Compare commits
8
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
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115547912d | ||
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zhaoli
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938192e900 | ||
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zhaoli
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507846cee1 | ||
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zhaoli
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7fad62a4ef | ||
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zhaoli
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f9ef005e9a | ||
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zhaoli
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c09997c7e2 | ||
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zhaoli
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32477c7bb8 | ||
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zhaoli
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e657c58758 |
@@ -1,46 +0,0 @@
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window,gate,start,end,trade_dates,gate_open,gate_closed,trip_rate,base_ann,base_sharpe,base_maxDD,gated_ann,gated_sharpe,gated_maxDD
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2026,hitrate_5d_0.50,2026-01-04,2026-08-19,157,92,65,0.414,0.255023,1.4465,-0.080671,0.649911,5.7351,-0.031795
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2026,hitrate_5d_0.60,2026-01-04,2026-08-19,157,49,108,0.6879,0.255023,1.4465,-0.080671,0.489313,6.0111,-0.014864
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2026,hitrate_5d_0.70,2026-01-04,2026-08-19,157,15,142,0.9045,0.255023,1.4465,-0.080671,0.229523,4.3611,-0.002029
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2026,hitrate_10d_0.50,2026-01-04,2026-08-19,157,109,48,0.3057,0.255023,1.4465,-0.080671,0.335822,2.6391,-0.060142
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2026,hitrate_10d_0.60,2026-01-04,2026-08-19,157,36,121,0.7707,0.255023,1.4465,-0.080671,0.251108,3.8579,-0.026842
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2026,hitrate_10d_0.70,2026-01-04,2026-08-19,157,9,148,0.9427,0.255023,1.4465,-0.080671,0.039314,1.3415,-0.012061
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2026,hitrate_20d_0.40,2026-01-04,2026-08-19,157,157,0,0.0,0.255023,1.4465,-0.080671,0.255023,1.4465,-0.080671
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2026,hitrate_20d_0.50,2026-01-04,2026-08-19,157,118,39,0.2484,0.255023,1.4465,-0.080671,0.343819,2.5185,-0.06259
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2026,hitrate_20d_0.60,2026-01-04,2026-08-19,157,28,129,0.8217,0.255023,1.4465,-0.080671,0.020214,0.3601,-0.028489
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2025,hitrate_5d_0.50,2025-01-02,2025-12-31,250,153,97,0.388,0.177515,0.8573,-0.217417,0.72055,6.9599,-0.048495
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2025,hitrate_5d_0.60,2025-01-02,2025-12-31,250,90,160,0.64,0.177515,0.8573,-0.217417,0.48564,5.3623,-0.046673
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2025,hitrate_5d_0.70,2025-01-02,2025-12-31,250,39,211,0.844,0.177515,0.8573,-0.217417,0.249221,4.9448,-0.009619
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2025,hitrate_10d_0.50,2025-01-02,2025-12-31,250,170,80,0.32,0.177515,0.8573,-0.217417,0.435847,3.5614,-0.055776
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2025,hitrate_10d_0.60,2025-01-02,2025-12-31,250,66,184,0.736,0.177515,0.8573,-0.217417,0.324574,5.2279,-0.016028
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2025,hitrate_10d_0.70,2025-01-02,2025-12-31,250,14,236,0.944,0.177515,0.8573,-0.217417,0.043002,1.4635,-0.010154
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2025,hitrate_20d_0.40,2025-01-02,2025-12-31,250,247,3,0.012,0.177515,0.8573,-0.217417,0.202949,0.9896,-0.217417
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2025,hitrate_20d_0.50,2025-01-02,2025-12-31,250,177,73,0.292,0.177515,0.8573,-0.217417,0.340772,2.8303,-0.071731
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2025,hitrate_20d_0.60,2025-01-02,2025-12-31,250,48,202,0.808,0.177515,0.8573,-0.217417,0.266028,4.1749,-0.021527
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2024,hitrate_5d_0.50,2024-01-02,2024-12-31,253,139,114,0.4506,0.08229,0.5594,-0.10685,0.30351,2.4275,-0.088219
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2024,hitrate_5d_0.60,2024-01-02,2024-12-31,253,71,182,0.7194,0.08229,0.5594,-0.10685,0.265365,2.4908,-0.078304
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2024,hitrate_5d_0.70,2024-01-02,2024-12-31,253,19,234,0.9249,0.08229,0.5594,-0.10685,0.143125,3.377,-0.003364
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2024,hitrate_10d_0.50,2024-01-02,2024-12-31,253,157,96,0.3794,0.08229,0.5594,-0.10685,0.277421,2.5767,-0.043146
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2024,hitrate_10d_0.60,2024-01-02,2024-12-31,253,37,216,0.8538,0.08229,0.5594,-0.10685,0.207161,3.6083,-0.011122
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2024,hitrate_10d_0.70,2024-01-02,2024-12-31,253,7,246,0.9723,0.08229,0.5594,-0.10685,0.031785,1.7408,-0.002083
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2024,hitrate_20d_0.40,2024-01-02,2024-12-31,253,247,6,0.0237,0.08229,0.5594,-0.10685,0.078007,0.5342,-0.10685
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2024,hitrate_20d_0.50,2024-01-02,2024-12-31,253,177,76,0.3004,0.08229,0.5594,-0.10685,0.210672,1.8469,-0.056207
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2024,hitrate_20d_0.60,2024-01-02,2024-12-31,253,13,240,0.9486,0.08229,0.5594,-0.10685,0.005953,0.3039,-0.014443
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2023,hitrate_5d_0.50,2023-01-03,2023-12-29,250,139,111,0.444,-0.047644,-0.2738,-0.197856,0.546654,4.2124,-0.035676
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2023,hitrate_5d_0.60,2023-01-03,2023-12-29,250,79,171,0.684,-0.047644,-0.2738,-0.197856,0.556291,5.1026,-0.025449
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2023,hitrate_5d_0.70,2023-01-03,2023-12-29,250,34,216,0.864,-0.047644,-0.2738,-0.197856,0.404269,4.4477,-0.013842
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2023,hitrate_10d_0.50,2023-01-03,2023-12-29,250,148,102,0.408,-0.047644,-0.2738,-0.197856,0.459869,3.5211,-0.046921
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2023,hitrate_10d_0.60,2023-01-03,2023-12-29,250,62,188,0.752,-0.047644,-0.2738,-0.197856,0.368232,4.0148,-0.022983
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2023,hitrate_10d_0.70,2023-01-03,2023-12-29,250,13,237,0.948,-0.047644,-0.2738,-0.197856,0.091481,2.1324,-0.010866
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2023,hitrate_20d_0.40,2023-01-03,2023-12-29,250,237,13,0.052,-0.047644,-0.2738,-0.197856,0.06639,0.3895,-0.146704
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||||||
2023,hitrate_20d_0.50,2023-01-03,2023-12-29,250,149,101,0.404,-0.047644,-0.2738,-0.197856,0.366016,2.5969,-0.063998
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||||||
2023,hitrate_20d_0.60,2023-01-03,2023-12-29,250,40,210,0.84,-0.047644,-0.2738,-0.197856,0.149844,2.2645,-0.032267
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||||||
2021,hitrate_5d_0.50,2021-01-04,2021-12-31,252,145,107,0.4246,0.18367,1.0979,-0.102651,0.556615,5.9167,-0.028062
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2021,hitrate_5d_0.60,2021-01-04,2021-12-31,252,68,184,0.7302,0.18367,1.0979,-0.102651,0.351867,5.8468,-0.011638
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2021,hitrate_5d_0.70,2021-01-04,2021-12-31,252,26,226,0.8968,0.18367,1.0979,-0.102651,0.148417,3.9593,-0.0041
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2021,hitrate_10d_0.50,2021-01-04,2021-12-31,252,163,89,0.3532,0.18367,1.0979,-0.102651,0.465023,4.2342,-0.040569
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2021,hitrate_10d_0.60,2021-01-04,2021-12-31,252,51,201,0.7976,0.18367,1.0979,-0.102651,0.21802,4.7978,-0.011134
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2021,hitrate_10d_0.70,2021-01-04,2021-12-31,252,13,239,0.9484,0.18367,1.0979,-0.102651,0.06108,2.7055,-0.000262
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2021,hitrate_20d_0.40,2021-01-04,2021-12-31,252,252,0,0.0,0.18367,1.0979,-0.102651,0.18367,1.0979,-0.102651
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||||||
2021,hitrate_20d_0.50,2021-01-04,2021-12-31,252,166,86,0.3413,0.18367,1.0979,-0.102651,0.289756,2.5787,-0.049244
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||||||
2021,hitrate_20d_0.60,2021-01-04,2021-12-31,252,38,214,0.8492,0.18367,1.0979,-0.102651,0.104685,2.394,-0.015084
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@@ -1,722 +0,0 @@
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[
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{
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"window": "2026",
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||||||
"gate": "hitrate_5d_0.50",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 92,
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||||||
"gate_closed": 65,
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||||||
"trip_rate": 0.414,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.649911,
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||||||
"gated_sharpe": 5.7351,
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||||||
"gated_maxDD": -0.031795
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||||||
},
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||||||
{
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||||||
"window": "2026",
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||||||
"gate": "hitrate_5d_0.60",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 49,
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||||||
"gate_closed": 108,
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||||||
"trip_rate": 0.6879,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.489313,
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||||||
"gated_sharpe": 6.0111,
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||||||
"gated_maxDD": -0.014864
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||||||
},
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||||||
{
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||||||
"window": "2026",
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||||||
"gate": "hitrate_5d_0.70",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 15,
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||||||
"gate_closed": 142,
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||||||
"trip_rate": 0.9045,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.229523,
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||||||
"gated_sharpe": 4.3611,
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||||||
"gated_maxDD": -0.002029
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||||||
},
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||||||
{
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||||||
"window": "2026",
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||||||
"gate": "hitrate_10d_0.50",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 109,
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||||||
"gate_closed": 48,
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||||||
"trip_rate": 0.3057,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.335822,
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||||||
"gated_sharpe": 2.6391,
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||||||
"gated_maxDD": -0.060142
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||||||
},
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||||||
{
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||||||
"window": "2026",
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||||||
"gate": "hitrate_10d_0.60",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 36,
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||||||
"gate_closed": 121,
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||||||
"trip_rate": 0.7707,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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|
||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.251108,
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||||||
"gated_sharpe": 3.8579,
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||||||
"gated_maxDD": -0.026842
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||||||
},
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||||||
{
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||||||
"window": "2026",
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|
||||||
"gate": "hitrate_10d_0.70",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 9,
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||||||
"gate_closed": 148,
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||||||
"trip_rate": 0.9427,
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||||||
"base_ann": 0.255023,
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||||||
"base_sharpe": 1.4465,
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||||||
"base_maxDD": -0.080671,
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||||||
"gated_ann": 0.039314,
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||||||
"gated_sharpe": 1.3415,
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||||||
"gated_maxDD": -0.012061
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||||||
},
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||||||
{
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||||||
"window": "2026",
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||||||
"gate": "hitrate_20d_0.40",
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||||||
"start": "2026-01-04",
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||||||
"end": "2026-08-19",
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||||||
"trade_dates": 157,
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||||||
"gate_open": 157,
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||||||
"gate_closed": 0,
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||||||
"trip_rate": 0.0,
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|
||||||
"base_ann": 0.255023,
|
|
||||||
"base_sharpe": 1.4465,
|
|
||||||
"base_maxDD": -0.080671,
|
|
||||||
"gated_ann": 0.255023,
|
|
||||||
"gated_sharpe": 1.4465,
|
|
||||||
"gated_maxDD": -0.080671
|
|
||||||
},
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|
||||||
{
|
|
||||||
"window": "2026",
|
|
||||||
"gate": "hitrate_20d_0.50",
|
|
||||||
"start": "2026-01-04",
|
|
||||||
"end": "2026-08-19",
|
|
||||||
"trade_dates": 157,
|
|
||||||
"gate_open": 118,
|
|
||||||
"gate_closed": 39,
|
|
||||||
"trip_rate": 0.2484,
|
|
||||||
"base_ann": 0.255023,
|
|
||||||
"base_sharpe": 1.4465,
|
|
||||||
"base_maxDD": -0.080671,
|
|
||||||
"gated_ann": 0.343819,
|
|
||||||
"gated_sharpe": 2.5185,
|
|
||||||
"gated_maxDD": -0.06259
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2026",
|
|
||||||
"gate": "hitrate_20d_0.60",
|
|
||||||
"start": "2026-01-04",
|
|
||||||
"end": "2026-08-19",
|
|
||||||
"trade_dates": 157,
|
|
||||||
"gate_open": 28,
|
|
||||||
"gate_closed": 129,
|
|
||||||
"trip_rate": 0.8217,
|
|
||||||
"base_ann": 0.255023,
|
|
||||||
"base_sharpe": 1.4465,
|
|
||||||
"base_maxDD": -0.080671,
|
|
||||||
"gated_ann": 0.020214,
|
|
||||||
"gated_sharpe": 0.3601,
|
|
||||||
"gated_maxDD": -0.028489
|
|
||||||
},
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||||||
{
|
|
||||||
"window": "2025",
|
|
||||||
"gate": "hitrate_5d_0.50",
|
|
||||||
"start": "2025-01-02",
|
|
||||||
"end": "2025-12-31",
|
|
||||||
"trade_dates": 250,
|
|
||||||
"gate_open": 153,
|
|
||||||
"gate_closed": 97,
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|
||||||
"trip_rate": 0.388,
|
|
||||||
"base_ann": 0.177515,
|
|
||||||
"base_sharpe": 0.8573,
|
|
||||||
"base_maxDD": -0.217417,
|
|
||||||
"gated_ann": 0.72055,
|
|
||||||
"gated_sharpe": 6.9599,
|
|
||||||
"gated_maxDD": -0.048495
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||||||
},
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||||||
{
|
|
||||||
"window": "2025",
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|
||||||
"gate": "hitrate_5d_0.60",
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||||||
"start": "2025-01-02",
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||||||
"end": "2025-12-31",
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|
||||||
"trade_dates": 250,
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|
||||||
"gate_open": 90,
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|
||||||
"gate_closed": 160,
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|
||||||
"trip_rate": 0.64,
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|
||||||
"base_ann": 0.177515,
|
|
||||||
"base_sharpe": 0.8573,
|
|
||||||
"base_maxDD": -0.217417,
|
|
||||||
"gated_ann": 0.48564,
|
|
||||||
"gated_sharpe": 5.3623,
|
|
||||||
"gated_maxDD": -0.046673
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|
||||||
},
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||||||
{
|
|
||||||
"window": "2025",
|
|
||||||
"gate": "hitrate_5d_0.70",
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||||||
"start": "2025-01-02",
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||||||
"end": "2025-12-31",
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|
||||||
"trade_dates": 250,
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|
||||||
"gate_open": 39,
|
|
||||||
"gate_closed": 211,
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|
||||||
"trip_rate": 0.844,
|
|
||||||
"base_ann": 0.177515,
|
|
||||||
"base_sharpe": 0.8573,
|
|
||||||
"base_maxDD": -0.217417,
|
|
||||||
"gated_ann": 0.249221,
|
|
||||||
"gated_sharpe": 4.9448,
|
|
||||||
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|
||||||
"gate": "hitrate_20d_0.60",
|
|
||||||
"start": "2023-01-03",
|
|
||||||
"end": "2023-12-29",
|
|
||||||
"trade_dates": 250,
|
|
||||||
"gate_open": 40,
|
|
||||||
"gate_closed": 210,
|
|
||||||
"trip_rate": 0.84,
|
|
||||||
"base_ann": -0.047644,
|
|
||||||
"base_sharpe": -0.2738,
|
|
||||||
"base_maxDD": -0.197856,
|
|
||||||
"gated_ann": 0.149844,
|
|
||||||
"gated_sharpe": 2.2645,
|
|
||||||
"gated_maxDD": -0.032267
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_5d_0.50",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 145,
|
|
||||||
"gate_closed": 107,
|
|
||||||
"trip_rate": 0.4246,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.556615,
|
|
||||||
"gated_sharpe": 5.9167,
|
|
||||||
"gated_maxDD": -0.028062
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_5d_0.60",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 68,
|
|
||||||
"gate_closed": 184,
|
|
||||||
"trip_rate": 0.7302,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.351867,
|
|
||||||
"gated_sharpe": 5.8468,
|
|
||||||
"gated_maxDD": -0.011638
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_5d_0.70",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 26,
|
|
||||||
"gate_closed": 226,
|
|
||||||
"trip_rate": 0.8968,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.148417,
|
|
||||||
"gated_sharpe": 3.9593,
|
|
||||||
"gated_maxDD": -0.0041
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_10d_0.50",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 163,
|
|
||||||
"gate_closed": 89,
|
|
||||||
"trip_rate": 0.3532,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.465023,
|
|
||||||
"gated_sharpe": 4.2342,
|
|
||||||
"gated_maxDD": -0.040569
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_10d_0.60",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 51,
|
|
||||||
"gate_closed": 201,
|
|
||||||
"trip_rate": 0.7976,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.21802,
|
|
||||||
"gated_sharpe": 4.7978,
|
|
||||||
"gated_maxDD": -0.011134
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_10d_0.70",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 13,
|
|
||||||
"gate_closed": 239,
|
|
||||||
"trip_rate": 0.9484,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.06108,
|
|
||||||
"gated_sharpe": 2.7055,
|
|
||||||
"gated_maxDD": -0.000262
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_20d_0.40",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 252,
|
|
||||||
"gate_closed": 0,
|
|
||||||
"trip_rate": 0.0,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.18367,
|
|
||||||
"gated_sharpe": 1.0979,
|
|
||||||
"gated_maxDD": -0.102651
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_20d_0.50",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 166,
|
|
||||||
"gate_closed": 86,
|
|
||||||
"trip_rate": 0.3413,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.289756,
|
|
||||||
"gated_sharpe": 2.5787,
|
|
||||||
"gated_maxDD": -0.049244
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"window": "2021",
|
|
||||||
"gate": "hitrate_20d_0.60",
|
|
||||||
"start": "2021-01-04",
|
|
||||||
"end": "2021-12-31",
|
|
||||||
"trade_dates": 252,
|
|
||||||
"gate_open": 38,
|
|
||||||
"gate_closed": 214,
|
|
||||||
"trip_rate": 0.8492,
|
|
||||||
"base_ann": 0.18367,
|
|
||||||
"base_sharpe": 1.0979,
|
|
||||||
"base_maxDD": -0.102651,
|
|
||||||
"gated_ann": 0.104685,
|
|
||||||
"gated_sharpe": 2.394,
|
|
||||||
"gated_maxDD": -0.015084
|
|
||||||
}
|
|
||||||
]
|
|
||||||
Binary file not shown.
@@ -1,293 +0,0 @@
|
|||||||
"""Signal-quality gate walk-forward backtest.
|
|
||||||
|
|
||||||
Gates trades based on whether the model's recent topk predictions were correct
|
|
||||||
(hit rate). This is a retrospective gate — it measures prediction accuracy,
|
|
||||||
not market state.
|
|
||||||
|
|
||||||
Usage:
|
|
||||||
cd /app && .venv/bin/python book/scripts/signal_quality_gate_bt.py
|
|
||||||
"""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import json
|
|
||||||
import pathlib
|
|
||||||
import sys
|
|
||||||
import time
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
LAKE_ROOT = "/home/data/lake"
|
|
||||||
MARKET = "US"
|
|
||||||
OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
|
|
||||||
|
|
||||||
WINDOWS = [
|
|
||||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
|
||||||
"pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
|
|
||||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
|
||||||
"pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
|
|
||||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
|
||||||
"pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
|
|
||||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
|
||||||
"pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
|
|
||||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
|
||||||
"pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
|
|
||||||
]
|
|
||||||
|
|
||||||
# Signal-quality gate configs: (lookback_days, threshold, name)
|
|
||||||
SIGNAL_GATE_CONFIGS = [
|
|
||||||
(5, 0.50, "hitrate_5d_0.50"),
|
|
||||||
(5, 0.60, "hitrate_5d_0.60"),
|
|
||||||
(5, 0.70, "hitrate_5d_0.70"),
|
|
||||||
(10, 0.50, "hitrate_10d_0.50"),
|
|
||||||
(10, 0.60, "hitrate_10d_0.60"),
|
|
||||||
(10, 0.70, "hitrate_10d_0.70"),
|
|
||||||
(20, 0.40, "hitrate_20d_0.40"),
|
|
||||||
(20, 0.50, "hitrate_20d_0.50"),
|
|
||||||
(20, 0.60, "hitrate_20d_0.60"),
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
def load_pred(path: str) -> pd.Series:
|
|
||||||
df = pd.read_pickle(path)
|
|
||||||
if isinstance(df, pd.DataFrame):
|
|
||||||
if "score" in df.columns:
|
|
||||||
s = df["score"]
|
|
||||||
else:
|
|
||||||
s = df.iloc[:, 0]
|
|
||||||
else:
|
|
||||||
s = df
|
|
||||||
idx = s.index
|
|
||||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
|
||||||
s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
|
|
||||||
return s
|
|
||||||
|
|
||||||
|
|
||||||
def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
|
|
||||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
|
||||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
|
|
||||||
sp = cfg.lake_root / "symbols.parquet"
|
|
||||||
if sp.exists():
|
|
||||||
syms = pd.read_parquet(sp)
|
|
||||||
col = "symbol" if "symbol" in syms.columns else syms.columns[0]
|
|
||||||
symbols = sorted(syms[col].astype(str).str.upper().tolist())
|
|
||||||
else:
|
|
||||||
return pd.DataFrame()
|
|
||||||
closes = {}
|
|
||||||
for sym in symbols:
|
|
||||||
p = cfg.bar_path("1d", sym)
|
|
||||||
if not p.exists():
|
|
||||||
continue
|
|
||||||
try:
|
|
||||||
df = pd.read_parquet(p)
|
|
||||||
except Exception:
|
|
||||||
continue
|
|
||||||
if not len(df):
|
|
||||||
continue
|
|
||||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
|
||||||
ts = pd.to_datetime(tcol)
|
|
||||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
|
||||||
warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
|
|
||||||
df = df.loc[warmup_start:end]
|
|
||||||
if len(df) >= 22:
|
|
||||||
closes[sym] = df["c"]
|
|
||||||
return pd.DataFrame(closes)
|
|
||||||
|
|
||||||
|
|
||||||
def compute_hit_rate_series(
|
|
||||||
pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
|
|
||||||
) -> pd.Series:
|
|
||||||
dt_idx = pred.index.get_level_values(0)
|
|
||||||
trade_dates = sorted(dt_idx.unique())
|
|
||||||
hit_rates = {}
|
|
||||||
for i in range(1, len(trade_dates)):
|
|
||||||
prev_date = trade_dates[i - 1]
|
|
||||||
curr_date = trade_dates[i]
|
|
||||||
try:
|
|
||||||
prev_scores = pred.loc[prev_date]
|
|
||||||
except KeyError:
|
|
||||||
continue
|
|
||||||
if isinstance(prev_scores, pd.DataFrame):
|
|
||||||
prev_scores = prev_scores.iloc[:, 0]
|
|
||||||
prev_scores = prev_scores.dropna().sort_values(ascending=False)
|
|
||||||
topk_syms = list(prev_scores.index[:topk])
|
|
||||||
if curr_date not in ret_df.index:
|
|
||||||
continue
|
|
||||||
today_ret = ret_df.loc[curr_date]
|
|
||||||
topk_rets = today_ret.reindex(topk_syms).dropna()
|
|
||||||
if len(topk_rets) > 0:
|
|
||||||
hit_rate = (topk_rets > 0).mean()
|
|
||||||
hit_rates[curr_date] = hit_rate
|
|
||||||
hit_series = pd.Series(hit_rates)
|
|
||||||
if len(hit_series) == 0:
|
|
||||||
return hit_series
|
|
||||||
rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
|
|
||||||
return rolling_hr
|
|
||||||
|
|
||||||
|
|
||||||
def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
|
|
||||||
if not isinstance(pred.index, pd.MultiIndex):
|
|
||||||
return {"error": "pred must have MultiIndex"}
|
|
||||||
ret_df = close_df.pct_change()
|
|
||||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
|
||||||
dt_idx = pred.index.get_level_values(0)
|
|
||||||
window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
|
|
||||||
window_pred = pred.loc[window_mask]
|
|
||||||
if len(window_pred) == 0:
|
|
||||||
return {"error": "no pred data in window"}
|
|
||||||
trade_dates = sorted(dt_idx[window_mask].unique())
|
|
||||||
gate_open = {}
|
|
||||||
for d in trade_dates:
|
|
||||||
known = hit_rate[hit_rate.index <= d]
|
|
||||||
if len(known) > 0 and not pd.isna(known.iloc[-1]):
|
|
||||||
gate_open[d] = bool(known.iloc[-1] >= threshold)
|
|
||||||
else:
|
|
||||||
gate_open[d] = True
|
|
||||||
n_total = len(trade_dates)
|
|
||||||
n_open = sum(1 for v in gate_open.values() if v)
|
|
||||||
n_closed = n_total - n_open
|
|
||||||
holdings_base = []
|
|
||||||
holdings_gated = []
|
|
||||||
equity_gated = 1_000_000.0
|
|
||||||
equity_base = 1_000_000.0
|
|
||||||
prev_week = None
|
|
||||||
prev_scores = None
|
|
||||||
daily_gated = []
|
|
||||||
daily_base = []
|
|
||||||
ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
|
|
||||||
for d in trade_dates:
|
|
||||||
try:
|
|
||||||
day_scores = window_pred.loc[d]
|
|
||||||
except KeyError:
|
|
||||||
daily_gated.append(equity_gated)
|
|
||||||
daily_base.append(equity_base)
|
|
||||||
prev_scores = None
|
|
||||||
continue
|
|
||||||
if isinstance(day_scores, pd.DataFrame):
|
|
||||||
day_scores = day_scores.iloc[:, 0]
|
|
||||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
|
||||||
if len(day_scores) == 0:
|
|
||||||
daily_gated.append(equity_gated)
|
|
||||||
daily_base.append(equity_base)
|
|
||||||
prev_scores = None
|
|
||||||
continue
|
|
||||||
ret_row = ret_by_date.get(d)
|
|
||||||
if ret_row is None:
|
|
||||||
daily_gated.append(equity_gated)
|
|
||||||
daily_base.append(equity_base)
|
|
||||||
prev_scores = day_scores
|
|
||||||
continue
|
|
||||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
|
|
||||||
gate_val = gate_open.get(d, True)
|
|
||||||
if cur_week != prev_week or not holdings_base:
|
|
||||||
if prev_scores is not None:
|
|
||||||
holdings_base = list(prev_scores.index[:topk])
|
|
||||||
if holdings_base:
|
|
||||||
base_rets = ret_row.reindex(holdings_base).dropna()
|
|
||||||
if len(base_rets) > 0:
|
|
||||||
equity_base *= (1 + base_rets.mean())
|
|
||||||
if gate_val:
|
|
||||||
if cur_week != prev_week or not holdings_gated:
|
|
||||||
if prev_scores is not None:
|
|
||||||
holdings_gated = list(prev_scores.index[:topk])
|
|
||||||
if holdings_gated:
|
|
||||||
hold_rets = ret_row.reindex(holdings_gated).dropna()
|
|
||||||
if len(hold_rets) > 0:
|
|
||||||
equity_gated *= (1 + hold_rets.mean())
|
|
||||||
else:
|
|
||||||
holdings_gated = []
|
|
||||||
prev_week = cur_week
|
|
||||||
prev_scores = day_scores
|
|
||||||
daily_gated.append(equity_gated)
|
|
||||||
daily_base.append(equity_base)
|
|
||||||
g_series = pd.Series(daily_gated, index=trade_dates)
|
|
||||||
b_series = pd.Series(daily_base, index=trade_dates)
|
|
||||||
def _metrics(eq):
|
|
||||||
if len(eq) < 2:
|
|
||||||
return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
|
|
||||||
rets = eq.pct_change().dropna()
|
|
||||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
|
||||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
|
||||||
sharpe = ann_ret / vol if vol > 0 else 0
|
|
||||||
peak = eq.cummax()
|
|
||||||
dd = (eq - peak) / peak
|
|
||||||
maxDD = float(dd.min())
|
|
||||||
return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
|
|
||||||
base_m = _metrics(b_series)
|
|
||||||
gated_m = _metrics(g_series)
|
|
||||||
return {
|
|
||||||
"trade_dates": n_total,
|
|
||||||
"gate_open_days": n_open,
|
|
||||||
"gate_closed_days": n_closed,
|
|
||||||
"trip_rate": round(n_closed / n_total, 4) if n_total else 0,
|
|
||||||
"base": base_m,
|
|
||||||
"gated": gated_m,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
|
||||||
full_start = "2015-01-03"
|
|
||||||
full_end = "2026-08-19"
|
|
||||||
print("Loading lake bars...")
|
|
||||||
close_df = load_bars_for_window(full_start, full_end)
|
|
||||||
print(f" {close_df.shape[1]} symbols, {close_df.shape[0]} days")
|
|
||||||
ret_df = close_df.pct_change()
|
|
||||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
|
||||||
results = []
|
|
||||||
for window in WINDOWS:
|
|
||||||
wl, ws, we = window["label"], window["start"], window["end"]
|
|
||||||
pred_path = window["pred"]
|
|
||||||
print(f"\n=== Window {wl} ({ws} to {we}) ===")
|
|
||||||
pred = load_pred(pred_path)
|
|
||||||
print(f" pred shape: {pred.shape}")
|
|
||||||
hit_rates = {}
|
|
||||||
for lookback, _, name in SIGNAL_GATE_CONFIGS:
|
|
||||||
if lookback not in hit_rates:
|
|
||||||
hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
|
|
||||||
hit_rates[lookback] = hr
|
|
||||||
print(f" lookback={lookback}: {len(hr)} days with hit rates")
|
|
||||||
for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
|
|
||||||
hr = hit_rates[lookback]
|
|
||||||
bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
|
|
||||||
if "error" in bt:
|
|
||||||
print(f" {name}: {bt['error']}")
|
|
||||||
continue
|
|
||||||
row = {
|
|
||||||
"window": wl,
|
|
||||||
"gate": name,
|
|
||||||
"start": ws,
|
|
||||||
"end": we,
|
|
||||||
"trade_dates": bt["trade_dates"],
|
|
||||||
"gate_open": bt["gate_open_days"],
|
|
||||||
"gate_closed": bt["gate_closed_days"],
|
|
||||||
"trip_rate": bt["trip_rate"],
|
|
||||||
"base_ann": bt["base"]["ann_return"],
|
|
||||||
"base_sharpe": bt["base"]["sharpe"],
|
|
||||||
"base_maxDD": bt["base"]["maxDD"],
|
|
||||||
"gated_ann": bt["gated"]["ann_return"],
|
|
||||||
"gated_sharpe": bt["gated"]["sharpe"],
|
|
||||||
"gated_maxDD": bt["gated"]["maxDD"],
|
|
||||||
}
|
|
||||||
results.append(row)
|
|
||||||
print(f" {name}: trip={bt['trip_rate']:.1%}, "
|
|
||||||
f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
|
|
||||||
f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f})")
|
|
||||||
df = pd.DataFrame(results)
|
|
||||||
out_path = OUT_DIR / "signal_quality_gate_results.csv"
|
|
||||||
df.to_csv(out_path, index=False)
|
|
||||||
with open(OUT_DIR / "signal_quality_gate_results.json", "w") as f:
|
|
||||||
json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
|
|
||||||
print(f"\nSaved to {out_path}")
|
|
||||||
print("\n=== Summary: Gated Return by Window ===")
|
|
||||||
for gate_name in df["gate"].unique():
|
|
||||||
gdf = df[df["gate"] == gate_name]
|
|
||||||
print(f"\n{gate_name}:")
|
|
||||||
for _, r in gdf.iterrows():
|
|
||||||
print(f" {r['window']}: base={r['base_ann']:+.1%}, gated={r['gated_ann']:+.1%}, "
|
|
||||||
f"trip={r['trip_rate']:.0%}, diff={r['gated_ann']-r['base_ann']:+.1%}pp")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
+16
-15
@@ -1,30 +1,31 @@
|
|||||||
# TradeAC custom-qlib-code snapshot (auto-generated)
|
# TradeAC custom-qlib-code snapshot (auto-generated)
|
||||||
# parent repo HEAD : ceb1e196e24761a87d1afe1bde00079b79e02693
|
# parent repo HEAD : 507846cee16eeee11daf33c4176e8aec79b985b2
|
||||||
# tac-qlib/tac_qlib/contrib
|
# tac-qlib/tac_qlib/contrib
|
||||||
# tac-qlib/tac_qlib/data
|
# tac-qlib/tac_qlib/data
|
||||||
# per-file hashes (git hash-object):
|
# per-file hashes (git hash-object):
|
||||||
1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
|
1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
|
||||||
861592c63edd6a0853a9cb174b5970435b135fc8 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
b419ee55ed455a1c45423d1c9025ca5cc0a98576 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
||||||
5c547a2ef92e075e550fe6d01508a2f1d3f536bc tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
2f6c67620aa2f9e6aaaef3369361d9b3eac3d6ca tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||||
4f656130d167e79dcaaeb7783a121f0b36852374 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
fdd5923a70a399e8680913593ff111641947898e tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||||
0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
||||||
b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
||||||
b1489f2fc0dee85f0a4f90b2e6ad545ed9c8967b tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
08dec87ccdf6bb5d2cf611ca3032a4280aaab8cf tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||||
121ef237da1df1b8e21a561c3ad0db200b901339 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
6fb61946ea9a83dfb560de3717f5fbf482c4c00e tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
||||||
74d0da348cbcc3700c96b6f4fe4391488e61efc5 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
3e80f2e08b661ddd2f58ffe5a6196063fa41ae51 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||||
d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
||||||
d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
||||||
2c2f167b693f4366a769998e3c9d4804f29e31e0 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
c4ef84ffda2a611262412fe1127689c667f3d0c1 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||||
c29e45e562256bf786c36f91a971b097467276e9 tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||||
6dd1c568a2961842793674390d5abffd1a0e71b8 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
8d684b3216b040071d9ee4fa920a0e0c7486d278 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
||||||
519a1f4c05dbe0ac018ab8b779eb33d53b4dd545 tac-qlib/tac_qlib/contrib/strategy/ic_gate.py
|
896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9 tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
|
||||||
79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
|
79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
|
||||||
7bcee5f0b09cfa721440f1354f16f2dd9a112b12 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
|
5b9acfb4340111b204249add7760bd53c6ae03f1 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
|
||||||
|
aa1ee880d52ceb5821d65973962099c2254f710a tac-qlib/tac_qlib/contrib/strategy/top_bottom.py
|
||||||
fe60bacdfedd48617863be31f24b7c7daebfac5a tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
|
fe60bacdfedd48617863be31f24b7c7daebfac5a tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
|
||||||
92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
|
92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
|
||||||
316bf4aa160cc8d15929ea648be03f4b4999667d tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
7c4e6c345fad1978efe8860c0d977d0c02d6f8d9 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||||
554a3f29d181b64effbf49a8161b32e7f93d8d3e tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
99e602392d51663cb06d5c425000b1ed1e5a916b tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||||
8b47f6d78ac046b6b7b2fb07bd7f3382773ffb73 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
020dcdcf288e4832c8cf2386351f78d5ceb4fe13 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||||
53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
||||||
8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
|
8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
|
||||||
|
|||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,11 +1,13 @@
|
|||||||
from .ic_gate import ICGateTopkDropoutStrategy # noqa: F401
|
from .kelly_dropout import FractionalKellyDropoutStrategy # noqa: F401
|
||||||
from .optimal_stop import OptimalStopControl # noqa: F401
|
from .optimal_stop import OptimalStopControl # noqa: F401
|
||||||
from .regime_gate import RegimeGateTopkDropoutStrategy # noqa: F401
|
from .regime_gate import RegimeGateDropoutStrategy # noqa: F401
|
||||||
|
from .top_bottom import TopBottomDropoutStrategy # noqa: F401
|
||||||
from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
|
from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
"ICGateTopkDropoutStrategy",
|
|
||||||
"OptimalStopControl",
|
"OptimalStopControl",
|
||||||
"RegimeGateTopkDropoutStrategy",
|
"FractionalKellyDropoutStrategy",
|
||||||
"WeeklyRebalanceDropoutStrategy",
|
"WeeklyRebalanceDropoutStrategy",
|
||||||
|
"TopBottomDropoutStrategy",
|
||||||
|
"RegimeGateDropoutStrategy",
|
||||||
]
|
]
|
||||||
|
|||||||
Binary file not shown.
Binary file not shown.
@@ -1,117 +0,0 @@
|
|||||||
"""Realized-IC circuit breaker TopkDropout strategy.
|
|
||||||
|
|
||||||
Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
|
|
||||||
holds the book (issues NO orders) while the streaming realized RankIC of the
|
|
||||||
deployed signal is below threshold — i.e. the model's cross-sectional
|
|
||||||
predictions are no longer earning against realized forward returns. When the
|
|
||||||
gate is open it behaves exactly like the reference TopkDropoutStrategy.
|
|
||||||
|
|
||||||
The gate is evaluated per trade step on the trailing mean realized RankIC of
|
|
||||||
the signal over the last ``ic_window`` trading days whose label is fully
|
|
||||||
realized as of the decision date (no lookahead — a 5d fwd label ``close[t+6]/
|
|
||||||
close[t+1]-1`` is only known at ``t+6``).
|
|
||||||
|
|
||||||
Two wiring modes:
|
|
||||||
|
|
||||||
* ``ic_gate``: a precomputed ``pd.Series`` indexed by datetime of booleans
|
|
||||||
(True = gate open / trade allowed). Computed once by the caller (e.g.
|
|
||||||
``rd_backtest``) and looked up per step. Missing dates default to open.
|
|
||||||
* realized-IC self-computation: when ``ic_min_rankic`` is given but no
|
|
||||||
``ic_gate``, the strategy computes the per-date realized RankIC itself from
|
|
||||||
``self.signal`` (the pred scores) and the lake 1d bars via
|
|
||||||
``tac_qlib.risk_limits.realized_rankic_series``, then applies the same
|
|
||||||
trailing-window comparison. Works when instantiated from a workflow YAML
|
|
||||||
PortAnaRecord config (``lake_root`` / ``market`` must be provided).
|
|
||||||
"""
|
|
||||||
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
|
|
||||||
from qlib.backtest.decision import TradeDecisionWO
|
|
||||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
|
||||||
|
|
||||||
from tac_qlib.risk_limits import ic_circuit_breaker, realized_rankic_series
|
|
||||||
|
|
||||||
__all__ = ["ICGateTopkDropoutStrategy"]
|
|
||||||
|
|
||||||
|
|
||||||
class ICGateTopkDropoutStrategy(TopkDropoutStrategy):
|
|
||||||
"""TopkDropout with a streaming realized-IC circuit breaker.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
|
||||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
|
||||||
ic_min_rankic : float — pause new trading while trailing realized RankIC is
|
|
||||||
below this threshold (0 disables the gate).
|
|
||||||
ic_window : int — trailing window for the realized RankIC mean (default 22).
|
|
||||||
ic_label_horizon : int — label horizon in trading days (default 6).
|
|
||||||
ic_min_obs : int — min realized labels before the gate arms (default 10).
|
|
||||||
ic_gate : pd.Series, optional — precomputed per-date gate (bool indexed by
|
|
||||||
datetime). When provided, it overrides self-computation.
|
|
||||||
lake_root, market : str — lake location for self-computed realized IC.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
*,
|
|
||||||
topk,
|
|
||||||
n_drop,
|
|
||||||
ic_min_rankic: float = 0.0,
|
|
||||||
ic_window: int = 22,
|
|
||||||
ic_label_horizon: int = 6,
|
|
||||||
ic_min_obs: int = 10,
|
|
||||||
ic_gate=None,
|
|
||||||
lake_root: str = "",
|
|
||||||
market: str = "US",
|
|
||||||
**kwargs,
|
|
||||||
):
|
|
||||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
|
||||||
self.ic_min_rankic = float(ic_min_rankic or 0.0)
|
|
||||||
self.ic_window = int(ic_window or 22)
|
|
||||||
self.ic_label_horizon = int(ic_label_horizon or 6)
|
|
||||||
self.ic_min_obs = int(ic_min_obs or 10)
|
|
||||||
self._ic_gate = ic_gate
|
|
||||||
self._realized_ic = None
|
|
||||||
self.lake_root = lake_root or ""
|
|
||||||
self.market = market or "US"
|
|
||||||
|
|
||||||
def _load_realized_ic(self):
|
|
||||||
if self._realized_ic is None:
|
|
||||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(
|
|
||||||
self.trade_calendar.get_trade_step(), shift=-self.ic_label_horizon
|
|
||||||
)
|
|
||||||
pred = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
|
||||||
if isinstance(pred, pd.DataFrame):
|
|
||||||
pred = pred.iloc[:, 0]
|
|
||||||
self._realized_ic = realized_rankic_series(
|
|
||||||
pred, self.lake_root, self.market, label_horizon=self.ic_label_horizon
|
|
||||||
)
|
|
||||||
return self._realized_ic
|
|
||||||
|
|
||||||
def _gate_open(self, trade_start_time) -> bool:
|
|
||||||
ts = pd.Timestamp(trade_start_time)
|
|
||||||
if self._ic_gate is not None:
|
|
||||||
# precomputed gate series: look up the latest known decision date <= ts
|
|
||||||
known = self._ic_gate[self._ic_gate.index <= ts]
|
|
||||||
if len(known):
|
|
||||||
return bool(known.iloc[-1])
|
|
||||||
return True
|
|
||||||
if self.ic_min_rankic <= 0:
|
|
||||||
return True
|
|
||||||
realized = self._load_realized_ic()
|
|
||||||
limits = {
|
|
||||||
"ic_min_rankic": self.ic_min_rankic,
|
|
||||||
"ic_window": self.ic_window,
|
|
||||||
"ic_min_obs": self.ic_min_obs,
|
|
||||||
}
|
|
||||||
tripped, _reason, _trail = ic_circuit_breaker(realized, ts, limits)
|
|
||||||
return not tripped
|
|
||||||
|
|
||||||
def generate_trade_decision(self, execute_result=None):
|
|
||||||
trade_step = self.trade_calendar.get_trade_step()
|
|
||||||
trade_start_time, _ = self.trade_calendar.get_step_time(trade_step)
|
|
||||||
if not self._gate_open(trade_start_time):
|
|
||||||
return TradeDecisionWO([], self)
|
|
||||||
return super().generate_trade_decision(execute_result)
|
|
||||||
@@ -0,0 +1,201 @@
|
|||||||
|
"""Fractional-Kelly dropout strategy for cross-sectional signals.
|
||||||
|
|
||||||
|
Sizing rule variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||||
|
the topk/n_drop SELECTION is identical to the reference, but the buy size is
|
||||||
|
proportional to the score MAGNITUDE (edge) instead of equal-weight, capped at a
|
||||||
|
fraction ``cap_frac`` of the equal-weight notional so a single name cannot
|
||||||
|
over-concentrate the book.
|
||||||
|
|
||||||
|
``cap_frac`` is the fraction of the equal-weight per-name notional that a top
|
||||||
|
signal can deploy at most (e.g. 0.5 = at most half the equal-weight size).
|
||||||
|
Names whose score is below the median of the buy set get a proportionally
|
||||||
|
smaller slice; the residual stays in cash (that is the point of the rule:
|
||||||
|
throw away less edge per name, deploy less capital when conviction is low).
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
from qlib.backtest import Order
|
||||||
|
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||||
|
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||||
|
|
||||||
|
__all__ = ["FractionalKellyDropoutStrategy"]
|
||||||
|
|
||||||
|
DEFAULT_CAP_FRAC = 0.5
|
||||||
|
|
||||||
|
|
||||||
|
class FractionalKellyDropoutStrategy(TopkDropoutStrategy):
|
||||||
|
"""TopkDropout selection with score-magnitude (fractional-Kelly) sizing.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||||
|
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||||
|
cap_frac : max buy notional as a fraction of the equal-weight notional.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, *, topk, n_drop, cap_frac: float = DEFAULT_CAP_FRAC, **kwargs):
|
||||||
|
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||||
|
self.cap_frac = cap_frac
|
||||||
|
|
||||||
|
def generate_trade_decision(self, execute_result=None):
|
||||||
|
import copy
|
||||||
|
|
||||||
|
trade_step = self.trade_calendar.get_trade_step()
|
||||||
|
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||||
|
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||||
|
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||||
|
if isinstance(pred_score, pd.DataFrame):
|
||||||
|
pred_score = pred_score.iloc[:, 0]
|
||||||
|
if pred_score is None:
|
||||||
|
return TradeDecisionWO([], self)
|
||||||
|
|
||||||
|
if self.only_tradable:
|
||||||
|
|
||||||
|
def get_first_n(li, n, reverse=False):
|
||||||
|
cur_n = 0
|
||||||
|
res = []
|
||||||
|
for si in reversed(li) if reverse else li:
|
||||||
|
if self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
):
|
||||||
|
res.append(si)
|
||||||
|
cur_n += 1
|
||||||
|
if cur_n >= n:
|
||||||
|
break
|
||||||
|
return res[::-1] if reverse else res
|
||||||
|
|
||||||
|
def get_last_n(li, n):
|
||||||
|
return get_first_n(li, n, reverse=True)
|
||||||
|
|
||||||
|
def filter_stock(li):
|
||||||
|
return [
|
||||||
|
si
|
||||||
|
for si in li
|
||||||
|
if self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
]
|
||||||
|
|
||||||
|
else:
|
||||||
|
|
||||||
|
def get_first_n(li, n):
|
||||||
|
return list(li)[:n]
|
||||||
|
|
||||||
|
def get_last_n(li, n):
|
||||||
|
return list(li)[-n:]
|
||||||
|
|
||||||
|
def filter_stock(li):
|
||||||
|
return li
|
||||||
|
|
||||||
|
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||||
|
sell_order_list: List[Order] = []
|
||||||
|
buy_order_list: List[Order] = []
|
||||||
|
cash = current_temp.get_cash()
|
||||||
|
current_stock_list = current_temp.get_stock_list()
|
||||||
|
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||||
|
|
||||||
|
if self.method_buy == "top":
|
||||||
|
today = get_first_n(
|
||||||
|
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||||
|
self.n_drop + self.topk - len(last),
|
||||||
|
)
|
||||||
|
elif self.method_buy == "random":
|
||||||
|
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||||
|
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||||
|
n = self.n_drop + self.topk - len(last)
|
||||||
|
try:
|
||||||
|
today = np.random.choice(candi, n, replace=False)
|
||||||
|
except ValueError:
|
||||||
|
today = candi
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f"This type of input is not supported")
|
||||||
|
|
||||||
|
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||||
|
|
||||||
|
if self.method_sell == "bottom":
|
||||||
|
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||||
|
elif self.method_sell == "random":
|
||||||
|
candi = filter_stock(last)
|
||||||
|
try:
|
||||||
|
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||||
|
except ValueError:
|
||||||
|
sell = candi
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f"This type of input is not supported")
|
||||||
|
|
||||||
|
buy = today[: len(sell) + self.topk - len(last)]
|
||||||
|
for code in current_stock_list:
|
||||||
|
if not self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||||
|
):
|
||||||
|
continue
|
||||||
|
if code in sell:
|
||||||
|
time_per_step = self.trade_calendar.get_freq()
|
||||||
|
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||||
|
continue
|
||||||
|
sell_amount = current_temp.get_stock_amount(code=code)
|
||||||
|
sell_order = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=sell_amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.SELL,
|
||||||
|
)
|
||||||
|
if self.trade_exchange.check_order(sell_order):
|
||||||
|
sell_order_list.append(sell_order)
|
||||||
|
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||||
|
sell_order, position=current_temp
|
||||||
|
)
|
||||||
|
cash += trade_val - trade_cost
|
||||||
|
|
||||||
|
if len(buy) == 0:
|
||||||
|
return TradeDecisionWO(sell_order_list, self)
|
||||||
|
|
||||||
|
# ---- fractional-Kelly sizing --------------------------------------
|
||||||
|
# equal-weight notional (reference baseline)
|
||||||
|
eq_notional = cash * self.risk_degree / len(buy)
|
||||||
|
buy_scores = pred_score.reindex(buy).astype(float)
|
||||||
|
lo, hi = buy_scores.min(), buy_scores.max()
|
||||||
|
if hi == lo:
|
||||||
|
w = pd.Series(1.0, index=buy_scores.index)
|
||||||
|
else:
|
||||||
|
w = (buy_scores - lo) / (hi - lo) # [0,1] edge magnitude
|
||||||
|
w = w.clip(lower=0.0)
|
||||||
|
w_max = w.max()
|
||||||
|
w = w / w_max if w_max > 0 else w # max == 1.0
|
||||||
|
for code in buy:
|
||||||
|
if not self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||||
|
):
|
||||||
|
continue
|
||||||
|
buy_price = self.trade_exchange.get_deal_price(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||||
|
)
|
||||||
|
notional = eq_notional * min(self.cap_frac, float(w.get(code, 0.0)))
|
||||||
|
buy_amount = notional / buy_price
|
||||||
|
factor = self.trade_exchange.get_factor(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||||
|
buy_order = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=buy_amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.BUY,
|
||||||
|
)
|
||||||
|
buy_order_list.append(buy_order)
|
||||||
|
|
||||||
|
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||||
@@ -1,215 +1,231 @@
|
|||||||
"""Regime-gate TopkDropout strategy.
|
"""HMM-regime overlay TopkDropout strategy.
|
||||||
|
|
||||||
Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
|
Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||||
holds the book (issues NO orders) while a regime detector says the market is in
|
selection and sizing are identical to the reference, but a name is only BOUGHT
|
||||||
an unfavorable state. When the gate is open it behaves exactly like the
|
(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
|
||||||
reference TopkDropoutStrategy.
|
the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
|
||||||
|
of being opened.
|
||||||
|
|
||||||
Three detector types are supported (all causal — no lookahead):
|
The regime posterior is read from the lake feature provider on the fly via
|
||||||
|
``qlib.data.D.features`` (field ``$sp_hmm_p_regime1``) for the signal window, so
|
||||||
|
no regime column needs to enter the model's ``feature_fields`` — the gate is a
|
||||||
|
pure overlay (book ch.01: regime flags regressed as model features, survived
|
||||||
|
only as an overlay). The HMM itself was fit with ``fit_end=<train end>`` when
|
||||||
|
the lake features were backfilled, so there is no lookahead.
|
||||||
|
|
||||||
* ``dispersion``: cross-sectional standard deviation of 22-day rolling returns
|
Names already held are NOT force-sold when the regime turns unfavourable
|
||||||
across the universe. Gate closes when CS dispersion < threshold (low
|
(entry gate only, matching the queue-10 design).
|
||||||
dispersion means the spread between winners and losers is too narrow for
|
|
||||||
TopkDropout to exploit).
|
|
||||||
* ``vol``: cross-sectional mean of 22-day rolling realized volatility. Gate
|
|
||||||
closes when avg vol is outside a band ``[vol_low, vol_high]`` (strategy
|
|
||||||
needs moderate vol — too calm or too turbulent both hurt).
|
|
||||||
* ``hmm``: pre-computed HMM posterior for regime 1 (``sp_hmm_p_regime1``).
|
|
||||||
Gate closes when posterior < threshold (model is not confident the calm
|
|
||||||
regime is active).
|
|
||||||
|
|
||||||
The gate is provided as a precomputed ``pd.Series`` of booleans indexed by
|
|
||||||
datetime (True = trade allowed). The companion ``compute_regime_gate``
|
|
||||||
function builds this series from lake bars; call it once before backtesting
|
|
||||||
and pass the result as the ``regime_gate`` parameter.
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
from __future__ import annotations
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
|
||||||
from qlib.backtest.decision import TradeDecisionWO
|
from qlib.backtest import Order
|
||||||
|
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||||
|
|
||||||
__all__ = ["RegimeGateTopkDropoutStrategy", "compute_regime_gate"]
|
try:
|
||||||
|
from qlib.data import D
|
||||||
|
except ImportError: # pragma: no cover - qlib always present in this stack
|
||||||
|
D = None
|
||||||
|
|
||||||
|
__all__ = ["RegimeGateDropoutStrategy"]
|
||||||
|
|
||||||
|
DEFAULT_REGIME_THRESHOLD = 0.5
|
||||||
|
REGIME_FIELD = "$sp_hmm_p_regime1"
|
||||||
|
|
||||||
|
|
||||||
class RegimeGateTopkDropoutStrategy(TopkDropoutStrategy):
|
class RegimeGateDropoutStrategy(TopkDropoutStrategy):
|
||||||
"""TopkDropout with a regime-gate circuit breaker.
|
"""TopkDropout with an HMM-regime entry gate on buy candidates.
|
||||||
|
|
||||||
Parameters
|
Parameters
|
||||||
----------
|
----------
|
||||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||||
regime_gate : pd.Series — precomputed per-date gate (bool indexed by
|
regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
|
||||||
datetime). True = trade allowed, False = no orders. Missing dates
|
new position (default 0.5).
|
||||||
default to open (trade allowed).
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(self, *, regime_gate=None, **kwargs):
|
def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
|
||||||
super().__init__(**kwargs)
|
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||||
self._regime_gate = regime_gate
|
self.regime_threshold = regime_threshold
|
||||||
|
|
||||||
def _gate_open(self, trade_start_time) -> bool:
|
def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
|
||||||
if self._regime_gate is None:
|
"""Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
|
||||||
return True
|
if D is None:
|
||||||
ts = pd.Timestamp(trade_start_time)
|
return pd.Series(dtype=float)
|
||||||
known = self._regime_gate[self._regime_gate.index <= ts]
|
try:
|
||||||
if len(known):
|
df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
|
||||||
return bool(known.iloc[-1])
|
except Exception: # noqa: BLE001 - a regime read failure should gate open, not crash
|
||||||
return True # default open if no history yet
|
return pd.Series(dtype=float)
|
||||||
|
if df is None or len(df) == 0:
|
||||||
|
return pd.Series(dtype=float)
|
||||||
|
# df index is MultiIndex (datetime, instrument); take the last day's values
|
||||||
|
df = df.reset_index()
|
||||||
|
ts_col = "datetime" if "datetime" in df.columns else df.columns[0]
|
||||||
|
sym_col = "instrument" if "instrument" in df.columns else df.columns[1]
|
||||||
|
last_ts = df[ts_col].max()
|
||||||
|
last = df[df[ts_col] == last_ts]
|
||||||
|
out = {}
|
||||||
|
for _, row in last.iterrows():
|
||||||
|
sym = str(row[sym_col]).split("/")[-1].upper()
|
||||||
|
val = row.iloc[-1]
|
||||||
|
out[sym] = float(val) if val == val else np.nan
|
||||||
|
return pd.Series(out)
|
||||||
|
|
||||||
def generate_trade_decision(self, execute_result=None):
|
def generate_trade_decision(self, execute_result=None):
|
||||||
|
import copy
|
||||||
|
|
||||||
trade_step = self.trade_calendar.get_trade_step()
|
trade_step = self.trade_calendar.get_trade_step()
|
||||||
trade_start_time, _ = self.trade_calendar.get_step_time(trade_step)
|
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||||
if not self._gate_open(trade_start_time):
|
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||||
|
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||||
|
if isinstance(pred_score, pd.DataFrame):
|
||||||
|
pred_score = pred_score.iloc[:, 0]
|
||||||
|
if pred_score is None:
|
||||||
return TradeDecisionWO([], self)
|
return TradeDecisionWO([], self)
|
||||||
return super().generate_trade_decision(execute_result)
|
|
||||||
|
|
||||||
|
if self.only_tradable:
|
||||||
|
|
||||||
# ---------------------------------------------------------------------------
|
def get_first_n(li, n, reverse=False):
|
||||||
# Precomputation helper
|
cur_n = 0
|
||||||
# ---------------------------------------------------------------------------
|
res = []
|
||||||
|
for si in reversed(li) if reverse else li:
|
||||||
def compute_regime_gate(
|
if self.trade_exchange.is_stock_tradable(
|
||||||
detector: str,
|
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||||
threshold: float = 0.0,
|
):
|
||||||
*,
|
res.append(si)
|
||||||
lake_root: str = "",
|
cur_n += 1
|
||||||
market: str = "US",
|
if cur_n >= n:
|
||||||
start: str = "2015-01-03",
|
|
||||||
end: str = "2026-08-19",
|
|
||||||
vol_low: float = 0.0,
|
|
||||||
vol_high: float = 999.0,
|
|
||||||
hmm_field: str = "sp_hmm_p_regime1",
|
|
||||||
) -> pd.Series:
|
|
||||||
"""Build a per-date regime gate series from lake bars.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
detector : str — ``"dispersion"``, ``"vol"``, or ``"hmm"``.
|
|
||||||
threshold : float — for ``dispersion``: min CS dispersion to allow trading.
|
|
||||||
For ``hmm``: min HMM posterior to allow trading.
|
|
||||||
Ignored for ``vol`` (uses ``vol_low``/``vol_high`` band instead).
|
|
||||||
lake_root, market : str — lake location.
|
|
||||||
start, end : str — date window.
|
|
||||||
vol_low, vol_high : float — annualized vol band for the ``vol`` detector.
|
|
||||||
hmm_field : str — HMM feature column name for the ``hmm`` detector.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
pd.Series — bool, indexed by datetime. True = trade allowed.
|
|
||||||
"""
|
|
||||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
|
||||||
|
|
||||||
cfg = LakeConfig(resolve_lake_root(lake_root or None), market)
|
|
||||||
symbols = _universe_symbols(cfg)
|
|
||||||
close_df, vol_df = _load_daily_bars(symbols, cfg, start, end)
|
|
||||||
if close_df.empty:
|
|
||||||
return pd.Series(dtype=bool)
|
|
||||||
|
|
||||||
if detector == "dispersion":
|
|
||||||
return _dispersion_gate(close_df, threshold)
|
|
||||||
elif detector == "vol":
|
|
||||||
return _vol_gate(close_df, vol_low, vol_high)
|
|
||||||
elif detector == "hmm":
|
|
||||||
return _hmm_gate(cfg, symbols, threshold, start, end, hmm_field)
|
|
||||||
else:
|
|
||||||
raise ValueError(f"Unknown detector: {detector!r}")
|
|
||||||
|
|
||||||
|
|
||||||
def _universe_symbols(cfg) -> list:
|
|
||||||
"""Read symbols from the lake symbols.parquet."""
|
|
||||||
import pathlib
|
|
||||||
|
|
||||||
sp = cfg.lake_root / "symbols.parquet"
|
|
||||||
if sp.exists():
|
|
||||||
df = pd.read_parquet(sp)
|
|
||||||
col = "symbol" if "symbol" in df.columns else df.columns[0]
|
|
||||||
return sorted(df[col].astype(str).str.upper().tolist())
|
|
||||||
return []
|
|
||||||
|
|
||||||
|
|
||||||
def _load_daily_bars(symbols, cfg, start, end):
|
|
||||||
"""Load daily close prices for all symbols into a wide DataFrame."""
|
|
||||||
closes = {}
|
|
||||||
vols = {}
|
|
||||||
for sym in symbols:
|
|
||||||
p = cfg.bar_path("1d", sym)
|
|
||||||
if not p.exists():
|
|
||||||
continue
|
|
||||||
try:
|
|
||||||
df = pd.read_parquet(p)
|
|
||||||
except Exception:
|
|
||||||
continue
|
|
||||||
if not len(df):
|
|
||||||
continue
|
|
||||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
|
||||||
ts = pd.to_datetime(tcol)
|
|
||||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
|
||||||
df = df.loc[start:end]
|
|
||||||
if len(df) < 22:
|
|
||||||
continue
|
|
||||||
closes[sym] = df["c"]
|
|
||||||
if "v" in df.columns:
|
|
||||||
vols[sym] = df["v"]
|
|
||||||
close_df = pd.DataFrame(closes)
|
|
||||||
vol_df = pd.DataFrame(vols) if vols else None
|
|
||||||
return close_df, vol_df
|
|
||||||
|
|
||||||
|
|
||||||
def _dispersion_gate(close_df, threshold):
|
|
||||||
"""Cross-sectional dispersion of 22-day rolling returns."""
|
|
||||||
if close_df.empty or close_df.shape[1] < 2:
|
|
||||||
return pd.Series(dtype=bool)
|
|
||||||
ret = close_df.pct_change(22)
|
|
||||||
cs_disp = ret.std(axis=1)
|
|
||||||
gate = cs_disp >= threshold
|
|
||||||
gate.iloc[:22] = True # warmup: allow trading
|
|
||||||
return gate
|
|
||||||
|
|
||||||
|
|
||||||
def _vol_gate(close_df, vol_low, vol_high):
|
|
||||||
"""Cross-sectional mean of 22-day rolling realized vol."""
|
|
||||||
if close_df.empty or close_df.shape[1] < 2:
|
|
||||||
return pd.Series(dtype=bool)
|
|
||||||
import numpy as np
|
|
||||||
log_ret = np.log(close_df / close_df.shift(1))
|
|
||||||
rv22 = log_ret.rolling(22).std() * (252 ** 0.5)
|
|
||||||
cs_mean_vol = rv22.mean(axis=1)
|
|
||||||
gate = (cs_mean_vol >= vol_low) & (cs_mean_vol <= vol_high)
|
|
||||||
gate.iloc[:22] = True # warmup
|
|
||||||
return gate
|
|
||||||
|
|
||||||
|
|
||||||
def _hmm_gate(cfg, symbols, threshold, start, end, hmm_field):
|
|
||||||
"""HMM regime posterior gate from persisted SP features."""
|
|
||||||
feat_root = cfg.lake_root / "features"
|
|
||||||
all_posteriors = {}
|
|
||||||
for sym in symbols:
|
|
||||||
# check both ta and sp family paths
|
|
||||||
for family in ("sp", "ta"):
|
|
||||||
p = feat_root / f"market=US" / f"timeframe=1d" / f"family={family}" / f"symbol={sym}.parquet"
|
|
||||||
if not p.exists():
|
|
||||||
continue
|
|
||||||
try:
|
|
||||||
df = pd.read_parquet(p)
|
|
||||||
except Exception:
|
|
||||||
continue
|
|
||||||
if hmm_field not in df.columns:
|
|
||||||
continue
|
|
||||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
|
||||||
ts = pd.to_datetime(tcol)
|
|
||||||
s = pd.Series(df[hmm_field].values, index=ts, name=sym)
|
|
||||||
s = s.loc[start:end].dropna()
|
|
||||||
if len(s) > 0:
|
|
||||||
all_posteriors[sym] = s
|
|
||||||
break
|
break
|
||||||
if not all_posteriors:
|
return res[::-1] if reverse else res
|
||||||
# no HMM features found — default open
|
|
||||||
idx = pd.date_range(start, end, freq="B")
|
def get_last_n(li, n):
|
||||||
return pd.Series(True, index=idx)
|
return get_first_n(li, n, reverse=True)
|
||||||
post_df = pd.DataFrame(all_posteriors)
|
|
||||||
cs_mean = post_df.mean(axis=1)
|
def filter_stock(li):
|
||||||
gate = cs_mean >= threshold
|
return [
|
||||||
return gate
|
si
|
||||||
|
for si in li
|
||||||
|
if self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
]
|
||||||
|
|
||||||
|
else:
|
||||||
|
|
||||||
|
def get_first_n(li, n):
|
||||||
|
return list(li)[:n]
|
||||||
|
|
||||||
|
def get_last_n(li, n):
|
||||||
|
return list(li)[-n:]
|
||||||
|
|
||||||
|
def filter_stock(li):
|
||||||
|
return li
|
||||||
|
|
||||||
|
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||||
|
sell_order_list: List[Order] = []
|
||||||
|
buy_order_list: List[Order] = []
|
||||||
|
cash = current_temp.get_cash()
|
||||||
|
current_stock_list = current_temp.get_stock_list()
|
||||||
|
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||||
|
|
||||||
|
if self.method_buy == "top":
|
||||||
|
today = get_first_n(
|
||||||
|
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||||
|
self.n_drop + self.topk - len(last),
|
||||||
|
)
|
||||||
|
elif self.method_buy == "random":
|
||||||
|
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||||
|
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||||
|
n = self.n_drop + self.topk - len(last)
|
||||||
|
try:
|
||||||
|
today = np.random.choice(candi, n, replace=False)
|
||||||
|
except ValueError:
|
||||||
|
today = candi
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f"This type of input is not supported")
|
||||||
|
|
||||||
|
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||||
|
|
||||||
|
if self.method_sell == "bottom":
|
||||||
|
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||||
|
elif self.method_sell == "random":
|
||||||
|
candi = filter_stock(last)
|
||||||
|
try:
|
||||||
|
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||||
|
except ValueError:
|
||||||
|
sell = candi
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f"This type of input is not supported")
|
||||||
|
|
||||||
|
buy = today[: len(sell) + self.topk - len(last)]
|
||||||
|
|
||||||
|
# ---- regime gate -----------------------------------------------------
|
||||||
|
if buy:
|
||||||
|
regime = self._regime_for(buy, pred_start_time, pred_end_time)
|
||||||
|
gated = [c for c in buy if regime.get(c, np.nan) >= self.regime_threshold]
|
||||||
|
else:
|
||||||
|
gated = []
|
||||||
|
|
||||||
|
for code in current_stock_list:
|
||||||
|
if not self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||||
|
):
|
||||||
|
continue
|
||||||
|
if code in sell:
|
||||||
|
time_per_step = self.trade_calendar.get_freq()
|
||||||
|
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||||
|
continue
|
||||||
|
sell_amount = current_temp.get_stock_amount(code=code)
|
||||||
|
sell_order = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=sell_amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.SELL,
|
||||||
|
)
|
||||||
|
if self.trade_exchange.check_order(sell_order):
|
||||||
|
sell_order_list.append(sell_order)
|
||||||
|
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||||
|
sell_order, position=current_temp
|
||||||
|
)
|
||||||
|
cash += trade_val - trade_cost
|
||||||
|
|
||||||
|
if len(gated) == 0:
|
||||||
|
return TradeDecisionWO(sell_order_list, self)
|
||||||
|
|
||||||
|
value = cash * self.risk_degree / len(gated)
|
||||||
|
for code in gated:
|
||||||
|
if not self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||||
|
):
|
||||||
|
continue
|
||||||
|
buy_price = self.trade_exchange.get_deal_price(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||||
|
)
|
||||||
|
buy_amount = value / buy_price
|
||||||
|
factor = self.trade_exchange.get_factor(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||||
|
buy_order = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=buy_amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.BUY,
|
||||||
|
)
|
||||||
|
buy_order_list.append(buy_order)
|
||||||
|
|
||||||
|
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||||
@@ -0,0 +1,169 @@
|
|||||||
|
"""Market-neutral top/bottom long-short strategy for cross-sectional signals.
|
||||||
|
|
||||||
|
Captures the cross-sectional long-short spread net of costs: buys the top-ranked
|
||||||
|
``topk`` names and shorts the bottom-ranked ``topk`` names, equal-weight per
|
||||||
|
side, sized to ``risk_degree`` of total value per side. Rebalances daily to the
|
||||||
|
current rank (dropout-free: the book converges to the latest top/bottom sets).
|
||||||
|
|
||||||
|
The long and short legs use equal notional per side (gross exposure ~2x
|
||||||
|
``risk_degree`` of NAV, i.e. approximately market neutral before transaction
|
||||||
|
costs). Benchmark neutrality (SPY beta ~ 0) is the secondary sanity metric.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import List
|
||||||
|
|
||||||
|
import copy
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
from qlib.backtest import Order
|
||||||
|
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||||
|
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||||
|
|
||||||
|
__all__ = ["TopBottomDropoutStrategy"]
|
||||||
|
|
||||||
|
DEFAULT_SHORT_LEG = True
|
||||||
|
DEFAULT_REBALANCE_DAILY = True
|
||||||
|
|
||||||
|
|
||||||
|
class TopBottomDropoutStrategy(BaseSignalStrategy):
|
||||||
|
"""Long top-k / short bottom-k equal-weight market-neutral book.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
topk : number of names on each side (long top-k and short bottom-k).
|
||||||
|
short_leg : whether to open the short side (if False, long-only topk).
|
||||||
|
rebalance_daily : if True rebalance to current rank every day; else keep
|
||||||
|
positions and only refresh on score changes (dropout-style).
|
||||||
|
risk_degree : fraction of total value deployed per side.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
*,
|
||||||
|
topk: int = 10,
|
||||||
|
short_leg: bool = DEFAULT_SHORT_LEG,
|
||||||
|
rebalance_daily: bool = DEFAULT_REBALANCE_DAILY,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
super().__init__(**kwargs)
|
||||||
|
self.topk = topk
|
||||||
|
self.short_leg = short_leg
|
||||||
|
self.rebalance_daily = rebalance_daily
|
||||||
|
self._prev_longs = set()
|
||||||
|
self._prev_shorts = set()
|
||||||
|
|
||||||
|
def generate_trade_decision(self, execute_result=None):
|
||||||
|
trade_step = self.trade_calendar.get_trade_step()
|
||||||
|
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||||
|
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||||
|
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||||
|
if isinstance(pred_score, pd.DataFrame):
|
||||||
|
pred_score = pred_score.iloc[:, 0]
|
||||||
|
if pred_score is None or len(pred_score) == 0:
|
||||||
|
return TradeDecisionWO([], self)
|
||||||
|
|
||||||
|
# rank all names; topk longs and topk shorts
|
||||||
|
ranked = pred_score.sort_values(ascending=False)
|
||||||
|
longs = list(ranked.index[: self.topk])
|
||||||
|
shorts = list(ranked.index[-self.topk :]) if self.short_leg else []
|
||||||
|
|
||||||
|
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||||
|
current_codes = set(current_temp.get_stock_list())
|
||||||
|
holdings = {c: current_temp for c in current_codes if abs(current_temp.get_stock_amount(c)) > 1e-6}
|
||||||
|
|
||||||
|
sell_orders: List[Order] = []
|
||||||
|
buy_orders: List[Order] = []
|
||||||
|
|
||||||
|
def _tradable(code, direction):
|
||||||
|
try:
|
||||||
|
return self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=direction
|
||||||
|
)
|
||||||
|
except TypeError:
|
||||||
|
return self.trade_exchange.is_stock_tradable(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
|
||||||
|
# determine target set (long/short)
|
||||||
|
target_longs = set(longs)
|
||||||
|
target_shorts = set(shorts)
|
||||||
|
|
||||||
|
# close positions not in the target book
|
||||||
|
for code in list(holdings):
|
||||||
|
if code in target_longs or code in target_shorts:
|
||||||
|
continue
|
||||||
|
amt = abs(current_temp.get_stock_amount(code))
|
||||||
|
o = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=amt,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.SELL if code in target_longs else Order.SELL,
|
||||||
|
)
|
||||||
|
if self.trade_exchange.check_order(o):
|
||||||
|
sell_orders.append(o)
|
||||||
|
self.trade_exchange.deal_order(o, position=current_temp)
|
||||||
|
|
||||||
|
# equal-weight notional per side
|
||||||
|
total_value = current_temp.get_cash()
|
||||||
|
for code, pos in holdings.items():
|
||||||
|
if code in target_longs or code in target_shorts:
|
||||||
|
mark = self.trade_exchange.get_deal_price(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||||
|
)
|
||||||
|
if mark is not None and mark == mark:
|
||||||
|
total_value += abs(current_temp.get_stock_amount(code)) * mark
|
||||||
|
|
||||||
|
side_notional = total_value * self.risk_degree / max(1, self.topk)
|
||||||
|
|
||||||
|
for code in longs:
|
||||||
|
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||||
|
continue
|
||||||
|
px = self.trade_exchange.get_deal_price(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.BUY
|
||||||
|
)
|
||||||
|
if px is None or px != px or px <= 0:
|
||||||
|
continue
|
||||||
|
amount = side_notional / px
|
||||||
|
factor = self.trade_exchange.get_factor(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||||
|
o = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.BUY,
|
||||||
|
)
|
||||||
|
if self.trade_exchange.check_order(o):
|
||||||
|
buy_orders.append(o)
|
||||||
|
|
||||||
|
if self.short_leg:
|
||||||
|
for code in shorts:
|
||||||
|
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||||
|
continue
|
||||||
|
px = self.trade_exchange.get_deal_price(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||||
|
)
|
||||||
|
if px is None or px != px or px <= 0:
|
||||||
|
continue
|
||||||
|
amount = side_notional / px
|
||||||
|
factor = self.trade_exchange.get_factor(
|
||||||
|
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||||
|
)
|
||||||
|
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||||
|
o = Order(
|
||||||
|
stock_id=code,
|
||||||
|
amount=amount,
|
||||||
|
start_time=trade_start_time,
|
||||||
|
end_time=trade_end_time,
|
||||||
|
direction=Order.SELL,
|
||||||
|
)
|
||||||
|
if self.trade_exchange.check_order(o):
|
||||||
|
sell_orders.append(o)
|
||||||
|
|
||||||
|
return TradeDecisionWO(sell_orders + buy_orders, self)
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,35 @@
|
|||||||
|
# Q08 — Risk-limit A/B re-validation (trace 40)
|
||||||
|
|
||||||
|
**Status:** DONE (verdict: REFUTED as an IR edge; safety-net value retained)
|
||||||
|
|
||||||
|
## Input
|
||||||
|
- Reference signal: exp-26 pred, run `21afc6afdb674a399b59dd76c97628ce` (mlflow exp 25)
|
||||||
|
- Window: 2026-01-04 → 2026-08-10, Topk10 n_drop1, SPY benchmark, $1M, 5/15bp/$5
|
||||||
|
- Tool: `rd_risk_calibrate` (A/B + sensitivity grid). Full JSON: `risk_calibration.json`
|
||||||
|
|
||||||
|
## Candidate spec (round-3 live spec)
|
||||||
|
`{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12, "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}`
|
||||||
|
|
||||||
|
## Results (net, with cost)
|
||||||
|
| Config | IR | Ann. return | Max DD |
|
||||||
|
|---|---|---|---|
|
||||||
|
| baseline (no limits) | 1.5804 | +27.50% | −6.91% |
|
||||||
|
| **candidate (5M floor + caps)** | **1.5121** | +2.20% | **−0.65%** |
|
||||||
|
| liquidity $10M | 1.5457 | +2.25% | −0.64% |
|
||||||
|
|
||||||
|
## Findings
|
||||||
|
- **Floor binds, not a no-op**: $5M liquidity floor dropped 8 symbols —
|
||||||
|
`DBA, DBC, ESPO, FDN, REM, TAN, UNG, XAR`.
|
||||||
|
- **No IR edge from the gate**: candidate IR (1.512) is BELOW baseline (1.580).
|
||||||
|
The exp-18 direction (floor IR 0.81→0.98) does NOT reproduce on the clean-lake
|
||||||
|
reference signal.
|
||||||
|
- **Drawdown cut is pure defunding**: size_cap 0.12 × concentration 0.95 fold
|
||||||
|
the effective risk_degree to ~0.0095 → ~$9.5k deployed of $1M (~100x less).
|
||||||
|
Sensitivity grid shows both caps are no-ops (conc 20–50% identical,
|
||||||
|
size_cap 5–20% identical); only the liquidity floor moves returns, marginally.
|
||||||
|
- **Conclusion**: keep the live spec as a safety net; there is no risk-limit
|
||||||
|
gate IR edge to harvest when the signal is the bottleneck (exp-20 pattern).
|
||||||
|
|
||||||
|
## Artifacts on this branch
|
||||||
|
- `evidence/q08-risklimit/risk_calibration.json` — full calibration dump
|
||||||
|
- `queue/designs/q08_risk_limit_ab.md` — the pre-registered design doc
|
||||||
@@ -0,0 +1,401 @@
|
|||||||
|
{
|
||||||
|
"rows": [
|
||||||
|
{
|
||||||
|
"label": "baseline (no limits)",
|
||||||
|
"mean": 0.001155,
|
||||||
|
"std": 0.011279,
|
||||||
|
"annualized_return": 0.274989,
|
||||||
|
"information_ratio": 1.580427,
|
||||||
|
"max_drawdown": -0.069145
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "liquidity $10,000,000",
|
||||||
|
"mean": 9.4e-05,
|
||||||
|
"std": 0.000942,
|
||||||
|
"annualized_return": 0.022464,
|
||||||
|
"information_ratio": 1.545736,
|
||||||
|
"max_drawdown": -0.006389
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "conc 20%",
|
||||||
|
"mean": 0.000115,
|
||||||
|
"std": 0.001168,
|
||||||
|
"annualized_return": 0.027285,
|
||||||
|
"information_ratio": 1.513718,
|
||||||
|
"max_drawdown": -0.008104
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "conc 30%",
|
||||||
|
"mean": 0.000115,
|
||||||
|
"std": 0.001168,
|
||||||
|
"annualized_return": 0.027285,
|
||||||
|
"information_ratio": 1.513718,
|
||||||
|
"max_drawdown": -0.008104
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "conc 40%",
|
||||||
|
"mean": 0.000115,
|
||||||
|
"std": 0.001168,
|
||||||
|
"annualized_return": 0.027285,
|
||||||
|
"information_ratio": 1.513718,
|
||||||
|
"max_drawdown": -0.008104
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "conc 50%",
|
||||||
|
"mean": 0.000115,
|
||||||
|
"std": 0.001168,
|
||||||
|
"annualized_return": 0.027285,
|
||||||
|
"information_ratio": 1.513718,
|
||||||
|
"max_drawdown": -0.008104
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "candidate {\"liquidity_floor_adv\": 5000000.0, \"size_cap_pct\": 0.12, \"concentration_cap_pct\": 0.95, \"drawdown_pause_pct\": 0.1}",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "size_cap 5%",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "size_cap 10%",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "size_cap 15%",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "size_cap 20%",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "liquidity $5,000,000",
|
||||||
|
"mean": 9.2e-05,
|
||||||
|
"std": 0.000943,
|
||||||
|
"annualized_return": 0.021991,
|
||||||
|
"information_ratio": 1.512051,
|
||||||
|
"max_drawdown": -0.00653
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "liquidity $1,000,000",
|
||||||
|
"mean": 9.1e-05,
|
||||||
|
"std": 0.000929,
|
||||||
|
"annualized_return": 0.021625,
|
||||||
|
"information_ratio": 1.508748,
|
||||||
|
"max_drawdown": -0.006376
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"label": "liquidity $2,500,000",
|
||||||
|
"mean": 7.1e-05,
|
||||||
|
"std": 0.000918,
|
||||||
|
"annualized_return": 0.017,
|
||||||
|
"information_ratio": 1.199721,
|
||||||
|
"max_drawdown": -0.007158
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"runs": {
|
||||||
|
"baseline": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 0.0011554172081987572,
|
||||||
|
"std": 0.01127853762493476,
|
||||||
|
"annualized_return": 0.27498929555130425,
|
||||||
|
"information_ratio": 1.5804272791471323,
|
||||||
|
"max_drawdown": -0.06914515336341577
|
||||||
|
},
|
||||||
|
"applied": {}
|
||||||
|
},
|
||||||
|
"candidate": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"size_cap 5%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"size_cap 10%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"size_cap 15%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"size_cap 20%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"conc 20%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 0.00011464156491316718,
|
||||||
|
"std": 0.0011683839517000441,
|
||||||
|
"annualized_return": 0.027284692449333788,
|
||||||
|
"information_ratio": 1.5137180903503433,
|
||||||
|
"max_drawdown": -0.008103887185240407
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"conc 30%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 0.00011464156491316718,
|
||||||
|
"std": 0.0011683839517000441,
|
||||||
|
"annualized_return": 0.027284692449333788,
|
||||||
|
"information_ratio": 1.5137180903503433,
|
||||||
|
"max_drawdown": -0.008103887185240407
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"conc 40%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 0.00011464156491316718,
|
||||||
|
"std": 0.0011683839517000441,
|
||||||
|
"annualized_return": 0.027284692449333788,
|
||||||
|
"information_ratio": 1.5137180903503433,
|
||||||
|
"max_drawdown": -0.008103887185240407
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"conc 50%": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 0.00011464156491316718,
|
||||||
|
"std": 0.0011683839517000441,
|
||||||
|
"annualized_return": 0.027284692449333788,
|
||||||
|
"information_ratio": 1.5137180903503433,
|
||||||
|
"max_drawdown": -0.008103887185240407
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"liquidity $1,000,000": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.086210454881382e-05,
|
||||||
|
"std": 0.0009290831160004576,
|
||||||
|
"annualized_return": 0.021625180882617688,
|
||||||
|
"information_ratio": 1.508747982736451,
|
||||||
|
"max_drawdown": -0.006376134679664126
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"ESPO"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"liquidity $2,500,000": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 7.142665167642606e-05,
|
||||||
|
"std": 0.0009184775632266332,
|
||||||
|
"annualized_return": 0.016999543098989402,
|
||||||
|
"information_ratio": 1.1997208834083914,
|
||||||
|
"max_drawdown": -0.0071582979845040825
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"REM",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"liquidity $5,000,000": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.239707947451976e-05,
|
||||||
|
"std": 0.0009427144352738658,
|
||||||
|
"annualized_return": 0.0219905049149357,
|
||||||
|
"information_ratio": 1.5120514373488407,
|
||||||
|
"max_drawdown": -0.006530482262119444
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"REM",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"liquidity $10,000,000": {
|
||||||
|
"risk": {
|
||||||
|
"mean": 9.438545151345752e-05,
|
||||||
|
"std": 0.0009420158170657147,
|
||||||
|
"annualized_return": 0.02246373746020289,
|
||||||
|
"information_ratio": 1.5457360696934006,
|
||||||
|
"max_drawdown": -0.006388809561209335
|
||||||
|
},
|
||||||
|
"applied": {
|
||||||
|
"dropped_liquidity": [
|
||||||
|
"DBA",
|
||||||
|
"DBC",
|
||||||
|
"ESPO",
|
||||||
|
"FDN",
|
||||||
|
"ICLN",
|
||||||
|
"ITA",
|
||||||
|
"MDY",
|
||||||
|
"REM",
|
||||||
|
"SHY",
|
||||||
|
"TAN",
|
||||||
|
"UNG",
|
||||||
|
"XAR"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"candidate": {
|
||||||
|
"liquidity_floor_adv": 5000000.0,
|
||||||
|
"size_cap_pct": 0.12,
|
||||||
|
"concentration_cap_pct": 0.95,
|
||||||
|
"drawdown_pause_pct": 0.1
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,34 @@
|
|||||||
|
# QUEUE-08 — Risk-limit A/B re-validation: $5M liquidity floor on the exp-26 reference
|
||||||
|
|
||||||
|
**Status:** QUEUED · **Priority:** P1 · **Effort:** tool-only (no new code)
|
||||||
|
|
||||||
|
## Hypothesis (prove)
|
||||||
|
The $5M liquidity floor improves net IR and cuts drawdown on the **post-reset**
|
||||||
|
reference signal (pre-reset exp 18, EVIDENCE#008: net IR 0.81→0.98, cumDD
|
||||||
|
7.93%→5.44%), while size/concentration caps hurt by cutting deployed capital.
|
||||||
|
Needs re-validation on the exp-26 lineage because exp 18 is pre-clean-lake and
|
||||||
|
not comparable (EVIDENCE#009/010). Source: `book/CLAIMS.md` open question +
|
||||||
|
`book/README.md` `TODO(evidence-needed: reconciliation of exp 18 risk-limit spec
|
||||||
|
on the post-reset reference signal)`.
|
||||||
|
|
||||||
|
## Change vs exp-26 reference (ONE variable)
|
||||||
|
- Reference: the saved exp-26 prediction (run `21afc6af…`, mlflow exp 25).
|
||||||
|
- A/B via `rd_risk_calibrate` (runs limit-vs-no-limit A/B + sensitivity grid
|
||||||
|
over size_cap_pct, concentration_cap_pct, liquidity_floor_adv) and/or
|
||||||
|
`rd_backtest` with `risk_limits` on the SAME saved `pred.pkl`:
|
||||||
|
- baseline: no limits (this must reproduce the exp-26 net +2.13% / IR 0.21);
|
||||||
|
- candidate: `{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12,
|
||||||
|
"concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}` (round-3 spec).
|
||||||
|
- Pick the spec (B2 calibration) that keeps live ≈ backtest.
|
||||||
|
|
||||||
|
## Acceptance
|
||||||
|
- Candidate spec: `net_IR > 0.21` AND `net_max_drawdown < 7.69%` vs no-limit on
|
||||||
|
the same pred. Size/concentration caps expected to REDUCE deployed capital
|
||||||
|
(record the direction as confirmation of exp 18).
|
||||||
|
- If the floor is a no-op (gates don't bind at this signal) → report that gates
|
||||||
|
are no-ops when the signal is the bottleneck (exp 20 pattern) as a PROVEN
|
||||||
|
clean-lake result.
|
||||||
|
|
||||||
|
## Execution prerequisites
|
||||||
|
- None (uses saved pred + `rd_risk_calibrate`/`rd_backtest`). Trace the A/B as
|
||||||
|
an experiment; record the spec chosen for the next live round.
|
||||||
@@ -0,0 +1,133 @@
|
|||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
|
||||||
|
# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
|
||||||
|
# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
|
||||||
|
# distinct experiment name so the ablation runs are isolated.
|
||||||
|
#
|
||||||
|
# Run:
|
||||||
|
# rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
|
||||||
|
# experiment_name=tac-rd-rank-ablate
|
||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
{%- set LAKE = TAC_LAKE_DIR %}
|
||||||
|
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||||
|
{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||||
|
|
||||||
|
qlib_init:
|
||||||
|
provider_uri: "{{ LAKE }}"
|
||||||
|
region: us
|
||||||
|
expression_cache: null
|
||||||
|
dataset_cache: null
|
||||||
|
|
||||||
|
calendar_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
instrument_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
markets: {}
|
||||||
|
feature_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
|
||||||
|
exp_manager:
|
||||||
|
class: MLflowExpManager
|
||||||
|
module_path: qlib.workflow.expm
|
||||||
|
kwargs:
|
||||||
|
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||||
|
default_exp_name: "tac-rd-rank-ablate"
|
||||||
|
|
||||||
|
task:
|
||||||
|
model:
|
||||||
|
class: RankICLGBModel
|
||||||
|
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||||
|
kwargs:
|
||||||
|
loss: mse
|
||||||
|
learning_rate: 0.02
|
||||||
|
num_leaves: 31
|
||||||
|
n_estimators: 3000
|
||||||
|
num_boost_round: 3000
|
||||||
|
early_stopping_rounds: 200
|
||||||
|
min_data_in_leaf: 20
|
||||||
|
lambda_l2: 0.5
|
||||||
|
colsample_bytree: 0.8
|
||||||
|
subsample: 0.8
|
||||||
|
subsample_freq: 1
|
||||||
|
reg_alpha: 0.1
|
||||||
|
reg_lambda: 1.0
|
||||||
|
seed: 42
|
||||||
|
|
||||||
|
dataset:
|
||||||
|
class: DatasetH
|
||||||
|
module_path: qlib.data.dataset
|
||||||
|
kwargs:
|
||||||
|
handler:
|
||||||
|
class: TACHandler
|
||||||
|
module_path: tac_qlib.contrib.data.handler
|
||||||
|
kwargs:
|
||||||
|
instruments: "{{ UNIVERSE }}"
|
||||||
|
start_time: 2015-01-03
|
||||||
|
end_time: 2026-08-10
|
||||||
|
fit_start_time: 2015-01-03
|
||||||
|
fit_end_time: 2025-09-01
|
||||||
|
freq: day
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||||
|
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||||
|
infer_processors:
|
||||||
|
- class: DropAllNaN
|
||||||
|
kwargs: {}
|
||||||
|
- class: ProcessInf
|
||||||
|
kwargs: {}
|
||||||
|
- class: CSRankNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: ZScoreNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: Fillna
|
||||||
|
kwargs: {}
|
||||||
|
segments:
|
||||||
|
train: [2015-01-03, 2025-09-01]
|
||||||
|
valid: [2025-09-03, 2026-01-03]
|
||||||
|
test: [2026-01-04, 2026-08-10]
|
||||||
|
|
||||||
|
record:
|
||||||
|
- class: SignalRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs: {}
|
||||||
|
- class: SigAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
ana_long_short: true
|
||||||
|
ann_scaler: 252
|
||||||
|
- class: PortAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
config:
|
||||||
|
strategy:
|
||||||
|
class: TopkDropoutStrategy
|
||||||
|
module_path: qlib.contrib.strategy
|
||||||
|
kwargs:
|
||||||
|
signal: "<PRED>"
|
||||||
|
topk: 10
|
||||||
|
n_drop: 2
|
||||||
|
only_tradable: true
|
||||||
|
risk_degree: 0.95
|
||||||
|
backtest:
|
||||||
|
start_time: 2026-01-04
|
||||||
|
end_time: 2026-08-10
|
||||||
|
account: 1000000
|
||||||
|
benchmark: SPY
|
||||||
|
exchange_kwargs:
|
||||||
|
codes: "{{ UNIVERSE }}"
|
||||||
|
deal_price: $close
|
||||||
|
freq: day
|
||||||
|
open_cost: 0.0005
|
||||||
|
close_cost: 0.0015
|
||||||
|
min_cost: 5.0
|
||||||
|
risk_analysis_freq: 1d
|
||||||
@@ -0,0 +1,134 @@
|
|||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
# ABLATION B (generic-only): same panel/model as the baseline, but feature
|
||||||
|
# fields restricted to the model-free / generic stochastic-process families
|
||||||
|
# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
|
||||||
|
# half-life) and hmm (2-state regime) families to test whether the generic
|
||||||
|
# families alone dominate the rank dimension.
|
||||||
|
#
|
||||||
|
# Run:
|
||||||
|
# rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
|
||||||
|
# experiment_name=tac-rd-rank-ablate
|
||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
{%- set LAKE = TAC_LAKE_DIR %}
|
||||||
|
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||||
|
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||||
|
|
||||||
|
qlib_init:
|
||||||
|
provider_uri: "{{ LAKE }}"
|
||||||
|
region: us
|
||||||
|
expression_cache: null
|
||||||
|
dataset_cache: null
|
||||||
|
|
||||||
|
calendar_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
instrument_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
markets: {}
|
||||||
|
feature_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
|
||||||
|
exp_manager:
|
||||||
|
class: MLflowExpManager
|
||||||
|
module_path: qlib.workflow.expm
|
||||||
|
kwargs:
|
||||||
|
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||||
|
default_exp_name: "tac-rd-rank-ablate"
|
||||||
|
|
||||||
|
task:
|
||||||
|
model:
|
||||||
|
class: RankICLGBModel
|
||||||
|
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||||
|
kwargs:
|
||||||
|
loss: mse
|
||||||
|
learning_rate: 0.02
|
||||||
|
num_leaves: 31
|
||||||
|
n_estimators: 3000
|
||||||
|
num_boost_round: 3000
|
||||||
|
early_stopping_rounds: 200
|
||||||
|
min_data_in_leaf: 20
|
||||||
|
lambda_l2: 0.5
|
||||||
|
colsample_bytree: 0.8
|
||||||
|
subsample: 0.8
|
||||||
|
subsample_freq: 1
|
||||||
|
reg_alpha: 0.1
|
||||||
|
reg_lambda: 1.0
|
||||||
|
seed: 42
|
||||||
|
|
||||||
|
dataset:
|
||||||
|
class: DatasetH
|
||||||
|
module_path: qlib.data.dataset
|
||||||
|
kwargs:
|
||||||
|
handler:
|
||||||
|
class: TACHandler
|
||||||
|
module_path: tac_qlib.contrib.data.handler
|
||||||
|
kwargs:
|
||||||
|
instruments: "{{ UNIVERSE }}"
|
||||||
|
start_time: 2015-01-03
|
||||||
|
end_time: 2026-08-10
|
||||||
|
fit_start_time: 2015-01-03
|
||||||
|
fit_end_time: 2025-09-01
|
||||||
|
freq: day
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||||
|
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||||
|
infer_processors:
|
||||||
|
- class: DropAllNaN
|
||||||
|
kwargs: {}
|
||||||
|
- class: ProcessInf
|
||||||
|
kwargs: {}
|
||||||
|
- class: CSRankNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: ZScoreNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: Fillna
|
||||||
|
kwargs: {}
|
||||||
|
segments:
|
||||||
|
train: [2015-01-03, 2025-09-01]
|
||||||
|
valid: [2025-09-03, 2026-01-03]
|
||||||
|
test: [2026-01-04, 2026-08-10]
|
||||||
|
|
||||||
|
record:
|
||||||
|
- class: SignalRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs: {}
|
||||||
|
- class: SigAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
ana_long_short: true
|
||||||
|
ann_scaler: 252
|
||||||
|
- class: PortAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
config:
|
||||||
|
strategy:
|
||||||
|
class: TopkDropoutStrategy
|
||||||
|
module_path: qlib.contrib.strategy
|
||||||
|
kwargs:
|
||||||
|
signal: "<PRED>"
|
||||||
|
topk: 10
|
||||||
|
n_drop: 2
|
||||||
|
only_tradable: true
|
||||||
|
risk_degree: 0.95
|
||||||
|
backtest:
|
||||||
|
start_time: 2026-01-04
|
||||||
|
end_time: 2026-08-10
|
||||||
|
account: 1000000
|
||||||
|
benchmark: SPY
|
||||||
|
exchange_kwargs:
|
||||||
|
codes: "{{ UNIVERSE }}"
|
||||||
|
deal_price: $close
|
||||||
|
freq: day
|
||||||
|
open_cost: 0.0005
|
||||||
|
close_cost: 0.0015
|
||||||
|
min_cost: 5.0
|
||||||
|
risk_analysis_freq: 1d
|
||||||
@@ -0,0 +1,141 @@
|
|||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
|
||||||
|
#
|
||||||
|
# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
|
||||||
|
# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
|
||||||
|
# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
|
||||||
|
# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
|
||||||
|
# that averages per-day predictions.
|
||||||
|
#
|
||||||
|
# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
|
||||||
|
# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
|
||||||
|
# features, so any change vs exp-15 is attributable to the feature set alone,
|
||||||
|
# and any change vs exp-9 is attributable to the ensemble + full history alone.
|
||||||
|
#
|
||||||
|
# Run:
|
||||||
|
# rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
|
||||||
|
# experiment_name=tac-rd-rank-ensemble-isolated
|
||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
{%- set LAKE = TAC_LAKE_DIR %}
|
||||||
|
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||||
|
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||||
|
|
||||||
|
qlib_init:
|
||||||
|
provider_uri: "{{ LAKE }}"
|
||||||
|
region: us
|
||||||
|
expression_cache: null
|
||||||
|
dataset_cache: null
|
||||||
|
|
||||||
|
calendar_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
instrument_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
markets: {}
|
||||||
|
feature_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
|
||||||
|
exp_manager:
|
||||||
|
class: MLflowExpManager
|
||||||
|
module_path: qlib.workflow.expm
|
||||||
|
kwargs:
|
||||||
|
uri: "sqlite:///mlruns.db"
|
||||||
|
default_exp_name: "tac-rd-rank-ensemble-isolated"
|
||||||
|
|
||||||
|
task:
|
||||||
|
model:
|
||||||
|
class: RankICEnsembleLGBModel
|
||||||
|
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||||
|
kwargs:
|
||||||
|
loss: mse
|
||||||
|
learning_rate: 0.02
|
||||||
|
num_leaves: 31
|
||||||
|
n_estimators: 3000
|
||||||
|
num_boost_round: 3000
|
||||||
|
early_stopping_rounds: 200
|
||||||
|
min_data_in_leaf: 20
|
||||||
|
lambda_l2: 0.5
|
||||||
|
colsample_bytree: 0.8
|
||||||
|
subsample: 0.8
|
||||||
|
subsample_freq: 1
|
||||||
|
reg_alpha: 0.1
|
||||||
|
reg_lambda: 1.0
|
||||||
|
seeds: "42,7,2026,99,123"
|
||||||
|
|
||||||
|
dataset:
|
||||||
|
class: DatasetH
|
||||||
|
module_path: qlib.data.dataset
|
||||||
|
kwargs:
|
||||||
|
handler:
|
||||||
|
class: TACHandler
|
||||||
|
module_path: tac_qlib.contrib.data.handler
|
||||||
|
kwargs:
|
||||||
|
instruments: "{{ UNIVERSE }}"
|
||||||
|
start_time: 2015-01-03
|
||||||
|
end_time: 2026-08-14
|
||||||
|
fit_start_time: 2016-01-04
|
||||||
|
fit_end_time: 2025-09-01
|
||||||
|
freq: day
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||||
|
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||||
|
infer_processors:
|
||||||
|
- class: DropAllNaN
|
||||||
|
kwargs: {}
|
||||||
|
- class: ProcessInf
|
||||||
|
kwargs: {}
|
||||||
|
- class: CSRankNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: ZScoreNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: Fillna
|
||||||
|
kwargs: {}
|
||||||
|
segments:
|
||||||
|
train: [2016-01-04, 2025-09-01]
|
||||||
|
valid: [2025-09-03, 2026-01-03]
|
||||||
|
test: [2026-01-04, 2026-08-10]
|
||||||
|
|
||||||
|
record:
|
||||||
|
- class: SignalRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs: {}
|
||||||
|
- class: SigAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
ana_long_short: true
|
||||||
|
ann_scaler: 252
|
||||||
|
- class: PortAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
config:
|
||||||
|
strategy:
|
||||||
|
class: TopkDropoutStrategy
|
||||||
|
module_path: qlib.contrib.strategy
|
||||||
|
kwargs:
|
||||||
|
signal: "<PRED>"
|
||||||
|
topk: 10
|
||||||
|
n_drop: 2
|
||||||
|
only_tradable: true
|
||||||
|
risk_degree: 0.95
|
||||||
|
backtest:
|
||||||
|
start_time: 2026-01-04
|
||||||
|
end_time: 2026-08-10
|
||||||
|
account: 1000000
|
||||||
|
benchmark: SPY
|
||||||
|
exchange_kwargs:
|
||||||
|
codes: "{{ UNIVERSE }}"
|
||||||
|
deal_price: $close
|
||||||
|
freq: day
|
||||||
|
open_cost: 0.0005
|
||||||
|
close_cost: 0.0015
|
||||||
|
min_cost: 5.0
|
||||||
|
risk_analysis_freq: 1d
|
||||||
@@ -0,0 +1,141 @@
|
|||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
|
||||||
|
#
|
||||||
|
# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
|
||||||
|
# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
|
||||||
|
# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
|
||||||
|
# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
|
||||||
|
# (same pred, liquidity/size/concentration caps) can be compared 1:1.
|
||||||
|
#
|
||||||
|
# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
|
||||||
|
# (tool-level param, not a YAML key); this run records the unconstrained
|
||||||
|
# baseline that the limit A/B is measured against.
|
||||||
|
#
|
||||||
|
# Run:
|
||||||
|
# rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
|
||||||
|
# experiment_name=tac-rd-risk-limit
|
||||||
|
# -----------------------------------------------------------------------------
|
||||||
|
{%- set LAKE = TAC_LAKE_DIR %}
|
||||||
|
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||||
|
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||||
|
|
||||||
|
qlib_init:
|
||||||
|
provider_uri: "{{ LAKE }}"
|
||||||
|
region: us
|
||||||
|
expression_cache: null
|
||||||
|
dataset_cache: null
|
||||||
|
|
||||||
|
calendar_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
instrument_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
markets: {}
|
||||||
|
feature_provider:
|
||||||
|
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||||
|
kwargs:
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
|
||||||
|
exp_manager:
|
||||||
|
class: MLflowExpManager
|
||||||
|
module_path: qlib.workflow.expm
|
||||||
|
kwargs:
|
||||||
|
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||||
|
default_exp_name: "tac-rd-risk-limit"
|
||||||
|
|
||||||
|
task:
|
||||||
|
model:
|
||||||
|
class: RankICEnsembleLGBModel
|
||||||
|
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||||
|
kwargs:
|
||||||
|
loss: mse
|
||||||
|
learning_rate: 0.02
|
||||||
|
num_leaves: 31
|
||||||
|
n_estimators: 3000
|
||||||
|
num_boost_round: 3000
|
||||||
|
early_stopping_rounds: 200
|
||||||
|
min_data_in_leaf: 20
|
||||||
|
lambda_l2: 0.5
|
||||||
|
colsample_bytree: 0.8
|
||||||
|
subsample: 0.8
|
||||||
|
subsample_freq: 1
|
||||||
|
reg_alpha: 0.1
|
||||||
|
reg_lambda: 1.0
|
||||||
|
seeds: "42,7,2026,99,123"
|
||||||
|
parallel: 5
|
||||||
|
|
||||||
|
dataset:
|
||||||
|
class: DatasetH
|
||||||
|
module_path: qlib.data.dataset
|
||||||
|
kwargs:
|
||||||
|
handler:
|
||||||
|
class: TACHandler
|
||||||
|
module_path: tac_qlib.contrib.data.handler
|
||||||
|
kwargs:
|
||||||
|
instruments: "{{ UNIVERSE }}"
|
||||||
|
start_time: 2015-01-03
|
||||||
|
end_time: 2026-08-14
|
||||||
|
fit_start_time: 2016-01-04
|
||||||
|
fit_end_time: 2025-09-01
|
||||||
|
freq: day
|
||||||
|
lake_root: "{{ LAKE }}"
|
||||||
|
market: US
|
||||||
|
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||||
|
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||||
|
infer_processors:
|
||||||
|
- class: DropAllNaN
|
||||||
|
kwargs: {}
|
||||||
|
- class: ProcessInf
|
||||||
|
kwargs: {}
|
||||||
|
- class: CSRankNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: ZScoreNorm
|
||||||
|
kwargs: {}
|
||||||
|
- class: Fillna
|
||||||
|
kwargs: {}
|
||||||
|
segments:
|
||||||
|
train: [2016-01-04, 2025-09-01]
|
||||||
|
valid: [2025-09-03, 2026-01-03]
|
||||||
|
test: [2026-01-04, 2026-08-10]
|
||||||
|
|
||||||
|
record:
|
||||||
|
- class: SignalRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs: {}
|
||||||
|
- class: SigAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
ana_long_short: true
|
||||||
|
ann_scaler: 252
|
||||||
|
- class: PortAnaRecord
|
||||||
|
module_path: qlib.workflow.record_temp
|
||||||
|
kwargs:
|
||||||
|
config:
|
||||||
|
strategy:
|
||||||
|
class: TopkDropoutStrategy
|
||||||
|
module_path: qlib.contrib.strategy
|
||||||
|
kwargs:
|
||||||
|
signal: "<PRED>"
|
||||||
|
topk: 10
|
||||||
|
n_drop: 2
|
||||||
|
only_tradable: true
|
||||||
|
risk_degree: 0.95
|
||||||
|
backtest:
|
||||||
|
start_time: 2026-01-04
|
||||||
|
end_time: 2026-08-10
|
||||||
|
account: 1000000
|
||||||
|
benchmark: SPY
|
||||||
|
exchange_kwargs:
|
||||||
|
codes: "{{ UNIVERSE }}"
|
||||||
|
deal_price: $close
|
||||||
|
freq: day
|
||||||
|
open_cost: 0.0005
|
||||||
|
close_cost: 0.0015
|
||||||
|
min_cost: 5.0
|
||||||
|
risk_analysis_freq: 1d
|
||||||
Reference in New Issue
Block a user