1159 lines
48 KiB
Plaintext
1159 lines
48 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "code",
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"id": "79a7758178bafdd3",
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"metadata": {
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"jupyter": {
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"source_hidden": true
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||
},
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"ExecuteTime": {
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"end_time": "2025-03-03T13:08:58.678627Z",
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"start_time": "2025-03-03T13:08:58.674698Z"
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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 pandas as pd\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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"\n",
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"pd.set_option('display.max_columns', None)\n"
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],
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"outputs": [],
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"execution_count": 3
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},
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{
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||
"cell_type": "code",
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||
"id": "a79cafb06a7e0e43",
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||
"metadata": {
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||
"ExecuteTime": {
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"end_time": "2025-03-03T13:09:40.479598Z",
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||
"start_time": "2025-03-03T13:08:58.750948Z"
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}
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},
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"source": [
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"from code.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'],\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', '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(df.info())"
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 8369855 entries, 0 to 8369854\n",
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"Data columns (total 21 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 turnover_rate float64 \n",
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" 8 pe_ttm float64 \n",
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" 9 circ_mv float64 \n",
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" 10 volume_ratio float64 \n",
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" 11 is_st bool \n",
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" 12 up_limit float64 \n",
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" 13 down_limit float64 \n",
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" 14 buy_sm_vol float64 \n",
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" 15 sell_sm_vol float64 \n",
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" 16 buy_lg_vol float64 \n",
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" 17 sell_lg_vol float64 \n",
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" 18 buy_elg_vol float64 \n",
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" 19 sell_elg_vol float64 \n",
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" 20 net_mf_vol float64 \n",
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"dtypes: bool(1), datetime64[ns](1), float64(18), object(1)\n",
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"memory usage: 1.3+ GB\n",
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"None\n"
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]
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}
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],
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"execution_count": 4
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},
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{
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-03-03T13:09:42.969235Z",
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||
"start_time": "2025-03-03T13:09:40.522863Z"
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}
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},
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"cell_type": "code",
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"source": [
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"print('industry')\n",
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"df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n",
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" columns=['ts_code', 'l2_code'],\n",
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" df=df, on=['ts_code'], join='left')\n"
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],
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"id": "38879273d3574ae3",
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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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"left merge on ['ts_code']\n"
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]
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}
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],
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"execution_count": 5
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},
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||
{
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-03-03T13:09:43.066598Z",
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||
"start_time": "2025-03-03T13:09:42.976241Z"
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}
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||
},
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\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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" 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', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\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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||
"id": "a4eec8c93f6a7cc3",
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||
"outputs": [],
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||
"execution_count": 6
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||
},
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||
{
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||
"cell_type": "code",
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||
"id": "c4e9e1d31da6dba6",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-03-03T13:09:43.133133Z",
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||
"start_time": "2025-03-03T13:09:43.090288Z"
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||
}
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||
},
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||
"source": [
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||
"import numpy as np\n",
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"import talib\n",
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"\n",
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"\n",
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||
"def get_technical_factor(df):\n",
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" # 按股票和日期排序\n",
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" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
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" grouped = df.groupby('ts_code', group_keys=False)\n",
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"\n",
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||
" # 计算 up 和 down\n",
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" df['log_close'] = np.log(df['close'])\n",
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"\n",
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" df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n",
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" df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n",
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"\n",
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" # 计算 ATR\n",
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" df['atr_14'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n",
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" index=x.index)\n",
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" )\n",
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" df['atr_6'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n",
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" index=x.index)\n",
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||
" )\n",
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"\n",
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" # 计算 OBV 及其均线\n",
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" df['obv'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
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" )\n",
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" df['maobv_6'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n",
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||
" )\n",
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||
" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
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"\n",
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||
" # 计算 RSI\n",
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" df['rsi_3'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n",
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||
" )\n",
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" df['rsi_6'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n",
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" )\n",
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" df['rsi_9'] = grouped.apply(\n",
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" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n",
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" )\n",
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"\n",
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" # 计算 return_10 和 return_20\n",
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" df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
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" df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n",
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" df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
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"\n",
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" # 计算 avg_close_5\n",
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" df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n",
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"\n",
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" # 计算标准差指标\n",
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" df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n",
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" df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n",
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" df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n",
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" df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n",
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" df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n",
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"\n",
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" # 计算比值指标\n",
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" df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n",
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" df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n",
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"\n",
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" # 计算标准差差值\n",
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" df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\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 get_act_factor(df, cat=True):\n",
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" # 按股票和日期排序\n",
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" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
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" grouped = df.groupby('ts_code', group_keys=False)\n",
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" # 计算 EMA 指标\n",
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" df['ema_5'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n",
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" )\n",
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" df['ema_13'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n",
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" )\n",
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" df['ema_20'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n",
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" )\n",
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" df['ema_60'] = grouped['close'].apply(\n",
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" lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n",
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" )\n",
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"\n",
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" # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
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" df['act_factor1'] = grouped['ema_5'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n",
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||
" )\n",
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" df['act_factor2'] = grouped['ema_13'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n",
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||
" )\n",
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" df['act_factor3'] = grouped['ema_20'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n",
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" )\n",
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||
" df['act_factor4'] = grouped['ema_60'].apply(\n",
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" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n",
|
||
" )\n",
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||
"\n",
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||
" if cat:\n",
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||
" df['cat_af1'] = df['act_factor1'] > 0\n",
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||
" df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n",
|
||
" df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n",
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||
" df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n",
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"\n",
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||
" # 计算 act_factor5 和 act_factor6\n",
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||
" df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n",
|
||
" df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n",
|
||
" df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n",
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"\n",
|
||
" # 根据 trade_date 截面计算排名\n",
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||
" df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n",
|
||
" df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n",
|
||
" df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n",
|
||
"\n",
|
||
" return df\n",
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||
"\n",
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||
"\n",
|
||
"def get_money_flow_factor(df):\n",
|
||
" # 计算资金流相关因子（字段名称见 tushare 数据说明）\n",
|
||
" df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n",
|
||
" df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n",
|
||
" df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n",
|
||
"\n",
|
||
" df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
|
||
" df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
|
||
"\n",
|
||
" df['log(circ_mv)'] = np.log(df['circ_mv'])\n",
|
||
" return df\n",
|
||
"\n",
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||
"\n",
|
||
"def get_alpha_factor(df):\n",
|
||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
" grouped = df.groupby('ts_code')\n",
|
||
"\n",
|
||
" # alpha_022: 当前 close 与 5 日前 close 差值\n",
|
||
" df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n",
|
||
"\n",
|
||
" # alpha_003: (close - open) / (high - low)\n",
|
||
" df['alpha_003'] = np.where(df['high'] != df['low'],\n",
|
||
" (df['close'] - df['open']) / (df['high'] - df['low']),\n",
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" 0)\n",
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||
"\n",
|
||
" # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n",
|
||
" df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n",
|
||
" df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n",
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||
"\n",
|
||
" # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n",
|
||
" df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n",
|
||
" df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "53f86ddc0677a6d7",
|
||
"metadata": {
|
||
"scrolled": true,
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:09:50.856330Z",
|
||
"start_time": "2025-03-03T13:09:43.137146Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"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.median())\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 = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(\n",
|
||
" columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
|
||
" return industry_data\n",
|
||
"\n",
|
||
"\n",
|
||
"industry_df = read_industry_data('../../data/sw_daily.h5')\n"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 8
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "dbe2fd8021b9417f",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"scrolled": true,
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:09:50.873842Z",
|
||
"start_time": "2025-03-03T13:09:50.870793Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"origin_columns = df.columns.tolist()\n",
|
||
"origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code']]\n",
|
||
"origin_columns = [col for col in origin_columns if col not in index_data.columns]\n"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 9
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "5f3d9aece75318cd",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:11:03.268415Z",
|
||
"start_time": "2025-03-03T13:09:50.890356Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
||
" df = df[~df['is_st']]\n",
|
||
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
||
" df = df.reset_index(drop=True)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"df = filter_data(df)\n",
|
||
"df = get_technical_factor(df)\n",
|
||
"df = get_act_factor(df)\n",
|
||
"df = get_money_flow_factor(df)\n",
|
||
"df = get_alpha_factor(df)\n",
|
||
"# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n",
|
||
"df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
|
||
"# df = df.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"print(df.info())"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"Index: 5732462 entries, 1964 to 5732460\n",
|
||
"Data columns (total 72 columns):\n",
|
||
" # Column Dtype \n",
|
||
"--- ------ ----- \n",
|
||
" 0 ts_code object \n",
|
||
" 1 trade_date datetime64[ns]\n",
|
||
" 2 open float64 \n",
|
||
" 3 close float64 \n",
|
||
" 4 high float64 \n",
|
||
" 5 low float64 \n",
|
||
" 6 vol float64 \n",
|
||
" 7 turnover_rate float64 \n",
|
||
" 8 pe_ttm float64 \n",
|
||
" 9 circ_mv float64 \n",
|
||
" 10 volume_ratio float64 \n",
|
||
" 11 is_st bool \n",
|
||
" 12 up_limit float64 \n",
|
||
" 13 down_limit float64 \n",
|
||
" 14 buy_sm_vol float64 \n",
|
||
" 15 sell_sm_vol float64 \n",
|
||
" 16 buy_lg_vol float64 \n",
|
||
" 17 sell_lg_vol float64 \n",
|
||
" 18 buy_elg_vol float64 \n",
|
||
" 19 sell_elg_vol float64 \n",
|
||
" 20 net_mf_vol float64 \n",
|
||
" 21 cat_l2_code object \n",
|
||
" 22 log_close float64 \n",
|
||
" 23 up float64 \n",
|
||
" 24 down float64 \n",
|
||
" 25 atr_14 float64 \n",
|
||
" 26 atr_6 float64 \n",
|
||
" 27 obv float64 \n",
|
||
" 28 maobv_6 float64 \n",
|
||
" 29 obv-maobv_6 float64 \n",
|
||
" 30 rsi_3 float64 \n",
|
||
" 31 rsi_6 float64 \n",
|
||
" 32 rsi_9 float64 \n",
|
||
" 33 return_5 float64 \n",
|
||
" 34 return_10 float64 \n",
|
||
" 35 return_20 float64 \n",
|
||
" 36 avg_close_5 float64 \n",
|
||
" 37 std_return_5 float64 \n",
|
||
" 38 std_return_15 float64 \n",
|
||
" 39 std_return_25 float64 \n",
|
||
" 40 std_return_90 float64 \n",
|
||
" 41 std_return_90_2 float64 \n",
|
||
" 42 std_return_5 / std_return_90 float64 \n",
|
||
" 43 std_return_5 / std_return_25 float64 \n",
|
||
" 44 std_return_90 - std_return_90_2 float64 \n",
|
||
" 45 ema_5 float64 \n",
|
||
" 46 ema_13 float64 \n",
|
||
" 47 ema_20 float64 \n",
|
||
" 48 ema_60 float64 \n",
|
||
" 49 act_factor1 float64 \n",
|
||
" 50 act_factor2 float64 \n",
|
||
" 51 act_factor3 float64 \n",
|
||
" 52 act_factor4 float64 \n",
|
||
" 53 cat_af1 bool \n",
|
||
" 54 cat_af2 bool \n",
|
||
" 55 cat_af3 bool \n",
|
||
" 56 cat_af4 bool \n",
|
||
" 57 act_factor5 float64 \n",
|
||
" 58 act_factor6 float64 \n",
|
||
" 59 rank_act_factor1 float64 \n",
|
||
" 60 rank_act_factor2 float64 \n",
|
||
" 61 rank_act_factor3 float64 \n",
|
||
" 62 active_buy_volume_large float64 \n",
|
||
" 63 active_buy_volume_big float64 \n",
|
||
" 64 active_buy_volume_small float64 \n",
|
||
" 65 buy_lg_vol_minus_sell_lg_vol float64 \n",
|
||
" 66 buy_elg_vol_minus_sell_elg_vol float64 \n",
|
||
" 67 log(circ_mv) float64 \n",
|
||
" 68 alpha_022 float64 \n",
|
||
" 69 alpha_003 float64 \n",
|
||
" 70 alpha_007 float64 \n",
|
||
" 71 alpha_013 float64 \n",
|
||
"dtypes: bool(5), datetime64[ns](1), float64(64), object(2)\n",
|
||
"memory usage: 2.9+ GB\n",
|
||
"None\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "f4f16d63ad18d1bc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:11:03.654681Z",
|
||
"start_time": "2025-03-03T13:11:03.638505Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"def create_deviation_within_dates(df, feature_columns):\n",
|
||
" groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n",
|
||
" new_columns = {}\n",
|
||
" ret_feature_columns = feature_columns[:]\n",
|
||
"\n",
|
||
" # 自动选择所有数值型特征\n",
|
||
" # num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n",
|
||
" num_features = [col for col in feature_columns if 'cat' not in col and 'industry' 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_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n",
|
||
" deviation_col_name = f'deviation_median_{feature}'\n",
|
||
" new_columns[deviation_col_name] = df[feature] - grouped_median\n",
|
||
" ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n",
|
||
" deviation_col_name = f'deviation_mean_{feature}'\n",
|
||
" new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
|
||
" ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" # 将新计算的偏差特征与原始 DataFrame 合并\n",
|
||
" df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n",
|
||
"\n",
|
||
" # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n",
|
||
" # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n",
|
||
"\n",
|
||
" return df, ret_feature_columns\n"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "0ebdfb92-d88b-4b5c-a715-675dab876fc0",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:11:03.763536Z",
|
||
"start_time": "2025-03-03T13:11:03.756900Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"def get_qcuts(series, quantiles):\n",
|
||
" q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n",
|
||
" return q[-1] # 返回窗口最后一个元素的分位数标签\n",
|
||
"\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
|
||
" log=True):\n",
|
||
" if not (0 <= lower_percentile < upper_percentile <= 1):\n",
|
||
" raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
|
||
"\n",
|
||
" # Calculate lower and upper bounds based on percentiles\n",
|
||
" lower_bound = label.quantile(lower_percentile)\n",
|
||
" upper_bound = label.quantile(upper_percentile)\n",
|
||
"\n",
|
||
" # Filter out values outside the bounds\n",
|
||
" filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
|
||
"\n",
|
||
" # Print the number of removed outliers\n",
|
||
" if log:\n",
|
||
" print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
|
||
" return filtered_label\n",
|
||
"\n",
|
||
"\n",
|
||
"def calculate_risk_adjusted_target(df, days=5):\n",
|
||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
" df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
|
||
" df['future_return'] = (df['future_close'] - df['close']) / df['close']\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",
|
||
" df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n",
|
||
" df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n",
|
||
"\n",
|
||
" return df['sharpe_ratio']\n",
|
||
"\n",
|
||
"\n"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 12
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "fbb968383f8cf2c7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:24:10.876647Z",
|
||
"start_time": "2025-03-03T13:22:30.406605Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"days = 5\n",
|
||
"future_close = df.groupby('ts_code')['close'].shift(-days)\n",
|
||
"future_open = df.groupby('ts_code')['open'].shift(-1)\n",
|
||
"future_return = (future_close - future_open) / future_open\n",
|
||
"df['label'] = future_return\n",
|
||
"df['label'] = remove_outliers_label_percentile(df['label'])\n",
|
||
"df['label'] = df.groupby('trade_date')['label'].transform(\n",
|
||
" lambda x: pd.qcut(x, q=5, labels=False, duplicates='drop')\n",
|
||
")\n",
|
||
"print(df['label'].unique())\n",
|
||
"\n",
|
||
"\n",
|
||
"# df['label'] = remove_outliers_label_percentile(df['label'])\n",
|
||
"\n",
|
||
"# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n",
|
||
"df = df.sort_values(by=['trade_date', 'ts_code'])\n",
|
||
"train_data = df[(df['trade_date'] <= '2024-03-01') & (df['trade_date'] >= '2016-01-01')]\n",
|
||
"test_data = df[(df['trade_date'] >= '2024-03-01')]\n",
|
||
"\n",
|
||
"train_data = train_data.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x.nsmallest(3000, 'log(circ_mv)')\n",
|
||
")\n",
|
||
"test_data = test_data.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x.nsmallest(3000, 'log(circ_mv)')\n",
|
||
")\n",
|
||
"train_data = train_data.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x.nlargest(1000, 'return_20')\n",
|
||
")\n",
|
||
"test_data = test_data.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x.nlargest(1000, 'return_20')\n",
|
||
")\n",
|
||
"\n",
|
||
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
||
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
||
"\n",
|
||
"train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"train_data = train_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"test_data = test_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
|
||
"\n",
|
||
"feature_columns = [col for col in train_data.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 'score' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
|
||
"feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"print(feature_columns)\n",
|
||
"\n",
|
||
"feature_columns_new = feature_columns[:]\n",
|
||
"train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n",
|
||
"print(f'feature_columns size: {len(feature_columns_new)}')\n",
|
||
"test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n",
|
||
"print(f'feature_columns size: {len(feature_columns_new)}')\n",
|
||
"\n",
|
||
"train_data = train_data.dropna(subset=feature_columns_new)\n",
|
||
"train_data = train_data.dropna(subset=['label'])\n",
|
||
"train_data = train_data.reset_index(drop=True)\n",
|
||
"\n",
|
||
"# print(test_data.tail())\n",
|
||
"test_data = test_data.dropna(subset=feature_columns_new)\n",
|
||
"# test_data = test_data.dropna(subset=['label'])\n",
|
||
"test_data = test_data.reset_index(drop=True)\n",
|
||
"\n",
|
||
"print(len(train_data))\n",
|
||
"print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(test_data))\n",
|
||
"print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"\n",
|
||
"cat_columns = [col for col in df.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')"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Removed 131011 outliers.\n",
|
||
"[ 1. 0. 3. 2. 4. nan]\n",
|
||
"['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'log_close', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', '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', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013', '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_obv_deviation', 'industry_return_5_deviation', 'industry_return_20_deviation', 'industry_act_factor1_deviation', 'industry_act_factor2_deviation', 'industry_act_factor3_deviation', 'industry_act_factor4_deviation', 'industry_return_5_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_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return']\n",
|
||
"feature_columns size: 221\n",
|
||
"feature_columns size: 221\n",
|
||
"1341891\n",
|
||
"最小日期: 2017-04-06\n",
|
||
"最大日期: 2024-03-01\n",
|
||
"181520\n",
|
||
"最小日期: 2024-03-01\n",
|
||
"最大日期: 2025-02-28\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 33
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:25:32.375790Z",
|
||
"start_time": "2025-03-03T13:24:11.128103Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def remove_highly_correlated_features(df, feature_columns, threshold=0.8):\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",
|
||
" return remaining_features\n",
|
||
"\n",
|
||
"\n",
|
||
"feature_columns_new = remove_highly_correlated_features(train_data, feature_columns_new)\n",
|
||
"keep_columns = [col for col in train_data.columns if\n",
|
||
" col in feature_columns_new or col in ['ts_code', 'trade_date', 'label']]\n",
|
||
"train_data = train_data[keep_columns]\n",
|
||
"test_data = test_data[keep_columns]\n",
|
||
"print(f'feature_columns size: {len(feature_columns_new)}')"
|
||
],
|
||
"id": "26c4c873b83ecc38",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"feature_columns size: 83\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 34
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "8f134d435f71e9e2",
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:29:03.972545Z",
|
||
"start_time": "2025-03-03T13:29:03.965555Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from catboost import Pool\n",
|
||
"import lightgbm as lgb\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"\n",
|
||
"def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n",
|
||
" print_feature_importance=True, num_boost_round=100,\n",
|
||
" use_optuna=False):\n",
|
||
" train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n",
|
||
" X_train = train_data_df[feature_columns]\n",
|
||
" y_train = train_data_df['label']\n",
|
||
"\n",
|
||
" X_val = test_data_df[feature_columns]\n",
|
||
" y_val = test_data_df['label']\n",
|
||
"\n",
|
||
" categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n",
|
||
" print(f'categorical_feature: {categorical_feature}')\n",
|
||
" train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n",
|
||
" val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n",
|
||
" model = lgb.train(\n",
|
||
" params, train_data, num_boost_round=num_boost_round,\n",
|
||
" valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n",
|
||
" callbacks=callbacks\n",
|
||
" )\n",
|
||
"\n",
|
||
" if print_feature_importance:\n",
|
||
" lgb.plot_metric(evals)\n",
|
||
" # lgb.plot_tree(model, figsize=(20, 8))\n",
|
||
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
|
||
" plt.show()\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"from catboost import CatBoostClassifier\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"def train_catboost(train_data_df, test_data_df, feature_columns, params=None, plot=False):\n",
|
||
" train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n",
|
||
" # train_data_df = train_data_df[train_data_df['label'].isin([0, 2, 4])]\n",
|
||
" # test_data_df = test_data_df[test_data_df['label'].isin([0, 2, 4])]\n",
|
||
" X_train = train_data_df[feature_columns]\n",
|
||
" y_train = train_data_df['label']\n",
|
||
"\n",
|
||
" X_val = test_data_df[feature_columns]\n",
|
||
" y_val = test_data_df['label']\n",
|
||
"\n",
|
||
" scaler = StandardScaler()\n",
|
||
" numeric_columns = X_train.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
" X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n",
|
||
" X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n",
|
||
"\n",
|
||
" cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n",
|
||
" print(f'cat_features: {cat_features}')\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",
|
||
" model = CatBoostClassifier(**params)\n",
|
||
" model.fit(train_pool,\n",
|
||
" eval_set=val_pool, plot=plot)\n",
|
||
"\n",
|
||
" return model, scaler"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 44
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "4a4542e1ed6afe7d",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:29:03.995376Z",
|
||
"start_time": "2025-03-03T13:29:03.991323Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"light_params = {\n",
|
||
" 'objective': 'binary',\n",
|
||
" 'metric': 'average_precision',\n",
|
||
" 'learning_rate': 0.05,\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': 0.7,\n",
|
||
" 'bagging_fraction': 0.7,\n",
|
||
" 'bagging_freq': 5,\n",
|
||
" # 'lambda_l1': 80,\n",
|
||
" # 'lambda_l2': 65,\n",
|
||
" 'verbosity': -1,\n",
|
||
" 'num_threads': 16\n",
|
||
"}"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 45
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "beeb098799ecfa6a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:29:04.196597Z",
|
||
"start_time": "2025-03-03T13:29:04.190141Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"print('train data size: ', len(train_data))\n",
|
||
"\n",
|
||
"evals = {}\n",
|
||
"# model, scaler = train_light_model(train_data, test_data, light_params, feature_columns_new,\n",
|
||
"# [lgb.log_evaluation(period=500),\n",
|
||
"# lgb.callback.record_evaluation(evals),\n",
|
||
"# lgb.early_stopping(50, first_metric_only=True)\n",
|
||
"# ], evals,\n",
|
||
"# num_boost_round=1000, use_optuna=False,\n",
|
||
"# print_feature_importance=True)"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"train data size: 1341891\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 46
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "445dff84-70b2-4fc9-a9b6-1251993324d6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:32:01.078551Z",
|
||
"start_time": "2025-03-03T13:29:04.236216Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"catboost_params = {\n",
|
||
" 'loss_function': 'MultiClass', # 适用于多分类任务\n",
|
||
" 'eval_metric': 'AUC', # 评估指标，也可以用 'Accuracy' 或 'TotalF1'\n",
|
||
" 'iterations': 5000, # 最大迭代次数\n",
|
||
" 'learning_rate': 0.01, # 学习率\n",
|
||
" 'depth': 10, # 树的深度，控制模型复杂度\n",
|
||
" 'l2_leaf_reg': 10, # L2 正则化\n",
|
||
" 'verbose': 500, # 每 500 次迭代输出一次日志\n",
|
||
" 'early_stopping_rounds': 100, # 提前停止的轮数\n",
|
||
" 'task_type': 'GPU', # 使用 GPU 加速训练\n",
|
||
"}\n",
|
||
"\n",
|
||
"feature_weights = {col: 2.0 if col in ['act_factor', 'af'] else 1.0 for col in feature_columns_new}\n",
|
||
"catboost_params['feature_weights'] = feature_weights\n",
|
||
"\n",
|
||
"model, scaler = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"cat_features: [3, 20, 21, 22, 23]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "7d14b6917a5a499ea87455ebd47a1138"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Default metric period is 5 because AUC is/are not implemented for GPU\n",
|
||
"AUC is not implemented on GPU. Will use CPU for metric computation, this could significantly affect learning time\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0:\ttest: 0.5831648\tbest: 0.5831648 (0)\ttotal: 115ms\tremaining: 9m 36s\n",
|
||
"500:\ttest: 0.6048420\tbest: 0.6048420 (500)\ttotal: 52.9s\tremaining: 7m 54s\n",
|
||
"1000:\ttest: 0.6071489\tbest: 0.6071489 (1000)\ttotal: 1m 45s\tremaining: 7m 1s\n",
|
||
"1500:\ttest: 0.6079927\tbest: 0.6080352 (1483)\ttotal: 2m 39s\tremaining: 6m 11s\n",
|
||
"bestTest = 0.6080667434\n",
|
||
"bestIteration = 1531\n",
|
||
"Shrink model to first 1532 iterations.\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 47
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "5d1522a7538db91b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:32:03.696119Z",
|
||
"start_time": "2025-03-03T13:32:01.217024Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# score_df = train_data\n",
|
||
"# score_df['score'] = model.predict_proba(score_df[feature_columns_new])[:, -1]\n",
|
||
"# # score_df['score'] = model.predict(score_df[feature_columns_new])\n",
|
||
"# predictions_train = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
|
||
"# # predictions_train = predictions_train[predictions_train['score'] > 0.]\n",
|
||
"# predictions_train[['trade_date', 'score', 'ts_code']].to_csv('predictions_train.tsv', index=False)"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 48
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "a3d7e881-c9b7-48a9-ba7f-20cd28c33f37",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:32:04.089341Z",
|
||
"start_time": "2025-03-03T13:32:03.700197Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"score_df = test_data\n",
|
||
"numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n",
|
||
"score_df['score'] = model.predict_proba(score_df[feature_columns_new])[:, -1]\n",
|
||
"# score_df['score'] = model.predict(score_df[feature_columns_new])\n",
|
||
"predictions_test = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
|
||
"# predictions_test = predictions_test[predictions_test['score'] > 0.5]\n",
|
||
"predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 49
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "ebae809e26a7b594",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-03-03T13:32:04.166384Z",
|
||
"start_time": "2025-03-03T13:32:04.160256Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"print(predictions_test[predictions_test['trade_date'] > '2018-01-01'][['label', 'score', 'ts_code', 'trade_date']].head(\n",
|
||
" 10))"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" label score ts_code trade_date\n",
|
||
"43 3.0 0.409653 601666.SH 2024-03-01\n",
|
||
"962 4.0 0.305911 000680.SZ 2024-03-04\n",
|
||
"2012 3.0 0.336820 001270.SZ 2024-03-05\n",
|
||
"3379 4.0 0.329924 605111.SH 2024-03-06\n",
|
||
"4176 4.0 0.354170 605111.SH 2024-03-07\n",
|
||
"4991 4.0 0.326872 605398.SH 2024-03-08\n",
|
||
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