Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
4f05d42ff1 | ||
|
zhaoli
|
ceb1e196e2 |
@@ -0,0 +1,46 @@
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
|
@@ -0,0 +1,722 @@
|
||||
[
|
||||
{
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||||
"window": "2026",
|
||||
"gate": "hitrate_5d_0.50",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 92,
|
||||
"gate_closed": 65,
|
||||
"trip_rate": 0.414,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.649911,
|
||||
"gated_sharpe": 5.7351,
|
||||
"gated_maxDD": -0.031795
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_5d_0.60",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 49,
|
||||
"gate_closed": 108,
|
||||
"trip_rate": 0.6879,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.489313,
|
||||
"gated_sharpe": 6.0111,
|
||||
"gated_maxDD": -0.014864
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_5d_0.70",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 15,
|
||||
"gate_closed": 142,
|
||||
"trip_rate": 0.9045,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.229523,
|
||||
"gated_sharpe": 4.3611,
|
||||
"gated_maxDD": -0.002029
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_10d_0.50",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 109,
|
||||
"gate_closed": 48,
|
||||
"trip_rate": 0.3057,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.335822,
|
||||
"gated_sharpe": 2.6391,
|
||||
"gated_maxDD": -0.060142
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_10d_0.60",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 36,
|
||||
"gate_closed": 121,
|
||||
"trip_rate": 0.7707,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.251108,
|
||||
"gated_sharpe": 3.8579,
|
||||
"gated_maxDD": -0.026842
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_10d_0.70",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 9,
|
||||
"gate_closed": 148,
|
||||
"trip_rate": 0.9427,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.039314,
|
||||
"gated_sharpe": 1.3415,
|
||||
"gated_maxDD": -0.012061
|
||||
},
|
||||
{
|
||||
"window": "2026",
|
||||
"gate": "hitrate_20d_0.40",
|
||||
"start": "2026-01-04",
|
||||
"end": "2026-08-19",
|
||||
"trade_dates": 157,
|
||||
"gate_open": 157,
|
||||
"gate_closed": 0,
|
||||
"trip_rate": 0.0,
|
||||
"base_ann": 0.255023,
|
||||
"base_sharpe": 1.4465,
|
||||
"base_maxDD": -0.080671,
|
||||
"gated_ann": 0.255023,
|
||||
"gated_sharpe": 1.4465,
|
||||
"gated_maxDD": -0.080671
|
||||
},
|
||||
{
|
||||
"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
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_5d_0.50",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 153,
|
||||
"gate_closed": 97,
|
||||
"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
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_5d_0.60",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 90,
|
||||
"gate_closed": 160,
|
||||
"trip_rate": 0.64,
|
||||
"base_ann": 0.177515,
|
||||
"base_sharpe": 0.8573,
|
||||
"base_maxDD": -0.217417,
|
||||
"gated_ann": 0.48564,
|
||||
"gated_sharpe": 5.3623,
|
||||
"gated_maxDD": -0.046673
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_5d_0.70",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 39,
|
||||
"gate_closed": 211,
|
||||
"trip_rate": 0.844,
|
||||
"base_ann": 0.177515,
|
||||
"base_sharpe": 0.8573,
|
||||
"base_maxDD": -0.217417,
|
||||
"gated_ann": 0.249221,
|
||||
"gated_sharpe": 4.9448,
|
||||
"gated_maxDD": -0.009619
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_10d_0.50",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 170,
|
||||
"gate_closed": 80,
|
||||
"trip_rate": 0.32,
|
||||
"base_ann": 0.177515,
|
||||
"base_sharpe": 0.8573,
|
||||
"base_maxDD": -0.217417,
|
||||
"gated_ann": 0.435847,
|
||||
"gated_sharpe": 3.5614,
|
||||
"gated_maxDD": -0.055776
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_10d_0.60",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 66,
|
||||
"gate_closed": 184,
|
||||
"trip_rate": 0.736,
|
||||
"base_ann": 0.177515,
|
||||
"base_sharpe": 0.8573,
|
||||
"base_maxDD": -0.217417,
|
||||
"gated_ann": 0.324574,
|
||||
"gated_sharpe": 5.2279,
|
||||
"gated_maxDD": -0.016028
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_10d_0.70",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 14,
|
||||
"gate_closed": 236,
|
||||
"trip_rate": 0.944,
|
||||
"base_ann": 0.177515,
|
||||
"base_sharpe": 0.8573,
|
||||
"base_maxDD": -0.217417,
|
||||
"gated_ann": 0.043002,
|
||||
"gated_sharpe": 1.4635,
|
||||
"gated_maxDD": -0.010154
|
||||
},
|
||||
{
|
||||
"window": "2025",
|
||||
"gate": "hitrate_20d_0.40",
|
||||
"start": "2025-01-02",
|
||||
"end": "2025-12-31",
|
||||
"trade_dates": 250,
|
||||
"gate_open": 247,
|
||||
"gate_closed": 3,
|
||||
"trip_rate": 0.012,
|
||||
"base_ann": 0.177515,
|
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||||
},
|
||||
{
|
||||
"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.
@@ -0,0 +1,293 @@
|
||||
"""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()
|
||||
+17
-12
@@ -1,25 +1,30 @@
|
||||
# TradeAC custom-qlib-code snapshot (auto-generated)
|
||||
# parent repo HEAD : 891bd0743d81f4a49cd91fc4d5dc1d97873e750e
|
||||
# parent repo HEAD : ceb1e196e24761a87d1afe1bde00079b79e02693
|
||||
# tac-qlib/tac_qlib/contrib
|
||||
# tac-qlib/tac_qlib/data
|
||||
# per-file hashes (git hash-object):
|
||||
1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
|
||||
2224424d0ff193be4f55d1b791f8fce89439c5d2 tac-qlib/tac_qlib/contrib/backtest/__init__.py
|
||||
0bf40dee440ddbded357d7bbb4efc67c62c4b084 tac-qlib/tac_qlib/contrib/backtest/tradeac_exchange.py
|
||||
861592c63edd6a0853a9cb174b5970435b135fc8 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
||||
fc3d01d530d4e84b3386614b6ac94324e811920e tac-qlib/tac_qlib/contrib/data/handler.py
|
||||
5c547a2ef92e075e550fe6d01508a2f1d3f536bc tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
4f656130d167e79dcaaeb7783a121f0b36852374 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
||||
b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
||||
b1489f2fc0dee85f0a4f90b2e6ad545ed9c8967b tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
121ef237da1df1b8e21a561c3ad0db200b901339 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
|
||||
d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
|
||||
d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
||||
184f80da8edf944bad3c8fb4d4d3d189bf4f082b tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||
896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9 tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
|
||||
9090fc6dfbd339f2f4df4b0c9b87f400ecb5c9d5 tac-qlib/tac_qlib/contrib/strategy/long_short.py
|
||||
2c2f167b693f4366a769998e3c9d4804f29e31e0 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||
c29e45e562256bf786c36f91a971b097467276e9 tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
6dd1c568a2961842793674390d5abffd1a0e71b8 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
||||
519a1f4c05dbe0ac018ab8b779eb33d53b4dd545 tac-qlib/tac_qlib/contrib/strategy/ic_gate.py
|
||||
79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
|
||||
5b9acfb4340111b204249add7760bd53c6ae03f1 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
|
||||
7bcee5f0b09cfa721440f1354f16f2dd9a112b12 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
|
||||
fe60bacdfedd48617863be31f24b7c7daebfac5a tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
|
||||
92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
|
||||
fa1090eb6f17b1b7835038e35fac86334294d165 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
48629a1f51bd4058036ce78d79ed49454982c051 tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
ad7c9b726c9c52773fa82031fcd681337be97fa8 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
1953fb2a6371525db7f7b0e1c9dfbf3492d82110 tac-qlib/tac_qlib/data/config.py
|
||||
316bf4aa160cc8d15929ea648be03f4b4999667d tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
554a3f29d181b64effbf49a8161b32e7f93d8d3e tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
8b47f6d78ac046b6b7b2fb07bd7f3382773ffb73 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
||||
8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
|
||||
|
||||
Binary file not shown.
@@ -1 +0,0 @@
|
||||
from .tradeac_exchange import TradeACExchange
|
||||
@@ -1,432 +0,0 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
"""
|
||||
TradeACExchange
|
||||
|
||||
A short/borrow enabled Exchange implementation built on top of qlib.backtest.exchange.Exchange.
|
||||
This exchange adds simple, configurable margin logic (initial/maintenance), borrowing support for
|
||||
shorts, a borrow fee, and a lightweight SMA (Special Memorandum Account) concept to emulate
|
||||
behaviors similar to brokers such as IBKR and Alpaca for backtesting purposes.
|
||||
|
||||
Notes / limitations
|
||||
- This implementation is intentionally lightweight and conservative: it implements the
|
||||
key behaviors needed for strategy/backtest experiments (allowing short selling, computing
|
||||
margin requirements, performing margin-call checks, and tracking SMA-like excess equity).
|
||||
- It makes some simplifying assumptions compared to real brokers (no per-product house margins,
|
||||
simplified SMA bookkeeping, borrow availability modeled only by a per-symbol boolean/limit).
|
||||
- The Position class in qlib.backtest.position was not changed. To support shorts we update the
|
||||
position.position dict directly when necessary. This keeps integration simple but bypasses some
|
||||
internal Position helpers. Use with care.
|
||||
|
||||
API additions
|
||||
- allow_short: enable short selling (bool)
|
||||
- initial_margin_long/short: fraction required to open a position
|
||||
- maintenance_margin_long/short: fraction required to keep a position
|
||||
- borrow_fee_rate: periodic borrow fee applied on short value (applied at trade time as additional cost)
|
||||
- borrowable: dict mapping stock_id -> bool or float (max borrowable shares). Symbols missing from
|
||||
the dict follow `borrow_default` (default True = unlimited; set False for a strict whitelist)
|
||||
- get_sma(position): returns SMA-like excess equity available as "buying power credit"
|
||||
- check_margin_call(position): returns True if position is below maintenance requirement
|
||||
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from qlib.backtest.decision import Order
|
||||
from qlib.backtest.exchange import Exchange
|
||||
from qlib.backtest.position import BasePosition
|
||||
|
||||
|
||||
class TradeACExchange(Exchange):
|
||||
"""An exchange that supports short selling / borrowing and basic margin rules.
|
||||
|
||||
The implementation aims to be compatible with the Exchange API used by Account and
|
||||
Position classes in qlib.backtest. It overrides only the minimum methods required to
|
||||
enable short/borrow behavior and margin calculations.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*args: Any,
|
||||
allow_short: bool = True,
|
||||
initial_margin_long: float = 0.5,
|
||||
initial_margin_short: float = 0.5,
|
||||
maintenance_margin_long: float = 0.25,
|
||||
maintenance_margin_short: float = 0.3,
|
||||
borrow_fee_rate: float = 0.0,
|
||||
borrowable: Optional[Dict[str, float]] = None,
|
||||
borrow_default: bool = True,
|
||||
sma_enabled: bool = True,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Create TradeACExchange.
|
||||
|
||||
Parameters mirror Exchange with additional tradeac-specific options.
|
||||
"""
|
||||
super().__init__(*args, **kwargs)
|
||||
self.allow_short = allow_short
|
||||
self.initial_margin_long = initial_margin_long
|
||||
self.initial_margin_short = initial_margin_short
|
||||
self.maintenance_margin_long = maintenance_margin_long
|
||||
self.maintenance_margin_short = maintenance_margin_short
|
||||
self.borrow_fee_rate = borrow_fee_rate
|
||||
# borrowable can be a dict with per-symbol max borrowable amount, or None (unlimited)
|
||||
self.borrowable = borrowable or {}
|
||||
# borrow_default: policy for symbols absent from `borrowable`.
|
||||
# True -> unlisted symbols are unlimited-borrowable (legacy behavior)
|
||||
# False -> unlisted symbols are NOT borrowable; only listed ones can be shorted
|
||||
self.borrow_default = bool(borrow_default)
|
||||
# sma_enabled: whether to expose lightweight SMA calculation
|
||||
self.sma_enabled = sma_enabled
|
||||
|
||||
# --------------------------- Helper calculations ---------------------------
|
||||
def _initial_margin_requirement(self, position: BasePosition) -> float:
|
||||
"""Compute the initial margin requirement (money) for the given position.
|
||||
|
||||
We treat longs and shorts separately and sum their required initial margins.
|
||||
"""
|
||||
im_req = 0.0
|
||||
for sid in position.get_stock_list():
|
||||
amt = position.get_stock_amount(sid)
|
||||
price = position.get_stock_price(sid)
|
||||
val = amt * price
|
||||
if val > 0:
|
||||
im_req += abs(val) * self.initial_margin_long
|
||||
elif val < 0:
|
||||
im_req += abs(val) * self.initial_margin_short
|
||||
return im_req
|
||||
|
||||
def _maintenance_margin_requirement(self, position: BasePosition) -> float:
|
||||
"""Compute the maintenance margin requirement (money) for the given position."""
|
||||
mm_req = 0.0
|
||||
for sid in position.get_stock_list():
|
||||
amt = position.get_stock_amount(sid)
|
||||
price = position.get_stock_price(sid)
|
||||
val = amt * price
|
||||
if val > 0:
|
||||
mm_req += abs(val) * self.maintenance_margin_long
|
||||
elif val < 0:
|
||||
mm_req += abs(val) * self.maintenance_margin_short
|
||||
return mm_req
|
||||
|
||||
def get_equity(self, position: BasePosition) -> float:
|
||||
"""Return account equity (position value + cash)."""
|
||||
return position.calculate_value()
|
||||
|
||||
def get_sma(self, position: BasePosition) -> float:
|
||||
"""Return a simplified SMA: excess equity above initial margin requirement.
|
||||
|
||||
Note: This is a synthetic/Simplified SMA used for strategy/backtest logic. Real-broker
|
||||
SMA accounting (e.g. credits/debits across days) can be more complex.
|
||||
"""
|
||||
if not self.sma_enabled:
|
||||
return 0.0
|
||||
equity = self.get_equity(position)
|
||||
im_req = self._initial_margin_requirement(position)
|
||||
return max(0.0, equity - im_req)
|
||||
|
||||
def check_margin_call(self, position: BasePosition) -> bool:
|
||||
"""Return True when the account is under maintenance margin (margin call).
|
||||
|
||||
Margin call condition here is simple: equity < maintenance requirement.
|
||||
"""
|
||||
equity = self.get_equity(position)
|
||||
mm_req = self._maintenance_margin_requirement(position)
|
||||
return equity < mm_req
|
||||
|
||||
def get_buying_power(self, position: BasePosition) -> float:
|
||||
"""Estimate buying power for new long positions assuming opening margin requirement.
|
||||
|
||||
Simplified: the maximum notional long value = equity / initial_margin_long.
|
||||
"""
|
||||
equity = self.get_equity(position)
|
||||
if self.initial_margin_long <= 0:
|
||||
return 0.0
|
||||
return equity / self.initial_margin_long
|
||||
|
||||
# --------------------------- Order / execution overrides ---------------------------
|
||||
def _borrow_headroom(self, stock_id: str, current_short: float) -> float:
|
||||
"""Remaining borrowable shares for `stock_id` given an already-open short of `current_short` shares.
|
||||
|
||||
borrowable values: bool (True=unlimited, False=not borrowable) or numeric max shares.
|
||||
Missing symbols follow `borrow_default` (True = unlimited when allow_short is enabled).
|
||||
"""
|
||||
if not self.allow_short:
|
||||
return 0.0
|
||||
v = self.borrowable.get(stock_id, self.borrow_default)
|
||||
if isinstance(v, bool):
|
||||
return float("inf") if v else 0.0
|
||||
try:
|
||||
limit = float(v)
|
||||
except (TypeError, ValueError):
|
||||
return float("inf")
|
||||
return max(0.0, limit - max(current_short, 0.0))
|
||||
|
||||
def _calc_trade_info_by_order(
|
||||
self,
|
||||
order: Order,
|
||||
position: Optional[BasePosition],
|
||||
dealt_order_amount: Dict[str, float],
|
||||
) -> Tuple[float, float, float]:
|
||||
"""Override to allow (optionally) short selling and to apply borrow fees.
|
||||
|
||||
The original Exchange implementation forbids selling more than you own. Here we allow
|
||||
sell orders to create/expand short positions when allow_short is True. We still rely on
|
||||
most base logic (price discovery, impact, cost calculation) by calling super(), but we
|
||||
adjust the sell-side clipping behavior before delegating to the base implementation.
|
||||
"""
|
||||
# When selling and shorts are allowed, temporarily relax the clipping logic in the base
|
||||
# implementation by monkey-patching current position check. Simpler: replicate minimal
|
||||
# parts of logic from Exchange._calc_trade_info_by_order with the key change.
|
||||
|
||||
# Get basic trade price & volume info using Exchange helpers
|
||||
trade_price = float(self.get_deal_price(order.stock_id, order.start_time, order.end_time, direction=order.direction))
|
||||
total_trade_val = float(self.get_volume(order.stock_id, order.start_time, order.end_time) or 0.0) * trade_price
|
||||
|
||||
order.factor = self.get_factor(order.stock_id, order.start_time, order.end_time)
|
||||
order.deal_amount = order.amount # attempt full
|
||||
|
||||
# volume clipping (same as base)
|
||||
self._clip_amount_by_volume(order, dealt_order_amount)
|
||||
|
||||
# approximate adjusted cost ratio based on liquidity
|
||||
if not total_trade_val or np.isnan(total_trade_val) or total_trade_val <= 0:
|
||||
adj_cost_ratio = self.impact_cost
|
||||
else:
|
||||
trade_val_tmp = order.deal_amount * trade_price
|
||||
adj_cost_ratio = self.impact_cost * (trade_val_tmp / total_trade_val) ** 2
|
||||
|
||||
# Differentiate buy / sell
|
||||
if order.direction == Order.SELL:
|
||||
cost_ratio = self.close_cost + adj_cost_ratio
|
||||
current_amount = (
|
||||
position.get_stock_amount(order.stock_id) if (position is not None and position.check_stock(order.stock_id)) else 0.0
|
||||
)
|
||||
long_held = max(current_amount, 0.0)
|
||||
short_open = max(-current_amount, 0.0)
|
||||
|
||||
if position is not None:
|
||||
if not self.allow_short:
|
||||
# clip by current holdings only
|
||||
if not np.isclose(order.deal_amount, current_amount):
|
||||
order.deal_amount = self.round_amount_by_trade_unit(
|
||||
min(long_held, order.deal_amount), order.factor
|
||||
)
|
||||
else:
|
||||
# allow selling beyond holdings up to the remaining borrow limit;
|
||||
# later when updating the position we create/expand a short if necessary.
|
||||
max_sell = long_held + self._borrow_headroom(order.stock_id, short_open)
|
||||
if order.deal_amount > max_sell and not np.isclose(order.deal_amount, max_sell):
|
||||
order.deal_amount = self.round_amount_by_trade_unit(max_sell, order.factor)
|
||||
|
||||
elif order.direction == Order.BUY:
|
||||
cost_ratio = self.open_cost + adj_cost_ratio
|
||||
if position is not None:
|
||||
cash = position.get_cash()
|
||||
trade_val = order.deal_amount * trade_price
|
||||
if cash < max(trade_val * cost_ratio, self.min_cost):
|
||||
order.deal_amount = 0
|
||||
self.logger.debug(f"Order clipped due to cost higher than cash: {order}")
|
||||
elif cash < trade_val + max(trade_val * cost_ratio, self.min_cost):
|
||||
max_buy_amount = self._get_buy_amount_by_cash_limit(trade_price, cash, cost_ratio)
|
||||
order.deal_amount = self.round_amount_by_trade_unit(min(max_buy_amount, order.deal_amount), order.factor)
|
||||
self.logger.debug(f"Order clipped due to cash limitation: {order}")
|
||||
else:
|
||||
order.deal_amount = self.round_amount_by_trade_unit(order.deal_amount, order.factor)
|
||||
else:
|
||||
order.deal_amount = self.round_amount_by_trade_unit(order.deal_amount, order.factor)
|
||||
else:
|
||||
raise NotImplementedError("order direction {} error".format(order.direction))
|
||||
|
||||
# compute final trade_val & trade_cost
|
||||
trade_val = order.deal_amount * trade_price
|
||||
# base trade_cost
|
||||
trade_cost = max(trade_val * cost_ratio, self.min_cost)
|
||||
# apply borrow fee only on the net-new short portion of the sell
|
||||
if order.direction == Order.SELL and self.allow_short:
|
||||
new_short = max(0.0, order.deal_amount - long_held)
|
||||
trade_cost += new_short * trade_price * self.borrow_fee_rate
|
||||
|
||||
if trade_val <= 1e-5:
|
||||
trade_cost = 0
|
||||
|
||||
return trade_price, trade_val, trade_cost
|
||||
|
||||
def deal_order(
|
||||
self,
|
||||
order: Order,
|
||||
trade_account: Optional[Any] = None,
|
||||
position: Optional[BasePosition] = None,
|
||||
dealt_order_amount: Dict[str, float] = None,
|
||||
) -> Tuple[float, float, float]:
|
||||
"""Deal order and handle short position bookkeeping.
|
||||
|
||||
This method mirrors Exchange.deal_order but when a position is provided and shorts are
|
||||
allowed it will update the Position.position dict directly to support negative amounts.
|
||||
"""
|
||||
if dealt_order_amount is None:
|
||||
dealt_order_amount = {}
|
||||
|
||||
if not self.check_order(order):
|
||||
order.deal_amount = 0.0
|
||||
self.logger.debug(f"Order failed due to trading limitation: {order}")
|
||||
return 0.0, 0.0, np.nan
|
||||
|
||||
if trade_account is not None and position is not None:
|
||||
raise ValueError("trade_account and position can only choose one")
|
||||
|
||||
pos = position or (trade_account.current_position if trade_account is not None else None)
|
||||
trade_price, trade_val, trade_cost = self._calc_trade_info_by_order(order, pos, dealt_order_amount)
|
||||
|
||||
if trade_val > 1e-5:
|
||||
if trade_account is not None:
|
||||
cp = trade_account.current_position
|
||||
if not cp.skip_update():
|
||||
held = cp.check_stock(order.stock_id)
|
||||
# Account-level bookkeeping (turnover/cost/returns). Mirrors
|
||||
# Account._update_state_from_order except for fresh short sales,
|
||||
# where no prior price exists to compute order profit from.
|
||||
if order.direction == Order.SELL and not held:
|
||||
trade_account.accum_info.add_turnover(trade_val)
|
||||
trade_account.accum_info.add_cost(trade_cost)
|
||||
trade_account.accum_info.add_return_value(0.0)
|
||||
if order.direction == Order.SELL:
|
||||
# sell: update account state first (stock entry may be deleted)
|
||||
if held:
|
||||
trade_account._update_state_from_order(order, trade_val, trade_cost, trade_price)
|
||||
self._position_sell(cp, order, trade_val, trade_cost, trade_price)
|
||||
else:
|
||||
# buy: update position first (entry may be created), then account state
|
||||
# A buy that covers a short to exactly flat deletes the entry inside
|
||||
# _position_buy; re-seed a transient zero-amount stub so the
|
||||
# account's order-profit lookup still finds the trade price,
|
||||
# then drop it (_update_state_from_order never mutates entries).
|
||||
sid = order.stock_id
|
||||
had_entry = isinstance(cp.position.get(sid), dict)
|
||||
self._position_buy(cp, order, trade_val, trade_cost, trade_price)
|
||||
covered_to_flat = had_entry and not isinstance(cp.position.get(sid), dict)
|
||||
if covered_to_flat:
|
||||
cp.position[sid] = {"amount": 0.0, "price": trade_price, "weight": 0}
|
||||
trade_account._update_state_from_order(order, trade_val, trade_cost, trade_price)
|
||||
if covered_to_flat:
|
||||
cp.position.pop(sid, None)
|
||||
elif position is not None:
|
||||
if order.direction == Order.BUY:
|
||||
self._position_buy(position, order, trade_val, trade_cost, trade_price)
|
||||
else:
|
||||
self._position_sell(position, order, trade_val, trade_cost, trade_price)
|
||||
return trade_val, trade_cost, trade_price
|
||||
|
||||
# --------------------------- Position mutation helpers ---------------------------
|
||||
def _position_buy(self, position: BasePosition, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
|
||||
"""Handle buy order bookkeeping against a BasePosition while supporting shorts.
|
||||
|
||||
Rules implemented (simplified):
|
||||
- If there is an existing short (amount < 0), the buy will first cover the short.
|
||||
- If covering closes the short completely, the remaining buy becomes a long
|
||||
- Cash updates mimic Position._buy_stock/_sell_stock (cash decreases by trade_val+cost for buys)
|
||||
"""
|
||||
trade_amount = trade_val / trade_price
|
||||
sid = order.stock_id
|
||||
current_amount = position.get_stock_amount(sid) if position.check_stock(sid) else 0.0
|
||||
|
||||
# covering existing short
|
||||
if current_amount < -1e-12:
|
||||
# amount is negative -> we are short. Buying reduces the short.
|
||||
new_amount = current_amount + trade_amount
|
||||
if abs(new_amount) <= 1e-8:
|
||||
# short fully covered exactly -> remove entry
|
||||
if sid in position.position:
|
||||
del position.position[sid]
|
||||
elif new_amount > 0:
|
||||
# short fully covered with leftover buy amount -> leftover becomes a long position
|
||||
position.position[sid] = {"amount": new_amount, "price": trade_price, "weight": 0}
|
||||
else:
|
||||
# partially cover
|
||||
position.position[sid]["amount"] = new_amount
|
||||
position.position[sid]["price"] = trade_price
|
||||
else:
|
||||
# normal or increasing long
|
||||
if sid not in position.position or not isinstance(position.position[sid], dict):
|
||||
# initialize stock
|
||||
position.position[sid] = {"amount": trade_amount, "price": trade_price, "weight": 0}
|
||||
else:
|
||||
position.position[sid]["amount"] = position.position[sid].get("amount", 0.0) + trade_amount
|
||||
position.position[sid]["price"] = trade_price
|
||||
|
||||
# cash effect same as Position._buy_stock
|
||||
position.position["cash"] -= trade_val + cost
|
||||
|
||||
def _position_sell(self, position: BasePosition, order: Order, trade_val: float, cost: float, trade_price: float) -> None:
|
||||
"""Handle sell order bookkeeping against a BasePosition while supporting shorts.
|
||||
|
||||
Rules implemented (simplified):
|
||||
- If holding enough long shares, sell will reduce/close the long position normally.
|
||||
- If not holding enough long shares and shorts are allowed, the remaining sold amount will create/expand a short position.
|
||||
- Cash update for sells follows Position._sell_stock logic (cash increases by trade_val - cost)
|
||||
"""
|
||||
trade_amount = trade_val / trade_price
|
||||
sid = order.stock_id
|
||||
current_amount = position.get_stock_amount(sid) if position.check_stock(sid) else 0.0
|
||||
|
||||
if current_amount > 1e-12:
|
||||
# we have long shares; sell from them first
|
||||
if trade_amount >= current_amount - 1e-8:
|
||||
# selling all or more than holdings
|
||||
# remove long position
|
||||
if sid in position.position:
|
||||
del position.position[sid]
|
||||
# remaining sold amount becomes short if allowed
|
||||
remain = trade_amount - current_amount
|
||||
if remain > 1e-8:
|
||||
if not self.allow_short:
|
||||
# should not happen due to clipping earlier, but guard anyway
|
||||
raise ValueError(f"Attempt to short {sid} while shorting disabled")
|
||||
# create short entry
|
||||
position.position[sid] = {"amount": -remain, "price": trade_price, "weight": 0}
|
||||
else:
|
||||
# partial sell
|
||||
position.position[sid]["amount"] = current_amount - trade_amount
|
||||
position.position[sid]["price"] = trade_price
|
||||
else:
|
||||
# currently flat or already short
|
||||
if not self.allow_short:
|
||||
raise ValueError(f"Attempt to short {sid} while shorting disabled")
|
||||
# expand short
|
||||
new_amount = current_amount - trade_amount
|
||||
if sid not in position.position or not isinstance(position.position[sid], dict):
|
||||
position.position[sid] = {"amount": new_amount, "price": trade_price, "weight": 0}
|
||||
else:
|
||||
position.position[sid]["amount"] = new_amount
|
||||
position.position[sid]["price"] = trade_price
|
||||
|
||||
# cash effect same as Position._sell_stock
|
||||
new_cash = trade_val - cost
|
||||
if getattr(position, "_settle_type", None) == position.ST_CASH:
|
||||
position.position["cash_delay"] = position.position.get("cash_delay", 0.0) + new_cash
|
||||
else:
|
||||
position.position["cash"] = position.position.get("cash", 0.0) + new_cash
|
||||
|
||||
# --------------------------- Borrow availability helpers ---------------------------
|
||||
def is_borrowable(self, stock_id: str, amount: float) -> bool:
|
||||
"""Check whether the requested amount is borrowable for the given stock.
|
||||
|
||||
If a borrowable dict is provided, it may contain either booleans or numeric limits (maximum borrowable shares).
|
||||
Symbols absent from the dict follow `borrow_default`.
|
||||
"""
|
||||
if not self.allow_short:
|
||||
return False
|
||||
if stock_id not in self.borrowable:
|
||||
return self.borrow_default
|
||||
v = self.borrowable[stock_id]
|
||||
if isinstance(v, bool):
|
||||
return v
|
||||
try:
|
||||
limit = float(v)
|
||||
return amount <= limit
|
||||
except Exception:
|
||||
return True
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -22,7 +22,6 @@ from qlib.utils import get_callable_kwargs
|
||||
from ...data.config import (
|
||||
LakeConfig,
|
||||
timeframe_for_freq,
|
||||
FEATURE_FAMILIES,
|
||||
NON_FEATURE_COLUMNS,
|
||||
)
|
||||
|
||||
@@ -93,7 +92,7 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
|
||||
|
||||
common: set = set()
|
||||
# family tier: features/market=*/timeframe=*/family=*/symbol=*.parquet
|
||||
for fam in FEATURE_FAMILIES:
|
||||
for fam in ("ta", "sp"):
|
||||
fam_dir = feat_dir / f"family={fam}"
|
||||
if fam_dir.is_dir():
|
||||
common |= _family_common(fam_dir)
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,4 +1,11 @@
|
||||
from .ic_gate import ICGateTopkDropoutStrategy # noqa: F401
|
||||
from .optimal_stop import OptimalStopControl # noqa: F401
|
||||
from .long_short import LongShortTopkStrategy # noqa: F401
|
||||
from .regime_gate import RegimeGateTopkDropoutStrategy # noqa: F401
|
||||
from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
|
||||
|
||||
__all__ = ["OptimalStopControl", "LongShortTopkStrategy"]
|
||||
__all__ = [
|
||||
"ICGateTopkDropoutStrategy",
|
||||
"OptimalStopControl",
|
||||
"RegimeGateTopkDropoutStrategy",
|
||||
"WeeklyRebalanceDropoutStrategy",
|
||||
]
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,117 @@
|
||||
"""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)
|
||||
@@ -1,201 +0,0 @@
|
||||
"""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)
|
||||
@@ -1,361 +0,0 @@
|
||||
"""Long-short Top-K strategy for cross-sectional signals.
|
||||
|
||||
Each day the strategy ranks the cross-section by prediction score and rebalances
|
||||
to an equal-weight two-sided book: the ``topk`` highest-ranked names go long and
|
||||
the ``k_short`` lowest-ranked names go short. Net-new shorts are opened by
|
||||
selling beyond current holdings, which requires a short-aware exchange such as
|
||||
``tac_qlib.contrib.backtest.tradeac_exchange.TradeACExchange`` with
|
||||
``allow_short=True`` (borrow limits, margin requirements and borrow fees are
|
||||
enforced there, not here).
|
||||
|
||||
Sizing deploys ``equity * risk_degree`` as gross notional split evenly across
|
||||
all long and short legs, so the book is approximately market neutral.
|
||||
``allow_short=False`` disables the short side entirely (long-only ``topk``).
|
||||
|
||||
Short eligibility can be restricted further, with static or dynamic gates:
|
||||
``short_whitelist`` limits shorts to an explicit symbol set; ``short_vol_top_pct``
|
||||
requires a candidate's trailing realized volatility to rank in the top fraction
|
||||
of that day's cross-section; ``short_max_mom`` (falling-knife filter) only
|
||||
allows shorting names whose own trailing momentum is at/below a threshold;
|
||||
``short_regime_sma`` disables shorts entirely while the benchmark trades above
|
||||
its moving average (risk-on). Borrow availability itself is enforced by the
|
||||
exchange (``borrowable`` whitelist / per-symbol caps via ``TradeACExchange``).
|
||||
|
||||
Wired into qrun workflows like any ``BaseStrategy`` (see ``PortAnaRecord``
|
||||
config). Mirrors the API usage of qlib's ``TopkDropoutStrategy``: ``Order`` /
|
||||
``OrderDir`` from ``qlib.backtest.decision``, ``trade_calendar`` /
|
||||
``trade_exchange`` / ``trade_position`` injected by the backtest executor.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||
from qlib.log import get_module_logger
|
||||
|
||||
__all__ = ["LongShortTopkStrategy"]
|
||||
|
||||
|
||||
class LongShortTopkStrategy(BaseSignalStrategy):
|
||||
"""Equal-weight long-short Top-K strategy over a cross-sectional signal.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk : number of long legs (highest-ranked names).
|
||||
k_short : number of short legs (lowest-ranked names).
|
||||
hold_thresh : minimum holding days before a leg may be closed/reduced.
|
||||
only_tradable : only select candidates tradable on the trade date.
|
||||
rebalance_tol : skip rebalances smaller than this fraction of a leg's
|
||||
target notional (turnover control).
|
||||
allow_short : enable/disable the short side. With ``False`` the bottom-ranked
|
||||
legs are dropped and the book is long-only ``topk``; pair with
|
||||
``allow_short=False`` on the exchange for a fully borrow-free run.
|
||||
Legacy alias ``enable_short`` is accepted.
|
||||
short_whitelist : optional list of symbols eligible for shorting; candidates
|
||||
outside the list are skipped (``None`` = all names eligible).
|
||||
short_vol_window : trailing window (trading days) for realized-vol estimation.
|
||||
short_vol_top_pct : if set, a short candidate's trailing realized volatility
|
||||
must rank at or above this percentile of that day's cross-section
|
||||
(e.g. ``0.5`` = only the more volatile half may be shorted). Candidates
|
||||
without measurable vol are never shorted.
|
||||
short_mom_window : trailing window (trading days) for the candidate momentum
|
||||
used by the falling-knife gate.
|
||||
short_max_mom : if set, a candidate's trailing ``short_mom_window``-day return
|
||||
must be <= this value to be shortable (e.g. ``0.0`` = only short names
|
||||
that are actually falling). Candidates without measurable momentum are
|
||||
never shorted.
|
||||
short_regime_symbol : benchmark symbol for the regime gate (default SPY).
|
||||
short_regime_sma : if set, shorts are only allowed on days where the regime
|
||||
symbol's last close (strictly before the execution bar) is BELOW its
|
||||
``short_regime_sma``-day moving average — i.e. shorts are disabled in
|
||||
risk-on regimes and enabled in drawdowns.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
signal=None,
|
||||
topk: int = 4,
|
||||
k_short: int = 2,
|
||||
hold_thresh: int = 1,
|
||||
only_tradable: bool = True,
|
||||
rebalance_tol: float = 0.05,
|
||||
allow_short: Optional[bool] = None,
|
||||
enable_short: Optional[bool] = None,
|
||||
short_whitelist: Optional[List[str]] = None,
|
||||
short_vol_window: int = 20,
|
||||
short_vol_top_pct: Optional[float] = None,
|
||||
short_mom_window: int = 20,
|
||||
short_max_mom: Optional[float] = None,
|
||||
short_regime_symbol: str = "SPY",
|
||||
short_regime_sma: Optional[int] = None,
|
||||
risk_degree: float = 0.95,
|
||||
trade_exchange=None,
|
||||
level_infra=None,
|
||||
common_infra=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
signal=signal,
|
||||
risk_degree=risk_degree,
|
||||
trade_exchange=trade_exchange,
|
||||
level_infra=level_infra,
|
||||
common_infra=common_infra,
|
||||
**kwargs,
|
||||
)
|
||||
if allow_short is None:
|
||||
allow_short = True if enable_short is None else bool(enable_short)
|
||||
self.allow_short = bool(allow_short)
|
||||
self.topk = topk
|
||||
self.k_short = k_short
|
||||
self.hold_thresh = hold_thresh
|
||||
self.only_tradable = only_tradable
|
||||
self.rebalance_tol = rebalance_tol
|
||||
self.short_whitelist = set(short_whitelist) if short_whitelist is not None else None
|
||||
if not 0 < float(short_vol_window) <= 1000:
|
||||
raise ValueError(f"short_vol_window must be in (0, 1000], got {short_vol_window}")
|
||||
self.short_vol_window = int(short_vol_window)
|
||||
if short_vol_top_pct is not None and not 0.0 < float(short_vol_top_pct) <= 1.0:
|
||||
raise ValueError(f"short_vol_top_pct must be in (0, 1], got {short_vol_top_pct}")
|
||||
self.short_vol_top_pct = None if short_vol_top_pct is None else float(short_vol_top_pct)
|
||||
if not 0 < float(short_mom_window) <= 1000:
|
||||
raise ValueError(f"short_mom_window must be in (0, 1000], got {short_mom_window}")
|
||||
self.short_mom_window = int(short_mom_window)
|
||||
self.short_max_mom = None if short_max_mom is None else float(short_max_mom)
|
||||
self.short_regime_symbol = str(short_regime_symbol)
|
||||
if short_regime_sma is not None and not 1 < int(short_regime_sma) <= 1000:
|
||||
raise ValueError(f"short_regime_sma must be in (1, 1000], got {short_regime_sma}")
|
||||
self.short_regime_sma = None if short_regime_sma is None else int(short_regime_sma)
|
||||
# per-day caches (keyed by trade date)
|
||||
self._vol_cache_key: Optional[str] = None
|
||||
self._vol_cache_val: Dict[str, Dict[str, float]] = {}
|
||||
self._regime_cache: Dict[str, bool] = {}
|
||||
|
||||
# ------------------------------------------------------------------ utils
|
||||
def _is_tradable(self, code, start, end) -> bool:
|
||||
if not self.only_tradable:
|
||||
return True
|
||||
try:
|
||||
return self.trade_exchange.is_stock_tradable(stock_id=code, start_time=start, end_time=end)
|
||||
except TypeError:
|
||||
return True
|
||||
|
||||
def _mark_price(self, code, start, end) -> Optional[float]:
|
||||
try:
|
||||
px = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=start, end_time=end, direction=OrderDir.BUY
|
||||
)
|
||||
except (KeyError, ValueError):
|
||||
return None
|
||||
if px is None or px != px or px <= 0:
|
||||
return None
|
||||
return float(px)
|
||||
|
||||
def _day_stats(self, codes: List[str], trade_start) -> Dict[str, Dict[str, float]]:
|
||||
"""Per-day cross-sectional stats used by the dynamic short gates.
|
||||
|
||||
For each code, returns ``{"vol_rank": r}`` (percentile of trailing
|
||||
realized vol over ``short_vol_window`` bars across that day's
|
||||
cross-section) when the vol gate is on, and ``{"mom": m}`` (trailing
|
||||
``short_mom_window``-bar return) when the falling-knife gate is on.
|
||||
All series end on the last bar strictly BEFORE the execution bar (no
|
||||
lookahead). Codes without measurable data are simply absent — such
|
||||
candidates are never shorted (fail-closed).
|
||||
"""
|
||||
if self.short_vol_top_pct is None and self.short_max_mom is None:
|
||||
return {}
|
||||
key = str(pd.Timestamp(trade_start))
|
||||
if self._vol_cache_key == key:
|
||||
return self._vol_cache_val
|
||||
out: Dict[str, Dict[str, float]] = {}
|
||||
try:
|
||||
from qlib.data import D
|
||||
|
||||
end = pd.Timestamp(trade_start)
|
||||
buf = max(self.short_vol_window, self.short_mom_window) * 3 + 30
|
||||
df = D.features(
|
||||
sorted(codes),
|
||||
["$close"],
|
||||
start_time=end - pd.Timedelta(days=buf),
|
||||
end_time=end - pd.Timedelta(days=1),
|
||||
)
|
||||
close = df["$close"].unstack(level="instrument") if isinstance(df.index, pd.MultiIndex) else df["$close"]
|
||||
if self.short_vol_top_pct is not None:
|
||||
vol = close.pct_change().rolling(self.short_vol_window).std().iloc[-1]
|
||||
for code, rank in vol.rank(pct=True).dropna().items():
|
||||
out.setdefault(str(code), {})["vol_rank"] = float(rank)
|
||||
if self.short_max_mom is not None:
|
||||
w = min(self.short_mom_window, len(close) - 1)
|
||||
mom = close.iloc[-1] / close.iloc[-(w + 1)] - 1
|
||||
for code, m in mom.items():
|
||||
if m == m:
|
||||
out.setdefault(str(code), {})["mom"] = float(m)
|
||||
except Exception as e: # noqa: BLE001 - degrade to fail-closed (no shorts)
|
||||
get_module_logger(self.__class__.__name__).warning(
|
||||
f"short gates unavailable ({type(e).__name__}: {e}); no shorts this step"
|
||||
)
|
||||
self._vol_cache_key, self._vol_cache_val = key, out
|
||||
return out
|
||||
|
||||
def _regime_ok(self, trade_start) -> bool:
|
||||
"""True when shorting is allowed by the benchmark-regime gate.
|
||||
|
||||
With ``short_regime_sma`` set, shorts are permitted only while the
|
||||
regime symbol's last close strictly before the execution bar sits below
|
||||
its moving average (risk-off). Data failure fails closed (no shorts).
|
||||
"""
|
||||
if self.short_regime_sma is None:
|
||||
return True
|
||||
key = str(pd.Timestamp(trade_start))
|
||||
cached = self._regime_cache.get(key)
|
||||
if cached is not None:
|
||||
return cached
|
||||
ok = False
|
||||
try:
|
||||
from qlib.data import D
|
||||
|
||||
end = pd.Timestamp(trade_start)
|
||||
df = D.features(
|
||||
[self.short_regime_symbol],
|
||||
["$close"],
|
||||
start_time=end - pd.Timedelta(days=int(self.short_regime_sma * 3 + 30)),
|
||||
end_time=end - pd.Timedelta(days=1),
|
||||
)
|
||||
s = df["$close"]
|
||||
if isinstance(s.index, pd.MultiIndex):
|
||||
s = s.droplevel("instrument")
|
||||
sma = s.rolling(self.short_regime_sma).mean().iloc[-1]
|
||||
px = s.iloc[-1]
|
||||
ok = bool(px < sma)
|
||||
except Exception as e: # noqa: BLE001 - degrade to fail-closed (no shorts)
|
||||
get_module_logger(self.__class__.__name__).warning(
|
||||
f"regime gate unavailable ({type(e).__name__}: {e}); no shorts this step"
|
||||
)
|
||||
self._regime_cache[key] = ok
|
||||
return ok
|
||||
|
||||
# ------------------------------------------------------------- decision
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start, trade_end = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start, pred_end = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start, end_time=pred_end)
|
||||
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)
|
||||
pred_score = pred_score.dropna()
|
||||
if pred_score.empty:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
current_temp = copy.deepcopy(self.trade_position)
|
||||
|
||||
# ---- signed current holdings ---------------------------------------
|
||||
cur_amount: Dict[str, float] = {}
|
||||
for code in current_temp.get_stock_list():
|
||||
amt = float(current_temp.get_stock_amount(code))
|
||||
if abs(amt) > 1e-6:
|
||||
cur_amount[code] = amt
|
||||
|
||||
# ---- targets: top-k long, bottom-k_short short ----------------------
|
||||
ranked = list(pred_score.sort_values(ascending=False).index)
|
||||
longs: List[str] = []
|
||||
for code in ranked:
|
||||
if len(longs) >= self.topk:
|
||||
break
|
||||
if self._is_tradable(code, trade_start, trade_end):
|
||||
longs.append(code)
|
||||
shorts: List[str] = []
|
||||
if self.allow_short and self._regime_ok(trade_start):
|
||||
stats = self._day_stats(list(ranked), trade_start)
|
||||
for code in reversed(ranked):
|
||||
if len(shorts) >= self.k_short:
|
||||
break
|
||||
if code in longs:
|
||||
continue
|
||||
if not self._is_tradable(code, trade_start, trade_end):
|
||||
continue
|
||||
if self.short_whitelist is not None and code not in self.short_whitelist:
|
||||
continue
|
||||
st = stats.get(code)
|
||||
if self.short_vol_top_pct is not None:
|
||||
rank = None if st is None else st.get("vol_rank")
|
||||
if rank is None or rank < self.short_vol_top_pct:
|
||||
continue
|
||||
if self.short_max_mom is not None:
|
||||
mom = None if st is None else st.get("mom")
|
||||
if mom is None or mom > self.short_max_mom:
|
||||
continue
|
||||
shorts.append(code)
|
||||
|
||||
# ---- marks & equity --------------------------------------------------
|
||||
marks: Dict[str, float] = {}
|
||||
for code in set(cur_amount) | set(longs) | set(shorts):
|
||||
px = self._mark_price(code, trade_start, trade_end)
|
||||
if px is not None:
|
||||
marks[code] = px
|
||||
|
||||
equity = current_temp.get_cash()
|
||||
for code, amt in cur_amount.items():
|
||||
if code in marks:
|
||||
equity += amt * marks[code]
|
||||
if equity <= 0:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
n_legs = len([c for c in longs if c in marks]) + len([c for c in shorts if c in marks])
|
||||
if n_legs == 0:
|
||||
return TradeDecisionWO([], self)
|
||||
per_leg = equity * self.risk_degree / n_legs
|
||||
|
||||
target_signed: Dict[str, float] = {}
|
||||
for code in longs:
|
||||
if code in marks:
|
||||
target_signed[code] = per_leg / marks[code]
|
||||
for code in shorts:
|
||||
if code in marks:
|
||||
target_signed[code] = -(per_leg / marks[code])
|
||||
|
||||
# ---- order generation -------------------------------------------------
|
||||
sell_orders: List[Order] = []
|
||||
buy_orders: List[Order] = []
|
||||
|
||||
def submit(code: str, amount: float, direction: int) -> None:
|
||||
factor = self.trade_exchange.get_factor(stock_id=code, start_time=trade_start, end_time=trade_end)
|
||||
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||
if amount <= 1e-6:
|
||||
return
|
||||
o = Order(stock_id=code, amount=amount, start_time=trade_start, end_time=trade_end, direction=direction)
|
||||
if self.trade_exchange.check_order(o):
|
||||
(buy_orders if direction == Order.BUY else sell_orders).append(o)
|
||||
|
||||
# close holdings that are no longer targeted (frees cash / unwinds shorts)
|
||||
for code, amt in cur_amount.items():
|
||||
if code in target_signed:
|
||||
continue
|
||||
if marks.get(code) is None:
|
||||
continue
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
submit(code, abs(amt), Order.SELL if amt > 0 else Order.BUY)
|
||||
|
||||
# rebalance targeted legs toward their signed target quantity
|
||||
for code, tgt in target_signed.items():
|
||||
cur = cur_amount.get(code, 0.0)
|
||||
delta = tgt - cur
|
||||
if abs(delta * marks[code]) < max(self.rebalance_tol * per_leg, 1.0):
|
||||
continue
|
||||
if delta > 0:
|
||||
submit(code, delta, Order.BUY)
|
||||
else:
|
||||
if cur > 0 and current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
submit(code, -delta, Order.SELL)
|
||||
|
||||
return TradeDecisionWO(sell_orders + buy_orders, self)
|
||||
@@ -1,231 +1,215 @@
|
||||
"""HMM-regime overlay TopkDropout strategy.
|
||||
"""Regime-gate TopkDropout strategy.
|
||||
|
||||
Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
selection and sizing are identical to the reference, but a name is only BOUGHT
|
||||
(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
|
||||
the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
|
||||
of being opened.
|
||||
Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
|
||||
holds the book (issues NO orders) while a regime detector says the market is in
|
||||
an unfavorable state. When the gate is open it behaves exactly like the
|
||||
reference TopkDropoutStrategy.
|
||||
|
||||
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.
|
||||
Three detector types are supported (all causal — no lookahead):
|
||||
|
||||
Names already held are NOT force-sold when the regime turns unfavourable
|
||||
(entry gate only, matching the queue-10 design).
|
||||
* ``dispersion``: cross-sectional standard deviation of 22-day rolling returns
|
||||
across the universe. Gate closes when CS dispersion < threshold (low
|
||||
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 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.backtest.decision import TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
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"
|
||||
__all__ = ["RegimeGateTopkDropoutStrategy", "compute_regime_gate"]
|
||||
|
||||
|
||||
class RegimeGateDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout with an HMM-regime entry gate on buy candidates.
|
||||
class RegimeGateTopkDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout with a regime-gate circuit breaker.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
|
||||
new position (default 0.5).
|
||||
regime_gate : pd.Series — precomputed per-date gate (bool indexed by
|
||||
datetime). True = trade allowed, False = no orders. Missing dates
|
||||
default to open (trade allowed).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.regime_threshold = regime_threshold
|
||||
def __init__(self, *, regime_gate=None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._regime_gate = regime_gate
|
||||
|
||||
def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
|
||||
"""Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
|
||||
if D is None:
|
||||
return pd.Series(dtype=float)
|
||||
try:
|
||||
df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
|
||||
except Exception: # noqa: BLE001 - a regime read failure should gate open, not crash
|
||||
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 _gate_open(self, trade_start_time) -> bool:
|
||||
if self._regime_gate is None:
|
||||
return True
|
||||
ts = pd.Timestamp(trade_start_time)
|
||||
known = self._regime_gate[self._regime_gate.index <= ts]
|
||||
if len(known):
|
||||
return bool(known.iloc[-1])
|
||||
return True # default open if no history yet
|
||||
|
||||
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:
|
||||
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)
|
||||
|
||||
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
|
||||
# ---------------------------------------------------------------------------
|
||||
# Precomputation helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
def compute_regime_gate(
|
||||
detector: str,
|
||||
threshold: float = 0.0,
|
||||
*,
|
||||
lake_root: str = "",
|
||||
market: str = "US",
|
||||
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.
|
||||
|
||||
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
|
||||
)
|
||||
]
|
||||
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.
|
||||
|
||||
else:
|
||||
Returns
|
||||
-------
|
||||
pd.Series — bool, indexed by datetime. True = trade allowed.
|
||||
"""
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
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)
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
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 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
|
||||
def _universe_symbols(cfg) -> list:
|
||||
"""Read symbols from the lake symbols.parquet."""
|
||||
import pathlib
|
||||
|
||||
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")
|
||||
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 []
|
||||
|
||||
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")
|
||||
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
|
||||
|
||||
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 = []
|
||||
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
|
||||
|
||||
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,
|
||||
):
|
||||
|
||||
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
|
||||
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,
|
||||
):
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except Exception:
|
||||
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)
|
||||
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
|
||||
if not all_posteriors:
|
||||
# no HMM features found — default open
|
||||
idx = pd.date_range(start, end, freq="B")
|
||||
return pd.Series(True, index=idx)
|
||||
post_df = pd.DataFrame(all_posteriors)
|
||||
cs_mean = post_df.mean(axis=1)
|
||||
gate = cs_mean >= threshold
|
||||
return gate
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -61,11 +61,6 @@ UNKNOWN_FIELD_NAMES = ("factor", "change", "trade_unit", "suspend_flag")
|
||||
#: columns in the parquet files that are not features
|
||||
NON_FEATURE_COLUMNS = ("t", "date", "market", "timeframe", "symbol")
|
||||
|
||||
#: Feature-family partitions merged by ``LakeConfig.load_features`` and scanned
|
||||
#: by the handler's field discovery. ``macro`` holds broadcast market-state
|
||||
#: columns (see skills/tac-qlib-custom/examples/persist_macro_broadcast.py).
|
||||
FEATURE_FAMILIES = ("ta", "sp", "macro")
|
||||
|
||||
|
||||
def timeframe_for_freq(freq: str) -> str:
|
||||
"""Map a qlib frequency (e.g. ``day``, ``1min``) to a lake timeframe (e.g. ``1d``)."""
|
||||
@@ -118,12 +113,12 @@ class LakeConfig:
|
||||
return self.features_dir(timeframe) / f"symbol={str(symbol).upper()}.parquet"
|
||||
|
||||
def load_features(self, timeframe: str, symbol: str) -> pd.DataFrame:
|
||||
"""All feature columns for a symbol, merging the `family=ta|sp|macro`
|
||||
partitions by timestamp. Returns an empty frame when no
|
||||
"""All feature columns for a symbol, merging the `family=ta` and
|
||||
`family=sp` partitions by timestamp. Returns an empty frame when no
|
||||
feature files exist (legacy flat layout falls back transparently)."""
|
||||
sym = str(symbol).upper()
|
||||
frames = []
|
||||
for family in FEATURE_FAMILIES:
|
||||
for family in ("ta", "sp"):
|
||||
p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
|
||||
if p.exists():
|
||||
frames.append(pd.read_parquet(p))
|
||||
|
||||
@@ -1,133 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# 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
|
||||
@@ -1,134 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# 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
|
||||
@@ -1,141 +0,0 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,97 +0,0 @@
|
||||
# Re-run of experiment 16 with validated family=ta and family=sp lake features.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sma_5,sma_20,ema_12,ema_26,rsi_14,macd,macd_signal,macd_hist,bb_upper,bb_middle,bb_lower,atr_14,adx_14,sp_ret,sp_ou_half_life,sp_ou_revert,sp_ou_zscore,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_down,sp_max_move,sp_max_up,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5" %}
|
||||
|
||||
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-exp16-db-ta-sp" }
|
||||
|
||||
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: 2026-08-10
|
||||
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: "{{ FEATURES }}"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,97 +0,0 @@
|
||||
# General stochastic-process feature ablation: no TA, HMM, or OU fields.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_down,sp_max_move,sp_max_up,sp_rv1,sp_rv5,sp_rv22,sp_rv_ac1,sp_rv_cv_22,sp_vol_ratio_1_22,sp_vol_ratio_5_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_1,sp_dsv_5,sp_dsv_22,sp_dsv_ratio_1,sp_dsv_ratio_5,sp_dsv_ratio_22,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_sig_level2_lead_lag_5,sp_sig_level2_lag_lead_5" %}
|
||||
|
||||
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-exp22-stochastic-general" }
|
||||
|
||||
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: 2026-08-10
|
||||
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: "{{ FEATURES }}"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,97 +0,0 @@
|
||||
# Exact compact stochastic feature set requested for a new run in MLflow exp 25.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,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-exp22-stochastic-general" }
|
||||
|
||||
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: 2026-08-10
|
||||
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: "{{ FEATURES }}"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { 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: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -1,98 +0,0 @@
|
||||
# Compact stochastic feature set with reduced turnover: n_drop=1 instead of 2.
|
||||
# Same setup as exp24 (compact baseline) but replacing the TopkDropout n_drop 2 with 1.
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set FEATURES = "$open,$high,$low,$close,$vwap,$volume,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-exp22-stochastic-general" }
|
||||
|
||||
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: 2026-08-10
|
||||
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: "{{ FEATURES }}"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { 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: 1, 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