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liaozhaorun
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code/train/ClassifyLR.ipynb
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code/train/ClassifyLR.ipynb
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code/train/DoubleQuntile.ipynb
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code/train/DoubleQuntile.ipynb
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code/train/DoubleRank.ipynb
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code/train/DoubleRank.ipynb
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code/train/PlUpdateClassify.ipynb
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code/train/PlUpdateClassify.ipynb
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code/train/RollingRank.py
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code/train/RollingRank.py
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code/train/TRank.ipynb
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code/train/TRank.ipynb
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code/train/Transformer.ipynb
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code/train/Transformer.ipynb
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code/train/UpdateRank.ipynb
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code/train/UpdateRank.ipynb
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code/train/V1-copy.ipynb
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code/train/V1-copy.ipynb
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{
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"cells": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-09T14:52:54.170824Z",
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"start_time": "2025-02-09T14:52:53.544850Z"
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}
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},
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"cell_type": "code",
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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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"from utils.utils import read_and_merge_h5_data"
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],
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"id": "79a7758178bafdd3",
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-09T14:53:36.873700Z",
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"start_time": "2025-02-09T14:52:54.170824Z"
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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('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_with_st',\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)\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)"
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],
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"id": "a79cafb06a7e0e43",
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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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"stk limit\n",
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"money flow\n"
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]
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}
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],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-09T14:53:37.426404Z",
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"start_time": "2025-02-09T14:53:36.955552Z"
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}
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},
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"cell_type": "code",
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"source": "origin_columns = df.columns.tolist()",
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"id": "c4e9e1d31da6dba6",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-09T14:53:38.164112Z",
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"start_time": "2025-02-09T14:53:38.070007Z"
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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 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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" 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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" df['atr_14'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)\n",
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" df['atr_6'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=6)\n",
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"\n",
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" df['obv'] = talib.OBV(df['close'], df['vol'])\n",
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" df['maobv_6'] = talib.SMA(df['obv'], timeperiod=6)\n",
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" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
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"\n",
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" df['rsi_3'] = talib.RSI(df['close'], timeperiod=3)\n",
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" df['rsi_6'] = talib.RSI(df['close'], timeperiod=6)\n",
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" df['rsi_9'] = talib.RSI(df['close'], timeperiod=9)\n",
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"\n",
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" df['return_10'] = df['close'] / df['close'].shift(10) - 1\n",
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" df['return_20'] = df['close'] / df['close'].shift(20) - 1\n",
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"\n",
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" # # 计算 _rank_return_10 和 _rank_return_20\n",
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" # df['_rank_return_10'] = df['return_10'].rank(pct=True)\n",
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" # df['_rank_return_20'] = df['return_20'].rank(pct=True)\n",
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"\n",
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" # 计算 avg_close_5\n",
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" df['avg_close_5'] = df['close'].rolling(window=5).mean() / df['close']\n",
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"\n",
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" # 计算 std_return_5, std_return_15, std_return_25, std_return_252, std_return_2522\n",
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" df['std_return_5'] = df['close'].pct_change().shift(-1).rolling(window=5).std()\n",
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" df['std_return_15'] = df['close'].pct_change().shift(-1).rolling(window=15).std()\n",
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" df['std_return_25'] = df['close'].pct_change().shift(-1).rolling(window=25).std()\n",
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" df['std_return_90'] = df['close'].pct_change().shift(-1).rolling(window=90).std()\n",
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" df['std_return_90_2'] = df['close'].shift(10).pct_change().shift(-1).rolling(window=90).std()\n",
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"\n",
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" # 计算 std_return_5 / std_return_252 和 std_return_5 / std_return_25\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 std_return_252 - std_return_2522\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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" return df\n",
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"\n",
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"\n",
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"def get_act_factor(df):\n",
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" # 计算 m_ta_ema(close, 5), m_ta_ema(close, 13), m_ta_ema(close, 20), m_ta_ema(close, 60)\n",
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" df['ema_5'] = talib.EMA(df['close'], timeperiod=5)\n",
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" df['ema_13'] = talib.EMA(df['close'], timeperiod=13)\n",
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" df['ema_20'] = talib.EMA(df['close'], timeperiod=20)\n",
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" df['ema_60'] = talib.EMA(df['close'], timeperiod=60)\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'] = np.arctan((df['ema_5'] / df['ema_5'].shift(1) - 1) * 100) * 57.3 / 50\n",
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" df['act_factor2'] = np.arctan((df['ema_13'] / df['ema_13'].shift(1) - 1) * 100) * 57.3 / 40\n",
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" df['act_factor3'] = np.arctan((df['ema_20'] / df['ema_20'].shift(1) - 1) * 100) * 57.3 / 21\n",
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" df['act_factor4'] = np.arctan((df['ema_60'] / df['ema_60'].shift(1) - 1) * 100) * 57.3 / 10\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",
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||||
" df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n",
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" df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n",
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"\n",
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||||
" # 根据 'trade_date' 进行分组，在每个组内分别计算 'act_factor1', 'act_factor2', 'act_factor3' 的排名\n",
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||||
" df['rank_act_factor1'] = df.groupby('trade_date')['act_factor1'].rank(ascending=False, pct=True)\n",
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||||
" df['rank_act_factor2'] = df.groupby('trade_date')['act_factor2'].rank(ascending=False, pct=True)\n",
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||||
" df['rank_act_factor3'] = df.groupby('trade_date')['act_factor3'].rank(ascending=False, pct=True)\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_money_flow_factor(df):\n",
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" df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n",
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||||
" df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n",
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" df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n",
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"\n",
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||||
" df['buy_lg_vol - sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
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||||
" df['buy_elg_vol - sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
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"\n",
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||||
" # # 你还提到了一些其他字段:\n",
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" # df['net_active_buy_volume_main'] = df['net_mf_vol'] / df['buy_sm_vol']\n",
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||||
" # df['netflow_amount_main'] = df['net_mf_vol'] / df['buy_sm_vol'] # 这里假设 'net_mf_vol' 是主流资金流\n",
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"\n",
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" # df['active_sell_volume_large'] = df['sell_lg_vol'] / df['sell_sm_vol']\n",
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" # df['active_sell_volume_big'] = df['sell_elg_vol'] / df['sell_sm_vol']\n",
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" # df['active_sell_volume_small'] = df['sell_sm_vol'] / df['sell_sm_vol']\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_alpha_factor(df):\n",
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" df['alpha_022'] = df['close'] - df['close'].shift(5)\n",
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"\n",
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" # alpha_003: (close - open) / (high - low)\n",
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" df['alpha_003'] = (df['close'] - df['open']) / (df['high'] - df['low'])\n",
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"\n",
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" # alpha_007: rank(correlation(close, volume, 5))\n",
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" df['alpha_007'] = df['close'].rolling(5).corr(df['vol']).rank(axis=1)\n",
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"\n",
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" # alpha_013: rank(sum(close, 5) - sum(close, 20))\n",
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" df['alpha_013'] = (df['close'].rolling(5).sum() - df['close'].rolling(20).sum()).rank(axis=1)\n",
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" return df\n",
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"\n",
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"\n",
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"def get_future_data(df):\n",
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" df['future_return1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
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" df['future_return2'] = (df['open'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
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" df['future_return3'] = (df['close'].shift(-2) - df['close'].shift(-1)) / df['close'].shift(-1)\n",
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" df['future_return4'] = (df['close'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
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" df['future_return5'] = (df['close'].shift(-5) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
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" df['future_return6'] = (df['close'].shift(-10) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
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" df['future_return7'] = (df['close'].shift(-20) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
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||||
" df['future_close1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
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" df['future_close2'] = (df['close'].shift(-2) - df['close']) / df['close']\n",
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" df['future_close3'] = (df['close'].shift(-3) - df['close']) / df['close']\n",
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" df['future_close4'] = (df['close'].shift(-4) - df['close']) / df['close']\n",
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" df['future_close5'] = (df['close'].shift(-5) - df['close']) / df['close']\n",
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" df['future_af11'] = df['act_factor1'].shift(-1)\n",
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" df['future_af12'] = df['act_factor1'].shift(-2)\n",
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" df['future_af13'] = df['act_factor1'].shift(-3)\n",
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" df['future_af14'] = df['act_factor1'].shift(-4)\n",
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" df['future_af15'] = df['act_factor1'].shift(-5)\n",
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" df['future_af21'] = df['act_factor2'].shift(-1)\n",
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" df['future_af22'] = df['act_factor2'].shift(-2)\n",
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" df['future_af23'] = df['act_factor2'].shift(-3)\n",
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" df['future_af24'] = df['act_factor2'].shift(-4)\n",
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" df['future_af25'] = df['act_factor2'].shift(-5)\n",
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" df['future_af31'] = df['act_factor3'].shift(-1)\n",
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" df['future_af32'] = df['act_factor3'].shift(-2)\n",
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" df['future_af33'] = df['act_factor3'].shift(-3)\n",
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" df['future_af34'] = df['act_factor3'].shift(-4)\n",
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" df['future_af35'] = df['act_factor3'].shift(-5)\n",
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"\n",
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||||
" return df\n"
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],
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||||
"id": "a735bc02ceb4d872",
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||||
"outputs": [],
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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": "2025-02-09T14:53:49.153376Z",
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||||
"start_time": "2025-02-09T14:53:38.164112Z"
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||||
}
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||||
},
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"cell_type": "code",
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"source": [
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"df = get_technical_factor(df)\n",
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"df = get_act_factor(df)\n",
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"df = get_money_flow_factor(df)\n",
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"df = get_future_data(df)\n",
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"# df = df.drop(columns=origin_columns)\n",
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"\n",
|
||||
"print(df.info())"
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],
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"id": "53f86ddc0677a6d7",
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 8364308 entries, 0 to 8364307\n",
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"Data columns (total 83 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",
|
||||
" 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 is_st object \n",
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||||
" 8 up_limit float64 \n",
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||||
" 9 down_limit float64 \n",
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||||
" 10 buy_sm_vol float64 \n",
|
||||
" 11 sell_sm_vol float64 \n",
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||||
" 12 buy_lg_vol float64 \n",
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||||
" 13 sell_lg_vol float64 \n",
|
||||
" 14 buy_elg_vol float64 \n",
|
||||
" 15 sell_elg_vol float64 \n",
|
||||
" 16 net_mf_vol float64 \n",
|
||||
" 17 up float64 \n",
|
||||
" 18 down float64 \n",
|
||||
" 19 atr_14 float64 \n",
|
||||
" 20 atr_6 float64 \n",
|
||||
" 21 obv float64 \n",
|
||||
" 22 maobv_6 float64 \n",
|
||||
" 23 obv-maobv_6 float64 \n",
|
||||
" 24 rsi_3 float64 \n",
|
||||
" 25 rsi_6 float64 \n",
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||||
" 26 rsi_9 float64 \n",
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||||
" 27 return_10 float64 \n",
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||||
" 28 return_20 float64 \n",
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||||
" 29 avg_close_5 float64 \n",
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||||
" 30 std_return_5 float64 \n",
|
||||
" 31 std_return_15 float64 \n",
|
||||
" 32 std_return_25 float64 \n",
|
||||
" 33 std_return_90 float64 \n",
|
||||
" 34 std_return_90_2 float64 \n",
|
||||
" 35 std_return_5 / std_return_90 float64 \n",
|
||||
" 36 std_return_5 / std_return_25 float64 \n",
|
||||
" 37 std_return_90 - std_return_90_2 float64 \n",
|
||||
" 38 ema_5 float64 \n",
|
||||
" 39 ema_13 float64 \n",
|
||||
" 40 ema_20 float64 \n",
|
||||
" 41 ema_60 float64 \n",
|
||||
" 42 act_factor1 float64 \n",
|
||||
" 43 act_factor2 float64 \n",
|
||||
" 44 act_factor3 float64 \n",
|
||||
" 45 act_factor4 float64 \n",
|
||||
" 46 act_factor5 float64 \n",
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||||
" 47 act_factor6 float64 \n",
|
||||
" 48 rank_act_factor1 float64 \n",
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||||
" 49 rank_act_factor2 float64 \n",
|
||||
" 50 rank_act_factor3 float64 \n",
|
||||
" 51 active_buy_volume_large float64 \n",
|
||||
" 52 active_buy_volume_big float64 \n",
|
||||
" 53 active_buy_volume_small float64 \n",
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||||
" 54 buy_lg_vol - sell_lg_vol float64 \n",
|
||||
" 55 buy_elg_vol - sell_elg_vol float64 \n",
|
||||
" 56 future_return1 float64 \n",
|
||||
" 57 future_return2 float64 \n",
|
||||
" 58 future_return3 float64 \n",
|
||||
" 59 future_return4 float64 \n",
|
||||
" 60 future_return5 float64 \n",
|
||||
" 61 future_return6 float64 \n",
|
||||
" 62 future_return7 float64 \n",
|
||||
" 63 future_close1 float64 \n",
|
||||
" 64 future_close2 float64 \n",
|
||||
" 65 future_close3 float64 \n",
|
||||
" 66 future_close4 float64 \n",
|
||||
" 67 future_close5 float64 \n",
|
||||
" 68 future_af11 float64 \n",
|
||||
" 69 future_af12 float64 \n",
|
||||
" 70 future_af13 float64 \n",
|
||||
" 71 future_af14 float64 \n",
|
||||
" 72 future_af15 float64 \n",
|
||||
" 73 future_af21 float64 \n",
|
||||
" 74 future_af22 float64 \n",
|
||||
" 75 future_af23 float64 \n",
|
||||
" 76 future_af24 float64 \n",
|
||||
" 77 future_af25 float64 \n",
|
||||
" 78 future_af31 float64 \n",
|
||||
" 79 future_af32 float64 \n",
|
||||
" 80 future_af33 float64 \n",
|
||||
" 81 future_af34 float64 \n",
|
||||
" 82 future_af35 float64 \n",
|
||||
"dtypes: datetime64[ns](1), float64(80), object(2)\n",
|
||||
"memory usage: 5.2+ GB\n",
|
||||
"None\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 5
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:55:28.712343Z",
|
||||
"start_time": "2025-02-09T14:53:49.279168Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"def filter_data(df):\n",
|
||||
" df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor3'))\n",
|
||||
" df = df[df['is_st'] == False]\n",
|
||||
" df = df[df['is_st'] == False]\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",
|
||||
"print(df.info())"
|
||||
],
|
||||
"id": "dbe2fd8021b9417f",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 1136157 entries, 0 to 1136156\n",
|
||||
"Data columns (total 83 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 ts_code 1136157 non-null object \n",
|
||||
" 1 trade_date 1136157 non-null datetime64[ns]\n",
|
||||
" 2 open 1136157 non-null float64 \n",
|
||||
" 3 close 1136157 non-null float64 \n",
|
||||
" 4 high 1136157 non-null float64 \n",
|
||||
" 5 low 1136157 non-null float64 \n",
|
||||
" 6 vol 1136157 non-null float64 \n",
|
||||
" 7 is_st 1136157 non-null object \n",
|
||||
" 8 up_limit 1135878 non-null float64 \n",
|
||||
" 9 down_limit 1135878 non-null float64 \n",
|
||||
" 10 buy_sm_vol 1135663 non-null float64 \n",
|
||||
" 11 sell_sm_vol 1135663 non-null float64 \n",
|
||||
" 12 buy_lg_vol 1135663 non-null float64 \n",
|
||||
" 13 sell_lg_vol 1135663 non-null float64 \n",
|
||||
" 14 buy_elg_vol 1135663 non-null float64 \n",
|
||||
" 15 sell_elg_vol 1135663 non-null float64 \n",
|
||||
" 16 net_mf_vol 1135663 non-null float64 \n",
|
||||
" 17 up 1136157 non-null float64 \n",
|
||||
" 18 down 1136157 non-null float64 \n",
|
||||
" 19 atr_14 1136157 non-null float64 \n",
|
||||
" 20 atr_6 1136157 non-null float64 \n",
|
||||
" 21 obv 1136157 non-null float64 \n",
|
||||
" 22 maobv_6 1136157 non-null float64 \n",
|
||||
" 23 obv-maobv_6 1136157 non-null float64 \n",
|
||||
" 24 rsi_3 1136157 non-null float64 \n",
|
||||
" 25 rsi_6 1136157 non-null float64 \n",
|
||||
" 26 rsi_9 1136157 non-null float64 \n",
|
||||
" 27 return_10 1136157 non-null float64 \n",
|
||||
" 28 return_20 1136157 non-null float64 \n",
|
||||
" 29 avg_close_5 1136157 non-null float64 \n",
|
||||
" 30 std_return_5 1136157 non-null float64 \n",
|
||||
" 31 std_return_15 1136157 non-null float64 \n",
|
||||
" 32 std_return_25 1136157 non-null float64 \n",
|
||||
" 33 std_return_90 1136131 non-null float64 \n",
|
||||
" 34 std_return_90_2 1136129 non-null float64 \n",
|
||||
" 35 std_return_5 / std_return_90 1136131 non-null float64 \n",
|
||||
" 36 std_return_5 / std_return_25 1136157 non-null float64 \n",
|
||||
" 37 std_return_90 - std_return_90_2 1136129 non-null float64 \n",
|
||||
" 38 ema_5 1136157 non-null float64 \n",
|
||||
" 39 ema_13 1136157 non-null float64 \n",
|
||||
" 40 ema_20 1136157 non-null float64 \n",
|
||||
" 41 ema_60 1136153 non-null float64 \n",
|
||||
" 42 act_factor1 1136157 non-null float64 \n",
|
||||
" 43 act_factor2 1136157 non-null float64 \n",
|
||||
" 44 act_factor3 1136157 non-null float64 \n",
|
||||
" 45 act_factor4 1136152 non-null float64 \n",
|
||||
" 46 act_factor5 1136152 non-null float64 \n",
|
||||
" 47 act_factor6 1136157 non-null float64 \n",
|
||||
" 48 rank_act_factor1 1136157 non-null float64 \n",
|
||||
" 49 rank_act_factor2 1136157 non-null float64 \n",
|
||||
" 50 rank_act_factor3 1136157 non-null float64 \n",
|
||||
" 51 active_buy_volume_large 1135659 non-null float64 \n",
|
||||
" 52 active_buy_volume_big 1135636 non-null float64 \n",
|
||||
" 53 active_buy_volume_small 1135663 non-null float64 \n",
|
||||
" 54 buy_lg_vol - sell_lg_vol 1135660 non-null float64 \n",
|
||||
" 55 buy_elg_vol - sell_elg_vol 1135640 non-null float64 \n",
|
||||
" 56 future_return1 1136157 non-null float64 \n",
|
||||
" 57 future_return2 1136157 non-null float64 \n",
|
||||
" 58 future_return3 1136157 non-null float64 \n",
|
||||
" 59 future_return4 1136157 non-null float64 \n",
|
||||
" 60 future_return5 1136157 non-null float64 \n",
|
||||
" 61 future_return6 1136157 non-null float64 \n",
|
||||
" 62 future_return7 1136157 non-null float64 \n",
|
||||
" 63 future_close1 1136157 non-null float64 \n",
|
||||
" 64 future_close2 1136157 non-null float64 \n",
|
||||
" 65 future_close3 1136157 non-null float64 \n",
|
||||
" 66 future_close4 1136157 non-null float64 \n",
|
||||
" 67 future_close5 1136157 non-null float64 \n",
|
||||
" 68 future_af11 1136157 non-null float64 \n",
|
||||
" 69 future_af12 1136157 non-null float64 \n",
|
||||
" 70 future_af13 1136157 non-null float64 \n",
|
||||
" 71 future_af14 1136157 non-null float64 \n",
|
||||
" 72 future_af15 1136157 non-null float64 \n",
|
||||
" 73 future_af21 1136157 non-null float64 \n",
|
||||
" 74 future_af22 1136157 non-null float64 \n",
|
||||
" 75 future_af23 1136157 non-null float64 \n",
|
||||
" 76 future_af24 1136157 non-null float64 \n",
|
||||
" 77 future_af25 1136157 non-null float64 \n",
|
||||
" 78 future_af31 1136157 non-null float64 \n",
|
||||
" 79 future_af32 1136157 non-null float64 \n",
|
||||
" 80 future_af33 1136157 non-null float64 \n",
|
||||
" 81 future_af34 1136157 non-null float64 \n",
|
||||
" 82 future_af35 1136157 non-null float64 \n",
|
||||
"dtypes: datetime64[ns](1), float64(80), object(2)\n",
|
||||
"memory usage: 719.5+ MB\n",
|
||||
"None\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 6
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T15:00:45.828404Z",
|
||||
"start_time": "2025-02-09T15:00:45.294830Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"def remove_outliers_iqr(series, lower_quantile=0.05, upper_quantile=0.95, threshold=1.5):\n",
|
||||
" Q1 = series.quantile(lower_quantile)\n",
|
||||
" Q3 = series.quantile(upper_quantile)\n",
|
||||
" IQR = Q3 - Q1\n",
|
||||
" lower_bound = Q1 - threshold * IQR\n",
|
||||
" upper_bound = Q3 + threshold * IQR\n",
|
||||
" # 过滤掉低于下边界或高于上边界的极值\n",
|
||||
" return (series >= lower_bound) & (series <= upper_bound)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def neutralize_labels(labels, features, feature_columns, z_threshold=3, method='regression'):\n",
|
||||
" labels_no_outliers = remove_outliers_iqr(labels)\n",
|
||||
" return labels_no_outliers\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"train_data = df[df['trade_date'] <= '2023-01-01']\n",
|
||||
"test_data = df[df['trade_date'] >= '2023-01-01']\n",
|
||||
"\n",
|
||||
"feature_columns = [col for col in df.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",
|
||||
"\n",
|
||||
"# for column in [column for column in train_data.columns if 'future' in column]:\n",
|
||||
"# label_index = neutralize_labels(train_data[column], train_data, feature_columns, z_threshold=3, method='regression')\n",
|
||||
"# train_data = train_data[label_index]\n",
|
||||
"# label_index = neutralize_labels(test_data[column], test_data, feature_columns, z_threshold=3, method='regression')\n",
|
||||
"# test_data = test_data[label_index]\n",
|
||||
"\n",
|
||||
"print(len(train_data))\n",
|
||||
"print(len(test_data))"
|
||||
],
|
||||
"id": "5f3d9aece75318cd",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', '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', '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 - sell_lg_vol', 'buy_elg_vol - sell_elg_vol']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 19
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:56:05.319915Z",
|
||||
"start_time": "2025-02-09T14:56:03.355725Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"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",
|
||||
"window = 5\n",
|
||||
"quantiles = 20\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_label(df):\n",
|
||||
" labels = df['future_af13'] - df['act_factor1']\n",
|
||||
" # labels = df['future_close3']\n",
|
||||
" return labels\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"train_data['label'], test_data['label'] = get_label(train_data), get_label(test_data)\n",
|
||||
"\n",
|
||||
"train_data, test_data = train_data.dropna(subset=['label']), test_data.dropna(subset=['label'])\n",
|
||||
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan).dropna(), test_data.replace([np.inf, -np.inf],\n",
|
||||
" np.nan).dropna()\n",
|
||||
"train_data, test_data = train_data.reset_index(drop=True), test_data.reset_index(drop=True)\n",
|
||||
"\n",
|
||||
"print(len(train_data))\n",
|
||||
"print(len(test_data))"
|
||||
],
|
||||
"id": "f4f16d63ad18d1bc",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"875004\n",
|
||||
"最小日期: 2017-01-03\n",
|
||||
"最大日期: 2022-12-30\n",
|
||||
"260581\n",
|
||||
"最小日期: 2023-01-03\n",
|
||||
"最大日期: 2025-01-27\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 13
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:56:05.480695Z",
|
||||
"start_time": "2025-02-09T14:56:05.367238Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"import lightgbm as lgb\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import optuna\n",
|
||||
"from sklearn.model_selection import KFold\n",
|
||||
"from sklearn.metrics import mean_absolute_error\n",
|
||||
"import os\n",
|
||||
"import json\n",
|
||||
"import pickle\n",
|
||||
"import hashlib\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def objective(trial, X, y, num_boost_round, params):\n",
|
||||
" # 参数网格\n",
|
||||
" X, y = X.reset_index(drop=True), y.reset_index(drop=True)\n",
|
||||
" param_grid = {\n",
|
||||
" \"n_estimators\": trial.suggest_categorical(\"n_estimators\", [10000]),\n",
|
||||
" \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.3),\n",
|
||||
" \"num_leaves\": trial.suggest_int(\"num_leaves\", 20, 3000, step=25),\n",
|
||||
" \"max_depth\": trial.suggest_int(\"max_depth\", 3, 16),\n",
|
||||
" \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 200, 10000, step=100),\n",
|
||||
" \"lambda_l1\": trial.suggest_int(\"lambda_l1\", 0, 100, step=5),\n",
|
||||
" \"lambda_l2\": trial.suggest_int(\"lambda_l2\", 0, 100, step=5),\n",
|
||||
" \"min_gain_to_split\": trial.suggest_float(\"min_gain_to_split\", 0, 15),\n",
|
||||
" \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.2, 0.95, step=0.1),\n",
|
||||
" \"bagging_freq\": trial.suggest_categorical(\"bagging_freq\", [1]),\n",
|
||||
" \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.2, 0.95, step=0.1),\n",
|
||||
" \"random_state\": 1,\n",
|
||||
" \"objective\": 'regression',\n",
|
||||
" 'verbosity': -1\n",
|
||||
" }\n",
|
||||
" # 5折交叉验证\n",
|
||||
" cv = KFold(n_splits=5, shuffle=False)\n",
|
||||
"\n",
|
||||
" cv_scores = np.empty(5)\n",
|
||||
" for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):\n",
|
||||
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
|
||||
" y_train, y_test = y[train_idx], y[test_idx]\n",
|
||||
"\n",
|
||||
" # LGBM建模\n",
|
||||
" model = lgb.LGBMRegressor(**param_grid, num_boost_round=num_boost_round)\n",
|
||||
" model.fit(\n",
|
||||
" X_train,\n",
|
||||
" y_train,\n",
|
||||
" eval_set=[(X_test, y_test)],\n",
|
||||
" eval_metric=\"l2\",\n",
|
||||
" callbacks=[\n",
|
||||
" # LightGBMPruningCallback(trial, \"l2\"),\n",
|
||||
" lgb.early_stopping(50, first_metric_only=True),\n",
|
||||
" lgb.log_evaluation(period=-1)\n",
|
||||
" ],\n",
|
||||
" )\n",
|
||||
" # 模型预测\n",
|
||||
" preds = model.predict(X_test)\n",
|
||||
" # 优化指标logloss最小\n",
|
||||
" cv_scores[idx] = mean_absolute_error(y_test, preds)\n",
|
||||
"\n",
|
||||
" return np.mean(cv_scores)\n",
|
||||
"\n",
|
||||
"def generate_key(params, feature_columns, num_boost_round):\n",
|
||||
" key_data = {\n",
|
||||
" \"params\": params,\n",
|
||||
" \"feature_columns\": feature_columns,\n",
|
||||
" \"num_boost_round\": num_boost_round\n",
|
||||
" }\n",
|
||||
" # 转换成排序后的 JSON 字符串，再生成 md5 hash\n",
|
||||
" key_str = json.dumps(key_data, sort_keys=True)\n",
|
||||
" return hashlib.md5(key_str.encode('utf-8')).hexdigest()\n",
|
||||
"\n",
|
||||
"def train_light_model(df, params, feature_columns, callbacks, evals,\n",
|
||||
" print_feature_importance=True, num_boost_round=100,\n",
|
||||
" use_optuna=False):\n",
|
||||
" cache_file = 'light_model.pkl'\n",
|
||||
" cache_key = generate_key(params, feature_columns, num_boost_round)\n",
|
||||
"\n",
|
||||
" # 检查缓存文件是否存在\n",
|
||||
" if os.path.exists(cache_file):\n",
|
||||
" try:\n",
|
||||
" with open(cache_file, 'rb') as f:\n",
|
||||
" cache_data = pickle.load(f)\n",
|
||||
" if cache_data.get('key') == cache_key:\n",
|
||||
" print(\"加载缓存模型...\")\n",
|
||||
" return cache_data.get('model')\n",
|
||||
" else:\n",
|
||||
" print(\"缓存模型的参数与当前参数不匹配，重新训练模型。\")\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"加载缓存失败: {e}，重新训练模型。\")\n",
|
||||
" else:\n",
|
||||
" print(\"未发现缓存模型，开始训练新模型。\")\n",
|
||||
" # 确保数据按照 date 和 label 排序\n",
|
||||
" df_sorted = df.sort_values(by=['trade_date', 'label'], ascending=[True, False]) # 按日期升序、标签降序排序\n",
|
||||
" df_sorted = df_sorted.sort_values(by='trade_date')\n",
|
||||
" unique_dates = df_sorted['trade_date'].unique()\n",
|
||||
" val_date_count = int(len(unique_dates) * 0.1)\n",
|
||||
" val_dates = unique_dates[-val_date_count:]\n",
|
||||
" val_indices = df_sorted[df_sorted['trade_date'].isin(val_dates)].index\n",
|
||||
" train_indices = df_sorted[~df_sorted['trade_date'].isin(val_dates)].index\n",
|
||||
"\n",
|
||||
" # 获取训练集和验证集的样本\n",
|
||||
" train_df = df_sorted.iloc[train_indices]\n",
|
||||
" val_df = df_sorted.iloc[val_indices]\n",
|
||||
"\n",
|
||||
" X_train = train_df[feature_columns]\n",
|
||||
" y_train = train_df['label']\n",
|
||||
"\n",
|
||||
" X_val = val_df[feature_columns]\n",
|
||||
" y_val = val_df['label']\n",
|
||||
"\n",
|
||||
" train_data = lgb.Dataset(X_train, label=y_train)\n",
|
||||
" val_data = lgb.Dataset(X_val, label=y_val)\n",
|
||||
" if use_optuna:\n",
|
||||
" # study = optuna.create_study(direction='minimize' if classify else 'maximize')\n",
|
||||
" study = optuna.create_study(direction='minimize')\n",
|
||||
" study.optimize(lambda trial: objective(trial, X_train, y_train, num_boost_round, params), n_trials=20)\n",
|
||||
"\n",
|
||||
" print(f\"Best parameters: {study.best_trial.params}\")\n",
|
||||
" print(f\"Best score: {study.best_trial.value}\")\n",
|
||||
"\n",
|
||||
" params.update(study.best_trial.params)\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",
|
||||
" # 打印特征重要性（如果需要）\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",
|
||||
" # with open(cache_file, 'wb') as f:\n",
|
||||
" # pickle.dump({'key': cache_key,\n",
|
||||
" # 'model': model,\n",
|
||||
" # 'feature_columns': feature_columns}, f)\n",
|
||||
" # print(\"模型训练完成并已保存缓存。\")\n",
|
||||
" return model\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from catboost import CatBoostRegressor\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def train_catboost(df, num_boost_round, params=None):\n",
|
||||
" \"\"\"\n",
|
||||
" 训练 CatBoost 排序模型\n",
|
||||
" - df: 包含因子、date、instrument 和 label 的 DataFrame\n",
|
||||
" - num_boost_round: 训练的轮数\n",
|
||||
" - print_feature_importance: 是否打印特征重要性\n",
|
||||
" - plot: 是否绘制特征重要性图\n",
|
||||
" - split_date: 用于划分训练集和验证集的日期（比如 '2020-01-01'）\n",
|
||||
"\n",
|
||||
" 返回训练好的模型\n",
|
||||
" \"\"\"\n",
|
||||
" df_sorted = df.sort_values(by=['date', 'label'], ascending=[True, False])\n",
|
||||
"\n",
|
||||
" # 提取特征和标签\n",
|
||||
" feature_columns = [col for col in df.columns if col not in ['date',\n",
|
||||
" 'instrument',\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",
|
||||
"\n",
|
||||
" df_sorted = df_sorted.sort_values(by='date')\n",
|
||||
" unique_dates = df_sorted['date'].unique()\n",
|
||||
" val_date_count = int(len(unique_dates) * 0.1)\n",
|
||||
" val_dates = unique_dates[-val_date_count:]\n",
|
||||
" val_indices = df_sorted[df_sorted['date'].isin(val_dates)].index\n",
|
||||
" train_indices = df_sorted[~df_sorted['date'].isin(val_dates)].index\n",
|
||||
"\n",
|
||||
" # 获取训练集和验证集的样本\n",
|
||||
" train_df = df_sorted.iloc[train_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n",
|
||||
" val_df = df_sorted.iloc[val_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n",
|
||||
"\n",
|
||||
" X_train = train_df[feature_columns]\n",
|
||||
" y_train = train_df['label']\n",
|
||||
"\n",
|
||||
" X_val = val_df[feature_columns]\n",
|
||||
" y_val = val_df['label']\n",
|
||||
"\n",
|
||||
" model = CatBoostRegressor(**params, iterations=num_boost_round)\n",
|
||||
" model.fit(X_train,\n",
|
||||
" y_train,\n",
|
||||
" eval_set=(X_val, y_val))\n",
|
||||
"\n",
|
||||
" return model"
|
||||
],
|
||||
"id": "8f134d435f71e9e2",
|
||||
"outputs": [],
|
||||
"execution_count": 14
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:56:05.576927Z",
|
||||
"start_time": "2025-02-09T14:56:05.480695Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"light_params = {\n",
|
||||
" 'objective': 'regression',\n",
|
||||
" 'metric': 'l2',\n",
|
||||
" 'learning_rate': 0.05,\n",
|
||||
" 'is_unbalance': True,\n",
|
||||
" 'num_leaves': 2048,\n",
|
||||
" 'min_data_in_leaf': 16,\n",
|
||||
" 'max_depth': 32,\n",
|
||||
" 'max_bin': 1024,\n",
|
||||
" 'nthread': 2,\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",
|
||||
"}"
|
||||
],
|
||||
"id": "4a4542e1ed6afe7d",
|
||||
"outputs": [],
|
||||
"execution_count": 15
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:57:25.341222Z",
|
||||
"start_time": "2025-02-09T14:56:05.640256Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"print('train data size: ', len(train_data))\n",
|
||||
"df = train_data\n",
|
||||
"\n",
|
||||
"evals = {}\n",
|
||||
"light_model = train_light_model(train_data, light_params, feature_columns,\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=False)"
|
||||
],
|
||||
"id": "beeb098799ecfa6a",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"train data size: 875004\n",
|
||||
"未发现缓存模型，开始训练新模型。\n",
|
||||
"Training until validation scores don't improve for 50 rounds\n",
|
||||
"Early stopping, best iteration is:\n",
|
||||
"[378]\ttrain's l2: 0.435049\tvalid's l2: 0.589178\n",
|
||||
"Evaluated only: l2\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 16
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:57:27.394697Z",
|
||||
"start_time": "2025-02-09T14:57:25.373274Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"test_data['score'] = light_model.predict(test_data[feature_columns])\n",
|
||||
"predictions = test_data.loc[test_data.groupby('trade_date')['score'].idxmax()]"
|
||||
],
|
||||
"id": "5bb96ca8492e74d",
|
||||
"outputs": [],
|
||||
"execution_count": 17
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-02-09T14:57:27.489570Z",
|
||||
"start_time": "2025-02-09T14:57:27.397368Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": "predictions[['trade_date', 'score', 'ts_code']].to_csv('predictions.csv', index=False)",
|
||||
"id": "5d1522a7538db91b",
|
||||
"outputs": [],
|
||||
"execution_count": 18
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
code/train/best_model.pth
Normal file
BIN
code/train/best_model.pth
Normal file
Binary file not shown.
5004
code/train/catboost_info/catboost_training.json
Normal file
5004
code/train/catboost_info/catboost_training.json
Normal file
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code/train/catboost_info/learn/events.out.tfevents
Normal file
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code/train/catboost_info/learn/events.out.tfevents
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code/train/catboost_info/learn_error.tsv
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5001
code/train/catboost_info/learn_error.tsv
Normal file
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code/train/catboost_info/test/events.out.tfevents
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code/train/catboost_info/test/events.out.tfevents
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code/train/catboost_info/test1/events.out.tfevents
Normal file
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code/train/catboost_info/test1/events.out.tfevents
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5001
code/train/catboost_info/test_error.tsv
Normal file
5001
code/train/catboost_info/test_error.tsv
Normal file
File diff suppressed because it is too large
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5001
code/train/catboost_info/time_left.tsv
Normal file
5001
code/train/catboost_info/time_left.tsv
Normal file
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384
code/train/code.ipynb
Normal file
384
code/train/code.ipynb
Normal file
@@ -0,0 +1,384 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"metadata": {},
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_technical_factor(df):\n",
|
||||
" # 按股票和日期排序\n",
|
||||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||||
" grouped = df.groupby('ts_code', group_keys=False)\n",
|
||||
"\n",
|
||||
" df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n",
|
||||
" df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n",
|
||||
"\n",
|
||||
" # 因子 1：短期成交量变化率\n",
|
||||
" df['volume_change_rate'] = (\n",
|
||||
" grouped['vol'].rolling(window=2).mean() /\n",
|
||||
" grouped['vol'].rolling(window=5).mean() - 1\n",
|
||||
" ).reset_index(level=0, drop=True) # 确保索引对齐\n",
|
||||
"\n",
|
||||
" # 因子 2：成交量突破信号\n",
|
||||
" max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n",
|
||||
" df['cat_volume_breakout'] = (df['vol'] > max_volume)\n",
|
||||
"\n",
|
||||
" # 因子 3：换手率均线偏离度\n",
|
||||
" mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n",
|
||||
" std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n",
|
||||
" df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n",
|
||||
"\n",
|
||||
" # 因子 4：换手率激增信号\n",
|
||||
" df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n",
|
||||
"\n",
|
||||
" # 因子 5：量比均值\n",
|
||||
" df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n",
|
||||
"\n",
|
||||
" # 因子 6：量比突破信号\n",
|
||||
" max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n",
|
||||
" df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n",
|
||||
"\n",
|
||||
" # 因子 7：成交量与换手率的综合动量因子\n",
|
||||
" alpha = 0.5\n",
|
||||
" df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n",
|
||||
"\n",
|
||||
" # 因子 8：量价共振因子\n",
|
||||
" df['price_change_rate'] = grouped['close'].pct_change()\n",
|
||||
" df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate']\n",
|
||||
"\n",
|
||||
" # 计算 up 和 down\n",
|
||||
" df['log_close'] = np.log(df['close'])\n",
|
||||
"\n",
|
||||
" df['vol_spike'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n",
|
||||
" df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n",
|
||||
"\n",
|
||||
" df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n",
|
||||
" df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n",
|
||||
"\n",
|
||||
" # 计算 ATR\n",
|
||||
" df['atr_14'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n",
|
||||
" index=x.index)\n",
|
||||
" )\n",
|
||||
" df['atr_6'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n",
|
||||
" index=x.index)\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # 计算 OBV 及其均线\n",
|
||||
" df['obv'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['maobv_6'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
|
||||
"\n",
|
||||
" # 计算 RSI\n",
|
||||
" df['rsi_3'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['rsi_6'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['rsi_9'] = grouped.apply(\n",
|
||||
" lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # 计算 return_10 和 return_20\n",
|
||||
" df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
|
||||
" df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n",
|
||||
" df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
|
||||
"\n",
|
||||
" # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n",
|
||||
"\n",
|
||||
" # 计算标准差指标\n",
|
||||
" df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n",
|
||||
" df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n",
|
||||
" df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n",
|
||||
" df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n",
|
||||
" df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n",
|
||||
"\n",
|
||||
" # 计算比值指标\n",
|
||||
" df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n",
|
||||
" df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n",
|
||||
"\n",
|
||||
" # 计算标准差差值\n",
|
||||
" df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n",
|
||||
"\n",
|
||||
" return df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_act_factor(df, cat=True):\n",
|
||||
" # 按股票和日期排序\n",
|
||||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||||
" grouped = df.groupby('ts_code', group_keys=False)\n",
|
||||
" # 计算 EMA 指标\n",
|
||||
" df['_ema_5'] = grouped['close'].apply(\n",
|
||||
" lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['_ema_13'] = grouped['close'].apply(\n",
|
||||
" lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['_ema_20'] = grouped['close'].apply(\n",
|
||||
" lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n",
|
||||
" )\n",
|
||||
" df['_ema_60'] = grouped['close'].apply(\n",
|
||||
" lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
|
||||
" df['act_factor1'] = grouped['_ema_5'].apply(\n",
|
||||
" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n",
|
||||
" )\n",
|
||||
" df['act_factor2'] = grouped['_ema_13'].apply(\n",
|
||||
" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n",
|
||||
" )\n",
|
||||
" df['act_factor3'] = grouped['_ema_20'].apply(\n",
|
||||
" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n",
|
||||
" )\n",
|
||||
" df['act_factor4'] = grouped['_ema_60'].apply(\n",
|
||||
" lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" if cat:\n",
|
||||
" df['cat_af1'] = df['act_factor1'] > 0\n",
|
||||
" df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n",
|
||||
" df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n",
|
||||
" df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n",
|
||||
"\n",
|
||||
" # 计算 act_factor5 和 act_factor6\n",
|
||||
" 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",
|
||||
"\n",
|
||||
" # 根据 trade_date 截面计算排名\n",
|
||||
" 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",
|
||||
"\n",
|
||||
"\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",
|
||||
"\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",
|
||||
" 0)\n",
|
||||
"\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",
|
||||
"\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",
|
||||
"\n",
|
||||
"def get_limit_factor(df):\n",
|
||||
" # 按股票和日期排序\n",
|
||||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||||
"\n",
|
||||
" # 分组处理\n",
|
||||
" grouped = df.groupby('ts_code', group_keys=False)\n",
|
||||
"\n",
|
||||
" # 1. 今日是否涨停/跌停\n",
|
||||
" df['cat_up_limit'] = (df['close'] == df['up_limit']).astype(int) # 是否涨停（1表示涨停，0表示未涨停）\n",
|
||||
" df['cat_down_limit'] = (df['close'] == df['down_limit']).astype(int) # 是否跌停（1表示跌停，0表示未跌停）\n",
|
||||
"\n",
|
||||
" # 2. 最近涨跌停次数（过去20个交易日）\n",
|
||||
" df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n",
|
||||
" drop=True)\n",
|
||||
" df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n",
|
||||
" drop=True)\n",
|
||||
"\n",
|
||||
" # 3. 最近连续涨跌停天数\n",
|
||||
" def calculate_consecutive_limits(series):\n",
|
||||
" \"\"\"\n",
|
||||
" 计算连续涨停/跌停天数。\n",
|
||||
" \"\"\"\n",
|
||||
" consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n",
|
||||
" consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n",
|
||||
" return consecutive_up, consecutive_down\n",
|
||||
"\n",
|
||||
" # 连续涨停天数\n",
|
||||
" df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n",
|
||||
" lambda x: calculate_consecutive_limits(x)[0]\n",
|
||||
" ).reset_index(level=0, drop=True)\n",
|
||||
"\n",
|
||||
" # 连续跌停天数\n",
|
||||
" # df['consecutive_down_limit'] = grouped['cat_down_limit'].apply(\n",
|
||||
" # lambda x: calculate_consecutive_limits(x)[1]\n",
|
||||
" # ).reset_index(level=0, drop=True)\n",
|
||||
"\n",
|
||||
" return df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_cyp_perf_factor(df):\n",
|
||||
" # 预处理：按股票代码和时间排序\n",
|
||||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||||
"\n",
|
||||
" # 按股票代码分组处理\n",
|
||||
" grouped = df.groupby('ts_code', group_keys=False)\n",
|
||||
"\n",
|
||||
" df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n",
|
||||
"\n",
|
||||
" df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n",
|
||||
"\n",
|
||||
" df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n",
|
||||
"\n",
|
||||
" df['lock_factor'] = df['turnover_rate'] * (\n",
|
||||
" 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n",
|
||||
"\n",
|
||||
" df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n",
|
||||
"\n",
|
||||
" df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n",
|
||||
"\n",
|
||||
" def rolling_corr(group):\n",
|
||||
" roc_close = group['close'].pct_change()\n",
|
||||
" roc_weight = group['weight_avg'].pct_change()\n",
|
||||
" return roc_close.rolling(10).corr(roc_weight)\n",
|
||||
"\n",
|
||||
" df['price_cost_divergence'] = grouped.apply(rolling_corr)\n",
|
||||
"\n",
|
||||
" def calc_atr(group):\n",
|
||||
" high, low, close = group['high'], group['low'], group['close']\n",
|
||||
" tr = np.maximum(high - low,\n",
|
||||
" np.maximum(abs(high - close.shift()),\n",
|
||||
" abs(low - close.shift())))\n",
|
||||
" return tr.rolling(14).mean()\n",
|
||||
"\n",
|
||||
" df['atr_14'] = grouped.apply(calc_atr)\n",
|
||||
" df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n",
|
||||
"\n",
|
||||
" # 12. 小盘股筹码集中度\n",
|
||||
" df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n",
|
||||
"\n",
|
||||
" # 16. 筹码稳定性指数 (20日波动率)\n",
|
||||
" df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n",
|
||||
" df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n",
|
||||
"\n",
|
||||
" # 17. 成本区间突破标记\n",
|
||||
" df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n",
|
||||
"\n",
|
||||
" # 18. 黄金筹码共振 (复合事件)\n",
|
||||
" df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n",
|
||||
" (df['volume_ratio'] > 1.5) &\n",
|
||||
" (df['winner_rate'] > 0.7))\n",
|
||||
"\n",
|
||||
" # 20. 筹码-流动性风险\n",
|
||||
" df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n",
|
||||
" 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n",
|
||||
"\n",
|
||||
" df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n",
|
||||
"\n",
|
||||
" return df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_mv_factors(df):\n",
|
||||
" \"\"\"\n",
|
||||
" 计算多个因子并生成最终的综合因子。\n",
|
||||
"\n",
|
||||
" 参数:\n",
|
||||
" df (pd.DataFrame): 包含 ts_code, trade_date, turnover_rate, pe_ttm, pb, ps, circ_mv, volume_ratio, vol 等列的数据框。\n",
|
||||
"\n",
|
||||
" 返回:\n",
|
||||
" pd.DataFrame: 包含新增因子和最终综合因子的数据框。\n",
|
||||
" \"\"\"\n",
|
||||
" # 按 ts_code 和 trade_date 排序\n",
|
||||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||||
"\n",
|
||||
" # 按 ts_code 分组\n",
|
||||
" grouped = df.groupby('ts_code', group_keys=False)\n",
|
||||
"\n",
|
||||
" # 1. 市值流动比因子\n",
|
||||
" df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']\n",
|
||||
"\n",
|
||||
" # 2. 市值调整成交量因子\n",
|
||||
" df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n",
|
||||
"\n",
|
||||
" # 3. 市值加权换手率因子\n",
|
||||
" df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n",
|
||||
"\n",
|
||||
" # 4. 非线性市值成交量因子\n",
|
||||
" df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n",
|
||||
"\n",
|
||||
" # 5. 市值量比因子\n",
|
||||
" df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n",
|
||||
"\n",
|
||||
" # 6. 市值动量因子\n",
|
||||
" df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']\n",
|
||||
"\n",
|
||||
" # 7. 市值波动率因子\n",
|
||||
" df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n",
|
||||
" df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n",
|
||||
"\n",
|
||||
" # 8. 市值成长性因子\n",
|
||||
" df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n",
|
||||
" df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n",
|
||||
"\n",
|
||||
" # # 标准化因子\n",
|
||||
" # factor_columns = [\n",
|
||||
" # 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover',\n",
|
||||
" # 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum',\n",
|
||||
" # 'mv_volatility', 'mv_growth'\n",
|
||||
" # ]\n",
|
||||
" # scaler = StandardScaler()\n",
|
||||
" # df[factor_columns] = scaler.fit_transform(df[factor_columns])\n",
|
||||
" #\n",
|
||||
" # # 加权合成因子\n",
|
||||
" # weights = [0.2, 0.15, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1] # 各因子权重\n",
|
||||
" # df['final_combined_factor'] = df[factor_columns].dot(weights)\n",
|
||||
"\n",
|
||||
" return df"
|
||||
],
|
||||
"id": "505e825945e4b8cf"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
1359
code/train/predictions.tsv
Normal file
1359
code/train/predictions.tsv
Normal file
File diff suppressed because it is too large
Load Diff
262
code/train/predictions_test.tsv
Normal file
262
code/train/predictions_test.tsv
Normal file
@@ -0,0 +1,262 @@
|
||||
trade_date,score,ts_code
|
||||
2024-03-01,0.48944956369028353,600515.SH
|
||||
2024-03-04,0.4910973129007265,601377.SH
|
||||
2024-03-05,0.49755441935533035,601377.SH
|
||||
2024-03-06,0.48530227310531543,000950.SZ
|
||||
2024-03-07,0.4852382924633875,002252.SZ
|
||||
2024-03-08,0.4295690224121115,000650.SZ
|
||||
2024-03-11,0.4772666567296332,002539.SZ
|
||||
2024-03-12,0.44912255948878027,002030.SZ
|
||||
2024-03-13,0.48036470172022555,603366.SH
|
||||
2024-03-14,0.47455902892485136,002616.SZ
|
||||
2024-03-15,0.4898609892396381,002616.SZ
|
||||
2024-03-18,0.4898609892396381,002616.SZ
|
||||
2024-03-19,0.4585803401938102,600749.SH
|
||||
2024-03-20,0.4717940942781549,601616.SH
|
||||
2024-03-21,0.4898609892396381,002616.SZ
|
||||
2024-03-22,0.4921881826907514,600268.SH
|
||||
2024-03-25,0.4529948903273269,000910.SZ
|
||||
2024-03-26,0.468535149074664,600163.SH
|
||||
2024-03-27,0.45345883846621166,600268.SH
|
||||
2024-03-28,0.4629678911934166,603689.SH
|
||||
2024-03-29,0.4576699667200496,600479.SH
|
||||
2024-04-01,0.4880682627555449,000407.SZ
|
||||
2024-04-02,0.4658882567202438,002228.SZ
|
||||
2024-04-03,0.46661866733947643,600279.SH
|
||||
2024-04-08,0.4348058590086616,601199.SH
|
||||
2024-04-09,0.440404827771838,600279.SH
|
||||
2024-04-10,0.45436095343545857,600279.SH
|
||||
2024-04-11,0.4860610115557819,002807.SZ
|
||||
2024-04-12,0.49251811801038575,002807.SZ
|
||||
2024-04-15,0.45282134310413785,002267.SZ
|
||||
2024-04-16,0.44573083040693495,601200.SH
|
||||
2024-04-17,0.4755082230960848,600681.SH
|
||||
2024-04-18,0.46969708080904654,600681.SH
|
||||
2024-04-19,0.4763960575203315,601963.SH
|
||||
2024-04-22,0.4797611675632624,601006.SH
|
||||
2024-04-23,0.4417060612494253,600905.SH
|
||||
2024-04-24,0.467078149060861,600681.SH
|
||||
2024-04-25,0.48593234463506524,601016.SH
|
||||
2024-04-26,0.485704669384595,600248.SH
|
||||
2024-04-29,0.4856232616952012,000088.SZ
|
||||
2024-04-30,0.47658127432737996,600717.SH
|
||||
2024-05-06,0.485599247202529,600057.SH
|
||||
2024-05-07,0.48502517567793635,600928.SH
|
||||
2024-05-08,0.4875800938455082,601997.SH
|
||||
2024-05-09,0.4764814479757618,601990.SH
|
||||
2024-05-10,0.4767299756060024,601187.SH
|
||||
2024-05-13,0.48339009051626075,002252.SZ
|
||||
2024-05-14,0.49804720312809037,601236.SH
|
||||
2024-05-15,0.4754440788180303,601669.SH
|
||||
2024-05-16,0.47188100886784723,002252.SZ
|
||||
2024-05-17,0.4738812084576224,000505.SZ
|
||||
2024-05-20,0.47680745811007763,600300.SH
|
||||
2024-05-21,0.4914822821325402,000919.SZ
|
||||
2024-05-22,0.43568399742751623,603777.SH
|
||||
2024-05-23,0.48230237417866073,000919.SZ
|
||||
2024-05-24,0.45217189085141946,600846.SH
|
||||
2024-05-27,0.4822386303700534,000012.SZ
|
||||
2024-05-28,0.4911043379651643,000012.SZ
|
||||
2024-05-29,0.4593967921412618,000012.SZ
|
||||
2024-05-30,0.46327623228814613,600033.SH
|
||||
2024-05-31,0.48434217554317305,601006.SH
|
||||
2024-06-03,0.49496311907072943,601158.SH
|
||||
2024-06-04,0.49311491712360267,601158.SH
|
||||
2024-06-05,0.4662101696226407,600681.SH
|
||||
2024-06-06,0.44598437609141306,600507.SH
|
||||
2024-06-07,0.46683069546117434,002936.SZ
|
||||
2024-06-11,0.48392216170099567,002936.SZ
|
||||
2024-06-12,0.48392216170099567,002936.SZ
|
||||
2024-06-13,0.4856066420239958,601555.SH
|
||||
2024-06-14,0.4626378515738043,600273.SH
|
||||
2024-06-17,0.4930611701154713,601868.SH
|
||||
2024-06-18,0.4850760483461876,600909.SH
|
||||
2024-06-19,0.48789755402053436,600909.SH
|
||||
2024-06-20,0.47168404865503394,600918.SH
|
||||
2024-06-21,0.4780215106901423,002939.SZ
|
||||
2024-06-24,0.4670168740500108,600507.SH
|
||||
2024-06-25,0.4462181161419812,600507.SH
|
||||
2024-06-26,0.47871585166988867,601108.SH
|
||||
2024-06-27,0.4550530100208326,601108.SH
|
||||
2024-06-28,0.4682668022638408,600056.SH
|
||||
2024-07-01,0.4956846242134755,601555.SH
|
||||
2024-07-02,0.4855061855012652,601375.SH
|
||||
2024-07-03,0.48208040944932096,000166.SZ
|
||||
2024-07-04,0.4741422882624783,002763.SZ
|
||||
2024-07-05,0.4207747223564723,600279.SH
|
||||
2024-07-08,0.4505916830616898,000778.SZ
|
||||
2024-07-09,0.47552387696457243,600108.SH
|
||||
2024-07-10,0.48433484852590997,601990.SH
|
||||
2024-07-11,0.4670143394143526,600597.SH
|
||||
2024-07-12,0.48598259773635294,601228.SH
|
||||
2024-07-15,0.46747598383004435,600704.SH
|
||||
2024-07-16,0.46517324677556354,601228.SH
|
||||
2024-07-17,0.4669329317249588,600681.SH
|
||||
2024-07-18,0.4342287048693295,603886.SH
|
||||
2024-07-19,0.4688897638752658,002936.SZ
|
||||
2024-07-22,0.4655336697859469,601008.SH
|
||||
2024-07-23,0.4385299287889017,000959.SZ
|
||||
2024-07-24,0.40980850428167526,600059.SH
|
||||
2024-07-25,0.4354808457559223,600928.SH
|
||||
2024-07-26,0.43863748866816843,002936.SZ
|
||||
2024-07-29,0.4757869671100767,002936.SZ
|
||||
2024-07-30,0.45055950278777573,600056.SH
|
||||
2024-07-31,0.43441391812299834,000672.SZ
|
||||
2024-08-01,0.46163952351711074,600597.SH
|
||||
2024-08-02,0.45912773679288216,601528.SH
|
||||
2024-08-05,0.42637336325271274,000012.SZ
|
||||
2024-08-06,0.44417354831155165,000498.SZ
|
||||
2024-08-07,0.46761174293945373,002029.SZ
|
||||
2024-08-08,0.44084265961702834,600061.SH
|
||||
2024-08-09,0.44756951600219536,600061.SH
|
||||
2024-08-12,0.43865468410238356,600517.SH
|
||||
2024-08-13,0.40119132713124916,600798.SH
|
||||
2024-08-14,0.4147687048594177,600279.SH
|
||||
2024-08-15,0.4285641100025751,600283.SH
|
||||
2024-08-16,0.43481178678490995,600279.SH
|
||||
2024-08-19,0.4334514206803078,000055.SZ
|
||||
2024-08-20,0.4271544196243579,600925.SH
|
||||
2024-08-21,0.44140030057394675,601718.SH
|
||||
2024-08-22,0.4260043286627239,002266.SZ
|
||||
2024-08-23,0.4272469992425721,002266.SZ
|
||||
2024-08-26,0.43298605818458036,601718.SH
|
||||
2024-08-27,0.42993335252901094,600308.SH
|
||||
2024-08-28,0.44014909636073596,601216.SH
|
||||
2024-08-29,0.4549086267823673,002108.SZ
|
||||
2024-08-30,0.4432950051114155,603817.SH
|
||||
2024-09-02,0.44924217940667366,603759.SH
|
||||
2024-09-03,0.45929491873592476,603759.SH
|
||||
2024-09-04,0.4555291720504659,603817.SH
|
||||
2024-09-05,0.45486187663776934,000581.SZ
|
||||
2024-09-06,0.458767486527876,600016.SH
|
||||
2024-09-09,0.42622859802922114,000725.SZ
|
||||
2024-09-10,0.47344109719180894,002239.SZ
|
||||
2024-09-11,0.4602775423090333,600050.SH
|
||||
2024-09-12,0.4581305095531178,603856.SH
|
||||
2024-09-13,0.48196532955068866,002697.SZ
|
||||
2024-09-18,0.48196532955068866,002697.SZ
|
||||
2024-09-19,0.4448663482243619,600210.SH
|
||||
2024-09-20,0.4894480847057984,600293.SH
|
||||
2024-09-23,0.48392216170099567,002239.SZ
|
||||
2024-09-24,0.43493260807615913,603182.SH
|
||||
2024-09-25,0.4279590881784511,601369.SH
|
||||
2024-09-26,0.457735727285402,600526.SH
|
||||
2024-09-27,0.464687096497251,002818.SZ
|
||||
2024-09-30,0.21397500413643297,600081.SH
|
||||
2024-10-08,0.22763716829204592,605333.SH
|
||||
2024-10-09,0.44498910797127905,600495.SH
|
||||
2024-10-10,0.4635725472634731,600251.SH
|
||||
2024-10-11,0.46083590103602623,000967.SZ
|
||||
2024-10-14,0.47310129519500743,600251.SH
|
||||
2024-10-15,0.4432757845922387,601727.SH
|
||||
2024-10-16,0.49142274407028197,002267.SZ
|
||||
2024-10-17,0.4969633584025033,600032.SH
|
||||
2024-10-18,0.49050625194789943,002267.SZ
|
||||
2024-10-21,0.4839204785725789,000709.SZ
|
||||
2024-10-22,0.47843667953797847,002237.SZ
|
||||
2024-10-23,0.4785259848937853,601577.SH
|
||||
2024-10-24,0.48592022305116356,601577.SH
|
||||
2024-10-25,0.4799806860019888,600820.SH
|
||||
2024-10-28,0.4616976069244002,002135.SZ
|
||||
2024-10-29,0.48907368099203313,600330.SH
|
||||
2024-10-30,0.47786058189302977,600516.SH
|
||||
2024-10-31,0.46572447890972274,600969.SH
|
||||
2024-11-01,0.46269570933167936,600261.SH
|
||||
2024-11-04,0.4819410916205754,600249.SH
|
||||
2024-11-05,0.4866489103957071,603167.SH
|
||||
2024-11-06,0.485704669384595,600423.SH
|
||||
2024-11-07,0.4906974157098039,600249.SH
|
||||
2024-11-08,0.47099968069944914,002328.SZ
|
||||
2024-11-11,0.47924756292999116,600103.SH
|
||||
2024-11-12,0.4864515748363791,600103.SH
|
||||
2024-11-13,0.4682636704183581,600419.SH
|
||||
2024-11-14,0.47314641913653194,000533.SZ
|
||||
2024-11-15,0.48200633444426155,600103.SH
|
||||
2024-11-18,0.4734312305146418,600300.SH
|
||||
2024-11-19,0.481591587838489,601113.SH
|
||||
2024-11-20,0.485704669384595,600493.SH
|
||||
2024-11-21,0.48103812387602335,600515.SH
|
||||
2024-11-22,0.48907368099203313,600284.SH
|
||||
2024-11-25,0.49351892138415066,603111.SH
|
||||
2024-11-26,0.49311491712360267,601187.SH
|
||||
2024-11-27,0.49823564043257407,601187.SH
|
||||
2024-11-28,0.49478522113574214,002091.SZ
|
||||
2024-11-29,0.4744243783704977,002390.SZ
|
||||
2024-12-02,0.47703304354620096,002390.SZ
|
||||
2024-12-03,0.48674050526244056,002566.SZ
|
||||
2024-12-04,0.4959275225246577,002753.SZ
|
||||
2024-12-05,0.48839819807517926,002753.SZ
|
||||
2024-12-06,0.4870618149295468,603128.SH
|
||||
2024-12-09,0.49249410351771356,600425.SH
|
||||
2024-12-10,0.4959275225246577,002772.SZ
|
||||
2024-12-11,0.48502517567793635,600035.SH
|
||||
2024-12-12,0.48907368099203313,600035.SH
|
||||
2024-12-13,0.4901095168698787,600035.SH
|
||||
2024-12-16,0.485704669384595,603577.SH
|
||||
2024-12-17,0.42271811250681296,000533.SZ
|
||||
2024-12-18,0.45253512664134277,000026.SZ
|
||||
2024-12-19,0.4668403018194925,601000.SH
|
||||
2024-12-20,0.47643624443298405,000026.SZ
|
||||
2024-12-23,0.4405301681206755,600305.SH
|
||||
2024-12-24,0.44052806294048863,000883.SZ
|
||||
2024-12-25,0.448810439825342,000589.SZ
|
||||
2024-12-26,0.47338854249265216,600582.SH
|
||||
2024-12-27,0.4495112908067394,000830.SZ
|
||||
2024-12-30,0.463164320301011,601006.SH
|
||||
2024-12-31,0.4773178744459276,600004.SH
|
||||
2025-01-02,0.47702414327290926,600572.SH
|
||||
2025-01-03,0.4474782636368997,601163.SH
|
||||
2025-01-06,0.4333771722554744,600821.SH
|
||||
2025-01-07,0.44770531932040636,600004.SH
|
||||
2025-01-08,0.47082512104142743,600116.SH
|
||||
2025-01-09,0.4541624257750102,600004.SH
|
||||
2025-01-10,0.4505883376349118,600905.SH
|
||||
2025-01-13,0.4505883376349118,600905.SH
|
||||
2025-01-14,0.4629277092655053,000088.SZ
|
||||
2025-01-15,0.4849467618585074,601222.SH
|
||||
2025-01-16,0.4904213884330184,600273.SH
|
||||
2025-01-17,0.49427977331421474,002267.SZ
|
||||
2025-01-20,0.4921881826907514,000088.SZ
|
||||
2025-01-21,0.49980885843191936,002233.SZ
|
||||
2025-01-22,0.49226959038014517,603817.SH
|
||||
2025-01-23,0.4845623679956794,600731.SH
|
||||
2025-01-24,0.4894480847057984,002443.SZ
|
||||
2025-01-27,0.480828304199342,600475.SH
|
||||
2025-02-05,0.49381191740852215,600475.SH
|
||||
2025-02-06,0.48051107405830695,600219.SH
|
||||
2025-02-07,0.4912337545022996,002365.SZ
|
||||
2025-02-10,0.4891404807504195,002606.SZ
|
||||
2025-02-11,0.48918316877240914,002454.SZ
|
||||
2025-02-12,0.48841224882795287,605138.SH
|
||||
2025-02-13,0.49427977331421474,603022.SH
|
||||
2025-02-14,0.4961990011809636,002454.SZ
|
||||
2025-02-17,0.49980885843191936,603172.SH
|
||||
2025-02-18,0.4859761480409009,600526.SH
|
||||
2025-02-19,0.47951904158629705,600526.SH
|
||||
2025-02-20,0.4865640468808261,002972.SZ
|
||||
2025-02-21,0.4878226668596109,002972.SZ
|
||||
2025-02-24,0.49427977331421474,002972.SZ
|
||||
2025-02-25,0.4959275225246577,002972.SZ
|
||||
2025-02-26,0.49559758720502334,000850.SZ
|
||||
2025-02-27,0.4962265008275085,600969.SH
|
||||
2025-02-28,0.45857905703475876,000931.SZ
|
||||
2025-03-03,0.475929514181944,600704.SH
|
||||
2025-03-04,0.4855766924008325,603176.SH
|
||||
2025-03-05,0.48947041607005387,600749.SH
|
||||
2025-03-06,0.48947041607005387,600749.SH
|
||||
2025-03-07,0.48852295338866114,002948.SZ
|
||||
2025-03-10,0.48013938012027435,600749.SH
|
||||
2025-03-11,0.4697442502219659,603916.SH
|
||||
2025-03-12,0.46825376442882116,600969.SH
|
||||
2025-03-13,0.485704669384595,601311.SH
|
||||
2025-03-14,0.47290759442027386,600784.SH
|
||||
2025-03-17,0.48236846890282975,002204.SZ
|
||||
2025-03-18,0.4753161955607809,600784.SH
|
||||
2025-03-19,0.4898609892396381,002627.SZ
|
||||
2025-03-20,0.4898609892396381,002627.SZ
|
||||
2025-03-21,0.47660773064492085,000589.SZ
|
||||
2025-03-24,0.4753538392607698,000589.SZ
|
||||
2025-03-25,0.4628733846203298,000589.SZ
|
||||
2025-03-26,0.45948501496487415,603367.SH
|
||||
2025-03-27,0.47591884404751766,600017.SH
|
||||
2025-03-28,0.4765671044505851,600925.SH
|
||||
|
1116
code/train/predictions_train.tsv
Normal file
1116
code/train/predictions_train.tsv
Normal file
File diff suppressed because it is too large
Load Diff
738
code/train/utils/factor.py
Normal file
738
code/train/utils/factor.py
Normal file
@@ -0,0 +1,738 @@
|
||||
import numpy as np
|
||||
import talib
|
||||
import pandas as pd
|
||||
|
||||
def get_technical_factor(df):
|
||||
# 按股票和日期排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
|
||||
df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)
|
||||
df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)
|
||||
|
||||
# 因子 1：短期成交量变化率
|
||||
df['volume_change_rate'] = (
|
||||
grouped['vol'].rolling(window=2).mean() /
|
||||
grouped['vol'].rolling(window=5).mean() - 1
|
||||
).reset_index(level=0, drop=True) # 确保索引对齐
|
||||
|
||||
# 因子 2：成交量突破信号
|
||||
max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐
|
||||
df['cat_volume_breakout'] = (df['vol'] > max_volume)
|
||||
|
||||
# 因子 3：换手率均线偏离度
|
||||
mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
||||
std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)
|
||||
df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover
|
||||
|
||||
# 因子 4：换手率激增信号
|
||||
df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)
|
||||
|
||||
# 因子 5：量比均值
|
||||
df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
||||
|
||||
# 因子 6：量比突破信号
|
||||
max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)
|
||||
df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)
|
||||
|
||||
# 因子 7：成交量与换手率的综合动量因子
|
||||
alpha = 0.5
|
||||
df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
|
||||
|
||||
# 因子 8：量价共振因子
|
||||
df['price_change_rate'] = grouped['close'].pct_change()
|
||||
df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate']
|
||||
|
||||
# 计算 up 和 down
|
||||
df['log_close'] = np.log(df['close'])
|
||||
|
||||
df['vol_spike'] = grouped.apply(
|
||||
lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
|
||||
)
|
||||
df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
|
||||
df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()
|
||||
|
||||
df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
|
||||
df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
|
||||
|
||||
# 计算 ATR
|
||||
df['atr_14'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),
|
||||
index=x.index)
|
||||
)
|
||||
df['atr_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),
|
||||
index=x.index)
|
||||
)
|
||||
|
||||
# 计算 OBV 及其均线
|
||||
df['obv'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
|
||||
)
|
||||
df['maobv_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)
|
||||
)
|
||||
df['obv-maobv_6'] = df['obv'] - df['maobv_6']
|
||||
|
||||
# 计算 RSI
|
||||
df['rsi_3'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)
|
||||
)
|
||||
df['rsi_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)
|
||||
)
|
||||
df['rsi_9'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)
|
||||
)
|
||||
|
||||
# 计算 return_10 和 return_20
|
||||
df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
|
||||
df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)
|
||||
df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
|
||||
|
||||
# df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)
|
||||
|
||||
# 计算标准差指标
|
||||
df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())
|
||||
df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())
|
||||
df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())
|
||||
df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())
|
||||
df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())
|
||||
|
||||
# 计算比值指标
|
||||
df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']
|
||||
df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']
|
||||
|
||||
# 计算标准差差值
|
||||
df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def get_act_factor(df, cat=True):
|
||||
# 按股票和日期排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
# 计算 EMA 指标
|
||||
df['_ema_5'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)
|
||||
)
|
||||
df['_ema_13'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)
|
||||
)
|
||||
df['_ema_20'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)
|
||||
)
|
||||
df['_ema_60'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)
|
||||
)
|
||||
|
||||
# 计算 act_factor1, act_factor2, act_factor3, act_factor4
|
||||
df['act_factor1'] = grouped['_ema_5'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
|
||||
)
|
||||
df['act_factor2'] = grouped['_ema_13'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
|
||||
)
|
||||
df['act_factor3'] = grouped['_ema_20'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
|
||||
)
|
||||
df['act_factor4'] = grouped['_ema_60'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
|
||||
)
|
||||
|
||||
if cat:
|
||||
df['cat_af1'] = df['act_factor1'] > 0
|
||||
df['cat_af2'] = df['act_factor2'] > df['act_factor1']
|
||||
df['cat_af3'] = df['act_factor3'] > df['act_factor2']
|
||||
df['cat_af4'] = df['act_factor4'] > df['act_factor3']
|
||||
|
||||
# 计算 act_factor5 和 act_factor6
|
||||
df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']
|
||||
df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(
|
||||
df['act_factor1'] ** 2 + df['act_factor2'] ** 2)
|
||||
|
||||
# 根据 trade_date 截面计算排名
|
||||
df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
|
||||
df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
|
||||
df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def get_money_flow_factor(df):
|
||||
# 计算资金流相关因子（字段名称见 tushare 数据说明）
|
||||
df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']
|
||||
df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']
|
||||
df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']
|
||||
|
||||
df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']
|
||||
df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']
|
||||
|
||||
df['log(circ_mv)'] = np.log(df['circ_mv'])
|
||||
return df
|
||||
|
||||
|
||||
def get_alpha_factor(df):
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
grouped = df.groupby('ts_code')
|
||||
|
||||
# alpha_022: 当前 close 与 5 日前 close 差值
|
||||
# df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))
|
||||
def rolling_covariance(x, y, window):
|
||||
return x.rolling(window).cov(y)
|
||||
|
||||
def delta(series, period):
|
||||
return series.diff(period)
|
||||
|
||||
def rank(series):
|
||||
return series.rank(pct=True)
|
||||
|
||||
def stddev(series, window):
|
||||
return series.rolling(window).std()
|
||||
|
||||
# 计算改进后的 Alpha 22 因子
|
||||
window_high_volume = 5
|
||||
window_close_stddev = 20
|
||||
period_delta = 5
|
||||
|
||||
df['cov'] = rolling_covariance(df['high'], df['volume'], window_high_volume)
|
||||
df['delta_cov'] = delta(df['cov'], period_delta)
|
||||
df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
|
||||
df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
|
||||
|
||||
# alpha_003: (close - open) / (high - low)
|
||||
df['alpha_003'] = np.where(df['high'] != df['low'],
|
||||
(df['close'] - df['open']) / (df['high'] - df['low']),
|
||||
0)
|
||||
|
||||
# alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名
|
||||
df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
|
||||
df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
|
||||
|
||||
# alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名
|
||||
df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
|
||||
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def get_limit_factor(df):
|
||||
# 按股票和日期排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
|
||||
# 分组处理
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
|
||||
# 1. 今日是否涨停/跌停
|
||||
df['cat_up_limit'] = (df['close'] == df['up_limit']).astype(int) # 是否涨停（1表示涨停，0表示未涨停）
|
||||
df['cat_down_limit'] = (df['close'] == df['down_limit']).astype(int) # 是否跌停（1表示跌停，0表示未跌停）
|
||||
|
||||
# 2. 最近涨跌停次数（过去20个交易日）
|
||||
df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
||||
drop=True)
|
||||
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
||||
drop=True)
|
||||
|
||||
# 3. 最近连续涨跌停天数
|
||||
def calculate_consecutive_limits(series):
|
||||
"""
|
||||
计算连续涨停/跌停天数。
|
||||
"""
|
||||
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
||||
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
||||
return consecutive_up, consecutive_down
|
||||
|
||||
# 连续涨停天数
|
||||
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
|
||||
lambda x: calculate_consecutive_limits(x)[0]
|
||||
).reset_index(level=0, drop=True)
|
||||
|
||||
# 连续跌停天数
|
||||
# df['consecutive_down_limit'] = grouped['cat_down_limit'].apply(
|
||||
# lambda x: calculate_consecutive_limits(x)[1]
|
||||
# ).reset_index(level=0, drop=True)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def get_cyp_perf_factor(df):
|
||||
# 预处理：按股票代码和时间排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
|
||||
# 按股票代码分组处理
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
|
||||
df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])
|
||||
|
||||
df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
||||
|
||||
df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
||||
|
||||
df['lock_factor'] = df['turnover_rate'] * (
|
||||
1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))
|
||||
|
||||
df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
|
||||
|
||||
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
|
||||
|
||||
def rolling_corr(group):
|
||||
roc_close = group['close'].pct_change()
|
||||
roc_weight = group['weight_avg'].pct_change()
|
||||
return roc_close.rolling(10).corr(roc_weight)
|
||||
|
||||
df['price_cost_divergence'] = grouped.apply(rolling_corr)
|
||||
|
||||
def calc_atr(group):
|
||||
high, low, close = group['high'], group['low'], group['close']
|
||||
tr = np.maximum(high - low,
|
||||
np.maximum(abs(high - close.shift()),
|
||||
abs(low - close.shift())))
|
||||
return tr.rolling(14).mean()
|
||||
|
||||
df['atr_14'] = grouped.apply(calc_atr)
|
||||
df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']
|
||||
|
||||
# 12. 小盘股筹码集中度
|
||||
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
||||
|
||||
# 16. 筹码稳定性指数 (20日波动率)
|
||||
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
|
||||
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
|
||||
|
||||
# 17. 成本区间突破标记
|
||||
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
|
||||
|
||||
# 18. 黄金筹码共振 (复合事件)
|
||||
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
|
||||
(df['volume_ratio'] > 1.5) &
|
||||
(df['winner_rate'] > 0.7))
|
||||
|
||||
# 20. 筹码-流动性风险
|
||||
df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (
|
||||
1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))
|
||||
|
||||
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def get_mv_factors(df):
|
||||
"""
|
||||
计算多个因子并生成最终的综合因子。
|
||||
|
||||
参数:
|
||||
df (pd.DataFrame): 包含 ts_code, trade_date, turnover_rate, pe_ttm, pb, ps, circ_mv, volume_ratio, vol 等列的数据框。
|
||||
|
||||
返回:
|
||||
pd.DataFrame: 包含新增因子和最终综合因子的数据框。
|
||||
"""
|
||||
# 按 ts_code 和 trade_date 排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
|
||||
# 按 ts_code 分组
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
|
||||
# 1. 市值流动比因子
|
||||
df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']
|
||||
|
||||
# 2. 市值调整成交量因子
|
||||
df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']
|
||||
|
||||
# 3. 市值加权换手率因子
|
||||
df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])
|
||||
|
||||
# 4. 非线性市值成交量因子
|
||||
df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']
|
||||
|
||||
# 5. 市值量比因子
|
||||
df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']
|
||||
|
||||
# 6. 市值动量因子
|
||||
df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']
|
||||
|
||||
# 7. 市值波动率因子
|
||||
df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)
|
||||
df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)
|
||||
|
||||
# 8. 市值成长性因子
|
||||
df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)
|
||||
df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)
|
||||
|
||||
# # 标准化因子
|
||||
# factor_columns = [
|
||||
# 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover',
|
||||
# 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum',
|
||||
# 'mv_volatility', 'mv_growth'
|
||||
# ]
|
||||
# scaler = StandardScaler()
|
||||
# df[factor_columns] = scaler.fit_transform(df[factor_columns])
|
||||
#
|
||||
# # 加权合成因子
|
||||
# weights = [0.2, 0.15, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1] # 各因子权重
|
||||
# df['final_combined_factor'] = df[factor_columns].dot(weights)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
import numpy as np
|
||||
import talib
|
||||
|
||||
|
||||
def get_rolling_factor(df):
|
||||
old_columns = df.columns.tolist()[:]
|
||||
# 按股票和日期排序
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
grouped = df.groupby('ts_code', group_keys=False)
|
||||
|
||||
df["gap_next_open"] = (df["open"].shift(-1) - df["close"]) / df["close"]
|
||||
|
||||
df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)
|
||||
df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)
|
||||
|
||||
# 因子 1：短期成交量变化率
|
||||
df['volume_change_rate'] = (
|
||||
grouped['vol'].rolling(window=2).mean() /
|
||||
grouped['vol'].rolling(window=10).mean() - 1
|
||||
).reset_index(level=0, drop=True) # 确保索引对齐
|
||||
|
||||
# 因子 2：成交量突破信号
|
||||
max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐
|
||||
df['cat_volume_breakout'] = (df['vol'] > max_volume)
|
||||
|
||||
# 因子 3：换手率均线偏离度
|
||||
mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
||||
std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)
|
||||
df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover
|
||||
|
||||
# 因子 4：换手率激增信号
|
||||
df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)
|
||||
|
||||
# 因子 5：量比均值
|
||||
df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
||||
|
||||
# 因子 6：量比突破信号
|
||||
max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)
|
||||
df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)
|
||||
|
||||
df['vol_spike'] = grouped.apply(
|
||||
lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
|
||||
)
|
||||
df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()
|
||||
|
||||
# 计算 ATR
|
||||
df['atr_14'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),
|
||||
index=x.index)
|
||||
)
|
||||
df['atr_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),
|
||||
index=x.index)
|
||||
)
|
||||
|
||||
# 计算 OBV 及其均线
|
||||
df['obv'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
|
||||
)
|
||||
df['maobv_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)
|
||||
)
|
||||
|
||||
df['rsi_3'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)
|
||||
)
|
||||
df['rsi_6'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)
|
||||
)
|
||||
df['rsi_9'] = grouped.apply(
|
||||
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)
|
||||
)
|
||||
|
||||
# 计算 return_10 和 return_20
|
||||
df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
|
||||
df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)
|
||||
df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
|
||||
|
||||
# df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)
|
||||
|
||||
# 计算标准差指标
|
||||
df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())
|
||||
df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())
|
||||
df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())
|
||||
df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())
|
||||
df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())
|
||||
|
||||
# 计算 EMA 指标
|
||||
df['_ema_5'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)
|
||||
)
|
||||
df['_ema_13'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)
|
||||
)
|
||||
df['_ema_20'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)
|
||||
)
|
||||
df['_ema_60'] = grouped['close'].apply(
|
||||
lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)
|
||||
)
|
||||
|
||||
# 计算 act_factor1, act_factor2, act_factor3, act_factor4
|
||||
df['act_factor1'] = grouped['_ema_5'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
|
||||
)
|
||||
df['act_factor2'] = grouped['_ema_13'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
|
||||
)
|
||||
df['act_factor3'] = grouped['_ema_20'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
|
||||
)
|
||||
df['act_factor4'] = grouped['_ema_60'].apply(
|
||||
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
|
||||
)
|
||||
|
||||
# 根据 trade_date 截面计算排名
|
||||
df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
|
||||
df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
|
||||
df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
|
||||
|
||||
df['log(circ_mv)'] = np.log(df['circ_mv'])
|
||||
|
||||
def rolling_covariance(x, y, window):
|
||||
return x.rolling(window).cov(y)
|
||||
|
||||
def delta(series, period):
|
||||
return series.diff(period)
|
||||
|
||||
def rank(series):
|
||||
return series.rank(pct=True)
|
||||
|
||||
def stddev(series, window):
|
||||
return series.rolling(window).std()
|
||||
|
||||
window_high_volume = 5
|
||||
window_close_stddev = 20
|
||||
period_delta = 5
|
||||
df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)
|
||||
df['delta_cov'] = delta(df['cov'], period_delta)
|
||||
df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
|
||||
df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
|
||||
|
||||
df['alpha_003'] = np.where(df['high'] != df['low'],
|
||||
(df['close'] - df['open']) / (df['high'] - df['low']),
|
||||
0)
|
||||
|
||||
df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
|
||||
df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
|
||||
|
||||
df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
|
||||
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
|
||||
|
||||
df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）
|
||||
df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）
|
||||
df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
||||
drop=True)
|
||||
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
|
||||
drop=True)
|
||||
|
||||
# 3. 最近连续涨跌停天数
|
||||
def calculate_consecutive_limits(series):
|
||||
"""
|
||||
计算连续涨停/跌停天数。
|
||||
"""
|
||||
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
||||
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
||||
return consecutive_up, consecutive_down
|
||||
|
||||
# 连续涨停天数
|
||||
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
|
||||
lambda x: calculate_consecutive_limits(x)[0]
|
||||
).reset_index(level=0, drop=True)
|
||||
|
||||
df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
|
||||
|
||||
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
|
||||
|
||||
def rolling_corr(group):
|
||||
roc_close = group['close'].pct_change()
|
||||
roc_weight = group['weight_avg'].pct_change()
|
||||
return roc_close.rolling(10).corr(roc_weight)
|
||||
|
||||
df['price_cost_divergence'] = grouped.apply(rolling_corr)
|
||||
|
||||
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
||||
|
||||
# 16. 筹码稳定性指数 (20日波动率)
|
||||
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
|
||||
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
|
||||
|
||||
# 17. 成本区间突破标记
|
||||
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
|
||||
|
||||
# 20. 筹码-流动性风险
|
||||
df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (
|
||||
1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))
|
||||
|
||||
# 7. 市值波动率因子
|
||||
df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)
|
||||
df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)
|
||||
|
||||
# 8. 市值成长性因子
|
||||
df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)
|
||||
df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)
|
||||
|
||||
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
|
||||
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
||||
|
||||
return df, new_columns
|
||||
|
||||
|
||||
def get_simple_factor(df):
|
||||
old_columns = df.columns.tolist()[:]
|
||||
df = df.sort_values(by=['ts_code', 'trade_date'])
|
||||
|
||||
alpha = 0.5
|
||||
df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
|
||||
df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']
|
||||
df['log_close'] = np.log(df['close'])
|
||||
|
||||
df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
|
||||
|
||||
df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
|
||||
df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
|
||||
|
||||
df['obv-maobv_6'] = df['obv'] - df['maobv_6']
|
||||
|
||||
# 计算比值指标
|
||||
df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']
|
||||
df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']
|
||||
|
||||
# 计算标准差差值
|
||||
df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']
|
||||
|
||||
df['cat_af1'] = df['act_factor1'] > 0
|
||||
df['cat_af2'] = df['act_factor2'] > df['act_factor1']
|
||||
df['cat_af3'] = df['act_factor3'] > df['act_factor2']
|
||||
df['cat_af4'] = df['act_factor4'] > df['act_factor3']
|
||||
|
||||
# 计算 act_factor5 和 act_factor6
|
||||
df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']
|
||||
df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(
|
||||
df['act_factor1'] ** 2 + df['act_factor2'] ** 2)
|
||||
|
||||
df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']
|
||||
df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']
|
||||
df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']
|
||||
|
||||
df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']
|
||||
df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']
|
||||
|
||||
df['log(circ_mv)'] = np.log(df['circ_mv'])
|
||||
|
||||
df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])
|
||||
|
||||
df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
||||
|
||||
df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
||||
|
||||
df['lock_factor'] = df['turnover_rate'] * (
|
||||
1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))
|
||||
|
||||
df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)
|
||||
|
||||
df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']
|
||||
|
||||
# 12. 小盘股筹码集中度
|
||||
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
||||
|
||||
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
|
||||
(df['volume_ratio'] > 1.5) &
|
||||
(df['winner_rate'] > 0.7))
|
||||
|
||||
df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']
|
||||
|
||||
df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']
|
||||
|
||||
df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])
|
||||
|
||||
df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']
|
||||
|
||||
df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']
|
||||
|
||||
df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']
|
||||
|
||||
drop_columns = [col for col in df.columns if col.startswith('_')]
|
||||
df.drop(columns=drop_columns, inplace=True, errors='ignore')
|
||||
|
||||
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
||||
return df, new_columns
|
||||
|
||||
|
||||
def calculate_indicators(df):
|
||||
"""
|
||||
计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。
|
||||
"""
|
||||
df = df.sort_values('trade_date')
|
||||
df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100
|
||||
# df['5_day_ma'] = df['close'].rolling(window=5).mean()
|
||||
delta = df['close'].diff()
|
||||
gain = delta.where(delta > 0, 0)
|
||||
loss = -delta.where(delta < 0, 0)
|
||||
avg_gain = gain.rolling(window=14).mean()
|
||||
avg_loss = loss.rolling(window=14).mean()
|
||||
rs = avg_gain / avg_loss
|
||||
df['RSI'] = 100 - (100 / (1 + rs))
|
||||
|
||||
# 计算MACD
|
||||
ema12 = df['close'].ewm(span=12, adjust=False).mean()
|
||||
ema26 = df['close'].ewm(span=26, adjust=False).mean()
|
||||
df['MACD'] = ema12 - ema26
|
||||
df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()
|
||||
df['MACD_hist'] = df['MACD'] - df['Signal_line']
|
||||
|
||||
# 4. 情绪因子1：市场上涨比例（Up Ratio）
|
||||
df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)
|
||||
df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例
|
||||
|
||||
# 5. 情绪因子2：成交量变化率（Volume Change Rate）
|
||||
df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量
|
||||
df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率
|
||||
|
||||
# 6. 情绪因子3：波动率（Volatility）
|
||||
df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差
|
||||
|
||||
# 7. 情绪因子4：成交额变化率（Amount Change Rate）
|
||||
df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额
|
||||
df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def generate_index_indicators(h5_filename):
|
||||
df = pd.read_hdf(h5_filename, key='index_data')
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
|
||||
df = df.sort_values('trade_date')
|
||||
|
||||
# 计算每个ts_code的相关指标
|
||||
df_indicators = []
|
||||
for ts_code in df['ts_code'].unique():
|
||||
df_index = df[df['ts_code'] == ts_code].copy()
|
||||
df_index = calculate_indicators(df_index)
|
||||
df_indicators.append(df_index)
|
||||
|
||||
# 合并所有指数的结果
|
||||
df_all_indicators = pd.concat(df_indicators, ignore_index=True)
|
||||
|
||||
# 保留trade_date列，并将同一天的数据按ts_code合并成一行
|
||||
df_final = df_all_indicators.pivot_table(
|
||||
index='trade_date',
|
||||
columns='ts_code',
|
||||
values=['daily_return', 'RSI', 'MACD', 'Signal_line',
|
||||
'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',
|
||||
'amount_change_rate', 'amount_mean'],
|
||||
aggfunc='last'
|
||||
)
|
||||
|
||||
df_final.columns = [f"{col[1]}_{col[0]}" for col in df_final.columns]
|
||||
df_final = df_final.reset_index()
|
||||
|
||||
return df_final
|
||||
Reference in New Issue
Block a user