feat: 完善 QMT 交易模块
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@@ -83,32 +83,32 @@
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"output_type": "stream",
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"text": [
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"text": [
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" ts_code trade_date close open high low \\\n",
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" ts_code trade_date close open high low \\\n",
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"0 000905.SH 20260206 8146.4128 8054.2180 8247.2968 8014.6599 \n",
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"0 000905.SH 20260213 8299.5850 8366.3327 8419.7618 8290.8388 \n",
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"1 000905.SH 20260205 8146.1073 8203.0627 8224.7847 8081.4283 \n",
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"1 000905.SH 20260212 8423.5695 8342.3650 8437.4202 8341.7705 \n",
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"2 000905.SH 20260204 8299.0574 8270.8420 8309.8579 8179.3522 \n",
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"2 000905.SH 20260211 8325.8107 8295.3326 8373.5033 8295.1223 \n",
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"3 000905.SH 20260203 8286.7005 8135.8699 8286.7005 8075.3738 \n",
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"3 000905.SH 20260210 8306.4448 8318.5325 8331.9858 8258.1519 \n",
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"4 000905.SH 20260202 8037.0456 8279.7152 8324.4341 8032.3500 \n",
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"4 000905.SH 20260209 8311.2829 8280.1088 8313.4692 8229.0026 \n",
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"... ... ... ... ... ... ... \n",
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"... ... ... ... ... ... ... \n",
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"14059 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
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"14074 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
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"14060 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
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"14075 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
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"14061 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
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"14076 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
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"14062 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
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"14077 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
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"14063 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
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"14078 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
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"\n",
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"\n",
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" pre_close change pct_chg vol amount \n",
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" pre_close change pct_chg vol amount \n",
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"0 8146.1073 0.3055 0.0038 2.186421e+08 4.183734e+08 \n",
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"0 8423.5695 -123.9845 -1.4719 2.027857e+08 4.063205e+08 \n",
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"1 8299.0574 -152.9501 -1.8430 2.318405e+08 4.352777e+08 \n",
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"1 8325.8107 97.7588 1.1742 2.121383e+08 4.383205e+08 \n",
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"2 8286.7005 12.3569 0.1491 2.688603e+08 5.018867e+08 \n",
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"2 8306.4448 19.3659 0.2331 2.040197e+08 3.886124e+08 \n",
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"3 8037.0456 249.6549 3.1063 2.722271e+08 5.432645e+08 \n",
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"3 8311.2829 -4.8381 -0.0582 1.958062e+08 3.942833e+08 \n",
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"4 8370.5203 -333.4747 -3.9839 2.996460e+08 5.460557e+08 \n",
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"4 8146.4128 164.8701 2.0238 2.116676e+08 4.279823e+08 \n",
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"... ... ... ... ... ... \n",
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"... ... ... ... ... ... \n",
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"14059 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
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"14074 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
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"14060 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
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"14075 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
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"14061 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
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"14076 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
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"14062 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
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"14077 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
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"14063 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
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"14078 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
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"\n",
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"\n",
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"[14064 rows x 11 columns]\n"
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"[14079 rows x 11 columns]\n"
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@@ -137,7 +137,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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"version": "3.13.2"
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}
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"nbformat": 4,
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"3 000006.SZ 20250312\n",
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"3 000006.SZ 20250312\n",
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"... ... ...\n",
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"27312 920978.BJ 20260126\n",
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"\n",
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"[8652365 rows x 2 columns]\n",
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"[8679695 rows x 2 columns]\n",
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"20260130\n",
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"20260206\n",
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"start_date: 20260202\n"
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@@ -86,27 +86,22 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"text": [
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"任务 20260309 完成\n",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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"3 801005.SI 20250221\n",
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"4 801010.SI 20250221\n",
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"2190 859811.SI 20260126\n",
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"2191 859821.SI 20260126\n",
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"2192 859822.SI 20260126\n",
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"2193 859852.SI 20260126\n",
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"2194 859951.SI 20260126\n",
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"\n",
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"[1145363 rows x 2 columns]\n",
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"20260130\n",
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"任务 20260309 完成\n",
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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"version": "3.13.2"
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}
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"output_type": "stream",
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"text": [
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 9602310 entries, 0 to 27316\n",
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"Index: 9629640 entries, 0 to 27329\n",
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"Data columns (total 2 columns):\n",
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"Data columns (total 2 columns):\n",
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" # Column Dtype \n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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"--- ------ ----- \n",
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" 0 ts_code object\n",
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" 0 ts_code object\n",
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" 1 trade_date object\n",
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" 1 trade_date object\n",
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"dtypes: object(2)\n",
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"dtypes: object(2)\n",
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"memory usage: 219.8+ MB\n",
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"memory usage: 220.4+ MB\n",
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"None\n",
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"None\n",
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"20260130\n",
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"20260202\n"
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@@ -146,8 +146,8 @@
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"任务 20260309 完成\n",
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"text": [
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"text": [
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" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
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" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
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"0 001389.SZ 20260206 99.50 6.1259 6.1259 \n",
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"0 001301.SZ 20260213 78.24 1.0939 1.3757 \n",
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"1 600841.SH 20260206 9.55 4.4177 14.0519 \n",
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"1 301050.SZ 20260213 53.61 2.8931 3.6211 \n",
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"2 300968.SZ 20260206 13.71 1.0258 2.1909 \n",
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"2 000829.SZ 20260213 10.58 2.2336 3.5271 \n",
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"3 300634.SZ 20260206 28.46 4.0862 6.8510 \n",
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"3 688498.SH 20260213 746.98 3.4302 4.7903 \n",
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"4 300295.SZ 20260206 11.99 3.5950 3.8451 \n",
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"4 920510.BJ 20260213 18.30 2.8519 4.1749 \n",
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"... ... ... ... ... ... \n",
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"27325 603766.SH 20260202 14.89 1.1630 1.9407 \n",
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"27350 000065.SZ 20260209 12.07 1.7167 3.2552 \n",
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"27326 603408.SH 20260202 12.92 0.7963 3.0049 \n",
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"27351 920249.BJ 20260209 13.86 3.2235 3.3218 \n",
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"27327 000004.SZ 20260202 11.99 0.2338 0.3161 \n",
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"27352 300824.SZ 20260209 11.36 1.2161 3.0644 \n",
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"27328 601628.SH 20260202 48.34 0.1384 1.9210 \n",
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"27353 000766.SZ 20260209 23.65 1.8680 2.3253 \n",
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"27329 301042.SZ 20260202 86.10 3.6207 5.4797 \n",
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"27354 000591.SZ 20260209 5.72 5.9341 9.0727 \n",
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"\n",
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"\n",
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" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
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" volume_ratio pe pe_ttm pb ps ps_ttm \\\n",
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"0 0.83 62.6474 46.6770 11.6304 11.3424 8.6639 0.4819 \n",
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"0 0.51 24.3404 20.9997 3.0175 3.9021 2.8680 \n",
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"1 1.02 NaN NaN 4.1908 2.0493 2.4531 NaN \n",
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"1 0.80 38.6753 105.5089 4.2033 11.2338 18.9830 \n",
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"2 0.80 106.8945 225.1215 2.9871 4.6434 4.4246 0.3647 \n",
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"2 0.65 347.7936 NaN 3.7189 0.1291 0.1352 \n",
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"3 0.68 55.7987 55.5119 4.4068 7.7725 7.2020 0.5590 \n",
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"3 0.66 NaN 640.0343 29.5658 254.5923 140.4090 \n",
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"4 0.40 NaN NaN 2.0169 18.5210 23.1598 NaN \n",
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"4 1.41 160.0964 NaN 8.4111 14.5063 16.3558 \n",
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"... ... ... ... ... ... ... ... \n",
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"... ... ... ... ... ... ... \n",
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"27325 1.04 27.2701 16.9932 3.2106 1.8177 1.5958 2.6864 \n",
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"27350 0.73 13.3573 18.0503 1.2589 0.7347 0.9425 \n",
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"27326 0.99 11.9989 13.3289 1.7275 1.1550 1.1674 3.8700 \n",
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"27351 0.85 NaN NaN 8.0159 2.9840 2.5496 \n",
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"27327 0.05 NaN NaN 42.9590 16.0879 21.5107 NaN \n",
|
"27352 0.87 53.3349 33.5338 4.9536 4.9192 3.9029 \n",
|
||||||
"27328 0.95 12.7771 8.0270 2.2069 2.5849 2.1373 1.4233 \n",
|
"27353 0.75 405.0086 397.6822 9.9329 17.2829 18.3415 \n",
|
||||||
"27329 1.92 84.1572 534.3244 5.7108 8.6920 12.7424 NaN \n",
|
"27354 1.27 18.3189 21.1797 0.9314 3.7171 4.1825 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" dv_ttm total_share float_share free_share total_mv \\\n",
|
" dv_ratio dv_ttm total_share float_share free_share total_mv \\\n",
|
||||||
"0 0.4819 4.256875e+04 1.511580e+04 15115.7957 4.235591e+06 \n",
|
"0 1.0183 1.0183 26080.2350 18605.2851 14794.9501 2.040518e+06 \n",
|
||||||
"1 NaN 1.387822e+05 1.043024e+05 32790.9410 1.325370e+06 \n",
|
"1 0.6398 0.6398 24721.2171 21036.5666 16807.4482 1.325304e+06 \n",
|
||||||
"2 0.3647 4.133800e+04 4.133800e+04 19355.6537 5.667440e+05 \n",
|
"2 0.0945 0.0945 102510.0438 102412.9669 64854.1551 1.084556e+06 \n",
|
||||||
"3 0.5590 4.512109e+04 4.345735e+04 25919.5274 1.284146e+06 \n",
|
"3 0.0665 0.0533 8594.7726 8400.0000 6014.9271 6.420123e+06 \n",
|
||||||
"4 NaN 1.896137e+04 1.675259e+04 15662.9042 2.273468e+05 \n",
|
"4 NaN NaN 18421.3929 9389.9078 6414.2078 3.371115e+05 \n",
|
||||||
"... ... ... ... ... ... \n",
|
"... ... ... ... ... ... ... \n",
|
||||||
"27325 4.0296 2.053542e+05 2.053542e+05 123065.9980 3.057724e+06 \n",
|
"27350 1.1303 1.1303 116144.2159 97496.2288 51416.3105 1.401861e+06 \n",
|
||||||
"27326 5.3406 4.475730e+04 4.475730e+04 11860.1633 5.782643e+05 \n",
|
"27351 NaN NaN 42163.0000 12466.9576 12097.9576 5.843792e+05 \n",
|
||||||
"27327 NaN 1.323803e+04 1.262878e+04 9339.3580 1.587240e+05 \n",
|
"27352 1.7937 1.7937 32634.1682 31649.5307 12560.3926 3.707242e+05 \n",
|
||||||
"27328 1.4233 2.826470e+06 2.082353e+06 150000.0000 1.366316e+08 \n",
|
"27353 NaN NaN 96649.4707 96600.7681 77600.7681 2.285760e+06 \n",
|
||||||
"27329 NaN 6.972358e+03 6.602083e+03 4362.2827 6.003200e+05 \n",
|
"27354 1.9711 2.0779 392444.2493 392354.1671 256620.6136 2.244781e+06 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" circ_mv is_st \n",
|
" circ_mv is_st \n",
|
||||||
"0 1.504022e+06 False \n",
|
"0 1.455678e+06 False \n",
|
||||||
"1 9.960884e+05 False \n",
|
"1 1.127770e+06 False \n",
|
||||||
"2 5.667440e+05 False \n",
|
"2 1.083529e+06 False \n",
|
||||||
"3 1.236796e+06 False \n",
|
"3 6.274632e+06 False \n",
|
||||||
"4 2.008635e+05 False \n",
|
"4 1.718353e+05 False \n",
|
||||||
"... ... ... \n",
|
"... ... ... \n",
|
||||||
"27325 3.057724e+06 False \n",
|
"27350 1.176779e+06 False \n",
|
||||||
"27326 5.782643e+05 False \n",
|
"27351 1.727920e+05 False \n",
|
||||||
"27327 1.514190e+05 True \n",
|
"27352 3.595387e+05 False \n",
|
||||||
"27328 1.006609e+08 False \n",
|
"27353 2.284608e+06 False \n",
|
||||||
"27329 5.684393e+05 False \n",
|
"27354 2.244266e+06 False \n",
|
||||||
"\n",
|
"\n",
|
||||||
"[27330 rows x 19 columns]\n"
|
"[27355 rows x 19 columns]\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -311,45 +306,58 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
|
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
|
||||||
"52 002713.SZ 20260206 9.52 4.6530 6.2693 \n",
|
"5 603261.SH 20260213 28.95 0.4664 1.3719 \n",
|
||||||
"107 000609.SZ 20260206 8.23 0.2427 0.3213 \n",
|
"14 002700.SZ 20260213 7.28 1.5973 1.6181 \n",
|
||||||
"113 300052.SZ 20260206 13.86 3.5983 4.3260 \n",
|
"34 300344.SZ 20260213 1.87 14.3407 25.9753 \n",
|
||||||
"116 600624.SH 20260206 5.31 0.6260 0.8827 \n",
|
"67 000430.SZ 20260213 7.26 0.8541 1.3705 \n",
|
||||||
"119 300555.SZ 20260206 14.31 1.4520 1.7474 \n",
|
"81 000752.SZ 20260213 10.86 1.0429 1.2849 \n",
|
||||||
"... ... ... ... ... ... \n",
|
"... ... ... ... ... ... \n",
|
||||||
"27182 000668.SZ 20260202 13.85 3.5014 5.9253 \n",
|
"27165 300301.SZ 20260209 2.49 1.9012 2.0554 \n",
|
||||||
"27240 003032.SZ 20260202 6.37 2.2066 3.1223 \n",
|
"27244 002822.SZ 20260209 3.53 1.0762 1.0762 \n",
|
||||||
"27305 000691.SZ 20260202 8.30 1.9108 2.2317 \n",
|
"27270 300147.SZ 20260209 9.44 1.7053 2.2069 \n",
|
||||||
"27312 300167.SZ 20260202 4.65 2.1705 2.3552 \n",
|
"27281 002501.SZ 20260209 2.16 3.6044 4.6543 \n",
|
||||||
"27327 000004.SZ 20260202 11.99 0.2338 0.3161 \n",
|
"27297 002620.SZ 20260209 5.98 2.2406 3.1755 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
|
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
|
||||||
"52 1.02 NaN NaN NaN 6.9890 12.8336 NaN NaN \n",
|
"5 0.49 NaN NaN 3.3744 7.7410 7.8754 NaN \n",
|
||||||
"107 0.05 NaN NaN NaN 8.0655 15.8232 NaN NaN \n",
|
"14 0.77 32.3153 40.4077 2.2014 4.2912 4.3540 0.9066 \n",
|
||||||
"113 1.51 NaN NaN 9.6004 15.9784 18.8191 NaN NaN \n",
|
"34 0.60 NaN NaN 5.3118 4.6416 4.9881 NaN \n",
|
||||||
"116 0.60 NaN NaN 5.7984 5.5900 5.4551 NaN NaN \n",
|
"67 0.49 NaN NaN 21.9522 13.6188 12.8340 NaN \n",
|
||||||
"119 1.24 NaN NaN 6.0224 16.1604 18.5602 NaN NaN \n",
|
"81 1.21 109.3444 19.4711 4.6597 6.7963 6.4062 NaN \n",
|
||||||
"... ... .. ... ... ... ... ... ... \n",
|
"... ... ... ... ... ... ... ... \n",
|
||||||
"27182 1.32 NaN NaN 2.8792 15.2387 7.2708 NaN NaN \n",
|
"27165 0.97 NaN NaN 12.5588 4.0907 4.7109 NaN \n",
|
||||||
"27240 1.15 NaN NaN 2.0451 10.4354 7.6772 NaN NaN \n",
|
"27244 0.95 NaN NaN 4.9828 2.9693 6.5620 NaN \n",
|
||||||
"27305 0.69 NaN NaN NaN 9.0850 9.0002 NaN NaN \n",
|
"27270 1.21 NaN NaN 9.0126 3.3586 4.2436 NaN \n",
|
||||||
"27312 0.71 NaN 134.128 63.0032 4.3517 3.8269 NaN NaN \n",
|
"27281 0.87 NaN NaN 29.0833 22.9884 26.1834 NaN \n",
|
||||||
"27327 0.05 NaN NaN 42.9590 16.0879 21.5107 NaN NaN \n",
|
"27297 1.30 NaN NaN NaN 2.8840 4.2972 NaN \n",
|
||||||
"\n",
|
"\n",
|
||||||
" total_share float_share free_share total_mv circ_mv is_st \n",
|
" dv_ttm total_share float_share free_share total_mv \\\n",
|
||||||
"52 95140.5184 52650.0831 39075.6229 905737.7352 501228.7911 True \n",
|
"5 NaN 7750.5022 7750.5022 2634.9858 224377.0387 \n",
|
||||||
"107 29926.5522 29105.8272 21991.3472 246295.5246 239540.9579 True \n",
|
"14 0.9066 41362.8185 26346.4874 26007.0037 301121.3187 \n",
|
||||||
"113 26185.8710 26185.1210 21780.6332 362936.1721 362925.7771 True \n",
|
"34 NaN 64170.6416 63999.4166 35333.5001 119999.0998 \n",
|
||||||
"116 67934.6942 67934.6942 48180.1806 360733.2262 360733.2262 True \n",
|
"67 NaN 80963.5372 37055.6486 23092.8156 587795.2801 \n",
|
||||||
"119 20000.0000 19798.4863 16450.7792 286200.0000 283316.3390 True \n",
|
"81 NaN 26375.8491 26375.8491 21407.3042 286441.7212 \n",
|
||||||
"... ... ... ... ... ... ... \n",
|
"... ... ... ... ... ... \n",
|
||||||
"27182 14684.1890 14684.1890 8677.2104 203376.0177 203376.0177 True \n",
|
"27165 NaN 82986.8769 78987.6719 73061.8561 206637.3235 \n",
|
||||||
"27240 40244.7500 27621.9885 19520.7308 256359.0575 175952.0667 True \n",
|
"27244 NaN 195094.2200 107059.0368 107059.0368 688682.5966 \n",
|
||||||
"27305 48490.5000 32327.0000 27679.8405 402471.1500 268314.1000 True \n",
|
"27270 NaN 66127.9045 65739.8353 50798.8432 624247.4185 \n",
|
||||||
"27312 38937.4000 35889.3250 33073.6637 181058.9100 166885.3613 True \n",
|
"27281 NaN 355000.0000 354646.9206 274646.9206 766800.0000 \n",
|
||||||
"27327 13238.0282 12628.7768 9339.3580 158723.9581 151419.0338 True \n",
|
"27297 NaN 37749.4000 31536.0303 22251.3747 225741.4120 \n",
|
||||||
"\n",
|
"\n",
|
||||||
"[872 rows x 19 columns]\n"
|
" circ_mv is_st \n",
|
||||||
|
"5 224377.0387 True \n",
|
||||||
|
"14 191802.4283 True \n",
|
||||||
|
"34 119678.9090 True \n",
|
||||||
|
"67 269024.0088 True \n",
|
||||||
|
"81 286441.7212 True \n",
|
||||||
|
"... ... ... \n",
|
||||||
|
"27165 196679.3030 True \n",
|
||||||
|
"27244 377918.3999 True \n",
|
||||||
|
"27270 620584.0452 True \n",
|
||||||
|
"27281 766037.3485 True \n",
|
||||||
|
"27297 188585.4612 True \n",
|
||||||
|
"\n",
|
||||||
|
"[870 rows x 19 columns]\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -399,7 +407,7 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"Index: 9629640 entries, 0 to 27329\n",
|
"Index: 9656995 entries, 0 to 27354\n",
|
||||||
"Data columns (total 3 columns):\n",
|
"Data columns (total 3 columns):\n",
|
||||||
" # Column Dtype \n",
|
" # Column Dtype \n",
|
||||||
"--- ------ ----- \n",
|
"--- ------ ----- \n",
|
||||||
@@ -407,7 +415,7 @@
|
|||||||
" 1 trade_date object\n",
|
" 1 trade_date object\n",
|
||||||
" 2 is_st bool \n",
|
" 2 is_st bool \n",
|
||||||
"dtypes: bool(1), object(2)\n",
|
"dtypes: bool(1), object(2)\n",
|
||||||
"memory usage: 229.6+ MB\n",
|
"memory usage: 230.2+ MB\n",
|
||||||
"None\n"
|
"None\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
@@ -34,17 +34,17 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"Index: 9387872 entries, 0 to 25863\n",
|
"Index: 9413748 entries, 0 to 25875\n",
|
||||||
"Data columns (total 2 columns):\n",
|
"Data columns (total 2 columns):\n",
|
||||||
" # Column Dtype \n",
|
" # Column Dtype \n",
|
||||||
"--- ------ ----- \n",
|
"--- ------ ----- \n",
|
||||||
" 0 ts_code object\n",
|
" 0 ts_code object\n",
|
||||||
" 1 trade_date object\n",
|
" 1 trade_date object\n",
|
||||||
"dtypes: object(2)\n",
|
"dtypes: object(2)\n",
|
||||||
"memory usage: 214.9+ MB\n",
|
"memory usage: 215.5+ MB\n",
|
||||||
"None\n",
|
"None\n",
|
||||||
"20260130\n",
|
"20260206\n",
|
||||||
"start_date: 20260202\n"
|
"start_date: 20260209\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -99,12 +99,7 @@
|
|||||||
"任务 20260212 完成\n",
|
"任务 20260212 完成\n",
|
||||||
"任务 20260211 完成\n",
|
"任务 20260211 完成\n",
|
||||||
"任务 20260210 完成\n",
|
"任务 20260210 完成\n",
|
||||||
"任务 20260209 完成\n",
|
"任务 20260209 完成\n"
|
||||||
"任务 20260206 完成\n",
|
|
||||||
"任务 20260205 完成\n",
|
|
||||||
"任务 20260204 完成\n",
|
|
||||||
"任务 20260203 完成\n",
|
|
||||||
"任务 20260202 完成\n"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -194,71 +189,71 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
|
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
|
||||||
"0 002397.SZ 20260206 143016 6566.04 142640 \n",
|
"0 300587.SZ 20260213 154110 9661.44 160598 \n",
|
||||||
"1 603882.SH 20260206 32234 10009.25 31819 \n",
|
"1 601000.SH 20260213 150959 6301.54 197344 \n",
|
||||||
"2 002824.SZ 20260206 58407 13856.33 47164 \n",
|
"2 002338.SZ 20260213 9215 5012.78 8260 \n",
|
||||||
"3 603379.SH 20260206 31545 21744.99 30955 \n",
|
"3 688373.SH 20260213 29166 1845.78 30329 \n",
|
||||||
"4 600223.SH 20260206 73778 5582.50 67552 \n",
|
"4 002226.SZ 20260213 101435 6137.98 79302 \n",
|
||||||
"... ... ... ... ... ... \n",
|
"... ... ... ... ... ... \n",
|
||||||
"25871 002774.SZ 20260202 32854 3675.61 20566 \n",
|
"25887 603713.SH 20260209 9898 6481.74 10208 \n",
|
||||||
"25872 300188.SZ 20260202 54005 7416.58 48176 \n",
|
"25888 300004.SZ 20260209 41923 5934.14 50255 \n",
|
||||||
"25873 688173.SH 20260202 64149 10373.20 68049 \n",
|
"25889 300975.SZ 20260209 198244 30367.70 159191 \n",
|
||||||
"25874 300145.SZ 20260202 132990 6043.37 133529 \n",
|
"25890 603381.SH 20260209 85934 22581.16 95505 \n",
|
||||||
"25875 300205.SZ 20260202 8361 364.75 6236 \n",
|
"25891 002836.SZ 20260209 27160 4238.25 22047 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
|
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
|
||||||
"0 6550.08 114346 5247.29 121777 5580.76 \n",
|
"0 10061.76 183752 11498.51 186971 11701.15 \n",
|
||||||
"1 9880.13 15560 4828.56 14519 4506.56 \n",
|
"1 8233.85 84549 3527.41 71932 3003.18 \n",
|
||||||
"2 11195.49 27988 6622.95 30015 7118.27 \n",
|
"2 4495.09 9488 5163.47 9035 4920.33 \n",
|
||||||
"3 21323.49 21708 14968.66 21044 14503.09 \n",
|
"3 1918.49 10043 635.49 10005 633.64 \n",
|
||||||
"4 5110.61 53914 4075.02 53354 4037.48 \n",
|
"4 4796.49 104000 6296.98 78239 4741.21 \n",
|
||||||
"... ... ... ... ... ... \n",
|
"... ... ... ... ... ... \n",
|
||||||
"25871 2300.99 20761 2320.61 21785 2437.00 \n",
|
"25887 6683.01 6735 4413.49 7402 4854.70 \n",
|
||||||
"25872 6623.07 52821 7250.13 53449 7347.52 \n",
|
"25888 7133.88 53004 7510.03 58384 8274.31 \n",
|
||||||
"25873 11000.03 35320 5706.28 30270 4895.56 \n",
|
"25889 24423.94 164520 25222.01 193044 29612.42 \n",
|
||||||
"25874 6066.65 114646 5208.71 97260 4422.03 \n",
|
"25890 25109.67 65977 17316.88 71008 18669.88 \n",
|
||||||
"25875 272.25 9374 408.60 9827 427.93 \n",
|
"25891 3438.16 21123 3293.94 20955 3271.07 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
|
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
|
||||||
"0 85477 3926.44 87204 3989.37 28400 \n",
|
"0 139274 8711.00 168037 10519.91 60907 \n",
|
||||||
"1 5474 1697.68 6390 1982.08 721 \n",
|
"1 57471 2398.28 37552 1567.23 21177 \n",
|
||||||
"2 11723 2778.17 15330 3630.71 3939 \n",
|
"2 5873 3195.64 5312 2890.59 514 \n",
|
||||||
"3 13069 9000.74 15789 10882.14 7583 \n",
|
"3 8238 521.88 7113 451.02 0 \n",
|
||||||
"4 27279 2062.59 22583 1709.48 10118 \n",
|
"4 50394 3048.72 69021 4177.39 9335 \n",
|
||||||
"... ... ... ... ... ... \n",
|
"... ... ... ... ... ... \n",
|
||||||
"25871 8975 1002.99 18793 2100.36 0 \n",
|
"25887 3515 2307.87 4056 2660.17 2867 \n",
|
||||||
"25872 31050 4271.36 35903 4923.97 7655 \n",
|
"25888 49767 7060.10 39566 5596.12 8820 \n",
|
||||||
"25873 11829 1915.52 12687 2050.68 2000 \n",
|
"25889 116306 17856.14 144536 22152.02 51550 \n",
|
||||||
"25874 81164 3682.66 113414 5149.25 41421 \n",
|
"25890 46270 12156.66 38176 10028.63 11944 \n",
|
||||||
"25875 6887 302.35 8559 375.51 0 \n",
|
"25891 9676 1508.43 11383 1772.18 1000 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
|
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
|
||||||
"0 1293.83 19618 913.39 42201 \n",
|
"0 3816.45 22437 1404.58 142435 \n",
|
||||||
"1 223.34 1261 390.07 -823 \n",
|
"1 882.70 7328 305.66 -48700 \n",
|
||||||
"2 935.53 9548 2248.51 2997 \n",
|
"2 280.52 2483 1346.40 -483 \n",
|
||||||
"3 5221.72 6117 4227.39 6215 \n",
|
"3 0.00 0 0.00 982 \n",
|
||||||
"4 766.07 21600 1628.62 -9839 \n",
|
"4 565.27 38602 2333.86 -103058 \n",
|
||||||
"... ... ... ... ... \n",
|
"... ... ... ... ... \n",
|
||||||
"25871 0.00 1446 160.86 -20182 \n",
|
"25887 1885.53 1349 890.75 1237 \n",
|
||||||
"25872 1053.35 8003 1096.85 -12903 \n",
|
"25888 1251.47 5309 751.42 8180 \n",
|
||||||
"25873 319.00 2291 367.73 -5670 \n",
|
"25889 7924.73 33850 5182.21 -41375 \n",
|
||||||
"25874 1881.32 26018 1178.15 -70082 \n",
|
"25890 3157.32 5436 1403.84 -11696 \n",
|
||||||
"25875 0.00 0 0.00 -1678 \n",
|
"25891 155.40 4574 714.61 1257 \n",
|
||||||
"\n",
|
"\n",
|
||||||
" net_mf_amount \n",
|
" net_mf_amount \n",
|
||||||
"0 1943.09 \n",
|
"0 8918.59 \n",
|
||||||
"1 -246.33 \n",
|
"1 -2025.73 \n",
|
||||||
"2 705.13 \n",
|
"2 -250.58 \n",
|
||||||
"3 4367.94 \n",
|
"3 64.60 \n",
|
||||||
"4 -741.40 \n",
|
"4 -6231.26 \n",
|
||||||
"... ... \n",
|
"... ... \n",
|
||||||
"25871 -2251.19 \n",
|
"25887 814.61 \n",
|
||||||
"25872 -1769.73 \n",
|
"25888 1173.63 \n",
|
||||||
"25873 -918.58 \n",
|
"25889 -6267.77 \n",
|
||||||
"25874 -3146.31 \n",
|
"25890 -3055.51 \n",
|
||||||
"25875 -68.42 \n",
|
"25891 191.86 \n",
|
||||||
"\n",
|
"\n",
|
||||||
"[25876 rows x 20 columns]\n"
|
"[25892 rows x 20 columns]\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -283,7 +278,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.11"
|
"version": "3.13.2"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -34,23 +34,23 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
" ts_code trade_date\n",
|
" ts_code trade_date\n",
|
||||||
"4971 600208.SH 20260130\n",
|
"4979 600202.SH 20260206\n",
|
||||||
"4972 600210.SH 20260130\n",
|
"4980 600203.SH 20260206\n",
|
||||||
"4973 600211.SH 20260130\n",
|
"4981 600206.SH 20260206\n",
|
||||||
"4961 600195.SH 20260130\n",
|
"4969 600189.SH 20260206\n",
|
||||||
"7441 920964.BJ 20260130\n",
|
"7454 920964.BJ 20260206\n",
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"Index: 11775527 entries, 0 to 37233\n",
|
"Index: 11812821 entries, 0 to 37293\n",
|
||||||
"Data columns (total 2 columns):\n",
|
"Data columns (total 2 columns):\n",
|
||||||
" # Column Dtype \n",
|
" # Column Dtype \n",
|
||||||
"--- ------ ----- \n",
|
"--- ------ ----- \n",
|
||||||
" 0 ts_code object\n",
|
" 0 ts_code object\n",
|
||||||
" 1 trade_date object\n",
|
" 1 trade_date object\n",
|
||||||
"dtypes: object(2)\n",
|
"dtypes: object(2)\n",
|
||||||
"memory usage: 269.5+ MB\n",
|
"memory usage: 270.4+ MB\n",
|
||||||
"None\n",
|
"None\n",
|
||||||
"20260130\n",
|
"20260206\n",
|
||||||
"20260202\n"
|
"20260209\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -95,8 +95,8 @@
|
|||||||
"任务 20260309 完成\n",
|
"任务 20260309 完成\n",
|
||||||
"任务 20260306 完成\n",
|
"任务 20260306 完成\n",
|
||||||
"任务 20260305 完成\n",
|
"任务 20260305 完成\n",
|
||||||
"任务 20260303 完成\n",
|
|
||||||
"任务 20260304 完成\n",
|
"任务 20260304 完成\n",
|
||||||
|
"任务 20260303 完成\n",
|
||||||
"任务 20260302 完成\n",
|
"任务 20260302 完成\n",
|
||||||
"任务 20260227 完成\n",
|
"任务 20260227 完成\n",
|
||||||
"任务 20260226 完成\n",
|
"任务 20260226 完成\n",
|
||||||
@@ -106,12 +106,7 @@
|
|||||||
"任务 20260212 完成\n",
|
"任务 20260212 完成\n",
|
||||||
"任务 20260211 完成\n",
|
"任务 20260211 完成\n",
|
||||||
"任务 20260210 完成\n",
|
"任务 20260210 完成\n",
|
||||||
"任务 20260209 完成\n",
|
"任务 20260209 完成\n"
|
||||||
"任务 20260206 完成\n",
|
|
||||||
"任务 20260205 完成\n",
|
|
||||||
"任务 20260204 完成\n",
|
|
||||||
"任务 20260203 完成\n",
|
|
||||||
"任务 20260202 完成\n"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -163,71 +158,71 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"[ trade_date ts_code up_limit down_limit\n",
|
"[ trade_date ts_code up_limit down_limit\n",
|
||||||
"0 20260206 000001.SZ 12.20 9.98\n",
|
"0 20260213 000001.SZ 12.06 9.86\n",
|
||||||
"1 20260206 000002.SZ 5.37 4.39\n",
|
"1 20260213 000002.SZ 5.41 4.43\n",
|
||||||
"2 20260206 000004.SZ 10.79 9.77\n",
|
"2 20260213 000004.SZ 8.36 7.56\n",
|
||||||
"3 20260206 000006.SZ 9.90 8.10\n",
|
"3 20260213 000006.SZ 10.12 8.28\n",
|
||||||
"4 20260206 000007.SZ 12.31 10.07\n",
|
"4 20260213 000007.SZ 13.44 11.00\n",
|
||||||
"... ... ... ... ...\n",
|
"... ... ... ... ...\n",
|
||||||
"7459 20260206 920978.BJ 41.60 22.40\n",
|
"7476 20260213 920978.BJ 39.09 21.05\n",
|
||||||
"7460 20260206 920981.BJ 41.63 22.43\n",
|
"7477 20260213 920981.BJ 41.61 22.41\n",
|
||||||
"7461 20260206 920982.BJ 298.63 160.81\n",
|
"7478 20260213 920982.BJ 284.06 152.96\n",
|
||||||
"7462 20260206 920985.BJ 11.37 6.13\n",
|
"7479 20260213 920985.BJ 11.34 6.12\n",
|
||||||
"7463 20260206 920992.BJ 22.32 12.02\n",
|
"7480 20260213 920992.BJ 21.77 11.73\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[7464 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
"[7481 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
||||||
"0 20260205 000001.SZ 12.07 9.87\n",
|
"0 20260212 000001.SZ 12.18 9.96\n",
|
||||||
"1 20260205 000002.SZ 5.39 4.41\n",
|
"1 20260212 000002.SZ 5.38 4.40\n",
|
||||||
"2 20260205 000004.SZ 11.36 10.28\n",
|
"2 20260212 000004.SZ 8.80 7.96\n",
|
||||||
"3 20260205 000006.SZ 9.97 8.15\n",
|
"3 20260212 000006.SZ 10.29 8.42\n",
|
||||||
"4 20260205 000007.SZ 12.32 10.08\n",
|
"4 20260212 000007.SZ 13.31 10.89\n",
|
||||||
"... ... ... ... ...\n",
|
"... ... ... ... ...\n",
|
||||||
"7454 20260205 920978.BJ 41.48 22.34\n",
|
"7476 20260212 920978.BJ 39.93 21.51\n",
|
||||||
"7455 20260205 920981.BJ 42.00 22.62\n",
|
"7477 20260212 920981.BJ 41.60 22.40\n",
|
||||||
"7456 20260205 920982.BJ 289.43 155.85\n",
|
"7478 20260212 920982.BJ 284.29 153.09\n",
|
||||||
"7457 20260205 920985.BJ 12.16 6.56\n",
|
"7479 20260212 920985.BJ 11.01 5.93\n",
|
||||||
"7458 20260205 920992.BJ 22.32 12.02\n",
|
"7480 20260212 920992.BJ 21.86 11.78\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[7459 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
"[7481 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
||||||
"0 20260204 000001.SZ 11.92 9.76\n",
|
"0 20260211 000001.SZ 12.17 9.95\n",
|
||||||
"1 20260204 000002.SZ 5.17 4.23\n",
|
"1 20260211 000002.SZ 5.37 4.39\n",
|
||||||
"2 20260204 000004.SZ 11.96 10.82\n",
|
"2 20260211 000004.SZ 9.26 8.38\n",
|
||||||
"3 20260204 000006.SZ 9.77 7.99\n",
|
"3 20260211 000006.SZ 10.18 8.33\n",
|
||||||
"4 20260204 000007.SZ 12.32 10.08\n",
|
"4 20260211 000007.SZ 13.37 10.94\n",
|
||||||
"... ... ... ... ...\n",
|
"... ... ... ... ...\n",
|
||||||
"7453 20260204 920978.BJ 38.15 20.55\n",
|
"7469 20260211 920978.BJ 41.02 22.10\n",
|
||||||
"7454 20260204 920981.BJ 43.01 23.17\n",
|
"7470 20260211 920981.BJ 41.91 22.57\n",
|
||||||
"7455 20260204 920982.BJ 279.50 150.50\n",
|
"7471 20260211 920982.BJ 286.35 154.19\n",
|
||||||
"7456 20260204 920985.BJ 10.66 5.74\n",
|
"7472 20260211 920985.BJ 11.34 6.12\n",
|
||||||
"7457 20260204 920992.BJ 22.28 12.00\n",
|
"7473 20260211 920992.BJ 22.04 11.88\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[7458 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
"[7474 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
||||||
"0 20260203 000001.SZ 11.95 9.77\n",
|
"0 20260210 000001.SZ 12.18 9.96\n",
|
||||||
"1 20260203 000002.SZ 5.15 4.21\n",
|
"1 20260210 000002.SZ 5.46 4.46\n",
|
||||||
"2 20260203 000004.SZ 12.59 11.39\n",
|
"2 20260210 000004.SZ 9.74 8.82\n",
|
||||||
"3 20260203 000006.SZ 9.58 7.84\n",
|
"3 20260210 000006.SZ 10.32 8.44\n",
|
||||||
"4 20260203 000007.SZ 12.09 9.89\n",
|
"4 20260210 000007.SZ 13.53 11.07\n",
|
||||||
"... ... ... ... ...\n",
|
"... ... ... ... ...\n",
|
||||||
"7453 20260203 920978.BJ 37.93 20.43\n",
|
"7466 20260210 920978.BJ 41.71 22.47\n",
|
||||||
"7454 20260203 920981.BJ 42.52 22.90\n",
|
"7467 20260210 920981.BJ 42.05 22.65\n",
|
||||||
"7455 20260203 920982.BJ 279.35 150.43\n",
|
"7468 20260210 920982.BJ 289.90 156.10\n",
|
||||||
"7456 20260203 920985.BJ 10.15 5.47\n",
|
"7469 20260210 920985.BJ 11.83 6.37\n",
|
||||||
"7457 20260203 920992.BJ 21.95 11.83\n",
|
"7470 20260210 920992.BJ 22.33 12.03\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[7458 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
"[7471 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
|
||||||
"0 20260202 000001.SZ 11.91 9.75\n",
|
"0 20260209 000001.SZ 12.16 9.95\n",
|
||||||
"1 20260202 000002.SZ 5.37 4.39\n",
|
"1 20260209 000002.SZ 5.29 4.33\n",
|
||||||
"2 20260202 000004.SZ 13.25 11.99\n",
|
"2 20260209 000004.SZ 10.26 9.28\n",
|
||||||
"3 20260202 000006.SZ 9.86 8.06\n",
|
"3 20260209 000006.SZ 9.88 8.08\n",
|
||||||
"4 20260202 000007.SZ 11.97 9.79\n",
|
"4 20260209 000007.SZ 13.00 10.64\n",
|
||||||
"... ... ... ... ...\n",
|
"... ... ... ... ...\n",
|
||||||
"7450 20260202 920978.BJ 40.36 21.74\n",
|
"7462 20260209 920978.BJ 42.45 22.87\n",
|
||||||
"7451 20260202 920981.BJ 45.50 24.50\n",
|
"7463 20260209 920981.BJ 41.40 22.30\n",
|
||||||
"7452 20260202 920982.BJ 287.80 154.98\n",
|
"7464 20260209 920982.BJ 291.70 157.08\n",
|
||||||
"7453 20260202 920985.BJ 10.33 5.57\n",
|
"7465 20260209 920985.BJ 11.32 6.10\n",
|
||||||
"7454 20260202 920992.BJ 22.63 12.19\n",
|
"7466 20260209 920992.BJ 22.13 11.93\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[7455 rows x 4 columns]]\n"
|
"[7467 rows x 4 columns]]\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -295,7 +290,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.11"
|
"version": "3.13.2"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -78,7 +78,7 @@
|
|||||||
"cyq perf\n",
|
"cyq perf\n",
|
||||||
"left merge on ['ts_code', 'trade_date']\n",
|
"left merge on ['ts_code', 'trade_date']\n",
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"RangeIndex: 9410807 entries, 0 to 9410806\n",
|
"RangeIndex: 9436343 entries, 0 to 9436342\n",
|
||||||
"Data columns (total 33 columns):\n",
|
"Data columns (total 33 columns):\n",
|
||||||
" # Column Dtype \n",
|
" # Column Dtype \n",
|
||||||
"--- ------ ----- \n",
|
"--- ------ ----- \n",
|
||||||
@@ -446,21 +446,21 @@
|
|||||||
"fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n",
|
"fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n",
|
||||||
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
|
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
|
||||||
" df=None)\n",
|
" df=None)\n",
|
||||||
"cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n",
|
"# cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n",
|
||||||
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
"# columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
||||||
" df=None)\n",
|
"# df=None)\n",
|
||||||
"balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n",
|
"# balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n",
|
||||||
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
"# columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
||||||
" df=None)\n",
|
"# df=None)\n",
|
||||||
"top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n",
|
"# top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n",
|
||||||
" columns=['ts_code', 'trade_date', 'reason'],\n",
|
"# columns=['ts_code', 'trade_date', 'reason'],\n",
|
||||||
" df=None)\n",
|
"# df=None)\n",
|
||||||
"\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",
|
"# 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",
|
||||||
"\n",
|
"\n",
|
||||||
"stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
|
"# stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
|
||||||
" columns=['ts_code', 'ann_date', 'in_de', 'change_ratio', 'after_ratio'],\n",
|
"# columns=['ts_code', 'ann_date', 'in_de', 'change_ratio', 'after_ratio'],\n",
|
||||||
" df=None)"
|
"# df=None)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -479,133 +479,27 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"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",
|
"警告: 从 financial_data_subset 中移除了 15 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",
|
"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",
|
"警告: 从 financial_data_subset 中移除了 15 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",
|
"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",
|
"警告: 从 financial_data_subset 中移除了 15 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",
|
"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",
|
"警告: 从 financial_data_subset 中移除了 15 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",
|
"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 计算成功。\n",
|
||||||
"因子 AR, BR 计算流程结束。\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",
|
"ename": "NameError",
|
||||||
"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",
|
"evalue": "name 'cashflow_df' is not defined",
|
||||||
"计算 BBI...\n",
|
"output_type": "error",
|
||||||
"--- 计算日级别偏离度 (使用 pct_chg) ---\n",
|
"traceback": [
|
||||||
"--- 计算日级别动量基准 (使用 pct_chg) ---\n",
|
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||||
"日级别动量基准计算完成 (使用 pct_chg)。\n",
|
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
|
||||||
"日级别偏离度计算完成 (使用 pct_chg)。\n",
|
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 43\u001b[39m\n\u001b[32m 41\u001b[39m calculate_arbr(df, N=\u001b[32m26\u001b[39m)\n\u001b[32m 42\u001b[39m df[\u001b[33m'\u001b[39m\u001b[33mlog_circ_mv\u001b[39m\u001b[33m'\u001b[39m] = np.log(df[\u001b[33m'\u001b[39m\u001b[33mcirc_mv\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m df = calculate_cashflow_to_ev_factor(df, \u001b[43mcashflow_df\u001b[49m, balancesheet_df)\n\u001b[32m 44\u001b[39m df = caculate_book_to_price_ratio(df, fina_indicator_df)\n\u001b[32m 46\u001b[39m df = turnover_rate_n(df, n=\u001b[32m5\u001b[39m)\n",
|
||||||
"--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
|
"\u001b[31mNameError\u001b[39m: name 'cashflow_df' is not defined"
|
||||||
"--- 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",
|
|
||||||
" 'amount', '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', '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",
|
|
||||||
" 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",
|
|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
|
||||||
"RangeIndex: 5072276 entries, 0 to 5072275\n",
|
|
||||||
"Columns: 181 entries, ts_code to cs_rank_size\n",
|
|
||||||
"dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
|
|
||||||
"memory usage: 6.5+ GB\n",
|
|
||||||
"None\n",
|
|
||||||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', '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"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -714,7 +608,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 11,
|
"execution_count": null,
|
||||||
"id": "b87b938028afa206",
|
"id": "b87b938028afa206",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"ExecuteTime": {
|
"ExecuteTime": {
|
||||||
@@ -752,7 +646,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 12,
|
"execution_count": null,
|
||||||
"id": "f4f16d63ad18d1bc",
|
"id": "f4f16d63ad18d1bc",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"ExecuteTime": {
|
"ExecuteTime": {
|
||||||
@@ -978,7 +872,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 13,
|
"execution_count": null,
|
||||||
"id": "40e6b68a91b30c79",
|
"id": "40e6b68a91b30c79",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"ExecuteTime": {
|
"ExecuteTime": {
|
||||||
@@ -1298,7 +1192,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 14,
|
"execution_count": null,
|
||||||
"id": "47c12bb34062ae7a",
|
"id": "47c12bb34062ae7a",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"ExecuteTime": {
|
"ExecuteTime": {
|
||||||
@@ -1332,7 +1226,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 15,
|
"execution_count": null,
|
||||||
"id": "29221dde",
|
"id": "29221dde",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -1375,7 +1269,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 16,
|
"execution_count": null,
|
||||||
"id": "03ee5daf",
|
"id": "03ee5daf",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
@@ -1388,7 +1282,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 17,
|
"execution_count": null,
|
||||||
"id": "b76ea08a",
|
"id": "b76ea08a",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -1396,10 +1290,10 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
" ts_code trade_date log_circ_mv\n",
|
" ts_code trade_date log_circ_mv\n",
|
||||||
"0 000001.SZ 2019-01-02 16.574219\n",
|
"5087381 605599.SH 2026-02-11 14.480487\n",
|
||||||
"1 000001.SZ 2019-01-03 16.583965\n",
|
"5087382 605599.SH 2026-02-12 14.482485\n",
|
||||||
"2 000001.SZ 2019-01-04 16.633371\n",
|
"5087383 605599.SH 2026-02-13 14.493206\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",
|
||||||
"去除极值\n",
|
"去除极值\n",
|
||||||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||||||
@@ -1409,7 +1303,7 @@
|
|||||||
"name": "stderr",
|
"name": "stderr",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 10.05it/s]\n"
|
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 9.90it/s]\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -1424,7 +1318,7 @@
|
|||||||
"name": "stderr",
|
"name": "stderr",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"MAD Filtering: 100%|██████████| 131/131 [00:16<00:00, 8.05it/s]\n"
|
"MAD Filtering: 100%|██████████| 131/131 [00:14<00:00, 8.90it/s]\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -1464,13 +1358,13 @@
|
|||||||
"截面 MAD 去极值处理完成。\n",
|
"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",
|
"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最小日期: 2019-01-02\n",
|
||||||
"df最大日期: 2026-02-06\n",
|
"df最大日期: 2026-02-13\n",
|
||||||
"2055944\n",
|
"2055886\n",
|
||||||
"train_data最小日期: 2020-01-02\n",
|
"train_data最小日期: 2020-01-02\n",
|
||||||
"train_data最大日期: 2022-12-30\n",
|
"train_data最大日期: 2022-12-30\n",
|
||||||
"2297633\n",
|
"2312688\n",
|
||||||
"test_data最小日期: 2023-01-03\n",
|
"test_data最小日期: 2023-01-03\n",
|
||||||
"test_data最大日期: 2026-02-06\n",
|
"test_data最大日期: 2026-02-13\n",
|
||||||
" ts_code trade_date log_circ_mv\n",
|
" ts_code trade_date log_circ_mv\n",
|
||||||
"0 000001.SZ 2019-01-02 16.574219\n",
|
"0 000001.SZ 2019-01-02 16.574219\n",
|
||||||
"1 000001.SZ 2019-01-03 16.583965\n",
|
"1 000001.SZ 2019-01-03 16.583965\n",
|
||||||
@@ -1483,7 +1377,7 @@
|
|||||||
"train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\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",
|
"test_data = df[(df['trade_date'] >= split_date)]\n",
|
||||||
"\n",
|
"\n",
|
||||||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
|
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].tail(3))\n",
|
||||||
"\n",
|
"\n",
|
||||||
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
||||||
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
||||||
@@ -1606,7 +1500,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 18,
|
"execution_count": null,
|
||||||
"id": "3ff2d1c5",
|
"id": "3ff2d1c5",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
@@ -1747,14 +1641,14 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 19,
|
"execution_count": null,
|
||||||
"id": "a5bbb8be",
|
"id": "a5bbb8be",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<catboost.core.CatBoostClassifier at 0x7b3eec333d70>"
|
"<catboost.core.CatBoostClassifier at 0x752dd29c0e30>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 19,
|
"execution_count": 19,
|
||||||
@@ -1772,7 +1666,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 20,
|
"execution_count": null,
|
||||||
"id": "5d1522a7538db91b",
|
"id": "5d1522a7538db91b",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"ExecuteTime": {
|
"ExecuteTime": {
|
||||||
@@ -1811,7 +1705,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 21,
|
"execution_count": null,
|
||||||
"id": "09b1799e",
|
"id": "09b1799e",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -1833,7 +1727,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 22,
|
"execution_count": null,
|
||||||
"id": "e53b209a",
|
"id": "e53b209a",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -1841,7 +1735,7 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"5584 2055944\n",
|
"5584 2055886\n",
|
||||||
" ts_code trade_date turnover_rate\n",
|
" ts_code trade_date turnover_rate\n",
|
||||||
"0 000001.SZ 2023-01-03 1.1307\n",
|
"0 000001.SZ 2023-01-03 1.1307\n",
|
||||||
"1 000001.SZ 2023-01-04 1.1284\n",
|
"1 000001.SZ 2023-01-04 1.1284\n",
|
||||||
@@ -1849,13 +1743,13 @@
|
|||||||
"3 000001.SZ 2023-01-06 0.6162\n",
|
"3 000001.SZ 2023-01-06 0.6162\n",
|
||||||
"4 000001.SZ 2023-01-09 0.5450\n",
|
"4 000001.SZ 2023-01-09 0.5450\n",
|
||||||
"... ... ... ...\n",
|
"... ... ... ...\n",
|
||||||
"2297628 605599.SH 2026-02-02 2.6968\n",
|
"2312683 605599.SH 2026-02-09 2.6607\n",
|
||||||
"2297629 605599.SH 2026-02-03 1.6084\n",
|
"2312684 605599.SH 2026-02-10 2.5722\n",
|
||||||
"2297630 605599.SH 2026-02-04 3.9328\n",
|
"2312685 605599.SH 2026-02-11 2.1806\n",
|
||||||
"2297631 605599.SH 2026-02-05 3.0072\n",
|
"2312686 605599.SH 2026-02-12 1.4513\n",
|
||||||
"2297632 605599.SH 2026-02-06 3.1129\n",
|
"2312687 605599.SH 2026-02-13 1.5153\n",
|
||||||
"\n",
|
"\n",
|
||||||
"[2297633 rows x 3 columns]\n"
|
"[2312688 rows x 3 columns]\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -1881,7 +1775,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.12.11"
|
"version": "3.13.2"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
@@ -1503,3 +1503,13 @@ trade_date,score,ts_code
|
|||||||
2026-02-05,0.6383257473564533,002193.SZ
|
2026-02-05,0.6383257473564533,002193.SZ
|
||||||
2026-02-06,0.45969830258066396,600493.SH
|
2026-02-06,0.45969830258066396,600493.SH
|
||||||
2026-02-06,0.6311025962732058,002193.SZ
|
2026-02-06,0.6311025962732058,002193.SZ
|
||||||
|
2026-02-09,0.4478161764829177,002719.SZ
|
||||||
|
2026-02-09,0.6157362204288197,603729.SH
|
||||||
|
2026-02-10,0.3999390029537349,002719.SZ
|
||||||
|
2026-02-10,0.5896469073813839,002193.SZ
|
||||||
|
2026-02-11,0.49232775247140015,002193.SZ
|
||||||
|
2026-02-11,0.628548611096168,603729.SH
|
||||||
|
2026-02-12,0.4321547861568006,002193.SZ
|
||||||
|
2026-02-12,0.5119718467949886,603729.SH
|
||||||
|
2026-02-13,0.39079335569325513,603729.SH
|
||||||
|
2026-02-13,0.46226975723447805,002193.SZ
|
||||||
|
|||||||
|
@@ -58,7 +58,13 @@ QMT 模块是 NewStock 量化交易系统的实盘交易执行模块，通过 `x
|
|||||||
|
|
||||||
交易信号通过 Redis 消息队列传递，每个策略对应一个独立的队列。消息格式为 JSON 对象，包含股票代码、操作类型、价格、时间戳等字段。系统对每条消息进行严格校验，包括日期校验、时间戳校验、必填字段校验等，确保只有当天的有效指令才会被执行。
|
交易信号通过 Redis 消息队列传递，每个策略对应一个独立的队列。消息格式为 JSON 对象，包含股票代码、操作类型、价格、时间戳等字段。系统对每条消息进行严格校验，包括日期校验、时间戳校验、必填字段校验等，确保只有当天的有效指令才会被执行。
|
||||||
|
|
||||||
买入逻辑支持槽位控制，通过 `total_slots` 参数限制同时持有的股票数量。系统根据可用资金和目标槽位自动计算每只股票的买入数量，金额过小或股数不足的请求会被拦截。卖出逻辑则根据 Redis 中的虚拟持仓和实盘可用持仓计算实际卖出数量，确保不会超卖。
|
买入逻辑支持两种模式：
|
||||||
|
|
||||||
|
1. **槽位控制模式**：通过 `total_slots` 参数限制同时持有的股票数量。系统根据可用资金和目标槽位自动计算每只股票的买入数量，金额过小或股数不足的请求会被拦截。
|
||||||
|
|
||||||
|
2. **仓位百分比模式**：通过 `position_pct` 参数指定目标持仓占账户总资产的比例。系统根据账户总资产计算目标金额，然后转换为具体股数进行下单。该模式无持仓数量限制。
|
||||||
|
|
||||||
|
卖出逻辑根据策略配置模式有所不同：槽位模式下根据 Redis 中的虚拟持仓和实盘可用持仓计算实际卖出数量；百分比模式下执行清仓操作。
|
||||||
|
|
||||||
### 3.3 持仓管理
|
### 3.3 持仓管理
|
||||||
|
|
||||||
@@ -193,9 +199,41 @@ QMT 模块是 NewStock 量化交易系统的实盘交易执行模块，通过 `x
|
|||||||
| `redis` | Object | Redis 连接配置，支持 host、port、db、password 等参数 |
|
| `redis` | Object | Redis 连接配置，支持 host、port、db、password 等参数 |
|
||||||
| `qmt_terminals` | Array | QMT 终端列表，每个终端包含唯一标识、别名、路径、账户信息 |
|
| `qmt_terminals` | Array | QMT 终端列表，每个终端包含唯一标识、别名、路径、账户信息 |
|
||||||
| `strategies` | Object | 策略配置，键为策略名，值包含使用的终端 ID 和执行参数 |
|
| `strategies` | Object | 策略配置，键为策略名，值包含使用的终端 ID 和执行参数 |
|
||||||
| `total_slots` | Integer | 策略的最大持仓股票数量 |
|
| `total_slots` | Integer | 策略的最大持仓股票数量（槽位模式） |
|
||||||
|
| `order_mode` | String | 下单模式，可选 `slots`（槽位）或 `percentage`（百分比），默认为 `slots` |
|
||||||
| `auto_reconnect` | Object | 自动重连配置，包含启用状态和执行时间 |
|
| `auto_reconnect` | Object | 自动重连配置，包含启用状态和执行时间 |
|
||||||
|
|
||||||
|
**槽位模式配置示例：**
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"strategies": {
|
||||||
|
"strategy_a": {
|
||||||
|
"qmt_id": "terminal_001",
|
||||||
|
"order_mode": "slots",
|
||||||
|
"total_slots": 5,
|
||||||
|
"execution": {
|
||||||
|
"buy_amount_per_stock": 20000,
|
||||||
|
"min_buy_amount": 2000
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
**百分比模式配置示例：**
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"strategies": {
|
||||||
|
"strategy_b": {
|
||||||
|
"qmt_id": "terminal_001",
|
||||||
|
"order_mode": "percentage"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
## 6. Web 仪表盘功能
|
## 6. Web 仪表盘功能
|
||||||
|
|
||||||
### 6.1 功能概览
|
### 6.1 功能概览
|
||||||
@@ -317,7 +355,152 @@ Web 仪表盘基于 Vue 3 和 Naive UI 组件库开发，提供可视化的系
|
|||||||
| 聚宽 | `.XSHE` / `.XSHG` | `000001.XSHE`、`600519.XSHG` |
|
| 聚宽 | `.XSHE` / `.XSHG` | `000001.XSHE`、`600519.XSHG` |
|
||||||
| QMT | `.SZ` / `.SH` | `000001.SZ`、`600519.SH` |
|
| QMT | `.SZ` / `.SH` | `000001.SZ`、`600519.SH` |
|
||||||
|
|
||||||
## 8. 系统架构
|
## 8. 百分比下单信号发送端（qmt_percentage_sender.py）
|
||||||
|
|
||||||
|
### 8.1 模块定位
|
||||||
|
|
||||||
|
`qmt_percentage_sender.py` 是基于**仓位百分比**的 QMT 信号发送端，与槽位模式的 `qmt_signal_sender.py` 并行存在。该模块用于配置为 `order_mode: "percentage"` 的策略，通过指定目标持仓占账户总资产的比例来触发交易。
|
||||||
|
|
||||||
|
### 8.2 核心函数
|
||||||
|
|
||||||
|
#### `send_qmt_percentage_signal(code, position_pct, action, price, is_backtest, timestamp, redis_config)`
|
||||||
|
|
||||||
|
| 参数 | 类型 | 说明 |
|
||||||
|
|------|------|------|
|
||||||
|
| `code` | str | 股票代码，聚宽格式（如 `000001.XSHE`、`600519.XSHG`） |
|
||||||
|
| `position_pct` | float | 目标持仓占总资产的比例，范围 0.0 ~ 1.0（如 0.2 表示 20%） |
|
||||||
|
| `action` | str | 交易动作，固定为 `"BUY"` 或 `"SELL"` |
|
||||||
|
| `price` | float | 当前最新价格，用于实盘限价单参考 |
|
||||||
|
| `is_backtest` | bool | 是否为回测模式（True/False） |
|
||||||
|
| `timestamp` | str | 时间戳字符串，格式 `"YYYY-MM-DD HH:MM:SS"` |
|
||||||
|
| `redis_config` | dict | Redis 连接配置，包含 `host`、`port`、`password`、`db`、`strategy_name` 等字段 |
|
||||||
|
|
||||||
|
### 8.3 处理流程
|
||||||
|
|
||||||
|
```
|
||||||
|
策略触发信号
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
1. 环境判断与流量控制
|
||||||
|
├─ 实盘模式 → 直接通过
|
||||||
|
└─ 回测模式 → 限制最多发送 10 条（防止回测刷爆队列）
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
2. 建立 Redis 连接（socket_timeout=1s）
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
3. 数据转换与规范化
|
||||||
|
└─ 股票代码格式转换：.XSHE → .SZ，.XSHG → .SH
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
4. 构建 JSON 消息体（包含 position_pct 字段）
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
5. 队列路由
|
||||||
|
├─ 回测 → {strategy_name}_backtest（TTL: 1 小时）
|
||||||
|
└─ 实盘 → {strategy_name}_real（TTL: 7 天）
|
||||||
|
│
|
||||||
|
▼
|
||||||
|
6. 控制台日志输出
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.4 消息格式
|
||||||
|
|
||||||
|
发送到 Redis 队列的 JSON 消息结构：
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"strategy_name": "my_strategy",
|
||||||
|
"stock_code": "000001.SZ",
|
||||||
|
"action": "BUY",
|
||||||
|
"price": 15.50,
|
||||||
|
"position_pct": 0.2,
|
||||||
|
"timestamp": "2026-02-17 14:30:00",
|
||||||
|
"is_backtest": false
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
| 字段 | 类型 | 说明 |
|
||||||
|
|------|------|------|
|
||||||
|
| `strategy_name` | str | 策略名称，来自 `redis_config['strategy_name']`，用于队列路由和持仓管理 |
|
||||||
|
| `stock_code` | str | QMT 格式的股票代码（`.SZ` / `.SH`） |
|
||||||
|
| `action` | str | 交易动作，`BUY` 或 `SELL` |
|
||||||
|
| `price` | float | 信号触发时的最新价格 |
|
||||||
|
| `position_pct` | float | 目标持仓占账户总资产的比例，范围 0.0 ~ 1.0 |
|
||||||
|
| `timestamp` | str | 信号生成时间，格式 `YYYY-MM-DD HH:MM:SS` |
|
||||||
|
| `is_backtest` | bool | 是否为回测环境发出的信号 |
|
||||||
|
|
||||||
|
### 8.5 买卖意图判定逻辑
|
||||||
|
|
||||||
|
与槽位模式不同，百分比模式需要**显式指定**交易动作：
|
||||||
|
|
||||||
|
- **`action = "BUY"`**：策略意向买入该股票，目标持仓占比为 `position_pct`。交易引擎根据账户总资产计算目标金额，然后转换为具体股数下单。
|
||||||
|
- **`action = "SELL"`**：策略意向清仓该股票。百分比模式下卖出采用简化逻辑，直接执行清仓操作。
|
||||||
|
|
||||||
|
### 8.6 买入计算公式
|
||||||
|
|
||||||
|
当 QMT 端接收到百分比模式的买入信号时，按以下公式计算买入股数：
|
||||||
|
|
||||||
|
```
|
||||||
|
目标金额 = 账户总资产 × position_pct
|
||||||
|
可用金额 = min(目标金额, 可用资金)
|
||||||
|
买入股数 = int(可用金额 / 价格 / 100) × 100
|
||||||
|
```
|
||||||
|
|
||||||
|
**边界处理：**
|
||||||
|
- 单笔金额 < 2000 元 → 拦截不下单
|
||||||
|
- 计算股数 < 100 股 → 拦截不下单
|
||||||
|
- 价格 ≤ 0 → 强制设为 1.0（仅测试用）
|
||||||
|
|
||||||
|
### 8.7 与槽位模式的对比
|
||||||
|
|
||||||
|
| 特性 | 槽位模式 (slots) | 百分比模式 (percentage) |
|
||||||
|
|------|------------------|------------------------|
|
||||||
|
| 核心参数 | `total_slots` | `position_pct` |
|
||||||
|
| 持仓限制 | 有（同时持仓数量限制） | 无 |
|
||||||
|
| 资金分配 | 按剩余槽位均分资金 | 按总资产比例计算 |
|
||||||
|
| 卖出逻辑 | 按持仓数量计算 | 清仓 |
|
||||||
|
| 配置方式 | 配置文件设置 `order_mode: "slots"` | 配置文件设置 `order_mode: "percentage"` |
|
||||||
|
| 信号发送 | `send_qmt_signal()` | `send_qmt_percentage_signal()` |
|
||||||
|
|
||||||
|
### 8.8 使用示例
|
||||||
|
|
||||||
|
```python
|
||||||
|
from qmt_percentage_sender import send_qmt_percentage_signal
|
||||||
|
|
||||||
|
# Redis 配置
|
||||||
|
redis_config = {
|
||||||
|
"host": "localhost",
|
||||||
|
"port": 6379,
|
||||||
|
"password": None,
|
||||||
|
"db": 0,
|
||||||
|
"strategy_name": "MyPercentageStrategy"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 买入信号：目标持仓占账户总资产的 20%
|
||||||
|
send_qmt_percentage_signal(
|
||||||
|
code="000001.XSHE",
|
||||||
|
position_pct=0.2,
|
||||||
|
action="BUY",
|
||||||
|
price=15.5,
|
||||||
|
is_backtest=False,
|
||||||
|
timestamp="2026-02-17 14:30:00",
|
||||||
|
redis_config=redis_config
|
||||||
|
)
|
||||||
|
|
||||||
|
# 卖出信号：清仓
|
||||||
|
send_qmt_percentage_signal(
|
||||||
|
code="000001.XSHE",
|
||||||
|
position_pct=0,
|
||||||
|
action="SELL",
|
||||||
|
price=15.8,
|
||||||
|
is_backtest=False,
|
||||||
|
timestamp="2026-02-17 14:35:00",
|
||||||
|
redis_config=redis_config
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
## 9. 系统架构
|
||||||
|
|
||||||
### 8.1 组件关系图
|
### 8.1 组件关系图
|
||||||
|
|
||||||
@@ -400,7 +583,7 @@ Web 仪表盘基于 Vue 3 和 Naive UI 组件库开发，提供可视化的系
|
|||||||
7. **订单执行**：调用 QMT API 下单，成功则缓存订单信息
|
7. **订单执行**：调用 QMT API 下单，成功则缓存订单信息
|
||||||
8. **状态更新**：标记虚拟持仓，异步等待成交回调
|
8. **状态更新**：标记虚拟持仓，异步等待成交回调
|
||||||
|
|
||||||
## 9. 启动与停止
|
## 10. 启动与停止
|
||||||
|
|
||||||
### 9.1 Windows 启动
|
### 9.1 Windows 启动
|
||||||
|
|
||||||
@@ -427,7 +610,7 @@ python run.py
|
|||||||
|------|----------|------|
|
|------|----------|------|
|
||||||
| API 服务 | 8001 | Web 仪表盘和 RESTful API 监听端口 |
|
| API 服务 | 8001 | Web 仪表盘和 RESTful API 监听端口 |
|
||||||
|
|
||||||
## 10. 注意事项
|
## 11. 注意事项
|
||||||
|
|
||||||
1. **QMT 终端要求**：确保 QMT 终端已登录且路径配置正确
|
1. **QMT 终端要求**：确保 QMT 终端已登录且路径配置正确
|
||||||
2. **Redis 服务**：系统依赖 Redis 运行，请确保 Redis 服务可用
|
2. **Redis 服务**：系统依赖 Redis 运行，请确保 Redis 服务可用
|
||||||
|
|||||||
102
qmt/qmt_percentage_sender.py
Normal file
102
qmt/qmt_percentage_sender.py
Normal file
@@ -0,0 +1,102 @@
|
|||||||
|
import redis
|
||||||
|
import json
|
||||||
|
|
||||||
|
# --- 模块级全局变量 ---
|
||||||
|
_BACKTEST_SEND_COUNT = 0
|
||||||
|
|
||||||
|
|
||||||
|
def send_qmt_percentage_signal(
|
||||||
|
code, position_pct, action, price, is_backtest, timestamp, redis_config
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
发送基于仓位百分比的信号到 Redis
|
||||||
|
|
||||||
|
参数:
|
||||||
|
- code: 股票代码 (聚宽格式: 000001.XSHE)
|
||||||
|
- position_pct: 目标持仓占总资产的比例 (0.0 ~ 1.0，如 0.2 表示 20%)
|
||||||
|
- action: 交易动作，"BUY" 或 "SELL"
|
||||||
|
- price: 当前最新价格 (用于实盘限价单参考)
|
||||||
|
- is_backtest: 是否为回测模式 (True/False)
|
||||||
|
- timestamp: 时间戳字符串，格式 "YYYY-MM-DD HH:MM:SS"
|
||||||
|
- redis_config: Redis配置字典，包含 host, port, password, db, strategy_name
|
||||||
|
"""
|
||||||
|
global _BACKTEST_SEND_COUNT
|
||||||
|
|
||||||
|
try:
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 1. 环境判断与流量控制
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
if is_backtest:
|
||||||
|
if _BACKTEST_SEND_COUNT >= 10:
|
||||||
|
return
|
||||||
|
_BACKTEST_SEND_COUNT += 1
|
||||||
|
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 2. 建立 Redis 连接
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
r = redis.Redis(
|
||||||
|
host=redis_config["host"],
|
||||||
|
port=redis_config["port"],
|
||||||
|
password=redis_config.get("password"),
|
||||||
|
db=redis_config.get("db", 0),
|
||||||
|
decode_responses=True,
|
||||||
|
socket_timeout=1,
|
||||||
|
)
|
||||||
|
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 3. 数据转换与规范化
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 股票代码格式转换: 聚宽(.XSHE/.XSHG) -> QMT(.SZ/.SH)
|
||||||
|
qmt_code = code
|
||||||
|
if code.endswith(".XSHE"):
|
||||||
|
qmt_code = code.replace(".XSHE", ".SZ")
|
||||||
|
elif code.endswith(".XSHG"):
|
||||||
|
qmt_code = code.replace(".XSHG", ".SH")
|
||||||
|
|
||||||
|
# 校验 action 参数
|
||||||
|
if action not in ["BUY", "SELL"]:
|
||||||
|
print(f"[Error] 无效的 action 参数: {action}，必须是 'BUY' 或 'SELL'")
|
||||||
|
return
|
||||||
|
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 4. 构建消息体
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
base_strategy_name = redis_config.get("strategy_name", "default_strategy")
|
||||||
|
|
||||||
|
msg = {
|
||||||
|
"strategy_name": base_strategy_name,
|
||||||
|
"stock_code": qmt_code,
|
||||||
|
"action": action,
|
||||||
|
"price": price,
|
||||||
|
"position_pct": float(position_pct),
|
||||||
|
"timestamp": timestamp,
|
||||||
|
"is_backtest": is_backtest,
|
||||||
|
}
|
||||||
|
|
||||||
|
json_payload = json.dumps(msg)
|
||||||
|
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 5. 队列路由
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
queue_key = (
|
||||||
|
f"{base_strategy_name}_backtest"
|
||||||
|
if is_backtest
|
||||||
|
else f"{base_strategy_name}_real"
|
||||||
|
)
|
||||||
|
expire_seconds = 3600 if is_backtest else 604800
|
||||||
|
|
||||||
|
r.rpush(queue_key, json_payload)
|
||||||
|
r.expire(queue_key, expire_seconds)
|
||||||
|
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
# 6. 控制台输出
|
||||||
|
# ---------------------------------------------------------
|
||||||
|
log_prefix = "【回测】" if is_backtest else "【实盘】"
|
||||||
|
pct_display = f"{position_pct * 100:.1f}%"
|
||||||
|
desc = f"目标仓位:{pct_display}" if action == "BUY" else "清仓"
|
||||||
|
print(
|
||||||
|
f"{log_prefix} 百分比信号 -> {qmt_code} | 动作:{action} | {desc} | 价格:{price} | 时间:{timestamp}"
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"[Error] 发送QMT百分比信号失败: {e}")
|
||||||
@@ -285,7 +285,251 @@ class MyXtQuantTraderCallback(XtQuantTraderCallback):
|
|||||||
|
|
||||||
|
|
||||||
# ================= 5. 核心消息处理 (重写版：拒绝静默失败) =================
|
# ================= 5. 核心消息处理 (重写版：拒绝静默失败) =================
|
||||||
def process_strategy_queue(strategy_name, r_client, xt_trader, acc, pos_manager):
|
def get_strategy_config(strategy_name, config):
|
||||||
|
"""获取策略配置，支持新旧配置格式兼容"""
|
||||||
|
strategies = config.get("strategies", {})
|
||||||
|
|
||||||
|
# 如果 strategies 是列表（旧格式），转换为默认配置
|
||||||
|
if isinstance(strategies, list):
|
||||||
|
return {"order_mode": "slots", "total_slots": 5}
|
||||||
|
|
||||||
|
# 获取策略配置
|
||||||
|
strategy_config = strategies.get(strategy_name, {})
|
||||||
|
|
||||||
|
# 设置默认值
|
||||||
|
result = {
|
||||||
|
"order_mode": strategy_config.get("order_mode", "slots"),
|
||||||
|
"total_slots": strategy_config.get("total_slots", 5),
|
||||||
|
}
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
def process_percentage_buy(
|
||||||
|
strategy_name, stock_code, price, position_pct, xt_trader, acc
|
||||||
|
):
|
||||||
|
"""处理百分比模式的买入逻辑"""
|
||||||
|
logger.info(f"[百分比模式] 处理买入: {stock_code}, 目标占比: {position_pct}")
|
||||||
|
|
||||||
|
# 查询资产
|
||||||
|
asset = xt_trader.query_stock_asset(acc)
|
||||||
|
if not asset:
|
||||||
|
logger.error("API 错误: query_stock_asset 返回 None，可能是 QMT 断连或未同步")
|
||||||
|
return
|
||||||
|
|
||||||
|
total_asset = asset.total_asset
|
||||||
|
available_cash = asset.cash
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"[百分比模式] 账户总资产: {total_asset:.2f}, 可用资金: {available_cash:.2f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 计算目标金额
|
||||||
|
target_amount = total_asset * position_pct
|
||||||
|
actual_amount = min(target_amount, available_cash)
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"[百分比模式] 目标金额: {target_amount:.2f}, 实际可用: {actual_amount:.2f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 检查最小金额限制
|
||||||
|
if actual_amount < 2000:
|
||||||
|
logger.warning(f"[百分比模式] 拦截买入: 金额过小 ({actual_amount:.2f} < 2000)")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 价格校验
|
||||||
|
if price <= 0:
|
||||||
|
logger.warning(
|
||||||
|
f"[百分比模式] 价格异常: {price}，强制设为1.0以计算股数(仅测试用)"
|
||||||
|
)
|
||||||
|
price = 1.0
|
||||||
|
|
||||||
|
# 计算股数
|
||||||
|
vol = int(actual_amount / price / 100) * 100
|
||||||
|
logger.info(
|
||||||
|
f"[百分比模式] 计算股数: 资金{actual_amount:.2f} / 价格{price} -> {vol}股"
|
||||||
|
)
|
||||||
|
|
||||||
|
if vol < 100:
|
||||||
|
logger.warning(f"[百分比模式] 拦截买入: 股数不足 100 ({vol})")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 执行下单
|
||||||
|
oid = xt_trader.order_stock(
|
||||||
|
acc,
|
||||||
|
stock_code,
|
||||||
|
xtconstant.STOCK_BUY,
|
||||||
|
vol,
|
||||||
|
xtconstant.FIX_PRICE,
|
||||||
|
price,
|
||||||
|
strategy_name,
|
||||||
|
"PyBuyPct",
|
||||||
|
)
|
||||||
|
|
||||||
|
if oid != -1:
|
||||||
|
logger.info(f"[百分比模式] √√√ 下单成功: ID={oid} {stock_code} 买入 {vol}")
|
||||||
|
ORDER_CACHE[oid] = (strategy_name, stock_code, "BUY")
|
||||||
|
else:
|
||||||
|
logger.error(
|
||||||
|
f"[百分比模式] XXX 下单请求被拒绝 (Result=-1)，请检查 QMT 终端报错"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def process_percentage_sell(strategy_name, stock_code, price, xt_trader, acc):
|
||||||
|
"""处理百分比模式的卖出逻辑（清仓）"""
|
||||||
|
logger.info(f"[百分比模式] 处理卖出: {stock_code} (清仓)")
|
||||||
|
|
||||||
|
# 查询实盘持仓
|
||||||
|
real_pos = xt_trader.query_stock_positions(acc)
|
||||||
|
if real_pos is None:
|
||||||
|
logger.error("[百分比模式] API 错误: query_stock_positions 返回 None")
|
||||||
|
return
|
||||||
|
|
||||||
|
rp = next((p for p in real_pos if p.stock_code == stock_code), None)
|
||||||
|
can_use = rp.can_use_volume if rp else 0
|
||||||
|
|
||||||
|
logger.info(f"[百分比模式] 股票 {stock_code} 实盘可用持仓: {can_use}")
|
||||||
|
|
||||||
|
if can_use <= 0:
|
||||||
|
logger.warning(f"[百分比模式] 拦截卖出: 无可用持仓")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 执行清仓
|
||||||
|
logger.info(f"[百分比模式] 执行清仓: {stock_code} @ {price}, 数量: {can_use}")
|
||||||
|
oid = xt_trader.order_stock(
|
||||||
|
acc,
|
||||||
|
stock_code,
|
||||||
|
xtconstant.STOCK_SELL,
|
||||||
|
can_use,
|
||||||
|
xtconstant.FIX_PRICE,
|
||||||
|
price,
|
||||||
|
strategy_name,
|
||||||
|
"PySellPct",
|
||||||
|
)
|
||||||
|
|
||||||
|
if oid != -1:
|
||||||
|
logger.info(f"[百分比模式] √√√ 下单成功: ID={oid} {stock_code} 卖出 {can_use}")
|
||||||
|
ORDER_CACHE[oid] = (strategy_name, stock_code, "SELL")
|
||||||
|
else:
|
||||||
|
logger.error(f"[百分比模式] XXX 下单请求被拒绝 (Result=-1)")
|
||||||
|
|
||||||
|
|
||||||
|
def process_slots_buy(
|
||||||
|
strategy_name, stock_code, price, total_slots, xt_trader, acc, pos_manager
|
||||||
|
):
|
||||||
|
"""处理槽位模式的买入逻辑（原有逻辑保持不变）"""
|
||||||
|
holding = pos_manager.get_holding_count(strategy_name)
|
||||||
|
empty = total_slots - holding
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"[槽位模式] 检查持仓: 当前占用 {holding} / 总槽位 {total_slots} -> 剩余 {empty}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if empty <= 0:
|
||||||
|
logger.warning(f"[槽位模式] 拦截买入: 槽位已满，不执行下单")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 查询资金
|
||||||
|
asset = xt_trader.query_stock_asset(acc)
|
||||||
|
if not asset:
|
||||||
|
logger.error(
|
||||||
|
"[槽位模式] API 错误: query_stock_asset 返回 None，可能是 QMT 断连或未同步"
|
||||||
|
)
|
||||||
|
return
|
||||||
|
|
||||||
|
logger.info(f"[槽位模式] 当前可用资金: {asset.cash:.2f}")
|
||||||
|
|
||||||
|
amt = asset.cash / empty
|
||||||
|
if amt < 2000:
|
||||||
|
logger.warning(f"[槽位模式] 拦截买入: 单笔金额过小 ({amt:.2f} < 2000)")
|
||||||
|
return
|
||||||
|
|
||||||
|
if price <= 0:
|
||||||
|
logger.warning(f"[槽位模式] 价格异常: {price}，强制设为1.0以计算股数(仅测试用)")
|
||||||
|
price = 1.0
|
||||||
|
|
||||||
|
vol = int(amt / price / 100) * 100
|
||||||
|
logger.info(f"[槽位模式] 计算股数: 资金{amt:.2f} / 价格{price} -> {vol}股")
|
||||||
|
|
||||||
|
if vol < 100:
|
||||||
|
logger.warning(f"[槽位模式] 拦截买入: 股数不足 100 ({vol})")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 执行下单
|
||||||
|
oid = xt_trader.order_stock(
|
||||||
|
acc,
|
||||||
|
stock_code,
|
||||||
|
xtconstant.STOCK_BUY,
|
||||||
|
vol,
|
||||||
|
xtconstant.FIX_PRICE,
|
||||||
|
price,
|
||||||
|
strategy_name,
|
||||||
|
"PyBuy",
|
||||||
|
)
|
||||||
|
|
||||||
|
if oid != -1:
|
||||||
|
logger.info(f"[槽位模式] √√√ 下单成功: ID={oid} {stock_code} 买入 {vol}")
|
||||||
|
ORDER_CACHE[oid] = (strategy_name, stock_code, "BUY")
|
||||||
|
pos_manager.mark_holding(strategy_name, stock_code)
|
||||||
|
else:
|
||||||
|
logger.error(f"[槽位模式] XXX 下单请求被拒绝 (Result=-1)，请检查 QMT 终端报错")
|
||||||
|
|
||||||
|
|
||||||
|
def process_slots_sell(strategy_name, stock_code, price, xt_trader, acc, pos_manager):
|
||||||
|
"""处理槽位模式的卖出逻辑（原有逻辑保持不变）"""
|
||||||
|
v_vol = pos_manager.get_position(strategy_name, stock_code)
|
||||||
|
logger.info(f"[槽位模式] 卖出 - Redis 记录虚拟持仓: {v_vol}")
|
||||||
|
|
||||||
|
if v_vol > 0:
|
||||||
|
logger.info(f"[槽位模式] 卖出 - 正在查询实盘持仓: {stock_code}")
|
||||||
|
real_pos = xt_trader.query_stock_positions(acc)
|
||||||
|
logger.info(
|
||||||
|
f"[槽位模式] 卖出 - 实盘持仓查询完成，获取到 {len(real_pos) if real_pos else 0} 条记录"
|
||||||
|
)
|
||||||
|
|
||||||
|
if real_pos is None:
|
||||||
|
logger.error("[槽位模式] API 错误: query_stock_positions 返回 None")
|
||||||
|
return
|
||||||
|
|
||||||
|
rp = next((p for p in real_pos if p.stock_code == stock_code), None)
|
||||||
|
can_use = rp.can_use_volume if rp else 0
|
||||||
|
logger.info(f"[槽位模式] 卖出 - 股票 {stock_code} 实盘可用持仓: {can_use}")
|
||||||
|
|
||||||
|
final = min(v_vol, can_use)
|
||||||
|
logger.info(f"[槽位模式] 卖出 - 计算卖出量: min({v_vol}, {can_use}) = {final}")
|
||||||
|
|
||||||
|
if final > 0:
|
||||||
|
logger.info(
|
||||||
|
f"[槽位模式] 卖出 - 执行卖出订单: {stock_code} @ {price}, 数量: {final}"
|
||||||
|
)
|
||||||
|
oid = xt_trader.order_stock(
|
||||||
|
acc,
|
||||||
|
stock_code,
|
||||||
|
xtconstant.STOCK_SELL,
|
||||||
|
final,
|
||||||
|
xtconstant.FIX_PRICE,
|
||||||
|
price,
|
||||||
|
strategy_name,
|
||||||
|
"PySell",
|
||||||
|
)
|
||||||
|
if oid != -1:
|
||||||
|
logger.info(
|
||||||
|
f"[槽位模式] √√√ 下单成功: ID={oid} {stock_code} 卖出 {final}"
|
||||||
|
)
|
||||||
|
ORDER_CACHE[oid] = (strategy_name, stock_code, "SELL")
|
||||||
|
else:
|
||||||
|
logger.error(f"[槽位模式] XXX 下单请求被拒绝 (Result=-1)")
|
||||||
|
else:
|
||||||
|
logger.warning(
|
||||||
|
f"[槽位模式] 拦截卖出: 最终计算卖出量为 0 (虚拟:{v_vol}, 实盘:{can_use})"
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
logger.warning(f"[槽位模式] 拦截卖出: Redis 中无此持仓记录，忽略")
|
||||||
|
|
||||||
|
|
||||||
|
def process_strategy_queue(
|
||||||
|
strategy_name, r_client, xt_trader, acc, pos_manager, config
|
||||||
|
):
|
||||||
queue_key = f"{strategy_name}_real"
|
queue_key = f"{strategy_name}_real"
|
||||||
|
|
||||||
# 1. 获取消息
|
# 1. 获取消息
|
||||||
@@ -326,130 +570,62 @@ def process_strategy_queue(strategy_name, r_client, xt_trader, acc, pos_manager)
|
|||||||
stock_code = data.get("stock_code")
|
stock_code = data.get("stock_code")
|
||||||
action = data.get("action")
|
action = data.get("action")
|
||||||
price = float(data.get("price", 0))
|
price = float(data.get("price", 0))
|
||||||
total_slots = int(data.get("total_slots", 1))
|
|
||||||
|
|
||||||
if not stock_code or not action:
|
if not stock_code or not action:
|
||||||
logger.error(f"缺少关键字段: Code={stock_code}, Action={action}")
|
logger.error(f"缺少关键字段: Code={stock_code}, Action={action}")
|
||||||
return
|
return
|
||||||
|
|
||||||
|
# 5. 获取策略配置，确定下单模式
|
||||||
|
strategy_config = get_strategy_config(strategy_name, config)
|
||||||
|
order_mode = strategy_config.get("order_mode", "slots")
|
||||||
|
|
||||||
logger.info(
|
logger.info(
|
||||||
f"解析成功: {action} {stock_code} @ {price}, 目标槽位: {total_slots}"
|
f"解析成功: {action} {stock_code} @ {price}, 下单模式: {order_mode}"
|
||||||
)
|
)
|
||||||
|
|
||||||
# 5. QMT 存活检查
|
# 6. QMT 存活检查
|
||||||
if xt_trader is None or acc is None:
|
if xt_trader is None or acc is None:
|
||||||
logger.error("严重错误: QMT 对象未初始化 (xt_trader is None)")
|
logger.error("严重错误: QMT 对象未初始化 (xt_trader is None)")
|
||||||
return
|
return
|
||||||
|
|
||||||
# 6. 买入逻辑
|
# 7. 根据下单模式执行相应逻辑
|
||||||
if action == "BUY":
|
if order_mode == "percentage":
|
||||||
holding = pos_manager.get_holding_count(strategy_name)
|
# 百分比模式
|
||||||
empty = total_slots - holding
|
position_pct = float(data.get("position_pct", 0))
|
||||||
|
|
||||||
logger.info(
|
if action == "BUY":
|
||||||
f"检查持仓: 当前占用 {holding} / 总槽位 {total_slots} -> 剩余 {empty}"
|
process_percentage_buy(
|
||||||
)
|
strategy_name, stock_code, price, position_pct, xt_trader, acc
|
||||||
|
|
||||||
if empty <= 0:
|
|
||||||
logger.warning(f"拦截买入: 槽位已满，不执行下单")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 查询资金
|
|
||||||
asset = xt_trader.query_stock_asset(acc)
|
|
||||||
if not asset:
|
|
||||||
logger.error(
|
|
||||||
"API 错误: query_stock_asset 返回 None，可能是 QMT 断连或未同步"
|
|
||||||
)
|
)
|
||||||
return
|
elif action == "SELL":
|
||||||
|
process_percentage_sell(
|
||||||
logger.info(f"当前可用资金: {asset.cash:.2f}")
|
strategy_name, stock_code, price, xt_trader, acc
|
||||||
|
|
||||||
amt = asset.cash / empty
|
|
||||||
if amt < 2000:
|
|
||||||
logger.warning(f"拦截买入: 单笔金额过小 ({amt:.2f} < 2000)")
|
|
||||||
return
|
|
||||||
|
|
||||||
if price <= 0:
|
|
||||||
logger.warning(f"价格异常: {price}，强制设为1.0以计算股数(仅测试用)")
|
|
||||||
price = 1.0
|
|
||||||
|
|
||||||
vol = int(amt / price / 100) * 100
|
|
||||||
logger.info(f"计算股数: 资金{amt:.2f} / 价格{price} -> {vol}股")
|
|
||||||
|
|
||||||
if vol < 100:
|
|
||||||
logger.warning(f"拦截买入: 股数不足 100 ({vol})")
|
|
||||||
return
|
|
||||||
|
|
||||||
# 执行下单
|
|
||||||
oid = xt_trader.order_stock(
|
|
||||||
acc,
|
|
||||||
stock_code,
|
|
||||||
xtconstant.STOCK_BUY,
|
|
||||||
vol,
|
|
||||||
xtconstant.FIX_PRICE,
|
|
||||||
price,
|
|
||||||
strategy_name,
|
|
||||||
"PyBuy",
|
|
||||||
)
|
|
||||||
|
|
||||||
if oid != -1:
|
|
||||||
logger.info(f"√√√ 下单成功: ID={oid} {stock_code} 买入 {vol}")
|
|
||||||
ORDER_CACHE[oid] = (strategy_name, stock_code, "BUY")
|
|
||||||
pos_manager.mark_holding(strategy_name, stock_code)
|
|
||||||
else:
|
|
||||||
logger.error(f"XXX 下单请求被拒绝 (Result=-1)，请检查 QMT 终端报错")
|
|
||||||
|
|
||||||
# 7. 卖出逻辑
|
|
||||||
elif action == "SELL":
|
|
||||||
v_vol = pos_manager.get_position(strategy_name, stock_code)
|
|
||||||
logger.info(f"卖出 - Redis 记录虚拟持仓: {v_vol}")
|
|
||||||
|
|
||||||
if v_vol > 0:
|
|
||||||
logger.info(f"卖出 - 正在查询实盘持仓: {stock_code}")
|
|
||||||
real_pos = xt_trader.query_stock_positions(acc)
|
|
||||||
logger.info(
|
|
||||||
f"卖出 - 实盘持仓查询完成，获取到 {len(real_pos) if real_pos else 0} 条记录"
|
|
||||||
)
|
)
|
||||||
|
|
||||||
if real_pos is None:
|
|
||||||
logger.error("API 错误: query_stock_positions 返回 None")
|
|
||||||
return
|
|
||||||
|
|
||||||
rp = next((p for p in real_pos if p.stock_code == stock_code), None)
|
|
||||||
can_use = rp.can_use_volume if rp else 0
|
|
||||||
logger.info(f"卖出 - 股票 {stock_code} 实盘可用持仓: {can_use}")
|
|
||||||
|
|
||||||
final = min(v_vol, can_use)
|
|
||||||
logger.info(f"卖出 - 计算卖出量: min({v_vol}, {can_use}) = {final}")
|
|
||||||
|
|
||||||
if final > 0:
|
|
||||||
logger.info(
|
|
||||||
f"卖出 - 执行卖出订单: {stock_code} @ {price}, 数量: {final}"
|
|
||||||
)
|
|
||||||
oid = xt_trader.order_stock(
|
|
||||||
acc,
|
|
||||||
stock_code,
|
|
||||||
xtconstant.STOCK_SELL,
|
|
||||||
final,
|
|
||||||
xtconstant.FIX_PRICE,
|
|
||||||
price,
|
|
||||||
strategy_name,
|
|
||||||
"PySell",
|
|
||||||
)
|
|
||||||
if oid != -1:
|
|
||||||
logger.info(f"√√√ 下单成功: ID={oid} {stock_code} 卖出 {final}")
|
|
||||||
ORDER_CACHE[oid] = (strategy_name, stock_code, "SELL")
|
|
||||||
else:
|
|
||||||
logger.error(f"XXX 下单请求被拒绝 (Result=-1)")
|
|
||||||
else:
|
|
||||||
logger.warning(
|
|
||||||
f"拦截卖出: 最终计算卖出量为 0 (虚拟:{v_vol}, 实盘:{can_use})"
|
|
||||||
)
|
|
||||||
else:
|
else:
|
||||||
logger.warning(f"拦截卖出: Redis 中无此持仓记录，忽略")
|
logger.error(f"未知的 Action: {action}")
|
||||||
|
|
||||||
else:
|
else:
|
||||||
logger.error(f"未知的 Action: {action}")
|
# 槽位模式（默认）
|
||||||
|
total_slots = int(
|
||||||
|
data.get("total_slots", strategy_config.get("total_slots", 5))
|
||||||
|
)
|
||||||
|
|
||||||
|
if action == "BUY":
|
||||||
|
process_slots_buy(
|
||||||
|
strategy_name,
|
||||||
|
stock_code,
|
||||||
|
price,
|
||||||
|
total_slots,
|
||||||
|
xt_trader,
|
||||||
|
acc,
|
||||||
|
pos_manager,
|
||||||
|
)
|
||||||
|
elif action == "SELL":
|
||||||
|
process_slots_sell(
|
||||||
|
strategy_name, stock_code, price, xt_trader, acc, pos_manager
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
logger.error(f"未知的 Action: {action}")
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(f"消息处理发生未捕获异常: {str(e)}", exc_info=True)
|
logger.error(f"消息处理发生未捕获异常: {str(e)}", exc_info=True)
|
||||||
@@ -662,7 +838,12 @@ def trading_loop():
|
|||||||
settler.reset_flag()
|
settler.reset_flag()
|
||||||
for s in watch_list:
|
for s in watch_list:
|
||||||
process_strategy_queue(
|
process_strategy_queue(
|
||||||
s, r, GLOBAL_STATE.xt_trader, GLOBAL_STATE.acc, pos_manager
|
s,
|
||||||
|
r,
|
||||||
|
GLOBAL_STATE.xt_trader,
|
||||||
|
GLOBAL_STATE.acc,
|
||||||
|
pos_manager,
|
||||||
|
CONFIG,
|
||||||
)
|
)
|
||||||
|
|
||||||
elif "150500" <= current_time_str <= "151000":
|
elif "150500" <= current_time_str <= "151000":
|
||||||
|
|||||||
Reference in New Issue
Block a user