diff --git a/main/data/cyq_perf.ipynb b/main/data/cyq_perf.ipynb index 70d220b..fa782de 100644 --- a/main/data/cyq_perf.ipynb +++ b/main/data/cyq_perf.ipynb @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "1b5a82fbf4e380de", "metadata": { "ExecuteTime": { @@ -88,7 +88,7 @@ "import pandas as pd\n", "import time\n", "\n", - "h5_filename = '../../../data/sw_daily.h5'\n", + "h5_filename = '../../../data/cyq_perf.h5'\n", "\n", "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", diff --git a/main/data/index_and_industry.ipynb b/main/data/index_and_industry.ipynb index a812726..525ec00 100644 --- a/main/data/index_and_industry.ipynb +++ b/main/data/index_and_industry.ipynb @@ -39,19 +39,16 @@ "text": [ "数据已经成功存储到index_data.h5文件中\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_16940\\1832869062.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " final_df = pd.concat(all_data, ignore_index=True)\n" - ] } ], "source": [ "# 定义四个指数\n", - "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", + "index_list = [\n", + " # '399300.SZ', \n", + " '000905.SH', \n", + " '000852.SH', \n", + " '399006.SZ'\n", + " ]\n", "\n", "# 获取并存储数据\n", "all_data = []\n", @@ -59,6 +56,7 @@ "for ts_code in index_list:\n", " df = pro.index_daily(ts_code=ts_code) # 可根据需要设置日期\n", " df['ts_code'] = ts_code # 添加ts_code列来区分数据\n", + " # print(df)\n", " all_data.append(df)\n", "\n", "# 合并所有数据\n", @@ -86,32 +84,32 @@ "output_type": "stream", "text": [ " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250509 5721.7225 5770.4410 5770.4410 5705.1654 \n", - "1 000905.SH 20250508 5773.8056 5731.7157 5783.7915 5724.9511 \n", - "2 000905.SH 20250507 5750.2911 5805.6560 5819.2422 5713.2734 \n", - "3 000905.SH 20250506 5740.3338 5668.8762 5740.3338 5666.4698 \n", - "4 000905.SH 20250430 5631.8249 5604.6537 5647.7821 5603.1718 \n", + "0 000905.SH 20250523 5653.0436 5697.1362 5738.0829 5653.0436 \n", + "1 000905.SH 20250522 5703.2797 5739.1909 5757.7946 5701.1614 \n", + "2 000905.SH 20250521 5757.9225 5741.6885 5763.0788 5733.8275 \n", + "3 000905.SH 20250520 5747.3670 5723.5055 5759.4582 5707.8101 \n", + "4 000905.SH 20250519 5720.7949 5719.4381 5729.0703 5669.7208 \n", "... ... ... ... ... ... ... \n", - "13501 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13502 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13503 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13504 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13505 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "13531 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13532 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13533 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13534 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13535 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", "\n", - " pre_close change pct_chg vol amount \n", - "0 5773.8056 -52.0831 -0.9021 1.239390e+08 1.781623e+08 \n", - "1 5750.2911 23.5145 0.4089 1.361403e+08 1.870326e+08 \n", - "2 5740.3338 9.9573 0.1735 1.710118e+08 2.275662e+08 \n", - "3 5631.8249 108.5089 1.9267 1.627736e+08 2.170600e+08 \n", - "4 5604.9057 26.9192 0.4803 1.383866e+08 1.816166e+08 \n", - "... ... ... ... ... ... \n", - "13501 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13502 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13503 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13504 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13505 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + " pre_close change pct_chg vol amount \n", + "0 5703.2797 -50.2361 -0.8808 1.143612e+08 1.481236e+08 \n", + "1 5757.9225 -54.6428 -0.9490 1.090577e+08 1.416209e+08 \n", + "2 5747.3670 10.5555 0.1837 1.158045e+08 1.551474e+08 \n", + "3 5720.7949 26.5721 0.4645 1.168966e+08 1.517512e+08 \n", + "4 5715.8491 4.9458 0.0865 1.153849e+08 1.410987e+08 \n", + "... ... ... ... ... ... \n", + "13531 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13532 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13533 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13534 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13535 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", "\n", - "[13506 rows x 11 columns]\n" + "[13536 rows x 11 columns]\n" ] } ], diff --git a/main/data/name_change.ipynb b/main/data/name_change.ipynb index fd3eea6..e1e4cda 100644 --- a/main/data/name_change.ipynb +++ b/main/data/name_change.ipynb @@ -183,7 +183,7 @@ "成功获取 000572.SZ 的数据\n", "成功获取 000573.SZ 的数据\n", "成功获取 000576.SZ 的数据\n", - "已调用300次API，等待 40.75 秒以满足速率限制...\n", + "已调用300次API，等待 40.21 秒以满足速率限制...\n", "成功获取 000581.SZ 的数据\n", "成功获取 000582.SZ 的数据\n", "成功获取 000584.SZ 的数据\n", @@ -334,7 +334,7 @@ "成功获取 000811.SZ 的数据\n", "成功获取 000812.SZ 的数据\n", "成功获取 000813.SZ 的数据\n", - "已调用300次API，等待 8.60 秒以满足速率限制...\n", + "已调用300次API，等待 40.67 秒以满足速率限制...\n", "成功获取 000815.SZ 的数据\n", "成功获取 000816.SZ 的数据\n", "成功获取 000818.SZ 的数据\n", @@ -485,7 +485,7 @@ "成功获取 001238.SZ 的数据\n", "成功获取 001239.SZ 的数据\n", "成功获取 001255.SZ 的数据\n", - "已调用300次API，等待 16.22 秒以满足速率限制...\n", + "已调用300次API，等待 40.55 秒以满足速率限制...\n", "成功获取 001256.SZ 的数据\n", "成功获取 001258.SZ 的数据\n", "成功获取 001259.SZ 的数据\n", @@ -636,7 +636,7 @@ "成功获取 002085.SZ 的数据\n", "成功获取 002086.SZ 的数据\n", "成功获取 002088.SZ 的数据\n", - "已调用300次API，等待 16.73 秒以满足速率限制...\n", + "已调用300次API，等待 40.85 秒以满足速率限制...\n", "成功获取 002090.SZ 的数据\n", "成功获取 002091.SZ 的数据\n", "成功获取 002092.SZ 的数据\n", @@ -787,7 +787,7 @@ "成功获取 002242.SZ 的数据\n", "成功获取 002243.SZ 的数据\n", "成功获取 002244.SZ 的数据\n", - "已调用300次API，等待 21.90 秒以满足速率限制...\n", + "已调用300次API，等待 39.17 秒以满足速率限制...\n", "成功获取 002245.SZ 的数据\n", "成功获取 002246.SZ 的数据\n", "成功获取 002247.SZ 的数据\n", @@ -938,7 +938,7 @@ "成功获取 002400.SZ 的数据\n", "成功获取 002401.SZ 的数据\n", "成功获取 002402.SZ 的数据\n", - "已调用300次API，等待 38.45 秒以满足速率限制...\n", + "已调用300次API，等待 39.14 秒以满足速率限制...\n", "成功获取 002403.SZ 的数据\n", "成功获取 002404.SZ 的数据\n", "成功获取 002405.SZ 的数据\n", @@ -1089,7 +1089,7 @@ "成功获取 002566.SZ 的数据\n", "成功获取 002567.SZ 的数据\n", "成功获取 002568.SZ 的数据\n", - "已调用300次API，等待 39.17 秒以满足速率限制...\n", + "已调用300次API，等待 39.79 秒以满足速率限制...\n", "成功获取 002569.SZ 的数据\n", "成功获取 002570.SZ 的数据\n", "成功获取 002571.SZ 的数据\n", @@ -1240,7 +1240,7 @@ "成功获取 002729.SZ 的数据\n", "成功获取 002730.SZ 的数据\n", "成功获取 002731.SZ 的数据\n", - "已调用300次API，等待 38.52 秒以满足速率限制...\n", + "已调用300次API，等待 40.41 秒以满足速率限制...\n", "成功获取 002732.SZ 的数据\n", "成功获取 002733.SZ 的数据\n", "成功获取 002734.SZ 的数据\n", @@ -1391,7 +1391,7 @@ "成功获取 002896.SZ 的数据\n", "成功获取 002897.SZ 的数据\n", "成功获取 002898.SZ 的数据\n", - "已调用300次API，等待 38.60 秒以满足速率限制...\n", + "已调用300次API，等待 40.53 秒以满足速率限制...\n", "成功获取 002899.SZ 的数据\n", "成功获取 002900.SZ 的数据\n", "成功获取 002901.SZ 的数据\n", @@ -1542,7 +1542,7 @@ "成功获取 300014.SZ 的数据\n", "成功获取 300015.SZ 的数据\n", "成功获取 300016.SZ 的数据\n", - "已调用300次API，等待 37.75 秒以满足速率限制...\n", + "已调用300次API，等待 40.55 秒以满足速率限制...\n", "成功获取 300017.SZ 的数据\n", "成功获取 300018.SZ 的数据\n", "成功获取 300019.SZ 的数据\n", @@ -1693,7 +1693,7 @@ "成功获取 300174.SZ 的数据\n", "成功获取 300175.SZ 的数据\n", "成功获取 300176.SZ 的数据\n", - "已调用300次API，等待 40.54 秒以满足速率限制...\n", + "已调用300次API，等待 38.94 秒以满足速率限制...\n", "成功获取 300177.SZ 的数据\n", "成功获取 300179.SZ 的数据\n", "成功获取 300180.SZ 的数据\n", @@ -1844,7 +1844,7 @@ "成功获取 300337.SZ 的数据\n", "成功获取 300338.SZ 的数据\n", "成功获取 300339.SZ 的数据\n", - "已调用300次API，等待 40.50 秒以满足速率限制...\n", + "已调用300次API，等待 37.41 秒以满足速率限制...\n", "成功获取 300340.SZ 的数据\n", "成功获取 300341.SZ 的数据\n", "成功获取 300342.SZ 的数据\n", @@ -1995,7 +1995,7 @@ "成功获取 300494.SZ 的数据\n", "成功获取 300496.SZ 的数据\n", "成功获取 300497.SZ 的数据\n", - "已调用300次API，等待 38.45 秒以满足速率限制...\n", + "已调用300次API，等待 39.25 秒以满足速率限制...\n", "成功获取 300498.SZ 的数据\n", "成功获取 300499.SZ 的数据\n", "成功获取 300500.SZ 的数据\n", @@ -2146,7 +2146,7 @@ "成功获取 300650.SZ 的数据\n", "成功获取 300651.SZ 的数据\n", "成功获取 300652.SZ 的数据\n", - "已调用300次API，等待 40.83 秒以满足速率限制...\n", + "已调用300次API，等待 39.97 秒以满足速率限制...\n", "成功获取 300653.SZ 的数据\n", "成功获取 300654.SZ 的数据\n", "成功获取 300655.SZ 的数据\n", @@ -2297,7 +2297,7 @@ "成功获取 300810.SZ 的数据\n", "成功获取 300811.SZ 的数据\n", "成功获取 300812.SZ 的数据\n", - "已调用300次API，等待 40.11 秒以满足速率限制...\n", + "已调用300次API，等待 37.73 秒以满足速率限制...\n", "成功获取 300813.SZ 的数据\n", "成功获取 300814.SZ 的数据\n", "成功获取 300815.SZ 的数据\n", @@ -2448,7 +2448,7 @@ "成功获取 300966.SZ 的数据\n", "成功获取 300967.SZ 的数据\n", "成功获取 300968.SZ 的数据\n", - "已调用300次API，等待 40.87 秒以满足速率限制...\n", + "已调用300次API，等待 41.01 秒以满足速率限制...\n", "成功获取 300969.SZ 的数据\n", "成功获取 300970.SZ 的数据\n", "成功获取 300971.SZ 的数据\n", @@ -2599,7 +2599,7 @@ "成功获取 301128.SZ 的数据\n", "成功获取 301129.SZ 的数据\n", "成功获取 301130.SZ 的数据\n", - "已调用300次API，等待 40.16 秒以满足速率限制...\n", + "已调用300次API，等待 40.96 秒以满足速率限制...\n", "成功获取 301131.SZ 的数据\n", "成功获取 301132.SZ 的数据\n", "成功获取 301133.SZ 的数据\n", @@ -2750,7 +2750,7 @@ "成功获取 301313.SZ 的数据\n", "成功获取 301314.SZ 的数据\n", "成功获取 301315.SZ 的数据\n", - "已调用300次API，等待 40.63 秒以满足速率限制...\n", + "已调用300次API，等待 40.38 秒以满足速率限制...\n", "成功获取 301316.SZ 的数据\n", "成功获取 301317.SZ 的数据\n", "成功获取 301318.SZ 的数据\n", @@ -2901,7 +2901,7 @@ "成功获取 301618.SZ 的数据\n", "成功获取 301622.SZ 的数据\n", "成功获取 301626.SZ 的数据\n", - "已调用300次API，等待 39.95 秒以满足速率限制...\n", + "已调用300次API，等待 39.97 秒以满足速率限制...\n", "成功获取 301628.SZ 的数据\n", "成功获取 301631.SZ 的数据\n", "成功获取 301633.SZ 的数据\n", @@ -3052,7 +3052,7 @@ "成功获取 600170.SH 的数据\n", "成功获取 600171.SH 的数据\n", "成功获取 600172.SH 的数据\n", - "已调用300次API，等待 39.18 秒以满足速率限制...\n", + "已调用300次API，等待 39.68 秒以满足速率限制...\n", "成功获取 600173.SH 的数据\n", "成功获取 600176.SH 的数据\n", "成功获取 600177.SH 的数据\n", @@ -3203,7 +3203,7 @@ "成功获取 600366.SH 的数据\n", "成功获取 600367.SH 的数据\n", "成功获取 600368.SH 的数据\n", - "已调用300次API，等待 40.48 秒以满足速率限制...\n", + "已调用300次API，等待 39.52 秒以满足速率限制...\n", "成功获取 600369.SH 的数据\n", "成功获取 600370.SH 的数据\n", "成功获取 600371.SH 的数据\n", @@ -3354,7 +3354,7 @@ "成功获取 600572.SH 的数据\n", "成功获取 600573.SH 的数据\n", "成功获取 600575.SH 的数据\n", - "已调用300次API，等待 39.24 秒以满足速率限制...\n", + "已调用300次API，等待 40.15 秒以满足速率限制...\n", "成功获取 600576.SH 的数据\n", "成功获取 600577.SH 的数据\n", "成功获取 600578.SH 的数据\n", @@ -3505,7 +3505,7 @@ "成功获取 600748.SH 的数据\n", "成功获取 600749.SH 的数据\n", "成功获取 600750.SH 的数据\n", - "已调用300次API，等待 39.49 秒以满足速率限制...\n", + "已调用300次API，等待 40.58 秒以满足速率限制...\n", "成功获取 600751.SH 的数据\n", "成功获取 600753.SH 的数据\n", "成功获取 600754.SH 的数据\n", @@ -3656,7 +3656,7 @@ "成功获取 600956.SH 的数据\n", "成功获取 600958.SH 的数据\n", "成功获取 600959.SH 的数据\n", - "已调用300次API，等待 40.83 秒以满足速率限制...\n", + "已调用300次API，等待 40.13 秒以满足速率限制...\n", "成功获取 600960.SH 的数据\n", "成功获取 600961.SH 的数据\n", "成功获取 600962.SH 的数据\n", @@ -3807,7 +3807,7 @@ "成功获取 601519.SH 的数据\n", "成功获取 601528.SH 的数据\n", "成功获取 601555.SH 的数据\n", - "已调用300次API，等待 40.73 秒以满足速率限制...\n", + "已调用300次API，等待 38.43 秒以满足速率限制...\n", "成功获取 601566.SH 的数据\n", "成功获取 601567.SH 的数据\n", "成功获取 601568.SH 的数据\n", @@ -3958,7 +3958,7 @@ "成功获取 603041.SH 的数据\n", "成功获取 603042.SH 的数据\n", "成功获取 603043.SH 的数据\n", - "已调用300次API，等待 40.08 秒以满足速率限制...\n", + "已调用300次API，等待 40.56 秒以满足速率限制...\n", "成功获取 603045.SH 的数据\n", "成功获取 603048.SH 的数据\n", "成功获取 603050.SH 的数据\n", @@ -4109,7 +4109,7 @@ "成功获取 603228.SH 的数据\n", "成功获取 603229.SH 的数据\n", "成功获取 603230.SH 的数据\n", - "已调用300次API，等待 40.23 秒以满足速率限制...\n", + "已调用300次API，等待 40.37 秒以满足速率限制...\n", "成功获取 603231.SH 的数据\n", "成功获取 603232.SH 的数据\n", "成功获取 603233.SH 的数据\n", @@ -4260,7 +4260,7 @@ "成功获取 603530.SH 的数据\n", "成功获取 603533.SH 的数据\n", "成功获取 603535.SH 的数据\n", - "已调用300次API，等待 40.89 秒以满足速率限制...\n", + "已调用300次API，等待 39.94 秒以满足速率限制...\n", "成功获取 603536.SH 的数据\n", "成功获取 603538.SH 的数据\n", "成功获取 603551.SH 的数据\n", @@ -4411,7 +4411,7 @@ "成功获取 603819.SH 的数据\n", "成功获取 603822.SH 的数据\n", "成功获取 603823.SH 的数据\n", - "已调用300次API，等待 38.75 秒以满足速率限制...\n", + "已调用300次API，等待 39.12 秒以满足速率限制...\n", "成功获取 603825.SH 的数据\n", "成功获取 603826.SH 的数据\n", "成功获取 603828.SH 的数据\n", @@ -4562,7 +4562,7 @@ "成功获取 605167.SH 的数据\n", "成功获取 605168.SH 的数据\n", "成功获取 605169.SH 的数据\n", - "已调用300次API，等待 40.37 秒以满足速率限制...\n", + "已调用300次API，等待 40.12 秒以满足速率限制...\n", "成功获取 605177.SH 的数据\n", "成功获取 605178.SH 的数据\n", "成功获取 605179.SH 的数据\n", @@ -4713,7 +4713,7 @@ "成功获取 688097.SH 的数据\n", "成功获取 688098.SH 的数据\n", "成功获取 688099.SH 的数据\n", - "已调用300次API，等待 39.89 秒以满足速率限制...\n", + "已调用300次API，等待 40.20 秒以满足速率限制...\n", "成功获取 688100.SH 的数据\n", "成功获取 688101.SH 的数据\n", "成功获取 688102.SH 的数据\n", @@ -4864,7 +4864,7 @@ "成功获取 688271.SH 的数据\n", "成功获取 688272.SH 的数据\n", "成功获取 688273.SH 的数据\n", - "已调用300次API，等待 39.50 秒以满足速率限制...\n", + "已调用300次API，等待 41.07 秒以满足速率限制...\n", "成功获取 688275.SH 的数据\n", "成功获取 688276.SH 的数据\n", "成功获取 688277.SH 的数据\n", @@ -5015,7 +5015,7 @@ "成功获取 688486.SH 的数据\n", "成功获取 688488.SH 的数据\n", "成功获取 688489.SH 的数据\n", - "已调用300次API，等待 39.30 秒以满足速率限制...\n", + "已调用300次API，等待 40.39 秒以满足速率限制...\n", "成功获取 688496.SH 的数据\n", "成功获取 688498.SH 的数据\n", "成功获取 688499.SH 的数据\n", @@ -5166,7 +5166,7 @@ "成功获取 688689.SH 的数据\n", "成功获取 688690.SH 的数据\n", "成功获取 688691.SH 的数据\n", - "已调用300次API，等待 40.48 秒以满足速率限制...\n", + "已调用300次API，等待 40.83 秒以满足速率限制...\n", "成功获取 688692.SH 的数据\n", "成功获取 688693.SH 的数据\n", "成功获取 688695.SH 的数据\n", @@ -5317,7 +5317,7 @@ "成功获取 835184.BJ 的数据\n", "成功获取 835185.BJ 的数据\n", "成功获取 835207.BJ 的数据\n", - "已调用300次API，等待 40.17 秒以满足速率限制...\n", + "已调用300次API，等待 41.07 秒以满足速率限制...\n", "成功获取 835237.BJ 的数据\n", "成功获取 835305.BJ 的数据\n", "成功获取 835368.BJ 的数据\n", @@ -5468,7 +5468,7 @@ "成功获取 000005.SZ 的数据\n", "成功获取 000013.SZ 的数据\n", "成功获取 000015.SZ 的数据\n", - "已调用300次API，等待 40.97 秒以满足速率限制...\n", + "已调用300次API，等待 41.06 秒以满足速率限制...\n", "成功获取 000018.SZ 的数据\n", "成功获取 000023.SZ 的数据\n", "成功获取 000024.SZ 的数据\n", @@ -5619,7 +5619,7 @@ "成功获取 300309.SZ 的数据\n", "成功获取 300312.SZ 的数据\n", "成功获取 300325.SZ 的数据\n", - "已调用300次API，等待 39.81 秒以满足速率限制...\n", + "已调用300次API，等待 41.04 秒以满足速率限制...\n", "成功获取 300330.SZ 的数据\n", "成功获取 300336.SZ 的数据\n", "成功获取 300356.SZ 的数据\n", @@ -5761,7 +5761,7 @@ "2 000001.SZ 深发展A 20070620 20120801 完成股改\n", "3 000001.SZ 深发展A 20070620 20120801 完成股改\n", "4 000001.SZ S深发展A 20061009 20070619 未股改加S\n", - "名称变化记录总数: 32259\n" + "名称变化记录总数: 32284\n" ] } ], diff --git a/main/data/top_list.ipynb b/main/data/top_list.ipynb new file mode 100644 index 0000000..b8ce057 --- /dev/null +++ b/main/data/top_list.ipynb @@ -0,0 +1,2538 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:25.004019Z", + "start_time": "2025-03-12T15:31:24.322440Z" + } + }, + "outputs": [], + "source": [ + "from operator import index\n", + "\n", + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "972a5ac9f79fe373", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:40.917015Z", + "start_time": "2025-03-12T15:31:35.958771Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " trade_date ts_code name close pct_change turnover_rate amount \\\n", + "0 20200826 000004.SZ 国农科技 23.8 -9.7117 7.21 149306898.0 \n", + "1 20200826 000004.SZ 国华网安 23.8 -9.7117 7.21 149306898.0 \n", + "\n", + " l_sell l_buy l_amount net_amount net_rate amount_rate \\\n", + "0 52431453.51 14154131.0 66585584.51 -38277322.51 -25.64 44.6 \n", + "1 52431453.51 14154131.0 66585584.51 -38277322.51 -25.64 44.6 \n", + "\n", + " float_values reason \n", + "0 1.997265e+09 日跌幅偏离值达到7%的前5只证券 \n", + "1 1.997265e+09 日跌幅偏离值达到7%的前5只证券 \n" + ] + } + ], + "source": [ + "\n", + "df = pro.top_list(trade_date='20200826')\n", + "print(df[df['ts_code'] == '000004.SZ'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1b5a82fbf4e380de", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:30:20.421604Z", + "start_time": "2025-03-12T15:30:20.224851Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import time\n", + "\n", + "h5_filename = '../../data/top_list.h5'\n", + "\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal['cal_date'].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f448da220816bf98", + "metadata": { + "ExecuteTime": { + "start_time": "2025-03-12T15:30:20.436796Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "任务 20250718 完成\n", + "任务 20250717 完成\n", + "任务 20250715 完成\n", + "任务 20250716 完成\n", + "任务 20250714 完成\n", + "任务 20250711 完成\n", + "任务 20250710 完成\n", + "任务 20250709 完成\n", + "任务 20250708 完成\n", + "任务 20250707 完成\n", + "任务 20250704 完成\n", + "任务 20250703 完成\n", + "任务 20250702 完成\n", + "任务 20250701 完成\n", + "任务 20250630 完成\n", + "任务 20250627 完成\n", + "任务 20250626 完成\n", + "任务 20250625 完成\n", + "任务 20250624 完成\n", + "任务 20250623 完成\n", + 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+ " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "907f732d3c397bf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:10.381348500Z", + "start_time": "2025-03-12T15:23:41.345460Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_12440\\3671378986.py:2: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有每日基础数据获取并保存完毕！\n" + ] + } + ], + "source": [ + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='top_list', mode='w', format='table', data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "73e829ac-ff3d-408e-beb3-0b87f5b00b19", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 000565.SZ 20250520\n", + "1 000609.SZ 20250520\n", + "2 000677.SZ 20250520\n", + "3 000722.SZ 20250520\n", + "4 001222.SZ 20250520\n", + "... ... ...\n", + "143695 603577.SH 20170103\n", + "143696 603823.SH 20170103\n", + "143697 603823.SH 20170103\n", + "143698 603878.SH 20170103\n", + "143699 603886.SH 20170103\n", + "\n", + "[143700 rows x 2 columns]\n" + ] + } + ], + "source": [ + "key = '/top_list'\n", + "max_date = None\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date']]\n", + " print(df)\n", + " max_date = df['trade_date'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ac8120e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "该列所有不同的数据为：\n", + "['日跌幅偏离值达到7%的前5只证券' '连续三个交易日内，涨幅偏离值累计达到12%的ST证券、*ST证券和未完成股改证券'\n", + " '连续三个交易日内，涨幅偏离值累计达到20%的证券' '日涨幅偏离值达到7%的前5只证券' '日振幅值达到15%的前5只证券'\n", + " '日换手率达到20%的前5只证券' '日涨幅达到15%的前5只可转债' '退市整理期' '连续三个交易日内，涨幅偏离值累计达到30%的证券'\n", + " '日涨幅达到15%的前5只证券' '日换手率达到30%的前5只证券'\n", + " '非ST、*ST和S证券连续三个交易日内收盘价格涨幅偏离值累计达到20%的证券' '有价格涨跌幅限制的日收盘价格涨幅偏离值达到7%的前五只证券'\n", + " '有价格涨跌幅限制的日换手率达到20%的前五只证券' '有价格涨跌幅限制的日收盘价格跌幅偏离值达到7%的前五只证券'\n", + " '有价格涨跌幅限制的日价格振幅达到15%的前五只证券' '有价格涨跌幅限制的日收盘价格涨幅达到15%的前五只证券'\n", + " '有价格涨跌幅限制的连续3个交易日内收盘价格涨幅偏离值累计达到30%的证券' '当日价格振幅达到30%的前5只股票'\n", + " '当日换手率达到20%的前5只股票' '当日收盘价涨幅达到20%的前5只股票'\n", + " '北交所股票最近3个有成交的交易日以内收盘价涨跌幅偏离值累计达到+40%(-40%)'\n", + " '非ST、*ST和S证券连续三个交易日内收盘价格跌幅偏离值累计达到20%的证券'\n", + " 'ST、*ST证券连续三个交易日内收盘价格跌幅偏离值累计达到12%的证券' '日振幅值达到30%的前5只证券' '无价格涨跌幅限制的证券'\n", + " 'ST、*ST证券连续三个交易日内收盘价格涨幅偏离值累计达到12%的证券' '严重异常期间日收盘价格涨幅偏离值累计达到100%的证券'\n", + " '严重异常期间4次出现正向异常波动的证券' '有价格涨跌幅限制的日换手率达到30%的前五只证券'\n", + " '连续10个交易日内4次出现同负向异常波动的证券' '连续三个交易日内，跌幅偏离值累计达到12%的ST证券、*ST证券和未完成股改证券'\n", + " '连续三个交易日内，跌幅偏离值累计达到20%的证券'\n", + " '连续三个交易日内，日均换手率与前五个交易日的日均换手率的比值达到30倍，且换手率累计达20%的证券'\n", + " '严重异常期间日收盘价格跌幅偏离值累计达到50%的证券' '非上市首日,当日收盘价涨幅达15%的前五只可转债'\n", + " '有价格涨跌幅限制的连续10个交易日内收盘价格涨幅偏离值累计达到100%的证券' '日跌幅达到15%的前5只证券'\n", + " '连续三个交易日内，跌幅偏离值累计达到30%的证券' '有价格涨跌幅限制的日收盘价格跌幅达到15%的前五只证券'\n", + " '当日收盘价跌幅达到-20%的前5只股票' '向不特定对象发行的可转债上市首日交易公开信息' '上市首日可转债'\n", + " '有价格涨跌幅限制的连续30个交易日内收盘价格涨幅偏离值累计达到200%的证券' '日跌幅达到15%的前5只可转债'\n", + " '有价格涨跌幅限制的日价格振幅达到30%的前五只证券' '非上市首日,当日收盘价跌幅达15%的前五只可转债'\n", + " '连续三个交易日内，涨幅偏离值累计达到30%的可转债' '连续10个交易日内4次出现同正向异常波动的证券'\n", + " '连续3个交易日内日收盘价格涨幅偏离值达到30%的可转债' '有价格涨跌幅限制的连续10个交易日内收盘价格跌幅偏离值累计达到-50%的证券'\n", + " '非上市首日,当日价格振幅达30%的前五只可转债' '严重异常期间日收盘价格涨幅偏离值累计达到200%的证券' '退市整理的证券'\n", + " '连续10个交易日内3次出现同正向异常波动的证券' '单只标的证券的当日融资买入数量达到当日该证券总交易量的50%以上'\n", + " '连续三个交易日内，跌幅偏离值累计达到30%的可转债' '日振幅值达到30%的前5只可转债' '日价格涨幅偏离值达到12.66%'\n", + " '有价格涨跌幅限制的连续30个交易日内收盘价格跌幅偏离值累计达到-70%的证券' 'S股连续三个交易日内收盘价格涨幅偏离值累计达到15%的证券'\n", + " '单只标的证券的当日融资买入数量达到当日该证券总交易量的50％以上' '连续3个交易日内日收盘价格跌幅偏离值达到30%的可转债'\n", + " '连续10个交易日内收盘价格涨幅偏离值累计达100%的可转债' '严重异常期间日收盘价格涨幅偏离值累计达到100%的可转债'\n", + " '有价格涨跌幅限制的连续3个交易日内收盘价格跌幅偏离值累计达到30%的证券' '严重异常期间4次出现负向异常波动的证券'\n", + " '严重异常期间日收盘价格跌幅偏离值累计达到70%的证券' '异常期间价格跌幅偏离值累计达到-21.46%'\n", + " '异常期间价格跌幅偏离值累计达到-21.48%' '连续三个交易日内，跌幅偏离值累计达到12%的ST证券、*ST证券'\n", + " '严重异常期间日收盘价格跌幅偏离值累计达到50%的可转债' '连续10个交易日内收盘价格跌幅偏离值累计达到-50%的可转债'\n", + " '日换手率达到30.08%' '单只标的证券的当日融券卖出数量达到当日该证券总交易量的50%以上' '退市整理期的可转债'\n", + " '连续三个交易日内,涨幅偏离值累计达到30%的可转债' '单只标的证券的当日融券卖出数量达到当日该证券总交易量的50％以上' '上市首日的可转债'\n", + " '日价格涨幅偏离值达到9.42%' '异常期间价格跌幅偏离值累计达到-14.01%' '日价格跌幅偏离值达到-8.95%'\n", + " '日价格跌幅偏离值达到-7.02%' '连续三个交易日内，涨幅偏离值累计达到12%的ST证券、*ST证券'\n", + " 'ST、*ST和S证券连续三个交易日内收盘价格跌幅偏离值累计达到15%的证券' '当日无价格涨跌幅限制的A股，出现异常波动停牌的'\n", + " 'ST、*ST和S证券连续三个交易日内收盘价格涨幅偏离值累计达到15%的证券' '日价格振幅达到33.72%' '日价格振幅达到35.76%'\n", + " '连续三个交易日内的日均换手率与前五个交易日日均换手率的比值到达30倍,并且该股票封闭式基金连续三个交易日内累计换手率达到20%'\n", + " '连续三个交易日内,涨幅偏离值累计达到20%的证券' '连续三个交易日内,跌幅偏离值累计达到20%的证券'\n", + " '连续三个交易日内,涨幅偏离值累计达到12%的ST证券、*ST证券和未完成股改证券'\n", + " '连续三个交易日内,跌幅偏离值累计达到12%的ST证券、*ST证券和未完成股改证券' '连续三个交易日内,涨幅偏离值累计达到30%的证券'\n", + " '异常期间价格涨幅偏离值累计达到30.72%' '异常期间价格涨幅偏离值累计达到32.42%' '异常期间价格涨幅偏离值累计达到31.45%'\n", + " '异常期间价格涨幅偏离值累计达到31.48%' '异常期间价格涨幅偏离值累计达到32.31%' '异常期间价格涨幅偏离值累计达到32.38%'\n", + " '异常期间价格涨幅偏离值累计达到32.28%' '异常期间价格涨幅偏离值累计达到32.27%' '异常期间价格涨幅偏离值累计达到30.49%'\n", + " '异常期间价格涨幅偏离值累计达到30.08%' '异常期间价格涨幅偏离值累计达到30.30%' '异常期间价格涨幅偏离值累计达到30.03%'\n", + " '当日无价格涨跌幅限制的B股，出现异常波动停牌的' '异常期间价格涨幅偏离值累计达到31.19%' '异常期间价格涨幅偏离值累计达到14.20%'\n", + " '异常期间价格涨幅偏离值累计达到29.20%' '日价格跌幅偏离值达到-9.52%' '日价格振幅达到18.73%'\n", + " '异常期间价格跌幅偏离值累计达到-20.19%' '异常期间价格涨幅偏离值累计达到21.50%' '日价格涨幅偏离值达到10.24%'\n", + " '日价格振幅达到21.77%' '日换手率达到47.05%' '异常期间价格涨幅偏离值累计达到21.77%'\n", + " '异常期间价格涨幅偏离值累计达到24.71%' '日价格跌幅偏离值达到-9.76%' '异常期间价格跌幅偏离值累计达到-20.80%'\n", + " '日价格跌幅偏离值达到-9.44%' '日价格振幅达到19.66%' '日换手率达到34.80%' '异常期间价格涨幅偏离值累计达到13.30%'\n", + " '日价格振幅达到17.86%' '日换手率达到33.98%' '异常期间价格涨幅偏离值累计达到20.45%' '日换手率达到47.96%'\n", + " '异常期间价格涨幅偏离值累计达到20.36%' '异常期间价格涨幅偏离值累计达到21.08%' '异常期间价格涨幅偏离值累计达到21.75%'\n", + " '异常期间价格涨幅偏离值累计达到20.62%' '异常期间价格涨幅偏离值累计达到22.14%' '日换手率达到40.73%'\n", + " '日价格涨幅偏离值达到7.14%' '异常期间价格涨幅偏离值累计达到32.18%' '日价格涨幅达到20.00%' '日换手率达到34.93%'\n", + " '日价格跌幅达到-17.66%' '异常期间价格跌幅偏离值累计达到-36.58%' '日换手率达到35.52%'\n", + " '异常期间价格涨幅偏离值累计达到34.10%' '异常期间价格跌幅偏离值累计达到-35.17%' '异常期间价格涨幅偏离值累计达到30.98%'\n", + " '异常期间价格涨幅偏离值累计达到41.08%' '日换手率达到57.97%' '日换手率达到44.08%' '日价格涨幅偏离值达到11.22%'\n", + " '异常期间价格涨幅偏离值累计达到12.29%' '日换手率达到32.12%' '异常期间价格涨幅偏离值累计达到16.13%'\n", + " '日换手率达到37.02%' '日换手率达到23.35%' '异常期间价格涨幅偏离值累计达到20.51%' '日价格跌幅偏离值达到-8.78%'\n", + " '异常期间价格涨幅偏离值累计达到20.25%' '日价格跌幅偏离值达到-8.13%' '日换手率达到26.60%'\n", + " '异常期间价格涨幅偏离值累计达到12.57%' '日价格跌幅偏离值达到-7.99%' '异常期间价格涨幅偏离值累计达到14.32%'\n", + " '日价格跌幅偏离值达到-7.68%' '日价格振幅达到17.57%' '异常期间价格涨幅偏离值累计达到25.27%'\n", + " '异常期间价格涨幅偏离值累计达到30.90%' '日换手率达到44.24%' '日换手率达到43.96%' '日换手率达到39.89%'\n", + " '日换手率达到45.45%' '异常期间价格涨幅偏离值累计达到33.64%' '日换手率达到39.99%' '无价格涨跌幅限制'\n", + " '严重异常期间3次出现正向异常波动的证券' '精选层股票最近3个有成交的交易日以内收盘价涨跌幅偏离值累计达到+40%(-40%)'\n", + " '日价格涨幅偏离值达到9.94%' '异常期间价格涨幅偏离值累计达到20.53%' '异常期间价格跌幅偏离值累计达到-12.31%'\n", + " '异常期间价格涨幅偏离值累计达到20.37%' '日价格振幅达到17.91%' '日价格跌幅偏离值达到-10.06%'\n", + " '日价格振幅达到19.94%' '日价格振幅达到17.82%' '日换手率达到23.94%' '日换手率达到21.95%'\n", + " '日换手率达到23.39%' '异常期间价格涨幅偏离值累计达到27.38%' '异常期间价格涨幅偏离值累计达到23.55%'\n", + " '日换手率达到23.02%' '异常期间价格跌幅偏离值累计达到-20.12%' '日换手率达到26.07%' '日价格跌幅达到-17.89%'\n", + " '日价格涨幅达到15.85%' '日价格涨幅达到16.00%' '日换手率达到41.01%' '日换手率达到55.53%'\n", + " '日换手率达到53.64%' '异常期间价格涨幅偏离值累计达到38.36%' '日换手率达到43.66%' '日换手率达到41.19%'\n", + " '有价格涨跌幅限制的日换手率达到20%的前三只证券' '有价格涨跌幅限制的日收盘价格跌幅偏离值达到7%的前三只证券'\n", + " '有价格涨跌幅限制的日收盘价格涨幅偏离值达到7%的前三只证券' '有价格涨跌幅限制的日价格振幅达到15%的前三只证券'\n", + " '日价格涨幅偏离值达到10.48%' '异常期间价格涨幅偏离值累计达到15.21%' '日换手率达到25.43%'\n", + " '异常期间价格跌幅偏离值累计达到-21.76%' '异常期间价格涨幅偏离值累计达到20.52%' '日换手率达到26.11%'\n", + " '日价格振幅达到15.08%' '日换手率达到29.60%' '日价格振幅达到16.58%' '日价格振幅达到15.71%'\n", + " '异常期间价格涨幅偏离值累计达到20.03%' '日换手率达到29.03%' '异常期间价格涨幅偏离值累计达到28.18%'\n", + " '异常期间价格涨幅偏离值累计达到21.60%' '日价格振幅达到15.51%' '异常期间价格涨幅偏离值累计达到24.15%'\n", + " '异常期间价格涨幅偏离值累计达到22.17%' '日换手率达到39.46%' '异常期间价格涨幅偏离值累计达到39.67%'\n", + " '异常期间价格涨幅偏离值累计达到31.06%' '日换手率达到52.63%' '日换手率达到47.81%' '日换手率达到47.75%'\n", + " '日换手率达到40.76%' '异常期间价格涨幅偏离值累计达到16.11%' '连续三个交易日内，涨幅偏离值累计达20%的证券'\n", + " '跌幅偏离值达7%的证券' '振幅值达15%的证券' '涨幅偏离值达7%的证券' '换手率达20%的证券'\n", + " '连续三个交易日内，跌幅偏离值累计达20%的证券' '连续三个交易日内，涨幅偏离值累计达到15%的ST证券、*ST证券和未完成股改证券'\n", + " '日价格涨幅偏离值达到11.43%' '日价格跌幅偏离值达到-8.45%' '异常期间价格涨幅偏离值累计达到20.86%'\n", + " '日换手率达到23.37%' '日价格跌幅偏离值达到-8.57%' '异常期间价格涨幅偏离值累计达到23.24%'\n", + " '异常期间价格涨幅偏离值累计达到20.97%' '日换手率达到21.93%' '日换手率达到22.54%' '日价格跌幅偏离值达到-7.45%'\n", + " '日价格振幅达到15.85%' '日换手率达到39.81%' '日换手率达到29.22%' '日换手率达到35.22%'\n", + " '异常期间价格涨幅偏离值累计达到33.04%' '日换手率达到32.99%' '异常期间价格涨幅偏离值累计达到32.93%'\n", + " '日换手率达到43.84%' '日换手率达到39.98%' '日换手率达到32.97%' '异常期间价格跌幅偏离值累计达到-15.62%'\n", + " '异常期间价格跌幅偏离值累计达到-16.87%' '异常期间价格涨幅偏离值累计达到16.08%' '日价格涨幅偏离值达到10.97%'\n", + " '日价格跌幅偏离值达到-9.03%' '日价格振幅达到19.42%' '异常期间价格涨幅偏离值累计达到16.06%' '日换手率达到34.95%'\n", + " '日价格振幅达到15.47%' '异常期间价格涨幅偏离值累计达到22.40%' '日换手率达到30.04%'\n", + " '异常期间价格跌幅偏离值累计达到-28.92%' '异常期间价格涨幅偏离值累计达到21.26%' '异常期间价格跌幅偏离值累计达到-22.22%'\n", + " '异常期间价格涨幅偏离值累计达到22.04%' '异常期间价格跌幅偏离值累计达到-13.94%' '日价格振幅达到16.22%'\n", + " '日价格振幅达到15.22%' '日价格振幅达到16.15%' '日换手率达到29.49%' '日换手率达到51.16%'\n", + " '日价格跌幅偏离值达到-9.69%' '日价格涨幅偏离值达到7.62%' '异常期间价格涨幅偏离值累计达到12.08%'\n", + " '日价格跌幅偏离值达到-9.71%' '异常期间价格涨幅偏离值累计达到20.09%' '日价格涨幅偏离值达到10.29%'\n", + " '日换手率达到29.42%' '异常期间价格跌幅偏离值累计达到-12.09%' '异常期间价格涨幅偏离值累计达到14.25%'\n", + " '异常期间价格跌幅偏离值累计达到-12.68%' '日价格振幅达到17.55%' '日换手率达到38.63%'\n", + " '异常期间价格跌幅偏离值累计达到-22.60%' '日换手率达到37.81%' '日换手率达到53.51%' '日价格跌幅达到-20.00%'\n", + " '异常期间价格跌幅偏离值累计达到-35.82%' '日价格涨幅达到16.10%' '日价格涨幅达到15.71%' '日换手率达到36.79%'\n", + " '日换手率达到60.06%' '异常期间价格涨幅偏离值累计达到32.14%' '日价格涨幅偏离值达到9.80%'\n", + " '异常期间价格涨幅偏离值累计达到22.56%' '日价格振幅达到15.93%' '异常期间价格涨幅偏离值累计达到13.93%'\n", + " '日价格振幅达到15.52%' '日换手率达到36.60%' '日价格跌幅偏离值达到-10.21%'\n", + " '异常期间价格跌幅偏离值累计达到-13.02%' '异常期间价格涨幅偏离值累计达到28.93%' '异常期间价格跌幅偏离值累计达到-14.22%'\n", + " '日价格振幅达到15.35%' '异常期间价格跌幅偏离值累计达到-12.10%' '日换手率达到45.16%'\n", + " '异常期间价格跌幅偏离值累计达到-12.82%' '异常期间价格涨幅偏离值累计达到24.12%' '日换手率达到34.34%'\n", + " '异常期间价格跌幅偏离值累计达到-21.04%' '日换手率达到49.77%' '日换手率达到33.85%'\n", + " '异常期间价格跌幅偏离值累计达到-31.38%' '异常期间价格跌幅偏离值累计达到-40.94%' '日换手率达到40.03%'\n", + " '异常期间价格跌幅偏离值累计达到-33.32%' '日换手率达到41.65%' '日换手率达到44.48%' '日换手率达到70.29%'\n", + " '日换手率达到35.39%' '异常期间价格涨幅偏离值累计达到24.43%' '日价格涨幅偏离值达到9.17%' '日价格振幅达到16.41%'\n", + " '日价格振幅达到18.23%' '异常期间价格跌幅偏离值累计达到-12.99%' '日价格振幅达到19.07%'\n", + " '日价格跌幅偏离值达到-10.83%' '异常期间价格涨幅偏离值累计达到22.98%' '异常期间价格涨幅偏离值累计达到21.18%'\n", + " '异常期间价格跌幅偏离值累计达到-20.86%' '日价格振幅达到16.40%' '日换手率达到37.89%'\n", + " '异常期间价格跌幅偏离值累计达到-20.47%' '日换手率达到33.15%' '日价格振幅达到20.69%' '日换手率达到38.30%'\n", + " '日换手率达到35.26%' '日价格振幅达到16.94%' '日价格涨幅偏离值达到10.75%' '异常期间价格涨幅偏离值累计达到20.30%'\n", + " '日价格跌幅偏离值达到-9.15%' '日价格跌幅偏离值达到-9.25%' '异常期间价格跌幅偏离值累计达到-22.02%'\n", + " '日价格振幅达到16.57%' '异常期间价格涨幅偏离值累计达到14.81%' '日价格振幅达到15.07%'\n", + " '异常期间价格涨幅偏离值累计达到24.40%' '日价格跌幅偏离值达到-9.16%' '日换手率达到44.86%' '日价格振幅达到15.33%'\n", + " '日换手率达到33.80%' '日换手率达到42.02%' '日价格振幅达到20.35%' '异常期间价格跌幅偏离值累计达到-12.70%'\n", + " '日价格涨幅偏离值达到9.97%' '异常期间价格涨幅偏离值累计达到12.19%' '异常期间价格涨幅偏离值累计达到15.27%'\n", + " '异常期间价格涨幅偏离值累计达到20.72%' '异常期间价格跌幅偏离值累计达到-20.53%' '日价格振幅达到15.95%'\n", + " '日价格跌幅偏离值达到-10.03%' '异常期间价格跌幅偏离值累计达到-22.33%' '异常期间价格跌幅偏离值累计达到-22.82%'\n", + " '日价格振幅达到22.19%' '异常期间价格跌幅偏离值累计达到-14.73%' '日价格振幅达到16.42%' '日价格振幅达到16.20%'\n", + " '日换手率达到36.69%' '日换手率达到35.23%' '日换手率达到44.23%' '异常期间价格跌幅偏离值累计达到-29.73%'\n", + " '日换手率达到39.75%' '日价格跌幅达到-16.93%' '日换手率达到40.71%' '日换手率达到36.04%'\n", + " '异常期间价格涨幅偏离值累计达到40.44%' '日价格跌幅偏离值达到-10.45%' '日价格涨幅偏离值达到9.55%'\n", + " '异常期间价格涨幅偏离值累计达到13.86%' '异常期间价格跌幅偏离值累计达到-20.94%' '异常期间价格涨幅偏离值累计达到21.34%'\n", + " '异常期间价格跌幅偏离值累计达到-23.98%' '日换手率达到51.98%' '日换手率达到45.44%' '日换手率达到36.40%'\n", + " '日价格振幅达到22.23%' '日换手率达到36.61%' '日换手率达到30.47%' '日价格跌幅达到-15.97%'\n", + " '异常期间价格涨幅偏离值累计达到14.39%' '异常期间价格跌幅偏离值累计达到-21.18%' '日价格涨幅偏离值达到9.52%'\n", + " '异常期间价格涨幅偏离值累计达到22.90%' '异常期间价格跌幅偏离值累计达到-26.48%' '异常期间价格涨幅偏离值累计达到29.39%'\n", + " '日价格振幅达到16.24%' '日价格跌幅偏离值达到-10.48%' '异常期间价格涨幅偏离值累计达到21.70%'\n", + " '异常期间价格涨幅偏离值累计达到20.64%' '日价格跌幅偏离值达到-8.62%' '日换手率达到43.36%'\n", + " '日价格跌幅偏离值达到-10.20%' '异常期间价格跌幅偏离值累计达到-20.40%' '日换手率达到52.28%'\n", + " '异常期间价格涨幅偏离值累计达到20.59%' '日换手率达到44.01%' '日价格涨幅偏离值达到8.37%'\n", + " '异常期间价格涨幅偏离值累计达到38.81%' '异常期间价格跌幅偏离值累计达到-41.19%' '日价格涨幅达到19.96%'\n", + " '异常期间价格涨幅偏离值累计达到34.07%' '日换手率达到31.59%' '日换手率达到30.68%' '日价格涨幅达到17.85%'\n", + " '日换手率达到30.75%' '日换手率达到30.87%' '日价格振幅达到17.07%' '日价格振幅达到15.24%'\n", + " '异常期间价格涨幅偏离值累计达到12.43%' '日价格振幅达到15.43%' '日价格振幅达到21.34%' '日换手率达到47.10%'\n", + " '日换手率达到56.87%' '日换手率达到54.95%' '严重异常期间日收盘价格跌幅偏离值累计达到-50.81%'\n", + " '严重异常期间日收盘价格跌幅偏离值累计达到-70.49%' '日价格涨幅达到15.30%' '日换手率达到30.09%'\n", + " '日换手率达到34.63%' '异常期间价格涨幅偏离值累计达到20.58%' '异常期间价格跌幅偏离值累计达到-25.68%'\n", + " '日价格振幅达到15.97%' '日价格振幅达到18.35%' '日价格涨幅偏离值达到10.07%'\n", + " '异常期间价格跌幅偏离值累计达到-20.25%' '日价格跌幅偏离值达到-9.93%' '异常期间价格涨幅偏离值累计达到27.02%'\n", + " '日价格跌幅偏离值达到-9.81%' '日换手率达到38.35%' '异常期间价格涨幅偏离值累计达到20.38%' '日换手率达到63.15%'\n", + " '异常期间价格涨幅偏离值累计达到20.34%' '日换手率达到39.94%' '异常期间价格跌幅偏离值累计达到-21.08%'\n", + " '日价格涨幅达到19.91%' '异常期间价格跌幅偏离值累计达到-30.43%' '日换手率达到42.95%' '日换手率达到41.73%'\n", + " '日换手率达到35.11%' '日换手率达到45.39%' '日换手率达到32.27%' '日价格涨幅偏离值达到7.56%'\n", + " '异常期间价格跌幅偏离值累计达到-13.89%' '日价格跌幅偏离值达到-12.44%' '日换手率达到37.30%'\n", + " '日价格振幅达到16.28%' '日价格振幅达到19.34%' '异常期间价格涨幅偏离值累计达到27.48%'\n", + " '异常期间价格涨幅偏离值累计达到12.48%' '异常期间价格涨幅偏离值累计达到20.18%' '异常期间价格涨幅偏离值累计达到20.26%'\n", + " '异常期间价格涨幅偏离值累计达到22.02%' '日价格跌幅偏离值达到-9.38%' '日价格跌幅偏离值达到-10.08%'\n", + " '日换手率达到58.15%' '日换手率达到43.57%' '日价格涨幅偏离值达到8.31%' '日换手率达到46.67%'\n", + " '日换手率达到32.90%' '日换手率达到33.19%' '日换手率达到41.68%' '异常期间价格涨幅偏离值累计达到20.05%'\n", + " '日价格涨幅偏离值达到10.36%' '日价格振幅达到15.29%' '日价格跌幅偏离值达到-8.96%' '日价格跌幅偏离值达到-9.64%'\n", + " '异常期间价格跌幅偏离值累计达到-22.28%' '日价格跌幅偏离值达到-7.65%' '日价格振幅达到16.34%'\n", + " '日价格振幅达到17.95%' '日换手率达到37.24%' '日换手率达到68.18%' '日换手率达到58.53%'\n", + " '日换手率达到47.28%' '异常期间价格涨幅偏离值累计达到40.35%' '异常期间价格涨幅偏离值累计达到30.70%'\n", + " '日换手率达到45.96%' '日换手率达到45.12%' '日换手率达到55.20%' '日换手率达到50.27%'\n", + " '异常期间价格涨幅偏离值累计达到15.59%' '异常期间价格涨幅偏离值累计达到15.15%' '异常期间价格跌幅偏离值累计达到-15.48%'\n", + " '连续三个交易日内,跌幅偏离值累计达到12%的ST证券、*ST证券' '严重异常期间日收盘价格涨幅偏离值累计达到108.96%'\n", + " '异常期间价格涨幅偏离值累计达到30.35%' '异常期间价格涨幅偏离值累计达到36.44%' '异常期间价格涨幅偏离值累计达到32.70%'\n", + " '日换手率达到20%的前五只证券' '日涨幅偏离值达到7%的前五只证券' '日振幅值达到15%的前五只证券' '日跌幅偏离值达到7%的前五只证券'\n", + " '异常期间价格涨幅偏离值累计达到16.78%' '异常期间价格涨幅偏离值累计达到15.39%' '异常期间价格跌幅偏离值累计达到-16.58%'\n", + " '异常期间价格涨幅偏离值累计达到16.84%' '异常期间价格跌幅偏离值累计达到-16.82%' '日价格跌幅偏离值达到-9.54%'\n", + " '日价格涨幅偏离值达到9.22%' '日价格涨幅偏离值达到10.46%' '日价格涨幅偏离值达到7.68%' '日价格涨幅偏离值达到10.62%'\n", + " '异常期间价格涨幅偏离值累计达到20.08%' '日价格振幅达到16.95%' '日换手率达到24.07%' '日换手率达到28.11%'\n", + " '日价格跌幅偏离值达到-9.14%' '异常期间价格涨幅偏离值累计达到20.78%' '日换手率达到27.40%' '日换手率达到25.58%'\n", + " '日换手率达到43.03%' '日价格涨幅偏离值达到11.19%' '日价格振幅达到18.21%' '异常期间价格涨幅偏离值累计达到21.46%'\n", + " '日价格跌幅偏离值达到-8.81%' '异常期间价格涨幅偏离值累计达到22.01%' '日价格振幅达到16.54%'\n", + " '日价格跌幅偏离值达到-8.05%' '日价格振幅达到16.56%' '异常期间价格跌幅偏离值累计达到-20.16%'\n", + " '日价格跌幅偏离值达到-7.92%' '异常期间价格跌幅偏离值累计达到-20.93%' '异常期间价格涨幅偏离值累计达到24.46%'\n", + " '日换手率达到32.82%' '日换手率达到31.87%' '日价格振幅达到22.24%'\n", + " '异常期间日均换手率放大196.25倍，并且累计换手率达到47.45%' '异常期间价格涨幅偏离值累计达到16.41%'\n", + " '异常期间价格跌幅偏离值累计达到-17.57%' '异常期间价格跌幅偏离值累计达到-16.00%'\n", + " '异常期间价格跌幅偏离值累计达到-15.90%' '异常期间价格涨幅偏离值累计达到15.50%' '异常期间价格涨幅偏离值累计达到15.47%'\n", + " '连续三个交易日内,涨幅偏离值累计达到12%的ST证券、*ST证券' '异常期间价格涨幅偏离值累计达到15.12%'\n", + " '异常期间价格涨幅偏离值累计达到17.33%' '异常期间价格跌幅偏离值累计达到-15.08%' '异常期间价格涨幅偏离值累计达到17.56%'\n", + " '异常期间价格跌幅偏离值累计达到-15.38%' '异常期间价格跌幅偏离值累计达到-15.11%'\n", + " '异常期间价格跌幅偏离值累计达到-15.41%' '异常期间价格跌幅偏离值累计达到-15.24%' '异常期间价格涨幅偏离值累计达到15.01%'\n", + " '异常期间价格跌幅偏离值累计达到-15.87%' '异常期间价格涨幅偏离值累计达到15.72%' '异常期间价格涨幅偏离值累计达到15.00%'\n", + " '异常期间价格跌幅偏离值累计达到-17.44%' '异常期间价格跌幅偏离值累计达到-15.04%'\n", + " '异常期间价格跌幅偏离值累计达到-15.36%' '异常期间价格跌幅偏离值累计达到-15.44%'\n", + " '异常期间价格跌幅偏离值累计达到-16.85%' '异常期间价格跌幅偏离值累计达到-15.66%' '新股首日交易信息'\n", + " '当日无价格涨跌幅限制的A股,出现异常波动停牌的' '日价格跌幅偏离值达到-8.51%' '日价格振幅达到18.39%'\n", + " '日价格涨幅偏离值达到11.49%' '异常期间价格涨幅偏离值累计达到20.41%' '日换手率达到33.69%' '日换手率达到30.27%'\n", + " '日价格振幅达到20.61%' '日换手率达到56.43%' '日换手率达到66.76%' '日换手率达到43.51%'\n", + " '日价格跌幅偏离值达到-10.00%' '日价格涨幅偏离值达到10.00%' '日价格涨幅偏离值达到9.00%'\n", + " '日价格涨幅偏离值达到7.79%' '日价格涨幅偏离值达到9.84%' '日价格振幅达到19.28%' '日价格跌幅偏离值达到-10.16%'\n", + " '异常期间价格涨幅偏离值累计达到12.13%' '异常期间价格涨幅偏离值累计达到21.01%' '日价格跌幅偏离值达到-8.47%'\n", + " '日换手率达到32.60%' '异常期间价格跌幅偏离值累计达到-22.11%' '日换手率达到27.97%' '日换手率达到28.78%'\n", + " '日换手率达到31.46%' '异常期间价格跌幅偏离值累计达到-15.40%' '日价格涨幅偏离值达到8.46%'\n", + " '异常期间价格涨幅偏离值累计达到23.98%' '异常期间价格跌幅偏离值累计达到-12.04%' '异常期间价格跌幅偏离值累计达到-15.46%'\n", + " '异常期间价格跌幅偏离值累计达到-15.18%' '异常期间价格涨幅偏离值累计达到21.10%' '异常期间价格跌幅偏离值累计达到-12.24%'\n", + " '日换手率达到48.90%' '异常期间价格涨幅偏离值累计达到20.71%' '日换手率达到42.14%' '日换手率达到32.49%'\n", + " '日换手率达到37.56%' '日换手率达到32.46%' '异常期间价格涨幅偏离值累计达到16.43%' '日价格振幅达到19.70%'\n", + " '日价格涨幅偏离值达到10.21%' '异常期间价格涨幅偏离值累计达到21.66%' '日价格跌幅偏离值达到-9.79%'\n", + " '日价格跌幅偏离值达到-9.33%' '日价格振幅达到16.90%' '日换手率达到69.01%' '日换手率达到46.87%'\n", + " '日换手率达到37.50%' '日换手率达到64.44%' '日换手率达到55.92%' '日价格振幅达到18.18%'\n", + " '日价格涨幅偏离值达到10.03%' '日价格振幅达到21.56%' '日价格跌幅偏离值达到-9.97%'\n", + " '异常期间价格跌幅偏离值累计达到-21.94%' '日价格振幅达到17.35%' '异常期间价格涨幅偏离值累计达到21.37%'\n", + " '日价格振幅达到18.88%' '异常期间价格跌幅偏离值累计达到-23.75%' '日换手率达到38.43%' '日换手率达到66.89%'\n", + " '日换手率达到37.97%' '异常期间价格跌幅偏离值累计达到-22.71%' '日换手率达到67.79%' '日换手率达到72.22%'\n", + " '连续三个交易日内的日均换手率与前五个交易日日均换手率的比值到达30倍,并且该股票封闭式']\n", + "\n", + "不同的数据共有：638 个\n" + ] + } + ], + "source": [ + "\n", + "# 假设您的 DataFrame 叫做 df，要查询的列名是 'column_name'\n", + "unique_values = all_daily_data_df['reason'].unique()\n", + "number_of_unique_values = len(unique_values)\n", + "\n", + "print(f\"该列所有不同的数据为：\\n{unique_values}\")\n", + "print(f\"\\n不同的数据共有：{number_of_unique_values} 个\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/data/update/cyq-perf.ipynb b/main/data/update/cyq-perf.ipynb index caf1ca7..6a8ad5a 100644 --- a/main/data/update/cyq-perf.ipynb +++ b/main/data/update/cyq-perf.ipynb @@ -32,22 +32,22 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date\n", - "0 000001.SZ 20250312\n", - "1 000002.SZ 20250312\n", - "2 000004.SZ 20250312\n", - "3 000006.SZ 20250312\n", - "4 000007.SZ 20250312\n", - "... ... ...\n", - "5384 920445.BJ 20250508\n", - "5385 920489.BJ 20250508\n", - "5386 920682.BJ 20250508\n", - "5387 920799.BJ 20250508\n", - "5388 920819.BJ 20250508\n", + " ts_code trade_date\n", + "0 000001.SZ 20250312\n", + "1 000002.SZ 20250312\n", + "2 000004.SZ 20250312\n", + "3 000006.SZ 20250312\n", + "4 000007.SZ 20250312\n", + "... ... ...\n", + "26947 920445.BJ 20250512\n", + "26948 920489.BJ 20250512\n", + "26949 920682.BJ 20250512\n", + "26950 920799.BJ 20250512\n", + "26951 920819.BJ 20250512\n", "\n", - "[7665071 rows x 2 columns]\n", - "20250508\n", - "start_date: 20250509\n" + "[7697412 rows x 2 columns]\n", + "20250516\n", + "start_date: 20250519\n" ] } ], @@ -86,36 +86,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250619 完成\n", "任务 20250620 完成\n", + "任务 20250619 完成\n", "任务 20250618 完成\n", "任务 20250617 完成\n", - "任务 20250616 完成\n", "任务 20250613 完成\n", - "任务 20250612 完成\n", + "任务 20250616 完成\n", "任务 20250611 完成\n", + "任务 20250612 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", "任务 20250606 完成\n", "任务 20250605 完成\n", "任务 20250604 完成\n", "任务 20250603 完成\n", - "任务 20250530 完成\n", "任务 20250529 完成\n", - "任务 20250528 完成\n", + "任务 20250530 完成\n", "任务 20250527 完成\n", - "任务 20250523 完成\n", + "任务 20250528 完成\n", "任务 20250526 完成\n", - "任务 20250521 完成\n", + "任务 20250523 完成\n", "任务 20250522 完成\n", - "任务 20250519 完成\n", + "任务 20250521 完成\n", "任务 20250520 完成\n", - "任务 20250516 完成\n", - "任务 20250515 完成\n", - "任务 20250514 完成\n", - "任务 20250513 完成\n", - "任务 20250512 完成\n", - "任务 20250509 完成\n" + "任务 20250519 完成\n" ] } ], diff --git a/main/data/update/sw_daily.ipynb b/main/data/update/sw_daily.ipynb index 6d3954e..4cca328 100644 --- a/main/data/update/sw_daily.ipynb +++ b/main/data/update/sw_daily.ipynb @@ -32,22 +32,22 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date\n", - "0 801001.SI 20250221\n", - "1 801002.SI 20250221\n", - "2 801003.SI 20250221\n", - "3 801005.SI 20250221\n", - "4 801010.SI 20250221\n", - ".. ... ...\n", - "434 859811.SI 20250508\n", - "435 859821.SI 20250508\n", - "436 859822.SI 20250508\n", - "437 859852.SI 20250508\n", - "438 859951.SI 20250508\n", + " ts_code trade_date\n", + "0 801001.SI 20250221\n", + "1 801002.SI 20250221\n", + "2 801003.SI 20250221\n", + "3 801005.SI 20250221\n", + "4 801010.SI 20250221\n", + "... ... ...\n", + "2190 859811.SI 20250512\n", + "2191 859821.SI 20250512\n", + "2192 859822.SI 20250512\n", + "2193 859852.SI 20250512\n", + "2194 859951.SI 20250512\n", "\n", - "[1066343 rows x 2 columns]\n", - "20250508\n", - "start_date: 20250509\n" + "[1068977 rows x 2 columns]\n", + "20250516\n", + "start_date: 20250519\n" ] } ], @@ -90,16 +90,16 @@ "任务 20250620 完成\n", "任务 20250617 完成\n", "任务 20250618 完成\n", - "任务 20250616 完成\n", "任务 20250613 完成\n", + "任务 20250616 完成\n", "任务 20250612 完成\n", "任务 20250611 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", - "任务 20250606 完成\n", "任务 20250605 完成\n", - "任务 20250604 完成\n", + "任务 20250606 完成\n", "任务 20250603 完成\n", + "任务 20250604 完成\n", "任务 20250530 完成\n", "任务 20250529 完成\n", "任务 20250528 完成\n", @@ -109,13 +109,7 @@ "任务 20250522 完成\n", "任务 20250521 完成\n", "任务 20250520 完成\n", - "任务 20250519 完成\n", - "任务 20250515 完成\n", - "任务 20250516 完成\n", - "任务 20250514 完成\n", - "任务 20250513 完成\n", - "任务 20250512 完成\n", - "任务 20250509 完成\n" + "任务 20250519 完成\n" ] } ], diff --git a/main/data/update/update_daily_basic.ipynb b/main/data/update/update_daily_basic.ipynb index 94332df..6a11381 100644 --- a/main/data/update/update_daily_basic.ipynb +++ b/main/data/update/update_daily_basic.ipynb @@ -94,17 +94,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8615301 entries, 0 to 5388\n", + "Index: 8647642 entries, 0 to 26951\n", "Data columns (total 2 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object\n", " 1 trade_date object\n", "dtypes: object(2)\n", - "memory usage: 197.2+ MB\n", + "memory usage: 197.9+ MB\n", "None\n", - "20250508\n", - "20250509\n" + "20250516\n", + "20250519\n" ] } ], @@ -144,14 +144,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250718 完成\n", "任务 20250717 完成\n", - "任务 20250716 完成\n", + "任务 20250718 完成\n", "任务 20250715 完成\n", - "任务 20250711 完成\n", + "任务 20250716 完成\n", "任务 20250714 完成\n", - "任务 20250710 完成\n", + "任务 20250711 完成\n", "任务 20250709 完成\n", + "任务 20250710 完成\n", "任务 20250708 完成\n", "任务 20250707 完成\n", "任务 20250704 完成\n", @@ -160,12 +160,12 @@ "任务 20250701 完成\n", "任务 20250630 完成\n", "任务 20250627 完成\n", - "任务 20250626 完成\n", "任务 20250625 完成\n", + "任务 20250626 完成\n", "任务 20250624 完成\n", "任务 20250623 完成\n", - "任务 20250620 完成\n", "任务 20250619 完成\n", + "任务 20250620 完成\n", "任务 20250618 完成\n", "任务 20250617 完成\n", "任务 20250616 完成\n", @@ -178,22 +178,16 @@ "任务 20250605 完成\n", "任务 20250604 完成\n", "任务 20250603 完成\n", - "任务 20250530 完成\n", "任务 20250529 完成\n", - "任务 20250528 完成\n", + "任务 20250530 完成\n", "任务 20250527 完成\n", + "任务 20250528 完成\n", "任务 20250526 完成\n", "任务 20250523 完成\n", "任务 20250522 完成\n", "任务 20250521 完成\n", "任务 20250520 完成\n", - "任务 20250519 完成\n", - "任务 20250516 完成\n", - "任务 20250515 完成\n", - "任务 20250514 完成\n", - "任务 20250513 完成\n", - "任务 20250512 完成\n", - "任务 20250509 完成\n" + "任务 20250519 完成\n" ] } ], @@ -263,59 +257,59 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 300575.SZ 20250509 6.05 1.9284 2.1880 \n", - "1 300247.SZ 20250509 3.77 2.1735 2.5437 \n", - "2 603038.SH 20250509 15.80 17.5702 32.3972 \n", - "3 002030.SZ 20250509 5.82 0.8252 1.2070 \n", - "4 600157.SH 20250509 1.36 0.8369 1.0222 \n", - "... ... ... ... ... ... \n", - "5384 600841.SH 20250509 5.57 1.0271 3.2670 \n", - "5385 300968.SZ 20250509 14.76 1.2857 2.7636 \n", - "5386 300634.SZ 20250509 25.79 5.2551 9.4581 \n", - "5387 300295.SZ 20250509 15.73 3.0347 3.2458 \n", - "5388 688370.SH 20250509 19.15 1.2008 1.2008 \n", + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "0 000839.SZ 20250523 2.67 0.8124 1.2782 \n", + "1 300274.SZ 20250523 60.60 3.2852 3.7071 \n", + "2 301356.SZ 20250523 17.59 5.0050 5.0698 \n", + "3 600152.SH 20250523 5.73 1.3359 2.0988 \n", + "4 300049.SZ 20250523 29.91 1.6066 1.7292 \n", + "... ... ... ... ... ... \n", + "26941 002458.SZ 20250519 8.36 2.1950 2.5416 \n", + "26942 600882.SH 20250519 27.18 2.2244 4.6853 \n", + "26943 001283.SZ 20250519 54.51 3.0453 3.0453 \n", + "26944 000718.SZ 20250519 2.20 1.4790 2.2404 \n", + "26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 0.71 239.8914 NaN 1.3451 1.1608 1.1259 1.9835 \n", - "1 0.96 64.6952 53.1680 2.7649 4.4008 3.9673 0.0000 \n", - "2 4.47 183.7603 154.4297 3.1047 4.0259 3.7692 0.2434 \n", - "3 0.62 NaN NaN 1.0296 9.5754 9.9145 0.2577 \n", - "4 0.55 19.3625 26.3896 0.6394 1.0656 1.1327 0.4044 \n", - "... ... ... ... ... ... ... ... \n", - "5384 0.77 NaN NaN 2.3362 1.1952 1.2860 0.0000 \n", - "5385 0.71 115.0812 181.8721 3.2254 4.9990 5.1146 0.3388 \n", - "5386 1.01 50.5639 52.9222 4.1166 7.0433 6.7806 0.8063 \n", - "5387 0.65 NaN NaN 2.6398 24.2982 28.1758 0.0000 \n", - "5388 1.25 29.1668 36.1111 0.9812 4.4106 4.4983 NaN \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "0 0.62 NaN NaN 7.4695 3.0824 3.1095 0.0000 \n", + "1 1.82 11.3840 9.8414 3.0807 1.6137 1.4907 1.1292 \n", + "2 1.43 NaN 18055.4366 1.2789 4.2618 3.3028 0.0000 \n", + "3 1.11 NaN NaN 1.7367 1.9844 2.0758 0.0000 \n", + "4 1.05 70.3242 80.3071 4.4707 5.9056 5.8725 0.0000 \n", + "... ... ... ... ... ... ... ... \n", + "26941 1.47 18.3588 24.2570 2.1403 2.9497 3.0116 2.3923 \n", + "26942 0.89 122.4919 89.9537 3.0986 2.8733 2.7144 0.0000 \n", + "26943 0.92 48.1520 36.6481 2.1043 0.8602 0.8229 0.8691 \n", + "26944 1.76 40.4178 55.0402 0.7058 3.1476 3.2425 3.6364 \n", + "26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 1.9835 4.647564e+04 3.427082e+04 3.020469e+04 2.811776e+05 \n", - "1 NaN 8.040403e+04 8.032753e+04 6.863630e+04 3.031232e+05 \n", - "2 0.2434 2.686771e+04 2.686771e+04 1.457134e+04 4.245098e+05 \n", - "3 0.2577 1.403446e+05 1.403446e+05 9.595371e+04 8.168056e+05 \n", - "4 0.4044 2.221776e+06 2.221776e+06 1.819047e+06 3.021616e+06 \n", - "... ... ... ... ... ... \n", - "5384 NaN 1.387822e+05 1.043024e+05 3.279094e+04 7.730167e+05 \n", - "5385 0.3388 4.133800e+04 4.133800e+04 1.923185e+04 6.101489e+05 \n", - "5386 0.8063 4.512109e+04 4.346809e+04 2.415175e+04 1.163673e+06 \n", - "5387 NaN 1.896137e+04 1.675486e+04 1.566518e+04 2.982624e+05 \n", - "5388 NaN 1.371079e+04 4.374912e+03 4.374912e+03 2.625616e+05 \n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "0 NaN 391982.6352 391982.6352 249133.8007 1.046594e+06 \n", + "1 1.1292 207321.1424 158970.9449 140880.3307 1.256366e+07 \n", + "2 NaN 21600.0000 5481.0000 5410.9920 3.799440e+05 \n", + "3 NaN 52907.9375 52907.9375 33676.4965 3.031625e+05 \n", + "4 NaN 26635.6100 23351.5217 21696.0562 7.966711e+05 \n", + "... ... ... ... ... ... \n", + "26941 2.3577 110641.2915 74886.8285 64675.1303 9.249612e+05 \n", + "26942 NaN 51205.3647 51205.3647 24310.0793 1.391762e+06 \n", + "26943 0.8691 8061.0011 5785.5721 5785.5721 4.394052e+05 \n", + "26944 3.6364 303463.6384 228209.3122 150654.2061 6.676200e+05 \n", + "26945 NaN 103293.5798 103159.2875 70714.2228 3.191772e+05 \n", "\n", - " circ_mv is_st \n", - "0 2.073385e+05 False \n", - "1 3.028348e+05 False \n", - "2 4.245098e+05 False \n", - "3 8.168056e+05 False \n", - "4 3.021616e+06 False \n", - "... ... ... \n", - "5384 5.809646e+05 False \n", - "5385 6.101489e+05 False \n", - "5386 1.121042e+06 False \n", - "5387 2.635540e+05 False \n", - "5388 8.377956e+04 False \n", + " circ_mv is_st \n", + "0 1.046594e+06 False \n", + "1 9.633639e+06 False \n", + "2 9.641079e+04 False \n", + "3 3.031625e+05 False \n", + "4 6.984440e+05 False \n", + "... ... ... \n", + "26941 6.260539e+05 False \n", + "26942 1.391762e+06 False \n", + "26943 3.153715e+05 False \n", + "26944 5.020605e+05 False \n", + "26945 3.187622e+05 True \n", "\n", - "[5389 rows x 19 columns]\n" + "[26946 rows x 19 columns]\n" ] } ], @@ -339,59 +333,46 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "54 002496.SZ 20250509 1.43 3.1262 3.2341 \n", - "148 603828.SH 20250509 5.04 3.5674 7.1692 \n", - "166 600599.SH 20250509 7.70 10.8623 27.2882 \n", - "193 000820.SZ 20250509 2.16 5.5698 5.7239 \n", - "203 300506.SZ 20250509 3.28 0.6710 0.9449 \n", - "... ... ... ... ... ... \n", - "5204 002602.SZ 20250509 8.00 1.3867 1.7044 \n", - "5253 300147.SZ 20250509 7.37 7.2159 9.3379 \n", - "5264 002501.SZ 20250509 2.08 2.4301 3.1371 \n", - "5317 600421.SH 20250509 5.27 2.7391 5.8971 \n", - "5345 600289.SH 20250509 5.78 1.3847 2.0115 \n", + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "23 002898.SZ 20250523 10.20 22.8874 36.4442 \n", + "35 000889.SZ 20250523 2.76 1.6609 2.2443 \n", + "53 300379.SZ 20250523 6.12 9.3935 9.5800 \n", + "58 300268.SZ 20250523 10.27 1.8178 2.5956 \n", + "155 000615.SZ 20250523 3.15 1.1640 1.7189 \n", + "... ... ... ... ... ... \n", + "26880 300147.SZ 20250519 8.80 6.8409 8.8527 \n", + "26891 002501.SZ 20250519 2.17 4.4260 5.7136 \n", + "26910 600421.SH 20250519 6.39 3.4329 7.3909 \n", + "26938 600289.SH 20250519 5.90 1.1380 1.6532 \n", + "26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "54 0.73 NaN NaN 1.6044 7.6992 7.2633 0.0 \n", - "148 1.65 349.9490 1691.0271 3.9734 1.2211 1.3170 0.0 \n", - "166 4.51 NaN NaN 11.5933 3.9468 4.0472 0.0 \n", - "193 1.00 NaN NaN 9.5443 11.2714 14.3393 0.0 \n", - "203 0.87 NaN NaN 28.5909 19.5183 19.3088 0.0 \n", - "... ... ... ... ... ... ... ... \n", - "5204 0.78 49.1432 31.1887 2.2169 2.6358 2.2496 0.0 \n", - "5253 1.74 NaN NaN 5.0393 2.6221 2.8487 0.0 \n", - "5264 0.87 NaN NaN 22.5816 22.1370 26.0255 0.0 \n", - "5317 0.74 NaN NaN 143.1934 8.7976 8.9449 0.0 \n", - "5345 0.55 NaN NaN 2.9752 11.3890 11.6628 0.0 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n", + "23 10.43 NaN NaN 3.6011 6.8112 7.2338 0.1961 0.1961 \n", + "35 0.52 NaN NaN 27.2957 1.7661 1.7554 0.0000 NaN \n", + "53 0.89 NaN NaN 1.0993 4.5062 4.1828 0.0000 NaN \n", + "58 0.99 NaN NaN NaN 0.5235 0.5833 0.0000 NaN \n", + "155 0.99 NaN NaN NaN 2.1957 2.2727 0.0000 NaN \n", + "... ... .. ... ... ... ... ... ... \n", + "26880 1.55 NaN NaN 6.0171 3.1309 3.4015 0.0000 NaN \n", + "26891 1.83 NaN NaN 23.5587 23.0948 27.1516 0.0000 NaN \n", + "26910 0.92 NaN NaN 173.6254 10.6672 10.8459 0.0000 NaN \n", + "26938 0.46 NaN NaN 3.0370 11.6255 11.9049 0.0000 NaN \n", + "26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 NaN \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "54 NaN 150758.9677 118138.6559 114196.4999 2.155853e+05 \n", - "148 NaN 59596.0158 59593.9625 29654.2988 3.003639e+05 \n", - "166 NaN 16600.0000 16600.0000 6607.7948 1.278200e+05 \n", - "193 NaN 64362.0201 29403.1899 28611.4718 1.390220e+05 \n", - "203 NaN 69559.6569 57572.5450 40880.9749 2.281557e+05 \n", - "... ... ... ... ... ... \n", - "5204 NaN 745255.6968 687870.8273 559649.7754 5.962046e+06 \n", - "5253 NaN 66127.9045 65745.9042 50804.9121 4.873627e+05 \n", - "5264 NaN 355000.0000 354999.9006 274999.9006 7.384000e+05 \n", - "5317 NaN 19560.0000 19560.0000 9085.2748 1.030812e+05 \n", - "5345 NaN 63105.2069 56592.2684 38956.2787 3.647481e+05 \n", + " total_share float_share free_share total_mv circ_mv is_st \n", + "23 17600.0000 10126.2561 6359.4096 179520.0000 103287.8122 True \n", + "35 93629.1116 86984.9676 64375.7658 258416.3480 240078.5106 True \n", + "53 55792.2828 52663.7564 51638.5483 341448.7707 322302.1892 True \n", + "58 17420.0000 13370.7500 9364.1581 178903.4000 137317.6025 True \n", + "155 76297.9719 76250.0287 51632.2709 240338.6115 240187.5904 True \n", + "... ... ... ... ... ... ... \n", + "26880 66127.9045 65745.9042 50804.9121 581925.5596 578563.9570 True \n", + "26891 355000.0000 354999.9006 274999.9006 770350.0000 770349.7843 True \n", + "26910 19560.0000 19560.0000 9085.2748 124988.4000 124988.4000 True \n", + "26938 63105.2069 56592.2684 38956.2787 372320.7207 333894.3836 True \n", + "26945 103293.5798 103159.2875 70714.2228 319177.1616 318762.1984 True \n", "\n", - " circ_mv is_st \n", - "54 1.689383e+05 True \n", - "148 3.003536e+05 True \n", - "166 1.278200e+05 True \n", - "193 6.351089e+04 True \n", - "203 1.888379e+05 True \n", - "... ... ... \n", - "5204 5.502967e+06 True \n", - "5253 4.845473e+05 True \n", - "5264 7.383998e+05 True \n", - "5317 1.030812e+05 True \n", - "5345 3.271033e+05 True \n", - "\n", - "[197 rows x 19 columns]\n" + "[944 rows x 19 columns]\n" ] } ], @@ -441,7 +422,7 @@ "output_type": "stream", "text": [ "\n", - "Index: 8620690 entries, 0 to 5388\n", + "Index: 8674588 entries, 0 to 26945\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -449,7 +430,7 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 205.5+ MB\n", + "memory usage: 206.8+ MB\n", "None\n" ] } diff --git a/main/data/update/update_daily_data.ipynb b/main/data/update/update_daily_data.ipynb index 37f7158..53739cb 100644 --- a/main/data/update/update_daily_data.ipynb +++ b/main/data/update/update_daily_data.ipynb @@ -38,17 +38,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8686674 entries, 0 to 5357\n", + "Index: 8718823 entries, 0 to 26790\n", "Data columns (total 2 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object\n", " 1 trade_date object\n", "dtypes: object(2)\n", - "memory usage: 198.8+ MB\n", + "memory usage: 199.6+ MB\n", "None\n", - "20250508\n", - "20250509\n" + "20250516\n", + "20250519\n" ] } ], @@ -87,12 +87,12 @@ 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"任务 301312.SZ 完成\n", "任务 301313.SZ 完成\n", - "任务 301314.SZ 完成\n", "任务 301315.SZ 完成\n", + "任务 301314.SZ 完成\n", "任务 301316.SZ 完成\n", "任务 301317.SZ 完成\n", - "任务 301318.SZ 完成\n", "任务 301319.SZ 完成\n", + "任务 301318.SZ 完成\n", "任务 301320.SZ 完成\n", "任务 301321.SZ 完成\n", "任务 301322.SZ 完成\n", "任务 301323.SZ 完成\n", "任务 301325.SZ 完成\n", "任务 301326.SZ 完成\n", - "任务 301327.SZ 完成\n", "任务 301328.SZ 完成\n", + "任务 301327.SZ 完成\n", "任务 301329.SZ 完成\n", "任务 301330.SZ 完成\n", "任务 301331.SZ 完成\n", "任务 301332.SZ 完成\n", - "任务 301333.SZ 完成\n", "任务 301335.SZ 完成\n", + "任务 301333.SZ 完成\n", "任务 301336.SZ 完成\n", "任务 301337.SZ 完成\n", "任务 301338.SZ 完成\n", @@ -2821,30 +2821,30 @@ "任务 301362.SZ 完成\n", "任务 301363.SZ 完成\n", "任务 301365.SZ 完成\n", - "任务 301366.SZ 完成\n", "任务 301367.SZ 完成\n", + "任务 301366.SZ 完成\n", "任务 301368.SZ 完成\n", "任务 301369.SZ 完成\n", - "任务 301370.SZ 完成\n", "任务 301371.SZ 完成\n", + "任务 301370.SZ 完成\n", "任务 301372.SZ 完成\n", "任务 301373.SZ 完成\n", "任务 301376.SZ 完成\n", "任务 301377.SZ 完成\n", - "任务 301378.SZ 完成\n", "任务 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"任务 600130.SH 完成\n", "任务 600131.SH 完成\n", - "任务 600133.SH 完成\n", "任务 600132.SH 完成\n", + "任务 600133.SH 完成\n", "任务 600135.SH 完成\n", "任务 600136.SH 完成\n", "任务 600137.SH 完成\n", "任务 600138.SH 完成\n", - "任务 600143.SH 完成\n", "任务 600141.SH 完成\n", + "任务 600143.SH 完成\n", "任务 600148.SH 完成\n", "任务 600149.SH 完成\n", "任务 600150.SH 完成\n", @@ -3114,8 +3114,8 @@ "任务 600203.SH 完成\n", "任务 600206.SH 完成\n", "任务 600207.SH 完成\n", - "任务 600210.SH 完成\n", "任务 600208.SH 完成\n", + "任务 600210.SH 完成\n", "任务 600211.SH 完成\n", "任务 600212.SH 完成\n", "任务 600215.SH 完成\n", @@ -3134,8 +3134,8 @@ "任务 600230.SH 完成\n", "任务 600231.SH 完成\n", "任务 600232.SH 完成\n", - "任务 600234.SH 完成\n", "任务 600233.SH 完成\n", + "任务 600234.SH 完成\n", "任务 600235.SH 完成\n", "任务 600236.SH 完成\n", "任务 600237.SH 完成\n", @@ -3162,11 +3162,11 @@ "任务 600268.SH 完成\n", "任务 600269.SH 完成\n", "任务 600271.SH 完成\n", - "任务 600273.SH 完成\n", "任务 600272.SH 完成\n", - "任务 600278.SH 完成\n", "任务 600276.SH 完成\n", + "任务 600273.SH 完成\n", "任务 600279.SH 完成\n", + "任务 600278.SH 完成\n", "任务 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完成\n", "任务 688618.SH 完成\n", "任务 688617.SH 完成\n", "任务 688619.SH 完成\n", @@ -5155,24 +5155,24 @@ "任务 688648.SH 完成\n", "任务 688651.SH 完成\n", "任务 688652.SH 完成\n", - "任务 688653.SH 完成\n", "任务 688655.SH 完成\n", - "任务 688656.SH 完成\n", + "任务 688653.SH 完成\n", "任务 688657.SH 完成\n", + "任务 688656.SH 完成\n", "任务 688658.SH 完成\n", "任务 688659.SH 完成\n", - "任务 688660.SH 完成\n", "任务 688661.SH 完成\n", - "任务 688662.SH 完成\n", + "任务 688660.SH 完成\n", "任务 688663.SH 完成\n", - "任务 688665.SH 完成\n", + "任务 688662.SH 完成\n", "任务 688667.SH 完成\n", + "任务 688665.SH 完成\n", "任务 688668.SH 完成\n", "任务 688669.SH 完成\n", "任务 688670.SH 完成\n", "任务 688671.SH 完成\n", - "任务 688676.SH 完成\n", "任务 688677.SH 完成\n", + "任务 688676.SH 完成\n", "任务 688678.SH 完成\n", "任务 688679.SH 完成\n", "任务 688680.SH 完成\n", @@ -5185,8 +5185,8 @@ "任务 688689.SH 完成\n", "任务 688690.SH 完成\n", "任务 688691.SH 完成\n", - "任务 688692.SH 完成\n", "任务 688693.SH 完成\n", + "任务 688692.SH 完成\n", "任务 688695.SH 完成\n", "任务 688696.SH 完成\n", "任务 688697.SH 完成\n", @@ -5215,18 +5215,18 @@ "任务 688750.SH 完成\n", "任务 688758.SH 完成\n", "任务 688766.SH 完成\n", - "任务 688768.SH 完成\n", "任务 688767.SH 完成\n", + "任务 688768.SH 完成\n", "任务 688772.SH 完成\n", "任务 688776.SH 完成\n", "任务 688777.SH 完成\n", "任务 688778.SH 完成\n", - "任务 688779.SH 完成\n", "任务 688786.SH 完成\n", + "任务 688779.SH 完成\n", "任务 688787.SH 完成\n", "任务 688788.SH 完成\n", - "任务 688789.SH 完成\n", "任务 688793.SH 完成\n", + "任务 688789.SH 完成\n", "任务 688798.SH 完成\n", "任务 688799.SH 完成\n", "任务 688800.SH 完成\n", @@ -5245,28 +5245,28 @@ "任务 831010.BJ 完成\n", "任务 831039.BJ 完成\n", "任务 831087.BJ 完成\n", - "任务 831167.BJ 完成\n", "任务 831152.BJ 完成\n", + "任务 831167.BJ 完成\n", "任务 831175.BJ 完成\n", "任务 831195.BJ 完成\n", "任务 831278.BJ 完成\n", "任务 831304.BJ 完成\n", - "任务 831370.BJ 完成\n", "任务 831305.BJ 完成\n", - "任务 831445.BJ 完成\n", + "任务 831370.BJ 完成\n", "任务 831396.BJ 完成\n", + "任务 831445.BJ 完成\n", "任务 831526.BJ 完成\n", "任务 831627.BJ 完成\n", "任务 831641.BJ 完成\n", "任务 831689.BJ 完成\n", "任务 831726.BJ 完成\n", "任务 831768.BJ 完成\n", - "任务 831834.BJ 完成\n", "任务 831832.BJ 完成\n", - "任务 831856.BJ 完成\n", + "任务 831834.BJ 完成\n", "任务 831855.BJ 完成\n", - "任务 831961.BJ 完成\n", + "任务 831856.BJ 完成\n", "任务 831906.BJ 完成\n", + "任务 831961.BJ 完成\n", "任务 832000.BJ 完成\n", "任务 832023.BJ 完成\n", "任务 832089.BJ 完成\n", @@ -5287,8 +5287,8 @@ "任务 832662.BJ 完成\n", "任务 832735.BJ 完成\n", "任务 832786.BJ 完成\n", - "任务 832876.BJ 完成\n", "任务 832802.BJ 完成\n", + "任务 832876.BJ 完成\n", "任务 832885.BJ 完成\n", "任务 832978.BJ 完成\n", "任务 832982.BJ 完成\n", @@ -5479,8 +5479,8 @@ "任务 920111.BJ 完成\n", "任务 920116.BJ 完成\n", "任务 920118.BJ 完成\n", - "任务 689009.SH 完成\n", "任务 920128.BJ 完成\n", + "任务 689009.SH 完成\n", "任务 000003.SZ 完成\n", "任务 000005.SZ 完成\n", "任务 000013.SZ 完成\n", @@ -5492,12 +5492,12 @@ "任务 000038.SZ 完成\n", "任务 000046.SZ 完成\n", "任务 000047.SZ 完成\n", - "任务 000150.SZ 完成\n", "任务 000405.SZ 完成\n", + "任务 000150.SZ 完成\n", "任务 000406.SZ 完成\n", "任务 000412.SZ 完成\n", - "任务 000413.SZ 完成\n", "任务 000416.SZ 完成\n", + "任务 000413.SZ 完成\n", "任务 000418.SZ 完成\n", "任务 000502.SZ 完成\n", "任务 000508.SZ 完成\n", @@ -5516,8 +5516,8 @@ "任务 000583.SZ 完成\n", "任务 000585.SZ 完成\n", "任务 000587.SZ 完成\n", - "任务 000588.SZ 完成\n", "任务 000594.SZ 完成\n", + "任务 000588.SZ 完成\n", "任务 000602.SZ 完成\n", "任务 000606.SZ 完成\n", "任务 000611.SZ 完成\n", @@ -5742,10 +5742,10 @@ "任务 600813.SH 完成\n", "任务 600823.SH 完成\n", "任务 600832.SH 完成\n", - "任务 600836.SH 完成\n", "任务 600840.SH 完成\n", - "任务 600842.SH 完成\n", + "任务 600836.SH 完成\n", "任务 600852.SH 完成\n", + "任务 600842.SH 完成\n", "任务 600856.SH 完成\n", "任务 600870.SH 完成\n", "任务 600878.SH 完成\n", @@ -5835,33 +5835,33 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date open high low close pre_close \\\n", - "0 000001.SZ 20250509 1414.57 1427.35 1413.29 1424.79 1415.85 \n", - "1 000002.SZ 20250509 1250.12 1251.94 1231.95 1231.95 1250.12 \n", - "2 000004.SZ 20250509 30.76 31.94 30.72 31.41 30.44 \n", - "3 000006.SZ 20250509 264.67 266.26 254.34 255.93 264.67 \n", - "4 000007.SZ 20250509 57.74 59.23 56.99 58.82 57.66 \n", - "... ... ... ... ... ... ... ... \n", - "5353 920111.BJ 20250509 29.35 29.35 28.60 28.96 29.30 \n", - "5354 920116.BJ 20250509 111.30 115.50 110.07 110.20 110.94 \n", - "5355 920118.BJ 20250509 27.88 28.30 27.51 27.94 27.84 \n", - "5356 689009.SH 20250509 63.54 64.81 62.70 62.93 63.57 \n", - "5357 920128.BJ 20250509 31.50 32.58 31.08 31.72 31.60 \n", + " ts_code trade_date open high low close pre_close \\\n", + "0 000001.SZ 20250523 1475.91 1482.30 1460.57 1464.41 1475.91 \n", + "1 000001.SZ 20250522 1463.13 1477.18 1461.85 1475.91 1466.96 \n", + "2 000001.SZ 20250521 1456.74 1481.02 1455.46 1466.96 1455.46 \n", + "3 000001.SZ 20250520 1456.74 1465.68 1452.91 1455.46 1452.91 \n", + "4 000001.SZ 20250519 1456.74 1465.68 1451.63 1452.91 1454.18 \n", + "... ... ... ... ... ... ... ... \n", + "26761 689009.SH 20250523 68.37 70.32 66.62 66.63 68.32 \n", + "26762 689009.SH 20250522 67.16 68.66 66.74 68.32 67.32 \n", + "26763 689009.SH 20250521 65.45 67.75 64.73 67.32 65.45 \n", + "26764 689009.SH 20250520 65.56 66.73 64.75 65.45 65.44 \n", + "26765 689009.SH 20250519 64.05 65.95 63.99 65.44 63.94 \n", "\n", - " change pct_chg vol amount \n", - "0 8.94 0.63 999897.47 1113688.153 \n", - "1 -18.17 -1.45 733417.84 499181.206 \n", - "2 0.97 3.19 158549.20 122842.337 \n", - "3 -8.74 -3.30 251217.00 162768.216 \n", - "4 1.16 2.01 98993.00 69539.895 \n", - "... ... ... ... ... \n", - "5353 -0.34 -1.16 21254.87 61411.666 \n", - "5354 -0.74 -0.67 46877.34 376432.396 \n", - "5355 0.10 0.36 8515.96 23741.902 \n", - "5356 -0.64 -1.01 45658.87 287802.257 \n", - "5357 0.12 0.38 13476.08 42753.432 \n", + " change pct_chg vol amount \n", + "0 -11.50 -0.78 962643.11 1108636.101 \n", + "1 8.95 0.61 1002968.06 1155265.064 \n", + "2 11.50 0.79 1328278.10 1528350.368 \n", + "3 2.55 0.18 643634.80 734455.266 \n", + "4 -1.27 -0.09 831390.60 949883.545 \n", + "... ... ... ... ... \n", + "26761 -1.69 -2.47 88999.73 599644.428 \n", + "26762 1.00 1.49 61685.15 417079.978 \n", + "26763 1.87 2.86 63715.11 420469.740 \n", + "26764 0.01 0.02 64488.23 421060.706 \n", + "26765 1.50 2.35 95078.93 615893.951 \n", "\n", - "[5358 rows x 11 columns]\n" + "[26766 rows x 11 columns]\n" ] } ], diff --git a/main/data/update/update_money_flow.ipynb b/main/data/update/update_money_flow.ipynb index 49a9058..ea6f435 100644 --- a/main/data/update/update_money_flow.ipynb +++ b/main/data/update/update_money_flow.ipynb @@ -34,17 +34,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8451068 entries, 0 to 5123\n", + "Index: 8481815 entries, 0 to 25622\n", "Data columns (total 2 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object\n", " 1 trade_date object\n", "dtypes: object(2)\n", - "memory usage: 193.4+ MB\n", + "memory usage: 194.1+ MB\n", "None\n", - "20250508\n", - "start_date: 20250509\n" + "20250516\n", + "start_date: 20250519\n" ] } ], @@ -84,8 +84,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250717 完成\n", "任务 20250718 完成\n", + "任务 20250717 完成\n", "任务 20250715 完成\n", "任务 20250716 完成\n", "任务 20250714 完成\n", @@ -100,18 +100,18 @@ "任务 20250701 完成\n", "任务 20250630 完成\n", "任务 20250627 完成\n", - "任务 20250625 完成\n", "任务 20250626 完成\n", + "任务 20250625 完成\n", "任务 20250624 完成\n", "任务 20250623 完成\n", "任务 20250620 完成\n", "任务 20250619 完成\n", - "任务 20250618 完成\n", "任务 20250617 完成\n", + "任务 20250618 完成\n", "任务 20250616 完成\n", "任务 20250613 完成\n", - "任务 20250612 完成\n", "任务 20250611 完成\n", + "任务 20250612 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", "任务 20250606 完成\n", @@ -127,13 +127,7 @@ "任务 20250522 完成\n", "任务 20250521 完成\n", "任务 20250520 完成\n", - "任务 20250519 完成\n", - "任务 20250516 完成\n", - "任务 20250515 完成\n", - "任务 20250514 完成\n", - "任务 20250513 完成\n", - "任务 20250512 完成\n", - "任务 20250509 完成\n" + "任务 20250519 完成\n" ] } ], diff --git a/main/factor/__pycache__/factor.cpython-311.pyc b/main/factor/__pycache__/factor.cpython-311.pyc index ba0ec93..c489e50 100644 Binary files a/main/factor/__pycache__/factor.cpython-311.pyc and b/main/factor/__pycache__/factor.cpython-311.pyc differ diff --git a/main/factor/__pycache__/index_factor.cpython-311.pyc b/main/factor/__pycache__/index_factor.cpython-311.pyc new file mode 100644 index 0000000..d12996a Binary files /dev/null and b/main/factor/__pycache__/index_factor.cpython-311.pyc differ diff --git a/main/factor/factor.py b/main/factor/factor.py index cabacb2..7d56f5a 100644 --- a/main/factor/factor.py +++ b/main/factor/factor.py @@ -2,6 +2,9 @@ import numpy as np import pandas as pd import talib +from pandarallel import pandarallel + +pandarallel.initialize() def get_rolling_factor(df): old_columns = df.columns.tolist()[:] @@ -2747,7 +2750,7 @@ def sentiment_panic_greed_index(df: pd.DataFrame, window_atr: int = 14, window_s print(f"Error: DataFrame 缺少必需的 OHLCV 列。将为 {factor_name} 填充 NaN。") df[factor_name] = np.nan return - + try: df['_prev_close'] = df['close'].shift(1) @@ -2848,4 +2851,186 @@ def sentiment_reversal_indicator(df: pd.DataFrame, window_ret: int = 5, window_v if cols_to_drop: df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") - return df \ No newline at end of file + return df + + +def price_minus_deduction_price(df, n=10): + """ + 因子 1 (定量): 计算当前收盘价与N周期前收盘价（均线抵扣价）的差值。 + + 该因子衡量当前价格相对于即将移出均线计算窗口的价格的差异。 + 正值表示当前价格高于抵扣价，下一周期均线倾向于上涨（如果价格不变）。 + + 参数: + df (pd.DataFrame): 包含股票日线数据的DataFrame。必须包含 'ts_code', 'close' 列。 + n (int): 均线计算的周期数。抵扣价是 n-1 周期前的数据点。 + + 返回: + pd.DataFrame: 增加了 'price_minus_deduction_price_n' 列的DataFrame。 + """ + if 'close' not in df.columns: + print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。") + return df + + if n <= 1: + print("错误: 均线周期 n 必须大于 1。") + df[f'price_minus_deduction_price_{n}'] = np.nan + return df + + grouped = df.groupby('ts_code', group_keys=False) + + # 抵扣价是当前窗口移除的最早的价格，即当前价格的 n-1 周期前的价格 + # 例如计算 SMA(10) for P_t, 窗口是 P_{t-9}, ..., P_t. 移除的是 P_{t-9}. + # P_{t-9} 是 P_t 的 shift(9). So shift(n-1). + deduction_price = grouped['close'].shift(n - 1) + + # 计算差值 + df[f'price_minus_deduction_price_{n}'] = df['close'] - deduction_price + + print(f"因子 price_minus_deduction_price_{n} 计算完成。") + return df + +def price_deduction_price_diff_ratio_to_sma(df, n=10): + """ + 因子 2 (定量): 计算当前收盘价与抵扣价的差值占N周期均线的比例。 + + 该因子衡量当前价格高于抵扣价的程度相对于均线水平的大小。 + + 参数: + df (pd.DataFrame): 包含股票日线数据的DataFrame。必须包含 'ts_code', 'close' 列。 + n (int): 均线计算的周期数。抵扣价是 n-1 周期前的数据点。 + + 返回: + pd.DataFrame: 增加了 'price_deduction_price_diff_ratio_to_sma_n' 列的DataFrame。 + """ + if 'close' not in df.columns: + print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。") + return df + if n <= 1: + print("错误: 均线周期 n 必须大于 1。") + df[f'price_deduction_price_diff_ratio_to_sma_{n}'] = np.nan + return df + + grouped = df.groupby('ts_code', group_keys=False) + + # 计算N周期SMA + # 使用 transform 可以保持与原始 df 的索引对齐 + sma = grouped['close'].transform(lambda x: x.rolling(window=n).mean()) + + # 抵扣价 + deduction_price = grouped['close'].shift(n - 1) + + # 计算比例，处理均线为零的情况 + diff = df['close'] - deduction_price + # 使用 np.divide 并指定 where 条件和 fill_value 来避免除以零警告和 NaN 结果 + # 如果 sma 为 0，则结果设为 NaN + df[f'price_deduction_price_diff_ratio_to_sma_{n}'] = np.divide( + diff, + sma, + out=np.full_like(diff, np.nan), # 输出数组形状与 diff 相同，NaN 填充 + where=(sma != 0) # 仅在 sma 不为 0 时执行除法 + ) + + # np.divide with where handles Inf/-Inf and 0/0 (as NaN), but explicitly replace might be slightly safer depending on numpy version + # df[f'price_deduction_price_diff_ratio_to_sma_{n}'].replace([np.inf, -np.inf], np.nan, inplace=True) # This is often redundant with np.divide(..., where=...) + + print(f"因子 price_deduction_price_diff_ratio_to_sma_{n} 计算完成。") + return df + + +def cat_price_vs_sma_vs_deduction_price(df, n=10): + """ + 因子 3 (分类): 基于当前收盘价、N周期均线和抵扣价的位置关系构建分类因子。 + + 捕捉当前价格和抵扣价相对于均线的位置，指示可能的趋势状态或变化。 + + 分类定义： + 0: 数据不足 (SMA 或抵扣价为 NaN) 或 均线为 NaN + 1: 当前价 > SMA 且 抵扣价 > SMA (两者都在均线之上) + 2: 当前价 < SMA 且 抵扣价 < SMA (两者都在均线之下) + 3: 当前价 > SMA 且 抵扣价 <= SMA (当前价上穿或位于均线上方，抵扣价在均线下方或正好在均线) + 4: 当前价 <= SMA 且 抵扣价 > SMA (当前价下穿或位于均线下方，抵扣价在均线上方) + + 参数: + df (pd.DataFrame): 包含股票日线数据的DataFrame。必须包含 'ts_code', 'close' 列。 + n (int): 均线计算的周期数。抵扣价是 n-1 周期前的数据点。 + + 返回: + pd.DataFrame: 增加了 'cat_price_vs_sma_vs_deduction_price_n' 列的DataFrame。 + """ + if 'close' not in df.columns: + print("错误: DataFrame中没有'close'列，无法计算抵扣价相关因子。") + return df + if n <= 1: + print("错误: 均线周期 n 必须大于 1。") + df[f'cat_price_vs_sma_vs_deduction_price_{n}'] = np.nan + return df + + grouped = df.groupby('ts_code', group_keys=False) + + # 计算N周期SMA + sma = grouped['close'].transform(lambda x: x.rolling(window=n).mean()) + + # 抵扣价 + deduction_price = grouped['close'].shift(n - 1) + + # 定义条件和分类值 + conditions = [ + (df['close'] > sma) & (deduction_price > sma), + (df['close'] < sma) & (deduction_price < sma), + (df['close'] > sma) & (deduction_price <= sma), # 包含等于的情况 + (df['close'] <= sma) & (deduction_price > sma) # 包含等于的情况 + # 注意：sma 或 deduction_price 为 NaN 的行，其条件结果为 False，会落入 default=0 + ] + choices = [1, 2, 3, 4] + + # 使用 np.select 进行分类 + # 默认值为 0，用于处理条件不满足或输入为 NaN 的情况 + df[f'cat_price_vs_sma_vs_deduction_price_{n}'] = np.select(conditions, choices, default=0) + + print(f"因子 cat_price_vs_sma_vs_deduction_price_{n} 计算完成。") + return df + +def cat_is_on_top_list(df: pd.DataFrame, top_list: pd.DataFrame) -> pd.DataFrame: + if 'cat_reason' not in df.columns: + print('计算因子cat_is_on_top_list失败，缺少cat_reason列') + return df + + df['cat_is_on_top_list'] = df['cat_reason'] + df['cat_is_on_top_list'] = df['cat_is_on_top_list'] * (df['pct_chg'] > 0).astype(int) + + return df + + +def cat_reason(df: pd.DataFrame, top_list: pd.DataFrame) -> pd.DataFrame: + """ + 高效地将龙虎榜的 reason 列转化为单一数值类型的因子列，并仅返回必要的列。 + + Args: + df (pd.DataFrame): 包含所有股票数据的 DataFrame，需包含 'ts_code' 和 'trade_date' 列。 + top_list (pd.DataFrame): 每日龙虎榜数据的 DataFrame，需包含 'ts_code', 'trade_date' 和 'reason' 列。 + + Returns: + pd.DataFrame: 包含 'ts_code', 'trade_date' 和 'cat_reason' 列。 + """ + # 提取所有唯一的 reason 并进行数值编码 + unique_reasons = top_list['reason'].unique() + reason_mapping = {reason: i + 1 for i, reason in enumerate(unique_reasons)} + + # 在 top_list 中创建数值型的 reason 列 + top_list['cat_reason'] = top_list['reason'].map(reason_mapping).astype('Int64') + + # 转换 trade_date 类型以进行合并 + df['trade_date'] = pd.to_datetime(df['trade_date']) + top_list['trade_date'] = pd.to_datetime(top_list['trade_date']) + + # 仅保留 top_list 中需要的列进行合并 + top_list_slim = top_list[['ts_code', 'trade_date', 'cat_reason']] + + # 合并 DataFrame + merged_df = df.merge(top_list_slim, on=['ts_code', 'trade_date'], how='left') + + # 填充 NaN 为 0 并转换为 int 类型 + merged_df['cat_reason'] = merged_df['cat_reason'].fillna(0).astype('int') + + return merged_df \ No newline at end of file diff --git a/main/factor/index_factor.py b/main/factor/index_factor.py new file mode 100644 index 0000000..66e3f60 --- /dev/null +++ b/main/factor/index_factor.py @@ -0,0 +1,193 @@ +import pandas as pd +import numpy as np +from scipy.stats import spearmanr # 用于因子3的原始思路，但实际简化了 + +epsilon = 1e-10 + +def _safe_divide(numerator, denominator, default_val=0.0): + """安全除法""" + with np.errstate(divide='ignore', invalid='ignore'): + result = numerator / denominator + result[~np.isfinite(result)] = default_val + return result + +# --- 修改后的因子计算函数 --- + +def calculate_size_style_strength_factor(df: pd.DataFrame, N: int = 5, factor_name_suffix: str = '') -> pd.DataFrame: + """ + 计算大小盘风格相对强度因子。 + 返回: 以 trade_date 为索引，因子值为列的 DataFrame。 + """ + factor_name = f'size_style_strength_{N}{factor_name_suffix}' + print(f"Calculating {factor_name}...") + + required_indices = ['399300.SZ', '000905.SH', '000852.SH'] + if not all(idx in df['ts_code'].unique() for idx in required_indices): + print(f"Error: DataFrame 中缺少部分必需的指数代码 ({required_indices})。返回空因子 Series。") + return pd.DataFrame(index=df['trade_date'].unique(), columns=[factor_name]).rename_axis('trade_date') + + # 1. 计算各指数N日收益率 + df_copy = df.copy() # 操作副本，避免修改原始传入df + df_copy['_ret_N'] = df_copy.groupby('ts_code')['close'].pct_change(periods=N) + + # 2. Pivot 以方便截面计算 + pivot_ret_N = df_copy.pivot_table(index='trade_date', columns='ts_code', values='_ret_N') + + # 确保列存在并获取 + large_ret = pivot_ret_N.get('399300.SZ', pd.Series(np.nan, index=pivot_ret_N.index)) + mid_ret = pivot_ret_N.get('000905.SH', pd.Series(np.nan, index=pivot_ret_N.index)) + small_ret = pivot_ret_N.get('000852.SH', pd.Series(np.nan, index=pivot_ret_N.index)) + + # 3. 计算因子 (结果是每日一个标量值) + large_small_diff = large_ret - small_ret + avg_large_small_ret = (large_ret + small_ret) / 2 + # 计算中盘偏离因子，处理NaN，如果中盘收益为NaN，则偏离因子不起调整作用（乘以1） + mid_deviation_raw = mid_ret - avg_large_small_ret + mid_deviation_factor = 1 + np.sign(mid_ret.fillna(0)) * np.abs(mid_deviation_raw.fillna(0)) + + daily_factor_values = large_small_diff * mid_deviation_factor + daily_factor_values.name = factor_name # 给 Series 命名 + + print(f"Finished {factor_name}.") + return daily_factor_values.to_frame() # 转换为 DataFrame 返回 + +def calculate_volatility_structure_factor(df: pd.DataFrame, N: int = 10, factor_name_suffix: str = '') -> pd.DataFrame: + """ + 计算市场波动结构因子。 + 返回: 以 trade_date 为索引，因子值为列的 DataFrame。 + """ + factor_name = f'vol_structure_idx_{N}{factor_name_suffix}' + print(f"Calculating {factor_name}...") + + required_indices = ['399300.SZ', '000905.SH', '000852.SH'] + if not all(idx in df['ts_code'].unique() for idx in required_indices): + print(f"Error: DataFrame 中缺少部分必需的指数代码 ({required_indices})。返回空因子 Series。") + return pd.DataFrame(index=df['trade_date'].unique(), columns=[factor_name]).rename_axis('trade_date') + + if 'pct_chg' not in df.columns: + print(f"Error: DataFrame 缺少 'pct_chg' 列。将为 {factor_name} 填充 NaN。") + return pd.DataFrame(index=df['trade_date'].unique(), columns=[factor_name]).rename_axis('trade_date') + + df_copy = df.copy() + # 1. 计算各指数N日波动率 + df_copy['_vol_N'] = df_copy.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(1, N//2)).std().reset_index(level=0, drop=True) + + # 2. Pivot + pivot_vol_N = df_copy.pivot_table(index='trade_date', columns='ts_code', values='_vol_N') + + large_vol = pivot_vol_N.get('399300.SZ', pd.Series(np.nan, index=pivot_vol_N.index)) + mid_vol = pivot_vol_N.get('000905.SH', pd.Series(np.nan, index=pivot_vol_N.index)) + small_vol = pivot_vol_N.get('000852.SH', pd.Series(np.nan, index=pivot_vol_N.index)) + + # 3. 计算因子 + daily_factor_values = _safe_divide((small_vol - mid_vol), large_vol) + daily_factor_values.name = factor_name + + print(f"Finished {factor_name}.") + return daily_factor_values.to_frame() + +def calculate_market_divergence_factor(df: pd.DataFrame, factor_name_suffix: str = '') -> pd.DataFrame: + """ + 计算市场分化度因子 (基于每日三个指数收益率符号的一致性)。 + 返回: 以 trade_date 为索引，因子值为列的 DataFrame。 + """ + factor_name = f'market_divergence_score{factor_name_suffix}' + print(f"Calculating {factor_name}...") + + required_indices = ['399300.SZ', '000905.SH', '000852.SH'] + if not all(idx in df['ts_code'].unique() for idx in required_indices): + print(f"Error: DataFrame 中缺少部分必需的指数代码 ({required_indices})。返回空因子 Series。") + return pd.DataFrame(index=df['trade_date'].unique(), columns=[factor_name]).rename_axis('trade_date') + + if 'pct_chg' not in df.columns: + print(f"Error: DataFrame 缺少 'pct_chg' 列。将为 {factor_name} 填充 NaN。") + return pd.DataFrame(index=df['trade_date'].unique(), columns=[factor_name]).rename_axis('trade_date') + + pivot_pct_chg = df.pivot_table(index='trade_date', columns='ts_code', values='pct_chg') + + # 确保列存在 + idx_large_col = '399300.SZ' + idx_mid_col = '000905.SH' + idx_small_col = '000852.SH' + + # 使用 reindex 确保所有期望的列都存在，缺失的填充NaN + pivot_pct_chg = pivot_pct_chg.reindex(columns=[idx_large_col, idx_mid_col, idx_small_col]) + + def daily_divergence_score_calc(row): + # 当天只有这三个指数的收益率 Series + valid_returns = row.dropna() # 获取非 NaN 的收益率 + if len(valid_returns) < 2: # 如果有效数据少于2个，无法判断分化 + return np.nan + + signs = np.sign(valid_returns) + unique_sign_count = len(signs.unique()) + + if unique_sign_count == 1: # 所有符号相同 (或都为0，sign后也是0) + return 0.0 # 分化度最低 (高度一致) + elif unique_sign_count == 2 and 0 in signs.unique(): # 一个方向，一个0 + return 0.25 # 较低分化 + elif unique_sign_count == 2: # 两个方向 (例如两正一负，或两负一正) + return 0.75 # 较高分化 + elif unique_sign_count == 3: # 三个不同方向 (+, -, 0) + return 1.0 # 分化度最高 + return np.nan # 其他未覆盖的情况 (理论上不应发生) + + daily_factor_values = pivot_pct_chg[[idx_large_col, idx_mid_col, idx_small_col]].apply(daily_divergence_score_calc, axis=1) + daily_factor_values.name = factor_name + + print(f"Finished {factor_name}.") + return daily_factor_values.to_frame() + +# --- 整合所有因子计算到一个主函数 --- +def generate_daily_index_relation_factors(df_input: pd.DataFrame) -> pd.DataFrame: + """ + 计算所有基于大中小盘指数关系的每日截面因子。 + + Args: + df_input (pd.DataFrame): 长格式的指数行情数据，包含 'ts_code', 'trade_date', 'close', 'pct_chg'。 + + Returns: + pd.DataFrame: 以 'trade_date' 为索引，各因子为列的 DataFrame。 + """ + # 确保输入 df 不被修改 + df = df_input.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + + # 计算各个因子 (每个函数返回一个单列或多列的 DataFrame，索引为 trade_date) + factor1_df = calculate_size_style_strength_factor(df, N=5) + factor2_df = calculate_volatility_structure_factor(df, N=10) + factor3_df = calculate_market_divergence_factor(df) + + # 还可以继续添加其他每日截面因子... + + # 合并所有因子 DataFrame + # 使用 functools.reduce 和 pd.merge 来优雅地合并多个 DataFrame + from functools import reduce + daily_factors_list = [factor1_df, factor2_df, factor3_df] + # 过滤掉可能因错误产生的完全为空或只有NaN的DataFrame + daily_factors_list = [f_df for f_df in daily_factors_list if not f_df.empty and not f_df.iloc[:,0].isna().all()] + + if not daily_factors_list: + print("警告: 未能成功计算任何因子。返回空 DataFrame。") + # 返回一个以日期为索引的空DataFrame，或者基于输入df的日期 + return pd.DataFrame(index=df['trade_date'].unique()).rename_axis('trade_date') + + # 使用 outer join 以保留所有日期，并确保索引是 trade_date + final_factors_df = reduce(lambda left, right: pd.merge(left, right, on='trade_date', how='outer'), + daily_factors_list) + + final_factors_df = final_factors_df.sort_index() # 按日期排序 + + return final_factors_df + +# --- 使用示例 --- +# 假设 all_indices_df 是你包含 '399300.SZ', '000905.SH', '000852.SH' 三个指数的长格式行情数据 +# 确保它有 'ts_code', 'trade_date', 'open', 'high', 'low', 'close', 'vol', 'pct_chg' 列 +# all_indices_df['trade_date'] = pd.to_datetime(all_indices_df['trade_date']) +# all_indices_df = all_indices_df.sort_values(['ts_code', 'trade_date']) + +# daily_market_factors = generate_daily_index_relation_factors(all_indices_df) +# print("\n每日市场风格/情绪因子:") +# print(daily_market_factors.tail()) + +# 后续，你可以将 daily_market_factors 与你的个股数据 pdf 按 'trade_date' 合并 +# pdf_with_market_factors = pd.merge(pdf, daily_market_factors, on='trade_date', how='left') \ No newline at end of file diff --git a/main/train/Classify2-HyperGBM.ipynb b/main/train/Classify2-HyperGBM.ipynb new file mode 100644 index 0000000..7b13ed3 --- /dev/null +++ b/main/train/Classify2-HyperGBM.ipynb @@ -0,0 +1,2550 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:46:06.987506Z", + "start_time": "2025-04-03T12:46:06.259551Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e:\\PyProject\\NewStock\\main\\train\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import gc\n", + "import os\n", + "import sys\n", + "sys.path.append('../../')\n", + "print(os.getcwd())\n", + "import pandas as pd\n", + "from main.factor.factor import get_rolling_factor, get_simple_factor\n", + "from main.utils.factor import read_industry_data\n", + "from main.utils.factor_processor import calculate_score\n", + "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:00.212859Z", + "start_time": "2025-04-03T12:46:06.998047Z" + }, + "scrolled": true + }, + "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", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8638639 entries, 0 to 8638638\n", + "Data columns (total 32 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 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 total_mv float64 \n", + " 12 volume_ratio float64 \n", + " 13 is_st bool \n", + " 14 up_limit float64 \n", + " 15 down_limit float64 \n", + " 16 buy_sm_vol float64 \n", + " 17 sell_sm_vol float64 \n", + " 18 buy_lg_vol float64 \n", + " 19 sell_lg_vol float64 \n", + " 20 buy_elg_vol float64 \n", + " 21 sell_elg_vol float64 \n", + " 22 net_mf_vol float64 \n", + " 23 his_low float64 \n", + " 24 his_high float64 \n", + " 25 cost_5pct float64 \n", + " 26 cost_15pct float64 \n", + " 27 cost_50pct float64 \n", + " 28 cost_85pct float64 \n", + " 29 cost_95pct float64 \n", + " 30 weight_avg float64 \n", + " 31 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n", + "memory usage: 2.0+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from main.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', 'pct_chg'],\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', 'total_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('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cac01788dac10678", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.527104Z", + "start_time": "2025-04-03T12:47:00.488715Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n" + ] + } + ], + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.719252Z", + "start_time": "2025-04-03T12:47:10.541247Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from main.factor.factor import *\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", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " # df = sentiment_panic_greed_index(df)\n", + " # df = sentiment_market_breadth_proxy(df)\n", + " # df = sentiment_reversal_indicator(df)\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', \n", + " 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n", + " # 'sentiment_panic_greed_index',\n", + " 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.821169Z", + "start_time": "2025-04-03T12:47:10.751831Z" + } + }, + "outputs": [], + "source": [ + "import talib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.944254Z", + "start_time": "2025-04-03T12:47:10.826179Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "from main.utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + "\n", + " # cs_rank_intraday_range(industry_data)\n", + " # cs_rank_close_pos_in_range(industry_data)\n", + "\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.969344Z", + "start_time": "2025-04-03T12:47:15.963327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85c3e3d0235ffffa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:16.089879Z", + "start_time": "2025-04-03T12:47:15.990101Z" + } + }, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:01.612695Z", + "start_time": "2025-04-03T12:47:16.121802Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n", + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", + "Calculating cs_rank_net_lg_flow_val...\n", + "Finished cs_rank_net_lg_flow_val.\n", + "Calculating cs_rank_flow_divergence...\n", + "Finished cs_rank_flow_divergence.\n", + "Calculating cs_rank_ind_adj_lg_flow...\n", + "Finished cs_rank_ind_adj_lg_flow.\n", + "Calculating cs_rank_elg_buy_ratio...\n", + "Finished cs_rank_elg_buy_ratio.\n", + "Calculating cs_rank_rel_profit_margin...\n", + "Finished cs_rank_rel_profit_margin.\n", + "Calculating cs_rank_cost_breadth...\n", + "Finished cs_rank_cost_breadth.\n", + "Calculating cs_rank_dist_to_upper_cost...\n", + "Finished cs_rank_dist_to_upper_cost.\n", + "Calculating cs_rank_winner_rate...\n", + "Finished cs_rank_winner_rate.\n", + "Calculating cs_rank_intraday_range...\n", + "Finished cs_rank_intraday_range.\n", + "Calculating cs_rank_close_pos_in_range...\n", + "Finished cs_rank_close_pos_in_range.\n", + "Calculating cs_rank_opening_gap...\n", + "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n", + "Calculating cs_rank_pos_in_hist_range...\n", + "Finished cs_rank_pos_in_hist_range.\n", + "Calculating cs_rank_vol_x_profit_margin...\n", + "Finished cs_rank_vol_x_profit_margin.\n", + "Calculating cs_rank_lg_flow_price_concordance...\n", + "Finished cs_rank_lg_flow_price_concordance.\n", + "Calculating cs_rank_turnover_per_winner...\n", + "Finished cs_rank_turnover_per_winner.\n", + "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n", + "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n", + "Calculating cs_rank_volume_ratio...\n", + "Finished cs_rank_volume_ratio.\n", + "Calculating cs_rank_elg_buy_sell_sm_ratio...\n", + "Finished cs_rank_elg_buy_sell_sm_ratio.\n", + "Calculating cs_rank_cost_dist_vol_ratio...\n", + "Finished cs_rank_cost_dist_vol_ratio.\n", + "Calculating cs_rank_size...\n", + "Finished cs_rank_size.\n", + "\n", + "RangeIndex: 4524625 entries, 0 to 4524624\n", + "Columns: 178 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n", + "memory usage: 5.7+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2019-01-01']\n", + " if 'in_date' in df.columns:\n", + " df = df.drop(columns=['in_date'])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "gc.collect()\n", + "\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df = daily_deviation(df)\n", + "df = daily_industry_deviation(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", + "\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b87b938028afa206", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.658725Z", + "start_time": "2025-04-03T13:08:02.469611Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.670700Z", + "start_time": "2025-04-03T13:08:03.665739Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm # 用于中性化回归\n", + "from tqdm import tqdm # 可选，用于显示进度条\n", + "\n", + "# --- 常量 ---\n", + "epsilon = 1e-10 # 防止除零\n", + "\n", + "# --- 1. 中位数去极值 (MAD) ---\n", + "\n", + "def cs_mad_filter(df: pd.DataFrame,\n", + " features: list,\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826):\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n", + "\n", + " 方法: 对每日截面数据，计算 median 和 MAD，\n", + " 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n", + " 替换为边界值 (Winsorization)。\n", + " scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n", + " scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"MAD Filtering\"):\n", + " try:\n", + " # 计算截面中位数\n", + " median = grouped[col].transform('median')\n", + " # 计算截面 MAD (Median Absolute Deviation from Median)\n", + " mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n", + "\n", + " # 计算上下边界\n", + " lower_bound = median - k * scale_factor * mad\n", + " upper_bound = median + k * scale_factor * mad\n", + "\n", + " # 原地应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\n", + "\n", + "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", + " features: list,\n", + " industry_col: str = 'cat_l2_code',\n", + " market_cap_col: str = 'circ_mv'):\n", + " \"\"\"\n", + " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", + " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", + " 将回归残差写回原特征列。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", + " industry_col, market_cap_col。\n", + " features (list): 需要处理的特征列名列表。\n", + " industry_col (str): 行业分类列名。\n", + " market_cap_col (str): 流通市值列名。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", + " 计算量较大，可能耗时较长。\n", + " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " \"\"\"\n", + " print(\"开始截面行业市值中性化...\")\n", + " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", + " return\n", + "\n", + " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " log_cap_col = '_log_market_cap'\n", + " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", + " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", + " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", + " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + "\n", + " dates = df['trade_date'].unique()\n", + " all_residuals = [] # 用于收集所有日期的残差\n", + "\n", + " for date in tqdm(dates, desc=\"Neutralizing\"):\n", + " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + "\n", + " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", + " X = daily_data[[log_cap_col]]\n", + " X = sm.add_constant(X, prepend=True) # 添加常数项\n", + " # 创建行业哑变量 (drop_first=True 避免共线性)\n", + " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", + " industry_dummies = industry_dummies.astype(int)\n", + " X = pd.concat([X, industry_dummies], axis=1)\n", + "\n", + " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + "\n", + " for col in features:\n", + " Y = daily_data[col]\n", + "\n", + " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", + " valid_mask = Y.notna() & X.notna().all(axis=1)\n", + " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", + " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " continue\n", + "\n", + " Y_valid = Y[valid_mask]\n", + " X_valid = X[valid_mask]\n", + "\n", + " # 执行 OLS 回归\n", + " try:\n", + " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", + " results = model.fit()\n", + " # 将残差填回对应位置\n", + " daily_residuals.loc[valid_mask, col] = results.resid\n", + " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " except Exception as e:\n", + " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " break\n", + "\n", + " all_residuals.append(daily_residuals)\n", + "\n", + " # 合并所有日期的残差结果\n", + " if all_residuals:\n", + " residuals_df = pd.concat(all_residuals)\n", + " # 将残差结果更新回原始 df (原地修改)\n", + " # 使用 update 比 merge 更适合基于索引的原地更新\n", + " # 确保 residuals_df 的索引与 df 中对应部分一致\n", + " df.update(residuals_df)\n", + " else:\n", + " print(\"没有有效的残差结果可以合并。\")\n", + "\n", + "\n", + " # 清理临时列\n", + " df.drop(columns=[log_cap_col], inplace=True)\n", + " print(\"截面行业市值中性化完成。\")\n", + "\n", + "\n", + "# --- 3. Z-Score 标准化 ---\n", + "\n", + "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " epsilon (float): 防止除以零的小常数。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"Standardizing\"):\n", + " try:\n", + " # 使用 transform 计算截面均值和标准差\n", + " mean = grouped[col].transform('mean')\n", + " std = grouped[col].transform('std')\n", + "\n", + " # 计算 Z-Score 并原地赋值\n", + " df[col] = (df[col] - mean) / (std + epsilon)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + "\n", + "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n", + " feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n", + " \"\"\"\n", + " processed_df = df.copy() # 在副本上操作，保留原始数据\n", + "\n", + " # 确保 trade_date 是 datetime 类型以便正确分组\n", + " processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n", + "\n", + " def _fill_daily_nan(group):\n", + " # group 是某一个交易日的 DataFrame\n", + "\n", + " # 遍历指定的特征列\n", + " for feature_col in feature_columns:\n", + " # 检查列是否存在于当前分组中\n", + " if feature_col in group.columns:\n", + " # 计算当日该特征的中位数\n", + " median_val = group[feature_col].median()\n", + "\n", + " # 使用当日中位数填充该特征列的 NaN 值\n", + " # inplace=True 会直接修改 group DataFrame\n", + " group[feature_col].fillna(median_val, inplace=True)\n", + " # else:\n", + " # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + "\n", + " return group\n", + "\n", + " # 按交易日期分组，并应用每日填充函数\n", + " # group_keys=False 避免将分组键添加到结果索引中\n", + " filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n", + "\n", + " return filled_df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:04.694262Z", + "start_time": "2025-04-03T13:08:03.694904Z" + } + }, + "outputs": [], + "source": [ + "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_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " # 使用已有的 pct_chg 字段计算波动率\n", + " volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + " return score\n", + "\n", + " # # 确保 DataFrame 按照股票代码和交易日期排序\n", + " # df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 对每个股票分别计算 score\n", + " df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n", + "\n", + " return df['score']\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n", + " 对特征在行业内部进行市值中性化。\n", + "\n", + " Args:\n", + " df: 输入的 DataFrame，包含特征、行业分类和市值列。\n", + " features: 需要进行中性化的特征列名列表。\n", + " industry_col: 行业分类列的列名。\n", + " mkt_cap_col: 市值列的列名。\n", + "\n", + " Returns:\n", + " 中性化后的 DataFrame。\n", + " \"\"\"\n", + "\n", + " df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n", + " df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n", + " df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n", + "\n", + " if df_cleaned.empty:\n", + " print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n", + " return df # 返回原始或空df，取决于清理前的状态\n", + "\n", + " processed_df = df\n", + "\n", + " for col in features:\n", + " if col not in df_cleaned.columns:\n", + " print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n", + " # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " # 跳过对控制变量本身进行中性化\n", + " if col == mkt_cap_col or col == industry_col:\n", + " print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n", + " # 在结果 df 中也保持原样\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n", + "\n", + " # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n", + " df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n", + "\n", + " if not df_subset_factor.empty:\n", + " for industry, group in df_subset_factor.groupby(industry_col):\n", + " x = group[mkt_cap_col] # 市值对数\n", + " y = group[col] # 因子值\n", + "\n", + " # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n", + " # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n", + " if len(group) > 1 and np.var(x) > 1e-9:\n", + " try:\n", + " beta = np.cov(y, x)[0, 1] / np.var(x)\n", + " alpha = np.mean(y) - beta * np.mean(x)\n", + "\n", + " # 计算残差\n", + " resid = y - (alpha + beta * x)\n", + "\n", + " # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n", + " residual_series.loc[resid.index] = resid\n", + "\n", + " except Exception as e:\n", + " # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n", + " print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n", + " # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n", + " # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n", + "\n", + " else:\n", + " residual_series.loc[group.index] = group[col] # 保留原始因子值\n", + " processed_df.loc[residual_series.index, col] = residual_series\n", + "\n", + "\n", + " else:\n", + " processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n", + "\n", + " return processed_df\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "\n", + "def mad_filter(df, features, n=3):\n", + " for col in features:\n", + " median = df[col].median()\n", + " mad = np.median(np.abs(df[col] - median))\n", + " upper = median + n * mad\n", + " lower = median - n * mad\n", + " df[col] = np.clip(df[col], lower, upper) # 截断极值\n", + " return df\n", + "\n", + "\n", + "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n", + " for col in features:\n", + " # 按日期分组计算上下百分位数\n", + " lower_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(lower_percentile)\n", + " )\n", + " upper_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(upper_percentile)\n", + " )\n", + " # 截断超出范围的值\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df\n", + "\n", + "\n", + "from scipy.stats import iqr\n", + "\n", + "\n", + "def iqr_filter(df, features):\n", + " for col in features:\n", + " df[col] = df.groupby('trade_date')[col].transform(\n", + " lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n", + " )\n", + " return df\n", + "\n", + "\n", + "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " for col in features:\n", + " # 计算 rolling 统计量，需要按日期进行 groupby\n", + " rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n", + " rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n", + "\n", + " # 对数据进行裁剪\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " \n", + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features\n", + "\n", + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:50.841165Z", + "start_time": "2025-04-03T14:49:25.889057Z" + } + }, + "outputs": [], + "source": [ + "days = 5\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + " df.groupby('ts_code')['open'].shift(-1)\n", + "\n", + "df['cat_up_limit'] = df['pct_chg'] > 5\n", + "# df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n", + "df['label'] = df['future_return']\n", + "\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "# for col in [col for col in df.columns]:\n", + "# train_data[col] = train_data[col].astype('str')\n", + "# test_data[col] = test_data[col].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "29221dde", + "metadata": {}, + "outputs": [], + "source": [ + "feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n", + "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", + "feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n", + "feature_columns = [col for col in feature_columns if 'code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", + "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n", + " 'cap_neutral_cost_metric', \n", + " 'hurst_net_mf_vol_60', \n", + " 'complex_factor_deap_1', \n", + " 'lg_buy_consolidation_20',\n", + " 'cs_rank_ind_cap_neutral_pe',\n", + " 'cs_rank_opening_gap',\n", + " 'cs_rank_ind_adj_lg_flow']]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "03ee5daf", + "metadata": {}, + "outputs": [], + "source": [ + "# df = fill_nan_with_daily_median(df, feature_columns)\n", + "for feature_col in [col for col in feature_columns if col in df.columns]:\n", + " # median_val = df[feature_col].median()\n", + " df[feature_col].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "去除极值\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 131/131 [00:28<00:00, 4.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 131/131 [00:23<00:00, 5.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "df最小日期: 2019-01-02\n", + "df最大日期: 2025-05-16\n", + "2057777\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2022-12-30\n", + "1751669\n", + "test_data最小日期: 2023-01-03\n", + "test_data最大日期: 2025-05-16\n", + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n" + ] + } + ], + "source": [ + "split_date = '2023-01-01'\n", + "train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n", + "test_data = df[(df['trade_date'] >= split_date)]\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n", + "# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n", + "\n", + "# feature_columns = [\n", + "# 'undist_profit_ps', \n", + "# 'AR_BR',\n", + "# 'pe_ttm',\n", + "# 'alpha_22_improved', \n", + "# 'alpha_003', \n", + "# 'alpha_007', \n", + "# 'alpha_013', \n", + "# 'cat_up_limit', \n", + "# 'cat_down_limit', \n", + "# 'up_limit_count_10d', \n", + "# 'down_limit_count_10d', \n", + "# 'consecutive_up_limit', \n", + "# 'vol_break', \n", + "# 'weight_roc5', \n", + "# 'price_cost_divergence', \n", + "# 'smallcap_concentration', \n", + "# 'cost_stability', \n", + "# 'high_cost_break_days', \n", + "# 'liquidity_risk', \n", + "# 'turnover_std', \n", + "# 'mv_volatility', \n", + "# 'volume_growth', \n", + "# 'mv_growth', \n", + "# 'lg_flow_mom_corr_20_60', \n", + "# 'lg_flow_accel', \n", + "# 'profit_pressure', \n", + "# 'underwater_resistance', \n", + "# 'cost_conc_std_20', \n", + "# 'profit_decay_20', \n", + "# 'vol_amp_loss_20', \n", + "# 'vol_drop_profit_cnt_5', \n", + "# 'lg_flow_vol_interact_20', \n", + "# 'cost_break_confirm_cnt_5', \n", + "# 'atr_norm_channel_pos_14', \n", + "# 'turnover_diff_skew_20', \n", + "# 'lg_sm_flow_diverge_20', \n", + "# 'pullback_strong_20_20', \n", + "# 'vol_wgt_hist_pos_20', \n", + "# 'vol_adj_roc_20',\n", + "# 'cashflow_to_ev_factor',\n", + "# 'ocfps',\n", + "# 'book_to_price_ratio',\n", + "# 'turnover_rate_mean_5',\n", + "# 'variance_20',\n", + "# 'bbi_ratio_factor'\n", + "# ]\n", + "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n", + "print(feature_columns)\n", + "\n", + "# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", + "# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", + "\n", + "train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "transform_feature_columns = feature_columns\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "# transform_feature_columns.remove('undist_profit_ps')\n", + "print('去除极值')\n", + "cs_mad_filter(train_data, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "\n", + "cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "cs_mad_filter(train_data, mad_filter_feature_columns)\n", + "cs_mad_filter(test_data, mad_filter_feature_columns)\n", + "\n", + "\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "\n", + "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(train_data))\n", + "print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3ff2d1c5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime # 用于日期计算\n", + "from catboost import CatBoostClassifier, CatBoostRegressor\n", + "from catboost import Pool\n", + "import lightgbm as lgb\n", + "from hypergbm import make_experiment\n", + "# from supervised.automl import AutoML\n", + "from flaml import AutoML, tune\n", + "from flaml.automl.model import LGBMEstimator, CatBoostEstimator\n", + "from lightgbm import LGBMClassifier, LGBMRegressor\n", + "from tabpfn import TabPFNClassifier, TabPFNRegressor\n", + "\n", + "class MyLGBM(LGBMEstimator):\n", + " def __init__(self, **config):\n", + " super().__init__(max_depth=-1, **config)\n", + "\n", + "class MyCat(CatBoostEstimator):\n", + " def __init__(self, **config):\n", + " config = {\n", + " 'depth': 10, # 控制模型复杂度\n", + " 'l2_leaf_reg': 50, # L2 正则化\n", + " # 'task_type': 'GPU',\n", + " }\n", + " super().__init__(**config)\n", + "\n", + "def train_model(train_data_df, feature_columns,\n", + " print_info=True, # 调整参数名，更通用\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " target_column='label', type='light'): # 增加目标列参数\n", + "\n", + " print('train data size: ', len(train_data_df))\n", + " print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " # 去除标签为空的样本\n", + " initial_len = len(train_data_df)\n", + " train_data_df = train_data_df.dropna(subset=[target_column])\n", + "\n", + " if print_info:\n", + " print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n", + "\n", + " # 提取特征和标签，只取数值型特征用于线性回归\n", + " \n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + " \n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[target_column]\n", + " train_data_split = pd.concat([X_train, y_train], axis=1)\n", + " \n", + " X_val = val_data_split[feature_columns]\n", + " y_val = val_data_split['label']\n", + " val_data_split = pd.concat([X_val, y_val], axis=1)\n", + "\n", + "\n", + " automl = AutoML()\n", + " automl.add_learner(learner_name=\"my_lgbm\", learner_class=MyLGBM)\n", + " automl.add_learner(learner_name=\"my_cat\", learner_class=MyCat)\n", + "\n", + " automl_settings = {\n", + " \"time_budget\": 600, # in seconds\n", + " \"metric\": \"r2\",\n", + " \"task\": \"regression\",\n", + " \"estimator_list\": [\n", + " \"catboost\",\n", + " \"lgbm\",\n", + " \"xgboost\"\n", + " ], \n", + " \"ensemble\": {\n", + " \"final_estimator\": LGBMRegressor(),\n", + " \"passthrough\": True,\n", + " },\n", + " }\n", + "\n", + " custom_hp = {\n", + " # \"lgbm\": {\n", + " # \"subsample\": {\n", + " # \"num_leaves\": tune.uniform(lower=10, upper=64),\n", + " # \"init_value\": 10,\n", + " # },\n", + " # },\n", + " }\n", + " # Train with labeled input data\n", + " automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, custom_hp=custom_hp, **automl_settings)\n", + " # model = TabPFNRegressor(model_path=\"../../model/tabpfn-v2-regressor.ckpt\", ignore_pretraining_limits=True)\n", + "\n", + " return automl" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:03:18.426481Z", + "start_time": "2025-04-03T15:02:19.926352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 364000\n", + " ts_code trade_date log_circ_mv\n", + "0 600306.SH 2020-01-02 11.552040\n", + "1 603269.SH 2020-01-02 11.324801\n", + "2 002633.SZ 2020-01-02 11.759023\n", + "3 603991.SH 2020-01-02 11.181150\n", + "4 000691.SZ 2020-01-02 11.677910\n", + "... ... ... ...\n", + "363995 603182.SH 2022-12-30 11.207510\n", + "363996 600749.SH 2022-12-30 12.594148\n", + "363997 605259.SH 2022-12-30 11.763909\n", + "363998 603600.SH 2022-12-30 12.594561\n", + "363999 603116.SH 2022-12-30 12.594781\n", + "\n", + "[364000 rows x 3 columns]\n", + "原始样本数: 364000, 去除标签为空后样本数: 364000\n", + "[flaml.automl.logger: 05-22 16:22:47] {1728} INFO - task = regression\n", + "[flaml.automl.logger: 05-22 16:22:47] {1736} INFO - Data split method: uniform\n", + "[flaml.automl.logger: 05-22 16:22:47] {1739} INFO - Evaluation method: holdout\n", + "[flaml.automl.logger: 05-22 16:22:47] {1838} INFO - Minimizing error metric: 1-r2\n", + "[flaml.automl.logger: 05-22 16:22:47] {1955} INFO - List of ML learners in AutoML Run: ['catboost', 'lgbm', 'xgboost']\n", + "[flaml.automl.logger: 05-22 16:22:47] {2258} INFO - iteration 0, current learner catboost\n", + "[flaml.automl.logger: 05-22 16:22:57] {2393} INFO - Estimated sufficient time budget=2563037s. 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Building ensemble with tuned estimators\n", + "[flaml.automl.logger: 05-22 16:33:39] {2631} INFO - ensemble: StackingRegressor(estimators=[('lgbm',\n", + " ),\n", + " ('xgboost',\n", + " ),\n", + " ('catboost',\n", + " )],\n", + " final_estimator=LGBMRegressor(), n_jobs=1, passthrough=True)\n", + "[flaml.automl.logger: 05-22 16:33:39] {1985} INFO - fit succeeded\n", + "[flaml.automl.logger: 05-22 16:33:39] {1986} INFO - Time taken to find the best model: 489.3946213722229\n", + "[flaml.automl.logger: 05-22 16:33:39] {1996} WARNING - Time taken to find the best model is 82% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" + ] + } + ], + "source": [ + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "type = 'cat'\n", + "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", + "# light_params['feature_contri'] = feature_contri\n", + "# print(f'feature_contri: {feature_contri}')\n", + "model = train_model(train_data\n", + " .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n", + " .apply(lambda x: x.nsmallest(500, 'total_mv'))\n", + " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + " .merge(index_data, on='trade_date', how='left'), feature_columns, type=type)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "59132b85", + "metadata": {}, + "outputs": [], + "source": [ + "# for e in model.model.estimators:\n", + "# print(e[1].estimator)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:04:39.656944Z", + "start_time": "2025-04-03T15:04:39.298483Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:01<00:00, 3.22it/s]\n" + ] + } + ], + "source": [ + "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv'))\n", + "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", + "# score_df = score_df[score_df['pe_ttm'] > 0]\n", + "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "score_df = score_df.merge(index_data, on='trade_date', how='left')\n", + "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n", + "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", + "# score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "\n", + "# score_df['score'] = model.predict_proba(score_df[feature_columns])[:, -1]\n", + "# score_df['score'] = model.predict(score_df[feature_columns])\n", + "chunk_size = 5000 # 设置您的切块大小\n", + "predictions_list = []\n", + "num_rows = len(score_df)\n", + "\n", + "from tqdm import tqdm\n", + "for i in tqdm(range(0, num_rows, chunk_size)):\n", + " chunk_df = score_df.iloc[i : i + chunk_size].copy()\n", + " chunk_features = chunk_df[feature_columns]\n", + " chunk_predictions_np = model.predict(chunk_features)\n", + " chunk_predictions_series = pd.Series(chunk_predictions_np, index=chunk_df.index)\n", + " predictions_list.append(chunk_predictions_series)\n", + " del chunk_df, chunk_features, chunk_predictions_np, chunk_predictions_series\n", + " gc.collect()\n", + "\n", + "combined_predictions = pd.concat(predictions_list)\n", + "score_df['score'] = combined_predictions\n", + "\n", + "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n", + "\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n", + ").reset_index(drop=True) # drop=True 避免添加旧索引列\n", + "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n", + "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index(drop=True)\n", + "save_df = save_df.sort_values(['trade_date', 'score'])\n", + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "09b1799e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n" + ] + } + ], + "source": [ + "print(len(feature_columns))\n", + "print(feature_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7e9023cc", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_factors(\n", + " df: pd.DataFrame,\n", + " feature_columns: list[str],\n", + " target_column: str = 'target', # 假设目标列默认为 'target'\n", + " trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n", + " mcap_col: str = 'total_mv', # 新增: 市值列名称\n", + " mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n", + ") -> pd.DataFrame:\n", + " \"\"\"\n", + " 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n", + " 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n", + " feature_columns (list[str]): 需要分析的特征列名称列表。\n", + " target_column (str): 目标变量列的名称。\n", + " trade_date_col (str): 交易日期列的名称。\n", + " mcap_col (str): 市值列的名称。\n", + " mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n", + "\n", + " Returns:\n", + " pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n", + " 同时打印因子在不同市值分位数上的平均IC表格。\n", + " 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n", + " \"\"\"\n", + "\n", + " # --- 数据校验 ---\n", + " required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: 输入DataFrame缺少必需的列: {missing}\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + " # 确保日期列是 datetime 类型\n", + " df = df.copy() # 在副本上操作\n", + " df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n", + " df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n", + "\n", + " # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n", + " # 直接在 df 副本上进行清洗\n", + " initial_rows_before_clean = len(df)\n", + " df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n", + " rows_dropped_clean = initial_rows_before_clean - len(df)\n", + " if rows_dropped_clean > 0:\n", + " print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n", + "\n", + " if df.empty:\n", + " print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + "\n", + " print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n", + "\n", + " # --- 1. 基本因子统计量 ---\n", + " basic_stats = df[feature_columns].describe().T\n", + "\n", + " print(\"\\n--- 基本因子统计量 ---\")\n", + " print(basic_stats)\n", + "\n", + " # --- 2. 因子与目标变量的整体相关性 ---\n", + " overall_correlation = {}\n", + " for feature in feature_columns:\n", + " # 在清理后的 df 上计算相关性\n", + " if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n", + " 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n", + " }\n", + " else:\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': np.nan,\n", + " 'Spearman_Correlation_with_Target': np.nan\n", + " }\n", + " overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n", + "\n", + " print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n", + " print(overall_corr_df)\n", + "\n", + " # --- 3. 因子之间的相关性矩阵 ---\n", + " # 在清理后的 df 上计算相关性\n", + " factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n", + "\n", + " print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n", + " print(factor_correlation_matrix)\n", + "\n", + " # --- 4. 日间 IC 和 ICIR ---\n", + " print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n", + "\n", + " # 直接在清理后的 df 上计算每日 IC\n", + " if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n", + " daily_ic_series = pd.Series(dtype=float) # 空 Series\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + " else:\n", + " daily_ic_series = df.groupby(trade_date_col).apply(\n", + " lambda day_group: {\n", + " feature: day_group[feature].corr(day_group[target_column], method='spearman')\n", + " for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n", + " }\n", + " ).apply(pd.Series) # 将字典结果转换为 DataFrame\n", + "\n", + " # 计算 IC 的统计量\n", + " if not daily_ic_series.empty:\n", + " ic_mean = daily_ic_series.mean()\n", + " ic_std = daily_ic_series.std()\n", + " # 避免除以零\n", + " ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n", + "\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': ic_mean,\n", + " 'Std_Dev_IC': ic_std,\n", + " 'ICIR': ic_ir\n", + " })\n", + " print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n", + " print(ic_stats)\n", + " else:\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + "\n", + "\n", + " # --- 5. 因子在不同市值分位数上的平均 IC ---\n", + " print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n", + "\n", + " # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n", + " # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n", + " # labels=False 返回整数 0 to mcap_bins-1\n", + " # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n", + " # 添加一个临时列来存储分位数\n", + " mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n", + " df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n", + " lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n", + " )\n", + "\n", + " # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n", + " # 创建一个临时 DataFrame df_binned_analysis\n", + " df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n", + "\n", + " if df_binned_analysis.empty:\n", + " print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n", + " avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n", + " else:\n", + " # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n", + " binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n", + " lambda group: {\n", + " feature: group[feature].corr(group[target_column], method='spearman')\n", + " for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n", + " }\n", + " ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n", + "\n", + " # 对每个分位数组的每日 IC 求平均\n", + " # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n", + " # mean(axis=0) 对日期索引求平均\n", + " avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n", + "\n", + " # 重命名索引和列，使表格更清晰\n", + " if not avg_ic_by_bin.empty:\n", + " # Index name will be the original column name used for grouping ('_mcap_bin_X')\n", + " # Rename the index name explicitly\n", + " avg_ic_by_bin.index.name = 'MarketCap_Bin'\n", + " avg_ic_by_bin.columns.name = 'Feature'\n", + " # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n", + " avg_ic_by_bin = avg_ic_by_bin.sort_index()\n", + "\n", + " print(avg_ic_by_bin)\n", + "\n", + "\n", + " # --- 6. 汇总所有指标 ---\n", + " # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n", + " # 注意：合并时需要根据索引进行对齐 (因子名称)\n", + " summary_df = basic_stats\n", + " summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n", + " summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n", + "\n", + " # print(\"\\n--- 因子分析汇总报告 ---\")\n", + " # print(summary_df)\n", + "\n", + " # --- 清理临时列 'mcap_bin' ---\n", + " # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n", + " if mcap_bin_col_name in df.columns:\n", + " df.drop(columns=[mcap_bin_col_name], inplace=True)\n", + "\n", + "\n", + " return summary_df # 主要返回汇总报告，分位数IC单独打印\n", + "\n", + "# # 运行分析函数\n", + "# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n", + "\n", + "# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n", + "# print(factor_analysis_report)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a0000d75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n", + "准备数据，处理 NaN 值...\n", + "原始数据 28550 行，移除 NaN 后剩余 28175 行用于分析。\n", + "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n", + "按二维分箱分组计算 Spearman Rank IC...\n", + "整理结果用于绘图...\n", + "circ_mv_bin 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 \\\n", + "future_return_bin ... \n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "... .. .. .. .. .. .. .. .. .. .. ... .. .. .. \n", + "94 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "96 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "98 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "\n", + "circ_mv_bin 93 94 95 96 97 98 99 \n", + "future_return_bin \n", + "0 NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN \n", + "... .. .. .. .. .. .. .. \n", + "94 NaN NaN NaN NaN NaN NaN NaN \n", + "95 NaN NaN NaN NaN NaN NaN NaN \n", + "96 NaN NaN NaN NaN NaN NaN NaN \n", + "97 NaN NaN NaN NaN NaN NaN NaN \n", + "98 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "[99 rows x 100 columns]\n", + "生成热力图...\n", + "分析完成。\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy.stats import spearmanr\n", + "from tqdm import tqdm # 用于显示进度条 (可选)\n", + "\n", + "# 设置 Matplotlib/Seaborn 样式 (可选)\n", + "sns.set_theme(style=\"whitegrid\")\n", + "plt.rcParams['font.sans-serif'] = ['SimHei'] # 或者其他支持中文的字体\n", + "plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题\n", + "\n", + "def analyze_score_performance_2d(score_df: pd.DataFrame,\n", + " score_col: str = 'score',\n", + " label_col: str = 'label',\n", + " condition1_col: str = 'circ_mv',\n", + " condition2_col: str = 'future_return',\n", + " n_bins: int = 100,\n", + " min_samples_per_bin: int = 30): # 每个格子最少样本数\n", + " \"\"\"\n", + " 分析 score 在两个条件下 (如市值、未来收益) 的二维分箱表现。\n", + "\n", + " Args:\n", + " score_df (pd.DataFrame): 包含分数、标签和条件列的 DataFrame。\n", + " score_col (str): 预测分数所在的列名。\n", + " label_col (str): 目标标签所在的列名 (应为数值或可排序类别)。\n", + " condition1_col (str): 第一个条件列名 (例如 'circ_mv')。\n", + " condition2_col (str): 第二个条件列名 (例如 'future_return')。\n", + " n_bins (int): 每个条件划分的箱数 (分位数数量)。\n", + " min_samples_per_bin (int): 计算指标所需的最小样本数，小于此数目的格子结果将被屏蔽。\n", + "\n", + " Returns:\n", + " tuple: 包含 (performance_pivot, count_pivot, fig)\n", + " performance_pivot: 以二维分箱为索引/列的 Spearman 相关系数矩阵。\n", + " count_pivot: 每个二维分箱的样本数量矩阵。\n", + " fig: 生成的热力图 Matplotlib Figure 对象。\n", + " \"\"\"\n", + " print(f\"开始分析 '{score_col}' 在 '{condition1_col}' 和 '{condition2_col}' 下的表现...\")\n", + "\n", + " required_cols = [score_col, label_col, condition1_col, condition2_col]\n", + " if not all(col in score_df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in score_df.columns]\n", + " raise ValueError(f\"输入 DataFrame 缺少必需列: {missing}\")\n", + "\n", + " # --- 1. 数据准备和清洗 ---\n", + " print(\"准备数据，处理 NaN 值...\")\n", + " # 只保留需要的列，并移除包含 NaN 的行，避免影响分箱和计算\n", + " analysis_df = score_df[required_cols].dropna().copy()\n", + " n_original = len(score_df)\n", + " n_after_drop = len(analysis_df)\n", + " print(f\"原始数据 {n_original} 行，移除 NaN 后剩余 {n_after_drop} 行用于分析。\")\n", + "\n", + " if n_after_drop < min_samples_per_bin * n_bins: # 检查数据量是否过少\n", + " print(f\"警告: 清理 NaN 后数据量 ({n_after_drop}) 可能不足以支持 {n_bins}x{n_bins} 的精细分箱分析。\")\n", + " if n_after_drop < min_samples_per_bin:\n", + " print(\"错误: 有效数据过少，无法进行分析。\")\n", + " return None, None, None\n", + "\n", + " # --- 2. 二维分箱 ---\n", + " print(f\"对 '{condition1_col}' 和 '{condition2_col}' 进行 {n_bins} 分位数分箱...\")\n", + " bin1_col = f'{condition1_col}_bin'\n", + " bin2_col = f'{condition2_col}_bin'\n", + "\n", + " try:\n", + " # 使用 qcut 进行分位数分箱，labels=False 返回 0 到 n_bins-1 的整数标签\n", + " # duplicates='drop' 会丢弃导致边界不唯一的重复值所在的箱子，可能导致某些箱号缺失\n", + " # 对于可视化，这通常可以接受，但如果需要严格的等分，需先 rank\n", + " analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col], q=n_bins, labels=False, duplicates='drop')\n", + " analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col], q=n_bins, labels=False, duplicates='drop')\n", + " except Exception as e:\n", + " print(f\"错误: 分箱失败，请检查数据分布或减少 n_bins。错误信息: {e}\")\n", + " # 可以尝试先 rank 再 qcut\n", + " # analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n", + " # analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n", + " return None, None, None\n", + "\n", + " # --- 3. 分组计算表现指标 (Spearman Rank IC) ---\n", + " print(\"按二维分箱分组计算 Spearman Rank IC...\")\n", + "\n", + " def safe_spearmanr(x, y):\n", + " \"\"\"安全计算 Spearman 相关性，处理数据量过少的情况\"\"\"\n", + " if len(x) < max(2, min_samples_per_bin): # 要求至少有 min_samples_per_bin 个点才计算\n", + " return np.nan\n", + " corr, p_value = spearmanr(x, y)\n", + " return corr if not np.isnan(corr) else np.nan # 确保返回 NaN 而不是 None 或其他\n", + "\n", + " # 按两个分箱列分组\n", + " grouped = analysis_df.groupby([bin1_col, bin2_col])\n", + "\n", + " # 计算每个格子的 Spearman 相关系数\n", + " # apply 可能较慢，但计算相关性通常需要 apply\n", + " performance_series = grouped.apply(lambda sub: safe_spearmanr(sub[score_col], sub[label_col]))\n", + "\n", + " # 计算每个格子的样本数量\n", + " count_series = grouped.size()\n", + "\n", + " # --- 4. 结果整理成 Pivot Table (用于绘图) ---\n", + " print(\"整理结果用于绘图...\")\n", + " try:\n", + " # 将 performance_series 转换成二维矩阵\n", + " # index 为 condition1_bin, columns 为 condition2_bin\n", + " performance_pivot = performance_series.unstack(level=0) # level=0 对应第一个 groupby key (bin1_col)\n", + " count_pivot = count_series.unstack(level=0)\n", + "\n", + " # 可选：按列和索引排序，确保顺序正确\n", + " performance_pivot = performance_pivot.sort_index(axis=0).sort_index(axis=1)\n", + " count_pivot = count_pivot.sort_index(axis=0).sort_index(axis=1)\n", + " \n", + " print(performance_pivot)\n", + "\n", + " except Exception as e:\n", + " print(f\"错误: 无法将结果转换为二维矩阵，可能因为分箱不均匀或数据问题: {e}\")\n", + " return None, None, None\n", + "\n", + " # --- 5. 可视化：绘制热力图 ---\n", + " print(\"生成热力图...\")\n", + " fig, ax = plt.subplots(figsize=(16, 12)) # 调整图像大小\n", + "\n", + " # 使用 count_pivot 创建一个 mask，屏蔽掉样本量过小的格子\n", + " mask = count_pivot < min_samples_per_bin\n", + "\n", + " # 绘制热力图\n", + " sns.heatmap(performance_pivot,\n", + " annot=False, # 100x100 个格子加注释会太密集\n", + " fmt=\".2f\",\n", + " cmap=\"viridis\", # 选择颜色映射, 'viridis', 'coolwarm', 'RdYlGn' 等都不错\n", + " linewidths=.5,\n", + " linecolor='lightgray',\n", + " # mask=mask, # 应用 mask\n", + " ax=ax,\n", + " cbar_kws={'label': f'Spearman Rank IC ({score_col} vs {label_col})'}) # 颜色条标签\n", + "\n", + " # 设置标题和轴标签\n", + " ax.set_title(f'{score_col} 表现分析 (Rank IC vs {label_col})\\n基于 {condition1_col} 和 {condition2_col} {n_bins}x{n_bins} 分箱', fontsize=16)\n", + " ax.set_xlabel(f'{condition1_col} 分位数 (0 -> 高)', fontsize=12)\n", + " ax.set_ylabel(f'{condition2_col} 分位数 (0 -> 高)', fontsize=12)\n", + "\n", + " # 可选：调整刻度标签，避免显示所有 100 个刻度\n", + " if n_bins > 20:\n", + " tick_interval = n_bins // 10 # 大约显示 10 个刻度\n", + " ax.set_xticks(np.arange(0, n_bins, tick_interval) + 0.5)\n", + " ax.set_yticks(np.arange(0, n_bins, tick_interval) + 0.5)\n", + " ax.set_xticklabels(np.arange(0, n_bins, tick_interval))\n", + " ax.set_yticklabels(np.arange(0, n_bins, tick_interval))\n", + "\n", + " plt.xticks(rotation=45, ha='right')\n", + " plt.yticks(rotation=0)\n", + " plt.tight_layout() # 调整布局\n", + "\n", + " print(\"分析完成。\")\n", + " return performance_pivot, count_pivot, fig\n", + "\n", + "# --- 如何使用 ---\n", + "# 假设你的包含预测结果和所需列的 DataFrame 是 final_predictions_df\n", + "# 确保它包含 'score', 'label', 'circ_mv', 'future_return'\n", + "\n", + "# # 示例调用 (你需要有实际的 score_df)\n", + "try:\n", + " # 确保数据类型正确\n", + " cols_to_numeric = ['score', 'label', 'circ_mv', 'future_return']\n", + " for col in cols_to_numeric:\n", + " if col in score_df.columns:\n", + " score_df[col] = pd.to_numeric(score_df[col], errors='coerce')\n", + "\n", + " # 调用分析函数\n", + " performance_matrix, count_matrix, heatmap_figure = analyze_score_performance_2d(\n", + " score_df,\n", + " n_bins=100, # 你要求的100分箱\n", + " min_samples_per_bin=50 # 每个格子至少需要50个样本才显示IC，可以调整\n", + " )\n", + "\n", + " # 显示图像\n", + " if heatmap_figure:\n", + " plt.show()\n", + "\n", + " # 可以查看具体的 performance_matrix 和 count_matrix\n", + " # print(\"\\nPerformance Matrix (Spearman IC):\")\n", + " # print(performance_matrix)\n", + " # print(\"\\nCount Matrix:\")\n", + " # print(count_matrix)\n", + "\n", + "except ValueError as ve:\n", + " print(f\"数据错误: {ve}\")\n", + "except Exception as e:\n", + " print(f\"发生未知错误: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a436dba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [ts_code, trade_date, is_st]\n", + "Index: []\n" + ] + } + ], + "source": [ + "print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb index dcc4c89..18c68ad 100644 --- a/main/train/Classify2.ipynb +++ b/main/train/Classify2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "79a7758178bafdd3", "metadata": { "ExecuteTime": { @@ -18,8 +18,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", "e:\\PyProject\\NewStock\\main\\train\n" ] } @@ -46,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -60,7 +58,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "daily data\n", + "daily data\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "daily basic\n", "inner merge on ['ts_code', 'trade_date']\n", "stk limit\n", @@ -70,7 +74,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8611848 entries, 0 to 8611847\n", + "RangeIndex: 8665405 entries, 0 to 8665404\n", "Data columns (total 32 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -145,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { @@ -213,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { @@ -313,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "a735bc02ceb4d872", "metadata": { "ExecuteTime": { @@ -329,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "id": "53f86ddc0677a6d7", "metadata": { "ExecuteTime": { @@ -396,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { @@ -424,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -435,19 +439,24 @@ "outputs": [], "source": [ "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", - " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n", " df=None)\n", "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", " df=None)\n", "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", - " df=None)" + " df=None)\n", + "top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n", + " columns=['ts_code', 'trade_date', 'reason'],\n", + " df=None)\n", + "\n", + "top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "id": "92d84ce15a562ec6", "metadata": { "ExecuteTime": { @@ -460,6 +469,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", "使用 'ann_date' 作为财务数据生效日期。\n", "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", "使用 'ann_date' 作为财务数据生效日期。\n", @@ -487,19 +500,20 @@ " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", - " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", - " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", - " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", - " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", - " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", - " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", - " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", - " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", - " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", - " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", - " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", - " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", - " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n", + " 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n", + " 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n", + " 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n", + " 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n", + " 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n", + " 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n", + " 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n", + " 'floating_chip_proxy', 'cost_support_15pct_change',\n", + " 'cat_winner_price_zone', 'flow_chip_consistency',\n", + " 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n", + " 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n", + " '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n", + " 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", @@ -577,12 +591,12 @@ "Calculating cs_rank_size...\n", "Finished cs_rank_size.\n", "\n", - "RangeIndex: 4509585 entries, 0 to 4509584\n", - "Columns: 178 entries, ts_code to cs_rank_size\n", - "dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n", - "memory usage: 5.6+ GB\n", + "RangeIndex: 4539678 entries, 0 to 4539677\n", + "Columns: 180 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(164), int32(3), object(2)\n", + "memory usage: 5.7+ GB\n", "None\n", - "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" ] } ], @@ -608,12 +622,23 @@ "\n", "df = filter_data(df)\n", "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + "# df = price_minus_deduction_price(df, n=120)\n", + "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n", + "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n", + "# df = cat_reason(df, top_list_df)\n", + "# df = cat_is_on_top_list(df, top_list_df)\n", + "\n", "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n", + "\n", "calculate_arbr(df, N=26)\n", "df['log_circ_mv'] = np.log(df['circ_mv'])\n", "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "\n", "df = turnover_rate_n(df, n=5)\n", "df = variance_n(df, n=20)\n", "df = bbi_ratio_factor(df)\n", @@ -672,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "id": "b87b938028afa206", "metadata": { "ExecuteTime": { @@ -710,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { @@ -782,98 +807,79 @@ "\n", "# --- 2. 行业市值中性化 ---\n", "\n", - "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", - " features: list,\n", - " industry_col: str = 'cat_l2_code',\n", - " market_cap_col: str = 'circ_mv'):\n", + "from tqdm import tqdm\n", + "\n", + "def cs_neutralize_market_cap_numpy(df: pd.DataFrame,\n", + " features: list,\n", + " market_cap_col: str = 'circ_mv'):\n", " \"\"\"\n", - " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", - " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", - " 将回归残差写回原特征列。\n", + " 对 DataFrame 中的指定特征进行截面市值中性化 (NumPy 优化)。\n", "\n", " Args:\n", - " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", - " industry_col, market_cap_col。\n", - " features (list): 需要处理的特征列名列表。\n", - " industry_col (str): 行业分类列名。\n", - " market_cap_col (str): 流通市值列名。\n", - "\n", - " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", - " 计算量较大，可能耗时较长。\n", - " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " df (pd.DataFrame): 包含数据的 DataFrame，需要有 'trade_date' 和 market_cap_col 列。\n", + " features (list): 需要进行市值中性化的特征列名列表。\n", + " market_cap_col (str): 包含市值数据的列名，默认为 'circ_mv'。\n", " \"\"\"\n", - " print(\"开始截面行业市值中性化...\")\n", - " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " print(\"开始截面市值中性化 (NumPy 优化)...\")\n", + " required_cols = features + ['trade_date', market_cap_col]\n", " if not all(col in df.columns for col in required_cols):\n", " missing = [col for col in required_cols if col not in df.columns]\n", " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", " return\n", "\n", - " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " df_copy = df\n", " log_cap_col = '_log_market_cap'\n", - " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", - " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", - " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", - " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + " df_copy[log_cap_col] = np.log1p(df_copy[market_cap_col])\n", "\n", - " dates = df['trade_date'].unique()\n", - " all_residuals = [] # 用于收集所有日期的残差\n", + " # 创建一个 DataFrame 来存储所有日期的残差结果\n", + " residuals_container = pd.DataFrame(index=df_copy.index, columns=features, dtype=float)\n", "\n", - " for date in tqdm(dates, desc=\"Neutralizing\"):\n", - " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + " for date, group_df in tqdm(df_copy.groupby('trade_date'), desc=\"Neutralizing by Date (NumPy)\"):\n", + " # 准备 X 矩阵 (自变量)：常数项和对数市值\n", + " X_daily = np.concatenate([np.ones((len(group_df), 1)), group_df[[log_cap_col]].values], axis=1)\n", "\n", - " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", - " X = daily_data[[log_cap_col]]\n", - " X = sm.add_constant(X, prepend=True) # 添加常数项\n", - " # 创建行业哑变量 (drop_first=True 避免共线性)\n", - " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", - " industry_dummies = industry_dummies.astype(int)\n", - " X = pd.concat([X, industry_dummies], axis=1)\n", + " for feature_col in features:\n", + " Y_daily = group_df[feature_col].values\n", "\n", - " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + " # 处理 NaN：只对有效数据对进行回归\n", + " valid_mask_y = ~np.isnan(Y_daily)\n", + " valid_mask_x = ~np.isnan(X_daily).any(axis=1)\n", + " valid_mask = valid_mask_y & valid_mask_x\n", "\n", - " for col in features:\n", - " Y = daily_data[col]\n", + " current_feature_indices = group_df.index[valid_mask]\n", "\n", - " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", - " valid_mask = Y.notna() & X.notna().all(axis=1)\n", - " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", - " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", - " daily_residuals[col] = np.nan\n", + " if np.sum(valid_mask) < X_daily.shape[1] + 1:\n", + " # 有效数据不足，此特征在此日期保持 NaN\n", " continue\n", "\n", - " Y_valid = Y[valid_mask]\n", - " X_valid = X[valid_mask]\n", + " Y_valid = Y_daily[valid_mask]\n", + " X_valid = X_daily[valid_mask, :]\n", "\n", - " # 执行 OLS 回归\n", " try:\n", - " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", - " results = model.fit()\n", - " # 将残差填回对应位置\n", - " daily_residuals.loc[valid_mask, col] = results.resid\n", - " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " # 使用 np.linalg.lstsq 进行 OLS 计算\n", + " beta, sum_sq_resid, rank, s = np.linalg.lstsq(X_valid, Y_valid, rcond=None)\n", + "\n", + " # 计算预测值 Y_hat = X_valid @ beta\n", + " Y_hat_valid = X_valid @ beta\n", + "\n", + " # 计算残差 residuals = Y_valid - Y_hat_valid\n", + " residuals_valid = Y_valid - Y_hat_valid\n", + "\n", + " # 将计算得到的残差放回 residuals_container\n", + " residuals_container.loc[current_feature_indices, feature_col] = residuals_valid\n", + "\n", + " except np.linalg.LinAlgError:\n", + " pass\n", " except Exception as e:\n", - " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", - " daily_residuals[col] = np.nan\n", - " break\n", - "\n", - " all_residuals.append(daily_residuals)\n", - "\n", - " # 合并所有日期的残差结果\n", - " if all_residuals:\n", - " residuals_df = pd.concat(all_residuals)\n", - " # 将残差结果更新回原始 df (原地修改)\n", - " # 使用 update 比 merge 更适合基于索引的原地更新\n", - " # 确保 residuals_df 的索引与 df 中对应部分一致\n", - " df.update(residuals_df)\n", - " else:\n", - " print(\"没有有效的残差结果可以合并。\")\n", + " pass\n", "\n", + " # 将所有计算得到的残差更新回原始的 df (原地修改)\n", + " for feature_col in features:\n", + " df[feature_col] = residuals_container[feature_col]\n", "\n", " # 清理临时列\n", - " df.drop(columns=[log_cap_col], inplace=True)\n", - " print(\"截面行业市值中性化完成。\")\n", - "\n", + " df.drop(columns=[log_cap_col], inplace=True, errors='ignore')\n", + " print(\"截面市值中性化完成 (NumPy 优化)。\")\n", "\n", "# --- 3. Z-Score 标准化 ---\n", "\n", @@ -956,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { @@ -1283,7 +1289,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "id": "47c12bb34062ae7a", "metadata": { "ExecuteTime": { @@ -1306,7 +1312,7 @@ "# df.groupby('ts_code')['open'].shift(-1)\n", "\n", "df['cat_up_limit'] = df['pct_chg'] > 5\n", - "df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n", + "df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().groupby('ts_code').shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n", "\n", "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", "\n", @@ -1317,10 +1323,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "29221dde", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191\n" + ] + } + ], "source": [ "feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n", "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", @@ -1346,7 +1360,17 @@ " 'cs_rank_ind_cap_neutral_pe',\n", " 'cs_rank_opening_gap',\n", " 'cs_rank_ind_adj_lg_flow']]\n", - "\n", + "feature_columns = [col for col in feature_columns if col not in ['roa', 'roe']]\n", + "print(len(feature_columns))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "03ee5daf", + "metadata": {}, + "outputs": [], + "source": [ "# df = fill_nan_with_daily_median(df, feature_columns)\n", "for feature_col in [col for col in feature_columns if col in df.columns]:\n", " # median_val = df[feature_col].median()\n", @@ -1355,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "id": "b76ea08a", "metadata": {}, "outputs": [ @@ -1367,7 +1391,7 @@ "0 000001.SZ 2019-01-02 16.574219\n", "1 000001.SZ 2019-01-03 16.583965\n", "2 000001.SZ 2019-01-04 16.633371\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', 'industry_cs_rank_intraday_range', 'industry_cs_rank_close_pos_in_range', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "去除极值\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] @@ -1376,7 +1400,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 131/131 [00:27<00:00, 4.69it/s]\n" + "MAD Filtering: 100%|██████████| 131/131 [00:28<00:00, 4.67it/s]\n" ] }, { @@ -1391,7 +1415,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 131/131 [00:23<00:00, 5.52it/s]\n" + "MAD Filtering: 100%|██████████| 131/131 [00:24<00:00, 5.43it/s]\n" ] }, { @@ -1429,15 +1453,15 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", - "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', 'industry_cs_rank_intraday_range', 'industry_cs_rank_close_pos_in_range', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "df最小日期: 2019-01-02\n", - "df最大日期: 2025-05-09\n", - "2057671\n", + "df最大日期: 2025-05-23\n", + "2057539\n", "train_data最小日期: 2020-01-02\n", "train_data最大日期: 2022-12-30\n", - "1736644\n", + "1766694\n", "test_data最小日期: 2023-01-03\n", - "test_data最大日期: 2025-05-09\n", + "test_data最大日期: 2025-05-23\n", " ts_code trade_date log_circ_mv\n", "0 000001.SZ 2019-01-02 16.574219\n", "1 000001.SZ 2019-01-03 16.583965\n", @@ -1538,12 +1562,12 @@ "print('去除极值')\n", "cs_mad_filter(train_data, transform_feature_columns)\n", "# print('中性化')\n", - "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# cs_neutralize_market_cap_numpy(train_data, transform_feature_columns)\n", "# print('标准化')\n", "# cs_zscore_standardize(train_data, transform_feature_columns)\n", "\n", "cs_mad_filter(test_data, transform_feature_columns)\n", - "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_neutralize_market_cap_numpy(test_data, transform_feature_columns)\n", "# cs_zscore_standardize(test_data, transform_feature_columns)\n", "\n", "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", @@ -1573,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "id": "3ff2d1c5", "metadata": {}, "outputs": [], @@ -1644,7 +1668,9 @@ " 'depth': 10, # 控制模型复杂度\n", " 'l2_leaf_reg': 50, # L2 正则化\n", " 'verbose': 5000,\n", - " 'early_stopping_rounds': 3000,\n", + " 'early_stopping_rounds': 300,\n", + " # 'od_type': 'Iter', # Overfitting detector type\n", + " # 'od_wait': 300, # Number of iterations to wait after the bes\n", " 'one_hot_max_size': 50,\n", " 'class_weights': [0.6, 1.2],\n", " 'task_type': 'GPU',\n", @@ -1714,7 +1740,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": { "ExecuteTime": { @@ -1748,7 +1774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0acc9aa66b564c16ba0dfdaa7dab6a9e", + "model_id": "b132fdab733b4d4e856d4688f2997dac", "version_major": 2, "version_minor": 0 }, @@ -1763,11 +1789,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.6886094\ttest: 0.6894541\tbest: 0.6894541 (0)\ttotal: 255ms\tremaining: 6m 22s\n", - "1499:\tlearn: 0.3197977\ttest: 0.5228570\tbest: 0.5197799 (414)\ttotal: 5m 23s\tremaining: 0us\n", - "bestTest = 0.5197798592\n", - "bestIteration = 414\n", - "Shrink model to first 415 iterations.\n" + "0:\tlearn: 0.6887138\ttest: 0.6894516\tbest: 0.6894516 (0)\ttotal: 328ms\tremaining: 8m 12s\n", + "bestTest = 0.5217666894\n", + "bestIteration = 487\n", + "Shrink model to first 488 iterations.\n" ] } ], @@ -1789,7 +1814,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { @@ -1799,7 +1824,7 @@ }, "outputs": [], "source": [ - "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv'))\n", + "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(300, 'total_mv'))\n", "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", "# score_df = score_df[score_df['pe_ttm'] > 0]\n", "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", @@ -1827,7 +1852,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "id": "09b1799e", "metadata": {}, "outputs": [ @@ -1836,18 +1861,207 @@ "output_type": "stream", "text": [ "191\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n" + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "[]\n" ] } ], "source": [ "print(len(feature_columns))\n", - "print(feature_columns)" + "print(feature_columns)\n", + "print([col for col in feature_columns if 'total_mv' in col])" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, + "id": "e53b209a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5595 2057539\n", + " ts_code trade_date turnover_rate\n", + "0 000001.SZ 2023-01-03 1.1307\n", + "1 000001.SZ 2023-01-04 1.1284\n", + "2 000001.SZ 2023-01-05 0.8582\n", + "3 000001.SZ 2023-01-06 0.6162\n", + "4 000001.SZ 2023-01-09 0.5450\n", + "... ... ... ...\n", + "1766689 605599.SH 2025-05-19 0.4952\n", + "1766690 605599.SH 2025-05-20 1.6447\n", + "1766691 605599.SH 2025-05-21 1.2658\n", + "1766692 605599.SH 2025-05-22 0.7522\n", + "1766693 605599.SH 2025-05-23 0.6051\n", + "\n", + "[1766694 rows x 3 columns]\n" + ] + } + ], + "source": [ + "print(len(train_data[train_data['pct_chg'] > 7]), len(train_data))\n", + "print(test_data[['ts_code', 'trade_date', 'turnover_rate']])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "364e821a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def calculate_binary_classification_metrics(df: pd.DataFrame, score_col: str, label_col: str, future_return_col: str = None, total_mv_col: str = None, n_mv_bins: int = 10, threshold: float = 0.5):\n", + " \"\"\"\n", + " 计算二分类模型的评估指标，可选择计算 score 和 future_return 的相关性，\n", + " 并可选择计算 score 在按总市值 (total_mv) 分为 n 份后的每个分组上的预测性能（ROC AUC）。\n", + "\n", + " Args:\n", + " df: 包含 score (预测概率或置信度), label (真实二分类标签), 可选的 future_return 和 total_mv 的 Pandas DataFrame。\n", + " score_col: 包含模型预测 score 的列名。\n", + " label_col: 包含真实二分类标签 (0 或 1) 的列名。\n", + " future_return_col: (可选) 包含未来收益率的列名，用于计算相关性。\n", + " total_mv_col: (可选) 包含总市值的列名，用于按市值分 n 份分析预测性能。\n", + " n_mv_bins: (可选) 将总市值分为多少份，默认为 5。\n", + " threshold: 将 score 转换为预测类别的阈值，默认为 0.5。\n", + "\n", + " Returns:\n", + " 一个包含以下评估指标的字典：\n", + " - accuracy: 准确率\n", + " - precision: 精确率\n", + " - recall: 召回率\n", + " - f1: F1 分数\n", + " - roc_auc: ROC AUC 值\n", + " - fpr: ROC 曲线的假正率 (False Positive Rate)\n", + " - tpr: ROC 曲线的真正率 (True Positive Rate)\n", + " - thresholds: ROC 曲线的阈值\n", + " - score_return_correlation: (如果 future_return_col 提供) score 和 future_return 的皮尔逊相关系数\n", + " - mv_roc_auc: (如果 total_mv_col 提供) 一个字典，包含按总市值分为 n 份后的每个市值分组对应的 ROC AUC 值\n", + " \"\"\"\n", + " y_true = df[label_col].values\n", + " y_score = df[score_col].values\n", + " y_pred = (y_score >= threshold).astype(int)\n", + "\n", + " metrics = {}\n", + " metrics['accuracy'] = accuracy_score(y_true, y_pred)\n", + " metrics['precision'] = precision_score(y_true, y_pred)\n", + " metrics['recall'] = recall_score(y_true, y_pred)\n", + " metrics['f1'] = f1_score(y_true, y_pred)\n", + " metrics['roc_auc'] = roc_auc_score(y_true, y_score)\n", + " metrics['fpr'], metrics['tpr'], metrics['thresholds'] = roc_curve(y_true, y_score)\n", + "\n", + " if future_return_col in df.columns:\n", + " metrics['score_return_correlation'] = df[score_col].corr(df[future_return_col])\n", + "\n", + " if total_mv_col in df.columns and n_mv_bins > 1:\n", + " metrics['mv_roc_auc'] = {}\n", + " df['mv_quantile'] = pd.cut(df[total_mv_col], bins=n_mv_bins, labels=False, duplicates='drop')\n", + " for i in range(df['mv_quantile'].nunique()):\n", + " mv_group = df[df['mv_quantile'] == i]\n", + " if len(mv_group) > 0 and len(np.unique(mv_group[label_col])) > 1 and len(np.unique(mv_group[score_col])) > 1:\n", + " roc_auc_mv = roc_auc_score(mv_group[label_col], mv_group[score_col])\n", + " lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n", + " upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n", + " metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = roc_auc_mv\n", + " else:\n", + " lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n", + " upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n", + " metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = np.nan\n", + " print(f'{lower_bound:.0e}-{upper_bound:.0e}')\n", + " df.drop(columns=['mv_quantile'], inplace=True)\n", + "\n", + " return metrics\n", + "\n", + "def plot_roc_curve(metrics: dict):\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(metrics['fpr'], metrics['tpr'], color='darkorange', lw=2, label=f'ROC curve (AUC = {metrics[\"roc_auc\"]:.2f})')\n", + " plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver Operating Characteristic (ROC)')\n", + " plt.legend(loc=\"lower right\")\n", + " plt.grid(True)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1f6e6336", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6e+04-9e+04\n", + "9e+04-1e+05\n", + "1e+05-1e+05\n", + "1e+05-1e+05\n", + "1e+05-2e+05\n", + "2e+05-2e+05\n", + "2e+05-2e+05\n", + "2e+05-2e+05\n", + "2e+05-3e+05\n", + "3e+05-3e+05\n", + "二分类评估指标:\n", + "accuracy: 0.6585\n", + "precision: 0.4597\n", + "recall: 0.1487\n", + "f1: 0.2247\n", + "roc_auc: 0.6122\n", + "fpr: (array of length 7368)\n", + "tpr: (array of length 7368)\n", + "thresholds: (array of length 7368)\n", + "score_return_correlation: -0.0355\n", + "mv_roc_auc: {'6e+04-9e+04': 0.5986394557823129, '9e+04-1e+05': 0.5806043519120943, '1e+05-1e+05': 0.5902634156155784, '1e+05-2e+05': 0.5821412014040664, '2e+05-2e+05': 0.5919906015824924, '2e+05-3e+05': 0.6058984399066272, '3e+05-3e+05': 0.5884844322344323}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "for sd in [\n", + " # save_df1, \n", + " # save_df2, \n", + " score_df\n", + " ]:\n", + " # 计算二分类指标\n", + " evaluation_metrics = calculate_binary_classification_metrics(sd, score_col='score', label_col='label', threshold=0.6,\n", + " future_return_col='future_return', total_mv_col='total_mv')\n", + "\n", + " # 打印指标\n", + " print(\"二分类评估指标:\")\n", + " for metric, value in evaluation_metrics.items():\n", + " if isinstance(value, (float, int)):\n", + " print(f\"{metric}: {value:.4f}\")\n", + " elif isinstance(value, (list, tuple, np.ndarray)):\n", + " print(f\"{metric}: (array of length {len(value)})\")\n", + " else:\n", + " print(f\"{metric}: {value}\")\n", + "\n", + " # 绘制 ROC 曲线\n", + " plot_roc_curve(evaluation_metrics)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "id": "7e9023cc", "metadata": {}, "outputs": [], @@ -2047,7 +2261,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 25, "id": "a0000d75", "metadata": {}, "outputs": [ @@ -2057,7 +2271,7 @@ "text": [ "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n", "准备数据，处理 NaN 值...\n", - "原始数据 28300 行，移除 NaN 后剩余 27850 行用于分析。\n", + "原始数据 17280 行，移除 NaN 后剩余 16976 行用于分析。\n", "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n", "按二维分箱分组计算 Spearman Rank IC...\n", "整理结果用于绘图...\n", @@ -2295,7 +2509,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 26, "id": "a436dba4", "metadata": {}, "outputs": [ diff --git a/main/train/Rank2.ipynb b/main/train/Rank2.ipynb new file mode 100644 index 0000000..cd8dbe6 --- /dev/null +++ b/main/train/Rank2.ipynb @@ -0,0 +1,2204 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:46:06.987506Z", + "start_time": "2025-04-03T12:46:06.259551Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e:\\PyProject\\NewStock\\main\\train\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import gc\n", + "import os\n", + "import sys\n", + "sys.path.append('../../')\n", + "print(os.getcwd())\n", + "import pandas as pd\n", + "from main.factor.factor import get_rolling_factor, get_simple_factor\n", + "from main.utils.factor import read_industry_data\n", + "from main.utils.factor_processor import calculate_score\n", + "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:00.212859Z", + "start_time": "2025-04-03T12:46:06.998047Z" + }, + "scrolled": true + }, + "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", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8665405 entries, 0 to 8665404\n", + "Data columns (total 32 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 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 total_mv float64 \n", + " 12 volume_ratio float64 \n", + " 13 is_st bool \n", + " 14 up_limit float64 \n", + " 15 down_limit float64 \n", + " 16 buy_sm_vol float64 \n", + " 17 sell_sm_vol float64 \n", + " 18 buy_lg_vol float64 \n", + " 19 sell_lg_vol float64 \n", + " 20 buy_elg_vol float64 \n", + " 21 sell_elg_vol float64 \n", + " 22 net_mf_vol float64 \n", + " 23 his_low float64 \n", + " 24 his_high float64 \n", + " 25 cost_5pct float64 \n", + " 26 cost_15pct float64 \n", + " 27 cost_50pct float64 \n", + " 28 cost_85pct float64 \n", + " 29 cost_95pct float64 \n", + " 30 weight_avg float64 \n", + " 31 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n", + "memory usage: 2.0+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from main.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', 'pct_chg'],\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', 'total_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('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cac01788dac10678", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.527104Z", + "start_time": "2025-04-03T12:47:00.488715Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n" + ] + } + ], + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.719252Z", + "start_time": "2025-04-03T12:47:10.541247Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from main.factor.factor import *\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", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " # df = sentiment_panic_greed_index(df)\n", + " # df = sentiment_market_breadth_proxy(df)\n", + " # df = sentiment_reversal_indicator(df)\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', \n", + " 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n", + " # 'sentiment_panic_greed_index',\n", + " 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.821169Z", + "start_time": "2025-04-03T12:47:10.751831Z" + } + }, + "outputs": [], + "source": [ + "import talib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.944254Z", + "start_time": "2025-04-03T12:47:10.826179Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "from main.utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + "\n", + " # cs_rank_intraday_range(industry_data)\n", + " # cs_rank_close_pos_in_range(industry_data)\n", + "\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.969344Z", + "start_time": "2025-04-03T12:47:15.963327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85c3e3d0235ffffa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:16.089879Z", + "start_time": "2025-04-03T12:47:15.990101Z" + } + }, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)\n", + "top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n", + " columns=['ts_code', 'trade_date', 'reason'],\n", + " df=None)\n", + "\n", + "top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:01.612695Z", + "start_time": "2025-04-03T12:47:16.121802Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[9], line 39\u001b[0m\n\u001b[0;32m 37\u001b[0m df \u001b[38;5;241m=\u001b[39m daily_deviation(df)\n\u001b[0;32m 38\u001b[0m df \u001b[38;5;241m=\u001b[39m daily_industry_deviation(df)\n\u001b[1;32m---> 39\u001b[0m df, _ \u001b[38;5;241m=\u001b[39m get_rolling_factor(df)\n\u001b[0;32m 40\u001b[0m df, _ \u001b[38;5;241m=\u001b[39m get_simple_factor(df)\n\u001b[0;32m 42\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml1_code\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcat_l1_code\u001b[39m\u001b[38;5;124m'\u001b[39m})\n", + "File \u001b[1;32me:\\PyProject\\NewStock\\main\\train\\../..\\main\\factor\\factor.py:340\u001b[0m, in \u001b[0;36mget_rolling_factor\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 336\u001b[0m \u001b[38;5;66;03m# 计算 act_factor1, act_factor2, act_factor3, act_factor4\u001b[39;00m\n\u001b[0;32m 337\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_5\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 338\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[0;32m 339\u001b[0m )\n\u001b[1;32m--> 340\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m 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\u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 1851\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_python_apply_general\u001b[39m(\n\u001b[0;32m 1852\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1857\u001b[0m is_agg: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 1858\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDFrameT:\n\u001b[0;32m 1859\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1860\u001b[0m \u001b[38;5;124;03m Apply function f in python space\u001b[39;00m\n\u001b[0;32m 1861\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1883\u001b[0m \u001b[38;5;124;03m data after applying f\u001b[39;00m\n\u001b[0;32m 1884\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1885\u001b[0m values, mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39mapply_groupwise(f, data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis)\n\u001b[0;32m 1886\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1887\u001b[0m not_indexed_same \u001b[38;5;241m=\u001b[39m mutated\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:919\u001b[0m, in \u001b[0;36mBaseGrouper.apply_groupwise\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m 917\u001b[0m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[0;32m 918\u001b[0m group_axes \u001b[38;5;241m=\u001b[39m group\u001b[38;5;241m.\u001b[39maxes\n\u001b[1;32m--> 919\u001b[0m res \u001b[38;5;241m=\u001b[39m f(group)\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[0;32m 921\u001b[0m mutated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32me:\\PyProject\\NewStock\\main\\train\\../..\\main\\factor\\factor.py:341\u001b[0m, in \u001b[0;36mget_rolling_factor..\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 336\u001b[0m \u001b[38;5;66;03m# 计算 act_factor1, act_factor2, act_factor3, act_factor4\u001b[39;00m\n\u001b[0;32m 337\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor1\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_5\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 338\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[0;32m 339\u001b[0m )\n\u001b[0;32m 340\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor2\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_13\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m--> 341\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[0;32m 342\u001b[0m )\n\u001b[0;32m 343\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor3\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_20\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 344\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m21\u001b[39m\n\u001b[0;32m 345\u001b[0m )\n\u001b[0;32m 346\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mact_factor4\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m grouped[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ema_60\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 347\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: np\u001b[38;5;241m.\u001b[39marctan((x \u001b[38;5;241m/\u001b[39m x\u001b[38;5;241m.\u001b[39mshift(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m57.3\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[0;32m 348\u001b[0m )\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\common.py:76\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer..new_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[0;32m 74\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[1;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m method(\u001b[38;5;28mself\u001b[39m, other)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\arraylike.py:194\u001b[0m, in \u001b[0;36mOpsMixin.__sub__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__sub__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 193\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__sub__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_arith_method(other, operator\u001b[38;5;241m.\u001b[39msub)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\series.py:6135\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m 6133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_arith_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, other, op):\n\u001b[0;32m 6134\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_for_op(other)\n\u001b[1;32m-> 6135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m base\u001b[38;5;241m.\u001b[39mIndexOpsMixin\u001b[38;5;241m.\u001b[39m_arith_method(\u001b[38;5;28mself\u001b[39m, other, op)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\base.py:1382\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m 1379\u001b[0m rvalues \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(rvalues\u001b[38;5;241m.\u001b[39mstart, rvalues\u001b[38;5;241m.\u001b[39mstop, rvalues\u001b[38;5;241m.\u001b[39mstep)\n\u001b[0;32m 1381\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 1382\u001b[0m result \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39marithmetic_op(lvalues, rvalues, op)\n\u001b[0;32m 1384\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(result, name\u001b[38;5;241m=\u001b[39mres_name)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py:283\u001b[0m, in \u001b[0;36marithmetic_op\u001b[1;34m(left, right, op)\u001b[0m\n\u001b[0;32m 279\u001b[0m _bool_arith_check(op, left, right) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m 281\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"_na_arithmetic_op\" has incompatible type\u001b[39;00m\n\u001b[0;32m 282\u001b[0m \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\"; expected \"ndarray[Any, Any]\"\u001b[39;00m\n\u001b[1;32m--> 283\u001b[0m res_values \u001b[38;5;241m=\u001b[39m _na_arithmetic_op(left, right, op) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\ops\\array_ops.py:218\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m 215\u001b[0m func \u001b[38;5;241m=\u001b[39m partial(expressions\u001b[38;5;241m.\u001b[39mevaluate, op)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 218\u001b[0m result \u001b[38;5;241m=\u001b[39m func(left, right)\n\u001b[0;32m 219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_cmp \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[0;32m 221\u001b[0m left\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(right, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m\n\u001b[0;32m 222\u001b[0m ):\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[38;5;66;03m# Don't do this for comparisons, as that will handle complex numbers\u001b[39;00m\n\u001b[0;32m 226\u001b[0m \u001b[38;5;66;03m# incorrectly, see GH#32047\u001b[39;00m\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:242\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m op_str \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_numexpr:\n\u001b[0;32m 241\u001b[0m \u001b[38;5;66;03m# error: \"None\" not callable\u001b[39;00m\n\u001b[1;32m--> 242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate(op, op_str, a, b) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 243\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate_standard(op, op_str, a, b)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:131\u001b[0m, in \u001b[0;36m_evaluate_numexpr\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m 128\u001b[0m _store_test_result(result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 131\u001b[0m result \u001b[38;5;241m=\u001b[39m _evaluate_standard(op, op_str, a, b)\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:73\u001b[0m, in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _TEST_MODE:\n\u001b[0;32m 72\u001b[0m _store_test_result(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op(a, b)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2019-01-01']\n", + " if 'in_date' in df.columns:\n", + " df = df.drop(columns=['in_date'])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "gc.collect()\n", + "\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + "# df = price_minus_deduction_price(df, n=120)\n", + "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n", + "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n", + "# df = cat_reason(df, top_list_df)\n", + "# df = cat_is_on_top_list(df, top_list_df)\n", + "\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df = daily_deviation(df)\n", + "df = daily_industry_deviation(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", + "\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b87b938028afa206", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.658725Z", + "start_time": "2025-04-03T13:08:02.469611Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.670700Z", + "start_time": "2025-04-03T13:08:03.665739Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm # 用于中性化回归\n", + "from tqdm import tqdm # 可选，用于显示进度条\n", + "\n", + "# --- 常量 ---\n", + "epsilon = 1e-10 # 防止除零\n", + "\n", + "# --- 1. 中位数去极值 (MAD) ---\n", + "\n", + "def cs_mad_filter(df: pd.DataFrame,\n", + " features: list,\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826):\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n", + "\n", + " 方法: 对每日截面数据，计算 median 和 MAD，\n", + " 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n", + " 替换为边界值 (Winsorization)。\n", + " scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n", + " scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"MAD Filtering\"):\n", + " try:\n", + " # 计算截面中位数\n", + " median = grouped[col].transform('median')\n", + " # 计算截面 MAD (Median Absolute Deviation from Median)\n", + " mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n", + "\n", + " # 计算上下边界\n", + " lower_bound = median - k * scale_factor * mad\n", + " upper_bound = median + k * scale_factor * mad\n", + "\n", + " # 原地应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\n", + "\n", + "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", + " features: list,\n", + " industry_col: str = 'cat_l2_code',\n", + " market_cap_col: str = 'circ_mv'):\n", + " \"\"\"\n", + " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", + " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", + " 将回归残差写回原特征列。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", + " industry_col, market_cap_col。\n", + " features (list): 需要处理的特征列名列表。\n", + " industry_col (str): 行业分类列名。\n", + " market_cap_col (str): 流通市值列名。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", + " 计算量较大，可能耗时较长。\n", + " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " \"\"\"\n", + " print(\"开始截面行业市值中性化...\")\n", + " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", + " return\n", + "\n", + " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " log_cap_col = '_log_market_cap'\n", + " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", + " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", + " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", + " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + "\n", + " dates = df['trade_date'].unique()\n", + " all_residuals = [] # 用于收集所有日期的残差\n", + "\n", + " for date in tqdm(dates, desc=\"Neutralizing\"):\n", + " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + "\n", + " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", + " X = daily_data[[log_cap_col]]\n", + " X = sm.add_constant(X, prepend=True) # 添加常数项\n", + " # 创建行业哑变量 (drop_first=True 避免共线性)\n", + " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", + " industry_dummies = industry_dummies.astype(int)\n", + " X = pd.concat([X, industry_dummies], axis=1)\n", + "\n", + " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + "\n", + " for col in features:\n", + " Y = daily_data[col]\n", + "\n", + " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", + " valid_mask = Y.notna() & X.notna().all(axis=1)\n", + " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", + " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " continue\n", + "\n", + " Y_valid = Y[valid_mask]\n", + " X_valid = X[valid_mask]\n", + "\n", + " # 执行 OLS 回归\n", + " try:\n", + " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", + " results = model.fit()\n", + " # 将残差填回对应位置\n", + " daily_residuals.loc[valid_mask, col] = results.resid\n", + " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " except Exception as e:\n", + " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " break\n", + "\n", + " all_residuals.append(daily_residuals)\n", + "\n", + " # 合并所有日期的残差结果\n", + " if all_residuals:\n", + " residuals_df = pd.concat(all_residuals)\n", + " # 将残差结果更新回原始 df (原地修改)\n", + " # 使用 update 比 merge 更适合基于索引的原地更新\n", + " # 确保 residuals_df 的索引与 df 中对应部分一致\n", + " df.update(residuals_df)\n", + " else:\n", + " print(\"没有有效的残差结果可以合并。\")\n", + "\n", + "\n", + " # 清理临时列\n", + " df.drop(columns=[log_cap_col], inplace=True)\n", + " print(\"截面行业市值中性化完成。\")\n", + "\n", + "\n", + "# --- 3. Z-Score 标准化 ---\n", + "\n", + "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " epsilon (float): 防止除以零的小常数。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"Standardizing\"):\n", + " try:\n", + " # 使用 transform 计算截面均值和标准差\n", + " mean = grouped[col].transform('mean')\n", + " std = grouped[col].transform('std')\n", + "\n", + " # 计算 Z-Score 并原地赋值\n", + " df[col] = (df[col] - mean) / (std + epsilon)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + "\n", + "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n", + " feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n", + " \"\"\"\n", + " processed_df = df.copy() # 在副本上操作，保留原始数据\n", + "\n", + " # 确保 trade_date 是 datetime 类型以便正确分组\n", + " processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n", + "\n", + " def _fill_daily_nan(group):\n", + " # group 是某一个交易日的 DataFrame\n", + "\n", + " # 遍历指定的特征列\n", + " for feature_col in feature_columns:\n", + " # 检查列是否存在于当前分组中\n", + " if feature_col in group.columns:\n", + " # 计算当日该特征的中位数\n", + " median_val = group[feature_col].median()\n", + "\n", + " # 使用当日中位数填充该特征列的 NaN 值\n", + " # inplace=True 会直接修改 group DataFrame\n", + " group[feature_col].fillna(median_val, inplace=True)\n", + " # else:\n", + " # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + "\n", + " return group\n", + "\n", + " # 按交易日期分组，并应用每日填充函数\n", + " # group_keys=False 避免将分组键添加到结果索引中\n", + " filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n", + "\n", + " return filled_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:04.694262Z", + "start_time": "2025-04-03T13:08:03.694904Z" + } + }, + "outputs": [], + "source": [ + "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_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " # 使用已有的 pct_chg 字段计算波动率\n", + " volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + " return score\n", + "\n", + " # # 确保 DataFrame 按照股票代码和交易日期排序\n", + " # df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 对每个股票分别计算 score\n", + " df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n", + "\n", + " return df['score']\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n", + " 对特征在行业内部进行市值中性化。\n", + "\n", + " Args:\n", + " df: 输入的 DataFrame，包含特征、行业分类和市值列。\n", + " features: 需要进行中性化的特征列名列表。\n", + " industry_col: 行业分类列的列名。\n", + " mkt_cap_col: 市值列的列名。\n", + "\n", + " Returns:\n", + " 中性化后的 DataFrame。\n", + " \"\"\"\n", + "\n", + " df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n", + " df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n", + " df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n", + "\n", + " if df_cleaned.empty:\n", + " print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n", + " return df # 返回原始或空df，取决于清理前的状态\n", + "\n", + " processed_df = df\n", + "\n", + " for col in features:\n", + " if col not in df_cleaned.columns:\n", + " print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n", + " # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " # 跳过对控制变量本身进行中性化\n", + " if col == mkt_cap_col or col == industry_col:\n", + " print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n", + " # 在结果 df 中也保持原样\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n", + "\n", + " # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n", + " df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n", + "\n", + " if not df_subset_factor.empty:\n", + " for industry, group in df_subset_factor.groupby(industry_col):\n", + " x = group[mkt_cap_col] # 市值对数\n", + " y = group[col] # 因子值\n", + "\n", + " # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n", + " # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n", + " if len(group) > 1 and np.var(x) > 1e-9:\n", + " try:\n", + " beta = np.cov(y, x)[0, 1] / np.var(x)\n", + " alpha = np.mean(y) - beta * np.mean(x)\n", + "\n", + " # 计算残差\n", + " resid = y - (alpha + beta * x)\n", + "\n", + " # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n", + " residual_series.loc[resid.index] = resid\n", + "\n", + " except Exception as e:\n", + " # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n", + " print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n", + " # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n", + " # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n", + "\n", + " else:\n", + " residual_series.loc[group.index] = group[col] # 保留原始因子值\n", + " processed_df.loc[residual_series.index, col] = residual_series\n", + "\n", + "\n", + " else:\n", + " processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n", + "\n", + " return processed_df\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "\n", + "def mad_filter(df, features, n=3):\n", + " for col in features:\n", + " median = df[col].median()\n", + " mad = np.median(np.abs(df[col] - median))\n", + " upper = median + n * mad\n", + " lower = median - n * mad\n", + " df[col] = np.clip(df[col], lower, upper) # 截断极值\n", + " return df\n", + "\n", + "\n", + "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n", + " for col in features:\n", + " # 按日期分组计算上下百分位数\n", + " lower_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(lower_percentile)\n", + " )\n", + " upper_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(upper_percentile)\n", + " )\n", + " # 截断超出范围的值\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df\n", + "\n", + "\n", + "from scipy.stats import iqr\n", + "\n", + "\n", + "def iqr_filter(df, features):\n", + " for col in features:\n", + " df[col] = df.groupby('trade_date')[col].transform(\n", + " lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n", + " )\n", + " return df\n", + "\n", + "\n", + "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " for col in features:\n", + " # 计算 rolling 统计量，需要按日期进行 groupby\n", + " rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n", + " rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n", + "\n", + " # 对数据进行裁剪\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " \n", + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features\n", + "\n", + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:50.841165Z", + "start_time": "2025-04-03T14:49:25.889057Z" + } + }, + "outputs": [], + "source": [ + "days = 5\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "\n", + "df['cat_up_limit'] = df['pct_chg'] > 5\n", + "# df['label'] = (df.groupby('ts_code')['cat_up_limit']\n", + "# .rolling(window=5, min_periods=1).sum()\n", + "# .groupby('ts_code') # 再次按 ts_code 分组\n", + "# .shift(-5)\n", + "# .fillna(0) # 填充每个股票组最后的 NaN\n", + "# .astype(int)\n", + "# .reset_index(level=0, drop=True))\n", + "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n", + " lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", + ")\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "# for col in [col for col in df.columns]:\n", + "# train_data[col] = train_data[col].astype('str')\n", + "# test_data[col] = test_data[col].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f4dc587", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date open close high low vol pct_chg \\\n", + "976171 002193.SZ 2019-01-02 18.02 17.82 18.21 17.73 22513.62 -1.00 \n", + "976172 002193.SZ 2019-01-03 17.82 17.78 17.94 17.65 19523.21 -0.22 \n", + "976173 002193.SZ 2019-01-04 17.47 18.04 18.10 17.34 28094.69 1.46 \n", + "976174 002193.SZ 2019-01-07 18.17 18.31 18.39 18.00 23866.88 1.50 \n", + "976175 002193.SZ 2019-01-08 18.25 18.52 18.66 18.13 22853.46 1.15 \n", + "... ... ... ... ... ... ... ... ... \n", + "977704 002193.SZ 2025-05-19 11.74 11.95 11.97 11.61 94249.32 2.49 \n", + "977705 002193.SZ 2025-05-20 11.99 12.20 12.22 11.84 103040.57 2.09 \n", + "977706 002193.SZ 2025-05-21 12.20 11.97 12.28 11.82 83112.00 -1.89 \n", + "977707 002193.SZ 2025-05-22 11.95 11.80 12.17 11.72 88811.00 -1.42 \n", + "977708 002193.SZ 2025-05-23 11.74 11.51 11.90 11.51 93013.00 -2.46 \n", + "\n", + " turnover_rate pe_ttm ... cs_rank_lg_flow_price_concordance \\\n", + "976171 0.9771 23.0129 ... 0.621118 \n", + "976172 0.8473 22.9598 ... 0.230095 \n", + "976173 1.2193 23.3049 ... 0.507119 \n", + "976174 1.0358 23.6499 ... 0.315078 \n", + "976175 0.9918 23.9154 ... 0.248631 \n", + "... ... ... ... ... \n", + "977704 3.6012 NaN ... 0.224402 \n", + "977705 3.9371 NaN ... 0.124834 \n", + "977706 3.1757 NaN ... 0.803720 \n", + "977707 3.3934 NaN ... 0.667220 \n", + "977708 3.5540 NaN ... 0.308867 \n", + "\n", + " cs_rank_turnover_per_winner cs_rank_ind_cap_neutral_pe \\\n", + "976171 0.990869 NaN \n", + "976172 0.934283 NaN \n", + "976173 0.925182 NaN \n", + "976174 0.843796 NaN \n", + "976175 0.834672 NaN \n", + "... ... ... \n", + "977704 0.624335 NaN \n", + "977705 0.641102 NaN \n", + "977706 0.624709 NaN \n", + "977707 0.563268 NaN \n", + "977708 0.661242 NaN \n", + "\n", + " cs_rank_volume_ratio cs_rank_elg_buy_sell_sm_ratio \\\n", + "976171 0.710344 0.341855 \n", + "976172 0.444444 0.318912 \n", + "976173 0.489226 0.260036 \n", + "976174 0.250000 0.251095 \n", + "976175 0.510588 0.286679 \n", + "... ... ... \n", + "977704 0.234043 0.397274 \n", + "977705 0.344124 0.116534 \n", + "977706 0.221189 0.126370 \n", + "977707 0.412155 0.130521 \n", + "977708 0.534540 0.134175 \n", + "\n", + " cs_rank_cost_dist_vol_ratio cs_rank_size future_return \\\n", + "976171 0.261603 0.262235 0.034231 \n", + "976172 0.185342 0.264695 0.029809 \n", + "976173 0.211959 0.259489 0.025499 \n", + "976174 0.145266 0.255474 0.014746 \n", + "976175 0.202299 0.259854 0.003240 \n", + "... ... ... ... \n", + "977704 0.299535 0.031250 NaN \n", + "977705 0.339641 0.032205 NaN \n", + "977706 0.291597 0.030555 NaN \n", + "977707 0.372634 0.030555 NaN \n", + "977708 0.430090 0.027898 NaN \n", + "\n", + " cat_up_limit label \n", + "976171 False 8.0 \n", + "976172 False 7.0 \n", + "976173 False 10.0 \n", + "976174 False 13.0 \n", + "976175 False 6.0 \n", + "... ... ... \n", + "977704 False NaN \n", + "977705 False NaN \n", + "977706 False NaN \n", + "977707 False NaN \n", + "977708 False NaN \n", + "\n", + "[1538 rows x 181 columns]\n" + ] + } + ], + "source": [ + "print(df[df['ts_code'] == '002193.SZ'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29221dde", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "192\n" + ] + } + ], + "source": [ + "feature_columns = [col for col in df.head(10)\n", + " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + " .merge(index_data, on='trade_date', how='left')\n", + " .columns\n", + " ]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n", + "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n", + "feature_columns = [col for col in feature_columns if 'code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", + "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n", + " 'cap_neutral_cost_metric', \n", + " 'hurst_net_mf_vol_60', \n", + " 'complex_factor_deap_1', \n", + " 'lg_buy_consolidation_20',\n", + " 'cs_rank_ind_cap_neutral_pe',\n", + " 'cs_rank_opening_gap',\n", + " 'cs_rank_ind_adj_lg_flow']]\n", + "feature_columns = [col for col in feature_columns if col not in ['cat_reason', 'cat_is_on_top_list']]\n", + "print(len(feature_columns))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03ee5daf", + "metadata": {}, + "outputs": [], + "source": [ + "# df = fill_nan_with_daily_median(df, feature_columns)\n", + "for feature_col in [col for col in feature_columns if col in df.columns]:\n", + " # median_val = df[feature_col].median()\n", + " df[feature_col].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "去除极值\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 132/132 [00:28<00:00, 4.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 132/132 [00:23<00:00, 5.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "df最小日期: 2019-01-02\n", + "df最大日期: 2025-05-23\n", + "2057539\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2022-12-30\n", + "1766694\n", + "test_data最小日期: 2023-01-03\n", + "test_data最大日期: 2025-05-23\n", + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n" + ] + } + ], + "source": [ + "split_date = '2023-01-01'\n", + "train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n", + "test_data = df[(df['trade_date'] >= split_date)]\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n", + "# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n", + "\n", + "# feature_columns = [\n", + "# 'undist_profit_ps', \n", + "# 'AR_BR',\n", + "# 'pe_ttm',\n", + "# 'alpha_22_improved', \n", + "# 'alpha_003', \n", + "# 'alpha_007', \n", + "# 'alpha_013', \n", + "# 'cat_up_limit', \n", + "# 'cat_down_limit', \n", + "# 'up_limit_count_10d', \n", + "# 'down_limit_count_10d', \n", + "# 'consecutive_up_limit', \n", + "# 'vol_break', \n", + "# 'weight_roc5', \n", + "# 'price_cost_divergence', \n", + "# 'smallcap_concentration', \n", + "# 'cost_stability', \n", + "# 'high_cost_break_days', \n", + "# 'liquidity_risk', \n", + "# 'turnover_std', \n", + "# 'mv_volatility', \n", + "# 'volume_growth', \n", + "# 'mv_growth', \n", + "# 'lg_flow_mom_corr_20_60', \n", + "# 'lg_flow_accel', \n", + "# 'profit_pressure', \n", + "# 'underwater_resistance', \n", + "# 'cost_conc_std_20', \n", + "# 'profit_decay_20', \n", + "# 'vol_amp_loss_20', \n", + "# 'vol_drop_profit_cnt_5', \n", + "# 'lg_flow_vol_interact_20', \n", + "# 'cost_break_confirm_cnt_5', \n", + "# 'atr_norm_channel_pos_14', \n", + "# 'turnover_diff_skew_20', \n", + "# 'lg_sm_flow_diverge_20', \n", + "# 'pullback_strong_20_20', \n", + "# 'vol_wgt_hist_pos_20', \n", + "# 'vol_adj_roc_20',\n", + "# 'cashflow_to_ev_factor',\n", + "# 'ocfps',\n", + "# 'book_to_price_ratio',\n", + "# 'turnover_rate_mean_5',\n", + "# 'variance_20',\n", + "# 'bbi_ratio_factor'\n", + "# ]\n", + "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n", + "print(feature_columns)\n", + "\n", + "# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", + "# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", + "\n", + "train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "transform_feature_columns = feature_columns\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "# transform_feature_columns.remove('undist_profit_ps')\n", + "print('去除极值')\n", + "cs_mad_filter(train_data, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "\n", + "cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "cs_mad_filter(train_data, mad_filter_feature_columns)\n", + "cs_mad_filter(test_data, mad_filter_feature_columns)\n", + "\n", + "\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "\n", + "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(train_data))\n", + "print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e4b027e", + "metadata": {}, + "outputs": [], + "source": [ + "class RmseObjective(object):\n", + "\n", + " def is_max_optimal(self):\n", + " return False\n", + "\n", + " def get_final_error(self, error, weight):\n", + " return np.sqrt(error / (weight + 1e-38))\n", + "\n", + "\n", + " def evaluate(self, approxes, target, weight):\n", + " assert len(approxes) == 1\n", + " assert len(target) == len(approxes[0])\n", + "\n", + " approx = approxes[0]\n", + "\n", + " error_sum = 0.0\n", + " weight_sum = 0.0\n", + "\n", + " for i in range(len(approx)):\n", + " w = 1.0 if weight is None else weight[i]\n", + " weight_sum += w\n", + " error_sum += w * ((approx[i] - target[i])**2)\n", + "\n", + " return error_sum, weight_sum\n", + "\n", + " def calc_ders_range(self, approxes, targets, weights):\n", + " assert len(approxes) == len(targets)\n", + " if weights is not None:\n", + " assert len(weights) == len(approxes)\n", + "\n", + " result = []\n", + " for index in range(len(targets)):\n", + " der1 = targets[index] - approxes[index]\n", + " der2 = -1\n", + "\n", + " if weights is not None:\n", + " der1 *= weights[index]\n", + " der2 *= weights[index]\n", + "\n", + " result.append((der1, der2))\n", + " return result\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ff2d1c5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime # 用于日期计算\n", + "from catboost import CatBoostClassifier, CatBoostRanker, CatBoostRegressor\n", + "from catboost import Pool\n", + "import lightgbm as lgb\n", + "from lightgbm import LGBMRanker, LGBMRegressor\n", + "\n", + "def train_model(train_data_df, feature_columns,\n", + " print_info=True, # 调整参数名，更通用\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " target_column='label', type='light'): # 增加目标列参数\n", + "\n", + " print('train data size: ', len(train_data_df))\n", + " print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " # 识别数值型特征列\n", + " numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n", + "\n", + " # 去除标签为空的样本\n", + " initial_len = len(train_data_df)\n", + " train_data_df = train_data_df.dropna(subset=[target_column])\n", + "\n", + " if print_info:\n", + " print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n", + "\n", + " # 提取特征和标签，只取数值型特征用于线性回归\n", + " \n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + "\n", + " train_data_split = train_data_split.sort_values('trade_date')\n", + " val_data_split = val_data_split.sort_values('trade_date')\n", + "\n", + " \n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[target_column]\n", + " \n", + " X_val = val_data_split[feature_columns]\n", + " y_val = val_data_split[target_column]\n", + "\n", + "\n", + " # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n", + " scaler = StandardScaler()\n", + " # X_train = scaler.fit_transform(X_train)\n", + "\n", + " # 训练线性回归模型\n", + " # model = LogisticRegression(random_state=42)\n", + " \n", + " # # 使用处理后的特征和样本权重进行训练\n", + " # model.fit(X_train, y_train)\n", + "\n", + "\n", + " if type == 'cat':\n", + " params = {\n", + " 'loss_function': 'QueryRMSE', # 适用于二分类\n", + " 'eval_metric': 'NDCG', # 评估指标\n", + " 'iterations': 1500,\n", + " 'learning_rate': 0.01,\n", + " 'depth': 10, # 控制模型复杂度\n", + " # 'l2_leaf_reg': 0.1, # L2 正则化\n", + " 'verbose': 5000,\n", + " 'early_stopping_rounds': 300,\n", + " 'one_hot_max_size': 50,\n", + " # 'class_weights': [0.6, 1.2],\n", + " 'task_type': 'GPU',\n", + " 'has_time': True,\n", + " 'random_seed': 7\n", + " }\n", + " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " group_train = train_data_split['trade_date'].factorize()[0]\n", + " group_val = val_data_split['trade_date'].factorize()[0]\n", + " train_pool = Pool(\n", + " data=X_train,\n", + " label=y_train,\n", + " group_id=group_train,\n", + " cat_features=cat_features\n", + " )\n", + " val_pool = Pool(\n", + " data=X_val,\n", + " label=y_val,\n", + " group_id=group_val,\n", + " cat_features=cat_features\n", + " )\n", + "\n", + "\n", + " model = CatBoostRanker(**params)\n", + " model.fit(train_pool,\n", + " eval_set=val_pool, \n", + " plot=True, \n", + " use_best_model=True\n", + " )\n", + " elif type == 'light':\n", + " label_gain = list(range(len(train_data_split['label'].unique())))\n", + " params = {\n", + " 'label_gain': [gain * gain for gain in label_gain],\n", + " 'objective': 'lambdarank',\n", + " 'metric': 'rank_xendcg',\n", + " 'learning_rate': 0.01,\n", + " 'num_leaves': 1024,\n", + " # 'min_data_in_leaf': 128,\n", + " 'max_depth': 10,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 1,\n", + " 'lambda_l2': 1,\n", + " 'boosting': 'gbdt',\n", + " 'verbosity': -1,\n", + " 'extra_trees': True,\n", + " # 'max_position': 5,\n", + " # 'ndcg_at': 1,\n", + " 'quant_train_renew_leaf': True,\n", + " # 'lambdarank_truncation_level': 1,\n", + " # 'lambdarank_position_bias_regularization': 1,\n", + " 'seed': 7\n", + " }\n", + " train_groups = train_data_split.groupby('trade_date').size().tolist()\n", + " val_groups = val_data_split.groupby('trade_date').size().tolist()\n", + "\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + " train_dataset = lgb.Dataset(\n", + " X_train, label=y_train, \n", + " group=train_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + " val_dataset = lgb.Dataset(\n", + " X_val, label=y_val, \n", + " group=val_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " evals = {}\n", + " callbacks = [lgb.log_evaluation(period=1000),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(100, first_metric_only=True)\n", + " ]\n", + " # # 训练模型\n", + " # model = lgb.train(\n", + " # params, train_dataset, num_boost_round=1000,\n", + " # valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n", + " # callbacks=callbacks\n", + " # )\n", + "\n", + " # # 打印特征重要性（如果需要）\n", + " # if True:\n", + " # lgb.plot_metric(evals)\n", + " # lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " # plt.show()\n", + "\n", + " from flaml import AutoML\n", + " from sklearn.datasets import fetch_california_housing\n", + "\n", + " # Initialize an AutoML instance\n", + " model = AutoML()\n", + " # Specify automl goal and constraint\n", + " automl_settings = {\n", + " \"time_budget\": 600, # in seconds\n", + " \"metric\": \"ndcg@1\",\n", + " \"task\": \"rank\",\n", + " \"estimator_list\": [\n", + " \"catboost\",\n", + " \"lgbm\",\n", + " \"xgboost\"\n", + " ], \n", + " \"ensemble\": {\n", + " \"final_estimator\": LGBMRanker(),\n", + " \"passthrough\": False,\n", + " },\n", + " }\n", + " model.fit(X_train=X_train, y_train=y_train, groups=train_groups,\n", + " X_val=X_val, y_val=y_val,groups_val=val_groups,\n", + " mlflow_logging=False, **automl_settings)\n", + "\n", + "\n", + " return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:03:18.426481Z", + "start_time": "2025-04-03T15:02:19.926352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 728000\n", + " ts_code trade_date log_circ_mv\n", + "0 600306.SH 2020-01-02 11.552040\n", + "1 603269.SH 2020-01-02 11.324801\n", + "2 002633.SZ 2020-01-02 11.759023\n", + "3 603991.SH 2020-01-02 11.181150\n", + "4 000691.SZ 2020-01-02 11.677910\n", + "... ... ... ...\n", + "727995 002235.SZ 2022-12-30 12.914708\n", + "727996 605598.SH 2022-12-30 11.783580\n", + "727997 002613.SZ 2022-12-30 12.489464\n", + "727998 600800.SH 2022-12-30 12.571911\n", + "727999 603068.SH 2022-12-30 12.967134\n", + "\n", + "[728000 rows x 3 columns]\n", + "原始样本数: 728000, 去除标签为空后样本数: 728000\n", + "[flaml.automl.logger: 05-26 20:16:56] {1728} INFO - task = rank\n", + "[flaml.automl.logger: 05-26 20:16:56] {1736} INFO - Data split method: group\n", + "[flaml.automl.logger: 05-26 20:16:56] {1739} INFO - Evaluation method: holdout\n", + "[flaml.automl.logger: 05-26 20:16:56] {1838} INFO - Minimizing error metric: 1-ndcg@1\n", + "[flaml.automl.logger: 05-26 20:16:56] {1955} INFO - List of ML learners in AutoML Run: ['catboost', 'lgbm', 'xgboost']\n", + "[flaml.automl.logger: 05-26 20:16:56] {2258} INFO - iteration 0, current learner catboost\n", + "[flaml.automl.logger: 05-26 20:17:16] {2393} INFO - Estimated sufficient time budget=203147s. 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feature_contri\n", + "# print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_model(train_data\n", + " .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n", + " .apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", + " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + " .merge(index_data, on='trade_date', how='left'), \n", + " feature_columns, type=type, target_column='label')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:04:39.656944Z", + "start_time": "2025-04-03T15:04:39.298483Z" + } + }, + "outputs": [], + "source": [ + "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", + "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", + "# score_df = score_df[score_df['pe_ttm'] > 0]\n", + "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "score_df = score_df.merge(index_data, on='trade_date', how='left')\n", + "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n", + "\n", + "if type == 'cat':\n", + " score_df['score'] = model.predict(score_df[feature_columns])\n", + "elif type == 'light':\n", + " score_df['score'] = model.predict(score_df[feature_columns])\n", + "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n", + "\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: \n", + " x[\n", + " # (x['score'] <= x['score'].quantile(0.99)) & \n", + " (x['score'] >= x['score'].quantile(0.90))\n", + " ] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n", + ").reset_index(drop=True) # drop=True 避免添加旧索引列\n", + "# df_to_drop = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", + "# score_df = score_df.drop(df_to_drop.index)\n", + "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n", + "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n", + "save_df = save_df.sort_values(['trade_date', 'score'])\n", + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09b1799e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "192\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n" + ] + } + ], + "source": [ + "print(len(feature_columns))\n", + "print(feature_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bceabd1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n", + "\n", + "NDCG 结果\n", + "{'ndcg@1': 0.16666666666666666, 'ndcg@3': 0.12073315639204953, 'ndcg@5': 0.16262598210797247}\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def calculate_ndcg(df: pd.DataFrame, score_col: str, label_col: str, group_id: str = 'trade_date', k_values: list = [1, 3, 5, 10]):\n", + " \"\"\"\n", + " 计算 DataFrame 中 score 列和 label 列的 NDCG 值。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 包含 score (排序学习预测分数) 和 label (相关性标签) 的 DataFrame。\n", + " 假设每个需要排序的组（例如，每天的股票）在 DataFrame 中是连续的。\n", + " score_col (str): 包含模型预测分数的列名。\n", + " label_col (str): 包含相关性标签的列名。标签值越高表示相关性越高。\n", + " k_values (list): 一个整数列表，表示计算 NDCG 的 top-k 值。\n", + " 例如，[1, 3, 5] 将计算 NDCG@1, NDCG@3 和 NDCG@5。\n", + "\n", + " Returns:\n", + " dict: 一个字典，包含每个 k 值对应的平均 NDCG 值。\n", + " 例如: {'ndcg@1': 0.85, 'ndcg@3': 0.78, 'ndcg@5': 0.72, 'ndcg@10': 0.65}\n", + " \"\"\"\n", + " ndcg_scores = {f'ndcg@{k}': [] for k in k_values}\n", + "\n", + " def dcg_at_k(r, k):\n", + " r = np.asfarray(r)[:k] if len(r) > 0 else np.zeros(k)\n", + " return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n", + "\n", + " def ndcg_at_k(r, k):\n", + " dcg_max = dcg_at_k(sorted(r, reverse=True), k)\n", + " if not dcg_max:\n", + " return 0.\n", + " return dcg_at_k(r, k) / dcg_max\n", + "\n", + " # 假设 DataFrame 已经按照需要排序的组（例如，'trade_date'）进行了分组，\n", + " # 并且每个组内的顺序不重要，我们只需要计算每个组的 NDCG。\n", + " # 如果需要按特定组计算 NDCG，请先对 DataFrame 进行分组。\n", + " if group_id not in df.columns:\n", + " print(\"警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\")\n", + " group_df = df.sort_values(by=score_col, ascending=False)\n", + " relevant_labels = group_df[label_col].values\n", + " for k in k_values:\n", + " ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n", + " else:\n", + " for _, group_df in df.groupby(group_id):\n", + " group_df_sorted = group_df.sort_values(by=score_col, ascending=False)\n", + " relevant_labels = group_df_sorted[label_col].values\n", + " for k in k_values:\n", + " ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n", + "\n", + " avg_ndcg = {k: np.mean(v) if v else np.nan for k, v in ndcg_scores.items()}\n", + " return avg_ndcg\n", + "\n", + "\n", + "ndcg_results_single_group = calculate_ndcg(score_df, score_col='score', label_col='label', k_values=[1, 3, 5], group_id=None)\n", + "print(\"\\nNDCG 结果\")\n", + "print(ndcg_results_single_group)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/Regression2.ipynb b/main/train/Regression2.ipynb new file mode 100644 index 0000000..b2304ef --- /dev/null +++ b/main/train/Regression2.ipynb @@ -0,0 +1,2151 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:46:06.987506Z", + "start_time": "2025-04-03T12:46:06.259551Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e:\\PyProject\\NewStock\\main\\train\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import gc\n", + "import os\n", + "import sys\n", + "sys.path.append('../../')\n", + "print(os.getcwd())\n", + "import pandas as pd\n", + "from main.factor.factor import get_rolling_factor, get_simple_factor\n", + "from main.utils.factor import read_industry_data\n", + "from main.utils.factor_processor import calculate_score\n", + "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:00.212859Z", + "start_time": "2025-04-03T12:46:06.998047Z" + }, + "scrolled": true + }, + "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", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8665405 entries, 0 to 8665404\n", + "Data columns (total 32 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 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 total_mv float64 \n", + " 12 volume_ratio float64 \n", + " 13 is_st bool \n", + " 14 up_limit float64 \n", + " 15 down_limit float64 \n", + " 16 buy_sm_vol float64 \n", + " 17 sell_sm_vol float64 \n", + " 18 buy_lg_vol float64 \n", + " 19 sell_lg_vol float64 \n", + " 20 buy_elg_vol float64 \n", + " 21 sell_elg_vol float64 \n", + " 22 net_mf_vol float64 \n", + " 23 his_low float64 \n", + " 24 his_high float64 \n", + " 25 cost_5pct float64 \n", + " 26 cost_15pct float64 \n", + " 27 cost_50pct float64 \n", + " 28 cost_85pct float64 \n", + " 29 cost_95pct float64 \n", + " 30 weight_avg float64 \n", + " 31 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n", + "memory usage: 2.0+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from main.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', 'pct_chg'],\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', 'total_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('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cac01788dac10678", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.527104Z", + "start_time": "2025-04-03T12:47:00.488715Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n" + ] + } + ], + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.719252Z", + "start_time": "2025-04-03T12:47:10.541247Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from main.factor.factor import *\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", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " # df = sentiment_panic_greed_index(df)\n", + " # df = sentiment_market_breadth_proxy(df)\n", + " # df = sentiment_reversal_indicator(df)\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', \n", + " 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n", + " # 'sentiment_panic_greed_index',\n", + " 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.821169Z", + "start_time": "2025-04-03T12:47:10.751831Z" + } + }, + "outputs": [], + "source": [ + "import talib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.944254Z", + "start_time": "2025-04-03T12:47:10.826179Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "from main.utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + "\n", + " # cs_rank_intraday_range(industry_data)\n", + " # cs_rank_close_pos_in_range(industry_data)\n", + "\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.969344Z", + "start_time": "2025-04-03T12:47:15.963327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85c3e3d0235ffffa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:16.089879Z", + "start_time": "2025-04-03T12:47:15.990101Z" + } + }, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)\n", + "top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n", + " columns=['ts_code', 'trade_date', 'reason'],\n", + " df=None)\n", + "\n", + "top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:01.612695Z", + "start_time": "2025-04-03T12:47:16.121802Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n", + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", + "Calculating cs_rank_net_lg_flow_val...\n", + "Finished cs_rank_net_lg_flow_val.\n", + "Calculating cs_rank_flow_divergence...\n", + "Finished cs_rank_flow_divergence.\n", + "Calculating cs_rank_ind_adj_lg_flow...\n", + "Finished cs_rank_ind_adj_lg_flow.\n", + "Calculating cs_rank_elg_buy_ratio...\n", + "Finished cs_rank_elg_buy_ratio.\n", + "Calculating cs_rank_rel_profit_margin...\n", + "Finished cs_rank_rel_profit_margin.\n", + "Calculating cs_rank_cost_breadth...\n", + "Finished cs_rank_cost_breadth.\n", + "Calculating cs_rank_dist_to_upper_cost...\n", + "Finished cs_rank_dist_to_upper_cost.\n", + "Calculating cs_rank_winner_rate...\n", + "Finished cs_rank_winner_rate.\n", + "Calculating cs_rank_intraday_range...\n", + "Finished cs_rank_intraday_range.\n", + "Calculating cs_rank_close_pos_in_range...\n", + "Finished cs_rank_close_pos_in_range.\n", + "Calculating cs_rank_opening_gap...\n", + "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n", + "Calculating cs_rank_pos_in_hist_range...\n", + "Finished cs_rank_pos_in_hist_range.\n", + "Calculating cs_rank_vol_x_profit_margin...\n", + "Finished cs_rank_vol_x_profit_margin.\n", + "Calculating cs_rank_lg_flow_price_concordance...\n", + "Finished cs_rank_lg_flow_price_concordance.\n", + "Calculating cs_rank_turnover_per_winner...\n", + "Finished cs_rank_turnover_per_winner.\n", + "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n", + "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n", + "Calculating cs_rank_volume_ratio...\n", + "Finished cs_rank_volume_ratio.\n", + "Calculating cs_rank_elg_buy_sell_sm_ratio...\n", + "Finished cs_rank_elg_buy_sell_sm_ratio.\n", + "Calculating cs_rank_cost_dist_vol_ratio...\n", + "Finished cs_rank_cost_dist_vol_ratio.\n", + "Calculating cs_rank_size...\n", + "Finished cs_rank_size.\n", + "\n", + "RangeIndex: 4539678 entries, 0 to 4539677\n", + "Columns: 178 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n", + "memory usage: 5.7+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2019-01-01']\n", + " if 'in_date' in df.columns:\n", + " df = df.drop(columns=['in_date'])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "gc.collect()\n", + "\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + "# df = price_minus_deduction_price(df, n=120)\n", + "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n", + "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n", + "# df = cat_reason(df, top_list_df)\n", + "# df = cat_is_on_top_list(df, top_list_df)\n", + "\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df = daily_deviation(df)\n", + "df = daily_industry_deviation(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", + "\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b87b938028afa206", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.658725Z", + "start_time": "2025-04-03T13:08:02.469611Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.670700Z", + "start_time": "2025-04-03T13:08:03.665739Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm # 用于中性化回归\n", + "from tqdm import tqdm # 可选，用于显示进度条\n", + "\n", + "# --- 常量 ---\n", + "epsilon = 1e-10 # 防止除零\n", + "\n", + "# --- 1. 中位数去极值 (MAD) ---\n", + "\n", + "def cs_mad_filter(df: pd.DataFrame,\n", + " features: list,\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826):\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n", + "\n", + " 方法: 对每日截面数据，计算 median 和 MAD，\n", + " 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n", + " 替换为边界值 (Winsorization)。\n", + " scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n", + " scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"MAD Filtering\"):\n", + " try:\n", + " # 计算截面中位数\n", + " median = grouped[col].transform('median')\n", + " # 计算截面 MAD (Median Absolute Deviation from Median)\n", + " mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n", + "\n", + " # 计算上下边界\n", + " lower_bound = median - k * scale_factor * mad\n", + " upper_bound = median + k * scale_factor * mad\n", + "\n", + " # 原地应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\n", + "\n", + "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", + " features: list,\n", + " industry_col: str = 'cat_l2_code',\n", + " market_cap_col: str = 'circ_mv'):\n", + " \"\"\"\n", + " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", + " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", + " 将回归残差写回原特征列。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", + " industry_col, market_cap_col。\n", + " features (list): 需要处理的特征列名列表。\n", + " industry_col (str): 行业分类列名。\n", + " market_cap_col (str): 流通市值列名。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", + " 计算量较大，可能耗时较长。\n", + " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " \"\"\"\n", + " print(\"开始截面行业市值中性化...\")\n", + " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", + " return\n", + "\n", + " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " log_cap_col = '_log_market_cap'\n", + " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", + " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", + " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", + " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + "\n", + " dates = df['trade_date'].unique()\n", + " all_residuals = [] # 用于收集所有日期的残差\n", + "\n", + " for date in tqdm(dates, desc=\"Neutralizing\"):\n", + " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + "\n", + " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", + " X = daily_data[[log_cap_col]]\n", + " X = sm.add_constant(X, prepend=True) # 添加常数项\n", + " # 创建行业哑变量 (drop_first=True 避免共线性)\n", + " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", + " industry_dummies = industry_dummies.astype(int)\n", + " X = pd.concat([X, industry_dummies], axis=1)\n", + "\n", + " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + "\n", + " for col in features:\n", + " Y = daily_data[col]\n", + "\n", + " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", + " valid_mask = Y.notna() & X.notna().all(axis=1)\n", + " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", + " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " continue\n", + "\n", + " Y_valid = Y[valid_mask]\n", + " X_valid = X[valid_mask]\n", + "\n", + " # 执行 OLS 回归\n", + " try:\n", + " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", + " results = model.fit()\n", + " # 将残差填回对应位置\n", + " daily_residuals.loc[valid_mask, col] = results.resid\n", + " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " except Exception as e:\n", + " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " break\n", + "\n", + " all_residuals.append(daily_residuals)\n", + "\n", + " # 合并所有日期的残差结果\n", + " if all_residuals:\n", + " residuals_df = pd.concat(all_residuals)\n", + " # 将残差结果更新回原始 df (原地修改)\n", + " # 使用 update 比 merge 更适合基于索引的原地更新\n", + " # 确保 residuals_df 的索引与 df 中对应部分一致\n", + " df.update(residuals_df)\n", + " else:\n", + " print(\"没有有效的残差结果可以合并。\")\n", + "\n", + "\n", + " # 清理临时列\n", + " df.drop(columns=[log_cap_col], inplace=True)\n", + " print(\"截面行业市值中性化完成。\")\n", + "\n", + "\n", + "# --- 3. Z-Score 标准化 ---\n", + "\n", + "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " epsilon (float): 防止除以零的小常数。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"Standardizing\"):\n", + " try:\n", + " # 使用 transform 计算截面均值和标准差\n", + " mean = grouped[col].transform('mean')\n", + " std = grouped[col].transform('std')\n", + "\n", + " # 计算 Z-Score 并原地赋值\n", + " df[col] = (df[col] - mean) / (std + epsilon)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + "\n", + "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n", + " feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n", + " \"\"\"\n", + " processed_df = df.copy() # 在副本上操作，保留原始数据\n", + "\n", + " # 确保 trade_date 是 datetime 类型以便正确分组\n", + " processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n", + "\n", + " def _fill_daily_nan(group):\n", + " # group 是某一个交易日的 DataFrame\n", + "\n", + " # 遍历指定的特征列\n", + " for feature_col in feature_columns:\n", + " # 检查列是否存在于当前分组中\n", + " if feature_col in group.columns:\n", + " # 计算当日该特征的中位数\n", + " median_val = group[feature_col].median()\n", + "\n", + " # 使用当日中位数填充该特征列的 NaN 值\n", + " # inplace=True 会直接修改 group DataFrame\n", + " group[feature_col].fillna(median_val, inplace=True)\n", + " # else:\n", + " # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + "\n", + " return group\n", + "\n", + " # 按交易日期分组，并应用每日填充函数\n", + " # group_keys=False 避免将分组键添加到结果索引中\n", + " filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n", + "\n", + " return filled_df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:04.694262Z", + "start_time": "2025-04-03T13:08:03.694904Z" + } + }, + "outputs": [], + "source": [ + "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_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " # 使用已有的 pct_chg 字段计算波动率\n", + " volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + " return score\n", + "\n", + " # # 确保 DataFrame 按照股票代码和交易日期排序\n", + " # df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 对每个股票分别计算 score\n", + " df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n", + "\n", + " return df['score']\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n", + " 对特征在行业内部进行市值中性化。\n", + "\n", + " Args:\n", + " df: 输入的 DataFrame，包含特征、行业分类和市值列。\n", + " features: 需要进行中性化的特征列名列表。\n", + " industry_col: 行业分类列的列名。\n", + " mkt_cap_col: 市值列的列名。\n", + "\n", + " Returns:\n", + " 中性化后的 DataFrame。\n", + " \"\"\"\n", + "\n", + " df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n", + " df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n", + " df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n", + "\n", + " if df_cleaned.empty:\n", + " print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n", + " return df # 返回原始或空df，取决于清理前的状态\n", + "\n", + " processed_df = df\n", + "\n", + " for col in features:\n", + " if col not in df_cleaned.columns:\n", + " print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n", + " # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " # 跳过对控制变量本身进行中性化\n", + " if col == mkt_cap_col or col == industry_col:\n", + " print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n", + " # 在结果 df 中也保持原样\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n", + "\n", + " # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n", + " df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n", + "\n", + " if not df_subset_factor.empty:\n", + " for industry, group in df_subset_factor.groupby(industry_col):\n", + " x = group[mkt_cap_col] # 市值对数\n", + " y = group[col] # 因子值\n", + "\n", + " # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n", + " # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n", + " if len(group) > 1 and np.var(x) > 1e-9:\n", + " try:\n", + " beta = np.cov(y, x)[0, 1] / np.var(x)\n", + " alpha = np.mean(y) - beta * np.mean(x)\n", + "\n", + " # 计算残差\n", + " resid = y - (alpha + beta * x)\n", + "\n", + " # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n", + " residual_series.loc[resid.index] = resid\n", + "\n", + " except Exception as e:\n", + " # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n", + " print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n", + " # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n", + " # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n", + "\n", + " else:\n", + " residual_series.loc[group.index] = group[col] # 保留原始因子值\n", + " processed_df.loc[residual_series.index, col] = residual_series\n", + "\n", + "\n", + " else:\n", + " processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n", + "\n", + " return processed_df\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "\n", + "def mad_filter(df, features, n=3):\n", + " for col in features:\n", + " median = df[col].median()\n", + " mad = np.median(np.abs(df[col] - median))\n", + " upper = median + n * mad\n", + " lower = median - n * mad\n", + " df[col] = np.clip(df[col], lower, upper) # 截断极值\n", + " return df\n", + "\n", + "\n", + "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n", + " for col in features:\n", + " # 按日期分组计算上下百分位数\n", + " lower_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(lower_percentile)\n", + " )\n", + " upper_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(upper_percentile)\n", + " )\n", + " # 截断超出范围的值\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df\n", + "\n", + "\n", + "from scipy.stats import iqr\n", + "\n", + "\n", + "def iqr_filter(df, features):\n", + " for col in features:\n", + " df[col] = df.groupby('trade_date')[col].transform(\n", + " lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n", + " )\n", + " return df\n", + "\n", + "\n", + "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " for col in features:\n", + " # 计算 rolling 统计量，需要按日期进行 groupby\n", + " rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n", + " rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n", + "\n", + " # 对数据进行裁剪\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " \n", + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features\n", + "\n", + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:50.841165Z", + "start_time": "2025-04-03T14:49:25.889057Z" + } + }, + "outputs": [], + "source": [ + "days = 5\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "df['future_return'] = np.log1p(df['future_return'])\n", + "\n", + "df['future_return_2'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-2) / x - 1)\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "df['future_return_2'] = np.log1p(df['future_return_2'])\n", + "\n", + "df['cat_up_limit'] = df['pct_chg'] > 5\n", + "# df['label'] = (df.groupby('ts_code')['cat_up_limit']\n", + "# .rolling(window=5, min_periods=1).sum()\n", + "# .groupby('ts_code') # 再次按 ts_code 分组\n", + "# .shift(-5)\n", + "# .fillna(0) # 填充每个股票组最后的 NaN\n", + "# .astype(int)\n", + "# .reset_index(level=0, drop=True))\n", + "df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n", + " lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", + ")\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "# for col in [col for col in df.columns]:\n", + "# train_data[col] = train_data[col].astype('str')\n", + "# test_data[col] = test_data[col].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f4dc587", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date open close high low vol pct_chg \\\n", + "976171 002193.SZ 2019-01-02 18.02 17.82 18.21 17.73 22513.62 -1.00 \n", + "976172 002193.SZ 2019-01-03 17.82 17.78 17.94 17.65 19523.21 -0.22 \n", + "976173 002193.SZ 2019-01-04 17.47 18.04 18.10 17.34 28094.69 1.46 \n", + "976174 002193.SZ 2019-01-07 18.17 18.31 18.39 18.00 23866.88 1.50 \n", + "976175 002193.SZ 2019-01-08 18.25 18.52 18.66 18.13 22853.46 1.15 \n", + "... ... ... ... ... ... ... ... ... \n", + "977704 002193.SZ 2025-05-19 11.74 11.95 11.97 11.61 94249.32 2.49 \n", + "977705 002193.SZ 2025-05-20 11.99 12.20 12.22 11.84 103040.57 2.09 \n", + "977706 002193.SZ 2025-05-21 12.20 11.97 12.28 11.82 83112.00 -1.89 \n", + "977707 002193.SZ 2025-05-22 11.95 11.80 12.17 11.72 88811.00 -1.42 \n", + "977708 002193.SZ 2025-05-23 11.74 11.51 11.90 11.51 93013.00 -2.46 \n", + "\n", + " turnover_rate pe_ttm ... cs_rank_lg_flow_price_concordance \\\n", + "976171 0.9771 23.0129 ... 0.621118 \n", + "976172 0.8473 22.9598 ... 0.230095 \n", + "976173 1.2193 23.3049 ... 0.507119 \n", + "976174 1.0358 23.6499 ... 0.315078 \n", + "976175 0.9918 23.9154 ... 0.248631 \n", + "... ... ... ... ... \n", + "977704 3.6012 NaN ... 0.224402 \n", + "977705 3.9371 NaN ... 0.124834 \n", + "977706 3.1757 NaN ... 0.803720 \n", + "977707 3.3934 NaN ... 0.667220 \n", + "977708 3.5540 NaN ... 0.308867 \n", + "\n", + " cs_rank_turnover_per_winner cs_rank_ind_cap_neutral_pe \\\n", + "976171 0.990869 NaN \n", + "976172 0.934283 NaN \n", + "976173 0.925182 NaN \n", + "976174 0.843796 NaN \n", + "976175 0.834672 NaN \n", + "... ... ... \n", + "977704 0.624335 NaN \n", + "977705 0.641102 NaN \n", + "977706 0.624709 NaN \n", + "977707 0.563268 NaN \n", + "977708 0.661242 NaN \n", + "\n", + " cs_rank_volume_ratio cs_rank_elg_buy_sell_sm_ratio \\\n", + "976171 0.710344 0.341855 \n", + "976172 0.444444 0.318912 \n", + "976173 0.489226 0.260036 \n", + "976174 0.250000 0.251095 \n", + "976175 0.510588 0.286679 \n", + "... ... ... \n", + "977704 0.234043 0.397274 \n", + "977705 0.344124 0.116534 \n", + "977706 0.221189 0.126370 \n", + "977707 0.412155 0.130521 \n", + "977708 0.534540 0.134175 \n", + "\n", + " cs_rank_cost_dist_vol_ratio cs_rank_size future_return \\\n", + "976171 0.261603 0.262235 0.033658 \n", + "976172 0.185342 0.264695 0.029373 \n", + "976173 0.211959 0.259489 0.025179 \n", + "976174 0.145266 0.255474 0.014638 \n", + "976175 0.202299 0.259854 0.003235 \n", + "... ... ... ... \n", + "977704 0.299535 0.031250 NaN \n", + "977705 0.339641 0.032205 NaN \n", + "977706 0.291597 0.030555 NaN \n", + "977707 0.372634 0.030555 NaN \n", + "977708 0.430090 0.027898 NaN \n", + "\n", + " cat_up_limit label \n", + "976171 False 8.0 \n", + "976172 False 7.0 \n", + "976173 False 10.0 \n", + "976174 False 13.0 \n", + "976175 False 6.0 \n", + "... ... ... \n", + "977704 False NaN \n", + "977705 False NaN \n", + "977706 False NaN \n", + "977707 False NaN \n", + "977708 False NaN \n", + "\n", + "[1538 rows x 181 columns]\n" + ] + } + ], + "source": [ + "print(df[df['ts_code'] == '002193.SZ'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "29221dde", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "162\n" + ] + } + ], + "source": [ + "feature_columns = [col for col in df.head(10)\n", + " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + " # .merge(index_data, on='trade_date', how='left')\n", + " .columns\n", + " ]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n", + "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n", + "feature_columns = [col for col in feature_columns if 'code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", + "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n", + " 'cap_neutral_cost_metric', \n", + " 'hurst_net_mf_vol_60', \n", + " 'complex_factor_deap_1', \n", + " 'lg_buy_consolidation_20',\n", + " 'cs_rank_ind_cap_neutral_pe',\n", + " 'cs_rank_opening_gap',\n", + " 'cs_rank_ind_adj_lg_flow']]\n", + "feature_columns = [col for col in feature_columns if col not in ['cat_reason', 'cat_is_on_top_list']]\n", + "print(len(feature_columns))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "03ee5daf", + "metadata": {}, + "outputs": [], + "source": [ + "# df = fill_nan_with_daily_median(df, feature_columns)\n", + "for feature_col in [col for col in feature_columns if col in df.columns]:\n", + " # median_val = df[feature_col].median()\n", + " df[feature_col].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile']\n", + "去除极值\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 132/132 [00:28<00:00, 4.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 132/132 [00:24<00:00, 5.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile']\n", + "df最小日期: 2019-01-02\n", + "df最大日期: 2025-05-23\n", + "2057539\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2022-12-30\n", + "1766694\n", + "test_data最小日期: 2023-01-03\n", + "test_data最大日期: 2025-05-23\n", + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "1 000001.SZ 2019-01-03 16.583965\n", + "2 000001.SZ 2019-01-04 16.633371\n" + ] + } + ], + "source": [ + "split_date = '2023-01-01'\n", + "train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n", + "test_data = df[(df['trade_date'] >= split_date)]\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n", + "# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n", + "\n", + "# feature_columns = [\n", + "# 'undist_profit_ps', \n", + "# 'AR_BR',\n", + "# 'pe_ttm',\n", + "# 'alpha_22_improved', \n", + "# 'alpha_003', \n", + "# 'alpha_007', \n", + "# 'alpha_013', \n", + "# 'cat_up_limit', \n", + "# 'cat_down_limit', \n", + "# 'up_limit_count_10d', \n", + "# 'down_limit_count_10d', \n", + "# 'consecutive_up_limit', \n", + "# 'vol_break', \n", + "# 'weight_roc5', \n", + "# 'price_cost_divergence', \n", + "# 'smallcap_concentration', \n", + "# 'cost_stability', \n", + "# 'high_cost_break_days', \n", + "# 'liquidity_risk', \n", + "# 'turnover_std', \n", + "# 'mv_volatility', \n", + "# 'volume_growth', \n", + "# 'mv_growth', \n", + "# 'lg_flow_mom_corr_20_60', \n", + "# 'lg_flow_accel', \n", + "# 'profit_pressure', \n", + "# 'underwater_resistance', \n", + "# 'cost_conc_std_20', \n", + "# 'profit_decay_20', \n", + "# 'vol_amp_loss_20', \n", + "# 'vol_drop_profit_cnt_5', \n", + "# 'lg_flow_vol_interact_20', \n", + "# 'cost_break_confirm_cnt_5', \n", + "# 'atr_norm_channel_pos_14', \n", + "# 'turnover_diff_skew_20', \n", + "# 'lg_sm_flow_diverge_20', \n", + "# 'pullback_strong_20_20', \n", + "# 'vol_wgt_hist_pos_20', \n", + "# 'vol_adj_roc_20',\n", + "# 'cashflow_to_ev_factor',\n", + "# 'ocfps',\n", + "# 'book_to_price_ratio',\n", + "# 'turnover_rate_mean_5',\n", + "# 'variance_20',\n", + "# 'bbi_ratio_factor'\n", + "# ]\n", + "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n", + "print(feature_columns)\n", + "\n", + "# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", + "# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", + "\n", + "train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "transform_feature_columns = feature_columns\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "# transform_feature_columns.remove('undist_profit_ps')\n", + "print('去除极值')\n", + "cs_mad_filter(train_data, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "\n", + "cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", + "cs_mad_filter(train_data, mad_filter_feature_columns)\n", + "cs_mad_filter(test_data, mad_filter_feature_columns)\n", + "\n", + "\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "\n", + "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(train_data))\n", + "print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "2e4b027e", + "metadata": {}, + "outputs": [], + "source": [ + "class RmseObjective(object):\n", + "\n", + " def is_max_optimal(self):\n", + " return False\n", + "\n", + " def get_final_error(self, error, weight):\n", + " return np.sqrt(error / (weight + 1e-38))\n", + "\n", + "\n", + " def evaluate(self, approxes, target, weight):\n", + " assert len(approxes) == 1\n", + " assert len(target) == len(approxes[0])\n", + "\n", + " approx = approxes[0]\n", + "\n", + " error_sum = 0.0\n", + " weight_sum = 0.0\n", + "\n", + " for i in range(len(approx)):\n", + " w = 1.0 if weight is None else weight[i]\n", + " weight_sum += w\n", + " error_sum += w * ((approx[i] - target[i])**2)\n", + "\n", + " return error_sum, weight_sum\n", + "\n", + " def calc_ders_range(self, approxes, targets, weights):\n", + " assert len(approxes) == len(targets)\n", + " if weights is not None:\n", + " assert len(weights) == len(approxes)\n", + "\n", + " result = []\n", + " for index in range(len(targets)):\n", + " der1 = targets[index] - approxes[index]\n", + " der2 = -1\n", + "\n", + " if weights is not None:\n", + " der1 *= weights[index]\n", + " der2 *= weights[index]\n", + "\n", + " result.append((der1, der2))\n", + " return result\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3ff2d1c5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime # 用于日期计算\n", + "from catboost import CatBoostClassifier, CatBoostRanker, CatBoostRegressor\n", + "from catboost import Pool\n", + "import lightgbm as lgb\n", + "from lightgbm import LGBMClassifier, LGBMRegressor\n", + "\n", + "def train_model(train_data_df, feature_columns,\n", + " print_info=True, # 调整参数名，更通用\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " target_column='label', type='light'): # 增加目标列参数\n", + "\n", + " print('train data size: ', len(train_data_df))\n", + " print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " # 识别数值型特征列\n", + " numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n", + "\n", + " # 去除标签为空的样本\n", + " initial_len = len(train_data_df)\n", + " train_data_df = train_data_df.dropna(subset=[target_column])\n", + "\n", + " if print_info:\n", + " print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n", + "\n", + " # 提取特征和标签，只取数值型特征用于线性回归\n", + " \n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + " \n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[target_column]\n", + " \n", + " X_val = val_data_split[feature_columns]\n", + " y_val = val_data_split[target_column]\n", + "\n", + "\n", + " # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n", + " scaler = StandardScaler()\n", + " # X_train = scaler.fit_transform(X_train)\n", + "\n", + " # 训练线性回归模型\n", + " # model = LogisticRegression(random_state=42)\n", + " \n", + " # # 使用处理后的特征和样本权重进行训练\n", + " # model.fit(X_train, y_train)\n", + "\n", + "\n", + " if type == 'cat':\n", + " params = {\n", + " 'loss_function': 'RMSE', # 适用于二分类\n", + " 'eval_metric': 'RMSE', # 评估指标\n", + " 'iterations': 1500,\n", + " 'learning_rate': 0.01,\n", + " 'depth': 8, # 控制模型复杂度\n", + " 'l2_leaf_reg': 1, # L2 正则化\n", + " 'verbose': 5000,\n", + " 'early_stopping_rounds': 300,\n", + " 'one_hot_max_size': 50,\n", + " # 'class_weights': [0.6, 1.2],\n", + " 'task_type': 'GPU',\n", + " 'has_time': True,\n", + " 'random_seed': 7\n", + " }\n", + " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " group_train = train_data_split['trade_date'].factorize()[0]\n", + " group_val = val_data_split['trade_date'].factorize()[0]\n", + " train_pool = Pool(\n", + " data=X_train,\n", + " label=y_train,\n", + " # group_id=group_train,\n", + " cat_features=cat_features\n", + " )\n", + " val_pool = Pool(\n", + " data=X_val,\n", + " label=y_val,\n", + " # group_id=group_val,\n", + " cat_features=cat_features\n", + " )\n", + "\n", + " model = CatBoostRegressor(**params)\n", + " model.fit(train_pool,\n", + " eval_set=val_pool, \n", + " plot=True, \n", + " use_best_model=True\n", + " )\n", + " elif type == 'light':\n", + " label_gain = list(range(len(train_data_split['label'].unique())))\n", + " params = {\n", + " 'label_gain': [gain * gain for gain in label_gain],\n", + " 'objective': 'rmse',\n", + " 'metric': 'rmse',\n", + " 'learning_rate': 0.01,\n", + " 'num_leaves': 1024,\n", + " # 'min_data_in_leaf': 128,\n", + " 'max_depth': 100,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 1,\n", + " 'lambda_l2': 1,\n", + " 'boosting': 'gbdt',\n", + " 'verbosity': -1,\n", + " 'extra_trees': True,\n", + " # 'max_position': 5,\n", + " # 'ndcg_at': 1,\n", + " 'quant_train_renew_leaf': True,\n", + " # 'lambdarank_truncation_level': 1,\n", + " # 'lambdarank_position_bias_regularization': 1,\n", + " 'seed': 7\n", + " }\n", + " train_groups = train_data_split.groupby('trade_date').size().tolist()\n", + " val_groups = val_data_split.groupby('trade_date').size().tolist()\n", + "\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + " train_dataset = lgb.Dataset(\n", + " X_train, label=y_train, \n", + " group=train_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + " val_dataset = lgb.Dataset(\n", + " X_val, label=y_val, \n", + " group=val_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " evals = {}\n", + " callbacks = [lgb.log_evaluation(period=1000),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(100, first_metric_only=True)\n", + " ]\n", + " # 训练模型\n", + " model = lgb.train(\n", + " params, train_dataset, num_boost_round=1000,\n", + " valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + " from flaml import AutoML\n", + " from sklearn.datasets import fetch_california_housing\n", + "\n", + " # # Initialize an AutoML instance\n", + " # model = AutoML()\n", + " # # Specify automl goal and constraint\n", + " # automl_settings = {\n", + " # \"time_budget\": 3000, # in seconds\n", + " # \"metric\": \"r2\",\n", + " # \"task\": \"regression\",\n", + " # \"estimator_list\": [\n", + " # # \"catboost\",\n", + " # \"lgbm\",\n", + " # # \"xgboost\"\n", + " # ], \n", + " # # \"ensemble\": {\n", + " # # \"final_estimator\": LGBMRegressor(),\n", + " # # \"passthrough\": False,\n", + " # # },\n", + " # }\n", + " # model.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, mlflow_logging=False, **automl_settings)\n", + "\n", + " # 打印特征重要性（如果需要）\n", + " if True:\n", + " lgb.plot_metric(evals)\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + "\n", + "\n", + " return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:03:18.426481Z", + "start_time": "2025-04-03T15:02:19.926352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 728000\n", + " ts_code trade_date log_circ_mv\n", + "0 600306.SH 2020-01-02 11.552040\n", + "1 603269.SH 2020-01-02 11.324801\n", + "2 002633.SZ 2020-01-02 11.759023\n", + "3 603991.SH 2020-01-02 11.181150\n", + "4 000691.SZ 2020-01-02 11.677910\n", + "... ... ... ...\n", + "727995 002235.SZ 2022-12-30 12.914708\n", + "727996 605598.SH 2022-12-30 11.783580\n", + "727997 002613.SZ 2022-12-30 12.489464\n", + "727998 600800.SH 2022-12-30 12.571911\n", + "727999 603068.SH 2022-12-30 12.967134\n", + "\n", + "[728000 rows x 3 columns]\n", + "原始样本数: 728000, 去除标签为空后样本数: 728000\n", + "Training until validation scores don't improve for 100 rounds\n", + "Early stopping, best iteration is:\n", + "[58]\ttrain's rmse: 0.0373704\tvalid's rmse: 0.040525\n", + "Evaluated only: rmse\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "type = 'light'\n", + "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", + "# light_params['feature_contri'] = feature_contri\n", + "# print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_model(train_data\n", + " .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n", + " .apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", + " .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + " .merge(index_data, on='trade_date', how='left'), \n", + " feature_columns, type=type, target_column='future_return_2')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:04:39.656944Z", + "start_time": "2025-04-03T15:04:39.298483Z" + } + }, + "outputs": [], + "source": [ + "score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))\n", + "# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", + "# score_df = score_df[score_df['pe_ttm'] > 0]\n", + "score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "score_df = score_df.merge(index_data, on='trade_date', how='left')\n", + "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n", + "\n", + "import pandas as pd\n", + "\n", + "def filter_high_volatility_daily(df: pd.DataFrame, threshold: float, window: int = 5,\n", + " date_col: str = 'trade_date', code_col: str = 'ts_code',\n", + " close_col: str = 'close') -> pd.DataFrame:\n", + " df = df.sort_values([code_col, date_col])\n", + "\n", + " def get_daily_std_rank(group):\n", + " rolling_std = group[close_col].rolling(window=window, min_periods=window).std()\n", + " # 在每个日期内计算标准差的百分比排名\n", + " return rolling_std.groupby(group[date_col]).rank(pct=True, ascending=False)\n", + "\n", + " # 直接在 apply 中计算并添加 'std_rank' 列\n", + " df_with_std = df.groupby(code_col, group_keys=False).apply(\n", + " lambda x: x.assign(std_rank=get_daily_std_rank(x))\n", + " )\n", + "\n", + " # 过滤掉每天标准差排名在前 top_percent 的股票\n", + " threshold_rank = threshold / 100.0\n", + " df_filtered = df_with_std[df_with_std['std_rank'] > threshold_rank].drop(\n", + " columns=['std_rank']\n", + " )\n", + "\n", + " return df_filtered\n", + "\n", + "# threshold = 10\n", + "# score_df = filter_high_volatility_daily(score_df, threshold=threshold)\n", + "# print(len(score_df))\n", + "\n", + "if type == 'cat':\n", + " score_df['score'] = model.predict(score_df[feature_columns])\n", + "elif type == 'light':\n", + " score_df['score'] = model.predict(score_df[feature_columns])\n", + "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n", + "\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: \n", + " x[\n", + " # (x['score'] <= x['score'].quantile(0.99)) & \n", + " (x['score'] >= x['score'].quantile(0.90))\n", + " ] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n", + ").reset_index(drop=True) # drop=True 避免添加旧索引列\n", + "# df_to_drop = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", + "# score_df = score_df.drop(df_to_drop.index)\n", + "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, 'score')).reset_index()\n", + "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n", + "save_df = save_df.sort_values(['trade_date', 'score'])\n", + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "09b1799e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "162\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile']\n" + ] + } + ], + "source": [ + "print(len(feature_columns))\n", + "print(feature_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "bceabd1f", + "metadata": {}, + "outputs": [], + "source": [ + "# print(model.get_feature_importance())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json index 20639b3..6c45bd2 100644 --- a/main/train/catboost_info/catboost_training.json +++ b/main/train/catboost_info/catboost_training.json @@ -1,1504 +1,792 @@ { "meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":1500,"learn_sets":["learn"],"name":"experiment"}, "iterations":[ -{"learn":[0.6886094006],"iteration":0,"passed_time":0.2548376797,"remaining_time":382.0016819,"test":[0.6894541077]}, 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