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liaozhaorun
2025-02-12 00:21:33 +08:00
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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:52:54.170824Z",
"start_time": "2025-02-09T14:52:53.544850Z"
}
},
"cell_type": "code",
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from utils.utils import read_and_merge_h5_data"
],
"id": "79a7758178bafdd3",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:36.873700Z",
"start_time": "2025-02-09T14:52:54.170824Z"
}
},
"cell_type": "code",
"source": [
"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_with_st',\n",
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
" 'is_st'], df=df)\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)"
],
"id": "a79cafb06a7e0e43",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"daily data\n",
"daily basic\n",
"stk limit\n",
"money flow\n"
]
}
],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:37.426404Z",
"start_time": "2025-02-09T14:53:36.955552Z"
}
},
"cell_type": "code",
"source": "origin_columns = df.columns.tolist()",
"id": "c4e9e1d31da6dba6",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:38.164112Z",
"start_time": "2025-02-09T14:53:38.070007Z"
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import talib\n",
"\n",
"\n",
"def get_technical_factor(df):\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",
" df['atr_14'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)\n",
" df['atr_6'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=6)\n",
"\n",
" df['obv'] = talib.OBV(df['close'], df['vol'])\n",
" df['maobv_6'] = talib.SMA(df['obv'], timeperiod=6)\n",
" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
"\n",
" df['rsi_3'] = talib.RSI(df['close'], timeperiod=3)\n",
" df['rsi_6'] = talib.RSI(df['close'], timeperiod=6)\n",
" df['rsi_9'] = talib.RSI(df['close'], timeperiod=9)\n",
"\n",
" df['return_10'] = df['close'] / df['close'].shift(10) - 1\n",
" df['return_20'] = df['close'] / df['close'].shift(20) - 1\n",
"\n",
" # # 计算 _rank_return_10 和 _rank_return_20\n",
" # df['_rank_return_10'] = df['return_10'].rank(pct=True)\n",
" # df['_rank_return_20'] = df['return_20'].rank(pct=True)\n",
"\n",
" # 计算 avg_close_5\n",
" df['avg_close_5'] = df['close'].rolling(window=5).mean() / df['close']\n",
"\n",
" # 计算 std_return_5, std_return_15, std_return_25, std_return_252, std_return_2522\n",
" df['std_return_5'] = df['close'].pct_change().shift(-1).rolling(window=5).std()\n",
" df['std_return_15'] = df['close'].pct_change().shift(-1).rolling(window=15).std()\n",
" df['std_return_25'] = df['close'].pct_change().shift(-1).rolling(window=25).std()\n",
" df['std_return_90'] = df['close'].pct_change().shift(-1).rolling(window=90).std()\n",
" df['std_return_90_2'] = df['close'].shift(10).pct_change().shift(-1).rolling(window=90).std()\n",
"\n",
" # 计算 std_return_5 / std_return_252 和 std_return_5 / std_return_25\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 std_return_252 - std_return_2522\n",
" df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n",
" return df\n",
"\n",
"\n",
"def get_act_factor(df):\n",
" # 计算 m_ta_ema(close, 5), m_ta_ema(close, 13), m_ta_ema(close, 20), m_ta_ema(close, 60)\n",
" df['ema_5'] = talib.EMA(df['close'], timeperiod=5)\n",
" df['ema_13'] = talib.EMA(df['close'], timeperiod=13)\n",
" df['ema_20'] = talib.EMA(df['close'], timeperiod=20)\n",
" df['ema_60'] = talib.EMA(df['close'], timeperiod=60)\n",
"\n",
" # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
" df['act_factor1'] = np.arctan((df['ema_5'] / df['ema_5'].shift(1) - 1) * 100) * 57.3 / 50\n",
" df['act_factor2'] = np.arctan((df['ema_13'] / df['ema_13'].shift(1) - 1) * 100) * 57.3 / 40\n",
" df['act_factor3'] = np.arctan((df['ema_20'] / df['ema_20'].shift(1) - 1) * 100) * 57.3 / 21\n",
" df['act_factor4'] = np.arctan((df['ema_60'] / df['ema_60'].shift(1) - 1) * 100) * 57.3 / 10\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' 进行分组，在每个组内分别计算 'act_factor1', 'act_factor2', 'act_factor3' 的排名\n",
" df['rank_act_factor1'] = df.groupby('trade_date')['act_factor1'].rank(ascending=False, pct=True)\n",
" df['rank_act_factor2'] = df.groupby('trade_date')['act_factor2'].rank(ascending=False, pct=True)\n",
" df['rank_act_factor3'] = df.groupby('trade_date')['act_factor3'].rank(ascending=False, pct=True)\n",
"\n",
" return df\n",
"\n",
"\n",
"def get_money_flow_factor(df):\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 - sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
" df['buy_elg_vol - sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
"\n",
" # # 你还提到了一些其他字段:\n",
" # df['net_active_buy_volume_main'] = df['net_mf_vol'] / df['buy_sm_vol']\n",
" # 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",
"\n",
" # df['active_sell_volume_large'] = df['sell_lg_vol'] / df['sell_sm_vol']\n",
" # df['active_sell_volume_big'] = df['sell_elg_vol'] / df['sell_sm_vol']\n",
" # df['active_sell_volume_small'] = df['sell_sm_vol'] / df['sell_sm_vol']\n",
"\n",
" return df\n",
"\n",
"\n",
"def get_alpha_factor(df):\n",
" df['alpha_022'] = df['close'] - df['close'].shift(5)\n",
"\n",
" # alpha_003: (close - open) / (high - low)\n",
" df['alpha_003'] = (df['close'] - df['open']) / (df['high'] - df['low'])\n",
"\n",
" # alpha_007: rank(correlation(close, volume, 5))\n",
" df['alpha_007'] = df['close'].rolling(5).corr(df['vol']).rank(axis=1)\n",
"\n",
" # alpha_013: rank(sum(close, 5) - sum(close, 20))\n",
" df['alpha_013'] = (df['close'].rolling(5).sum() - df['close'].rolling(20).sum()).rank(axis=1)\n",
" return df\n",
"\n",
"\n",
"def get_future_data(df):\n",
" df['future_return1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
" df['future_return2'] = (df['open'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return3'] = (df['close'].shift(-2) - df['close'].shift(-1)) / df['close'].shift(-1)\n",
" df['future_return4'] = (df['close'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return5'] = (df['close'].shift(-5) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return6'] = (df['close'].shift(-10) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return7'] = (df['close'].shift(-20) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_close1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
" df['future_close2'] = (df['close'].shift(-2) - df['close']) / df['close']\n",
" df['future_close3'] = (df['close'].shift(-3) - df['close']) / df['close']\n",
" df['future_close4'] = (df['close'].shift(-4) - df['close']) / df['close']\n",
" df['future_close5'] = (df['close'].shift(-5) - df['close']) / df['close']\n",
" df['future_af11'] = df['act_factor1'].shift(-1)\n",
" df['future_af12'] = df['act_factor1'].shift(-2)\n",
" df['future_af13'] = df['act_factor1'].shift(-3)\n",
" df['future_af14'] = df['act_factor1'].shift(-4)\n",
" df['future_af15'] = df['act_factor1'].shift(-5)\n",
" df['future_af21'] = df['act_factor2'].shift(-1)\n",
" df['future_af22'] = df['act_factor2'].shift(-2)\n",
" df['future_af23'] = df['act_factor2'].shift(-3)\n",
" df['future_af24'] = df['act_factor2'].shift(-4)\n",
" df['future_af25'] = df['act_factor2'].shift(-5)\n",
" df['future_af31'] = df['act_factor3'].shift(-1)\n",
" df['future_af32'] = df['act_factor3'].shift(-2)\n",
" df['future_af33'] = df['act_factor3'].shift(-3)\n",
" df['future_af34'] = df['act_factor3'].shift(-4)\n",
" df['future_af35'] = df['act_factor3'].shift(-5)\n",
"\n",
" return df\n"
],
"id": "a735bc02ceb4d872",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:49.153376Z",
"start_time": "2025-02-09T14:53:38.164112Z"
}
},
"cell_type": "code",
"source": [
"df = get_technical_factor(df)\n",
"df = get_act_factor(df)\n",
"df = get_money_flow_factor(df)\n",
"df = get_future_data(df)\n",
"# df = df.drop(columns=origin_columns)\n",
"\n",
"print(df.info())"
],
"id": "53f86ddc0677a6d7",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8364308 entries, 0 to 8364307\n",
"Data columns (total 83 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 is_st object \n",
" 8 up_limit float64 \n",
" 9 down_limit float64 \n",
" 10 buy_sm_vol float64 \n",
" 11 sell_sm_vol float64 \n",
" 12 buy_lg_vol float64 \n",
" 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",
" 26 rsi_9 float64 \n",
" 27 return_10 float64 \n",
" 28 return_20 float64 \n",
" 29 avg_close_5 float64 \n",
" 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",
" 47 act_factor6 float64 \n",
" 48 rank_act_factor1 float64 \n",
" 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",
" 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
}

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code/train/predictions.csv Normal file
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@@ -0,0 +1,486 @@
trade_date,score,ts_code
2023-01-03,0.6847274162535452,600965.SH
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2024-10-11,1.0343311919140215,600657.SH
2024-10-14,0.47180187869811324,603383.SH
2024-10-15,0.8703255693900985,603106.SH
2024-10-16,0.8602575981884926,002987.SZ
2024-10-17,0.8415090474760394,000567.SZ
2024-10-18,0.15292428350076237,002542.SZ
2024-10-21,0.4922089945140812,002457.SZ
2024-10-22,0.4176771447393169,002423.SZ
2024-10-23,0.8519606263347589,000158.SZ
2024-10-24,0.5430046541942165,000935.SZ
2024-10-25,0.18041199007445233,603016.SH
2024-10-28,0.5571845419116188,000503.SZ
2024-10-29,0.4049481417593221,002851.SZ
2024-10-30,0.7134818524722009,600463.SH
2024-10-31,0.5829203522209173,603117.SH
2024-11-01,1.2246655429458404,002851.SZ
2024-11-04,0.8176452500049061,600843.SH
2024-11-05,0.896531400955133,002514.SZ
2024-11-06,0.8080466337179094,600292.SH
2024-11-07,0.8536978345761432,600481.SH
2024-11-08,0.735404763610977,002047.SZ
2024-11-11,0.7749352489908305,002428.SZ
2024-11-12,0.8967548353595826,002428.SZ
2024-11-13,1.0199850479686239,603859.SH
2024-11-14,0.9361081815051415,603559.SH
2024-11-15,1.5997213068383358,002086.SZ
2024-11-18,1.5789594007558525,002709.SZ
2024-11-19,1.5249787983366927,603825.SH
2024-11-20,0.736292965730012,002808.SZ
2024-11-21,0.7544341372210354,603859.SH
2024-11-22,1.2744170240551567,600593.SH
2024-11-25,0.8324187454861728,600787.SH
2024-11-26,1.0752006153991913,600736.SH
2024-11-27,0.6500606694243171,002245.SZ
2024-11-28,0.8456761058883665,000833.SZ
2024-11-29,0.8489157795122361,002354.SZ
2024-12-02,0.6832024025795977,002611.SZ
2024-12-03,0.8939114413301544,603366.SH
2024-12-04,1.0277665925094743,002822.SZ
2024-12-05,0.9103466279163851,002175.SZ
2024-12-06,0.7686238693084577,002467.SZ
2024-12-09,1.0846053237578963,601933.SH
2024-12-10,1.0700371757624498,600193.SH
2024-12-11,0.7673727634361369,002520.SZ
2024-12-12,0.8497906144901559,002362.SZ
2024-12-13,1.254346634364545,603429.SH
2024-12-16,1.0386293779387827,000727.SZ
2024-12-17,1.2458109349303497,600157.SH
2024-12-18,1.3409657396440136,003002.SZ
2024-12-19,1.1408332938187322,600593.SH
2024-12-20,0.8248161307709121,002512.SZ
2024-12-23,1.1576027817247634,002336.SZ
2024-12-24,0.8652560164174093,603610.SH
2024-12-25,0.8928903216570524,002045.SZ
2024-12-26,0.7436802683703838,603214.SH
2024-12-27,0.8278524197563604,002945.SZ
2024-12-30,0.6846259683364331,600223.SH
2024-12-31,1.0514482797503029,001309.SZ
2025-01-02,1.4104671874229635,603668.SH
1 trade_date score ts_code
2 2023-01-03 0.6847274162535452 600965.SH
3 2023-01-04 0.7414192412896188 000615.SZ
4 2023-01-05 0.5542654290126795 000430.SZ
5 2023-01-06 0.6959513776962396 002640.SZ
6 2023-01-09 0.7412055912703203 600785.SH
7 2023-01-10 0.6855907318251123 002762.SZ
8 2023-01-11 0.7292345443148791 002719.SZ
9 2023-01-12 0.7309633018913224 600996.SH
10 2023-01-13 0.8190324397886146 002279.SZ
11 2023-01-16 0.784606092607123 000850.SZ
12 2023-01-17 0.7241262895910806 002441.SZ
13 2023-01-18 0.7040191348893544 002467.SZ
14 2023-01-19 1.4428906481799044 002195.SZ
15 2023-01-20 0.8043769403690574 000561.SZ
16 2023-01-30 0.8713260866140435 000913.SZ
17 2023-01-31 0.7091214216170246 002560.SZ
18 2023-02-01 0.8674507984004312 002576.SZ
19 2023-02-02 0.8435316672358789 002576.SZ
20 2023-02-03 0.3637794960484866 603698.SH
21 2023-02-06 0.8498784343039595 002401.SZ
22 2023-02-07 0.5210597972482388 002767.SZ
23 2023-02-08 0.9567872514253289 002368.SZ
24 2023-02-09 0.5267235816880836 002155.SZ
25 2023-02-10 0.8402723790587989 002348.SZ
26 2023-02-13 0.4846072502710197 002348.SZ
27 2023-02-14 0.7626025684565886 002877.SZ
28 2023-02-15 0.697045569808066 600225.SH
29 2023-02-16 0.8282986278708487 002748.SZ
30 2023-02-17 1.8315250204409614 601020.SH
31 2023-02-20 0.9746443150927466 003021.SZ
32 2023-02-21 1.33028636760986 002339.SZ
33 2023-02-22 0.7778102046341324 002167.SZ
34 2023-02-23 1.1313865958453686 600100.SH
35 2023-02-24 0.5636940136703907 000716.SZ
36 2023-02-27 1.1864163478203489 600557.SH
37 2023-02-28 1.0001756905854564 603129.SH
38 2023-03-01 0.859829916936714 603025.SH
39 2023-03-02 0.7059826582301507 603186.SH
40 2023-03-03 0.6713484518505006 002942.SZ
41 2023-03-06 0.7514650334919161 002362.SZ
42 2023-03-07 0.9199279895136431 002350.SZ
43 2023-03-08 0.7172896853160168 601872.SH
44 2023-03-09 1.2558830649002728 002808.SZ
45 2023-03-10 1.2172505980712502 003020.SZ
46 2023-03-13 0.5215320654292881 600363.SH
47 2023-03-14 1.0463998807907229 601872.SH
48 2023-03-15 0.583641209552904 603966.SH
49 2023-03-16 0.9789347178467226 603803.SH
50 2023-03-17 0.6572400306835567 600780.SH
51 2023-03-20 0.9514948440683256 601003.SH
52 2023-03-21 0.842214622933076 002439.SZ
53 2023-03-22 1.1462525936633026 601698.SH
54 2023-03-23 0.48812639918095935 601199.SH
55 2023-03-24 0.4727906220196696 601698.SH
56 2023-03-27 0.7956407883949915 000810.SZ
57 2023-03-28 0.9780023176799034 601698.SH
58 2023-03-29 0.7581739222176508 002558.SZ
59 2023-03-30 1.2088597249444364 002425.SZ
60 2023-03-31 1.0383208709472522 000938.SZ
61 2023-04-03 0.7742369597473725 001872.SZ
62 2023-04-04 0.8024470755073208 002858.SZ
63 2023-04-06 0.7607284837753079 002261.SZ
64 2023-04-07 0.9927088245634893 002261.SZ
65 2023-04-10 0.8693657271995023 603019.SH
66 2023-04-11 1.3756495540495381 600228.SH
67 2023-04-12 0.6041603413288005 002222.SZ
68 2023-04-13 0.7744980911161148 002777.SZ
69 2023-04-14 0.8672190988257731 600633.SH
70 2023-04-17 1.4449313652608706 603083.SH
71 2023-04-18 0.6773096694945689 002517.SZ
72 2023-04-19 2.078581022345213 002908.SZ
73 2023-04-20 0.9806780152085147 600203.SH
74 2023-04-21 0.8344159521107176 000686.SZ
75 2023-04-24 1.210049105329498 600749.SH
76 2023-04-25 1.4048193576090038 603699.SH
77 2023-04-26 1.4845584717530398 600750.SH
78 2023-04-27 1.7290167418990006 002351.SZ
79 2023-04-28 0.6169128309035838 002343.SZ
80 2023-05-04 0.406077908055111 603019.SH
81 2023-05-05 0.9080152593104391 603258.SH
82 2023-05-08 0.796876165601434 603083.SH
83 2023-05-09 0.9700970682165659 002291.SZ
84 2023-05-10 1.3258832446428042 002291.SZ
85 2023-05-11 0.8900850443360986 600750.SH
86 2023-05-12 1.0650432251092237 601318.SH
87 2023-05-15 0.6787099027648815 002760.SZ
88 2023-05-16 0.722076527194143 603357.SH
89 2023-05-17 1.053645140729648 603357.SH
90 2023-05-18 0.6034913204660662 601518.SH
91 2023-05-19 0.5868824391890941 000600.SZ
92 2023-05-22 0.7653788907870708 002315.SZ
93 2023-05-23 0.8498831870683509 003021.SZ
94 2023-05-24 0.8222394685174235 600337.SH
95 2023-05-25 0.9415393784400905 600587.SH
96 2023-05-26 0.520220425480583 000600.SZ
97 2023-05-29 0.6595697603731597 002334.SZ
98 2023-05-30 0.606477318736669 603790.SH
99 2023-05-31 0.694237121673502 603662.SH
100 2023-06-01 0.5921959927449983 603196.SH
101 2023-06-02 0.6895081721399422 603019.SH
102 2023-06-05 0.5566382152721012 001309.SZ
103 2023-06-06 1.892873809852157 002587.SZ
104 2023-06-07 0.7678242983656595 605011.SH
105 2023-06-08 1.0539866095101476 000938.SZ
106 2023-06-09 0.8147649527362582 002292.SZ
107 2023-06-12 0.7188933998461803 002173.SZ
108 2023-06-13 0.681977313547429 603097.SH
109 2023-06-14 0.8733642333159071 002865.SZ
110 2023-06-15 0.8968643954524321 603108.SH
111 2023-06-16 0.7840028396319321 002897.SZ
112 2023-06-19 0.8775653925608153 603319.SH
113 2023-06-20 1.0844187891842454 002902.SZ
114 2023-06-21 1.2927896026027148 603629.SH
115 2023-06-26 1.2292848291237288 002261.SZ
116 2023-06-27 1.3781866321602365 000938.SZ
117 2023-06-28 0.8540277846763247 601138.SH
118 2023-06-29 0.8523211757782663 603779.SH
119 2023-06-30 1.1754329601349267 601127.SH
120 2023-07-03 1.2249906293712542 603015.SH
121 2023-07-04 1.1084820336750372 000936.SZ
122 2023-07-05 1.2710980206868936 000936.SZ
123 2023-07-06 0.747949676257483 603728.SH
124 2023-07-07 0.9061602512086774 002835.SZ
125 2023-07-10 0.9551342600022579 002126.SZ
126 2023-07-11 0.966971776030953 603767.SH
127 2023-07-12 0.9443518376543841 601886.SH
128 2023-07-13 0.6769606633633086 603085.SH
129 2023-07-14 0.918333951173531 002036.SZ
130 2023-07-17 0.6569232106980998 603045.SH
131 2023-07-18 0.7121594789903711 002409.SZ
132 2023-07-19 0.7294037039105077 002548.SZ
133 2023-07-20 0.7381883846308512 002703.SZ
134 2023-07-21 0.7652204062690017 002997.SZ
135 2023-07-24 0.9003480376547147 000550.SZ
136 2023-07-25 0.6357345607310146 605020.SH
137 2023-07-26 0.5810885924302148 605020.SH
138 2023-07-27 0.8761890730266239 002316.SZ
139 2023-07-28 0.9028509127260252 000716.SZ
140 2023-07-31 0.4708956316796065 601777.SH
141 2023-08-01 0.5561891737811866 600763.SH
142 2023-08-02 0.8760196470112966 002719.SZ
143 2023-08-03 0.7648464404493108 002400.SZ
144 2023-08-04 0.7791782042380738 601799.SH
145 2023-08-07 0.8061135186567487 000797.SZ
146 2023-08-08 1.1666879365515435 600322.SH
147 2023-08-09 1.4355955291322207 000006.SZ
148 2023-08-10 1.2886157376213954 000656.SZ
149 2023-08-11 1.3479230675318692 002941.SZ
150 2023-08-14 1.0490220109983646 601377.SH
151 2023-08-15 1.0632174359473308 600376.SH
152 2023-08-16 1.050421116938835 002126.SZ
153 2023-08-17 0.6717173215978612 000948.SZ
154 2023-08-18 0.9668301556287141 002907.SZ
155 2023-08-21 1.1746853792925736 003005.SZ
156 2023-08-22 0.9584954141170795 600838.SH
157 2023-08-23 1.2457408517312187 001234.SZ
158 2023-08-24 1.0823923917882998 002999.SZ
159 2023-08-25 0.9378153059525566 603918.SH
160 2023-08-28 1.2421433405918902 603767.SH
161 2023-08-29 0.616127073238452 002696.SZ
162 2023-08-30 0.9031479500764501 000010.SZ
163 2023-08-31 0.8845829289455852 000010.SZ
164 2023-09-01 0.6016754849643945 600272.SH
165 2023-09-04 0.7558571185023197 600272.SH
166 2023-09-05 0.4028980705656072 000678.SZ
167 2023-09-06 0.659374499556469 603080.SH
168 2023-09-07 0.6245495405404559 000609.SZ
169 2023-09-08 0.6710722697700532 603919.SH
170 2023-09-11 0.6278048802992457 600546.SH
171 2023-09-12 0.8256329238016435 600546.SH
172 2023-09-13 0.9974851537103986 601127.SH
173 2023-09-14 1.2529240943908486 603005.SH
174 2023-09-15 0.6551278623697204 603306.SH
175 2023-09-18 0.62491397271537 000851.SZ
176 2023-09-19 0.9590430560641906 600293.SH
177 2023-09-20 0.9192992329949209 600895.SH
178 2023-09-21 1.303323539412371 603667.SH
179 2023-09-22 0.6392556165728372 002406.SZ
180 2023-09-25 0.550363061204975 603933.SH
181 2023-09-26 0.8885810012289143 000766.SZ
182 2023-09-27 1.3119504188158804 605588.SH
183 2023-09-28 0.6383963467007131 002885.SZ
184 2023-10-09 0.5559833174181884 603277.SH
185 2023-10-10 0.6746335129218759 002855.SZ
186 2023-10-11 0.48076279797688554 001319.SZ
187 2023-10-12 0.4648032423789084 002953.SZ
188 2023-10-13 0.7973569102606312 605588.SH
189 2023-10-16 0.6753500013003589 603929.SH
190 2023-10-17 0.939032197442827 002786.SZ
191 2023-10-18 1.3316765134484991 603005.SH
192 2023-10-19 1.5107094655109568 002728.SZ
193 2023-10-20 1.8033472200834302 603890.SH
194 2023-10-23 2.0156758075487904 000526.SZ
195 2023-10-24 1.733095955568728 600186.SH
196 2023-10-25 1.0763644734039597 002456.SZ
197 2023-10-26 0.6254989123134431 600155.SH
198 2023-10-27 0.9189894086467906 000712.SZ
199 2023-10-30 0.82608918468284 600839.SH
200 2023-10-31 0.7212314254570089 001319.SZ
201 2023-11-01 0.7217175319235539 002657.SZ
202 2023-11-02 0.7778599932153227 002456.SZ
203 2023-11-03 1.3422820103526423 603985.SH
204 2023-11-06 0.5239681122691597 600595.SH
205 2023-11-07 0.5067988914432168 000520.SZ
206 2023-11-08 0.8068628626883659 603009.SH
207 2023-11-09 0.795444145270753 601127.SH
208 2023-11-10 0.7853290033363176 600630.SH
209 2023-11-13 0.6675304106579922 601595.SH
210 2023-11-14 0.2361369659661822 000828.SZ
211 2023-11-15 1.3945937102768664 603598.SH
212 2023-11-16 0.9576360953800751 000628.SZ
213 2023-11-17 0.6890730621251102 603108.SH
214 2023-11-20 0.7909511281469108 000628.SZ
215 2023-11-21 0.8142228770378246 000925.SZ
216 2023-11-22 0.797507102177366 000550.SZ
217 2023-11-23 0.7063630358338048 000676.SZ
218 2023-11-24 1.036008941153352 002181.SZ
219 2023-11-27 1.0291531391754778 000503.SZ
220 2023-11-28 0.9076486100273481 600775.SH
221 2023-11-29 0.7882491062137358 000669.SZ
222 2023-11-30 1.0967518981113826 000669.SZ
223 2023-12-01 0.7311870736612255 000625.SZ
224 2023-12-04 1.0691371238894785 603286.SH
225 2023-12-05 1.3131270390024472 605188.SH
226 2023-12-06 0.7752257820655966 000628.SZ
227 2023-12-07 0.8197027217611897 600107.SH
228 2023-12-08 1.3967047074635028 603488.SH
229 2023-12-11 0.687045285463665 002277.SZ
230 2023-12-12 0.665884682887716 600593.SH
231 2023-12-13 0.654047737248992 600476.SH
232 2023-12-14 1.0532572750390772 603106.SH
233 2023-12-15 1.0658083351666034 603358.SH
234 2023-12-18 0.962400311858336 000766.SZ
235 2023-12-19 0.7287547482546808 603103.SH
236 2023-12-20 1.2148728495986132 002647.SZ
237 2023-12-21 0.7541763251442525 002858.SZ
238 2023-12-22 0.8726322448001688 600178.SH
239 2023-12-25 0.8241484608574933 605011.SH
240 2023-12-26 0.9109440061301425 603992.SH
241 2023-12-27 0.8418823218408088 002660.SZ
242 2023-12-28 0.6918104133614209 002238.SZ
243 2023-12-29 0.587505902687491 002495.SZ
244 2024-01-02 0.9211111544777676 002587.SZ
245 2024-01-03 0.7226235020970133 000691.SZ
246 2024-01-04 0.9569550769001772 002962.SZ
247 2024-01-05 1.1926365483735857 605117.SH
248 2024-01-08 1.2688927804343633 603032.SH
249 2024-01-09 0.658120719671456 002862.SZ
250 2024-01-10 1.0753659753822198 000698.SZ
251 2024-01-11 0.8894494773121738 603097.SH
252 2024-01-12 0.9087175987157813 600593.SH
253 2024-01-15 0.5892323752732013 603212.SH
254 2024-01-16 0.7839193778147607 000698.SZ
255 2024-01-17 1.0625258983405035 603172.SH
256 2024-01-18 1.2631347573071583 002033.SZ
257 2024-01-19 1.178331671274573 603579.SH
258 2024-01-22 1.6352965224693876 605268.SH
259 2024-01-23 1.4266068895392103 605117.SH
260 2024-01-24 0.8002314972730927 600138.SH
261 2024-01-25 0.6269522471780932 603099.SH
262 2024-01-26 1.1309900405971738 605111.SH
263 2024-01-29 1.125066361142788 002253.SZ
264 2024-01-30 1.351423375727843 003007.SZ
265 2024-01-31 1.5739771441257069 601138.SH
266 2024-02-01 1.499054147286471 002033.SZ
267 2024-02-02 1.5167645269468597 601038.SH
268 2024-02-05 1.903436398968963 000998.SZ
269 2024-02-06 0.8212170920332696 600705.SH
270 2024-02-07 0.8351364611756162 603199.SH
271 2024-02-08 0.6822013314565247 601998.SH
272 2024-02-19 0.5444839345994206 000999.SZ
273 2024-02-20 0.5734873097155654 603369.SH
274 2024-02-21 0.43260992475178217 600023.SH
275 2024-02-22 0.4837658423417254 000526.SZ
276 2024-02-23 0.25932845682914313 000157.SZ
277 2024-02-26 0.37391625217883473 000983.SZ
278 2024-02-27 0.06249364085384111 002039.SZ
279 2024-02-28 1.038623993098607 000680.SZ
280 2024-02-29 0.11792404338777529 603871.SH
281 2024-03-01 0.30810066963043975 603605.SH
282 2024-03-04 0.17622933681947947 600375.SH
283 2024-03-05 1.0199617166369774 002009.SZ
284 2024-03-06 0.7025950679251926 002209.SZ
285 2024-03-07 0.8130361341508562 002317.SZ
286 2024-03-08 0.6714948117029352 603960.SH
287 2024-03-11 0.7527103580484402 603960.SH
288 2024-03-12 0.773794092704635 603960.SH
289 2024-03-13 0.6395980350337678 603499.SH
290 2024-03-14 0.6983134888804106 603660.SH
291 2024-03-15 0.990357376475744 601138.SH
292 2024-03-18 0.4182273803259301 002075.SZ
293 2024-03-19 0.8116021296004745 603050.SH
294 2024-03-20 0.6916582321872963 605580.SH
295 2024-03-21 0.7126108686308776 002698.SZ
296 2024-03-22 0.795964501966808 603190.SH
297 2024-03-25 1.0652068531193497 603286.SH
298 2024-03-26 1.2060727717193194 002331.SZ
299 2024-03-27 1.8308956146424886 001696.SZ
300 2024-03-28 1.5762953683649072 002645.SZ
301 2024-03-29 0.8133804882766478 002055.SZ
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303 2024-04-02 0.9728619403924941 002085.SZ
304 2024-04-03 0.9735850429701983 002625.SZ
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306 2024-04-09 1.10422053943107 002085.SZ
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311 2024-04-16 2.1135975645782725 002521.SZ
312 2024-04-17 1.0448007042761251 000737.SZ
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trade_date,score,ts_code
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2024-09-02,1.093224090711538,603826.SH
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2024-09-09,1.2278175192855338,000999.SZ
2024-09-10,0.7463596223821397,600804.SH
2024-09-11,0.7470176349514708,002808.SZ
2024-09-12,0.7243651180373872,603559.SH
2024-09-13,1.474732794777432,603615.SH
2024-09-18,0.883126816576788,000659.SZ
2024-09-19,0.640225572474989,600898.SH
2024-09-20,1.051164703847969,603398.SH
2024-09-23,0.9312327237656652,601162.SH
2024-09-24,0.47774549082415085,603559.SH
2024-09-25,0.9563618822865794,600756.SH
2024-09-26,0.5275946326737218,002686.SZ
2024-09-27,0.18984202360702415,002016.SZ
2024-09-30,0.035786009050673036,603106.SH
2024-10-08,0.032889649456072596,603106.SH
2024-10-09,0.12465071252054723,601336.SH
2024-10-10,0.6969210259884471,000402.SZ
2024-10-11,1.0531806211526256,600099.SH
2024-10-14,0.46171010596010975,601162.SH
2024-10-15,1.050584350922452,000402.SZ
2024-10-16,0.9133292498947153,002987.SZ
2024-10-17,0.7700882254413255,000567.SZ
2024-10-18,0.1201641254984537,600895.SH
2024-10-21,0.6976186419462845,002457.SZ
2024-10-22,0.29432613116032685,002423.SZ
2024-10-23,0.7806425787966057,000158.SZ
2024-10-24,0.6036172569478745,002199.SZ
2024-10-25,0.12282827583399647,603016.SH
2024-10-28,0.5245178749249334,000503.SZ
2024-10-29,0.5076972792469883,002851.SZ
2024-10-30,0.7895092176015108,600463.SH
2024-10-31,0.6192784720087864,603117.SH
2024-11-01,1.204091078793982,002134.SZ
2024-11-04,0.8093625376363384,002570.SZ
2024-11-05,0.8638488115176264,600172.SH
2024-11-06,0.8186577511607894,600292.SH
2024-11-07,0.8635644200307799,600481.SH
2024-11-08,0.747868586080844,002047.SZ
2024-11-11,0.8396084711761063,002428.SZ
2024-11-12,0.7919805059954543,603859.SH
2024-11-13,0.9060991217118458,603859.SH
2024-11-14,0.9421847563274262,600966.SH
2024-11-15,1.5178035825387006,002086.SZ
2024-11-18,1.5376931721833804,600212.SH
2024-11-19,1.4060097628439219,603825.SH
2024-11-20,0.8890834002457785,002808.SZ
2024-11-21,0.8855345297456824,603859.SH
2024-11-22,1.119413451276471,600593.SH
2024-11-25,0.7785122491322624,600787.SH
2024-11-26,1.0000210964516405,600736.SH
2024-11-27,0.8847088850937169,002245.SZ
2024-11-28,0.8621169938483969,601360.SH
2024-11-29,0.7042168573520408,002354.SZ
2024-12-02,0.6426744321671465,002611.SZ
2024-12-03,0.9021248369663103,603366.SH
2024-12-04,0.9942733603597254,002822.SZ
2024-12-05,0.8594169937974554,002175.SZ
2024-12-06,0.6985253068214117,603366.SH
2024-12-09,0.9476416369172441,601933.SH
2024-12-10,1.053999067291189,600193.SH
2024-12-11,0.6996323878339654,002193.SZ
2024-12-12,0.9535599508119768,002362.SZ
2024-12-13,1.3279717542253164,603429.SH
2024-12-16,1.0260681164941636,000727.SZ
2024-12-17,1.3063049942413876,600157.SH
2024-12-18,1.3121814535335503,002878.SZ
2024-12-19,1.219098477203033,600593.SH
2024-12-20,0.8480675770196239,002512.SZ
2024-12-23,1.1806404660060321,600724.SH
2024-12-24,0.8537658533885591,603610.SH
2024-12-25,0.8752079792242901,002965.SZ
2024-12-26,0.720498836899636,603214.SH
2024-12-27,0.8552518755027023,002945.SZ
2024-12-30,0.7853260336927593,600223.SH
2024-12-31,1.0774196590782728,600183.SH
2025-01-02,1.345874429894366,603225.SH
2025-01-03,1.4595000226870254,603379.SH
2025-01-06,1.8567194520891437,002130.SZ
2025-01-07,1.327995034218316,002881.SZ
2025-01-08,0.7854520495476546,600126.SH
2025-01-09,0.8656051617404842,000756.SZ
2025-01-10,1.1141535494224937,605016.SH
2025-01-13,2.0375745364278695,605080.SH
2025-01-14,0.6228152667370752,603269.SH
2025-01-15,0.7099046974063614,600673.SH
2025-01-16,0.7230959774435842,600381.SH
2025-01-17,0.8735560458074921,603007.SH
2025-01-20,0.6265446536616674,002164.SZ
2025-01-21,1.4687297319348953,000534.SZ
2025-01-22,0.7347201431708319,000408.SZ
2025-01-23,0.7698608009850573,603121.SH
2025-01-24,0.6733337270251429,603360.SH
2025-01-27,1.3722279750234074,002484.SZ
2025-02-05,1.0995625926629562,603667.SH
2025-02-06,0.9540560778289151,603308.SH
2025-02-07,0.8152925075270021,000856.SZ
2025-02-10,0.4527649047879118,603166.SH
1 trade_date,score,ts_code
2 2024-01-02,0.8906433047229376,002587.SZ
3 2024-01-03,0.800255773815545,000691.SZ
4 2024-01-04,0.918203870395468,002962.SZ
5 2024-01-05,1.1734063865615825,605117.SH
6 2024-01-08,1.2784720379037475,600761.SH
7 2024-01-09,0.5936470874291284,002862.SZ
8 2024-01-10,0.9080905815108482,000698.SZ
9 2024-01-11,0.827458720223193,603097.SH
10 2024-01-12,0.994883205877543,600593.SH
11 2024-01-15,0.6804254263110727,603212.SH
12 2024-01-16,0.7194593431343859,000698.SZ
13 2024-01-17,1.0860069907228742,603172.SH
14 2024-01-18,1.3749235527137786,603828.SH
15 2024-01-19,1.2391308950507334,603579.SH
16 2024-01-22,1.7603168299560354,605268.SH
17 2024-01-23,1.5656552549163458,605117.SH
18 2024-01-24,0.7379915949457881,600138.SH
19 2024-01-25,0.7346719736914655,603099.SH
20 2024-01-26,1.0310897640701377,605111.SH
21 2024-01-29,1.1768938262108766,603398.SH
22 2024-01-30,1.235487562255028,003007.SZ
23 2024-01-31,1.5371910050217372,601138.SH
24 2024-02-01,1.3629464247750829,600551.SH
25 2024-02-02,1.52270944692793,601038.SH
26 2024-02-05,1.847344110860692,000550.SZ
27 2024-02-06,0.8127749986671006,600705.SH
28 2024-02-07,0.7565034143929377,603199.SH
29 2024-02-08,0.7247153170440155,600188.SH
30 2024-02-19,0.41560847492124364,002032.SZ
31 2024-02-20,0.5419618315007714,603369.SH
32 2024-02-21,0.44193316268825533,600023.SH
33 2024-02-22,0.45382033625301066,000526.SZ
34 2024-02-23,0.25689917770287357,000157.SZ
35 2024-02-26,0.4253231714991775,000983.SZ
36 2024-02-27,0.03172161439110529,605151.SH
37 2024-02-28,1.074800376390378,000680.SZ
38 2024-02-29,0.11241663388214615,002467.SZ
39 2024-03-01,0.317478967758629,603605.SH
40 2024-03-04,0.17878811429242739,600860.SH
41 2024-03-05,1.0151694508153393,002009.SZ
42 2024-03-06,0.8229010452846762,002209.SZ
43 2024-03-07,0.8017175629386889,600584.SH
44 2024-03-08,0.6810433978551881,603960.SH
45 2024-03-11,0.9532104338812376,603960.SH
46 2024-03-12,0.7055297835013503,603960.SH
47 2024-03-13,0.5920124881579221,603499.SH
48 2024-03-14,0.661232064922907,603660.SH
49 2024-03-15,0.8778620305552904,601138.SH
50 2024-03-18,0.42042942845890563,002075.SZ
51 2024-03-19,0.7527868193603998,603050.SH
52 2024-03-20,0.5531908723666995,605580.SH
53 2024-03-21,0.7900117288163369,002698.SZ
54 2024-03-22,0.8285381778407641,603190.SH
55 2024-03-25,1.0749381159867608,603286.SH
56 2024-03-26,1.2654734266422276,002331.SZ
57 2024-03-27,1.8684480159293833,001696.SZ
58 2024-03-28,1.6075301389782366,002645.SZ
59 2024-03-29,0.8465441903404123,002055.SZ
60 2024-04-01,0.7568317810951942,002735.SZ
61 2024-04-02,1.0341346018053856,002085.SZ
62 2024-04-03,1.0122022102013215,002130.SZ
63 2024-04-08,0.8881305473937254,002805.SZ
64 2024-04-09,1.0559556356983075,002085.SZ
65 2024-04-10,0.6554344664442165,603822.SH
66 2024-04-11,1.2760784980841757,002544.SZ
67 2024-04-12,1.0181838249663664,605198.SH
68 2024-04-15,1.221720496054648,002290.SZ
69 2024-04-16,2.0663546208214703,002521.SZ
70 2024-04-17,1.1065962300439527,000737.SZ
71 2024-04-18,1.34853784445544,603619.SH
72 2024-04-19,0.6639505828915956,000933.SZ
73 2024-04-22,1.1652613644520093,600529.SH
74 2024-04-23,1.0507483721309534,002078.SZ
75 2024-04-24,0.7225763953314781,000949.SZ
76 2024-04-25,1.9430192587586146,600066.SH
77 2024-04-26,1.0817360300030114,603556.SH
78 2024-04-29,0.21577435079395113,600480.SH
79 2024-04-30,0.5290265764148879,605098.SH
80 2024-05-06,0.7885258258967485,001696.SZ
81 2024-05-07,0.415812996822765,002225.SZ
82 2024-05-08,0.5596574674012184,603232.SH
83 2024-05-09,0.8548632655231382,002590.SZ
84 2024-05-10,0.5787850519196119,001696.SZ
85 2024-05-13,0.9751906596140552,000952.SZ
86 2024-05-14,0.7644462578838344,600645.SH
87 2024-05-15,0.8589488842170756,600682.SH
88 2024-05-16,1.049953727857974,600644.SH
89 2024-05-17,1.1220964730505885,000702.SZ
90 2024-05-20,0.8027292772970297,002851.SZ
91 2024-05-21,1.1153910838352858,603683.SH
92 2024-05-22,0.6413250933571519,002922.SZ
93 2024-05-23,1.570388967019694,002225.SZ
94 2024-05-24,1.2444795042063028,002968.SZ
95 2024-05-27,0.9627736773164858,600675.SH
96 2024-05-28,0.9448048863120843,002761.SZ
97 2024-05-29,0.6810520099963742,600979.SH
98 2024-05-30,0.842272857355848,600530.SH
99 2024-05-31,1.0001129400930693,600101.SH
100 2024-06-03,1.3637566909343166,001267.SZ
101 2024-06-04,0.9588694721783405,600530.SH
102 2024-06-05,1.0953534292665954,002922.SZ
103 2024-06-06,0.8110235687535462,603360.SH
104 2024-06-07,1.2134023901747366,002889.SZ
105 2024-06-11,1.0772961141495465,002655.SZ
106 2024-06-12,1.1183914831029496,603009.SH
107 2024-06-13,1.0362186103086477,600996.SH
108 2024-06-14,0.7480333968482387,600039.SH
109 2024-06-17,1.0007287301926653,000528.SZ
110 2024-06-18,0.5381156056733658,600996.SH
111 2024-06-19,0.7036814608094294,600830.SH
112 2024-06-20,0.8499910179916197,605598.SH
113 2024-06-21,1.2150720829980681,603936.SH
114 2024-06-24,1.0311244474924908,600992.SH
115 2024-06-25,1.2484419002632245,600584.SH
116 2024-06-26,0.7320854040096385,000819.SZ
117 2024-06-27,1.9029808384109885,002452.SZ
118 2024-06-28,0.8803270724492669,600889.SH
119 2024-07-01,0.6059109111833119,000622.SZ
120 2024-07-02,0.656778781716391,600584.SH
121 2024-07-03,0.8605418473204086,601179.SH
122 2024-07-04,1.073683249192727,603225.SH
123 2024-07-05,0.8800051248743536,603936.SH
124 2024-07-08,1.8061114289786495,603328.SH
125 2024-07-09,0.5826571385994789,000679.SZ
126 2024-07-10,0.8413277231762297,605117.SH
127 2024-07-11,0.6665748975268276,000622.SZ
128 2024-07-12,1.0278922272860618,002452.SZ
129 2024-07-15,0.6403011305936952,605081.SH
130 2024-07-16,0.592445020815451,002420.SZ
131 2024-07-17,0.5442518053370551,002355.SZ
132 2024-07-18,0.8022024783282671,002384.SZ
133 2024-07-19,0.9433013682690108,002384.SZ
134 2024-07-22,0.6240394772580439,000421.SZ
135 2024-07-23,1.237721650844904,002428.SZ
136 2024-07-24,1.186379759584258,605111.SH
137 2024-07-25,0.8926351872925693,002496.SZ
138 2024-07-26,0.8499067690354271,600171.SH
139 2024-07-29,0.7013969819645556,002750.SZ
140 2024-07-30,0.615258963090716,600563.SH
141 2024-07-31,0.5799237504937365,002700.SZ
142 2024-08-01,0.5070284832062075,600834.SH
143 2024-08-02,0.6485822834772664,600604.SH
144 2024-08-05,1.3222325162798954,605117.SH
145 2024-08-06,0.9935887583155018,000584.SZ
146 2024-08-07,0.7440148514526516,603032.SH
147 2024-08-08,0.8934326068252262,600611.SH
148 2024-08-09,0.9907610203863012,000659.SZ
149 2024-08-12,0.7143811940598249,000880.SZ
150 2024-08-13,0.5948683342786406,600266.SH
151 2024-08-14,0.7780755309120447,000159.SZ
152 2024-08-15,0.8738359592762805,000880.SZ
153 2024-08-16,0.7460601727356423,603050.SH
154 2024-08-19,0.8904872743308099,603444.SH
155 2024-08-20,1.077089845741329,002488.SZ
156 2024-08-21,1.330096420372438,002488.SZ
157 2024-08-22,1.04117609681098,002208.SZ
158 2024-08-23,1.2634267858516914,002369.SZ
159 2024-08-26,0.8745811905917017,000532.SZ
160 2024-08-27,0.836754154047108,002760.SZ
161 2024-08-28,0.7231326607988842,002547.SZ
162 2024-08-29,0.8735120215139563,003001.SZ
163 2024-08-30,0.6615140980616735,601898.SH
164 2024-09-02,1.093224090711538,603826.SH
165 2024-09-03,0.41407989346203866,002309.SZ
166 2024-09-04,0.7919233491318468,001298.SZ
167 2024-09-05,0.6449753890047838,000908.SZ
168 2024-09-06,1.17109797325565,002622.SZ
169 2024-09-09,1.2278175192855338,000999.SZ
170 2024-09-10,0.7463596223821397,600804.SH
171 2024-09-11,0.7470176349514708,002808.SZ
172 2024-09-12,0.7243651180373872,603559.SH
173 2024-09-13,1.474732794777432,603615.SH
174 2024-09-18,0.883126816576788,000659.SZ
175 2024-09-19,0.640225572474989,600898.SH
176 2024-09-20,1.051164703847969,603398.SH
177 2024-09-23,0.9312327237656652,601162.SH
178 2024-09-24,0.47774549082415085,603559.SH
179 2024-09-25,0.9563618822865794,600756.SH
180 2024-09-26,0.5275946326737218,002686.SZ
181 2024-09-27,0.18984202360702415,002016.SZ
182 2024-09-30,0.035786009050673036,603106.SH
183 2024-10-08,0.032889649456072596,603106.SH
184 2024-10-09,0.12465071252054723,601336.SH
185 2024-10-10,0.6969210259884471,000402.SZ
186 2024-10-11,1.0531806211526256,600099.SH
187 2024-10-14,0.46171010596010975,601162.SH
188 2024-10-15,1.050584350922452,000402.SZ
189 2024-10-16,0.9133292498947153,002987.SZ
190 2024-10-17,0.7700882254413255,000567.SZ
191 2024-10-18,0.1201641254984537,600895.SH
192 2024-10-21,0.6976186419462845,002457.SZ
193 2024-10-22,0.29432613116032685,002423.SZ
194 2024-10-23,0.7806425787966057,000158.SZ
195 2024-10-24,0.6036172569478745,002199.SZ
196 2024-10-25,0.12282827583399647,603016.SH
197 2024-10-28,0.5245178749249334,000503.SZ
198 2024-10-29,0.5076972792469883,002851.SZ
199 2024-10-30,0.7895092176015108,600463.SH
200 2024-10-31,0.6192784720087864,603117.SH
201 2024-11-01,1.204091078793982,002134.SZ
202 2024-11-04,0.8093625376363384,002570.SZ
203 2024-11-05,0.8638488115176264,600172.SH
204 2024-11-06,0.8186577511607894,600292.SH
205 2024-11-07,0.8635644200307799,600481.SH
206 2024-11-08,0.747868586080844,002047.SZ
207 2024-11-11,0.8396084711761063,002428.SZ
208 2024-11-12,0.7919805059954543,603859.SH
209 2024-11-13,0.9060991217118458,603859.SH
210 2024-11-14,0.9421847563274262,600966.SH
211 2024-11-15,1.5178035825387006,002086.SZ
212 2024-11-18,1.5376931721833804,600212.SH
213 2024-11-19,1.4060097628439219,603825.SH
214 2024-11-20,0.8890834002457785,002808.SZ
215 2024-11-21,0.8855345297456824,603859.SH
216 2024-11-22,1.119413451276471,600593.SH
217 2024-11-25,0.7785122491322624,600787.SH
218 2024-11-26,1.0000210964516405,600736.SH
219 2024-11-27,0.8847088850937169,002245.SZ
220 2024-11-28,0.8621169938483969,601360.SH
221 2024-11-29,0.7042168573520408,002354.SZ
222 2024-12-02,0.6426744321671465,002611.SZ
223 2024-12-03,0.9021248369663103,603366.SH
224 2024-12-04,0.9942733603597254,002822.SZ
225 2024-12-05,0.8594169937974554,002175.SZ
226 2024-12-06,0.6985253068214117,603366.SH
227 2024-12-09,0.9476416369172441,601933.SH
228 2024-12-10,1.053999067291189,600193.SH
229 2024-12-11,0.6996323878339654,002193.SZ
230 2024-12-12,0.9535599508119768,002362.SZ
231 2024-12-13,1.3279717542253164,603429.SH
232 2024-12-16,1.0260681164941636,000727.SZ
233 2024-12-17,1.3063049942413876,600157.SH
234 2024-12-18,1.3121814535335503,002878.SZ
235 2024-12-19,1.219098477203033,600593.SH
236 2024-12-20,0.8480675770196239,002512.SZ
237 2024-12-23,1.1806404660060321,600724.SH
238 2024-12-24,0.8537658533885591,603610.SH
239 2024-12-25,0.8752079792242901,002965.SZ
240 2024-12-26,0.720498836899636,603214.SH
241 2024-12-27,0.8552518755027023,002945.SZ
242 2024-12-30,0.7853260336927593,600223.SH
243 2024-12-31,1.0774196590782728,600183.SH
244 2025-01-02,1.345874429894366,603225.SH
245 2025-01-03,1.4595000226870254,603379.SH
246 2025-01-06,1.8567194520891437,002130.SZ
247 2025-01-07,1.327995034218316,002881.SZ
248 2025-01-08,0.7854520495476546,600126.SH
249 2025-01-09,0.8656051617404842,000756.SZ
250 2025-01-10,1.1141535494224937,605016.SH
251 2025-01-13,2.0375745364278695,605080.SH
252 2025-01-14,0.6228152667370752,603269.SH
253 2025-01-15,0.7099046974063614,600673.SH
254 2025-01-16,0.7230959774435842,600381.SH
255 2025-01-17,0.8735560458074921,603007.SH
256 2025-01-20,0.6265446536616674,002164.SZ
257 2025-01-21,1.4687297319348953,000534.SZ
258 2025-01-22,0.7347201431708319,000408.SZ
259 2025-01-23,0.7698608009850573,603121.SH
260 2025-01-24,0.6733337270251429,603360.SH
261 2025-01-27,1.3722279750234074,002484.SZ
262 2025-02-05,1.0995625926629562,603667.SH
263 2025-02-06,0.9540560778289151,603308.SH
264 2025-02-07,0.8152925075270021,000856.SZ
265 2025-02-10,0.4527649047879118,603166.SH