{ "cells": [ { "cell_type": "code", "id": "79a7758178bafdd3", "metadata": { "jupyter": { "source_hidden": true }, "ExecuteTime": { "end_time": "2025-03-03T13:08:58.678627Z", "start_time": "2025-03-03T13:08:58.674698Z" } }, "source": [ "# %load_ext autoreload\n", "# %autoreload 2\n", "\n", "import pandas as pd\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option('display.max_columns', None)\n" ], "outputs": [], "execution_count": 3 }, { "cell_type": "code", "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:09:40.479598Z", "start_time": "2025-03-03T13:08:58.750948Z" } }, "source": [ "from utils.utils import read_and_merge_h5_data\n", "\n", "print('daily data')\n", "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", " df=None)\n", "\n", "print('daily basic')\n", "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", " 'is_st'], df=df, join='inner')\n", "\n", "print('stk limit')\n", "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", " df=df)\n", "print('money flow')\n", "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", " df=df)\n", "print(df.info())" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "daily data\n", "daily basic\n", "inner merge on ['ts_code', 'trade_date']\n", "stk limit\n", "left merge on ['ts_code', 'trade_date']\n", "money flow\n", "left merge on ['ts_code', 'trade_date']\n", "\n", "RangeIndex: 8369855 entries, 0 to 8369854\n", "Data columns (total 21 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object \n", " 1 trade_date datetime64[ns]\n", " 2 open float64 \n", " 3 close float64 \n", " 4 high float64 \n", " 5 low float64 \n", " 6 vol float64 \n", " 7 turnover_rate float64 \n", " 8 pe_ttm float64 \n", " 9 circ_mv float64 \n", " 10 volume_ratio float64 \n", " 11 is_st bool \n", " 12 up_limit float64 \n", " 13 down_limit float64 \n", " 14 buy_sm_vol float64 \n", " 15 sell_sm_vol float64 \n", " 16 buy_lg_vol float64 \n", " 17 sell_lg_vol float64 \n", " 18 buy_elg_vol float64 \n", " 19 sell_elg_vol float64 \n", " 20 net_mf_vol float64 \n", "dtypes: bool(1), datetime64[ns](1), float64(18), object(1)\n", "memory usage: 1.3+ GB\n", "None\n" ] } ], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:09:42.969235Z", "start_time": "2025-03-03T13:09:40.522863Z" } }, "cell_type": "code", "source": [ "print('industry')\n", "df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", " columns=['ts_code', 'l2_code'],\n", " df=df, on=['ts_code'], join='left')\n" ], "id": "38879273d3574ae3", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "industry\n", "left merge on ['ts_code']\n" ] } ], "execution_count": 5 }, { "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:09:43.066598Z", "start_time": "2025-03-03T13:09:42.976241Z" } }, "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def calculate_indicators(df):\n", " \"\"\"\n", " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", " \"\"\"\n", " df = df.sort_values('trade_date')\n", " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", " delta = df['close'].diff()\n", " gain = delta.where(delta > 0, 0)\n", " loss = -delta.where(delta < 0, 0)\n", " avg_gain = gain.rolling(window=14).mean()\n", " avg_loss = loss.rolling(window=14).mean()\n", " rs = avg_gain / avg_loss\n", " df['RSI'] = 100 - (100 / (1 + rs))\n", "\n", " # 计算MACD\n", " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", " df['MACD'] = ema12 - ema26\n", " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", "\n", " return df\n", "\n", "\n", "def generate_index_indicators(h5_filename):\n", " df = pd.read_hdf(h5_filename, key='index_data')\n", " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", " df = df.sort_values('trade_date')\n", "\n", " # 计算每个ts_code的相关指标\n", " df_indicators = []\n", " for ts_code in df['ts_code'].unique():\n", " df_index = df[df['ts_code'] == ts_code].copy()\n", " df_index = calculate_indicators(df_index)\n", " df_indicators.append(df_index)\n", "\n", " # 合并所有指数的结果\n", " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", "\n", " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", " df_final = df_all_indicators.pivot_table(\n", " index='trade_date',\n", " columns='ts_code',\n", " values=['daily_return', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\n", " aggfunc='last'\n", " )\n", "\n", " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", " df_final = df_final.reset_index()\n", "\n", " return df_final\n", "\n", "\n", "# 使用函数\n", "h5_filename = '../../data/index_data.h5'\n", "index_data = generate_index_indicators(h5_filename)\n", "index_data = index_data.dropna()\n" ], "id": "a4eec8c93f6a7cc3", "outputs": [], "execution_count": 6 }, { "cell_type": "code", "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:09:43.133133Z", "start_time": "2025-03-03T13:09:43.090288Z" } }, "source": [ "import numpy as np\n", "import talib\n", "\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", " # 计算 up 和 down\n", " df['log_close'] = np.log(df['close'])\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", " # 计算 avg_close_5\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" ], "outputs": [], "execution_count": 7 }, { "cell_type": "code", "id": "53f86ddc0677a6d7", "metadata": { "scrolled": true, "ExecuteTime": { "end_time": "2025-03-03T13:09:50.856330Z", "start_time": "2025-03-03T13:09:43.137146Z" } }, "source": [ "def read_industry_data(h5_filename):\n", " # 读取 H5 文件中所有的行业数据\n", " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", " ]) # 假设 H5 文件的键是 'industry_data'\n", " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", " industry_data = industry_data.reindex()\n", " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", "\n", " grouped = industry_data.groupby('ts_code', group_keys=False)\n", " industry_data['obv'] = grouped.apply(\n", " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", " )\n", " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", "\n", " industry_data = get_act_factor(industry_data, cat=False)\n", " # industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", "\n", " # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", " factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", "\n", " for factor in factor_columns:\n", " if factor in industry_data.columns:\n", " # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", " industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", " lambda x: x - x.median())\n", "\n", " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", " lambda x: x.rank(pct=True))\n", " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", "\n", " industry_data = industry_data.rename(\n", " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", "\n", " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", " return industry_data\n", "\n", "\n", "industry_df = read_industry_data('../../data/sw_daily.h5')\n" ], "outputs": [], "execution_count": 8 }, { "cell_type": "code", "id": "dbe2fd8021b9417f", "metadata": { "jupyter": { "source_hidden": true }, "scrolled": true, "ExecuteTime": { "end_time": "2025-03-03T13:09:50.873842Z", "start_time": "2025-03-03T13:09:50.870793Z" } }, "source": [ "origin_columns = df.columns.tolist()\n", "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code']]\n", "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n" ], "outputs": [], "execution_count": 9 }, { "cell_type": "code", "id": "5f3d9aece75318cd", "metadata": { "jupyter": { "source_hidden": true }, "ExecuteTime": { "end_time": "2025-03-03T13:11:03.268415Z", "start_time": "2025-03-03T13:09:50.890356Z" } }, "source": [ "def filter_data(df):\n", " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", " df = df[~df['is_st']]\n", " df = df[~df['ts_code'].str.endswith('BJ')]\n", " df = df[~df['ts_code'].str.startswith('30')]\n", " df = df[~df['ts_code'].str.startswith('68')]\n", " df = df[~df['ts_code'].str.startswith('8')]\n", " df = df.reset_index(drop=True)\n", " return df\n", "\n", "\n", "df = filter_data(df)\n", "df = get_technical_factor(df)\n", "df = get_act_factor(df)\n", "df = get_money_flow_factor(df)\n", "df = get_alpha_factor(df)\n", "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", "# df = df.merge(index_data, on='trade_date', how='left')\n", "\n", "print(df.info())" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5732462 entries, 1964 to 5732460\n", "Data columns (total 72 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object \n", " 1 trade_date datetime64[ns]\n", " 2 open float64 \n", " 3 close float64 \n", " 4 high float64 \n", " 5 low float64 \n", " 6 vol float64 \n", " 7 turnover_rate float64 \n", " 8 pe_ttm float64 \n", " 9 circ_mv float64 \n", " 10 volume_ratio float64 \n", " 11 is_st bool \n", " 12 up_limit float64 \n", " 13 down_limit float64 \n", " 14 buy_sm_vol float64 \n", " 15 sell_sm_vol float64 \n", " 16 buy_lg_vol float64 \n", " 17 sell_lg_vol float64 \n", " 18 buy_elg_vol float64 \n", " 19 sell_elg_vol float64 \n", " 20 net_mf_vol float64 \n", " 21 cat_l2_code object \n", " 22 log_close float64 \n", " 23 up float64 \n", " 24 down float64 \n", " 25 atr_14 float64 \n", " 26 atr_6 float64 \n", " 27 obv float64 \n", " 28 maobv_6 float64 \n", " 29 obv-maobv_6 float64 \n", " 30 rsi_3 float64 \n", " 31 rsi_6 float64 \n", " 32 rsi_9 float64 \n", " 33 return_5 float64 \n", " 34 return_10 float64 \n", " 35 return_20 float64 \n", " 36 avg_close_5 float64 \n", " 37 std_return_5 float64 \n", " 38 std_return_15 float64 \n", " 39 std_return_25 float64 \n", " 40 std_return_90 float64 \n", " 41 std_return_90_2 float64 \n", " 42 std_return_5 / std_return_90 float64 \n", " 43 std_return_5 / std_return_25 float64 \n", " 44 std_return_90 - std_return_90_2 float64 \n", " 45 ema_5 float64 \n", " 46 ema_13 float64 \n", " 47 ema_20 float64 \n", " 48 ema_60 float64 \n", " 49 act_factor1 float64 \n", " 50 act_factor2 float64 \n", " 51 act_factor3 float64 \n", " 52 act_factor4 float64 \n", " 53 cat_af1 bool \n", " 54 cat_af2 bool \n", " 55 cat_af3 bool \n", " 56 cat_af4 bool \n", " 57 act_factor5 float64 \n", " 58 act_factor6 float64 \n", " 59 rank_act_factor1 float64 \n", " 60 rank_act_factor2 float64 \n", " 61 rank_act_factor3 float64 \n", " 62 active_buy_volume_large float64 \n", " 63 active_buy_volume_big float64 \n", " 64 active_buy_volume_small float64 \n", " 65 buy_lg_vol_minus_sell_lg_vol float64 \n", " 66 buy_elg_vol_minus_sell_elg_vol float64 \n", " 67 log(circ_mv) float64 \n", " 68 alpha_022 float64 \n", " 69 alpha_003 float64 \n", " 70 alpha_007 float64 \n", " 71 alpha_013 float64 \n", "dtypes: bool(5), datetime64[ns](1), float64(64), object(2)\n", "memory usage: 2.9+ GB\n", "None\n" ] } ], "execution_count": 10 }, { "cell_type": "code", "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:11:03.654681Z", "start_time": "2025-03-03T13:11:03.638505Z" } }, "source": [ "def create_deviation_within_dates(df, feature_columns):\n", " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", " new_columns = {}\n", " ret_feature_columns = feature_columns[:]\n", "\n", " # 自动选择所有数值型特征\n", " # num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", " num_features = [col for col in feature_columns if 'cat' not in col and 'industry' not in col]\n", "\n", " # 遍历所有数值型特征\n", " for feature in num_features:\n", " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", " continue\n", "\n", " grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", " deviation_col_name = f'deviation_median_{feature}'\n", " new_columns[deviation_col_name] = df[feature] - grouped_median\n", " ret_feature_columns.append(deviation_col_name)\n", "\n", " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", " deviation_col_name = f'deviation_mean_{feature}'\n", " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", " ret_feature_columns.append(deviation_col_name)\n", "\n", " # 将新计算的偏差特征与原始 DataFrame 合并\n", " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", "\n", " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", "\n", " return df, ret_feature_columns\n" ], "outputs": [], "execution_count": 11 }, { "cell_type": "code", "id": "0ebdfb92-d88b-4b5c-a715-675dab876fc0", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:11:03.763536Z", "start_time": "2025-03-03T13:11:03.756900Z" } }, "source": [ "def get_qcuts(series, quantiles):\n", " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", " return q[-1] # 返回窗口最后一个元素的分位数标签\n", "\n", "\n", "import pandas as pd\n", "\n", "\n", "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", " log=True):\n", " if not (0 <= lower_percentile < upper_percentile <= 1):\n", " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", "\n", " # Calculate lower and upper bounds based on percentiles\n", " lower_bound = label.quantile(lower_percentile)\n", " upper_bound = label.quantile(upper_percentile)\n", "\n", " # Filter out values outside the bounds\n", " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", "\n", " # Print the number of removed outliers\n", " if log:\n", " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", " return filtered_label\n", "\n", "\n", "def calculate_risk_adjusted_target(df, days=5):\n", " df = df.sort_values(by=['ts_code', 'trade_date'])\n", "\n", " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", "\n", " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", " level=0, drop=True)\n", " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", "\n", " return df['sharpe_ratio']\n", "\n", "\n" ], "outputs": [], "execution_count": 12 }, { "cell_type": "code", "id": "fbb968383f8cf2c7", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:24:10.876647Z", "start_time": "2025-03-03T13:22:30.406605Z" } }, "source": [ "days = 5\n", "future_close = df.groupby('ts_code')['close'].shift(-days)\n", "future_open = df.groupby('ts_code')['open'].shift(-1)\n", "future_return = (future_close - future_open) / future_open\n", "df['label'] = future_return\n", "df['label'] = remove_outliers_label_percentile(df['label'])\n", "df['label'] = df.groupby('trade_date')['label'].transform(\n", " lambda x: pd.qcut(x, q=5, labels=False, duplicates='drop')\n", ")\n", "print(df['label'].unique())\n", "\n", "\n", "# df['label'] = remove_outliers_label_percentile(df['label'])\n", "\n", "# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n", "df = df.sort_values(by=['trade_date', 'ts_code'])\n", "train_data = df[(df['trade_date'] <= '2024-03-01') & (df['trade_date'] >= '2016-01-01')]\n", "test_data = df[(df['trade_date'] >= '2024-03-01')]\n", "\n", "train_data = train_data.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nsmallest(3000, 'log(circ_mv)')\n", ")\n", "test_data = test_data.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nsmallest(3000, 'log(circ_mv)')\n", ")\n", "train_data = train_data.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nlargest(1000, 'return_20')\n", ")\n", "test_data = test_data.groupby('trade_date', group_keys=False).apply(\n", " lambda x: x.nlargest(1000, 'return_20')\n", ")\n", "\n", "industry_df = industry_df.sort_values(by=['trade_date'])\n", "index_data = index_data.sort_values(by=['trade_date'])\n", "\n", "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", "\n", "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", "\n", "feature_columns = [col for col in train_data.columns if col not in ['trade_date',\n", " 'ts_code',\n", " 'label']]\n", "feature_columns = [col for col in feature_columns if 'future' not in col]\n", "feature_columns = [col for col in feature_columns if 'score' not in col]\n", "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", "print(feature_columns)\n", "\n", "feature_columns_new = feature_columns[:]\n", "train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n", "print(f'feature_columns size: {len(feature_columns_new)}')\n", "test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n", "print(f'feature_columns size: {len(feature_columns_new)}')\n", "\n", "train_data = train_data.dropna(subset=feature_columns_new)\n", "train_data = train_data.dropna(subset=['label'])\n", "train_data = train_data.reset_index(drop=True)\n", "\n", "# print(test_data.tail())\n", "test_data = test_data.dropna(subset=feature_columns_new)\n", "# test_data = test_data.dropna(subset=['label'])\n", "test_data = test_data.reset_index(drop=True)\n", "\n", "print(len(train_data))\n", "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", "print(len(test_data))\n", "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", "\n", "cat_columns = [col for col in df.columns if col.startswith('cat')]\n", "for col in cat_columns:\n", " train_data[col] = train_data[col].astype('category')\n", " test_data[col] = test_data[col].astype('category')" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Removed 131011 outliers.\n", "[ 1. 0. 3. 2. 4. nan]\n", "['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'log_close', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry_ema_5', 'industry_ema_13', 'industry_ema_20', 'industry_ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_obv_deviation', 'industry_return_5_deviation', 'industry_return_20_deviation', 'industry_act_factor1_deviation', 'industry_act_factor2_deviation', 'industry_act_factor3_deviation', 'industry_act_factor4_deviation', 'industry_return_5_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return']\n", "feature_columns size: 221\n", "feature_columns size: 221\n", "1341891\n", "最小日期: 2017-04-06\n", "最大日期: 2024-03-01\n", "181520\n", "最小日期: 2024-03-01\n", "最大日期: 2025-02-28\n" ] } ], "execution_count": 33 }, { "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:25:32.375790Z", "start_time": "2025-03-03T13:24:11.128103Z" } }, "cell_type": "code", "source": [ "def remove_highly_correlated_features(df, feature_columns, threshold=0.8):\n", " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", " if not numeric_features:\n", " raise ValueError(\"No numeric features found in the provided data.\")\n", "\n", " corr_matrix = df[numeric_features].corr().abs()\n", " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", " remaining_features = [col for col in feature_columns if col not in to_drop]\n", " return remaining_features\n", "\n", "\n", "feature_columns_new = remove_highly_correlated_features(train_data, feature_columns_new)\n", "keep_columns = [col for col in train_data.columns if\n", " col in feature_columns_new or col in ['ts_code', 'trade_date', 'label']]\n", "train_data = train_data[keep_columns]\n", "test_data = test_data[keep_columns]\n", "print(f'feature_columns size: {len(feature_columns_new)}')" ], "id": "26c4c873b83ecc38", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "feature_columns size: 83\n" ] } ], "execution_count": 34 }, { "cell_type": "code", "id": "8f134d435f71e9e2", "metadata": { "jupyter": { "source_hidden": true }, "ExecuteTime": { "end_time": "2025-03-03T13:29:03.972545Z", "start_time": "2025-03-03T13:29:03.965555Z" } }, "source": [ "from sklearn.preprocessing import StandardScaler\n", "from catboost import Pool\n", "import lightgbm as lgb\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n", " print_feature_importance=True, num_boost_round=100,\n", " use_optuna=False):\n", " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", " X_train = train_data_df[feature_columns]\n", " y_train = train_data_df['label']\n", "\n", " X_val = test_data_df[feature_columns]\n", " y_val = test_data_df['label']\n", "\n", " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", " print(f'categorical_feature: {categorical_feature}')\n", " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", " val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n", " model = lgb.train(\n", " params, train_data, num_boost_round=num_boost_round,\n", " valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n", " callbacks=callbacks\n", " )\n", "\n", " if print_feature_importance:\n", " lgb.plot_metric(evals)\n", " # lgb.plot_tree(model, figsize=(20, 8))\n", " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", " plt.show()\n", " return model\n", "\n", "\n", "from catboost import CatBoostClassifier\n", "import pandas as pd\n", "\n", "\n", "def train_catboost(train_data_df, test_data_df, feature_columns, params=None, plot=False):\n", " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", " # train_data_df = train_data_df[train_data_df['label'].isin([0, 2, 4])]\n", " # test_data_df = test_data_df[test_data_df['label'].isin([0, 2, 4])]\n", " X_train = train_data_df[feature_columns]\n", " y_train = train_data_df['label']\n", "\n", " X_val = test_data_df[feature_columns]\n", " y_val = test_data_df['label']\n", "\n", " scaler = StandardScaler()\n", " numeric_columns = X_train.select_dtypes(include=['float64', 'int64']).columns\n", " X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n", " X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n", "\n", " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", " print(f'cat_features: {cat_features}')\n", " train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n", " val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n", "\n", " model = CatBoostClassifier(**params)\n", " model.fit(train_pool,\n", " eval_set=val_pool, plot=plot)\n", "\n", " return model, scaler" ], "outputs": [], "execution_count": 44 }, { "cell_type": "code", "id": "4a4542e1ed6afe7d", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:29:03.995376Z", "start_time": "2025-03-03T13:29:03.991323Z" } }, "source": [ "light_params = {\n", " 'objective': 'binary',\n", " 'metric': 'average_precision',\n", " 'learning_rate': 0.05,\n", " 'is_unbalance': True,\n", " 'num_leaves': 2048,\n", " 'min_data_in_leaf': 1024,\n", " 'max_depth': 32,\n", " 'max_bin': 1024,\n", " 'feature_fraction': 0.7,\n", " 'bagging_fraction': 0.7,\n", " 'bagging_freq': 5,\n", " # 'lambda_l1': 80,\n", " # 'lambda_l2': 65,\n", " 'verbosity': -1,\n", " 'num_threads': 16\n", "}" ], "outputs": [], "execution_count": 45 }, { "cell_type": "code", "id": "beeb098799ecfa6a", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:29:04.196597Z", "start_time": "2025-03-03T13:29:04.190141Z" } }, "source": [ "print('train data size: ', len(train_data))\n", "\n", "evals = {}\n", "# model, scaler = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", "# [lgb.log_evaluation(period=500),\n", "# lgb.callback.record_evaluation(evals),\n", "# lgb.early_stopping(50, first_metric_only=True)\n", "# ], evals,\n", "# num_boost_round=1000, use_optuna=False,\n", "# print_feature_importance=True)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train data size: 1341891\n" ] } ], "execution_count": 46 }, { "cell_type": "code", "id": "445dff84-70b2-4fc9-a9b6-1251993324d6", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:32:01.078551Z", "start_time": "2025-03-03T13:29:04.236216Z" } }, "source": [ "catboost_params = {\n", " 'loss_function': 'MultiClass', # 适用于多分类任务\n", " 'eval_metric': 'AUC', # 评估指标，也可以用 'Accuracy' 或 'TotalF1'\n", " 'iterations': 5000, # 最大迭代次数\n", " 'learning_rate': 0.01, # 学习率\n", " 'depth': 10, # 树的深度，控制模型复杂度\n", " 'l2_leaf_reg': 10, # L2 正则化\n", " 'verbose': 500, # 每 500 次迭代输出一次日志\n", " 'early_stopping_rounds': 100, # 提前停止的轮数\n", " 'task_type': 'GPU', # 使用 GPU 加速训练\n", "}\n", "\n", "feature_weights = {col: 2.0 if col in ['act_factor', 'af'] else 1.0 for col in feature_columns_new}\n", "catboost_params['feature_weights'] = feature_weights\n", "\n", "model, scaler = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cat_features: [3, 20, 21, 22, 23]\n" ] }, { "data": { "text/plain": [ "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "7d14b6917a5a499ea87455ebd47a1138" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Default metric period is 5 because AUC is/are not implemented for GPU\n", "AUC is not implemented on GPU. Will use CPU for metric computation, this could significantly affect learning time\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0:\ttest: 0.5831648\tbest: 0.5831648 (0)\ttotal: 115ms\tremaining: 9m 36s\n", "500:\ttest: 0.6048420\tbest: 0.6048420 (500)\ttotal: 52.9s\tremaining: 7m 54s\n", "1000:\ttest: 0.6071489\tbest: 0.6071489 (1000)\ttotal: 1m 45s\tremaining: 7m 1s\n", "1500:\ttest: 0.6079927\tbest: 0.6080352 (1483)\ttotal: 2m 39s\tremaining: 6m 11s\n", "bestTest = 0.6080667434\n", "bestIteration = 1531\n", "Shrink model to first 1532 iterations.\n" ] } ], "execution_count": 47 }, { "cell_type": "code", "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:32:03.696119Z", "start_time": "2025-03-03T13:32:01.217024Z" } }, "source": [ "# score_df = train_data\n", "# score_df['score'] = model.predict_proba(score_df[feature_columns_new])[:, -1]\n", "# # score_df['score'] = model.predict(score_df[feature_columns_new])\n", "# predictions_train = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", "# # predictions_train = predictions_train[predictions_train['score'] > 0.]\n", "# predictions_train[['trade_date', 'score', 'ts_code']].to_csv('predictions_train.tsv', index=False)" ], "outputs": [], "execution_count": 48 }, { "cell_type": "code", "id": "a3d7e881-c9b7-48a9-ba7f-20cd28c33f37", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:32:04.089341Z", "start_time": "2025-03-03T13:32:03.700197Z" } }, "source": [ "score_df = test_data\n", "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", "score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", "score_df['score'] = model.predict_proba(score_df[feature_columns_new])[:, -1]\n", "# score_df['score'] = model.predict(score_df[feature_columns_new])\n", "predictions_test = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", "# predictions_test = predictions_test[predictions_test['score'] > 0.5]\n", "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)" ], "outputs": [], "execution_count": 49 }, { "cell_type": "code", "id": "ebae809e26a7b594", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:32:04.166384Z", "start_time": "2025-03-03T13:32:04.160256Z" } }, "source": [ "print(predictions_test[predictions_test['trade_date'] > '2018-01-01'][['label', 'score', 'ts_code', 'trade_date']].head(\n", " 10))" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " label score ts_code trade_date\n", "43 3.0 0.409653 601666.SH 2024-03-01\n", "962 4.0 0.305911 000680.SZ 2024-03-04\n", "2012 3.0 0.336820 001270.SZ 2024-03-05\n", "3379 4.0 0.329924 605111.SH 2024-03-06\n", "4176 4.0 0.354170 605111.SH 2024-03-07\n", "4991 4.0 0.326872 605398.SH 2024-03-08\n", "5154 3.0 0.341976 603283.SH 2024-03-11\n", "6000 0.0 0.355384 603283.SH 2024-03-12\n", "6790 0.0 0.358717 603283.SH 2024-03-13\n", "7507 0.0 0.350276 603283.SH 2024-03-14\n" ] } ], "execution_count": 50 }, { "cell_type": "code", "id": "751a6df9-d90b-4053-8769-c6c3b6654406", "metadata": { "ExecuteTime": { "end_time": "2025-03-03T13:32:04.238552Z", "start_time": "2025-03-03T13:32:04.235960Z" } }, "source": [], "outputs": [], "execution_count": null } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 5 }