2568 lines
286 KiB
Plaintext
2568 lines
286 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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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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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n",
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"/mnt/d/PyProject/NewStock\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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"# %load_ext cudf.pandas\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('/mnt/d/PyProject/NewStock/')\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\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4a481c60",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 设置使用核心\n",
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"import os\n",
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"os.environ[\"MODIN_CPUS\"] = \"4\"\n"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 6,
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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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"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: 8692146 entries, 0 to 8692145\n",
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"Data columns (total 33 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 amount float64 \n",
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" 8 pct_chg float64 \n",
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" 9 turnover_rate float64 \n",
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" 10 pe_ttm float64 \n",
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" 11 circ_mv float64 \n",
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" 12 total_mv float64 \n",
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" 13 volume_ratio float64 \n",
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" 14 is_st bool \n",
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" 15 up_limit float64 \n",
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" 16 down_limit float64 \n",
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" 17 buy_sm_vol float64 \n",
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" 18 sell_sm_vol float64 \n",
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" 19 buy_lg_vol float64 \n",
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" 20 sell_lg_vol float64 \n",
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" 21 buy_elg_vol float64 \n",
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" 22 sell_elg_vol float64 \n",
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" 23 net_mf_vol float64 \n",
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" 24 his_low float64 \n",
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" 25 his_high float64 \n",
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" 26 cost_5pct float64 \n",
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" 27 cost_15pct float64 \n",
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" 28 cost_50pct float64 \n",
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" 29 cost_85pct float64 \n",
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" 30 cost_95pct float64 \n",
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" 31 weight_avg float64 \n",
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" 32 winner_rate float64 \n",
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"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
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"memory usage: 2.1+ 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('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n",
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" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', '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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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": 7,
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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('/mnt/d/PyProject/NewStock/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": 8,
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||
"id": "c4e9e1d31da6dba6",
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||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
|
||
"start_time": "2025-04-03T12:47:10.541247Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.factor.factor import *\n",
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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 = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n",
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"index_data = generate_index_indicators(h5_filename)\n",
|
||
"index_data = index_data.dropna()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "a735bc02ceb4d872",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.821169Z",
|
||
"start_time": "2025-04-03T12:47:10.751831Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import talib\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"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('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"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', 'amount', '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": 12,
|
||
"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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/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",
|
||
"\n",
|
||
"stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
|
||
" columns=['ts_code', 'ann_date', 'in_de', 'change_ratio', 'after_ratio'],\n",
|
||
" df=None)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"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",
|
||
" 'amount', '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: 4554725 entries, 0 to 4554724\n",
|
||
"Columns: 181 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
|
||
"memory usage: 5.8+ GB\n",
|
||
"None\n",
|
||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', '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",
|
||
"from main.factor.money_factor import *\n",
|
||
"\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 = ts_turnover_rate_acceleration_5_20(df)\n",
|
||
"# df = ts_vol_sustain_10_30(df)\n",
|
||
"# df = cs_turnover_rate_relative_strength_20(df)\n",
|
||
"# df = cs_amount_outlier_10(df)\n",
|
||
"# df = holder_trade_factors(stk_holdertrade_df, 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": 14,
|
||
"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": 15,
|
||
"id": "f4f16d63ad18d1bc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.670700Z",
|
||
"start_time": "2025-04-03T13:08:03.665739Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"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": 16,
|
||
"id": "40e6b68a91b30c79",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:04.694262Z",
|
||
"start_time": "2025-04-03T13:08:03.694904Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"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",
|
||
"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": 17,
|
||
"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": 18,
|
||
"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": 19,
|
||
"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": 20,
|
||
"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 [00:16<00:00, 7.78it/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 [00:11<00:00, 11.06it/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: 2025-05-30\n",
|
||
"2057465\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2022-12-30\n",
|
||
"1781706\n",
|
||
"test_data最小日期: 2023-01-03\n",
|
||
"test_data最大日期: 2025-05-30\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": 21,
|
||
"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 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": 22,
|
||
"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": "182bcb05602b45a586d931fa2bcb3dd1",
|
||
"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: 0.6890148\ttest: 0.6905108\tbest: 0.6905108 (0)\ttotal: 114ms\tremaining: 2m 50s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"bestTest = 0.5226514078\n",
|
||
"bestIteration = 527\n",
|
||
"Shrink model to first 528 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": 23,
|
||
"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": 24,
|
||
"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": 25,
|
||
"id": "e53b209a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"5595 2057465\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",
|
||
"1781701 605599.SH 2025-05-26 0.6188\n",
|
||
"1781702 605599.SH 2025-05-27 1.2576\n",
|
||
"1781703 605599.SH 2025-05-28 2.0432\n",
|
||
"1781704 605599.SH 2025-05-29 2.0954\n",
|
||
"1781705 605599.SH 2025-05-30 1.4392\n",
|
||
"\n",
|
||
"[1781706 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": 26,
|
||
"id": "364e821a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"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": 27,
|
||
"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: 0.6543\n",
|
||
"precision: 0.4629\n",
|
||
"recall: 0.1754\n",
|
||
"f1: 0.2544\n",
|
||
"roc_auc: 0.6206\n",
|
||
"fpr: (array of length 7484)\n",
|
||
"tpr: (array of length 7484)\n",
|
||
"thresholds: (array of length 7484)\n",
|
||
"score_return_correlation: -0.0293\n",
|
||
"mv_roc_auc: {'6e+04-9e+04': np.float64(0.5687499999999999), '9e+04-1e+05': np.float64(0.5662772981208735), '1e+05-1e+05': np.float64(0.5818040450154087), '1e+05-2e+05': np.float64(0.5694847679411162), '2e+05-2e+05': np.float64(0.6027478841082352), '2e+05-3e+05': np.float64(0.6169522445569707), '3e+05-3e+05': np.float64(0.6131663537577515)}\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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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": 28,
|
||
"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": 29,
|
||
"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 17430 行，移除 NaN 后剩余 17123 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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",
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"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": 30,
|
||
"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": "stock",
|
||
"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": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|