n",
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"df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n",
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" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
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" 'cost_50pct',\n",
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" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
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" df=df)\n",
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"print(df.info())"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "cac01788dac10678",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:47:10.527104Z",
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||
"start_time": "2025-04-03T12:47:00.488715Z"
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||
}
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||
},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"industry\n"
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]
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||
}
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],
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"source": [
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"print('industry')\n",
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"industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n",
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" columns=['ts_code', 'l2_code', 'in_date'],\n",
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" df=None, on=['ts_code'], join='left')\n",
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"\n",
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"\n",
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"def merge_with_industry_data(df, industry_df):\n",
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" # 确保日期字段是 datetime 类型\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
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" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
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"\n",
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" # 对 industry_df 按 ts_code 和 in_date 排序\n",
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" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
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"\n",
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" # 对原始 df 按 ts_code 和 trade_date 排序\n",
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" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
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"\n",
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" # 使用 merge_asof 进行向后合并\n",
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" merged = pd.merge_asof(\n",
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" df_sorted,\n",
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" industry_df_sorted,\n",
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" by='ts_code', # 按 ts_code 分组\n",
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" left_on='trade_date',\n",
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" right_on='in_date',\n",
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" direction='backward'\n",
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" )\n",
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"\n",
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" # 获取每个 ts_code 的最早 in_date 记录\n",
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" min_in_date_per_ts = (industry_df_sorted\n",
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" .groupby('ts_code')\n",
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" .first()\n",
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" .reset_index()[['ts_code', 'l2_code']])\n",
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"\n",
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" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
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" merged['l2_code'] = merged['l2_code'].fillna(\n",
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" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
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" )\n",
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"\n",
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" # 保留需要的列并重置索引\n",
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" result = merged.reset_index(drop=True)\n",
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" return result\n",
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"\n",
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"\n",
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"# 使用示例\n",
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"df = merge_with_industry_data(df, industry_df)\n",
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"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "c4e9e1d31da6dba6",
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||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
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||
"start_time": "2025-04-03T12:47:10.541247Z"
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||
},
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||
"jupyter": {
|
||
"source_hidden": true
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||
}
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"from main.factor.factor import *\n",
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"\n",
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"def calculate_indicators(df):\n",
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" \"\"\"\n",
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" 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
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" \"\"\"\n",
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" df = df.sort_values('trade_date')\n",
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" df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
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||
" # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
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||
" delta = df['close'].diff()\n",
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" gain = delta.where(delta > 0, 0)\n",
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" loss = -delta.where(delta < 0, 0)\n",
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||
" avg_gain = gain.rolling(window=14).mean()\n",
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||
" avg_loss = loss.rolling(window=14).mean()\n",
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||
" rs = avg_gain / avg_loss\n",
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||
" df['RSI'] = 100 - (100 / (1 + rs))\n",
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"\n",
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||
" # 计算MACD\n",
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||
" ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
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||
" ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
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||
" df['MACD'] = ema12 - ema26\n",
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||
" df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
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||
" df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
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"\n",
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||
" # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
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" df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
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" df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n",
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"\n",
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||
" # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
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" df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n",
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" df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n",
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"\n",
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" # 6. 情绪因子3：波动率（Volatility）\n",
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" df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n",
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"\n",
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" # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
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" df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n",
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" df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n",
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"\n",
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" # df = sentiment_panic_greed_index(df)\n",
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" # df = sentiment_market_breadth_proxy(df)\n",
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" # df = sentiment_reversal_indicator(df)\n",
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"\n",
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" return df\n",
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"\n",
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"\n",
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"def generate_index_indicators(h5_filename):\n",
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" df = pd.read_hdf(h5_filename, key='index_data')\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
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" df = df.sort_values('trade_date')\n",
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"\n",
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" # 计算每个ts_code的相关指标\n",
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" df_indicators = []\n",
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" for ts_code in df['ts_code'].unique():\n",
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" df_index = df[df['ts_code'] == ts_code].copy()\n",
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" df_index = calculate_indicators(df_index)\n",
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" df_indicators.append(df_index)\n",
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"\n",
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" # 合并所有指数的结果\n",
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" df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
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"\n",
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" # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
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" df_final = df_all_indicators.pivot_table(\n",
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" index='trade_date',\n",
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" columns='ts_code',\n",
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" values=['daily_return', \n",
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" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
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" # 'sentiment_panic_greed_index',\n",
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" 'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
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" 'amount_change_rate', 'amount_mean'],\n",
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" aggfunc='last'\n",
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" )\n",
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"\n",
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" df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
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" df_final = df_final.reset_index()\n",
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"\n",
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" return df_final\n",
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"\n",
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"\n",
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"# 使用函数\n",
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"h5_filename = '../../data/index_data.h5'\n",
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"index_data = generate_index_indicators(h5_filename)\n",
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"index_data = index_data.dropna()\n"
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||
]
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||
},
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||
{
|
||
"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"
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||
]
|
||
},
|
||
{
|
||
"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', 'roa', 'roe'],\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",
|
||
"execution_count": 9,
|
||
"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",
|
||
"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",
|
||
"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",
|
||
"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",
|
||
"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', 'roa', 'roe',\n",
|
||
" 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
|
||
" 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
|
||
" 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
|
||
" 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
|
||
" 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
|
||
" 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
|
||
" 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
|
||
" 'floating_chip_proxy', 'cost_support_15pct_change',\n",
|
||
" 'cat_winner_price_zone', 'flow_chip_consistency',\n",
|
||
" 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
|
||
" 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
|
||
" '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
|
||
" '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",
|
||
" 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: 180 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(164), 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', 'roa', 'roe', '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"
|
||
]
|
||
}
|
||
],
|
||
"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",
|
||
"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",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n",
|
||
"\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",
|
||
"\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",
|
||
"execution_count": 10,
|
||
"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",
|
||
"execution_count": 11,
|
||
"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，\n",
|
||
" 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，需包含 'trade_date' 和 features 列。\n",
|
||
" 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",
|
||
"from tqdm import tqdm\n",
|
||
"\n",
|
||
"def cs_neutralize_market_cap_numpy(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" market_cap_col: str = 'circ_mv'):\n",
|
||
" \"\"\"\n",
|
||
" 对 DataFrame 中的指定特征进行截面市值中性化 (NumPy 优化)。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含数据的 DataFrame，需要有 'trade_date' 和 market_cap_col 列。\n",
|
||
" features (list): 需要进行市值中性化的特征列名列表。\n",
|
||
" market_cap_col (str): 包含市值数据的列名，默认为 'circ_mv'。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面市值中性化 (NumPy 优化)...\")\n",
|
||
" required_cols = features + ['trade_date', 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",
|
||
" df_copy = df\n",
|
||
" log_cap_col = '_log_market_cap'\n",
|
||
" df_copy[log_cap_col] = np.log1p(df_copy[market_cap_col])\n",
|
||
"\n",
|
||
" # 创建一个 DataFrame 来存储所有日期的残差结果\n",
|
||
" residuals_container = pd.DataFrame(index=df_copy.index, columns=features, dtype=float)\n",
|
||
"\n",
|
||
" for date, group_df in tqdm(df_copy.groupby('trade_date'), desc=\"Neutralizing by Date (NumPy)\"):\n",
|
||
" # 准备 X 矩阵 (自变量)：常数项和对数市值\n",
|
||
" X_daily = np.concatenate([np.ones((len(group_df), 1)), group_df[[log_cap_col]].values], axis=1)\n",
|
||
"\n",
|
||
" for feature_col in features:\n",
|
||
" Y_daily = group_df[feature_col].values\n",
|
||
"\n",
|
||
" # 处理 NaN：只对有效数据对进行回归\n",
|
||
" valid_mask_y = ~np.isnan(Y_daily)\n",
|
||
" valid_mask_x = ~np.isnan(X_daily).any(axis=1)\n",
|
||
" valid_mask = valid_mask_y & valid_mask_x\n",
|
||
"\n",
|
||
" current_feature_indices = group_df.index[valid_mask]\n",
|
||
"\n",
|
||
" if np.sum(valid_mask) < X_daily.shape[1] + 1:\n",
|
||
" # 有效数据不足，此特征在此日期保持 NaN\n",
|
||
" continue\n",
|
||
"\n",
|
||
" Y_valid = Y_daily[valid_mask]\n",
|
||
" X_valid = X_daily[valid_mask, :]\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # 使用 np.linalg.lstsq 进行 OLS 计算\n",
|
||
" beta, sum_sq_resid, rank, s = np.linalg.lstsq(X_valid, Y_valid, rcond=None)\n",
|
||
"\n",
|
||
" # 计算预测值 Y_hat = X_valid @ beta\n",
|
||
" Y_hat_valid = X_valid @ beta\n",
|
||
"\n",
|
||
" # 计算残差 residuals = Y_valid - Y_hat_valid\n",
|
||
" residuals_valid = Y_valid - Y_hat_valid\n",
|
||
"\n",
|
||
" # 将计算得到的残差放回 residuals_container\n",
|
||
" residuals_container.loc[current_feature_indices, feature_col] = residuals_valid\n",
|
||
"\n",
|
||
" except np.linalg.LinAlgError:\n",
|
||
" pass\n",
|
||
" except Exception as e:\n",
|
||
" pass\n",
|
||
"\n",
|
||
" # 将所有计算得到的残差更新回原始的 df (原地修改)\n",
|
||
" for feature_col in features:\n",
|
||
" df[feature_col] = residuals_container[feature_col]\n",
|
||
"\n",
|
||
" # 清理临时列\n",
|
||
" df.drop(columns=[log_cap_col], inplace=True, errors='ignore')\n",
|
||
" print(\"截面市值中性化完成 (NumPy 优化)。\")\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，需包含 'trade_date' 和 features 列。\n",
|
||
" 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，需要包含 'trade_date' 和 feature_columns 中的列。\n",
|
||
" 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",
|
||
"execution_count": 12,
|
||
"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，包含特征、行业分类和市值列。\n",
|
||
" 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，取决于清理前的状态\n",
|
||
"\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（可能全是NaN）\n",
|
||
" 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，只对有因子值的行进行回归计算\n",
|
||
" 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，例如np.cov或np.var因为极端数据报错\n",
|
||
" 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：residual_series.loc[group.index] = group[col]\n",
|
||
"\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，并返回 RankIC 绝对值最高的 n 个特征。\n",
|
||
"\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，RankIC设为0或跳过\n",
|
||
"\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",
|
||
"execution_count": 13,
|
||
"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",
|
||
"df['cat_up_limit'] = df['pct_chg'] > 5\n",
|
||
"df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().groupby('ts_code').shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n",
|
||
"\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",
|
||
"execution_count": 14,
|
||
"id": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"191\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').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 '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 ['roa', 'roe']]\n",
|
||
"print(len(feature_columns))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"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",
|
||
"execution_count": 16,
|
||
"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",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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', 'cat_up_limit', '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",
|
||
"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%|██████████| 131/131 [00:28<00:00, 4.67it/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: 100%|██████████| 131/131 [00:24<00:00, 5.43it/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",
|
||
"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",
|
||
"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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', 'cat_up_limit', '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",
|
||
"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_market_cap_numpy(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_market_cap_numpy(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",
|
||
"execution_count": 17,
|
||
"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，尽管LightGBM的绘图功能已移除\n",
|
||
"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\n",
|
||
"from catboost import Pool\n",
|
||
"import lightgbm as lgb\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",
|
||
" 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['label']\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': 'Logloss', # 适用于二分类\n",
|
||
" 'eval_metric': 'Logloss', # 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': 50, # L2 正则化\n",
|
||
" 'verbose': 5000,\n",
|
||
" 'early_stopping_rounds': 300,\n",
|
||
" # 'od_type': 'Iter', # Overfitting detector type\n",
|
||
" # 'od_wait': 300, # Number of iterations to wait after the bes\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",
|
||
" train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n",
|
||
" val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n",
|
||
"\n",
|
||
"\n",
|
||
" model = CatBoostClassifier(**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",
|
||
" params = {\n",
|
||
" 'objective': 'binary',\n",
|
||
" 'metric': 'average_precision',\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" 'is_unbalance': True,\n",
|
||
" 'num_leaves': 2048,\n",
|
||
" 'min_data_in_leaf': 1024,\n",
|
||
" 'max_depth': 32,\n",
|
||
" 'max_bin': 1024,\n",
|
||
" 'feature_fraction': 0.5,\n",
|
||
" 'bagging_fraction': 0.5,\n",
|
||
" 'bagging_freq': 1,\n",
|
||
" 'lambda_l1': 50,\n",
|
||
" 'lambda_l2': 50,\n",
|
||
" 'verbosity': -1,\n",
|
||
" 'num_threads' : 8\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",
|
||
" categorical_feature=categorical_feature\n",
|
||
" )\n",
|
||
" val_dataset = lgb.Dataset(\n",
|
||
" X_val, label=y_val,\n",
|
||
" categorical_feature=categorical_feature\n",
|
||
" )\n",
|
||
"\n",
|
||
" evals = {}\n",
|
||
" callbacks = [lgb.log_evaluation(period=1000),\n",
|
||
" lgb.callback.record_evaluation(evals),\n",
|
||
" lgb.early_stopping(100, first_metric_only=True)\n",
|
||
" ]\n",
|
||
" # 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",
|
||
"\n",
|
||
" return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"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": [
|
||
"train data size: 36400\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",
|
||
"36395 600615.SH 2022-12-30 12.027909\n",
|
||
"36396 603829.SH 2022-12-30 12.034572\n",
|
||
"36397 603037.SH 2022-12-30 12.035767\n",
|
||
"36398 002767.SZ 2022-12-30 11.896427\n",
|
||
"36399 600561.SH 2022-12-30 11.858571\n",
|
||
"\n",
|
||
"[36400 rows x 3 columns]\n",
|
||
"原始样本数: 36400, 去除标签为空后样本数: 36400\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "b132fdab733b4d4e856d4688f2997dac",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0:\tlearn: 0.6887138\ttest: 0.6894516\tbest: 0.6894516 (0)\ttotal: 328ms\tremaining: 8m 12s\n",
|
||
"bestTest = 0.5217666894\n",
|
||
"bestIteration = 487\n",
|
||
"Shrink model to first 488 iterations.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"use_pca = False\n",
|
||
"type = 'cat'\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",
|
||
" .apply(lambda x: x.nsmallest(50, 'total_mv'))\n",
|
||
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
" .merge(index_data, on='trade_date', how='left'), feature_columns, type=type)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"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(300, '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",
|
||
"# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n",
|
||
"# score_df = cross_sectional_standardization(score_df, numeric_columns)\n",
|
||
"\n",
|
||
"if type == 'cat':\n",
|
||
" score_df['score'] = model.predict_proba(score_df[feature_columns])[:, 1]\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: x[x['score'] >= x['score'].quantile(0.90)] # 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",
|
||
"# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, '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",
|
||
"execution_count": 20,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"191\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', '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', 'cat_up_limit', '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",
|
||
"[]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(feature_columns))\n",
|
||
"print(feature_columns)\n",
|
||
"print([col for col in feature_columns if 'total_mv' in col])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "e53b209a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"5595 2057539\n",
|
||
" ts_code trade_date turnover_rate\n",
|
||
"0 000001.SZ 2023-01-03 1.1307\n",
|
||
"1 000001.SZ 2023-01-04 1.1284\n",
|
||
"2 000001.SZ 2023-01-05 0.8582\n",
|
||
"3 000001.SZ 2023-01-06 0.6162\n",
|
||
"4 000001.SZ 2023-01-09 0.5450\n",
|
||
"... ... ... ...\n",
|
||
"1766689 605599.SH 2025-05-19 0.4952\n",
|
||
"1766690 605599.SH 2025-05-20 1.6447\n",
|
||
"1766691 605599.SH 2025-05-21 1.2658\n",
|
||
"1766692 605599.SH 2025-05-22 0.7522\n",
|
||
"1766693 605599.SH 2025-05-23 0.6051\n",
|
||
"\n",
|
||
"[1766694 rows x 3 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(train_data[train_data['pct_chg'] > 7]), len(train_data))\n",
|
||
"print(test_data[['ts_code', 'trade_date', 'turnover_rate']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "364e821a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"def calculate_binary_classification_metrics(df: pd.DataFrame, score_col: str, label_col: str, future_return_col: str = None, total_mv_col: str = None, n_mv_bins: int = 10, threshold: float = 0.5):\n",
|
||
" \"\"\"\n",
|
||
" 计算二分类模型的评估指标，可选择计算 score 和 future_return 的相关性，\n",
|
||
" 并可选择计算 score 在按总市值 (total_mv) 分为 n 份后的每个分组上的预测性能（ROC AUC）。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 包含 score (预测概率或置信度), label (真实二分类标签), 可选的 future_return 和 total_mv 的 Pandas DataFrame。\n",
|
||
" score_col: 包含模型预测 score 的列名。\n",
|
||
" label_col: 包含真实二分类标签 (0 或 1) 的列名。\n",
|
||
" future_return_col: (可选) 包含未来收益率的列名，用于计算相关性。\n",
|
||
" total_mv_col: (可选) 包含总市值的列名，用于按市值分 n 份分析预测性能。\n",
|
||
" n_mv_bins: (可选) 将总市值分为多少份，默认为 5。\n",
|
||
" threshold: 将 score 转换为预测类别的阈值，默认为 0.5。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 一个包含以下评估指标的字典：\n",
|
||
" - accuracy: 准确率\n",
|
||
" - precision: 精确率\n",
|
||
" - recall: 召回率\n",
|
||
" - f1: F1 分数\n",
|
||
" - roc_auc: ROC AUC 值\n",
|
||
" - fpr: ROC 曲线的假正率 (False Positive Rate)\n",
|
||
" - tpr: ROC 曲线的真正率 (True Positive Rate)\n",
|
||
" - thresholds: ROC 曲线的阈值\n",
|
||
" - score_return_correlation: (如果 future_return_col 提供) score 和 future_return 的皮尔逊相关系数\n",
|
||
" - mv_roc_auc: (如果 total_mv_col 提供) 一个字典，包含按总市值分为 n 份后的每个市值分组对应的 ROC AUC 值\n",
|
||
" \"\"\"\n",
|
||
" y_true = df[label_col].values\n",
|
||
" y_score = df[score_col].values\n",
|
||
" y_pred = (y_score >= threshold).astype(int)\n",
|
||
"\n",
|
||
" metrics = {}\n",
|
||
" metrics['accuracy'] = accuracy_score(y_true, y_pred)\n",
|
||
" metrics['precision'] = precision_score(y_true, y_pred)\n",
|
||
" metrics['recall'] = recall_score(y_true, y_pred)\n",
|
||
" metrics['f1'] = f1_score(y_true, y_pred)\n",
|
||
" metrics['roc_auc'] = roc_auc_score(y_true, y_score)\n",
|
||
" metrics['fpr'], metrics['tpr'], metrics['thresholds'] = roc_curve(y_true, y_score)\n",
|
||
"\n",
|
||
" if future_return_col in df.columns:\n",
|
||
" metrics['score_return_correlation'] = df[score_col].corr(df[future_return_col])\n",
|
||
"\n",
|
||
" if total_mv_col in df.columns and n_mv_bins > 1:\n",
|
||
" metrics['mv_roc_auc'] = {}\n",
|
||
" df['mv_quantile'] = pd.cut(df[total_mv_col], bins=n_mv_bins, labels=False, duplicates='drop')\n",
|
||
" for i in range(df['mv_quantile'].nunique()):\n",
|
||
" mv_group = df[df['mv_quantile'] == i]\n",
|
||
" if len(mv_group) > 0 and len(np.unique(mv_group[label_col])) > 1 and len(np.unique(mv_group[score_col])) > 1:\n",
|
||
" roc_auc_mv = roc_auc_score(mv_group[label_col], mv_group[score_col])\n",
|
||
" lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n",
|
||
" upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n",
|
||
" metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = roc_auc_mv\n",
|
||
" else:\n",
|
||
" lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n",
|
||
" upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n",
|
||
" metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = np.nan\n",
|
||
" print(f'{lower_bound:.0e}-{upper_bound:.0e}')\n",
|
||
" df.drop(columns=['mv_quantile'], inplace=True)\n",
|
||
"\n",
|
||
" return metrics\n",
|
||
"\n",
|
||
"def plot_roc_curve(metrics: dict):\n",
|
||
" plt.figure(figsize=(8, 6))\n",
|
||
" plt.plot(metrics['fpr'], metrics['tpr'], color='darkorange', lw=2, label=f'ROC curve (AUC = {metrics[\"roc_auc\"]:.2f})')\n",
|
||
" plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
|
||
" plt.xlabel('False Positive Rate')\n",
|
||
" plt.ylabel('True Positive Rate')\n",
|
||
" plt.title('Receiver Operating Characteristic (ROC)')\n",
|
||
" plt.legend(loc=\"lower right\")\n",
|
||
" plt.grid(True)\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "1f6e6336",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"6e+04-9e+04\n",
|
||
"9e+04-1e+05\n",
|
||
"1e+05-1e+05\n",
|
||
"1e+05-1e+05\n",
|
||
"1e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-3e+05\n",
|
||
"3e+05-3e+05\n",
|
||
"二分类评估指标:\n",
|
||
"accuracy: 0.6585\n",
|
||
"precision: 0.4597\n",
|
||
"recall: 0.1487\n",
|
||
"f1: 0.2247\n",
|
||
"roc_auc: 0.6122\n",
|
||
"fpr: (array of length 7368)\n",
|
||
"tpr: (array of length 7368)\n",
|
||
"thresholds: (array of length 7368)\n",
|
||
"score_return_correlation: -0.0355\n",
|
||
"mv_roc_auc: {'6e+04-9e+04': 0.5986394557823129, '9e+04-1e+05': 0.5806043519120943, '1e+05-1e+05': 0.5902634156155784, '1e+05-2e+05': 0.5821412014040664, '2e+05-2e+05': 0.5919906015824924, '2e+05-3e+05': 0.6058984399066272, '3e+05-3e+05': 0.5884844322344323}\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"for sd in [\n",
|
||
" # save_df1, \n",
|
||
" # save_df2, \n",
|
||
" score_df\n",
|
||
" ]:\n",
|
||
" # 计算二分类指标\n",
|
||
" evaluation_metrics = calculate_binary_classification_metrics(sd, score_col='score', label_col='label', threshold=0.6,\n",
|
||
" future_return_col='future_return', total_mv_col='total_mv')\n",
|
||
"\n",
|
||
" # 打印指标\n",
|
||
" print(\"二分类评估指标:\")\n",
|
||
" for metric, value in evaluation_metrics.items():\n",
|
||
" if isinstance(value, (float, int)):\n",
|
||
" print(f\"{metric}: {value:.4f}\")\n",
|
||
" elif isinstance(value, (list, tuple, np.ndarray)):\n",
|
||
" print(f\"{metric}: (array of length {len(value)})\")\n",
|
||
" else:\n",
|
||
" print(f\"{metric}: {value}\")\n",
|
||
"\n",
|
||
" # 绘制 ROC 曲线\n",
|
||
" plot_roc_curve(evaluation_metrics)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "7e9023cc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def analyze_factors(\n",
|
||
" df: pd.DataFrame,\n",
|
||
" feature_columns: list[str],\n",
|
||
" target_column: str = 'target', # 假设目标列默认为 'target'\n",
|
||
" trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n",
|
||
" mcap_col: str = 'total_mv', # 新增: 市值列名称\n",
|
||
" mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n",
|
||
") -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n",
|
||
" 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n",
|
||
" feature_columns (list[str]): 需要分析的特征列名称列表。\n",
|
||
" target_column (str): 目标变量列的名称。\n",
|
||
" trade_date_col (str): 交易日期列的名称。\n",
|
||
" mcap_col (str): 市值列的名称。\n",
|
||
" mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n",
|
||
" 同时打印因子在不同市值分位数上的平均IC表格。\n",
|
||
" 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" # --- 数据校验 ---\n",
|
||
" required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\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: 输入DataFrame缺少必需的列: {missing}\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
" # 确保日期列是 datetime 类型\n",
|
||
" df = df.copy() # 在副本上操作\n",
|
||
" df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n",
|
||
" df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n",
|
||
"\n",
|
||
" # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n",
|
||
" # 直接在 df 副本上进行清洗\n",
|
||
" initial_rows_before_clean = len(df)\n",
|
||
" df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n",
|
||
" rows_dropped_clean = initial_rows_before_clean - len(df)\n",
|
||
" if rows_dropped_clean > 0:\n",
|
||
" print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n",
|
||
"\n",
|
||
" if df.empty:\n",
|
||
" print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
"\n",
|
||
" print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n",
|
||
"\n",
|
||
" # --- 1. 基本因子统计量 ---\n",
|
||
" basic_stats = df[feature_columns].describe().T\n",
|
||
"\n",
|
||
" print(\"\\n--- 基本因子统计量 ---\")\n",
|
||
" print(basic_stats)\n",
|
||
"\n",
|
||
" # --- 2. 因子与目标变量的整体相关性 ---\n",
|
||
" overall_correlation = {}\n",
|
||
" for feature in feature_columns:\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n",
|
||
" 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n",
|
||
" }\n",
|
||
" else:\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': np.nan,\n",
|
||
" 'Spearman_Correlation_with_Target': np.nan\n",
|
||
" }\n",
|
||
" overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n",
|
||
" print(overall_corr_df)\n",
|
||
"\n",
|
||
" # --- 3. 因子之间的相关性矩阵 ---\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n",
|
||
" print(factor_correlation_matrix)\n",
|
||
"\n",
|
||
" # --- 4. 日间 IC 和 ICIR ---\n",
|
||
" print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n",
|
||
"\n",
|
||
" # 直接在清理后的 df 上计算每日 IC\n",
|
||
" if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n",
|
||
" daily_ic_series = pd.Series(dtype=float) # 空 Series\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
" else:\n",
|
||
" daily_ic_series = df.groupby(trade_date_col).apply(\n",
|
||
" lambda day_group: {\n",
|
||
" feature: day_group[feature].corr(day_group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将字典结果转换为 DataFrame\n",
|
||
"\n",
|
||
" # 计算 IC 的统计量\n",
|
||
" if not daily_ic_series.empty:\n",
|
||
" ic_mean = daily_ic_series.mean()\n",
|
||
" ic_std = daily_ic_series.std()\n",
|
||
" # 避免除以零\n",
|
||
" ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n",
|
||
"\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': ic_mean,\n",
|
||
" 'Std_Dev_IC': ic_std,\n",
|
||
" 'ICIR': ic_ir\n",
|
||
" })\n",
|
||
" print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n",
|
||
" print(ic_stats)\n",
|
||
" else:\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 5. 因子在不同市值分位数上的平均 IC ---\n",
|
||
" print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n",
|
||
"\n",
|
||
" # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n",
|
||
" # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n",
|
||
" # labels=False 返回整数 0 to mcap_bins-1\n",
|
||
" # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n",
|
||
" # 添加一个临时列来存储分位数\n",
|
||
" mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n",
|
||
" df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n",
|
||
" lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n",
|
||
" # 创建一个临时 DataFrame df_binned_analysis\n",
|
||
" df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n",
|
||
"\n",
|
||
" if df_binned_analysis.empty:\n",
|
||
" print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n",
|
||
" avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n",
|
||
" else:\n",
|
||
" # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n",
|
||
" binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n",
|
||
" lambda group: {\n",
|
||
" feature: group[feature].corr(group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n",
|
||
"\n",
|
||
" # 对每个分位数组的每日 IC 求平均\n",
|
||
" # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n",
|
||
" # mean(axis=0) 对日期索引求平均\n",
|
||
" avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n",
|
||
"\n",
|
||
" # 重命名索引和列，使表格更清晰\n",
|
||
" if not avg_ic_by_bin.empty:\n",
|
||
" # Index name will be the original column name used for grouping ('_mcap_bin_X')\n",
|
||
" # Rename the index name explicitly\n",
|
||
" avg_ic_by_bin.index.name = 'MarketCap_Bin'\n",
|
||
" avg_ic_by_bin.columns.name = 'Feature'\n",
|
||
" # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n",
|
||
" avg_ic_by_bin = avg_ic_by_bin.sort_index()\n",
|
||
"\n",
|
||
" print(avg_ic_by_bin)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 6. 汇总所有指标 ---\n",
|
||
" # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n",
|
||
" # 注意：合并时需要根据索引进行对齐 (因子名称)\n",
|
||
" summary_df = basic_stats\n",
|
||
" summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n",
|
||
" summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n",
|
||
"\n",
|
||
" # print(\"\\n--- 因子分析汇总报告 ---\")\n",
|
||
" # print(summary_df)\n",
|
||
"\n",
|
||
" # --- 清理临时列 'mcap_bin' ---\n",
|
||
" # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n",
|
||
" if mcap_bin_col_name in df.columns:\n",
|
||
" df.drop(columns=[mcap_bin_col_name], inplace=True)\n",
|
||
"\n",
|
||
"\n",
|
||
" return summary_df # 主要返回汇总报告，分位数IC单独打印\n",
|
||
"\n",
|
||
"# # 运行分析函数\n",
|
||
"# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n",
|
||
"\n",
|
||
"# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n",
|
||
"# print(factor_analysis_report)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "a0000d75",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
|
||
"准备数据，处理 NaN 值...\n",
|
||
"原始数据 17280 行，移除 NaN 后剩余 16976 行用于分析。\n",
|
||
"对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
|
||
"按二维分箱分组计算 Spearman Rank IC...\n",
|
||
"整理结果用于绘图...\n",
|
||
"circ_mv_bin 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 \\\n",
|
||
"future_return_bin ... \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. .. .. .. ... .. .. .. \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"\n",
|
||
"circ_mv_bin 93 94 95 96 97 98 99 \n",
|
||
"future_return_bin \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"99 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"\n",
|
||
"[100 rows x 100 columns]\n",
|
||
"生成热力图...\n",
|
||
"分析完成。\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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f++OPP7R582b7LxFSVa9eXcOHD9e6devUokWLNO/z9ddf6/nnn9dbb70lPz8/Wa1WPfroo1q3bp2uXLmS7hc9kjR8+HDNnz/fbQvo0rWfVUkKCQlx+Xu3atXKvnv/+kX0H3/80f56qosXL+rnn39WuXLlNGfOHPvPbIMGDfThhx9q27ZtbllE/+qrr2S1WjVu3Dg1b97cfv2pp57S/Pnz9dZbb6XZVX/27FlFR0fbf/litVr1/PPPa+3atVq9erWaNGkiSfrmm2+0ceNGvfzyy3r99dft46dPn66RI0dq2rRpevPNN2+p9tSf9ev/EgQAAAC+iUV0AABMlrpb8tChQ4qPj1e+fPkkSW+88YbeeOONNNnFixdLurYQfP3id9OmTTVmzJg0x0d89913kqT+/funWfhs0KCBmjZtqpUrV2rDhg165JFH0swRFxen6dOn64EHHsiw3vXr1+vs2bOKiIjQnDlz0ryWL18+nThxQtu3b89wUfhmMnog6OXLl9WzZ0+dPXtWvXv3VocOHdSwYUNFRUXp7bfftucuXbqkDz74QAMGDMjyw0Cvd/1Z6qlnkN945I10bYf19Qu2oaGheuihhyRdWzhNXTw9evSoJKlcuXLp3uP06dOSlOmu7qSkJI0bN05BQUFq166dJk+erEKFCumFF17QpEmTNGnSJD399NOSpKJFi2bnVp0WGhqq//77L9NMt27dJF07P33//v0aNWpUmsVdSXrooYfsfyWQ6siRI9q1a5eka0fW3KhkyZJ644037DuRw8PDVbNmTS1fvlz//fdfugXhESNGaPbs2apatarbFtAl2eu7/pcyrlK1alVVqVJFK1eu1LBhwxQQEKCUlBQtX75cFSpUULVq1ezZPHnyKDw8XOfOndORI0dUvnx5SdKjjz6qRx991OW1OfLJJ5/ok08+sf87KSlJ27Zt04ULF5SYmKiTJ0/afxEnScHBwWn+IiM8PFydO3fW66+/rjVr1tgX0ZcuXSrp2pE1Y8eOtefj4+MlXfvl5K06e/asJPf9kgoAAACei0V0AABMdu+996ply5ZaunSpHnzwQVWpUkURERGqU6eOmjZtmmax/NChQwoJCbEviKUKCQnR448/nubaX3/9JUkZnn189913a+XKlfrrr7/SLaI3atTI4QK6JB0+fFjStYXm1MXmG508edLxDWdDXFycevbsqX379qlevXrq1auXpGtnFS9atEjdunVTiRIlZLPZ9Nprr+mXX37R3r17NXv27GwvfN1zzz32Hf+//PKLpGu7WmfMmKG5c+fq/vvvl81mU0pKSpbeLyYmRlL6ndlS1hbRJ0yYoGPHjqlDhw5pFhk7duyov//+W23btrX34rbbbstSTbeqVKlSN+1tv379JEk1a9bUyy+/rEWLFqVbRJeufQ/mzp2rLVu2aO/evTp//rx9R3JG3+NmzZql+6uJ1B4bhpHm+vz583Xy5ElVrFhRe/fu1S+//JLu5zynpP4SLHUx90bt27fXnj17tGbNGqeOG2ndurVGjRqljRs36qGHHtLWrVt16tSpNDv9pWvHv7z55psaPny4oqKiVL58eUVERKhGjRqKiopy6hdNztq0aZOWLVumHTt26NChQ0pOTk5zRNX1SpYsme4on0qVKkn6v19MSdLff/8tSWmOO7qeKz6DTp48qXz58vEAWAAAAMj/5hEAAJDTPv74Yy1YsEC9evVS2bJl9fvvv6t///5q2bKlLly4kKX3uHLlihISEtItKGb3gXh33XVXpq+nvv/gwYO1b9++DL+effbZbM2Zke+++05PPfWUDhw4IElpjofp2bOnrly5omHDhikxMVF9+vTRL7/8oqZNm+qbb75xaudov3799M4778hms2nFihWqVq2aAgICdOzYMUnXFnGHDRuW5pgZR1JSUvT7778rLCxMkZGR6V5PXUQvXrx4huPXrVunKVOmqGDBgunOly9UqJDGjh2rihUr2h/ambrImNPuuecebdiwIUvZBg0a6O6779bGjRu1Y8eONK/FxMSoWbNm+vLLL1WoUCG9/PLLmjJlisOzzaWMd/Q7cvLkSb3yyiuaN2+eihUrpvfff99tR3Kk/vXCsWPHMtyNfvr0aSUnJ6dbKM6qFi1aKCAgwH6Ey48//ig/Pz+1bNkyXfaZZ57Rzz//rCFDhuj+++/X4cOH9d5776lJkybauXOnU/Nn1yeffKKOHTtq/fr1qlWrloYOHarFixdn+IsV6dri/41SF9yv/2wzDEP58+d3+Bm0fv36W659/fr1uueee275fQAAAOD9WEQHAMBkcXFx+vPPP3XnnXfqlVde0ejRo7VmzRp17txZhw8f1rfffmvPVqhQQYmJifYdyNd7/PHHdd999+nixYuS/m9hNaPd4rey+Jq6SHjw4MEM33fWrFn2XdjO2LFjh6KjozV48GCFhIRo+vTp6TIRERF6/vnn9csvv+jxxx/Xzz//rE6dOmnChAn2ncA3c/DgQU2fPl3du3dPc/3777/XsWPH7EfLXH9ec1b9/PPPOnbsmBo3bpzmQY+pTp06JcnxTvRChQopf/78GjBgQKYPNNy0aZOCgoLctojeuHFjbdq0Kcu7fF955RVJ0sSJE9Nc//TTT5WQkKCvvvpK48aNU5cuXdSgQYN0vwC6XkbfR0datWql1157TXnz5lXfvn0VFxeX5uz1nFSmTBmVLl1aly9f1vbt29O8dvbsWZ04cUJly5Z1+sz0YsWK6YEHHtCqVat0+fJlrVy5UrVq1VLJkiXTzfXnn38qNDRUHTp00PDhw7Vw4UJNmjRJCQkJaY5AySmnT5/WlClTVL58eS1btkxDhw5V+/btFRERYT87/kZHjx5VQkJCmmuHDh2SpDR/kVG+fHldvHjR/t/S9ebMmaPZs2ffUu1JSUlauXKl/fgYAAAA+DYW0QEAMNm8efPUvn17rVmzJs31ihUrSpLOnDljv5a6e/PDDz9UUlKS/frmzZt17NgxVa1a1X70wFNPPSVJGjt2rH1hXbq2u3LlypUqWbKk6tWrl+16H3zwQRUuXFg//PCD9u/fb79us9k0fPhwvffeezp//ny231eSJk+erGeeeUabN2/WY489piVLlqhOnTrpcsuWLVOrVq109913Ky4uTrVr19bAgQMd7rpPSEhQTEyM5s6dq927dys+Pl5RUVEaOXKkfVe4JJ0/f17jx49XhQoV7N+/7IqPj9fIkSPl5+enF154IcNMak8dLaJXq1ZN33zzjf3M84yknj1fvXr1dMec5JRHHnlEpUuX1qeffpql/KOPPqoKFSro999/t593Lv3fLxGuXxSNi4vTqFGjXFLn9Q+EbdOmje6++25NmTJFR44cccn730zHjh0lXduFff0O+IkTJyolJSXNAzad0bp1a128eFEjR47U2bNn1bp163SZ3bt3q3379ul+gVG5cmVJaT9Xcsrp06dlGIaKFSuW5md0yZIl+vnnnzMck5iYqM8//zzNv1N/kXb9kTypD54dP358mh3/69at09tvv60lS5bcUu1ffvmlAgICHO6YBwAAgG/hTHQAAEzWpk0bzZo1SwMHDtSKFStUsmRJnTlzRitWrJC/v7+aNWtmzz7//PNav3691q1bpxYtWqh+/fo6d+6cVqxYoeDgYA0bNsyeffjhh/XCCy9oxowZeuKJJ9SoUSOdPXtWq1atUlhYmEaPHu3U4mtYWJhGjBih3r1766mnnlKTJk1UvHhx/fHHH9q7d6+aNWum+vXrO/W96Ny5s/bv36+nnnoqwwX+uLg4ffDBB1q7dq169OihyZMnq0uXLtq8ebOeeeYZPffcc2rYsGG6ozKio6O1e/duSdceVPjoo4/q4Ycf1oMPPmhfyE1OTtaAAQN0/PhxffPNNxl+b5YuXapt27bpmWeeyfCs+cTERPXs2VMnTpzQc889l+ZBj9c7fvy4QkJC0hxRc6OqVava/++MdmhPmTJFKSkpatiwocP3cLWAgAANHDhQPXr0UFRUlP2Bqo74+/vr5Zdf1qBBgzRp0iT7cS3169fXvn371KVLFz3yyCM6fvy4fvnlF/sDW6//pc+t8vf31+DBg9WhQwe99957mjp1qsve25Ho6Ght3bpVK1euVMuWLfXII4/IYrFo69atqlSpkrp06XJL79+kSRPlyZNHc+fOVWhoaJrPiFR16tTRnXfeqdmzZ+vvv/9W1apVdeXKFfvi9a0u5GdFpUqVdPvtt2vz5s3q2bOnSpcurW3btikmJkYFChTQhQsX0p0dX6BAAU2bNk27d+9WhQoVtHHjRh08eFB169ZNc58dO3bUunXrNH/+fO3cuVN16tTRxYsXtXz5coWHh2vw4MFO171//3598cUXGjRoUJqHNQMAAMB3sRMdAACTlS9fXt99952eeOIJ7d69W7NmzdJvv/2mWrVqadq0aWl2YgcGBurzzz/Xm2++qcDAQM2ZM0e///67GjZsqPnz56tmzZpp3nvw4MH66KOPVKxYMS1cuFCbNm3So48+qu+++y5dNjsaNmyoOXPmqH79+lq/fr3mzp0rf39/DR8+/JaOiQgODtYnn3ySZgE9dSfvli1bFBUVpbVr16pp06Z67LHHVKxYMc2ePVvPPfecYmJi9MYbb6h27dqKiopSr1697At0jz32mOrWrauPP/5YGzZs0IQJE/T000/bF9D//fdfdenSRb/++qsGDBig2rVr2+e/fpfrnj17NGfOHPtDW6935MgRPfvss9q0aZPq1auX4SKeYRjavHmz9u3bl2a39M0kJydL+r8Hbm7btk1z5sxReHi4/diZ69lstkyPRrkVjRs3Vrt27dS/f3/t3bv3pvmWLVuqVKlSWrt2rf0Yob59+6p79+5KSkrSrFmztHv3bj3//POaOXOm/P39tWbNGpeeYX7//ferefPm+u2337Ry5UqXva8j/v7+GjdunIYMGaLg4GB9++23OnLkiJ5//nnNnj37lhdmw8LC7AvKTZo0yfD9goODNWvWLPXq1cv+ENelS5eqRIkSeu+999SjR49bqiErgoODNW3aNDVp0kRbt27VnDlzFBAQoLFjx9r/SmPZsmVpxpQrV06ffvqpveZLly6pS5cu9p3h17/3V199pf79+8tms2nu3Ln6448/1LRpUy1atEj33nuvUzUfP35c3bp1U926dfXcc885f/MAAADIVfyMnPr/YQEAALjAqlWr1LNnT0nSnXfeqWHDhqlWrVrpcgcPHtS3336r77//XhcvXtSTTz6pDz/8UNK1xeeMzjY/e/asZs6cqenTpys5OVnDhg1Tu3bt7K937txZGzdu1NChQxUSEqKPP/5YSUlJ+vXXX+1nr584cUIzZszQt99+q6SkJDVr1izDXf7R0dHatm2bfVH+jTfe0EsvvZSl78HUqVP18ccfa9q0aSpVqpSeffZZnT17Vj169FCfPn3S5Vu0aKFz587p999/z9L7Z1dSUpK6dOmiv/76S8uXL3f6IZmAJ7l69apatmypgIAAzZ49O8vPVwAAAEDux3EuAADAozVo0ED33nuvatWqpT59+jg8gqZixYoaNmyYBg8erD///DPNUSmOHg76yy+/aOrUqapSpYref//9NEeoSFKHDh104MABDR8+XNK1vxro169fmsW1lJQU/frrrwoMDNSbb76p559/PsO5OnTooMOHDysyMlItW7ZUVFRUlr8HqeffJyYmqmzZsho+fLi+/PJL+4M7M8pfvnw5y++fXcHBwfriiy+0YcMGFtCRawQGBuqtt95S1apVWUAHAABAGuxEBwAAHi8pKSnHHp55+PBhlStXzuFDSbPi6NGjCg4OVvHixV1Y2f85e/aszpw5o5IlS9qP7jAM45ZqBgAAAABkDYvoAAAAAAAAAAA4wINFAQAAAAAAAABwgEV0AAAAAAAAAAAcYBEdAAAAALxMYmLiTTOc3AkAAOAaLKIDAHKVn376Sd99953D1xcvXqwffvghS++VkJCgpKQkpaSkZHn+q1evKiEhQefPn8/yGHiu5ORkXblyxewyAOQCP/zwgy5cuGD/95UrVzR8+HC99dZb2X6v8+fP68EHH1Tnzp0VFxeXYcZms+nxxx9Xt27d9N9//zldNwAAAKRAswsAAMCVPv/8c8XFxempp57K8PWRI0eqQIECeuKJJ276XkOGDNGyZcucqqNo0aJav369U2NhjpSUFJ0+fVqHDh3Svn37tHPnTv3666/q06ePOnbsaHZ5ALzYyZMnNXDgQBUpUkSrVq1SUFCQQkNDtX//fm3fvl0vvviiKlasmOX3mzhxouLj42Wz2ZSUlKSDBw9KuvaLv+DgYFWoUEE//vijDh48qHvvvVdFihSxjzUMQ5cvX1ZoaKj8/dlTBQAAkBUsogMAvNq5c+f0999/KyQkRCEhIQoICFBQUJB9QeFGgYGB9tcNw1BiYqIKFiyoUqVKpcvWrl1bhQoVUmBgoAICAtK89vPPPysuLk7t2rVT3rx57devXr2qpKQkhYaGuvZGkSNOnz6t7t276+zZszp16pSSk5Ptr4WGhqpUqVI6cOCAiRUCyA2mTJmi5ORkde7cWUFBQfbrffv2VYcOHTR8+HDNmDEjS++1detWzZ49W5K0adMmRUVFpXm9WbNmGj16tMaNGyfp2l9gLVmyxP66zWaTJK1evVqlS5e+ldsCAADwGX4GB+UBALzYqlWr1LdvX/sCekJCgmw2mwoUKJBhPj4+Xn5+fsqbN68Mw1BSUpI6d+6sPn36ZGverl276pdffvHIRYjo6Ght3rxZ+/btM7sUr/Dqq6/KZrPp9ttvV0hIiKZOnao33nhDL774ovz8/Oy57du3a9euXRm+R0BAgKKjo2+5luTkZH355ZdavHixTpw4oTx58qhu3br69NNPb/m9gRsdPHhQzzzzjAYNGqQ2bdpkmImPj9ekSZO0YsUKnTlzRvfcc48GDRqkyMjIdNmkpCR99dVX9p/fihUr6vXXX9eDDz7ospqHDRum33//XWvWrHGY2bBhgyZOnKi9e/cqPDxcTz31lHr37q3AwPT7hywWi8aNG6ddu3bJz89PzZs314ABA5QnTx6X1fz333/riSeeULFixbRixQoFBweneb1Pnz5avny5Bg0apE6dOmX6XkePHlXbtm118eJFTZkyRdu3b9eECRP0ySefKCIiQklJSQoPD9f8+fM1ZcoUde3aVTVq1NC0adO0e/duffTRR0pOTlZiYqKaNm3q0vsEAADIzdiJDgDwak2aNJHFYrH/Ozo6WocOHXJ4lMoTTzyh8PBwzZs3z10lwsONHz/e/n8fPHhQU6dOVXh4eJoFdOnawtyECRMyfI/w8HCXLKKPGzdOU6dO1QMPPKBHH31U586d059//nnL7wvc6OzZs+rWrZsuXrzoMJOQkKDOnTsrJiZG1apVU7NmzfTrr78qOjpaCxYsSHP8iM1m06uvvqq1a9eqYsWK6tChg7Zt26aXX35Z06ZNU926dW+55unTp2vu3LkZ/uVQqh9++EEDBgxQaGionnjiCSUlJWnq1Kk6ffq0RowYkSa7efNmdenSRTabTVFRUQoLC9PixYv1999/6+uvv073GeCMlJQUDR48WMnJyerfv3+6BXTp2tFhGzdu1OjRo1WpUiU99NBDGb5XTEyMevbsqbNnz6pPnz6qV6+eQkJCNGHCBB08eNB+TNn69es1bdo03X333erbt6/8/f31zz//aPPmzSpQoIBq1ap1y/cFAADga1hEBwDkOmfOnFGVKlUcvn7vvfe6sRr3GzVqlC5fvmx2GblO6hE9P//8s0qWLGm/3qlTJ/31118umeP777/XHXfckWYBLzsPts3I0aNH1bhxYz355JP68MMPXVGmT9i0aZM6duyoXr16qXfv3maX41IHDhxQjx499M8//2SamzRpkmJiYvT4449r9OjR8vf3V+/evdW6dWu99dZbmjNnjj07d+5crV27Vvfff7++/vprBQcHy2azKTo6WoMHD9aKFSvSHGOSHSkpKRo3bpwmT56cae7MmTMaNmyYgoKCNGvWLN19992SpBo1aujtt99WVFSUfYE6MTFRAwcOVFJSkr744gs98sgjkqSmTZuqS5cumjdvntq3b+9UvdebNm2atm3bpho1ajh8FkexYsX04YcfqkePHurdu7fGjx+v+vXrp8kYhqHPP/9cJ0+eVOvWrdW9e3dJ0l133aWAgABt3bpV0rXvVeov+9599137med169aVn5+fLBYLi+gAAABO4EkyAIBcJ2/evBo5cmSGXyVKlDC7vBxXsmTJbD2gzpddunRJycnJcnS6XVJSki5duiSr1Wo/CsLf31+BgYH2Lz8/v3Rn5jvr5MmTuu2229LsgOXBf3ClQ4cOqV27dpKuHUvlyNWrVzV37lwFBQVpyJAh9p/D8PBwvfTSS9qxY4cOHTpkz3/77beSpKFDh9p3WwcEBKhHjx46duyYNm7c6HTNb7/9tiZPnqzu3btnugt98eLFSkhI0LPPPmtfQJekp59+Wrfffru+++47+7U1a9bo2LFjatKkiX0BXZLq16+vGjVqpMk6a8+ePfr0008VHh6ukSNHZrqzvVGjRho6dKisVqu6du2qKVOmpPkFmp+fnyZMmKB3333X/l6tW7fWwIEDNXXqVH311VeSJKvVquHDh+vdd99VeHi4Dh48qIMHDyowMFCfffaZGjRokKZvAAAAyBp2ogMAcp2goCCHu83DwsJuOj4hIUFBQUEKCgpy+s/5DcOQzWZTYmKiQkJCMjyLF+Z78sknM9yN+8477+idd96x//vll19W0aJF3VgZkDPOnDljX7DN7Fzx2NhYxcfHq169eipcuHCa11LPOF+/fr0qVKigs2fP6q+//lLZsmVVtWrVNNnatWsrKChIGzZsUIMGDZyqOSEhQZ999pmaNGmipUuXOsxt2bJFkvToo4+muR4YGKi6detq7dq1N82m3t9nn32mS5cupXlwdHYcP35cXbt2VVJSkoYPH67y5cvfdEyHDh0UFhamoUOHavTo0Vq9erWGDBliP38+ICBAzzzzjD1/9OhR5cmTR/Xq1bNfW7dunfr375/pPOHh4dqxY4dT9wUAAOCr+P/RAwBynXPnzikqKsrh6zc7zqV9+/Y6cOBAluZq3LjxTTPffPON6tSpk6X3u9H27ds1adIk7dixQ/7+/ipfvryio6P1xBNPOFzgz8qDRRs1aiTp2m7MQ4cO6auvvtKGDRv02GOP6c0330yTPX/+vD777DP9/PPPOnv2rEqWLKkGDRqoR48eKliwYLbvaeHChRo0aJAGDx6smJgYrVq1SuXLl9f48eM1ffp0LVy4UEWLFtXIkSN1//33q2/fvvrpp5/0008/qUKFCmnea+DAgVq0aJEWLVqku+66K9u19OzZU8nJyfad5IsWLdLmzZvVunVr1alTRzabTcnJybrzzjvTnL3vSjcePbR58+Y0167/+Um934weaHt9T1OPcLle6vcp1fXvkdqTkSNHpnvAZEZz3njUybJlyzRnzhzt27dPM2bMSLeQumfPHk2aNElbtmxRQkKCypcvr44dO6pt27bZ/n45uueb/RxfunRJX375pZYvX67jx4+rYMGCatSokfr166dChQqlua/rTZw4URMnTrT/+/r/riZMmKCJEydm+N94Rv8dXv99bt26tebNm6dFixbp4MGDWrt2rfLly5emjl69eqldu3YaNWqU1q9fr5SUFNWoUUNDhw5VmTJlnPqe1axZU7Vr175p7tSpU5KU4X9XpUuXVlBQkP0XUJllg4ODVbJkyTS/rJo4caImTJigzp07a+DAgfbrY8eO1RdffKFu3bqpX79+9usff/xxlv7a49SpU/Lz88uwjrJly+r8+fOKj49Xvnz57DVfv2P9+qxhGIqLi1NERMRN571RXFycXnjhBZ06dUrt27fXypUr9dtvvykwMDBLf1kyYsQIffjhh9qxY4dee+01LVmyxH6c1PUCAwPTfV9Sf1E8ZcqUDH9pER0dneX/9w0AAAD/h0V0AECuU7RoUYcPFk1ddMtMkyZNVLt27QwXKFL9/PPPiouLU7t27TLcqZiSkiKbzabLly87vYN56dKlGjhwoIKCgtSsWTPlz59fq1ev1uuvvy6LxaJBgwY59b7X++233/Tqq69KkipXrpzuuJtTp07pueeeU1xcnO6//341a9bMvlC6Zs0aLVy4UPnz53dq7nHjxum+++5T9erVtXHjRj399NMqVqyYHnvsMS1cuFBjx47Vt99+q1atWumnn37SsmXL1KtXL/v4pKQkrVq1SnfeeadTC+iS1Lp1a/v/ffXqVX366aeSpOrVq6tZs2YKCgqyH02RU4vo3bp1s//fX3zxhUqWLKmWLVvar11//npW5c+f3/6+ly5d0qxZs1SlShU1bNgwTcYVhgwZovnz56t06dIqW7ZsusW+devWqVevXgoPD1fTpk0VGhqqX375RUOGDNG///7rkvPGb/ZzHB8fr+eee04HDhxQ/fr11bhxY+3fv19z587Vtm3btGDBAoWFhalkyZL279vx48e1dOlS3X///br//vtvucbrXb16Va+88op+++03VahQQRUqVMhwcfX06dP2z5jWrVtr+/btWrdunU6ePKlFixY5ddRPVo8eunTpkiQ5/PzKly+fTp48maVs/vz57Vnp2s/8mjVrNHPmTLVp00Z33nmnDh8+rK+++kp33nmnevbs6XTN4eHhGf7FUerP+7///qt8+fLZay5SpEi6bIECBSRdO17JmUX0n376yX5UzJtvvqn77rsvW+PfeOMN1a1bV4MGDdIbb7xh/29qyZIl+vjjjxUcHCx/f39dvHhRO3fuVMOGDZWUlKTo6Gj7Z+HFixd1+vTpdO99/S8NAQAAkHUsogMAvNY///yjCxcu2I9ekaTLly/LZrPp4MGDGY5JTk5WYmJimteTk5Nls9nsOxL79u1707kPHTqkuLg4de3aNd2OYFc4deqUhg4dqpCQEC1YsMB+xvmrr76q5s2b65tvvlGXLl1UrFgxp+eIj4/Xa6+9pg4dOqh79+7KkydPusy7776ruLg49enTRz169LBfHzZsmObOnau5c+fq5Zdfdmr+atWq6auvvtLevXvVqlUrSdLs2bPtvyw4ceKEpGtnFBcuXDjdIvqvv/6q+Pj4NIvQt2LBggX6999/JV078uKZZ57R3XffneMP47x+x+0XX3yh0qVLp7nmjPz589vf4+jRo5o1a5buuuuuW37fGy1ZskTx8fGaOnVqugchStf+exw4cKDy5MmjRYsW6fbbb5d07b+xJ554Ql9++aWio6Od+ouGVFn5OR47dqz279+vd955R88++6z9+gcffKBvvvlGCxcuVIcOHVSmTBn792jTpk1aunSp6tat6/IHi3755ZcyDEPz589XtWrVHObmzZunpk2basyYMQoKCpLNZtNTTz2l2NhY/f333+n+MsOVUhdaM9oBLV3bYX7lypUsZy9cuGD/d2BgoD788EO1adNGw4cP18yZM/Xee+/JMAyNGjXK/our7PL393d4ZFfqe6Y+dDm15ozyN2az65VXXlHVqlVVt25dBQcHa/PmzQoLC1NgYKAaN24sq9WqTZs2pRv3xBNP6MiRI/bjc6ZNm5bm9Vq1aumDDz5QcHCwfvzxR82fP18REREaPHiwrl69qhIlSth3/L/22msO6+NoKgAAgOxjER0A4LUmT56sBQsWZPhaZse5nDp1Kt3rme1eN8Py5ct15coVvfLKK2keEpo/f36NHTtWp0+ftv/iwFkXL15Uq1at9Prrr2f4+oULF7RmzRqVKFEi3QMIu3Xrpjp16qhs2bJOz5963EjqTv5atWrZd4tev7s/MDBQjz/+uGbOnKm9e/fajwpZtmyZ/P391aJFC6drSBUfH6/x48crLCxMly9fVuHChVW0aFEtWrRIlStX1ksvvXTLc+RGcXFxmj59uh544IEMX1+/fr3Onj2riIgIzZkzJ81r+fLl04kTJ7R9+/Ys/YWIIzf7OU5JSdEPP/ygwMBAnThxQmPHjrW/du7cOUnSH3/8oQ4dOjhdQ3adPHlSixcvvukDgPPkyaN3333X/t96QECA6tWrp9jYWJ05cyZHF9HDw8MlXfuLj4wkJycrOTk529lUd955p3r16qWxY8eqX79+Wr9+vXr27On0X5VI175fZ8+edVjD9f97fc0hISFpsqn3cWPN2XH9USqpO9uTkpL077//OvzFyenTpzN9+HXJkiXtf5kycuRISdf+O4qNjVXVqlVVrlw5+yL6+PHjM/zvsmvXrhk+BwIAAACZYxEdAOC1Bg4cqAEDBig0NFQhISFKTk5Wnz59tG7dOk2ZMkUPPvigli5dqvj4eD377LMyDEPdunXTn3/+aV9gSD3z2tkdhznl0KFDktKfly0pS+cZZ0VwcLDDhUdJOnLkiFJSUlS5cuV0f/5//WKOs248BiezB/i1bt1aM2fO1LJly1S1alVdvnxZa9eu1QMPPJDpolNWvfvuu/rvv//Ut29fjRs3Tv7+/hozZoxatmypsWPH2h+k6MlsNluOHNNgs9kcvtaoUSOHC+iSdPjwYUnXHlIZGxubYeb6Yz6ccbOf43Pnztl3QU+ePDlHashIZt+39u3b33QBXbr2gMsbH+qZunPaMIxbK/AmbrvtNknXflFyo6tXr+r8+fP2X3pllpWk//77L919SNce2Pvzzz/rp59+UpUqVdS9e/dbqrlEiRLavXu3Lly4YF+4vr4G6f8WtFM/N+Li4tIt3N+YdZXdu3crJSUlw8/1pKQknT9/XpUqVbrp+6xfv1779u2Tn5+fTp8+rUmTJik5OVnffvutPRMWFpbhkU085BoAAMA52T9IEQAAD5EvXz4VLFhQoaGhiomJ0TPPPKN169bp3XfftS96fvXVV/ZF0YCAAI0dO1YVK1ZUly5dNGbMGCUlJSk0NNT+YEFvcPXqVSUkJNzSLklJKlasmIoXL+70+ISEBLf98iEyMlKVKlXSsmXLJElr166V1WpNc3a4s5YtW6bvv/9eTZs21aOPPmq/XqhQIQ0aNEjJyclatWrVLc+Tk5KSkjI8/9gVjh075vC1m+0aTl3oHTx4sPbt25fh1/XHqzjjZj/HqTVEREQ4rGHevHm3VENGbuX7lqpcuXIuqib7KlasqJCQEMXExKR7LSYmRjabzX6cVIECBVSqVClZLJZ0i/unTp3SsWPHMjx6KiEhwb5gffbsWfs55c5K/b7u2rUr3Wt//vmnpP87yiQr2Vs5Lisj69atk3Ttr25ulPrf780+k202mz766CPdddddKlKkiIoVK6YpU6bo6tWr6t69u+Lj4yVd+wVFlSpV0n1t3rzZpfcEAADgK1hEBwB4rStXrmjVqlV66aWX1K5dOx09elTPPPOMChcurPXr12vDhg1KTExUQECANmzYoA0bNmjz5s2Kjo5W48aNNXnyZD388MN6//33tW7dOodHEZgh9ZiGffv2pXvtiy++0H333Zdm12FOKFu2rPz9/XXgwIF0u2qPHz+u++67T+3bt8/RGq7XqlUrxcXFadeuXVq2bJnCw8PTLHo7Y9euXXrrrbdUrFgxDR8+PN3rUVFR+vrrr9Ocxb5q1SotWbLE/nXmzJlbqiE7/Pz8JKXf5bxlyxaHO5+z8vBJR+97/vx57d2715lSJf3fz3FGzyjYs2ePZs2aleEirSsVLlxYBQoU0JEjR9L9N37lyhXNmjVLS5YsSTfuVr5vhw8fdsnu9ls9sulWhISEqH79+vrzzz/TPVR30aJFkqR69erZrzVp0kSnTp3Szz//fNNsqnfffVcnTpxQly5ddPr0ab399tu3VHPTpk0lKd1n47Fjx7Rp0ybdfffd9vP3H374YQUFBWnOnDlKSUmxZy9duqSVK1eqcOHCGe4Yd9bly5c1f/58hYWF6ZFHHkn3euoi+s3+smbSpEnau3dvmmdR3H333Ro8eLCio6Ptf9Hz/vvva9myZem+7rnnHpfdEwAAgC9hER0A4LVefPFF9ezZU+vXr1fLli31008/6bffflOPHj304osvqnPnzjp8+LDOnTunzp07q3PnzurevbtGjBih8ePH66OPPpK/v79mzpyZ4w+PzK7HHntMISEhmjVrVpoFyCtXrmj58uWSpDp16uRoDQUKFFDDhg118uTJdMdgLFy40C01XK9ly5by9/fX7Nmz9euvv+rRRx+1n2vsjMOHD+uVV15RYmKiPvnkkwyPm5DSL/6NHDlSb7zxhv0r9egdd0it8cCBA/ZrSUlJGj16tMMxxYsXV1BQULqjNq5evZrufffv358mM27cOCUkJDhdb+pxJD/88EOa97bZbBo+fLjee+89nT9/3un3zwp/f389/vjjslqt+vLLL9O8tnDhQr333nv2HcLXK1WqlKT0R5Tc7PtmGIY+/PDDHD9uxR1eeukl+fv7q3///vrrr7/sD0P97rvvdPvtt+uhhx6yZ6OjoxUWFqa3335b27ZtkyStWbNGkydPVt68efXYY4+lee8ff/xRP/zwg1q1aqUBAwaobdu2WrFihX3R3RlVq1ZV/fr1tXbtWk2YMEHJyck6efKkXnvtNSUnJ+upp56yZ4sUKWJ/SOvbb78tq9Wqixcv6o033tC5c+f05JNPuvR4pE8++URnzpxRmzZtlC9fvnSvp/4yLrNF9HXr1unzzz9X9erV1bx58zSvtW3bVp06dbL/YqdEiRKqWLFiui9HD14FAABA5jgUDwDgtfr376+lS5eqU6dO9h2vkydPVlBQkAIDA+Xn56fu3bvr7Nmzmjt3rmw2my5cuCCr1Srp2s7mJk2a6Ntvv9Xtt9+u4OBgM28njeLFi2v48OEaPHiwnnrqKT366KPKmzevfvnlFx07dkzR0dGKiIjI8Trefvtt7d27V59++qk2bNigu+++W/v379eGDRtUpkyZWz7DODtuu+021a5d277I1qpVq1t6v+LFi+v+++/X/fffn6VfBqTuNv7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",
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from scipy.stats import spearmanr\n",
|
||
"from tqdm import tqdm # 用于显示进度条 (可选)\n",
|
||
"\n",
|
||
"# 设置 Matplotlib/Seaborn 样式 (可选)\n",
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"plt.rcParams['font.sans-serif'] = ['SimHei'] # 或者其他支持中文的字体\n",
|
||
"plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题\n",
|
||
"\n",
|
||
"def analyze_score_performance_2d(score_df: pd.DataFrame,\n",
|
||
" score_col: str = 'score',\n",
|
||
" label_col: str = 'label',\n",
|
||
" condition1_col: str = 'circ_mv',\n",
|
||
" condition2_col: str = 'future_return',\n",
|
||
" n_bins: int = 100,\n",
|
||
" min_samples_per_bin: int = 30): # 每个格子最少样本数\n",
|
||
" \"\"\"\n",
|
||
" 分析 score 在两个条件下 (如市值、未来收益) 的二维分箱表现。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" score_df (pd.DataFrame): 包含分数、标签和条件列的 DataFrame。\n",
|
||
" score_col (str): 预测分数所在的列名。\n",
|
||
" label_col (str): 目标标签所在的列名 (应为数值或可排序类别)。\n",
|
||
" condition1_col (str): 第一个条件列名 (例如 'circ_mv')。\n",
|
||
" condition2_col (str): 第二个条件列名 (例如 'future_return')。\n",
|
||
" n_bins (int): 每个条件划分的箱数 (分位数数量)。\n",
|
||
" min_samples_per_bin (int): 计算指标所需的最小样本数，小于此数目的格子结果将被屏蔽。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" tuple: 包含 (performance_pivot, count_pivot, fig)\n",
|
||
" performance_pivot: 以二维分箱为索引/列的 Spearman 相关系数矩阵。\n",
|
||
" count_pivot: 每个二维分箱的样本数量矩阵。\n",
|
||
" fig: 生成的热力图 Matplotlib Figure 对象。\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始分析 '{score_col}' 在 '{condition1_col}' 和 '{condition2_col}' 下的表现...\")\n",
|
||
"\n",
|
||
" required_cols = [score_col, label_col, condition1_col, condition2_col]\n",
|
||
" if not all(col in score_df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in score_df.columns]\n",
|
||
" raise ValueError(f\"输入 DataFrame 缺少必需列: {missing}\")\n",
|
||
"\n",
|
||
" # --- 1. 数据准备和清洗 ---\n",
|
||
" print(\"准备数据，处理 NaN 值...\")\n",
|
||
" # 只保留需要的列，并移除包含 NaN 的行，避免影响分箱和计算\n",
|
||
" analysis_df = score_df[required_cols].dropna().copy()\n",
|
||
" n_original = len(score_df)\n",
|
||
" n_after_drop = len(analysis_df)\n",
|
||
" print(f\"原始数据 {n_original} 行，移除 NaN 后剩余 {n_after_drop} 行用于分析。\")\n",
|
||
"\n",
|
||
" if n_after_drop < min_samples_per_bin * n_bins: # 检查数据量是否过少\n",
|
||
" print(f\"警告: 清理 NaN 后数据量 ({n_after_drop}) 可能不足以支持 {n_bins}x{n_bins} 的精细分箱分析。\")\n",
|
||
" if n_after_drop < min_samples_per_bin:\n",
|
||
" print(\"错误: 有效数据过少，无法进行分析。\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 2. 二维分箱 ---\n",
|
||
" print(f\"对 '{condition1_col}' 和 '{condition2_col}' 进行 {n_bins} 分位数分箱...\")\n",
|
||
" bin1_col = f'{condition1_col}_bin'\n",
|
||
" bin2_col = f'{condition2_col}_bin'\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # 使用 qcut 进行分位数分箱，labels=False 返回 0 到 n_bins-1 的整数标签\n",
|
||
" # duplicates='drop' 会丢弃导致边界不唯一的重复值所在的箱子，可能导致某些箱号缺失\n",
|
||
" # 对于可视化，这通常可以接受，但如果需要严格的等分，需先 rank\n",
|
||
" analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 分箱失败，请检查数据分布或减少 n_bins。错误信息: {e}\")\n",
|
||
" # 可以尝试先 rank 再 qcut\n",
|
||
" # analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" # analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 3. 分组计算表现指标 (Spearman Rank IC) ---\n",
|
||
" print(\"按二维分箱分组计算 Spearman Rank IC...\")\n",
|
||
"\n",
|
||
" def safe_spearmanr(x, y):\n",
|
||
" \"\"\"安全计算 Spearman 相关性，处理数据量过少的情况\"\"\"\n",
|
||
" if len(x) < max(2, min_samples_per_bin): # 要求至少有 min_samples_per_bin 个点才计算\n",
|
||
" return np.nan\n",
|
||
" corr, p_value = spearmanr(x, y)\n",
|
||
" return corr if not np.isnan(corr) else np.nan # 确保返回 NaN 而不是 None 或其他\n",
|
||
"\n",
|
||
" # 按两个分箱列分组\n",
|
||
" grouped = analysis_df.groupby([bin1_col, bin2_col])\n",
|
||
"\n",
|
||
" # 计算每个格子的 Spearman 相关系数\n",
|
||
" # apply 可能较慢，但计算相关性通常需要 apply\n",
|
||
" performance_series = grouped.apply(lambda sub: safe_spearmanr(sub[score_col], sub[label_col]))\n",
|
||
"\n",
|
||
" # 计算每个格子的样本数量\n",
|
||
" count_series = grouped.size()\n",
|
||
"\n",
|
||
" # --- 4. 结果整理成 Pivot Table (用于绘图) ---\n",
|
||
" print(\"整理结果用于绘图...\")\n",
|
||
" try:\n",
|
||
" # 将 performance_series 转换成二维矩阵\n",
|
||
" # index 为 condition1_bin, columns 为 condition2_bin\n",
|
||
" performance_pivot = performance_series.unstack(level=0) # level=0 对应第一个 groupby key (bin1_col)\n",
|
||
" count_pivot = count_series.unstack(level=0)\n",
|
||
"\n",
|
||
" # 可选：按列和索引排序，确保顺序正确\n",
|
||
" performance_pivot = performance_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" count_pivot = count_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" \n",
|
||
" print(performance_pivot)\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 无法将结果转换为二维矩阵，可能因为分箱不均匀或数据问题: {e}\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 5. 可视化：绘制热力图 ---\n",
|
||
" print(\"生成热力图...\")\n",
|
||
" fig, ax = plt.subplots(figsize=(16, 12)) # 调整图像大小\n",
|
||
"\n",
|
||
" # 使用 count_pivot 创建一个 mask，屏蔽掉样本量过小的格子\n",
|
||
" mask = count_pivot < min_samples_per_bin\n",
|
||
"\n",
|
||
" # 绘制热力图\n",
|
||
" sns.heatmap(performance_pivot,\n",
|
||
" annot=False, # 100x100 个格子加注释会太密集\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"viridis\", # 选择颜色映射, 'viridis', 'coolwarm', 'RdYlGn' 等都不错\n",
|
||
" linewidths=.5,\n",
|
||
" linecolor='lightgray',\n",
|
||
" # mask=mask, # 应用 mask\n",
|
||
" ax=ax,\n",
|
||
" cbar_kws={'label': f'Spearman Rank IC ({score_col} vs {label_col})'}) # 颜色条标签\n",
|
||
"\n",
|
||
" # 设置标题和轴标签\n",
|
||
" ax.set_title(f'{score_col} 表现分析 (Rank IC vs {label_col})\\n基于 {condition1_col} 和 {condition2_col} {n_bins}x{n_bins} 分箱', fontsize=16)\n",
|
||
" ax.set_xlabel(f'{condition1_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
" ax.set_ylabel(f'{condition2_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
"\n",
|
||
" # 可选：调整刻度标签，避免显示所有 100 个刻度\n",
|
||
" if n_bins > 20:\n",
|
||
" tick_interval = n_bins // 10 # 大约显示 10 个刻度\n",
|
||
" ax.set_xticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_yticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_xticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
" ax.set_yticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
"\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
" plt.yticks(rotation=0)\n",
|
||
" plt.tight_layout() # 调整布局\n",
|
||
"\n",
|
||
" print(\"分析完成。\")\n",
|
||
" return performance_pivot, count_pivot, fig\n",
|
||
"\n",
|
||
"# --- 如何使用 ---\n",
|
||
"# 假设你的包含预测结果和所需列的 DataFrame 是 final_predictions_df\n",
|
||
"# 确保它包含 'score', 'label', 'circ_mv', 'future_return'\n",
|
||
"\n",
|
||
"# # 示例调用 (你需要有实际的 score_df)\n",
|
||
"try:\n",
|
||
" # 确保数据类型正确\n",
|
||
" cols_to_numeric = ['score', 'label', 'circ_mv', 'future_return']\n",
|
||
" for col in cols_to_numeric:\n",
|
||
" if col in score_df.columns:\n",
|
||
" score_df[col] = pd.to_numeric(score_df[col], errors='coerce')\n",
|
||
"\n",
|
||
" # 调用分析函数\n",
|
||
" performance_matrix, count_matrix, heatmap_figure = analyze_score_performance_2d(\n",
|
||
" score_df,\n",
|
||
" n_bins=100, # 你要求的100分箱\n",
|
||
" min_samples_per_bin=50 # 每个格子至少需要50个样本才显示IC，可以调整\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 显示图像\n",
|
||
" if heatmap_figure:\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # 可以查看具体的 performance_matrix 和 count_matrix\n",
|
||
" # print(\"\\nPerformance Matrix (Spearman IC):\")\n",
|
||
" # print(performance_matrix)\n",
|
||
" # print(\"\\nCount Matrix:\")\n",
|
||
" # print(count_matrix)\n",
|
||
"\n",
|
||
"except ValueError as ve:\n",
|
||
" print(f\"数据错误: {ve}\")\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"发生未知错误: {e}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "a436dba4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Empty DataFrame\n",
|
||
"Columns: [ts_code, trade_date, is_st]\n",
|
||
"Index: []\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|