2841 lines
310 KiB
Plaintext
2841 lines
310 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "79a7758178bafdd3",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-03T12:46:06.987506Z",
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"start_time": "2025-04-03T12:46:06.259551Z"
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},
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"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/mnt/d/PyProject/NewStock/main/train\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import gc\n",
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"import os\n",
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"import sys\n",
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"sys.path.append('../../')\n",
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"print(os.getcwd())\n",
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"import pandas as pd\n",
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"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
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"from main.utils.factor import read_industry_data\n",
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"from main.utils.factor_processor import calculate_score\n",
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"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "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": 3,
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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: 8665405 entries, 0 to 8665404\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('../../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('../../data/daily_basic.h5', key='daily_basic',\n",
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" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
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" 'is_st'], df=df, join='inner')\n",
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"\n",
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"print('stk limit')\n",
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"df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n",
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" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
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" df=df)\n",
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"print('money flow')\n",
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"df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n",
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" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
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" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
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" df=df)\n",
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"print('cyq perf')\n",
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"df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n",
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" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
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" 'cost_50pct',\n",
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" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
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" df=df)\n",
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"print(df.info())"
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]
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},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "cac01788dac10678",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:47:10.527104Z",
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||
"start_time": "2025-04-03T12:47:00.488715Z"
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||
}
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||
},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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"output_type": "stream",
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"text": [
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"industry\n"
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]
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}
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||
],
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"source": [
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"print('industry')\n",
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"industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n",
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" columns=['ts_code', 'l2_code', 'in_date'],\n",
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" df=None, on=['ts_code'], join='left')\n",
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"\n",
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"\n",
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"def merge_with_industry_data(df, industry_df):\n",
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" # 确保日期字段是 datetime 类型\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
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" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
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"\n",
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" # 对 industry_df 按 ts_code 和 in_date 排序\n",
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" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
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"\n",
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" # 对原始 df 按 ts_code 和 trade_date 排序\n",
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" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
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"\n",
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" # 使用 merge_asof 进行向后合并\n",
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" merged = pd.merge_asof(\n",
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" df_sorted,\n",
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" industry_df_sorted,\n",
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" by='ts_code', # 按 ts_code 分组\n",
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" left_on='trade_date',\n",
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" right_on='in_date',\n",
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" direction='backward'\n",
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" )\n",
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"\n",
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" # 获取每个 ts_code 的最早 in_date 记录\n",
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" min_in_date_per_ts = (industry_df_sorted\n",
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" .groupby('ts_code')\n",
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" .first()\n",
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" .reset_index()[['ts_code', 'l2_code']])\n",
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"\n",
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" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
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" merged['l2_code'] = merged['l2_code'].fillna(\n",
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" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
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" )\n",
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"\n",
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" # 保留需要的列并重置索引\n",
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" result = merged.reset_index(drop=True)\n",
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" return result\n",
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"\n",
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"\n",
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"# 使用示例\n",
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"df = merge_with_industry_data(df, industry_df)\n",
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"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 5,
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||
"id": "c4e9e1d31da6dba6",
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||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
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||
"start_time": "2025-04-03T12:47:10.541247Z"
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||
},
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||
"jupyter": {
|
||
"source_hidden": true
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||
}
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"from main.factor.factor import *\n",
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"\n",
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||
"def calculate_indicators(df):\n",
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" \"\"\"\n",
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||
" 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
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" \"\"\"\n",
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" df = df.sort_values('trade_date')\n",
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" df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
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" # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
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||
" delta = df['close'].diff()\n",
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" gain = delta.where(delta > 0, 0)\n",
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||
" loss = -delta.where(delta < 0, 0)\n",
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||
" avg_gain = gain.rolling(window=14).mean()\n",
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||
" avg_loss = loss.rolling(window=14).mean()\n",
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||
" rs = avg_gain / avg_loss\n",
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" df['RSI'] = 100 - (100 / (1 + rs))\n",
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"\n",
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||
" # 计算MACD\n",
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" ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
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" ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
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||
" df['MACD'] = ema12 - ema26\n",
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||
" df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
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" df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
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"\n",
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||
" # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
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" df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
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" df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n",
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"\n",
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" # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
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" df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n",
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" df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n",
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"\n",
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" # 6. 情绪因子3：波动率（Volatility）\n",
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" df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n",
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"\n",
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" # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
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" df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n",
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" df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n",
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"\n",
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" # df = sentiment_panic_greed_index(df)\n",
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" # df = sentiment_market_breadth_proxy(df)\n",
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" # df = sentiment_reversal_indicator(df)\n",
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"\n",
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" return df\n",
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"\n",
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"\n",
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"def generate_index_indicators(h5_filename):\n",
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" df = pd.read_hdf(h5_filename, key='index_data')\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
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" df = df.sort_values('trade_date')\n",
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"\n",
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" # 计算每个ts_code的相关指标\n",
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" df_indicators = []\n",
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" for ts_code in df['ts_code'].unique():\n",
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" df_index = df[df['ts_code'] == ts_code].copy()\n",
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" df_index = calculate_indicators(df_index)\n",
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" df_indicators.append(df_index)\n",
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"\n",
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" # 合并所有指数的结果\n",
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" df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
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"\n",
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" # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
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" df_final = df_all_indicators.pivot_table(\n",
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" index='trade_date',\n",
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" columns='ts_code',\n",
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" values=['daily_return', \n",
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" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
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" # 'sentiment_panic_greed_index',\n",
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" 'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
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" 'amount_change_rate', 'amount_mean'],\n",
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" aggfunc='last'\n",
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" )\n",
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"\n",
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" df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
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" df_final = df_final.reset_index()\n",
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"\n",
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" return df_final\n",
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"\n",
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"\n",
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||
"# 使用函数\n",
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"h5_filename = '../../data/index_data.h5'\n",
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"index_data = generate_index_indicators(h5_filename)\n",
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||
"index_data = index_data.dropna()\n"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"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": 7,
|
||
"id": "53f86ddc0677a6d7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:15.944254Z",
|
||
"start_time": "2025-04-03T12:47:10.826179Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.utils.factor import get_act_factor\n",
|
||
"\n",
|
||
"\n",
|
||
"def read_industry_data(h5_filename):\n",
|
||
" # 读取 H5 文件中所有的行业数据\n",
|
||
" industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n",
|
||
" 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n",
|
||
" ]) # 假设 H5 文件的键是 'industry_data'\n",
|
||
" industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
" industry_data = industry_data.reindex()\n",
|
||
" industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n",
|
||
"\n",
|
||
" grouped = industry_data.groupby('ts_code', group_keys=False)\n",
|
||
" industry_data['obv'] = grouped.apply(\n",
|
||
" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
|
||
" )\n",
|
||
" industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
|
||
" industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
|
||
"\n",
|
||
" industry_data = get_act_factor(industry_data, cat=False)\n",
|
||
" industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n",
|
||
" # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n",
|
||
" # \n",
|
||
" # for factor in factor_columns:\n",
|
||
" # if factor in industry_data.columns:\n",
|
||
" # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n",
|
||
" # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n",
|
||
" # lambda x: x - x.mean())\n",
|
||
"\n",
|
||
" industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
" industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
"\n",
|
||
" # cs_rank_intraday_range(industry_data)\n",
|
||
" # cs_rank_close_pos_in_range(industry_data)\n",
|
||
"\n",
|
||
" industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(\n",
|
||
" columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
|
||
" return industry_data\n",
|
||
"\n",
|
||
"\n",
|
||
"industry_df = read_industry_data('../../data/sw_daily.h5')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"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": 9,
|
||
"id": "85c3e3d0235ffffa",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:16.089879Z",
|
||
"start_time": "2025-04-03T12:47:15.990101Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n",
|
||
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
|
||
" df=None)\n",
|
||
"cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n",
|
||
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
||
" df=None)\n",
|
||
"balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n",
|
||
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
||
" df=None)\n",
|
||
"top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n",
|
||
" columns=['ts_code', 'trade_date', 'reason'],\n",
|
||
" df=None)\n",
|
||
"\n",
|
||
"top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"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": [
|
||
"计算因子 ts_turnover_rate_acceleration_5_20\n",
|
||
"计算因子 ts_vol_sustain_10_30\n",
|
||
"计算因子 cs_amount_outlier_10\n",
|
||
"计算因子 ts_ff_to_total_turnover_ratio\n",
|
||
"计算因子 ts_price_volume_trend_coherence_5_20\n",
|
||
"计算因子 ts_ff_turnover_rate_surge_10\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",
|
||
"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', 'ts_turnover_rate_acceleration_5_20',\n",
|
||
" 'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n",
|
||
" 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n",
|
||
" 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'roa',\n",
|
||
" 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
|
||
" 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
|
||
" 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
|
||
" 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
|
||
" 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
|
||
" 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
|
||
" 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
|
||
" 'floating_chip_proxy', 'cost_support_15pct_change',\n",
|
||
" 'cat_winner_price_zone', 'flow_chip_consistency',\n",
|
||
" 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
|
||
" 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
|
||
" '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
|
||
" 'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
|
||
" 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
|
||
" 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
|
||
" 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
|
||
" dtype='object')\n",
|
||
"Calculating lg_flow_mom_corr_20_60...\n",
|
||
"Finished lg_flow_mom_corr_20_60.\n",
|
||
"Calculating lg_flow_accel...\n",
|
||
"Finished lg_flow_accel.\n",
|
||
"Calculating profit_pressure...\n",
|
||
"Finished profit_pressure.\n",
|
||
"Calculating underwater_resistance...\n",
|
||
"Finished underwater_resistance.\n",
|
||
"Calculating cost_conc_std_20...\n",
|
||
"Finished cost_conc_std_20.\n",
|
||
"Calculating profit_decay_20...\n",
|
||
"Finished profit_decay_20.\n",
|
||
"Calculating vol_amp_loss_20...\n",
|
||
"Finished vol_amp_loss_20.\n",
|
||
"Calculating vol_drop_profit_cnt_5...\n",
|
||
"Finished vol_drop_profit_cnt_5.\n",
|
||
"Calculating lg_flow_vol_interact_20...\n",
|
||
"Finished lg_flow_vol_interact_20.\n",
|
||
"Calculating cost_break_confirm_cnt_5...\n",
|
||
"Finished cost_break_confirm_cnt_5.\n",
|
||
"Calculating atr_norm_channel_pos_14...\n",
|
||
"Finished atr_norm_channel_pos_14.\n",
|
||
"Calculating turnover_diff_skew_20...\n",
|
||
"Finished turnover_diff_skew_20.\n",
|
||
"Calculating lg_sm_flow_diverge_20...\n",
|
||
"Finished lg_sm_flow_diverge_20.\n",
|
||
"Calculating pullback_strong_20_20...\n",
|
||
"Finished pullback_strong_20_20.\n",
|
||
"Calculating vol_wgt_hist_pos_20...\n",
|
||
"Finished vol_wgt_hist_pos_20.\n",
|
||
"Calculating vol_adj_roc_20...\n",
|
||
"Finished vol_adj_roc_20.\n",
|
||
"Calculating cs_rank_net_lg_flow_val...\n",
|
||
"Finished cs_rank_net_lg_flow_val.\n",
|
||
"Calculating cs_rank_flow_divergence...\n",
|
||
"Finished cs_rank_flow_divergence.\n",
|
||
"Calculating cs_rank_ind_adj_lg_flow...\n",
|
||
"Finished cs_rank_ind_adj_lg_flow.\n",
|
||
"Calculating cs_rank_elg_buy_ratio...\n",
|
||
"Finished cs_rank_elg_buy_ratio.\n",
|
||
"Calculating cs_rank_rel_profit_margin...\n",
|
||
"Finished cs_rank_rel_profit_margin.\n",
|
||
"Calculating cs_rank_cost_breadth...\n",
|
||
"Finished cs_rank_cost_breadth.\n",
|
||
"Calculating cs_rank_dist_to_upper_cost...\n",
|
||
"Finished cs_rank_dist_to_upper_cost.\n",
|
||
"Calculating cs_rank_winner_rate...\n",
|
||
"Finished cs_rank_winner_rate.\n",
|
||
"Calculating cs_rank_intraday_range...\n",
|
||
"Finished cs_rank_intraday_range.\n",
|
||
"Calculating cs_rank_close_pos_in_range...\n",
|
||
"Finished cs_rank_close_pos_in_range.\n",
|
||
"Calculating cs_rank_opening_gap...\n",
|
||
"Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
|
||
"Calculating cs_rank_pos_in_hist_range...\n",
|
||
"Finished cs_rank_pos_in_hist_range.\n",
|
||
"Calculating cs_rank_vol_x_profit_margin...\n",
|
||
"Finished cs_rank_vol_x_profit_margin.\n",
|
||
"Calculating cs_rank_lg_flow_price_concordance...\n",
|
||
"Finished cs_rank_lg_flow_price_concordance.\n",
|
||
"Calculating cs_rank_turnover_per_winner...\n",
|
||
"Finished cs_rank_turnover_per_winner.\n",
|
||
"Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
|
||
"Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
|
||
"Calculating cs_rank_volume_ratio...\n",
|
||
"Finished cs_rank_volume_ratio.\n",
|
||
"Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
|
||
"Finished cs_rank_elg_buy_sell_sm_ratio.\n",
|
||
"Calculating cs_rank_cost_dist_vol_ratio...\n",
|
||
"Finished cs_rank_cost_dist_vol_ratio.\n",
|
||
"Calculating cs_rank_size...\n",
|
||
"Finished cs_rank_size.\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4539678 entries, 0 to 4539677\n",
|
||
"Columns: 187 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(171), int64(3), object(2)\n",
|
||
"memory usage: 6.0+ 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', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from main.factor.factor import *\n",
|
||
"\n",
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
||
" df = df[~df['is_st']]\n",
|
||
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
||
" df = df[df['trade_date'] >= '2019-01-01']\n",
|
||
" if 'in_date' in df.columns:\n",
|
||
" df = df.drop(columns=['in_date'])\n",
|
||
" df = df.reset_index(drop=True)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"df = filter_data(df)\n",
|
||
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
"# df = price_minus_deduction_price(df, n=120)\n",
|
||
"# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
|
||
"# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
|
||
"# df = cat_reason(df, top_list_df)\n",
|
||
"# df = cat_is_on_top_list(df, top_list_df)\n",
|
||
"\n",
|
||
"# df = cat_senti_mom_vol_spike(\n",
|
||
"# df,\n",
|
||
"# return_period=3,\n",
|
||
"# return_threshold=0.03, # 近3日涨幅超3%\n",
|
||
"# volume_ratio_threshold=1.3,\n",
|
||
"# current_pct_chg_min=0.0, # 当日必须收红\n",
|
||
"# current_pct_chg_max=0.05,\n",
|
||
"# ) # 当日涨幅不宜过大\n",
|
||
"\n",
|
||
"# df = cat_senti_pre_breakout(\n",
|
||
"# df,\n",
|
||
"# atr_short_N=10,\n",
|
||
"# atr_long_M=40,\n",
|
||
"# vol_atrophy_N=10,\n",
|
||
"# vol_atrophy_M=40,\n",
|
||
"# price_stab_N=5,\n",
|
||
"# price_stab_threshold=0.06,\n",
|
||
"# current_pct_chg_min_signal=0.002,\n",
|
||
"# current_pct_chg_max_signal=0.05,\n",
|
||
"# volume_ratio_signal_threshold=1.1,\n",
|
||
"# )\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 = ts_ff_to_total_turnover_ratio(df)\n",
|
||
"df = ts_price_volume_trend_coherence_5_20(df)\n",
|
||
"# df = ts_turnover_rate_trend_strength_5(df)\n",
|
||
"df = ts_ff_turnover_rate_surge_10(df)\n",
|
||
"\n",
|
||
"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": 11,
|
||
"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": 12,
|
||
"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": 13,
|
||
"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": 14,
|
||
"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": 15,
|
||
"id": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"197\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": 16,
|
||
"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": 17,
|
||
"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', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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%|██████████| 137/137 [00:14<00:00, 9.48it/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%|██████████| 137/137 [00:15<00:00, 8.83it/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', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"df最小日期: 2019-01-02\n",
|
||
"df最大日期: 2025-05-23\n",
|
||
"2057539\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2022-12-30\n",
|
||
"1766694\n",
|
||
"test_data最小日期: 2023-01-03\n",
|
||
"test_data最大日期: 2025-05-23\n",
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||
"2 000001.SZ 2019-01-04 16.633371\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"split_date = '2023-01-01'\n",
|
||
"train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n",
|
||
"test_data = df[(df['trade_date'] >= split_date)]\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
|
||
"\n",
|
||
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
||
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
||
"\n",
|
||
"# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# train_data = train_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# test_data = test_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
|
||
"\n",
|
||
"# feature_columns_new = feature_columns[:]\n",
|
||
"# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"\n",
|
||
"# feature_columns = [\n",
|
||
"# 'undist_profit_ps', \n",
|
||
"# 'AR_BR',\n",
|
||
"# 'pe_ttm',\n",
|
||
"# 'alpha_22_improved', \n",
|
||
"# 'alpha_003', \n",
|
||
"# 'alpha_007', \n",
|
||
"# 'alpha_013', \n",
|
||
"# 'cat_up_limit', \n",
|
||
"# 'cat_down_limit', \n",
|
||
"# 'up_limit_count_10d', \n",
|
||
"# 'down_limit_count_10d', \n",
|
||
"# 'consecutive_up_limit', \n",
|
||
"# 'vol_break', \n",
|
||
"# 'weight_roc5', \n",
|
||
"# 'price_cost_divergence', \n",
|
||
"# 'smallcap_concentration', \n",
|
||
"# 'cost_stability', \n",
|
||
"# 'high_cost_break_days', \n",
|
||
"# 'liquidity_risk', \n",
|
||
"# 'turnover_std', \n",
|
||
"# 'mv_volatility', \n",
|
||
"# 'volume_growth', \n",
|
||
"# 'mv_growth', \n",
|
||
"# 'lg_flow_mom_corr_20_60', \n",
|
||
"# 'lg_flow_accel', \n",
|
||
"# 'profit_pressure', \n",
|
||
"# 'underwater_resistance', \n",
|
||
"# 'cost_conc_std_20', \n",
|
||
"# 'profit_decay_20', \n",
|
||
"# 'vol_amp_loss_20', \n",
|
||
"# 'vol_drop_profit_cnt_5', \n",
|
||
"# 'lg_flow_vol_interact_20', \n",
|
||
"# 'cost_break_confirm_cnt_5', \n",
|
||
"# 'atr_norm_channel_pos_14', \n",
|
||
"# 'turnover_diff_skew_20', \n",
|
||
"# 'lg_sm_flow_diverge_20', \n",
|
||
"# 'pullback_strong_20_20', \n",
|
||
"# 'vol_wgt_hist_pos_20', \n",
|
||
"# 'vol_adj_roc_20',\n",
|
||
"# 'cashflow_to_ev_factor',\n",
|
||
"# 'ocfps',\n",
|
||
"# 'book_to_price_ratio',\n",
|
||
"# 'turnover_rate_mean_5',\n",
|
||
"# 'variance_20',\n",
|
||
"# 'bbi_ratio_factor'\n",
|
||
"# ]\n",
|
||
"# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
|
||
"# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"\n",
|
||
"numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
"# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
|
||
"print(feature_columns)\n",
|
||
"\n",
|
||
"# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n",
|
||
"# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n",
|
||
"\n",
|
||
"train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"train_data = train_data.dropna(subset=['label'])\n",
|
||
"train_data = train_data.reset_index(drop=True)\n",
|
||
"# print(test_data.tail())\n",
|
||
"test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data = test_data.dropna(subset=['label'])\n",
|
||
"test_data = test_data.reset_index(drop=True)\n",
|
||
"\n",
|
||
"transform_feature_columns = feature_columns\n",
|
||
"transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"# transform_feature_columns.remove('undist_profit_ps')\n",
|
||
"print('去除极值')\n",
|
||
"cs_mad_filter(train_data, transform_feature_columns)\n",
|
||
"# print('中性化')\n",
|
||
"# cs_neutralize_market_cap_numpy(train_data, transform_feature_columns)\n",
|
||
"# print('标准化')\n",
|
||
"# cs_zscore_standardize(train_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"cs_mad_filter(test_data, transform_feature_columns)\n",
|
||
"# cs_neutralize_market_cap_numpy(test_data, transform_feature_columns)\n",
|
||
"# cs_zscore_standardize(test_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"cs_mad_filter(train_data, mad_filter_feature_columns)\n",
|
||
"cs_mad_filter(test_data, mad_filter_feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f'feature_columns: {feature_columns}')\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(train_data))\n",
|
||
"print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(test_data))\n",
|
||
"print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"\n",
|
||
"cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
|
||
"for col in cat_columns:\n",
|
||
" train_data[col] = train_data[col].astype('category')\n",
|
||
" test_data[col] = test_data[col].astype('category')\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"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': 100,\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": 19,
|
||
"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": "306af53af6f24cce84719f44ae19a9b0",
|
||
"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: 0.6889525\ttest: 0.6893155\tbest: 0.6893155 (0)\ttotal: 1.83s\tremaining: 45m 41s\n",
|
||
"100:\tlearn: 0.5065717\ttest: 0.5493420\tbest: 0.5493420 (100)\ttotal: 14.7s\tremaining: 3m 23s\n",
|
||
"200:\tlearn: 0.4728006\ttest: 0.5277064\tbest: 0.5277064 (200)\ttotal: 27.7s\tremaining: 2m 59s\n",
|
||
"300:\tlearn: 0.4572238\ttest: 0.5226125\tbest: 0.5225487 (298)\ttotal: 43s\tremaining: 2m 51s\n",
|
||
"400:\tlearn: 0.4478623\ttest: 0.5208601\tbest: 0.5208601 (400)\ttotal: 57.6s\tremaining: 2m 37s\n",
|
||
"500:\tlearn: 0.4408993\ttest: 0.5198696\tbest: 0.5198644 (463)\ttotal: 1m 12s\tremaining: 2m 23s\n",
|
||
"600:\tlearn: 0.4349650\ttest: 0.5201926\tbest: 0.5198644 (463)\ttotal: 1m 26s\tremaining: 2m 9s\n",
|
||
"700:\tlearn: 0.4287194\ttest: 0.5200216\tbest: 0.5198644 (463)\ttotal: 1m 40s\tremaining: 1m 54s\n",
|
||
"800:\tlearn: 0.4220811\ttest: 0.5194681\tbest: 0.5194258 (799)\ttotal: 1m 54s\tremaining: 1m 40s\n",
|
||
"900:\tlearn: 0.4156361\ttest: 0.5195695\tbest: 0.5193854 (845)\ttotal: 2m 9s\tremaining: 1m 26s\n",
|
||
"1000:\tlearn: 0.4083819\ttest: 0.5195108\tbest: 0.5193854 (845)\ttotal: 2m 25s\tremaining: 1m 12s\n",
|
||
"1100:\tlearn: 0.4002510\ttest: 0.5196046\tbest: 0.5193854 (845)\ttotal: 2m 41s\tremaining: 58.5s\n",
|
||
"bestTest = 0.5193854246\n",
|
||
"bestIteration = 845\n",
|
||
"Shrink model to first 846 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": 20,
|
||
"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": 21,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"197\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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": 22,
|
||
"id": "e53b209a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"5595 2057539\n",
|
||
" ts_code trade_date turnover_rate\n",
|
||
"0 000001.SZ 2023-01-03 1.1307\n",
|
||
"1 000001.SZ 2023-01-04 1.1284\n",
|
||
"2 000001.SZ 2023-01-05 0.8582\n",
|
||
"3 000001.SZ 2023-01-06 0.6162\n",
|
||
"4 000001.SZ 2023-01-09 0.5450\n",
|
||
"... ... ... ...\n",
|
||
"1766689 605599.SH 2025-05-19 0.4952\n",
|
||
"1766690 605599.SH 2025-05-20 1.6447\n",
|
||
"1766691 605599.SH 2025-05-21 1.2658\n",
|
||
"1766692 605599.SH 2025-05-22 0.7522\n",
|
||
"1766693 605599.SH 2025-05-23 0.6051\n",
|
||
"\n",
|
||
"[1766694 rows x 3 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(train_data[train_data['pct_chg'] > 7]), len(train_data))\n",
|
||
"print(test_data[['ts_code', 'trade_date', 'turnover_rate']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"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": 24,
|
||
"id": "1f6e6336",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"6e+04-9e+04"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\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: 0.6525\n",
|
||
"precision: 0.4625\n",
|
||
"recall: 0.2437\n",
|
||
"f1: 0.3192\n",
|
||
"roc_auc: 0.6190\n",
|
||
"fpr: (array of length 7459)\n",
|
||
"tpr: (array of length 7459)\n",
|
||
"thresholds: (array of length 7459)\n",
|
||
"score_return_correlation: -0.0381\n",
|
||
"mv_roc_auc: {'6e+04-9e+04': np.float64(0.5297001153402537), '9e+04-1e+05': np.float64(0.5480807161280534), '1e+05-1e+05': np.float64(0.5803400535039577), '1e+05-2e+05': np.float64(0.5801592577513709), '2e+05-2e+05': np.float64(0.6041226723862076), '2e+05-3e+05': np.float64(0.6108816749042437), '3e+05-3e+05': np.float64(0.6029078699377564)}\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": 25,
|
||
"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": 27,
|
||
"id": "a0000d75",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
|
||
"准备数据，处理 NaN 值...\n",
|
||
"原始数据 17280 行，移除 NaN 后剩余 16971 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"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
|
||
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分析完成。\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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ffDIiIhYuXBhTp05dbiJ9r732imnTpsV7771X1M+07bbbxr333hvz5s1bZS6fz8e9994bAwYMKOr6y6qoCfbf/va30bdv3+X+XHLJJY1yd955ZwwdOjS22GKLGD58eDz++ONlqhgAAAAAoDpNnz690eR3xH9XmPfo0SOmT5++yvMiYrlz+/TpE++//37Mnz9/pddPpVKx4YYbNlzjnXfeiUWLFq3wWkuPtbpGjhwZ//nPf+L444+PDz74YIWZDz74IE444YT4z3/+E8ccc0xR119WRW4Rc91118Waa67Z8HrpX0X4/e9/H+edd16ccMIJMWjQoJgyZUqMHj06br755ujXr18ZqgUAAAAAKJ8hQ4as8vijjz66wvdnz54d9fX1y73ftWvXmDVr1kqvN3v27Gjfvn106NCh0fv19fVRKBRi1qxZ0bFjx5g9e3ajed4VXf/zfy5bx+evV1XHiqTT6RgzZkyMGTMmhgwZEl//+tdjk002ic6dO8enn34a//jHP+Ivf/lLFAqFOP/88yOdThd1/WWlCoVC0bsOFQqFuP322+Ouu+6Kd999N2bPnr38hVOpePXVV4u67m9/+9s466yz4plnnlnp3jdDhw6NTCYTl156acN7hx56aKy55poxYcKE4n6QpWSz2ZLPBQAAAIByyWQy5S6hqvS+4hflLiFxG/7f71d5fGUT7Jtvvnmccsopcfzxxzd6f5999on+/fvHT3/60xWe96tf/Sp++ctfxssvv9zo/QceeCBOOeWUeOqpp2LttdeOPfbYIwYNGtSwt/rnRo0aFYsWLYpf//rX8de//jW+853vxO23395oAfWMGTNi8ODBcckll8S+++67yp9vRV544YUYN25c/PnPf47PPvus4f011lgjBg4cGKNHj47+/fsXfd1llbSCfezYsfGb3/wm0ul0DB8+PLp27drkQlbHu+++G2+99VaceeaZjd7fa6+9YuzYsbFw4cJo3759yddf9maUz+cjl8tFOp2Ourq6Rsey2WzRN69iz5Fv/nySPZZPNp/UGHpc2/la7m8l1qTH8knnfdeq/bzPcMvly1WTHtd2vpr6W4k1VUO+mnosD6u2sgn0L1JfXx9z5sxZ7v1Zs2atcs63vr4+Fi5cGAsWLGi0in327NmRSqUazq2vr4+5c+eu8Ppf+9rXIiIassvW8fmi7lLnnvv16xfXXXddzJ8/P95+++2YO3dudOnSJTbYYIPo2LFjSddckZIm2O+5557YY4894oorrkiskKXts88+8cknn8Q666wT3/rWt+LYY4+Ntm3bNuy3s+GGGzbK9+nTJxYtWhTvvvtuw948AAAAAACsXO/evZfb43zOnDnx0UcfLbcn+rLnRUS8+eabsemmmza8P3369FhnnXUaJrB79+4d//jHPxqdWygU4s0334ztt98+IiLWX3/9aNeuXUyfPj123HHHRtdaeqxSdezYMfr27duka6xKSRPs8+fPj+222y7pWqJHjx5x8sknx1ZbbRWpVCoee+yxuPzyy+ODDz6I888/P/H9eAAAAAAAWquddtoprrnmmkZ7sT/wwAPRpk2bhgnwFdl6662jS5cucf/99zdMsC9atCgeeuih2GmnnRpd///+7//irbfeil69ekVExDPPPBMzZ86MnXfeOSIi2rdvHwMHDowHH3wwjjzyyIZzp0yZEn369In11luvqJ/plVdeKSof8d+tckpV0gT74MGD4+WXX45DDjmk5IFXZMcdd2z0txQ77LBDdOjQIW688cY44YQTEh1rRfL5fKPX8+bNa/TPL8qXMoZ8efNJ91g+2XwSY+hxbedrvb8tMUal52u9x60977tW7ed9hls23xJj6HHryldbf1tijFrLV1uP5Vdu2S1++AJFP5Gydh166KExceLEOOmkk2LUqFHxwQcfxNixY+PQQw+NtddeuyF35JFHxvvvvx8PP/xwRER06NAhRo0aFVdddVV07949Ntlkk7j11ltj5syZMXLkyIbzhg4dGtdee22cfPLJcfrpp8e8efNi7Nixscsuu8SWW27ZkDvxxBNjxIgRMWbMmBg2bFhMnTo17rvvvrjsssuK/pkOOuigSKVSq5UtFAqRSqUil8sVPc7nSnrI6QcffBDHHnts7L333nHIIYfEl770pZIL+CIvvfRSHHzwwTF+/PiIiDj++OPj/vvvb/SrAU8//XQcc8wxDX+rUYpsNhsLFixY7XyHDh2Kypdyjrx8a85XYk3y8i2Zr8Sa5OUrOV+JNcnLt2S+EmuSl2/JfCXWJC/fkvltttlmtbNE9L689h5yOv3U00s+d9q0afHTn/40/va3v0Xnzp1jv/32i9NOO63Rsy6POOKI+Oc//xmPPfZYw3uFQiHGjx8ft9xyS8yYMSPS6XScddZZyz049IMPPoif/exn8cc//jHWWGON2H333ePss8+OLl26NMo9+uijcfnll8ebb74Z66yzThx//PHxzW9+s+if5+677y76nAMOOKDocz5X0gR7//79o1AoNHzQO3ToEG3atGl84VQq/vrXv5Zc2OeWnmDv3bt37LbbbnH11VfHbrvt1pCZOHFi/PznP4/nn3++5IecZrPZ5fbzmTdvXsOvL3Tq1KnRsenTpxe9/0+x58g3fz7JHssnm09qDD2u7Xwt97cSa9Jj+aTzvmvVft5nuOXy5apJj2s7X039rcSaqiFfTT2WXzUr2Itjgp0klbRFzNChQ1d7mX1TTZkyJdq2bRubbbZZ9OjRI3r16hUPPPBAown2KVOmxODBg0ueXP/cym5GnTp1WuGxUm5exZ4j3zL5pHosn2w+yTH0uLbztdrflhijWvK12mP5//Jdq/bzPsMtk2+JMfS4dearpb8tMUat5qulx/JAJSp6gr1QKMS5554ba6yxRsPTYJMycuTIGDhwYMNTXR999NG44447YsSIEdGjR4+IiDj55JPjjDPOiPXXXz8GDhwYU6ZMiZdeeikmTZqUaC0AAAAAQA2yBzsJKnqCfdGiRTFgwIA4/fTT49hjj020mA033DAmT54c//73v2PJkiXRq1evOPvss+OII45oyOyzzz4xb968mDBhQowfPz423HDDGDdu3HJ7+wAAAAAAQHMqeoK9ffv2sdZaa0W7du0SL+bcc89drdzBBx8cBx98cOLjAwAAAADA6irpIaeXXXZZ/OEPf4jbbrutyfueV4psNlvuEgAAAACgaJlMptwlVJXel9XgQ05P85DTcinpIad9+/aNRx99NPbZZ5844IADYt11113hfux77LFHkwtsScvejPL5fORyuUin08s9WCKbzRZ98yr2HPnmzyfZY/lk80mNoce1na/l/lZiTXosn3Ted63az/sMt1y+XDXpcW3nq6m/lVhTNeSrqcfyJCllD/ZWY8aMGdG9e/dVZl566aXYcsstSx6jpAn200//f38jcsUVV6wwk0qlIpfLlVYVAAAAAAA0wZFHHhmTJk2Krl27rvD4n//85zjppJPir3/9a8ljlDTBftNNN5U8IAAAAAAANLf58+fH0UcfHTfeeGOsueaajY49/vjjccopp0S/fv2aNEZJE+wDBgxo0qAAAAAAANCcfvOb38Thhx8exx57bPz617+Ozp07R0TE73//+/jBD34Q22+/fVx55ZVNGqNNEoUCAAAAAEAlWXfddePGG2+Mf/3rX3H88cfHvHnz4vbbb48zzzwzdt9997j66qujQ4cOTRqjpBXsI0aM+MJMKpWKG2+8sZTLAwAAAAA0Dw85bVXWX3/9uOGGG2LEiBGx//77xzvvvBMHHXRQ/PSnP41UKtXk65c0wV4oLP9/4ZIlS+L999+Pf/3rX7HBBhvEV77ylSYXBwAAAAAAq2PmzJkrfP/LX/5yXHbZZXHCCSfE/vvvH9///vdj1qxZDce7detW8pglTbBPnDhxpccef/zxOO+88+Kss84quSgAAAAAACjGoEGDVrkqvVAoxD333BP33HNPo/dzuVzJY5Y0wb4q3/jGN2L48OFxwQUXxKRJk5K+PAAAAAAALOekk05KZNuXYqQKK9rvpYluu+22uPDCC+PFF19M+tLNJpvNlrsEAAAAAChaJpMpdwlVpc8lvyh3CYmbdsbp5S6h1Up8Bftnn30W999/f3zpS19K+tLNbtmbUT6fj1wuF+l0Ourq6hody2azRd+8ij1HvvnzSfZYPtl8UmPocW3na7m/lViTHssnnfddq/bzPsMtly9XTXpc2/lq6m8l1lQN+WrqsTxQqUqaYF/Z/upz5syJF154If7zn//ED3/4wyYVBgAAAAAATTFr1qy477774r333otZs2bFshu6pFKpuOCCC0q+fkkT7FOnTl3uvVQqFV27do1tttkmDj744Nhhhx1KLgoAAAAAAJriD3/4Q3zve9+LefPmRZcuXaK+vn65TFP3bC9pgv2xxx5r0qAAAAAAAOWQSvyJlFSqn//859GjR4+46qqrom/fvs0yRptSTrrnnnvivffeW+nx9957L+65555SawIAAAAAgCZ5++2344gjjmi2yfWIEifYzzrrrPjb3/620uMvvfTSSvdpBwAAAACA5tarV6/49NNPm3WMkibYl90Ifln5fD7atm1bUkEAAAAAANBUp5xyStxyyy2r3I2lqVZ7D/bXXnstXnvttYbXzz33XCxevHi53OzZs+O2226LDTfcMJkKAQAAAACSUmjaQy2pHn/+85+je/fusddee8V2220XX/va11a4MPzcc88teYzVnmB/5JFHYty4cRHx3yer3n777XH77bevMFtfXx8///nPSy4KAAAAAACaYtKkSQ3//sQTT6wwk0qlWmaC/Vvf+lbssssuUSgU4uCDD47vfe97sdNOOy1XTKdOnWL99dePNdZY7UsDAAAAAECilt6RpbmkCl+0ofoKPPvss9GnT5/48pe/3Bw1lUU2my13CQAAAABQtEwmU+4SqspGYy8rdwmJe+N/Tit3Ca1WScvMBwwYEBERCxcujFdeeSU+/vjj2HrrraN79+6JFtfSlr0Z5fP5yOVykU6no66urtGxbDZb9M2r2HPkmz+fZI/lk80nNYYe13a+lvtbiTXpsXzSed+1aj/vM9xy+XLVpMe1na+m/lZiTdWQr6Yey5Ooopcbw8qVvI/LTTfdFOPGjYs5c+ZERMSvf/3rGDx4cMyYMSOGDRsWZ555Znzzm99MrFAAAAAAACjGk08+Gb/5zW/i1VdfjTlz5sSKNnTJ5XIlX79NKSdNnjw5Lrjggthxxx3jf//3fxsV1b179xg0aFBMmTKl5KIAAAAAAKApHnzwwTjhhBPiP//5T+y1116xZMmS2HvvvWOvvfaKjh07Rt++feOkk05q0hglrWC/4YYbYsiQIXHppZfGJ598stzxzTffPCZOnNikwgAAAAAAoFTXXnttbLnllnHLLbfErFmz4tZbb42DDjooBg8eHO+9914ccsghsd566zVpjJJWsL/99tux0047rfR4t27dYubMmaXWBAAAAADQLFKF2vvDik2bNi322muvaNu2bayxxn/Xmn/22WcREbHeeuvFt7/97ZgwYUKTxihpgr2+vn6FK9c/98Ybb0SPHj1KLgoAAAAAAJqiY8eO0a5du4j475x2+/bt46OPPmo4vtZaa8V7773XpDFKmmDfaaed4o477ojZs2cvd+z111+PO++8M3bdddcmFQYAAAAAAKXacMMNY9q0aQ2v0+l03HvvvfHZZ5/FggUL4r777ouvfe1rTRqjpAn2U089NRYvXhz77LNPXH755ZFKpeKee+6JM844Iw466KDo3r17fPe7321SYQAAAAAAUKrdd989Hn300Vi4cGFERJxwwgnx7LPPxte//vUYNGhQPPfcc3H88cc3aYySHnK69tprx29/+9v4xS9+Effff38UCoW49957o3PnzrH33nvHGWecEd27d29SYQAAAAAAibNneasxcuTIGDlyZMPrb3zjGzFx4sR46KGHom3btrHzzjvHoEGDmjRGqlAoFPW/1MKFC+MPf/hDrLvuurHppptGRMSMGTNiyZIl0b1792jTpqRF8WWXzWbLXQIAAAAAFC2TyZS7hKqy8YWXlbuExL1+1mnlLqFqzZ07N7p06VLy+UWvYG/Xrl2ccsopcc455zRMsNfKavVlb0b5fD5yuVyk0+moq6trdCybzRZ98yr2HPnmzyfZY/lk80mNoce1na/l/lZiTXosn3Ted63az/sMt1y+XDXpcW3nq6m/lVhTNeSrqcfyQNI+/vjjuPHGG+PWW2+Nv/zlLyVfp+gJ9lQqFb169YpPPvmk5EEBAAAAAKA5fPzxx3HPPffEO++8E127do099tij4S+tPvjgg/jVr34Vd999dyxYsCAGDBjQpLFK2oN91KhRcdFFF8Wee+4ZvXv3blIBAAAAAAAtJWUP9po2bdq0OPzww2PmzJnx+e7o1113XVx88cWRSqXinHPOiYULF8Yee+wRI0eObPJvi5Q0wf7iiy9Gt27dYt99940BAwbEuuuuGx07dlwud+655zapOAAAAAAAWF1XXHFF5PP5+NGPfhTbbrttvPfee3HhhRfGBRdcEHPmzIlvfOMbccYZZ0TPnj0TGa+kCfZJkyY1/PszzzyzwkwqlTLBDgAAAABAi3nuuefi29/+dhx66KEREbHRRhtF27Zt47jjjosDDjggLrzwwkTHK2mC/bXXXku0CAAAAAAAaKqZM2dG3759G7236aabRkTEbrvtlvh4bRK/4grMnTs3zjrrrJg2bVpLDAcAAAAAQCu0ZMmSWGONxuvKP39dV1eX+HglrWAv1vz58+Oee+6J4cOHR58+fVpiSAAAAACA5XnIac3LZrPRoUOHhteffvpppFKp+Otf/xpz5sxZLr/HHnuUPFaLTLBHRMMTWwEAAAAAoLnceOONceONNy73/rhx45Z7L5VKRS6XK3msFptgBwAAAACA5nTTTTe16HipQgssLf/Pf/4TO+ywQ9xwww0xePDg5h6uJNlsttwlAAAAAEDRMplMuUuoKpv872XlLiFx/zjntHKX0GpZwb6UZW9G+Xw+crlcpNPp5TbAz2azRd+8ij1HvvnzSfZYPtl8UmPocW3na7m/lViTHssnnfddq/bzPsMtly9XTXpc2/lq6m8l1lQN+WrqsTyJspM1CWpT7gIAAAAAAKAatdgEeyqVaqmhAAAAAACg2bXYBHsLbPUOAAAAAAAtpsl7sH/44YcxY8aMWH/99Zfbr+tza621Vrz22mtNHQoAAAAAoElS1gGToJJXsD/yyCOx5557xs477xwHHHBAvPjiixERMWPGjNh///3jkUceSaxIAAAAAACoNCWtYH/sscfi5JNPjn79+sU+++wT48aNazjWvXv3WHvttWPy5Mmx2267JVYoAAAAAAAUY/HixfHHP/4x3n333Zg1a9ZyW5mnUqk46aSTSr5+SRPsV199dWy77bYxceLE+OSTTxpNsEdE9OvXL26//faSiwIAAAAAgKZ4+eWX43vf+178+9//XukzQssywf7666/HD3/4w5UeX2utteLjjz8uuSgAAAAAAGiKH//4xzF//vyGBeP19fWJj5EqrGzqfhUGDhwYxx9/fIwcOTI++eSTGDx4cNxwww0xePDgiIj4xS9+EXfffXf84Q9/SLzg5pLNZstdAgAAAAAULZPJlLuEqtL3p5eVu4TE/f2808pdQkXaYost4rTTTotjjjmm2cYoaQX7wIED45577okjjzxyuWMfffRR3HHHHfGNb3yjycW1tGVvRvl8PnK5XKTT6airq2t0LJvNFn3zKvYc+ebPJ9lj+WTzSY2hx7Wdr+X+VmJNeiyfdN53rdrP+wy3XL5cNelxbeerqb+VWFM15Kupx/JAKb761a+udGuYpLQp5aRTTjkl/v3vf8c3v/nNuP322yOVSsUf//jHuOyyy2LfffeNQqHQpH1rAAAAAACgKY477ri44447Yu7cuc02Rkkr2Pv06RO33npr/OxnP4srrrgiCoVCXH/99RERMWDAgPjRj34U6623XqKFAgAAAAA0WfMuaKaCfPrpp9G5c+fYfffdY++9946vfvWr0bZt20aZVCoVRx11VMljFD3BvmjRopg2bVp069YtfvOb38SsWbPi7bffjkKhED179ozu3buXXAwAAAAAACTh5z//ecO/T5o0aYWZFp9gb9OmTRx00EHxgx/8IEaMGBFdu3aNLbfcsuQCAAAAAAAgaY8++mizj1H0BHvbtm1jnXXWiYULFzZHPQAAAAAA0GTrrrtus49R0kNODz/88Ljjjjti5syZCZcDAAAAANB8UoXa+0P5lPSQ0yVLlkT79u1j9913j6FDh8a6664bHTt2bJRp6t41AAAAAADQFK+99lpMmjQpXn311ZgzZ04sWbKk0fFUKhWPPPJIydcvaYJ96c3h77rrrhVmTLADAAAAAFAuU6dOjWOPPTa6du0amUwmXn311Rg0aFAsWLAgXnjhhdhoo40ik8k0aYySJthbYnN4AAAAAAAo1ZVXXhk9e/aMO+64IxYuXBjbbbddjBo1KgYPHhwvvvhiHHfccXHGGWc0aYxUoVCwS09EZLPZcpcAAAAAAEVr6grc1mbTMZeVu4TEvTbmtHKXUJH69+8fJ598chxzzDExa9asGDhwYFx//fWx/fbbR0TEpZdeGk8//XT89re/LXmMklaw16plb0b5fD5yuVyk0+moq6trdCybzRZ98yr2HPnmzyfZY/lk80mNoce1na/l/lZiTXosn3Ted63az/sMt1y+XDXpcW3nq6m/lVhTNeSrqcfyQCnatm0bnTt3joiI+vr6WGONNeLjjz9uON6zZ8+YNm1ak8YoaYJ91113jVQqtcpMUzeHBwAAAACAUq2//vrx1ltvRcR/56t79+4djzzySAwfPjwiIp544olYa621mjRGSRPsAwYMWG6CffHixfH+++/H888/HxtvvHFsttlmTSoMAAAAAABKtfPOO8fkyZPj+9//fqyxxhpx9NFHx1lnnRV77LFHRES88847cfrppzdpjJIm2C+66KKVHnvttddi5MiRse+++5ZcFAAAAABAs/BEylbju9/9bowYMSLatm0bEREHHHBAtGnTJh566KFo27ZtnHDCCXHggQc2aYzE92DfdNNN45BDDolLLrmkSZvDAwAAAABAqdq1axdf+tKXGr233377xX777ZfYGG0Su9JSvvzlL8cbb7zRHJcGAAAAAICKkPgK9k8++SQmT54cX/3qV5O+NAAAAAAArLbnnnsuJk+eHO+9917MmjUrCoXGewSlUqn4v//7v5KvX9IE+4gRI1b4/pw5c2L69OmxaNGiGDt2bMlFAQAAAABAU9xwww0xduzY6NChQ2y44YbRtWvXxMcoaYJ92Vn+iP/O9K+33noxePDgOOigg6JPnz5NLg4AAAAAIEkpDzltNa6//vrYeuut45prrok111yzWcYoaYJ94sSJSdcBAAAAAACJmTdvXuy7777NNrkeEZEqrGg5+hcYN25c7LHHHrHJJpus8Pjrr78eDz74YIwePbrJBbaUbDZb7hIAAAAAoGiZTKbcJVSV9PmXlbuExOV+clq5S6hI3/3ud+OrX/1qnH/++c02Rkkr2MeNGxcbbLDBKifYr7766qqaYI9Y/maUz+cjl8tFOp2Ourq6Rsey2WzRN69iz5Fv/nySPZZPNp/UGHpc2/la7m8l1qTH8knnfdeq/bzPcMvly1WTHtd2vpr6W4k1VUO+mnosD5TivPPOi2OOOSauv/76OOigg6Jbt26Jj1HSBPsXmTlzZrRr1645Lg0AAAAAUDp7sLcaX/va1+KQQw6JsWPHxiWXXBIdOnSINm3aNMqkUqn461//WvIYqz3B/pe//CWmTp3a8Prhhx+Ot99+e7ncnDlzYsqUKStd3Q4AAAAAAM3tiiuuiGuuuSbWXnvtyGQyzbIX+2pPsE+dOjXGjRsXEf+d1X/ooYfioYceWmF2o402ivPOOy+ZCgEAAAAAoEi33XZb7LzzzvHLX/5yuZXrSVntCfZjjz02DjvssCgUCrHddtvFj3/849hjjz0aZVKpVHTq1Ck6dOiQeKEAAAAAALC6Fi1aFLvsskuzTa5HFDHB3rFjx+jYsWNERDz66KPRvXv36NSpU7MVBgAAAACQtJQ92FuNXXbZJZ577rk49NBDm22Mkqbu1113XZPrAAAAAABUrNGjR8e0adNizJgxkc1mY8aMGTFz5szl/jTFaq9gX9quu+4aqVRqlZlUKhWPPPJISUUBAAAAAEBT7LnnnhERkcvl4vbbb19pLpfLlTxGSRPsAwYMWG6CffHixfH+++/H888/HxtvvHFsttlmJRcFAAAAAABNcdJJJ33hQvGmShUKhUR3HXrttddi5MiRcfHFF8d2222X5KWbVTabLXcJAAAAAFC0TCZT7hKqymbnXFbuEhL36v+eVu4SKs6iRYti2rRp0a1bt/jqV7/abOOUtIJ9VTbddNM45JBD4pJLLonf/va3SV++WS17M8rn85HL5SKdTkddXV2jY9lstuibV7HnyDd/PskeyyebT2oMPa7tfC33txJr0mP5pPO+a9V+3me45fLlqkmPaztfTf2txJqqIV9NPZYHitWmTZs46KCD4gc/+EGMGDGi+cZpjot++ctfjjfeeKM5Lg0AAAAAAKvUtm3bWGeddWLhwoXNOk7iE+yffPJJTJ48uVmX3QMAAAAAwKocfvjhcccdd8TMmTObbYyStohZ2ZL6OXPmxPTp02PRokUxduzYJhUGAAAAAJC4RJ9ISSVbsmRJtG/fPnbfffcYOnRorLvuutGxY8dGmVQqFUcddVTJY5Q0wb6i56KmUqlYb731YvDgwXHQQQdFnz59Si4KAAAAAACa4uc//3nDv991110rzLTIBPtrr70W6667bqy55poRETFx4sSSBwQAAAAAgOb26KOPNvsYqzXBfsABB8TYsWNj3333jYj/bhFz4oknxuDBg5u1OAAAAAAAKMW6667b7GOs1kNOO3bsGPPnz294/eyzz8Z//vOfZivq7rvvjv333z+22GKLGDhwYBx77LGNxn/sscdi+PDhscUWW8TQoUNj8uTJzVYLAAAAAFA7UoXa+0P5rNYK9r59+8YNN9wQbdq0adgm5uWXX44OHTqs8rw99tij6IJ+9atfxYQJE+KEE06Ifv36xSeffBLPPPNMLF68OCIinnvuuRg9enR885vfjLPPPjv+/Oc/xznnnBOdO3eOPffcs+jxAAAAAACoTa+99lpMmjQpXn311ZgzZ04sWbKk0fFUKhWPPPJIyddfrQn2c845J0455ZQ455xzGga96aab4qabblrpOalUKnK5XFHFTJ8+PcaNGxe//OUvY+edd254f+jQoQ3//qtf/Sq23HLL+MlPfhIREYMGDYp33303rrzyShPsAAAAAABERMTUqVPj2GOPja5du0Ymk4lXX301Bg0aFAsWLIgXXnghNtpoo8hkMk0aI1UoFFbrlwg+++yzeOedd+Ljjz+OI444Ik444YTYbrvtVnnOgAEDiirmkksuiYcffjgefPDBFR5fuHBhbL311nHGGWc0erLro48+Gt/97nfj0UcfjfXWW6+oMT+XzWZLOg8AAAAAyqmpE4StzeZnXVbuEhL3yoWnlbuEinTYYYfFJ598EnfccUcsXLgwtttuu7jhhhti8ODB8eKLL8Zxxx0XF198caPF3sVarRXsERFrrLFG9O7dO3r37h0HHHBAfOMb34itttqq5IFX5MUXX4xNNtkkfvnLX8bEiRNjzpw5kclk4qyzzoqtttoq3nnnnVi0aFH07t270Xl9+vSJiP+ugC91gj1i+ZtRPp+PXC4X6XQ66urqGh3LZrNF37yKPUe++fNJ9lg+2XxSY+hxbedrub+VWJMeyyed912r9vM+wy2XL1dNelzb+WrqbyXWVA35auqxPImyZ3mr8eqrr8bJJ58cXbp0iVmzZkVENGwRs9VWW8UhhxwSV1xxRctMsC/twgsvLHnAVfnoo48im83GP/7xj/jRj34UnTp1imuuuSaOOeaYeOihhxr+I9TX1zc67/PXnx8HAAAAAKB1a9u2bXTu3Dki/juHvMYaa8THH3/ccLxnz54xbdq0Jo3RpklnJ6xQKEQ+n48rrrgi9txzz9h5553jV7/6VRQKhZg0aVK5ywMAAAAAoEqsv/768dZbb0XEf58Z2rt370YPNH3iiSdirbXWatIYJa1gby719fXRrVu32HTTTRve69atW2y22WbxxhtvxN577x0REXPmzGl03uzZsyMiomvXrk0aP5/PN3o9b968Rv/8onwpY8iXN590j+WTzScxhh7Xdr7W+9sSY1R6vtZ73NrzvmvVft5nuGXzLTGGHreufLX1tyXGqLV8tfVYfuWW3eIH+K+dd945Jk+eHN///vdjjTXWiKOPPjrOOuus2GOPPSIi4p133onTTz+9SWOs9kNOW8JZZ50Vjz32WEydOrXR+4cffnjU1dXFuHHjYuutt44zzzwzjjzyyIbjjz32WJx44olNfsjpggULVjvfoUOHovKlnCMv35rzlViTvHxL5iuxJnn5Ss5XYk3y8i2Zr8Sa5OVbMl+JNcnLt2R+m222We0sEZv/oAYfcvpzDzldkUWLFsXcuXOjW7dukUqlIiLi3nvvjYceeijatm0bu+yySxx44IFNGqOiJtgfeuihOPnkk+Oee+6JdDodERGffPJJ7LrrrnHUUUfFKaecEiNHjox58+bFLbfc0nDeGWecEa+++mpMmTKl5LGz2exyD0+dN29evPXWW9GrV6/o1KlTo2PTp09fLv9Fij1HvvnzSfZYPtl8UmPocW3na7m/lViTHssnnfddq/bzPsMtly9XTXpc2/lq6m8l1lQN+WrqsfyqWcFeHBPsJKmitojZbbfdYosttojvfe97cdppp0WHDh1i/Pjx0b59+/jOd74TEREnnnhijBgxIsaMGRPDhg2LqVOnxn333ReXXdb0D8bKbkadOnVa4bFSbl7FniPfMvmkeiyfbD7JMfS4tvO12t+WGKNa8rXaY/n/8l2r9vM+wy2Tb4kx9Lh15qulvy0xRq3mq6XH8kCpFi5cGK+88kp8/PHHsfXWW0f37t0Tu3ZFPeS0TZs2MX78+OjXr1+cf/75cfrpp0eXLl3i5ptvjh49ekRExLbbbhtXXXVV/PWvf42RI0fGfffdFz/72c9i2LBhZa4eAAAAAIBKctNNN8UOO+wQ3/nOd+Lkk0+Ov//97xERMWPGjBg4cGDcddddTbp+Ra1gj4jo3r17XHzxxavMDBkyJIYMGdJCFQEAAAAAUG0mT54cF1xwQey9996x/fbbx9lnn91wrHv37jFo0KCYMmVKfPOb3yx5jIrag72cstlsuUsAAAAAgKJlMplyl1BVMv9Te3uwZ8fag31F9tlnn9hggw3i6quvjk8++SQGDx4cN9xwQwwePDgiIsaPHx8TJ06MP/zhDyWPUXEr2Mtp2ZtRPp+PXC4X6XR6uX2vstls0TevYs+Rb/58kj2WTzaf1Bh6XNv5Wu5vJdakx/JJ533Xqv28z3DL5ctVkx7Xdr6a+luJNVVDvpp6LA+U4u23344jjjhipce7desWM2fObNIYFbUHOwAAAAAAJKG+vj4++eSTlR5/4403Gp79WSoT7AAAAAAA1Jyddtop7rjjjpg9e/Zyx15//fW48847Y9ddd23SGLaIAQAAAABaD0+kbDVOPfXU+Na3vhX77LNPfOMb34hUKhX33HNPTJ48OR566KHo0aNHfPe7323SGFawAwAAAABQc9Zee+347W9/GzvuuGPcf//9USgU4t57743HH3889t5777jjjjuie/fuTRrDCnYAAAAAAGrSl7/85fjf//3f+N///d+YMWNGLFmyJLp37x5t2iSz9twKdgAAAAAAalqhUIhCoRCpVCpSqVRi17WCHQAAAABoNVL2YG9V3njjjbjyyivjD3/4Q8yfPz8iIjp27Bg77rhjjB49OjbZZJMmXd8EOwAAAAAANee5556L4447LpYsWRJDhgyJXr16RUTEm2++GY899lg89dRTcd1118W2225b8hgm2AEAAAAAaFAoFGLChAlxyy23xIwZMyKdTsdZZ50V/fr1+8Jzp02bFj/72c/ib3/7W3Tu3Dn222+/OPXUU6N9+/YRETF37ty44YYb4sknn4y33nor2rdvH1tuuWWcdtpp0bdv34brvPfeezFkyJDlrr/VVlvFHXfcsVo/xwUXXBDdu3ePSZMmxde+9rVGx/71r3/FYYcdFhdeeGFMnjx5ta63IqlCoeCXIiIim82WuwQAAAAAKFomkyl3CVVlizMuK3cJiXv5ktMSvd748ePjyiuvjDPOOCP69u0bN998c/zpT3+Ke++9N3r27LnS82bNmhV777139OrVK0aNGhUffPBBXHTRRTF8+PA4//zzIyLiH//4RxxzzDFx0EEHxde//vVYsGBB/PrXv45XXnklJk+eHH369ImI/zfBfvrpp8fAgQMbxujcuXNsvPHGq/VzbLnllnHKKafEyJEjV3h8woQJMW7cuHjxxRdX9z/NcqxgX8qyN6N8Ph+5XC7S6XTU1dU1OpbNZou+eRV7jnzz55PssXyy+aTG0OPaztdyfyuxJj2WTzrvu1bt532GWy5frpr0uLbz1dTfSqypGvLV1GN5EmW58SotWLAgrr322jjmmGPiqKOOioiIbbbZJvbcc8+4/vrrY8yYMSs997bbbotPP/00xo0bF926dYuIiMWLF8ePf/zjGDVqVKy99tqx3nrrxcMPPxydOnVqOG/QoEGx6667xi233BLnnXdeo2tusMEGq7VyfkXWWWedWLhw4UqPL1q0KL761a+WdO3PtWnS2QAAAAAA1Iznn38+5s6dG8OGDWt4r3379rH77rvHU089tcpzn3rqqRg8eHDD5HpExLBhw2LJkiXx9NNPR0REXV1do8n1iP+uSl9//fXjww8/TO4HiYiTTjopJk6cGLlcbrljr776akyaNClOPvnkJo1hBTsAAAAAABERMX369IiI6N27d6P3+/TpEzfeeGPMnz8/OnbsuNJzDzrooEbv1dfXR48ePRquuyKzZ8+O119/Pbbbbrvljo0ZMyZOO+206NatWwwZMiTOOOOMRhP4q/Liiy/Gl7/85TjwwAOjf//+scEGG0RExFtvvRUvvPBCbLzxxvHCCy/ECy+80Oi8c889d7WuH2GCHQAAAACgqq3oYaBLe/TRR1f7WrNnz4727dtHhw4dGr1fX18fhUIhZs2atdIJ9tmzZ0d9ff1y73ft2jVmzZq10jEvvvjiSKVS8e1vf7vhvfbt28e3v/3t2GGHHaK+vj5efPHFuOaaayKbzcadd94Z7dq1+8KfZdKkSQ3//vzzz8fzzz/f6Pg//vGP+Mc//tHovVQqZYIdAAAAAGCF7MHeoFAoxOLFixtep1KpFq9h8uTJcccdd8RFF13UaD/0r3zlK432ex8wYEBsvPHGMWrUqHj44Ydjr732+sJrv/baa81RciMm2AEAAAAAqlgxK9SX9uyzz8aIESMaXg8YMCD23HPPWLhwYSxYsKDRKvbZs2dHKpWKrl27rvR69fX1MWfOnOXenzVr1grPe/LJJ+P888+P7373u3HAAQd8Yb0777xz1NXVxSuvvLJaE+wtwQQ7AAAAAEArtPnmm8ddd93V8Lpz587xwQcfRETEm2++GZtuumnDsenTp8c666yz0u1hIv67b/uye63PmTMnPvroo+X2dH/hhRfilFNOif333z9OOeWUJH6cLzRt2rR44IEH4qOPPooNN9wwDjrooOjSpUuTrmmCHQAAAACgFerSpUtsscUWjd5bd911o0uXLnH//fc3TLAvWrQoHnroodhpp51Web2ddtoprrnmmkZ7sT/wwAPRpk2b2H777Rtyb7zxRowaNSoGDRoUP/7xj1e73scffzzy+fxyNS9t0qRJMXHixLj11luje/fuDe8/9thjccopp8SiRYsaZW+//fZGuWKZYAcAAAAAWo2W32W8unTo0CFGjRoVV111VXTv3j022WSTuPXWW2PmzJkxcuTIhtyzzz4bRx11VFxwwQWx//77R0TEoYceGhMnToyTTjopRo0aFR988EGMHTs2Dj300Fh77bUjIuLjjz+OkSNHRocOHeLII4+MbDbbcM0uXbrERhttFBERF110UaRSqejXr1/U19fHSy+9FNdee21kMpnYbbfdVlr/Y489Fj179mw0af7ZZ5/FueeeG23bto2f/OQnkclk4oknnojLL788rrnmmjj77LNL/u+VKhQKtvWPaNRIAAAAAKgWmUym3CVUlS1Pv6zcJSTupV+cluj1CoVCjB8/Pm655ZaYMWNGpNPpOOuss6J///4NmalTp8aIESPiwgsvjAMPPLDh/WnTpsVPf/rT+Nvf/hadO3eO/fbbL0477bRo3759o/NWZMCAATFx4sSIiLjzzjvj1ltvjbfffjvmz58fa6+9duy2227xve99b5Xbuuy0007xrW99K0aPHt3w3tNPPx0jR46MUaNGxWmn/b//Vt///vcjm83Ggw8+WNp/qLCCvZFlb0b5fD5yuVyk0+moq6trdCybzRZ98yr2HPnmzyfZY/lk80mNoce1na/l/lZiTXosn3Ted63az/sMt1y+XDXpcW3nq6m/lVhTNeSrqcfy0LJSqVSMGjUqRo0atdLMwIED4+9///ty7/fp0yd+85vfFH3esg4++OA4+OCDV6vepc2cOTO++tWvNnrvmWeeiVQqFbvvvnuj97feeut4+OGHix5jaW2adDYAAAAAAFSItdZaK/7zn/80eu+5556Ljh07Nnpoa0RE+/bto127dk0azwQ7AAAAANB6FGrwDw0ymUzcfffdMXfu3IiIeP311+Pll1+OHXfcMdZYo/GGLtOnT19utXuxbBEDAAAAAEBNOOmkk+Kb3/xmDB06NDbaaKN45ZVXIpVKxfHHH79c9uGHH45BgwY1aTwr2AEAAAAAqAl9+/aNG2+8MTbffPP48MMPY6uttorx48cv91yDqVOnRqdOnWLPPfds0nhWsAMAAAAAUDO23nrrGD9+/CozAwcOjN/97ndNHssEOwAAAADQaqTsWU6CbBEDAAAAAAAlMMEOAAAAAAAlMMEOAAAAAAAlSBUKBbsORUQ2my13CQAAAABQtEwmU+4SqspWp1xW7hIS9+IVp5W7hFbLQ06XsuzNKJ/PRy6Xi3Q6HXV1dY2OZbPZom9exZ4j3/z5JHssn2w+qTH0uLbztdzfSqxJj+WTzvuuVft5n+GWy5erJj2u7Xw19bcSa6qGfDX1WB6oVLaIAQAAAACAEphgBwAAAACAEphgBwAAAACAEtiDHQAAAABoPQrlLoBaYgU7AAAAAACUwAQ7AAAAAACUwAQ7AAAAAACUwB7sAAAAAECrkbIHOwlKFQoF/0tFRDabLXcJAAAAAFC0TCZT7hKqSr+TLyt3CYl74arTyl1Cq2UF+1KWvRnl8/nI5XKRTqejrq6u0bFsNlv0zavYc+SbP59kj+WTzSc1hh7Xdr6W+1uJNemxfNJ537VqP+8z3HL5ctWkx7Wdr6b+VmJN1ZCvph7LA5XKHuwAAAAAAFACK9gBAAAAgNbDhtkkyAp2AAAAAAAogQl2AAAAAAAogQl2AAAAAAAogT3YAQAAAIBWI2UPdhJkBTsAAAAAAJTABDsAAAAAAJTABDsAAAAAAJQgVSgU7DoUEdlsttwlAAAAAEDRMplMuUuoKv2/e1m5S0jc3355WrlLaLU85HQpy96M8vl85HK5SKfTUVdX1+hYNpst+uZV7DnyzZ9PssfyyeaTGkOPaztfy/2txJr0WD7pvO9atZ/3GW65fLlq0uPazldTfyuxpmrIV1OP5YFKZYsYAAAAAAAogQl2AAAAAAAogS1iAAAAAIBWI+WJlCTICnYAAAAAACiBCXYAAAAAACiBCXYAAAAAACiBPdgBAAAAgNbDHuwkyAp2AAAAAAAoQapQKPg7m4jIZrPlLgEAAAAAipbJZMpdQlXZ+oTLyl1C4p6/5rRyl9Bq2SJmKcvejPL5fORyuUin01FXV9foWDabLfrmVew58s2fT7LH8snmkxpDj2s7X8v9rcSa9Fg+6bzvWrWf9xluuXy5atLj2s5XU38rsaZqyFdTj+WBSmWCHQAAAABoPeznQYLswQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACXwkFMAAAAAoNVIecgpCbKCHQAAAAAASmCCHQAAAAAASmCCHQAAAAAASpAqFAp2HYqIbDZb7hIAAAAAoGiZTKbcJVSVbY67rNwlJO6vE04rdwmtloecLmXZm1E+n49cLhfpdDrq6uoaHctms0XfvIo9R77580n2WD7ZfFJj6HFt52u5v5VYkx7LJ533Xav28z7DLZcvV016XNv5aupvJdZUDflq6rE8UKlsEQMAAAAAACUwwQ4AAAAAACWwRQwAAAAA0GqkPJKSBFnBDgAAAAAAJTDBDgAAAAAAJTDBDgAAAAAAJbAHOwAAAADQetiCnQRZwQ4AAAAAACUwwQ4AAAAAACVIFQoFvxQREdlsttwlAAAAAEDRMplMuUuoKtuO/EW5S0jcc9efXu4SWi17sC9l2ZtRPp+PXC4X6XQ66urqGh3LZrNF37yKPUe++fNJ9lg+2XxSY+hxbedrub+VWJMeyyed912r9vM+wy2XL1dNelzb+WrqbyXWVA35auqxPElKWW5MgmwRAwAAAAAAJTDBDgAAAAAAJTDBDgAAAAAAJbAHOwAAAADQetiDnQRZwQ4AAAAAACUwwQ4AAAAAACWoqAn2I444Ivr27bvCP7///e8bcnfeeWcMHTo0tthiixg+fHg8/vjjZawaAAAAAIDWqKL2YP/Rj34Uc+fObfTejTfeGA899FAMHjw4IiJ+//vfx3nnnRcnnHBCDBo0KKZMmRKjR4+Om2++Ofr161eGqgEAAACAapGyBzsJShUKhYr+X2rIkCHRp0+fGD9+fEREDB06NDKZTFx66aUNmUMPPTTWXHPNmDBhQsnjZLPZJtcKAAAAAC0tk8mUu4SqMuCoX5S7hMQ9+5vTy11Cq1VRK9iX9fzzz8d7770Xp556akREvPvuu/HWW2/FmWee2Si31157xdixY2PhwoXRvn37ksdb9maUz+cjl8tFOp2Ourq6Rsey2WzRN69iz5Fv/nySPZZPNp/UGHpc2/la7m8l1qTH8knnfdeq/bzPcMvly1WTHtd2vpr6W4k1VUO+mnosD1SqitqDfVn33Xdf1NXVxZAhQyIiYvr06RERseGGGzbK9enTJxYtWhTvvvtui9cIAAAAAEDrVLEr2D/77LO4//77Y9ddd234W9RZs2ZFRER9fX2j7OevPz8OAAAAALBCFb1hNtWmYifYn3766ZgxY0bss88+LTZmPp9v9HrevHmN/vlF+VLGkC9vPukeyyebT2IMPa7tfK33tyXGqPR8rfe4ted916r9vM9wy+ZbYgw9bl35autvS4xRa/lq67H8yi27xQ/Qcir2IadnnnlmPPXUU/HHP/4x2rVrFxERTz75ZBx//PFx//33R+/evRuyTz/9dBxzzDExZcqU6NOnT0njZbPZWLBgwWrnO3ToUFS+lHPk5VtzvhJrkpdvyXwl1iQvX8n5SqxJXr4l85VYk7x8S+YrsSZ5+ZbMb7PNNqudJWLAkTX4kNMbPeS0XCpyBfv8+fPjkUceieHDhzdMrkdEw6T69OnTG02wT58+Pdq1axc9e/Zs0rjpdLrR63nz5sVbb70VvXr1ik6dOjU6Nn369OXyX6TYc+SbP59kj+WTzSc1hh7Xdr6W+1uJNemxfNJ537VqP+8z3HL5ctWkx7Wdr6b+VmJN1ZCvph7LA5WqIifYH3vsscjn87Hvvvs2er9nz57Rq1eveOCBB2K33XZreH/KlCkxePDgaN++fZPGXdmv03Tq1GmFx0r59Ztiz5FvmXxSPZZPNp/kGHpc2/la7W9LjFEt+Vrtsfx/+a5V+3mf4ZbJt8QYetw689XS35YYo1bz1dJjeaASVeQE++9+97tYZ511VvjrLSeffHKcccYZsf7668fAgQNjypQp8dJLL8WkSZPKUCkAAAAAUE1SFblhNtWq4ibYZ82aFX/4wx/iyCOPjFQqtdzxffbZJ+bNmxcTJkyI8ePHx4Ybbhjjxo2L/v37l6FaAAAAAABaq4qbYO/atWtks9lVZg4++OA4+OCDW6giAAAAAABYXqpQKPiliIgvnNQHAAAAgEqUyWTKXUJVGTjiF+UuIXFTbzq93CW0WhW3gr2clr0Z5fP5yOVykU6nl3uwRDabLfrmVew58s2fT7LH8snmkxpDj2s7X8v9rcSa9Fg+6bzvWrWf9xluuXy5atLj2s5XU38rsaZqyFdTj+VJlOXGJKhNuQsAAAAAAIBqZIIdAAAAAABKYIIdAAAAAABKYA92AAAAAKDVSNmDnQRZwQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACWwBzsAAAAA0HoUbMJOclKFgv+jIiKy2Wy5SwAAAACAomUymXKXUFUGHXZpuUtI3J9v/n65S2i1rGBfyrI3o3w+H7lcLtLpdNTV1TU6ls1mi755FXuOfPPnk+yxfLL5pMbQ49rO13J/K7EmPZZPOu+7Vu3nfYZbLl+umvS4tvPV1N9KrKka8tXUY3mgUtmDHQAAAAAASmAFOwAAAADQaqRsmE2CrGAHAAAAAIASmGAHAAAAAIASmGAHAAAAAIAS2IMdAAAAAGg97MFOgqxgBwAAAACAEphgBwAAAACAEphgBwAAAACAEqQKhYJdhyIim82WuwQAAAAAKFomkyl3CVVlu0MuLXcJifvT7d8vdwmtloecLmXZm1E+n49cLhfpdDrq6uoaHctms0XfvIo9R77580n2WD7ZfFJj6HFt52u5v5VYkx7LJ533Xav28z7DLZcvV016XNv5aupvJdZUDflq6rE8UKlsEQMAAAAAACUwwQ4AAAAAACWwRQwAAAAA0Hp4IiUJsoIdAAAAAABKYIIdAAAAAABKYIIdAAAAAABKYIIdAAAAAABK4CGnAAAAAECrkfKQUxKUKhQK/peKiGw2W+4SAAAAAKBomUym3CVUle0PvrTcJSTu6Tu/X+4SWi0r2Jey7M0on89HLpeLdDoddXV1jY5ls9mib17FniPf/PkkeyyfbD6pMfS4tvO13N9KrEmP5ZPO+65V+3mf4ZbLl6smPa7tfDX1txJrqoZ8NfVYHqhU9mAHAAAAAIASWMEOAAAAALQedswmQVawAwAAAABACUywAwAAAADQoFAoxPjx42OXXXaJLbfcMg455JB44YUXVuvcadOmxdFHHx39+vWL7bffPsaOHRsLFy5slDniiCOib9++y/2ZNm1ao9ycOXPi7LPPjgEDBkT//v3je9/7Xnz44YdJ/ZiJsEUMAAAAAAANJkyYEFdeeWWcccYZ0bdv37j55pvjmGOOiXvvvTd69uy50vNmzZoVRx55ZPTq1Suuuuqq+OCDD+Kiiy6K+fPnx/nnn98ou/XWW8cPfvCDRu+tt956jV6feuqp8cYbb8SYMWOiQ4cOcfnll8dxxx0XkydPjjXWqIyp7cqoAgAAAACgBaRswb5KCxYsiGuvvTaOOeaYOOqooyIiYptttok999wzrr/++hgzZsxKz73tttvi008/jXHjxkW3bt0iImLx4sXx4x//OEaNGhVrr712Q7a+vj769eu30mv97W9/iz/+8Y9x/fXXxw477BARERtuuGHstdde8dBDD8Vee+3V1B81EbaIAQAAAAAgIiKef/75mDt3bgwbNqzhvfbt28fuu+8eTz311CrPfeqpp2Lw4MENk+sREcOGDYslS5bE008/XVQdTz31VNTX18f222/f8F7v3r0jnU5/YR0tyQQ7AAAAAAARETF9+vSI+O9k9tL69OkT77//fsyfP3+V5y57Xn19ffTo0aPhup979tlno1+/frHFFlvE4YcfHn/5y1+Wu9aGG24YqVSq0fu9e/de7lrlZIsYAAAAAIAqNmTIkFUef/TRR1f7WrNnz4727dtHhw4dGr1fX18fhUIhZs2aFR07dlzpufX19cu937Vr15g1a1bD669//eux3377Ra9eveLDDz+M66+/Po4++uiYOHFi9O/fv+Faa6655gqvlc1mV/vnaW6pQqFg16GIimoKAAAAAKyuTCZT7hKqyg4HXlLuEhLXYdb9qzy+sgn2QqEQixcvbnidSqVi/Pjx8ctf/jJefvnlRtkHHnggTjnllHjqqaca7aW+tM033zxOOeWUOP744xu9v88++0T//v3jpz/96QrPy+fzsc8++0SfPn1iwoQJERFx9NFHR5s2beL6669vlP3JT34STz/9dDz44IOr/JlbihXsS1n2ZpTP5yOXy0U6nY66urpGx7LZbNE3r2LPkW/+fJI9lk82n9QYelzb+VrubyXWpMfySed916r9vM9wy+XLVZMe13a+mvpbiTVVQ76aeiwPq1bMCvWlPfvsszFixIiG1wMGDIg999wzFi5cGAsWLGi0in327NmRSqWia9euK71efX19zJkzZ7n3Z82atcrz6urqYuedd240aV5fXx///ve/i75WSzPBDgAAAADQCm2++eZx1113Nbzu3LlzfPDBBxER8eabb8amm27acGz69OmxzjrrrHR7mIgV748+Z86c+Oijj5bbm/2L9O7dO5555pkoFAqN9mF/8803Y5NNNinqWs3JQ04BAAAAAFqhLl26xBZbbNHwp3fv3rH11ltHly5d4v77/9+2M4sWLYqHHnoodtppp1Veb6eddoo//elPMXv27Ib3HnjggWjTpk1sv/32Kz0vn8/HE088EVtssUWja82aNSueeeaZhvfefPPNePXVV7+wjpZkBTsAAAAA0GqkPJFylTp06BCjRo2Kq666Krp37x6bbLJJ3HrrrTFz5swYOXJkQ+7ZZ5+No446Ki644ILYf//9IyLi0EMPjYkTJ8ZJJ50Uo0aNig8++CDGjh0bhx56aMO+7c8991xcd911sfvuu8e6664bH374Ydxwww3x0UcfxRVXXNFw/f79+8cOO+wQZ599dvzgBz+IDh06xGWXXRZ9+/aNPfbYo0X/m6yKCXYAAAAAABocd9xxUSgU4te//nXMmDEj0ul0XH/99dGzZ8+GzOcPSF2yZEnDe127do0bb7wxfvrTn8ZJJ50UnTt3jm9+85tx2mmnNWR69OgRixYtissuuyxmzpwZnTp1iv79+8ePf/zj2HLLLRvVcfnll8eFF14Y559/fnz22Wexww47xLnnnhtrrFE509qVUwkAAAAAAGWXSqVi1KhRMWrUqJVmBg4cGH//+9+Xe79Pnz7xm9/8ZqXnbbDBBnH99devVh1rrrlmXHDBBXHBBResVr4c7MEOAAAAAAAlsIIdAAAAAGg9CjZhJzlWsAMAAAAAQAlShYK/somIyGaz5S4BAAAAAIqWyWTKXUJV2XH/i8tdQuL+cM+Z5S6h1bJFzFKWvRnl8/nI5XKRTqejrq6u0bFsNlv0zavYc+SbP59kj+WTzSc1hh7Xdr6W+1uJNemxfNJ537VqP+8z3HL5ctWkx7Wdr6b+VmJN1ZCvph7LA5XKBDsAAAAA0Gqk7OdBguzBDgAAAAAAJTDBDgAAAAAAJTDBDgAAAAAAJbAHOwAAAADQetiDnQRZwQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACWwBzsAAAAA0Gqk7MFOglKFQsH/UhGRzWbLXQIAAAAAFC2TyZS7hKqy874Xl7uExD35uzPLXUKrZQX7Upa9GeXz+cjlcpFOp6Ourq7RsWw2W/TNq9hz5Js/n2SP5ZPNJzWGHtd2vpb7W4k16bF80nnftWo/7zPccvly1aTHtZ2vpv5WYk3VkK+mHssDlcoe7AAAAAAAUAIT7AAAAAAAUAJbxAAAAAAArccSj6QkOVawAwAAAABACUywAwAAAABACUywAwAAAABACezBDgAAAAC0HrZgJ0FWsAMAAAAAQAlMsAMAAAAAQAlShULBL0VERDabLXcJAAAAAFC0TCZT7hKqys57jS13CYl7csr/lLuEVsse7EtZ9maUz+cjl8tFOp2Ourq6Rsey2WzRN69iz5Fv/nySPZZPNp/UGHpc2/la7m8l1qTH8knnfdeq/bzPcMvly1WTHtd2vpr6W4k1VUO+mnosT5JSlhuTIFvEAAAAAABACUywAwAAAABACUywAwAAAABACezBDgAAAAC0HgWbsJMcK9gBAAAAAKAEJtgBAAAAAKAEJtgBAAAAAKAE9mAHAAAAAFqNlC3YSVCqULCrf0RENpstdwkAAAAAULRMJlPuEqrKN4b+vNwlJO7xB39Q7hJaLSvYl7LszSifz0cul4t0Oh11dXWNjmWz2aJvXsWeI9/8+SR7LJ9sPqkx9Li287Xc30qsSY/lk877rlX7eZ/hlsuXqyY9ru18NfW3Emuqhnw19VgeqFT2YAcAAAAAgBJYwQ4AAAAAtB42zCZBVrADAAAAAEAJTLADAAAAAEAJTLADAAAAAEAJ7MEOAAAAALQaqYJN2ElOxa1gf/TRR+Pggw+O/v37xw477BCnnHJKvPvuu8vl7rzzzhg6dGhsscUWMXz48Hj88cfLUC0AAAAAAK1VRU2wT506NUaPHh0bbbRRXH311XH22WfHa6+9Fsccc0zMnz+/Iff73/8+zjvvvBg2bFhMmDAh+vXrF6NHj44XXnihfMUDAAAAANCqVNQWMb///e9jnXXWiQsuuCBSqVRERHTv3j2OPPLIyGazse2220ZExJVXXhl77713nHrqqRERMWjQoPjHP/4RV199dUyYMKFc5QMAAAAA0IqkCoXK2XTo7LPPjmw2G//3f//X8N4rr7wSBx54YEyaNCm+/vWvx7vvvhu77bZbXH311bHbbrs15G666aYYO3ZsPP/889G+ffuix85ms4n8DAAAAADQkjKZTLlLqCq7Drmo3CUk7rFHf1juElqtilrBfuCBB8a9994bN998cwwfPjxmzpwZv/jFL2KzzTaLrbfeOiIipk+fHhERG264YaNz+/TpE4sWLYp33303+vTpU9L4y96M8vl85HK5SKfTUVdX1+hYNpst+uZV7DnyzZ9PssfyyeaTGkOPaztfy/2txJr0WD7pvO9atZ/3GW65fLlq0uPazldTfyuxpmrIV1OP5YFKVVF7sG+77bYxbty4uPTSS2PbbbeN3XbbLT7++OOYMGFCtG3bNiIiZs2aFRER9fX1jc79/PXnxwEAAAAAoDlV1AT7888/H//zP/8T3/rWt+LGG2+MK664IpYsWRLHH398o4ecAgAAAABAuVXUFjE/+9nPYtCgQfHDH/6/PYP69esXu+yyS9x7771xyCGHRNeuXSMiYs6cOdGjR4+G3OzZsyMiGo6XIp/PN3o9b968Rv/8onwpY8iXN590j+WTzScxhh7Xdr7W+9sSY1R6vtZ73NrzvmvVft5nuGXzLTGGHreufLX1tyXGqLV8tfVYfuWW3eIHaDkV9ZDTrbbaKo4//vg46aSTGr0/ePDgOPDAA+PMM89c6UNOJ06cGD//+c+b9JDTBQsWrHa+Q4cOReVLOUdevjXnK7EmefmWzFdiTfLylZyvxJrk5VsyX4k1ycu3ZL4Sa5KXb8n8Nttss9pZIobsemG5S0jco4+dVe4SWq2KWsG+zjrrxKuvvtrovX/+85/xySefxLrrrhsRET179oxevXrFAw880GiCfcqUKTF48OCSJtc/l06nG72eN29evPXWW9GrV6/o1KlTo2PTp09fLv9Fij1HvvnzSfZYPtl8UmPocW3na7m/lViTHssnnfddq/bzPsMtly9XTXpc2/lq6m8l1lQN+WrqsTxQqSpqgv3QQw+NCy64IH72s5/FrrvuGjNnzoxf/epX8eUvfzmGDRvWkDv55JPjjDPOiPXXXz8GDhwYU6ZMiZdeeikmTZrUpPFX9us0nTp1WuGxUn79pthz5Fsmn1SP5ZPNJzmGHtd2vlb72xJjVEu+Vnss/1++a9V+3me4ZfItMYYet858tfS3Jcao1Xy19FgeqEQVNcE+YsSIaN++fdx6660xefLk6Ny5c/Tr1y8uv/zy+NKXvtSQ22effWLevHkxYcKEGD9+fGy44YYxbty46N+/fxmrBwAAAACgNamoCfZUKhXf/va349vf/vYXZg8++OA4+OCDW6AqAAAAAKBmVMwTKakFFfWQ03LKZrPlLgEAAAAAipbJZMpdQlUZ8o0afMjp4x5yWi4VtYK93Ja9GeXz+cjlcpFOp5fb9yqbzRZ98yr2HPnmzyfZY/lk80mNoce1na/l/lZiTXosn3Ted63az/sMt1y+XDXpcW3nq6m/lVhTNeSrqcfyQKVqU+4CAAAAAACgGlnBDgAAAAC0HnbMJkFWsAMAAAAAQAlMsAMAAAAAQAlMsAMAAAAAQAnswQ4AAAAAtBopW7CTICvYAQAAAACgBCbYAQAAAACgBCbYAQAAAACgBKlCoWDXoYjIZrPlLgEAAAAAipbJZMpdQlXZbaf/LXcJiXvkqXPKXUKr5SGnS1n2ZpTP5yOXy0U6nY66urpGx7LZbNE3r2LPkW/+fJI9lk82n9QYelzb+VrubyXWpMfySed916r9vM9wy+XLVZMe13a+mvpbiTVVQ76aeiwPVCpbxAAAAAAAQAlMsAMAAAAAQAlsEQMAAAAAtBqpJeWugFpiBTsAAAAAAJTABDsAAAAAAJTABDsAAAAAAJTAHuwAAAAAQOtRKJS7AmqIFewAAAAAAFCCVKHgr2wiIrLZbLlLAAAAAICiZTKZcpdQVXbf/mflLiFxDz99brlLaLVsEbOUZW9G+Xw+crlcpNPpqKura3Qsm80WffMq9hz55s8n2WP5ZPNJjaHHtZ2v5f5WYk16LJ903net2s/7DLdcvlw16XFt56upv5VYUzXkq6nH8kClMsEOAAAAALQe9vMgQfZgBwAAAACAEphgBwAAAACAEphgBwAAAACAEphgBwAAAACAEnjIKQAAAADQaqQKnnJKcqxgBwAAAACAEphgBwAAAACAEphgBwAAAACAEqQKBZsORURks9lylwAAAAAARctkMuUuoarsMegn5S4hcQ/9+fxyl9BqecjpUpa9GeXz+cjlcpFOp6Ourq7RsWw2W/TNq9hz5Js/n2SP5ZPNJzWGHtd2vpb7W4k16bF80nnftWo/7zPccvly1aTHtZ2vpv5WYk3VkK+mHssDlcoWMQAAAAAAUAIT7AAAAAAAUAJbxAAAAAAArceSchdALbGCHQAAAAAASmCCHQAAAAAASmCCHQAAAAAASmAPdgAAAACg1UgVCuUugRpiBTsAAAAAAJQgVSj4K5uIiGw2W+4SAAAAAKBomUym3CVUlaFf/3G5S0jcg3/5UblLaLVsEbOUZW9G+Xw+crlcpNPpqKura3Qsm80WffMq9hz55s8n2WP5ZPNJjaHHtZ2v5f5WYk16LJ903net2s/7DLdcvlw16XFt56upv5VYUzXkq6nH8kClMsEOAAAAALQeNvQgQfZgBwAAAACAEphgBwAAAACAEphgBwAAAACAEtiDHQAAAABoPezBToKsYAcAAAAAgBKYYAcAAAAAgBKYYAcAAAAAgBLYgx0AAAAAaD2WlLsAakmqULCrf0RENpstdwkAAAAAULRMJlPuEqrK0P4/KncJiXvwbz8udwmtlhXsS1n2ZpTP5yOXy0U6nY66urpGx7LZbNE3r2LPkW/+fJI9lk82n9QYelzb+VrubyXWpMfySed916r9vM9wy+XLVZMe13a+mvpbiTVVQ76aeiwPVCp7sAMAAAAAQAmsYAcAAAAAWo2UHbNJkBXsAAAAAABQAhPsAAAAAABQAhPsAAAAAABQAhPsAAAAAABQAg85BQAAAABaDw85JUFWsAMAAAAAQAmsYAcAAAAAoEGhUIgJEybELbfcEjNmzIh0Oh1nnXVW9OvX7wvPnTZtWvzsZz+Lv/3tb9G5c+fYb7/94tRTT4327dtHRMR7770XQ4YMWeG57du3j5dffnmVua222iruuOOO0n+4hKUKBb8TERGRzWbLXQIAAAAAFC2TyZS7hKqy51bnlbuExD3w4k8Tvd748ePjyiuvjDPOOCP69u0bN998c/zpT3+Ke++9N3r27LnS82bNmhV777139OrVK0aNGhUffPBBXHTRRTF8+PA4//zzIyJi4cKF8eqrrzY6r1AoxLHHHhuDBg2Kq6++OiL+3wT76aefHgMHDmzIdu7cOTbeeONEf96msIJ9KcvejPL5fORyuUin01FXV9foWDabLfrmVew58s2fT7LH8snmkxpDj2s7X8v9rcSa9Fg+6bzvWrWf9xluuXy5atLj2s5XU38rsaZqyFdTj+VJlPXGq7RgwYK49tpr45hjjomjjjoqIiK22Wab2HPPPeP666+PMWPGrPTc2267LT799NMYN25cdOvWLSIiFi9eHD/+8Y9j1KhRsfbaa0f79u2XWwk/derUmDt3buyzzz7LXXODDTZYrZXz5WIPdgAAAAAAIiLi+eefj7lz58awYcMa3mvfvn3svvvu8dRTT63y3KeeeioGDx7cMLkeETFs2LBYsmRJPP300ys977777osuXbrErrvu2uT6W5oJdgAAAAAAIiJi+vTpERHRu3fvRu/36dMn3n///Zg/f/4qz132vPr6+ujRo0fDdZe1aNGieOihh2L33XePDh06LHd8zJgxkU6nY/DgwXHuuefGzJkzi/yJmpctYgAAAAAAqtjKHhr6uUcffXS1rzV79uxo3779cpPd9fX1USgUYtasWdGxY8eVnltfX7/c+127do1Zs2at8JynnnoqZs6cudz2MO3bt49vf/vbscMOO0R9fX28+OKLcc0110Q2m40777wz2rVrt9o/U3MywQ4AAAAAtB72YG9QKBRi8eLFDa9TqVSL1/C73/0u1lprrRg8eHCj97/yla802u99wIABsfHGG8eoUaPi4Ycfjr322quFK10xE+wAAAAAAFWsmBXqS3v22WdjxIgRDa8HDBgQe+65ZyxcuDAWLFjQaBX77NmzI5VKRdeuXVd6vfr6+pgzZ85y78+aNWuF53366afx+OOPx8EHHxxt27b9wnp33nnnqKuri1deecUEOwAAAAAA5bP55pvHXXfd1fC6c+fO8cEHH0RExJtvvhmbbrppw7Hp06fHOuuss9LtYSL+u2/7snutz5kzJz766KPl9maPiHj44Ydj/vz5se+++zb1RykbE+wAAAAAAK1Qly5dYosttmj03rrrrhtdunSJ+++/v2GC/fMHke60006rvN5OO+0U11xzTaO92B944IFo06ZNbL/99svl77vvvlh//fVjq622Wq16H3/88cjn88vVXE4m2AEAAACA1mNJuQuobB06dIhRo0bFVVddFd27d49NNtkkbr311pg5c2aMHDmyIffss8/GUUcdFRdccEHsv//+ERFx6KGHxsSJE+Okk06KUaNGxQcffBBjx46NQw89NNZee+1G48yYMSOeeeaZOO6441ZYx0UXXRSpVCr69esX9fX18dJLL8W1114bmUwmdtttt2b7+YuVKhTs6h8Rkc1my10CAAAAABQtk8mUu4Sqsufm55S7hMQ98Mr/Jnq9QqEQ48ePj1tuuSVmzJgR6XQ6zjrrrOjfv39DZurUqTFixIi48MIL48ADD2x4f9q0afHTn/40/va3v0Xnzp1jv/32i9NOOy3at2/faIybb745fvKTn8SUKVOiT58+y9Vw5513xq233hpvv/12zJ8/P9Zee+3Ybbfd4nvf+1506dIl0Z+3KUyw//+y2exyN6N8Ph+5XC7S6XTU1dV9Yb6UMeTLm0+yx/LJ5pMaQ49rO1/L/a3EmvRYPum871q1n/cZbrl8uWrS49rOV1N/K7GmashXU4/lSZIJdpLUptwFAAAAAABANbIHOwAAAADQaqRs6EGCrGAHAAAAAIASmGAHAAAAAIASmGAHAAAAAIAS2IMdAAAAAGg97MFOgqxgBwAAAACAEphgBwAAAACAEphgBwAAAACAEqQKBZsORURks9lylwAAAAAARctkMuUuoaoM6/vDcpeQuPv/flG5S2i1POR0KcvejPL5fORyuUin01FXV9foWDabLfrmVew58s2fT7LH8snmkxpDj2s7X8v9rcSa9Fg+6bzvWrWf9xluuXy5atLj2s5XU38rsaZqyFdTj+WBSmWLGAAAAAAAKIEJdgAAAAAAKIEtYgAAAACA1sMjKUmQFewAAAAAAFACE+wAAAAAAFACE+wAAAAAAFCCiptgf/zxx+OAAw6ITCYTO++8c1x55ZWxePHi5XKPPfZYDB8+PLbYYosYOnRoTJ48uQzVAgAAAADQWlXUBPsLL7wQ3/3ud6NPnz7xq1/9Ko466qi4/vrr45JLLmmUe+6552L06NHRr1+/mDBhQgwbNizOOeeceOCBB8pUOQAAAABQFQqF2vtD2aQKhcrpwMiRI+OTTz6J3/72tw3v/frXv45f/OIX8cQTT8Raa63VkPv000/jtttua8h9//vfj1wuF1OmTClp7Gw227TiAQAAAKAMMplMuUuoKsM2/p9yl5C4+18fW+4SWq01yl3A0nK5XBx00EGN3tthhx3i5z//efzxj3+M/fffPxYuXBhTp06NM844o1Fur732ivvuuy/ee++9WG+99Uoaf9mbUT6fj1wuF+l0Ourq6hody2azRd+8ij1HvvnzSfZYPtl8UmPocW3na7m/lViTHssnnfddq/bzPsMtly9XTXpc2/lq6m8l1lQN+WrqsTxQqSpqi5gFCxZE+/btG733+etp06ZFRMQ777wTixYtit69ezfK9enTJyIipk+f3gKVAgAAAADQ2lXUCvYNNtggXnrppUbvvfDCCxERMWvWrEb/rK+vb5T7/PXnxwEAAAAAllM5O2ZTAypqgv073/lOnHPOOXHjjTfGfvvtF2+88UZcfvnl0bZt2xYZP5/PN3o9b968Rv/8onwpY8iXN590j+WTzScxhh7Xdr7W+9sSY1R6vtZ73NrzvmvVft5nuGXzLTGGHreufLX1tyXGqLV8tfVYfuWW3eIHaDkV9ZDTJUuWxEUXXRQ333xzfPbZZ9GuXbsYPXp03HjjjXHYYYfF6NGj44033oi99947rrvuuthxxx0bzn3rrbdi6NChMWHChNhpp52KHjubzcaCBQtWO9+hQ4ei8qWcIy/fmvOVWJO8fEvmK7EmeflKzldiTfLyLZmvxJrk5VsyX4k1ycu3ZH6bbbZZ7SwRwzY6s9wlJO7+Ny4udwmtVkVNsH9uzpw58c9//jPWWWed+Oyzz2Lw4MENE+oLFy6MrbfeOs4888w48sgjG8557LHH4sQTT4xHH320pIecZrPZ5fZ1nzdvXrz11lvRq1ev6NSpU6Nj06dPXy7/RYo9R77580n2WD7ZfFJj6HFt52u5v5VYkx7LJ533Xav28z7DLZcvV016XNv5aupvJdZUDflq6rH8qlnBXhwT7CSporaI+dyaa64Zm266aUREXHHFFbHeeuvFdtttFxH/fejpwIED48EHH2w0wT5lypTo06dPSZPrn1vZzahTp04rPFbKzavYc+RbJp9Uj+WTzSc5hh7Xdr5W+9sSY1RLvlZ7LP9fvmvVft5nuGXyLTGGHrfOfLX0tyXGqNV8tfRYnsQsqbj1xlSxippgf+mll+LZZ5+NdDod8+fPj8ceeyzuvffemDBhQqN92E888cQYMWJEjBkzJoYNGxZTp06N++67Ly677LIyVg8AAAAAQGtSURPs7dq1i4ceeiiuvvrqiIjYaqutYuLEidG/f/9GuW233TauuuqquPzyy+Ouu+6KddZZJ372s5/FsGHDylE2AAAAAACtUEVNsKfT6bjjjjtWKztkyJAYMmRIM1cEAAAAAAArVpEPOS2HbDZb7hIAAAAAoGiZTKbcJVSVYRueXu4SEnf/m78odwmtVkWtYC+3ZW9G+Xw+crlcpNPp5R4skc1mi755FXuOfPPnk+yxfLL5pMbQ49rO13J/K7EmPZZPOu+7Vu3nfYZbLl+umvS4tvPV1N9KrKka8tXUY3mgUrUpdwEAAAAAAFCNTLADAAAAAEAJbBEDAAAAALQeHklJgqxgBwAAAACAEphgBwAAAACAEphgBwAAAACAEtiDHQAAAABoPZbYg53kWMEOAAAAAAAlSBUKHpsbEZHNZstdAgAAAAAULZPJlLuEqjJs/VPLXULi7n/n8nKX0GrZImYpy96M8vl85HK5SKfTUVdX1+hYNpst+uZV7DnyzZ9PssfyyeaTGkOPaztfy/2txJr0WD7pvO9atZ/3GW65fLlq0uPazldTfyuxpmrIV1OP5YFKZYIdAAAAAGg9bOhBguzBDgAAAAAAJTDBDgAAAAAAJTDBDgAAAAAAJbAHOwAAAADQetiDnQRZwQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACWwBzsAAAAA0HrYg50EpQoF/0dFRGSz2XKXAAAAAABFy2Qy5S6hqgxb9+Ryl5C4+/95VblLaLWsYF/KsjejfD4fuVwu0ul01NXVNTqWzWaLvnkVe4588+eT7LF8svmkxtDj2s7Xcn8rsSY9lk8677tW7ed9hlsuX66a9Li289XU30qsqRry1dRjeaBS2YMdAAAAAABKYIIdAAAAAABKYIsYAAAAAKD1WLKk3BVQQ6xgBwAAAACAEphgBwAAAACAEphgBwAAAACAEtiDHQAAAABoPQqFcldADbGCHQAAAAAASpAqFPyVTURENpstdwkAAAAAULRMJlPuEqrKsK9+t9wlJO7+f/+y3CW0WraIWcqyN6N8Ph+5XC7S6XTU1dU1OpbNZou+eRV7jnzz55PssXyy+aTG0OPaztdyfyuxJj2WTzrvu1bt532GWy5frpr0uLbz1dTfSqypGvLV1GN5oFKZYAcAAAAAWg8bepAge7ADAAAAAEAJTLADAAAAAEAJTLADAAAAAEAJ7MEOAAAAALQeS+zBTnKsYAcAAAAAgBKYYAcAAAAAgBKYYAcAAAAAgBLYgx0AAAAAaDUKhSXlLoEakioUCnb1j4hsNlvuEgAAAACgaJlMptwlVJU91zq+3CUk7oH/jC93Ca2WFexLWfZmlM/nI5fLRTqdjrq6ukbHstls0TevYs+Rb/58kj2WTzaf1Bh6XNv5Wu5vJdakx/JJ533Xqv28z3DL5ctVkx7Xdr6a+luJNVVDvpp6LA9UKnuwAwAAAABACaxgBwAAAABajyV2zCY5VrADAAAAAEAJTLADAAAAAEAJTLADAAAAAEAJ7MEOAAAAALQeBXuwkxwr2AEAAAAAoAQm2AEAAAAAoASpQsHvREREZLPZcpcAAAAAAEXLZDLlLqGq7PmlY8tdQuIe+OS6cpfQatmDfSnL3ozy+XzkcrlIp9NRV1fX6Fg2my365lXsOfLNn0+yx/LJ5pMaQ49rO1/L/a3EmvRYPum871q1n/cZbrl8uWrS49rOV1N/K7GmashXU4/lSdSSJeWugBpiixgAAAAAACiBCXYAAAAAACiBCXYAAAAAACiBCXYAAAAAACiBh5wCAAAAAK1HoVDuCqghVrADAAAAAEAJTLADAAAAAEAJTLADAAAAAEAJ7MEOAAAAALQahSVLyl0CNSRVKNjVPyIim82WuwQAAAAAKFomkyl3CVVlaJcjy11C4h6ce2O5S2i1rGBfyrI3o3w+H7lcLtLpdNTV1TU6ls1mi755FXuOfPPnk+yxfLL5pMbQ49rO13J/K7EmPZZPOu+7Vu3nfYZbLl+umvS4tvPV1N9KrKka8tXUY3mgUtmDHQAAAAAASmAFOwAAAADQetgxmwRZwQ4AAAAAACUwwQ4AAAAAACUwwQ4AAAAAACWwBzsAAAAA0HossQc7ybGCHQAAAAAASmCCHQAAAAAASmCCHQAAAAAASpAqFAo2HYqIbDZb7hIAAAAAoGiZTKbcJVSVoR0PK3cJiXtw/s3lLqHV8pDTpSx7M8rn85HL5SKdTkddXV2jY9lstuibV7HnyDd/PskeyyebT2oMPa7tfC33txJr0mP5pPO+a9V+3me45fLlqkmPaztfTf2txJqqIV9NPZYHKpUtYgAAAAAAoAQm2AEAAAAAoAS2iAEAAAAAWo3CEo+kJDlWsAMAAAAAQAlMsAMAAAAAQAlMsAMAAAAAQAnswQ4AAAAAtB6FJeWugBpiBTsAAAAAAJQgVSgUPDY3IrLZbLlLAAAAAICiZTKZcpdQVfZod2i5S0jcQ4tuK3cJrZYtYpay7M0on89HLpeLdDoddXV1jY5ls9mib17FniPf/PkkeyyfbD6pMfS4tvO13N9KrEmP5ZPO+65V+3mf4ZbLl6smPa7tfDX1txJrqoZ8NfVYHqhUJtgBAAAAgFajsMSGHiTHHuwAAAAAAFACE+wAAAAAAFACE+wAAAAAADQoFAoxfvz42GWXXWLLLbeMQw45JF544YUvPO/tt9+O888/P/bbb7/YbLPNYp999llp9s4774yhQ4fGFltsEcOHD4/HH398ucycOXPi7LPPjgEDBkT//v3je9/7Xnz44YdN+dESZ4IdAAAAAIAGEyZMiCuvvDKOOuqouPbaa6NHjx5xzDHHxLvvvrvK815//fV48sknY4MNNog+ffqsNPf73/8+zjvvvBg2bFhMmDAh+vXrF6NHj15uEv/UU0+Np59+OsaMGROXXHJJvPnmm3HcccfFZ599lsSPmQgPOQUAAAAAWo/CknJXUNEWLFgQ1157bRxzzDFx1FFHRUTENttsE3vuuWdcf/31MWbMmJWeu+uuu8Zuu+0WERE//OEPI5vNrjB35ZVXxt577x2nnnpqxP/X3p2H13Ttfxx/Z5RIIoZGIjJKJRRRUw1XaVUaFFXBz1BuCbeiSilVpCVatOlF1VCkNVSKVq/OaaRub+mQlrZXUap+YkipiEQSJ3Nyzu8PT85PaqoIxzk+r+fxPM7ae6/z3ZY9ffc6awEdOnTgt99+Y9myZSQkJADw3//+l6+//po333yTzp07AxAcHEyvXr1ISUmhV69e1bPD10k92EVEREREREREREQEgJ9++gmDwUDPnj3NZc7OzkRERLBjx44rbmtvf/V0c3p6OkePHq1UP0CvXr1ITU2lpKQEgB07dlCrVi3+9re/mddp1KgRTZs2vWocN5MS7CIiIiIiIiIiIiICQFpaGnA+mX2hkJAQTp48SVFRUbXUHxwcfFH9paWl5mFo0tLSCA4Oxs7OrtJ6jRo1MtdxK9AQMSIiIiIiIiIiIiJW7IEHHrji8n//+99/ua68vDycnZ2pUaNGpfJatWphMpnIzc3FxcWlSnEC5Obmmuv7c/0XLs/Ly8PDw+Oi7T09PS879Iwl2JlMJpOlgxARERERERERERGRqqlqgt1kMlFeXm7+bGdnx6pVq1i+fDl79+6ttG5ycjITJ05kx44deHt7XzWmijHYP/nkk0rlH330EVOnTuXrr7/Gy8vLXL53714GDBjAxo0bad26NSNHjsTe3p4333yz0vZz5szhm2++YevWrVeN4WZQD3YRERERERERERERK3YtPdQvtHPnTkaMGGH+fM8999CjRw9KSkooLi6u1Is9Ly8POzs7PD09ryvWiu3PnTtXKcGel5dXaXmtWrU4derURdvn5uZedwzVSQl2ERERERERERERkdtQs2bNeO+998yf3dzcyMjIAODIkSM0adLEvCwtLQ1fX9/rGh4G/n9s97S0tErjvKelpeHk5IS/v795vdTUVEwmU6Vx2I8cOUJoaOh1xVCdNMmpiIiIiIiIiIiIyG3I3d2dFi1amP80atSI1q1b4+7uzmeffWZer7S0lJSUFLp06XLd3+nv709QUBDJycmVypOSkujYsSPOzs4AdOnShdzcXFJTU83rHDlyhP3791dLHNVFPdhFREREREREREREBIAaNWrw+OOPs2TJEurWrUtoaCgbN24kJyeH6Oho83o7d+7kscceY968efTr1w+AwsJCtm/fDsCJEycwGAzmRPo999xD3bp1AXjyySeZMmUKAQEBtG/fnqSkJPbs2UNiYqK5/latWtG5c2dmzJjBtGnTqFGjBosWLSIsLIwHH3zwJv1rXJ0S7CIiIiIiIiIiIiJiNmbMGEwmE6tXryY7O5umTZvy5ptvmodvgf+fINVoNJrLsrKymDhxYqW6Kj6/9dZbtG/fHoDevXtTWFhIQkICq1atIjg4mKVLl9KqVatK27766qvMnz+f559/nrKyMjp37kxsbCyOjrdOWtvOZDKZLB2EiIiIiIiIiIiIiIi10RjsIiIiIiIiIiIiIiJVoAS7iIiIiIiIiIiIiEgVKMEuIiIiIiIiIiIiIlIFSrCLiIiIiIiIiIiIiFSBEuwiIiIiIiIiIiIiIlWgBLuIiIiIiIiIiIiISBUowS4iIiIiIiIiIiIiUgW3XYLdaDRaOgQRERERERERERERsQG3TYK9IrFeVFRk4UhERESkgslksnQIIiIitzVdi22HyWRSe4qIWICd6TY4+xoMBl544QXS09MB6Ny5M1FRUXh7e1s4MqlO5eXlFBQU4OTkhKOjI46OjhiNRuztb5v3SDZN7Wv7ysrKyMrKwsnJCRcXF2rWrInJZMLOzs7SoUk1yM/PZ82aNRw/fpyAgADCwsKIiIiwdFhSjXSetm06R9s+HcO2T9di25afn8/ixYs5evQo3t7eNG3alKFDh1o6LBGR24LNJ9gLCwsZMGAA9erVIywsjLKyMjZv3kyzZs34+9//Tq9evSwdolQDg8HA1KlTOX36NAaDgdatWzN8+HDuuusuS4cm1UDta/sMBgMTJkwgIyODvLw8AgMDiYmJ4W9/+5ulQ5NqkJ+fT1RUFC4uLtSpU4dTp06RkZFBZGQkM2fOxN3d3dIhynXSedq26Rxt+3QM2z5di21bQUEBUVFR1KpVi4YNG3L27Fl++uknOnbsyNNPP82dd96pF6IiIjeQzXdH2Lp1KyUlJcyZM4eZM2cya9YsPv74Y4qKinj99dd5++23LR2iXKeioiKGDBnCuXPneOSRR+jatSuHDh1i0KBBfPDBB5SVlVk6RLkOal/bV1xczPDhwykrK2PcuHGMHDkSNzc3Ro8ezbJlyzhz5oylQ5TrtHr1atzc3FiyZAlr1qxh3bp1TJs2jW3btjFx4kROnDhh6RDlOug8bdt0jrZ9OoZvD7oW27bNmzfj6OjIK6+8wsKFC1m2bBlLly7lwIEDPPPMM/z4448aOkZE5AZytHQAN9rp06cpLy8nKCgIgJKSEoKDg1m1ahXTpk1j06ZNeHh40LdvX8sGKlWWmppKeXk5cXFxhISEAJCWlsa6deuYMWMGOTk5DBs2DCcnJwtHKlWh9rV9Bw4coKCggLi4OMLDwwEYOHAgiYmJLF68mNzcXMaMGYOXl5eFI5WqOnbsGDVr1sTf3x+A+vXrExUVRUBAAFOmTOH5559n4cKFeHp6asgJK6TztG3TOdr26Ri+PdJhchsAAB5YSURBVOhabNsqhsMNCAgAoGbNmtx7770kJiYycuRIXnzxRV566SWaNGliyTBFRGyWzfdgDwsLIyMjg++++w4AZ2dnysrK8Pb25uWXX8bJyYm33nrLfEES65OXl8fx48dxdXU1lzVq1Ihnn32W6Oho4uPj+fDDD4H/n+xWrIfa1/bl5uZy7NgxPDw8gPOTM3l4eBATE8Nzzz3HW2+9xaZNmzRpkxVr2LAh+fn5nD592lzm6OhIx44dWbhwIfv27WPu3LkAeqC3QjpP2zado21fbm6ujmEbVnFc6lps2xo3bkx+fj6//fabucxoNOLv78/atWvJzc3ln//8pwUjFBGxbTafYG/ZsiXNmjVj48aNHD9+HDh/I1GRZH/11Vc5dOgQ7777roUjlapq0KABtWvX5ueff670YOfq6soTTzzB4MGDmT17Nnv37tUkTVak4gHO29tb7WujKtrY398fPz8/tm3bRnFxMXZ2dua2HjZsGJMnT2bZsmXs2LGj0jK5dZWUlGAwGMyfw8PD+fXXX/nPf/4DUKkN27Zty7Rp0/j888/NCRy59V3YhroO254Lk6gBAQH4+/vrHG1jSkpKOHjwIKB7LVtXkSzXtdj2XHiuvvPOO8nNzeXTTz+loKAAAHt7e4xGI35+fsyfP58ffviBN954w1LhiojYNJu/Q6pduzaxsbF89dVXbNq0iYyMDOB8kr2kpISAgABGjx7Nl19+SXZ2th4KrEh5eTkA99xzD35+fqxatYqzZ88C/3+z4eLiwsiRIwkPD2fFihUUFxdbLF65NqWlpQB06NCBBg0aqH1tSHl5OaWlpWRlZQHne8mFhoayYcMGc6+bC5M0w4cP58EHH2TRokWcO3dOvapucQUFBfTp04f4+HhycnIAeOCBBxg8eDDz5s0jNTW1Uvs6ODjQtWtXQkND2b9/vwUjl7/KYDCwYMEC86//dB22Lfn5+Tz77LN8/PHHAAQHBxMaGsrGjRt1jrYR+fn5PPLII6xatQqAjh074uvrq2PYhpSWlpKdnc3hw4fNZboW246K47OkpMRc1qZNG0aPHs0bb7xBUlKSuW3t7e0xmUzcfffd3HfffezZs8f8HC0iItXH5hPscP5t/bJly1i9ejXr1683PxA6OzsD55Pt5eXluLm56aHgFldQUGB+4HNwcDDfVMyZM4fMzEyeeeYZysrKzDcScL537L333sv+/fv1UHCLKygoYNmyZUyYMIFJkyaxadMmAObOnUtGRoba1wbk5+fz/PPPM3ToUAYMGMCiRYsAiI+Px9nZmbi4OPOvjSoe/FxdXenWrRsZGRnk5uZaMnz5C3bt2sWxY8d49913WbBgAefOnQNgzJgxdOjQgXHjxvHdd99Vut7Wq1ePhg0b8uuvv+qh7xZnMBjo0aMHv/32G3Xq1DG3l67DtsFgMDBw4ECOHj3KyZMnze318ssv6xxtIwwGA/379+fYsWOkpKTw5ZdfAvDiiy/qGLYR+fn5TJw4kaFDh9KnTx9mzJhhnsB01KhRdOzYUddiK5afn8/s2bMZPnw4Y8eOZdWqVeZOhDExMfTr14+4uDi2bNlCUVERcP587eLiwh133MHvv/+u9hURuQFuiwQ7nO+ZsXr1at5++20WL17MDz/8AEBWVhbHjx/Hx8eHsrIyC0cpV1JYWMjQoUOZOnUqq1evBs6/JDEajTRq1IiZM2eyZ88eYmJiyM3NrXTDGBwcjIODA4WFhZYKX64iPz+fQYMGsX37dgoLC8nLy2P27NmsWrWK0NBQnnvuOX7++We1rxUzGAxERUVx7NgxWrVqRUREBCtXrmTJkiW4u7vzyiuvkJWVxeTJkzlw4AAlJSXmdvb29sbFxUUP9lagSZMmtGnThgkTJvDJJ58wb948CgoKaNCgAU8//TRt27Zl7Nix/Otf/yI7OxuAnJwc8vLyCAoK0ovuW5jBYODhhx+mcePGzJkzB3d3dxwcHAAICgpi5syZ7N69m7Fjx+o8bYXKysp45pln8PLyYuHChURHR1OjRg1MJhPu7u7Ex8dz9uxZnnrqKZ2jrZTBYKBv3740bNiQZcuW4enpyRdffEF5ebn5GNa9tHUrKChg8ODBFBcXM3jwYKZNm8Znn31mHhbEz8+Pp556inbt2jF27Fi2bNmia7EVKSgoICoqit9++w0/Pz98fHx49dVXmTBhAsnJyQDExsbSp08fZs2axRtvvEFaWhoAZ8+e5fTp0wQGBlpyF0REbJajpQO4mTp16sTatWuJi4tj9OjR5puHkydPsm7dOtzc3CwdolxGWVkZ8fHxnD59mlatWrFu3TrKy8sZM2YM9vb22Nvbc//992MymZg3bx7R0dE8+eST3H333ZSWlvLFF19Qu3ZttfEtqqSkhKeffpr69esza9YsAgMDOXPmDCtWrCAhIYFu3brRrVs3ta8VKy4uJiYmBh8fH1544QX8/f2B8z9h3rlzJ3D+10aLFy/m2Wef5YknniA6OprIyEgKCwv55JNPcHNzo169epbcDfkL6tWrR15eHnZ2dsyePZuZM2diZ2fHjBkzCA0NZfbs2SQmJjJz5kzat2+Pp6cnpaWl7N69m2nTpml831tUxdA/Fcn1+vXrA+cnR6z4RVnPnj0xmUzMnz+fkSNHMnHiRJ2nrUhOTg5//PEHY8aMwc/PD4Bff/2VjIwMSktLadGiBQsXLmT27NmMGzeO0aNH6xxtRSqS6wEBAbz88st4eXnRr18/EhMTiY6OJjAwkC5dujB79mzda1mxxMRE7O3tef75582J1KKiIt5//31KS0txcnKiSZMmxMXFsXz5cmbOnMk999yja7GVWL9+Pfb29syfP5/g4GAA+vbty1NPPUV8fDy5ubn8z//8D/PmzaNBgwYkJCTw2WefUbduXezs7Dhw4ACJiYnmX/KLiEj1ua0S7HB+0tNVq1bx3Xff8dNPP+Hr60v37t0JCgqydGhyBenp6aSmptKlSxf+/ve/s2bNGtavXw+cH3YAzo8RGRERQWBgIM8//zwzZsygtLQUPz8/Tpw4wbp163B3d7fkbshlfPfdd5w+fZqxY8eaE6933HEHDz74IJs3b+bo0aPceeeddO/eXe1rpb755huMRiOjRo0yJ24A6tevj8FgYOvWrZSWltKuXTs2bNjA9OnTSUhIYO7cuQQHB5Obm0tCQgK1a9e23E7IVRmNRhwdHenWrZu5t3NxcTEvvPAC9vb2zJo1i3fffZfBgwfTunVrvvrqK44dO0ZgYCAbN26kcePGlt4FuYwPPviAP/74g0ceeQQfHx8Atm/fzrp160hLS8POzo5+/frRv39/VqxYwaxZs5g+fTplZWU6T1uJ/Px8srOzadSoEQBJSUnMnj0bR0dHsrOzady4Mf369WP16tXMmjWLVatW6RxtJYxGIzExMQQEBBAfH88dd9wBQJ8+ffj4449Zvnw5cXFxuLm56V7ayh0+fBhXV1cCAwMxmUzY2dlRp04dGjZsyCeffEJBQQEdOnQgJCSEF154gQ4dOvD999/rWmwljh49iru7uzm5Xl5eTqdOnZg+fTozZsxg06ZNeHh40KtXL5588knatGnDvn372LdvH0FBQcyaNYuQkBAL74WIiG2yM2lWT7ECRUVFfPzxx0RERFC7dm0OHTrEihUr2LVrF8OHDzcn2Y1Go7nHxbZt20hPT6dWrVrcc8895sSt3HrS0tKYPXs2y5cvx93dvVI79ujRg8jISCZNmmR+UAC1r7U5e/YsX331FT169DD3miksLKRfv34UFRXh4ODAmTNnCAkJIS4ujvDwcPbu3cvhw4fx9PQkLCwMX19fC++F/FVJSUnEx8ezZcsWatasSXJyMs899xxeXl7k5+eTkJBAeHg45eXlODg4UFZWhqPjbffO36rk5uaycuVKVq9ezdy5c/H19WXUqFF0796dBg0aUFhYyPvvv0/Xrl2Jj4/Hzc2NlJQUTp48iYeHh87TViAzM5PevXszZcoUIiIiiIyMZMSIETzwwAPUrFmT+fPnc/jwYUaMGMGjjz7KL7/8wqFDh3SOthLp6em4ublRt25dc5nJZGLKlCns2rWLdevWERwcfNG91u+//65j2IosW7aMxMREVq5cSXh4OOfOnWPw4MHk5eVRp04djh8/jp+fH+PGjaNXr14A5jbXtfjWt3LlSt555x3WrFlTaaiX1NRUJkyYgJubG0FBQSxdulQvw0REbjIl2MVq/Pnm7/DhwyxfvvyiJHtJSYl+9maFCgsLcXV1rZRcB+jfvz+tW7cmNjYWwJyQE+tTcQwbjUZMJhMPPfQQHh4ePPfccwQHB5OVlcWjjz5KaGioeZ4FsT4mk4n09HSeeOIJlixZQlBQEEajkUGDBrF//346derEggUL8PT0tHSoco0MBgNLly5l7dq1ODs7M378eIYMGYKHhwcmk4kdO3YQExPDmDFjmDRpkqXDlSp46aWX+OGHH7jvvvv49ttvee2118y9nTMzM4mNjSU9PZ3NmzdrqBArV3G/lZ6eTlRUFH379jXfaynRar1OnjzJpEmTOHLkCHfffTcHDhygfv36xMfHExwczOnTpxk1ahS1atVizZo1uLq6VnqpIre2tLQ0hgwZQlRUFMOGDaNhw4bA+V8DL1++nJiYGB5//HFiY2MZNGiQhaMVEbm9aHA1sRoVN34VN/whISGMGzeOdu3asX79evPkPZmZmWzcuJFTp05ZLFa5dq6urgDm5HrF7PZubm4UFRWZ1yssLOSLL75A7watT8UxbG9vj4ODA4MHD2bJkiWEh4fj4eFBUFAQsbGxpKamcujQIYxGo4Ujlqqws7MjICAAV1dXUlJSAJg8eTLp6emMGDGCH3/8kbi4OAwGg4UjlWvl7u7O+PHjefzxx4mIiKBHjx54eHgA58/Z9957Lz169OA///kPZ8+e1TFshYYMGUJpaSlbtmyhqKjInFwvLi7Gy8uLSZMmkZaWxt69ey0cqVwve3t7TCaTeUi+zz//nH379gEouW7FfH19ee2115gyZQq9e/fGx8eH8ePHExISgslkwsfHhzlz5rB7925++eUXACXXrUijRo147bXXePvtt3nppZd4++23ef/99xk/fjx+fn507NiRfv36ceTIEUuHKiJy29Hdk1i1kJAQYmJisLOz46233iIvL4+jR4+SkpJCt27dLB2eXIeKXuoeHh6cPXsWgHPnzjF//ny2bNnCV199hZeXlyVDlCqq6Cn12GOPXbTsxIkTBAQE4Ovrqwm2rFRFr8g2bdrw+++/M2XKFFJTU3n11Vdp3bo1gYGBLF26lIKCAv182Qq5u7sTHR3NyZMnCQgIAM4f0xcm5BwdHaldu7aSNlYoMDCQF198kWHDhnHy5Em2bt1KZGQkNWrUACA7O5v69etrMlMbYWdnh6urK/369eO9997j22+/pXnz5hf9mlCsi7e3N4MGDSIzM5O4uDhzWzo4OGAymcjKyuKOO+6gTp06Fo5UqqJ9+/asXbuW+Ph4XnnlFWrUqEHfvn159tlnATh16hQuLi4WjlJE5PajBLtYNaPRyJ133klMTAxFRUWsWrUKT09PtmzZgre3t6XDk2rg7OxMXl4eRUVFxMfHk5yczHvvvafkuhW7MOl24c+Sz5w5wy+//MJdd92lB3srVtF2nTt3Jjo6Gk9PTxYuXEiHDh2ws7MjKiqKhx56iFq1alk4UqmqWrVqmdvvwmG7Tp06RWZmJnfddRelpaU4OTkpyW6FWrRowcaNGxk5ciQzZ86kuLiYhx56iBMnTpCUlISrq2ulcbzF+rVt25aoqCjWrFlDjx49zC/PxLp5eHjQsGFDtm/fTpMmTfD29iY7O5uvv/4aHx8fJditWKtWrVi9ejVnzpyhoKCAsLAwAH7//XeKi4vp1KmThSMUEbn9aAx2sQmZmZk888wz7Nu3j40bN3LnnXdaOiS5ThW9p6ZPn86JEycICgriww8/ZOPGjdx1112WDk+q2eHDh3njjTf48ssvSUxMJCQkxNIhyXUyGo0kJydTt25d2rVrp7kTbNCFL8iOHTvGihUr+OKLL9iwYYOOYRtw+PBh4uPj2b59Oz4+Pjg7O1NQUEBCQgJNmza1dHhSzb788kvGjh3L1KlTGTVqlF6O2Yjvv/+e6Oho2rdvT/369Tlz5gx79+5l3bp15qSs2IaDBw+SmJhovg5fOAmqiIjceEqwi9UrLCwkNjaWTz/9lA8++IAmTZpYOiSpRosWLWLlypW4u7uzbt06mjVrZumQpJotWbKE3bt3c+TIEZYvX65j2IZo4rTbw6JFi8zH8MqVK5V8tSGFhYX88ssv7N69G29vb1q3bm2eVE9sz+TJk3niiSf0gszG/PzzzyxevJjs7GyaNGnCmDFj1MY25tdff2XFihX897//ZeXKlbqXFhGxACXYxSZ8/fXXeHl5qSeGDdq/fz+jR49m/fr1ehiwUQcPHiQ5OZn+/fvj7+9v6XBE5Brt27ePd955h+joaIKCgiwdjohcI70MtX0lJSXmvzs7O1swErkRioqK+OGHHwgMDNS9tIiIhSjBLiK3vKKiIk3WY+MuHMdZRKxPWVlZpYlORUREREREbhdKsIuIiIiIiIiIiIiIVIG9pQMQEREREREREREREbFGSrCLiIiIiIiIiIiIiFSBEuwiIiIiIiIiIiIiIlWgBLuIiIiIiIiIiIiISBUowS4iIiIiIiIiIiIiUgVKsIuIiIiIiIiIiIiIVIES7CIiIiLX4fvvvycsLIzvv//e0qHctv744w9atGjBjz/+aOlQbohBgwYRHx9v6TBEREREROQSlGAXEREREau2bNkyWrZsSZs2bSqVZ2RkMHHiRNq2bUvr1q2JiYkhPT3dQlFW3ZgxY9iwYQOZmZmWDkVERERERP7EzmQymSwdhIiIiIi1MhqNlJaW4uTkhL29bfddKCgooF27djg7O19yeWlpKQkJCbRs2bJa1+vYseNlY8rOzqZLly689NJL9O7d21yen59P//79OXfuHCNHjsTJyYm1a9diMpn44IMPqFOnzjXsefV75ZVXWL9+PQ4ODhctM5lMtGjRgvXr1wPn/4916dKFgQMHMnHixJsdqoiIiIiIXIGjpQMQERERsWb29vbUqFHjqusVFhbi6up6EyK6cUwmE/Xq1WPHjh2XXD5p0iRMJlO1r3clH330EQ4ODtx///2Vyjds2MDRo0fZvHkz4eHhANx777306dOHNWvWMHny5Kvt7iUdPHiQsLCwKm17IaPRyHPPPcfAgQMvWnb48GFiY2PNn+3t7YmMjOTDDz9kwoQJ2NnZXff3i4iIiIhI9bDtblYiIiIi1ykjI4MZM2bQuXNnmjdvTrdu3Zg1axYlJSXApcdgHz58OL1792bfvn0MGzaMli1bsnDhQgCKi4tZsmQJkZGRtGjRgs6dOzN+/HiOHz/+l2OqqP/XX3/l0UcfpWXLlkRERJCcnAzAzp07GThwIOHh4URGRvLtt9+at01OTiYsLIydO3deVO+mTZsICwvjt99+q9K/lSVs27aN8PBw3NzcKpVv3bqVFi1amJPrACEhIXTs2JHPPvusyt8XExNDr169WL16NVlZWVWu51p16tSJEydOcODAgZv2nSIiIiIicnVKsIuIiIhcRkZGBgMGDCApKYlevXoRGxvLww8/zK5duygqKrritjk5OYwZM4amTZsyY8YM2rdvT3l5OY8//jhLly6lWbNmPPvss4wYMYJz585dc1I7NzeXsWPHEh4eztSpU3F2dmby5MkkJSUxefJkunbtytNPP01hYSETJkzAYDAAcN9991GzZs1LJpmTkpJo3LgxoaGh1xSLpZSWlrJ3716aNWtWqdxoNHLw4EGaN29+0TYtWrTg+PHj5n+PazVt2jS8vb155ZVX6Nq1K08++STbt2+nvLy8SvX9VRX78tNPP93Q7xERERERkWujIWJERERELmPhwoWcOXOGd999lxYtWpjLJ06ceNWhSzIzM4mLi2Pw4MHmsn/961+kpqYyffp0HnvsMXP5P/7xj6vW92enT59mwYIF5nHHO3XqRM+ePXn66afZtGkTLVu2BM732o6OjiYlJYX+/fvj4uJCt27d2Lp1K7GxseYxwDMzM9m1axfjx4+/pjgs6Y8//qCoqAg/P79K5Tk5OZSUlODl5XXRNhVlp0+fxt3d/Zq/MzIyksjISE6ePMmWLVt4//33+cc//oGPjw+PPPIIUVFR+Pv7V22HrsDb2xsnJyf+93//t9rrFhERERGRqlMPdhEREZFLMBqNbNu2jfvvv79Scr3C1cbBdnZ2pn///pXKUlJSqFOnDo8++ug11/dnNWvW5KGHHjJ/btSoEbVq1SIkJMScXAfMf09PTzeX9ezZk6ysrErDxGzduhWj0UivXr2uKQ5LysnJAaBWrVqVyouLiwEuOXlqxXj5FetUla+vL+PHj2fbtm2sXbuWdu3asWbNGiIiInjsscfYtWvXddV/KZ6enpw9e7ba6xURERERkapTgl1ERETkErKzszEYDDRu3LhK23t7e1+U4D1+/DjBwcE4Ol7/jwh9fHwuSsp7eHjg4+NzURlAXl6euaxLly54eHiQlJRkLktKSqJp06YEBwdfd2w32597/1ck0SvGyb9QRWL9ShPT5uTkkJmZaf5z7ty5y65rZ2dHx44d+ec//8mKFSvw8vIiNTWVlJSUquzKFZlMJk1wKiIiIiJyi1GCXUREROQGcHFxuaH1Vwzt8lfLL0xCOzs70717dz7//HPKysrIyMjgp59+sqre6wC1a9cGKr88qCh3dnYmMzPzom0qyurXr3/Zep988kk6d+5s/jN37tzLrpuVlcWaNWvo06cPjz32GEajkejoaEaMGFGFPbqyvLw86tSpU+31ioiIiIhI1WkMdhEREZFLqFu3Lu7u7hw6dKja6gwICODnn3+mtLQUJyenaqu3Knr27Mn7779Pamoqhw8fxmQy0bNnT4vGdK0aNGiAi4sLv//+e6Vye3t7QkND2bdv30Xb7NmzB39//yuOvz5t2rRKSfs/J+PLysrYvn07W7ZsYfv27RiNRjp37syECRO47777bkjbZmRkUFpaSkhISLXXLSIiIiIiVacEu4iIiMgl2Nvb0717dz766CP27t170TjsVRmu48EHH+TLL7/k7bffrjTJaVXrux6dOnWidu3aJCUlkZaWRnh4+A2ZnPNGcnJyonnz5pdMpEdGRrJgwYJKbZeWlsZ3333HqFGjrlhv8+bNL7tsyZIlbNq0iTNnzuDn58e4ceOIiorC29v7+nbmKir2sVWrVjf0e0RERERE5NoowS4iIiJyGZMnT+abb75h+PDhDBo0iJCQEDIzM0lOTmbDhg0XTa55Nf369eODDz5g/vz57NmzhzZt2lBYWEhqaipDhgyhe/fuN2hPLubk5ERERASffvophYWFTJs27aZ9d3V64IEHWLRoEQaDoVKv9KFDh7J582Yef/xxRo0ahaOjI2vXrqVevXpXTbBfyaeffkr79u0ZMGAAHTt2vGkvRb799lt8fX256667bsr3iYiIiIjIX6MEu4iIiMhleHt78+6777J48WI+/vhjDAYD3t7edOnSpUpjrDs4OJCQkMDrr7/OJ598QkpKCrVr16Z169aEhYXdgD24sl69erF582bs7OysbniYCg8//DALFizg3//+Nw8//LC53N3dnfXr1zNv3jxef/11jEYj7du3Z/r06dStW7fK37dlyxZq1qxZHaH/ZUajka1btzJgwABNcioiIiIicotRgl1ERETkCnx9fXn55Zcvu7x9+/YcPHiwUtn69esvu76LiwuTJk1i0qRJVY7pcvV/8cUXlyz/c3wVOnXqdNll1qJevXo8/PDDvPPOO5US7AA+Pj689tpr1fp9Nzu5Dufb9dy5cwwdOvSmf7eIiIiIiFyZvaUDEBERERG5HuPHj2fv3r38+OOPlg7lhkhISGDYsGEXTbYqIiIiIiKWpx7sIiIiIreInJwcSktLL7vcwcHhuoY3qQ6nT5+mbdu2l1xWVFTEwIEDb8h6V+Lr68vevXv/Svi3lBdffPGSv44wGo2Vhgx65513bmZYIiIiIiJyDexMJpPJ0kGIiIiICAwfPpydO3dednnDhg0vOwyMiIiIiIiI3HxKsIuIiIjcIvbt20deXt5ll9eoUYM2bdrcxIhERERERETkSpRgFxERERERERERERGpAk1yKiIiIiIiIiIiIiJSBUqwi4iIiIiIiIiIiIhUgRLsIiIiIiIiIiIiIiJVoAS7iIiIiIiIiIiIiEgVKMEuIiIiIiIiIiIiIlIFSrCLiIiIiIiIiIiIiFSBEuwiIiIiIiIiIiIiIlWgBLuIiIiIiIiIiIiISBX8Hzz8s7303n9CAAAAAElFTkSuQmCC",
|
||
"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}\")"
|
||
]
|
||
}
|
||
],
|
||
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"name": "ipython",
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|
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|
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