Compare commits
5
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
7fad62a4ef | ||
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zhaoli
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f9ef005e9a | ||
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zhaoli
|
c09997c7e2 | ||
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zhaoli
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32477c7bb8 | ||
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zhaoli
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e657c58758 |
@@ -1,46 +0,0 @@
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||||
window,gate,start,end,trade_dates,gate_open,gate_closed,trip_rate,base_ann,base_sharpe,base_maxDD,gated_ann,gated_sharpe,gated_maxDD
|
||||
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
|
||||
2026,hitrate_10d_0.60,2026-01-04,2026-08-19,157,36,121,0.7707,0.255023,1.4465,-0.080671,0.251108,3.8579,-0.026842
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2026,hitrate_10d_0.70,2026-01-04,2026-08-19,157,9,148,0.9427,0.255023,1.4465,-0.080671,0.039314,1.3415,-0.012061
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||||
2026,hitrate_20d_0.40,2026-01-04,2026-08-19,157,157,0,0.0,0.255023,1.4465,-0.080671,0.255023,1.4465,-0.080671
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||||
2026,hitrate_20d_0.50,2026-01-04,2026-08-19,157,118,39,0.2484,0.255023,1.4465,-0.080671,0.343819,2.5185,-0.06259
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||||
2026,hitrate_20d_0.60,2026-01-04,2026-08-19,157,28,129,0.8217,0.255023,1.4465,-0.080671,0.020214,0.3601,-0.028489
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||||
2025,hitrate_5d_0.50,2025-01-02,2025-12-31,250,153,97,0.388,0.177515,0.8573,-0.217417,0.72055,6.9599,-0.048495
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||||
2025,hitrate_5d_0.60,2025-01-02,2025-12-31,250,90,160,0.64,0.177515,0.8573,-0.217417,0.48564,5.3623,-0.046673
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||||
2025,hitrate_5d_0.70,2025-01-02,2025-12-31,250,39,211,0.844,0.177515,0.8573,-0.217417,0.249221,4.9448,-0.009619
|
||||
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
|
||||
|
@@ -1,722 +0,0 @@
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||||
[
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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,
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|
||||
"gated_ann": 0.289756,
|
||||
"gated_sharpe": 2.5787,
|
||||
"gated_maxDD": -0.049244
|
||||
},
|
||||
{
|
||||
"window": "2021",
|
||||
"gate": "hitrate_20d_0.60",
|
||||
"start": "2021-01-04",
|
||||
"end": "2021-12-31",
|
||||
"trade_dates": 252,
|
||||
"gate_open": 38,
|
||||
"gate_closed": 214,
|
||||
"trip_rate": 0.8492,
|
||||
"base_ann": 0.18367,
|
||||
"base_sharpe": 1.0979,
|
||||
"base_maxDD": -0.102651,
|
||||
"gated_ann": 0.104685,
|
||||
"gated_sharpe": 2.394,
|
||||
"gated_maxDD": -0.015084
|
||||
}
|
||||
]
|
||||
Binary file not shown.
@@ -1,293 +0,0 @@
|
||||
"""Signal-quality gate walk-forward backtest.
|
||||
|
||||
Gates trades based on whether the model's recent topk predictions were correct
|
||||
(hit rate). This is a retrospective gate — it measures prediction accuracy,
|
||||
not market state.
|
||||
|
||||
Usage:
|
||||
cd /app && .venv/bin/python book/scripts/signal_quality_gate_bt.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import pathlib
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
LAKE_ROOT = "/home/data/lake"
|
||||
MARKET = "US"
|
||||
OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
|
||||
|
||||
WINDOWS = [
|
||||
{"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
|
||||
{"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
|
||||
{"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
|
||||
{"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
|
||||
{"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
|
||||
"pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
|
||||
]
|
||||
|
||||
# Signal-quality gate configs: (lookback_days, threshold, name)
|
||||
SIGNAL_GATE_CONFIGS = [
|
||||
(5, 0.50, "hitrate_5d_0.50"),
|
||||
(5, 0.60, "hitrate_5d_0.60"),
|
||||
(5, 0.70, "hitrate_5d_0.70"),
|
||||
(10, 0.50, "hitrate_10d_0.50"),
|
||||
(10, 0.60, "hitrate_10d_0.60"),
|
||||
(10, 0.70, "hitrate_10d_0.70"),
|
||||
(20, 0.40, "hitrate_20d_0.40"),
|
||||
(20, 0.50, "hitrate_20d_0.50"),
|
||||
(20, 0.60, "hitrate_20d_0.60"),
|
||||
]
|
||||
|
||||
|
||||
def load_pred(path: str) -> pd.Series:
|
||||
df = pd.read_pickle(path)
|
||||
if isinstance(df, pd.DataFrame):
|
||||
if "score" in df.columns:
|
||||
s = df["score"]
|
||||
else:
|
||||
s = df.iloc[:, 0]
|
||||
else:
|
||||
s = df
|
||||
idx = s.index
|
||||
new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
|
||||
s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
|
||||
return s
|
||||
|
||||
|
||||
def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
|
||||
sp = cfg.lake_root / "symbols.parquet"
|
||||
if sp.exists():
|
||||
syms = pd.read_parquet(sp)
|
||||
col = "symbol" if "symbol" in syms.columns else syms.columns[0]
|
||||
symbols = sorted(syms[col].astype(str).str.upper().tolist())
|
||||
else:
|
||||
return pd.DataFrame()
|
||||
closes = {}
|
||||
for sym in symbols:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists():
|
||||
continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except Exception:
|
||||
continue
|
||||
if not len(df):
|
||||
continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
|
||||
df = df.loc[warmup_start:end]
|
||||
if len(df) >= 22:
|
||||
closes[sym] = df["c"]
|
||||
return pd.DataFrame(closes)
|
||||
|
||||
|
||||
def compute_hit_rate_series(
|
||||
pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
|
||||
) -> pd.Series:
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
trade_dates = sorted(dt_idx.unique())
|
||||
hit_rates = {}
|
||||
for i in range(1, len(trade_dates)):
|
||||
prev_date = trade_dates[i - 1]
|
||||
curr_date = trade_dates[i]
|
||||
try:
|
||||
prev_scores = pred.loc[prev_date]
|
||||
except KeyError:
|
||||
continue
|
||||
if isinstance(prev_scores, pd.DataFrame):
|
||||
prev_scores = prev_scores.iloc[:, 0]
|
||||
prev_scores = prev_scores.dropna().sort_values(ascending=False)
|
||||
topk_syms = list(prev_scores.index[:topk])
|
||||
if curr_date not in ret_df.index:
|
||||
continue
|
||||
today_ret = ret_df.loc[curr_date]
|
||||
topk_rets = today_ret.reindex(topk_syms).dropna()
|
||||
if len(topk_rets) > 0:
|
||||
hit_rate = (topk_rets > 0).mean()
|
||||
hit_rates[curr_date] = hit_rate
|
||||
hit_series = pd.Series(hit_rates)
|
||||
if len(hit_series) == 0:
|
||||
return hit_series
|
||||
rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
|
||||
return rolling_hr
|
||||
|
||||
|
||||
def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
|
||||
if not isinstance(pred.index, pd.MultiIndex):
|
||||
return {"error": "pred must have MultiIndex"}
|
||||
ret_df = close_df.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
dt_idx = pred.index.get_level_values(0)
|
||||
window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
|
||||
window_pred = pred.loc[window_mask]
|
||||
if len(window_pred) == 0:
|
||||
return {"error": "no pred data in window"}
|
||||
trade_dates = sorted(dt_idx[window_mask].unique())
|
||||
gate_open = {}
|
||||
for d in trade_dates:
|
||||
known = hit_rate[hit_rate.index <= d]
|
||||
if len(known) > 0 and not pd.isna(known.iloc[-1]):
|
||||
gate_open[d] = bool(known.iloc[-1] >= threshold)
|
||||
else:
|
||||
gate_open[d] = True
|
||||
n_total = len(trade_dates)
|
||||
n_open = sum(1 for v in gate_open.values() if v)
|
||||
n_closed = n_total - n_open
|
||||
holdings_base = []
|
||||
holdings_gated = []
|
||||
equity_gated = 1_000_000.0
|
||||
equity_base = 1_000_000.0
|
||||
prev_week = None
|
||||
prev_scores = None
|
||||
daily_gated = []
|
||||
daily_base = []
|
||||
ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
|
||||
for d in trade_dates:
|
||||
try:
|
||||
day_scores = window_pred.loc[d]
|
||||
except KeyError:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = None
|
||||
continue
|
||||
if isinstance(day_scores, pd.DataFrame):
|
||||
day_scores = day_scores.iloc[:, 0]
|
||||
day_scores = day_scores.dropna().sort_values(ascending=False)
|
||||
if len(day_scores) == 0:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = None
|
||||
continue
|
||||
ret_row = ret_by_date.get(d)
|
||||
if ret_row is None:
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
prev_scores = day_scores
|
||||
continue
|
||||
cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
|
||||
gate_val = gate_open.get(d, True)
|
||||
if cur_week != prev_week or not holdings_base:
|
||||
if prev_scores is not None:
|
||||
holdings_base = list(prev_scores.index[:topk])
|
||||
if holdings_base:
|
||||
base_rets = ret_row.reindex(holdings_base).dropna()
|
||||
if len(base_rets) > 0:
|
||||
equity_base *= (1 + base_rets.mean())
|
||||
if gate_val:
|
||||
if cur_week != prev_week or not holdings_gated:
|
||||
if prev_scores is not None:
|
||||
holdings_gated = list(prev_scores.index[:topk])
|
||||
if holdings_gated:
|
||||
hold_rets = ret_row.reindex(holdings_gated).dropna()
|
||||
if len(hold_rets) > 0:
|
||||
equity_gated *= (1 + hold_rets.mean())
|
||||
else:
|
||||
holdings_gated = []
|
||||
prev_week = cur_week
|
||||
prev_scores = day_scores
|
||||
daily_gated.append(equity_gated)
|
||||
daily_base.append(equity_base)
|
||||
g_series = pd.Series(daily_gated, index=trade_dates)
|
||||
b_series = pd.Series(daily_base, index=trade_dates)
|
||||
def _metrics(eq):
|
||||
if len(eq) < 2:
|
||||
return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
|
||||
rets = eq.pct_change().dropna()
|
||||
ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
|
||||
vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
|
||||
sharpe = ann_ret / vol if vol > 0 else 0
|
||||
peak = eq.cummax()
|
||||
dd = (eq - peak) / peak
|
||||
maxDD = float(dd.min())
|
||||
return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
|
||||
base_m = _metrics(b_series)
|
||||
gated_m = _metrics(g_series)
|
||||
return {
|
||||
"trade_dates": n_total,
|
||||
"gate_open_days": n_open,
|
||||
"gate_closed_days": n_closed,
|
||||
"trip_rate": round(n_closed / n_total, 4) if n_total else 0,
|
||||
"base": base_m,
|
||||
"gated": gated_m,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
full_start = "2015-01-03"
|
||||
full_end = "2026-08-19"
|
||||
print("Loading lake bars...")
|
||||
close_df = load_bars_for_window(full_start, full_end)
|
||||
print(f" {close_df.shape[1]} symbols, {close_df.shape[0]} days")
|
||||
ret_df = close_df.pct_change()
|
||||
ret_df.index = pd.to_datetime(ret_df.index).normalize()
|
||||
results = []
|
||||
for window in WINDOWS:
|
||||
wl, ws, we = window["label"], window["start"], window["end"]
|
||||
pred_path = window["pred"]
|
||||
print(f"\n=== Window {wl} ({ws} to {we}) ===")
|
||||
pred = load_pred(pred_path)
|
||||
print(f" pred shape: {pred.shape}")
|
||||
hit_rates = {}
|
||||
for lookback, _, name in SIGNAL_GATE_CONFIGS:
|
||||
if lookback not in hit_rates:
|
||||
hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
|
||||
hit_rates[lookback] = hr
|
||||
print(f" lookback={lookback}: {len(hr)} days with hit rates")
|
||||
for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
|
||||
hr = hit_rates[lookback]
|
||||
bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
|
||||
if "error" in bt:
|
||||
print(f" {name}: {bt['error']}")
|
||||
continue
|
||||
row = {
|
||||
"window": wl,
|
||||
"gate": name,
|
||||
"start": ws,
|
||||
"end": we,
|
||||
"trade_dates": bt["trade_dates"],
|
||||
"gate_open": bt["gate_open_days"],
|
||||
"gate_closed": bt["gate_closed_days"],
|
||||
"trip_rate": bt["trip_rate"],
|
||||
"base_ann": bt["base"]["ann_return"],
|
||||
"base_sharpe": bt["base"]["sharpe"],
|
||||
"base_maxDD": bt["base"]["maxDD"],
|
||||
"gated_ann": bt["gated"]["ann_return"],
|
||||
"gated_sharpe": bt["gated"]["sharpe"],
|
||||
"gated_maxDD": bt["gated"]["maxDD"],
|
||||
}
|
||||
results.append(row)
|
||||
print(f" {name}: trip={bt['trip_rate']:.1%}, "
|
||||
f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
|
||||
f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f})")
|
||||
df = pd.DataFrame(results)
|
||||
out_path = OUT_DIR / "signal_quality_gate_results.csv"
|
||||
df.to_csv(out_path, index=False)
|
||||
with open(OUT_DIR / "signal_quality_gate_results.json", "w") as f:
|
||||
json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
|
||||
print(f"\nSaved to {out_path}")
|
||||
print("\n=== Summary: Gated Return by Window ===")
|
||||
for gate_name in df["gate"].unique():
|
||||
gdf = df[df["gate"] == gate_name]
|
||||
print(f"\n{gate_name}:")
|
||||
for _, r in gdf.iterrows():
|
||||
print(f" {r['window']}: base={r['base_ann']:+.1%}, gated={r['gated_ann']:+.1%}, "
|
||||
f"trip={r['trip_rate']:.0%}, diff={r['gated_ann']-r['base_ann']:+.1%}pp")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+17
-20
@@ -1,30 +1,27 @@
|
||||
# TradeAC custom-qlib-code snapshot (auto-generated)
|
||||
# parent repo HEAD : ceb1e196e24761a87d1afe1bde00079b79e02693
|
||||
# parent repo HEAD : f9ef005e9aa05e546c11eef760046648cf5a6334
|
||||
# tac-qlib/tac_qlib/contrib
|
||||
# tac-qlib/tac_qlib/data
|
||||
# per-file hashes (git hash-object):
|
||||
1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
|
||||
861592c63edd6a0853a9cb174b5970435b135fc8 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
b8112569f9b2537c45b6535e1a505a207878d322 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.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
|
||||
8d5333ebd2b44165c50cba639ca2d4ac3fc7cfec tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
18cb37c0354184c49fa2e598396d7df0634cce0f tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
||||
871ff1e163c29261f140c3f53d42a41e6504c779 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
|
||||
ab958203f33a99d12c7d923b6efb435189231666 tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
9dc36de7e343073b7d511349ee5aede086c38f94 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
||||
9f9014ddd9bce37490061312d51e8e6fe540fec4 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
|
||||
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
|
||||
ccfe7d554989aa7f3e5a2128ae663e51b2207149 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
|
||||
4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
||||
74e5ecbbbb20bb71fd5cd083383de4ce88476712 tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
afaf562aeaa12cebc8529cd916153252e7e3c38a tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
||||
79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.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
|
||||
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
|
||||
0ed1ead6c1314a3f25784d453e54a15a8a04baaa tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
9609782800944c45b78bb58eaa7b51ba1b7f8f43 tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
a85628d71d12cfe5b18b1c884c5d829c89594579 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
686d36f6d101c547491ca866aa143aa542e17518 tac-qlib/tac_qlib/data/config.py
|
||||
d9f839be30026f337754a3f015425a8efdbe8e2a tac-qlib/tac_qlib/data/providers.py
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -64,13 +64,9 @@ def check_transform_proc(proc_l, fit_start_time, fit_end_time):
|
||||
|
||||
|
||||
def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> List[str]:
|
||||
"""Discover feature columns present in *every* feature file of the lake.
|
||||
"""Discover ta-lib columns present in *every* features parquet file of the lake.
|
||||
|
||||
Walks the `family=ta|sp` partition layout (plus any legacy flat files).
|
||||
TA and SP columns are disjoint by construction, so the common set is
|
||||
computed per family (columns shared by all symbol files of that family),
|
||||
then the per-family results are unioned. Returns sorted field names
|
||||
(without the ``$`` prefix). Empty if no features are persisted.
|
||||
Returns sorted field names (without the ``$`` prefix). Empty if no features are persisted.
|
||||
"""
|
||||
cfg = LakeConfig(lake_root, market)
|
||||
feat_dir = cfg.features_dir(timeframe)
|
||||
@@ -78,9 +74,8 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
|
||||
return []
|
||||
import pyarrow.parquet as pq
|
||||
|
||||
def _family_common(fam_dir: Path) -> set:
|
||||
common = None
|
||||
for p in sorted(fam_dir.glob("symbol=*.parquet")):
|
||||
for p in sorted(feat_dir.glob("symbol=*.parquet")):
|
||||
try:
|
||||
cols = set(pq.read_schema(p).names) - set(NON_FEATURE_COLUMNS)
|
||||
except Exception: # pragma: no cover - skip unreadable files
|
||||
@@ -88,20 +83,7 @@ def get_common_feature_fields(lake_root=None, market="US", timeframe="1d") -> Li
|
||||
common = cols if common is None else (common & cols)
|
||||
if not common:
|
||||
break
|
||||
return common or set()
|
||||
|
||||
common: set = set()
|
||||
# family tier: features/market=*/timeframe=*/family=*/symbol=*.parquet
|
||||
for fam in ("ta", "sp"):
|
||||
fam_dir = feat_dir / f"family={fam}"
|
||||
if fam_dir.is_dir():
|
||||
common |= _family_common(fam_dir)
|
||||
# legacy flat: features/market=*/timeframe=*/symbol=*.parquet
|
||||
if (feat_dir / "family=ta").exists() or (feat_dir / "family=sp").exists():
|
||||
pass # family layout already covered
|
||||
else:
|
||||
common |= _family_common(feat_dir)
|
||||
return sorted(common)
|
||||
return sorted(common) if common else []
|
||||
|
||||
|
||||
class DropAllNaN(processor_module.Processor):
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -53,61 +53,23 @@ from qlib.workflow import R
|
||||
__all__ = ["RankICLGBModel", "rankic_feval"]
|
||||
|
||||
|
||||
def _group_averaged_rank(values: np.ndarray, gid: np.ndarray, offs: np.ndarray) -> np.ndarray:
|
||||
"""Averaged (tie-corrected) rank of ``values`` within each group, vectorized.
|
||||
|
||||
``gid`` maps each row to its group id; ``offs`` holds the cumulative row
|
||||
offsets so that group ``i`` occupies rows ``[offs[i], offs[i+1])``. Returns
|
||||
the same result as ``pandas.Series.rank(method='average')`` applied per
|
||||
group, but in one pass (``np.lexsort`` is the only non-linear step).
|
||||
"""
|
||||
n = len(values)
|
||||
order = np.lexsort((values, gid))
|
||||
ord_rank = np.empty(n, dtype=np.float64)
|
||||
ord_rank[order] = np.arange(n, dtype=np.float64) - offs[gid[order]] + 1.0
|
||||
sg = gid[order]
|
||||
sv = values[order]
|
||||
newblock = np.empty(n, dtype=bool)
|
||||
newblock[0] = True
|
||||
newblock[1:] = (sg[1:] != sg[:-1]) | (sv[1:] != sv[:-1])
|
||||
blockid = np.cumsum(newblock) - 1
|
||||
block_mean = np.bincount(blockid, weights=ord_rank[order]) / np.bincount(blockid)
|
||||
out = np.empty(n)
|
||||
out[order] = block_mean[blockid]
|
||||
return out
|
||||
|
||||
|
||||
def _per_day_spearman(preds: np.ndarray, labels: np.ndarray, group: np.ndarray) -> float:
|
||||
"""Mean per-day Spearman rank correlation of preds vs labels.
|
||||
|
||||
``group`` holds the number of rows of each trading day (query group), in
|
||||
order. Days with <3 valid rows or a constant pred/label are skipped.
|
||||
|
||||
Vectorized: per-day Spearman == Pearson of the per-day rank transforms,
|
||||
and the Pearson moments (``sum``, ``sum`` of products/squares) aggregate
|
||||
over each day with ``np.bincount``. Runs ~10x faster than the per-day
|
||||
``pd.Series.rank()`` loop that preceded it — this feval is invoked on the
|
||||
train and valid panels every boosting round, per seed.
|
||||
"""
|
||||
if group is None or len(group) == 0:
|
||||
return 0.0
|
||||
offs = np.concatenate([[0], np.cumsum(group.astype(int))])
|
||||
gid = np.repeat(np.arange(len(group)), group.astype(int))
|
||||
rp = _group_averaged_rank(preds, gid, offs)
|
||||
rl = _group_averaged_rank(labels, gid, offs)
|
||||
n_g = group.astype(float)
|
||||
s_p = np.bincount(gid, weights=rp)
|
||||
s_l = np.bincount(gid, weights=rl)
|
||||
s_pl = np.bincount(gid, weights=rp * rl)
|
||||
s_pp = np.bincount(gid, weights=rp * rp)
|
||||
s_ll = np.bincount(gid, weights=rl * rl)
|
||||
cov = n_g * s_pl - s_p * s_l
|
||||
var_p = n_g * s_pp - s_p ** 2
|
||||
var_l = n_g * s_ll - s_l ** 2
|
||||
denom = np.sqrt(var_p * var_l)
|
||||
valid = (n_g >= 3) & (denom > 0)
|
||||
corr = np.where(valid, cov / np.where(denom == 0, 1, denom), 0.0)
|
||||
return float(corr[valid].mean()) if valid.any() else 0.0
|
||||
vals = []
|
||||
for i in range(len(group)):
|
||||
s = slice(offs[i], offs[i + 1])
|
||||
p, l = preds[s], labels[s]
|
||||
if len(p) < 3 or np.std(p) == 0 or np.std(l) == 0:
|
||||
continue
|
||||
vals.append(np.corrcoef(pd.Series(p).rank(), pd.Series(l).rank())[0, 1])
|
||||
return float(np.mean(vals)) if vals else 0.0
|
||||
|
||||
|
||||
def rankic_feval(preds, dataset):
|
||||
|
||||
@@ -1,11 +1,3 @@
|
||||
from .ic_gate import ICGateTopkDropoutStrategy # noqa: F401
|
||||
from .optimal_stop import OptimalStopControl # noqa: F401
|
||||
from .regime_gate import RegimeGateTopkDropoutStrategy # noqa: F401
|
||||
from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
|
||||
|
||||
__all__ = [
|
||||
"ICGateTopkDropoutStrategy",
|
||||
"OptimalStopControl",
|
||||
"RegimeGateTopkDropoutStrategy",
|
||||
"WeeklyRebalanceDropoutStrategy",
|
||||
]
|
||||
__all__ = ["OptimalStopControl"]
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -1,117 +0,0 @@
|
||||
"""Realized-IC circuit breaker TopkDropout strategy.
|
||||
|
||||
Subclass of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`` that
|
||||
holds the book (issues NO orders) while the streaming realized RankIC of the
|
||||
deployed signal is below threshold — i.e. the model's cross-sectional
|
||||
predictions are no longer earning against realized forward returns. When the
|
||||
gate is open it behaves exactly like the reference TopkDropoutStrategy.
|
||||
|
||||
The gate is evaluated per trade step on the trailing mean realized RankIC of
|
||||
the signal over the last ``ic_window`` trading days whose label is fully
|
||||
realized as of the decision date (no lookahead — a 5d fwd label ``close[t+6]/
|
||||
close[t+1]-1`` is only known at ``t+6``).
|
||||
|
||||
Two wiring modes:
|
||||
|
||||
* ``ic_gate``: a precomputed ``pd.Series`` indexed by datetime of booleans
|
||||
(True = gate open / trade allowed). Computed once by the caller (e.g.
|
||||
``rd_backtest``) and looked up per step. Missing dates default to open.
|
||||
* realized-IC self-computation: when ``ic_min_rankic`` is given but no
|
||||
``ic_gate``, the strategy computes the per-date realized RankIC itself from
|
||||
``self.signal`` (the pred scores) and the lake 1d bars via
|
||||
``tac_qlib.risk_limits.realized_rankic_series``, then applies the same
|
||||
trailing-window comparison. Works when instantiated from a workflow YAML
|
||||
PortAnaRecord config (``lake_root`` / ``market`` must be provided).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest.decision import TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
from tac_qlib.risk_limits import ic_circuit_breaker, realized_rankic_series
|
||||
|
||||
__all__ = ["ICGateTopkDropoutStrategy"]
|
||||
|
||||
|
||||
class ICGateTopkDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout with a streaming realized-IC circuit breaker.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
ic_min_rankic : float — pause new trading while trailing realized RankIC is
|
||||
below this threshold (0 disables the gate).
|
||||
ic_window : int — trailing window for the realized RankIC mean (default 22).
|
||||
ic_label_horizon : int — label horizon in trading days (default 6).
|
||||
ic_min_obs : int — min realized labels before the gate arms (default 10).
|
||||
ic_gate : pd.Series, optional — precomputed per-date gate (bool indexed by
|
||||
datetime). When provided, it overrides self-computation.
|
||||
lake_root, market : str — lake location for self-computed realized IC.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
topk,
|
||||
n_drop,
|
||||
ic_min_rankic: float = 0.0,
|
||||
ic_window: int = 22,
|
||||
ic_label_horizon: int = 6,
|
||||
ic_min_obs: int = 10,
|
||||
ic_gate=None,
|
||||
lake_root: str = "",
|
||||
market: str = "US",
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.ic_min_rankic = float(ic_min_rankic or 0.0)
|
||||
self.ic_window = int(ic_window or 22)
|
||||
self.ic_label_horizon = int(ic_label_horizon or 6)
|
||||
self.ic_min_obs = int(ic_min_obs or 10)
|
||||
self._ic_gate = ic_gate
|
||||
self._realized_ic = None
|
||||
self.lake_root = lake_root or ""
|
||||
self.market = market or "US"
|
||||
|
||||
def _load_realized_ic(self):
|
||||
if self._realized_ic is None:
|
||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(
|
||||
self.trade_calendar.get_trade_step(), shift=-self.ic_label_horizon
|
||||
)
|
||||
pred = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if isinstance(pred, pd.DataFrame):
|
||||
pred = pred.iloc[:, 0]
|
||||
self._realized_ic = realized_rankic_series(
|
||||
pred, self.lake_root, self.market, label_horizon=self.ic_label_horizon
|
||||
)
|
||||
return self._realized_ic
|
||||
|
||||
def _gate_open(self, trade_start_time) -> bool:
|
||||
ts = pd.Timestamp(trade_start_time)
|
||||
if self._ic_gate is not None:
|
||||
# precomputed gate series: look up the latest known decision date <= ts
|
||||
known = self._ic_gate[self._ic_gate.index <= ts]
|
||||
if len(known):
|
||||
return bool(known.iloc[-1])
|
||||
return True
|
||||
if self.ic_min_rankic <= 0:
|
||||
return True
|
||||
realized = self._load_realized_ic()
|
||||
limits = {
|
||||
"ic_min_rankic": self.ic_min_rankic,
|
||||
"ic_window": self.ic_window,
|
||||
"ic_min_obs": self.ic_min_obs,
|
||||
}
|
||||
tripped, _reason, _trail = ic_circuit_breaker(realized, ts, limits)
|
||||
return not tripped
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, _ = self.trade_calendar.get_step_time(trade_step)
|
||||
if not self._gate_open(trade_start_time):
|
||||
return TradeDecisionWO([], self)
|
||||
return super().generate_trade_decision(execute_result)
|
||||
@@ -1,215 +0,0 @@
|
||||
"""Regime-gate TopkDropout strategy.
|
||||
|
||||
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.
|
||||
|
||||
Three detector types are supported (all causal — no lookahead):
|
||||
|
||||
* ``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
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest.decision import TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
__all__ = ["RegimeGateTopkDropoutStrategy", "compute_regime_gate"]
|
||||
|
||||
|
||||
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_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, *, regime_gate=None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._regime_gate = regime_gate
|
||||
|
||||
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):
|
||||
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)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Precomputation helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
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.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
detector : str — ``"dispersion"``, ``"vol"``, or ``"hmm"``.
|
||||
threshold : float — for ``dispersion``: min CS dispersion to allow trading.
|
||||
For ``hmm``: min HMM posterior to allow trading.
|
||||
Ignored for ``vol`` (uses ``vol_low``/``vol_high`` band instead).
|
||||
lake_root, market : str — lake location.
|
||||
start, end : str — date window.
|
||||
vol_low, vol_high : float — annualized vol band for the ``vol`` detector.
|
||||
hmm_field : str — HMM feature column name for the ``hmm`` detector.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series — bool, indexed by datetime. True = trade allowed.
|
||||
"""
|
||||
from tac_qlib.data.config import LakeConfig, resolve_lake_root
|
||||
|
||||
cfg = LakeConfig(resolve_lake_root(lake_root or None), market)
|
||||
symbols = _universe_symbols(cfg)
|
||||
close_df, vol_df = _load_daily_bars(symbols, cfg, start, end)
|
||||
if close_df.empty:
|
||||
return pd.Series(dtype=bool)
|
||||
|
||||
if detector == "dispersion":
|
||||
return _dispersion_gate(close_df, threshold)
|
||||
elif detector == "vol":
|
||||
return _vol_gate(close_df, vol_low, vol_high)
|
||||
elif detector == "hmm":
|
||||
return _hmm_gate(cfg, symbols, threshold, start, end, hmm_field)
|
||||
else:
|
||||
raise ValueError(f"Unknown detector: {detector!r}")
|
||||
|
||||
|
||||
def _universe_symbols(cfg) -> list:
|
||||
"""Read symbols from the lake symbols.parquet."""
|
||||
import pathlib
|
||||
|
||||
sp = cfg.lake_root / "symbols.parquet"
|
||||
if sp.exists():
|
||||
df = pd.read_parquet(sp)
|
||||
col = "symbol" if "symbol" in df.columns else df.columns[0]
|
||||
return sorted(df[col].astype(str).str.upper().tolist())
|
||||
return []
|
||||
|
||||
|
||||
def _load_daily_bars(symbols, cfg, start, end):
|
||||
"""Load daily close prices for all symbols into a wide DataFrame."""
|
||||
closes = {}
|
||||
vols = {}
|
||||
for sym in symbols:
|
||||
p = cfg.bar_path("1d", sym)
|
||||
if not p.exists():
|
||||
continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except Exception:
|
||||
continue
|
||||
if not len(df):
|
||||
continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
df = df.assign(_t=ts).set_index("_t").sort_index()
|
||||
df = df.loc[start:end]
|
||||
if len(df) < 22:
|
||||
continue
|
||||
closes[sym] = df["c"]
|
||||
if "v" in df.columns:
|
||||
vols[sym] = df["v"]
|
||||
close_df = pd.DataFrame(closes)
|
||||
vol_df = pd.DataFrame(vols) if vols else None
|
||||
return close_df, vol_df
|
||||
|
||||
|
||||
def _dispersion_gate(close_df, threshold):
|
||||
"""Cross-sectional dispersion of 22-day rolling returns."""
|
||||
if close_df.empty or close_df.shape[1] < 2:
|
||||
return pd.Series(dtype=bool)
|
||||
ret = close_df.pct_change(22)
|
||||
cs_disp = ret.std(axis=1)
|
||||
gate = cs_disp >= threshold
|
||||
gate.iloc[:22] = True # warmup: allow trading
|
||||
return gate
|
||||
|
||||
|
||||
def _vol_gate(close_df, vol_low, vol_high):
|
||||
"""Cross-sectional mean of 22-day rolling realized vol."""
|
||||
if close_df.empty or close_df.shape[1] < 2:
|
||||
return pd.Series(dtype=bool)
|
||||
import numpy as np
|
||||
log_ret = np.log(close_df / close_df.shift(1))
|
||||
rv22 = log_ret.rolling(22).std() * (252 ** 0.5)
|
||||
cs_mean_vol = rv22.mean(axis=1)
|
||||
gate = (cs_mean_vol >= vol_low) & (cs_mean_vol <= vol_high)
|
||||
gate.iloc[:22] = True # warmup
|
||||
return gate
|
||||
|
||||
|
||||
def _hmm_gate(cfg, symbols, threshold, start, end, hmm_field):
|
||||
"""HMM regime posterior gate from persisted SP features."""
|
||||
feat_root = cfg.lake_root / "features"
|
||||
all_posteriors = {}
|
||||
for sym in symbols:
|
||||
# check both ta and sp family paths
|
||||
for family in ("sp", "ta"):
|
||||
p = feat_root / f"market=US" / f"timeframe=1d" / f"family={family}" / f"symbol={sym}.parquet"
|
||||
if not p.exists():
|
||||
continue
|
||||
try:
|
||||
df = pd.read_parquet(p)
|
||||
except Exception:
|
||||
continue
|
||||
if hmm_field not in df.columns:
|
||||
continue
|
||||
tcol = df["t"] if "t" in df.columns else df["date"]
|
||||
ts = pd.to_datetime(tcol)
|
||||
s = pd.Series(df[hmm_field].values, index=ts, name=sym)
|
||||
s = s.loc[start:end].dropna()
|
||||
if len(s) > 0:
|
||||
all_posteriors[sym] = s
|
||||
break
|
||||
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
|
||||
@@ -1,202 +0,0 @@
|
||||
"""Weekly-rebalance TopkDropout strategy.
|
||||
|
||||
Turnover-reduction variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
the topk/n_drop selection and sizing are identical to the reference, but the
|
||||
target book is recomputed only on the first trading day of each ISO week; on the
|
||||
other days the strategy issues NO orders (holds the book untouched).
|
||||
|
||||
The weekly cadence is derived from the qlib trade calendar: a rebalance happens
|
||||
when the current trade step's date belongs to a different ISO ``(year, week)``
|
||||
than the previous trade step. ``hold_band_pct`` (default 0) optionally skips
|
||||
tiny rebalances: when a name's existing position differs from the new target by
|
||||
less than this fraction, no order is generated for it.
|
||||
"""
|
||||
|
||||
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__ = ["WeeklyRebalanceDropoutStrategy"]
|
||||
|
||||
DEFAULT_HOLD_BAND_PCT = 0.0
|
||||
|
||||
|
||||
class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout rebalanced once per ISO week; holds otherwise.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
hold_band_pct : skip order for a name whose deviation from target weight is
|
||||
below this fraction of the target (no-trade buffer band).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.hold_band_pct = hold_band_pct
|
||||
|
||||
@staticmethod
|
||||
def _iso_week(ts) -> tuple:
|
||||
return (ts.year, ts.week)
|
||||
|
||||
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)
|
||||
|
||||
cur_week = self._iso_week(trade_start_time)
|
||||
prev_week = getattr(self, "_last_week", None)
|
||||
self._last_week = cur_week
|
||||
|
||||
if prev_week is not None and prev_week == cur_week:
|
||||
# not the first trading day of this ISO week -> hold
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
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)
|
||||
|
||||
value = cash * self.risk_degree / len(buy)
|
||||
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
|
||||
)
|
||||
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)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -6,11 +6,10 @@ The lake is a hive-partitioned parquet store (see ``tac-engine/skills/tradeac-la
|
||||
├── market=US/
|
||||
│ └── timeframe=1d/
|
||||
│ └── symbol=AAPL.parquet # OHLCV bars: t, date, o, h, l, c, v, n, vw
|
||||
├── features/ # indicators, wide format, family tier
|
||||
├── features/ # ta-lib indicators, wide format
|
||||
│ └── market=US/
|
||||
│ └── timeframe=1d/
|
||||
│ ├── family=ta/symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
|
||||
│ └── family=sp/symbol=AAPL.parquet # t, sp_ou_*, sp_hmm_*, ...
|
||||
│ └── symbol=AAPL.parquet # t, sma_5, sma_20, rsi_14, ...
|
||||
├── calendar.parquet # trading days per market
|
||||
├── coverage.parquet # per (market,timeframe,symbol) loaded windows
|
||||
└── symbols.parquet # asset master
|
||||
@@ -108,34 +107,8 @@ class LakeConfig:
|
||||
return self.lake_root / "features" / f"market={self.market}" / f"timeframe={timeframe}"
|
||||
|
||||
def features_path(self, timeframe: str, symbol: str) -> Path:
|
||||
# Legacy flat path (no family tier). Prefer `load_features` which
|
||||
# resolves the family=ta|sp partition layout.
|
||||
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` 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 ("ta", "sp"):
|
||||
p = self.features_dir(timeframe) / f"family={family}" / f"symbol={sym}.parquet"
|
||||
if p.exists():
|
||||
frames.append(pd.read_parquet(p))
|
||||
if not frames:
|
||||
flat = self.features_dir(timeframe) / f"symbol={sym}.parquet"
|
||||
if flat.exists():
|
||||
return pd.read_parquet(flat)
|
||||
return pd.DataFrame()
|
||||
if len(frames) == 1:
|
||||
return frames[0]
|
||||
merged = frames[0]
|
||||
for extra in frames[1:]:
|
||||
merged = merged.merge(extra, on="t", how="outer", suffixes=("", "_dup"))
|
||||
for c in [c for c in merged.columns if c.endswith("_dup")]:
|
||||
merged = merged.drop(columns=c)
|
||||
return merged
|
||||
|
||||
def calendar_path(self) -> Path:
|
||||
return self.lake_root / "calendar.parquet"
|
||||
|
||||
|
||||
@@ -173,7 +173,8 @@ class LakeFeatureProvider(FeatureProvider):
|
||||
def _load_feature_df(self, instrument: str, timeframe: str) -> pd.DataFrame:
|
||||
key = (instrument, timeframe)
|
||||
if key not in self._feature_cache:
|
||||
self._feature_cache[key] = self.cfg.load_features(timeframe, instrument)
|
||||
p = self.cfg.features_path(timeframe, instrument)
|
||||
self._feature_cache[key] = pd.read_parquet(p) if p.exists() else pd.DataFrame()
|
||||
return self._feature_cache[key]
|
||||
|
||||
@staticmethod
|
||||
|
||||
@@ -0,0 +1,133 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# ABLATION A (baseline): LightGBM with RankIC early-stopping on the 50-ETF SP-5d
|
||||
# panel, using ALL 24 sp_* feature columns (ou,hmm,jump,har,trend,hurst,
|
||||
# signature). Copy of the canonical workflow_lgb_sp5d_rankic.yaml with a
|
||||
# distinct experiment name so the ablation runs are isolated.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=tac-qlib/workflows/ablate_baseline_all_sp_fields.yaml \
|
||||
# experiment_name=tac-rd-rank-ablate
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_ou_zscore,sp_ou_half_life,sp_ou_revert,sp_hmm_p_regime1,sp_hmm_state,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
markets: {}
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-rank-ablate"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seed: 42
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-10
|
||||
fit_start_time: 2015-01-03
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2015-01-03, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,134 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# ABLATION B (generic-only): same panel/model as the baseline, but feature
|
||||
# fields restricted to the model-free / generic stochastic-process families
|
||||
# (jump,har,trend,hurst,signature). Drops the model-specific ou (OU/AR-1
|
||||
# half-life) and hmm (2-state regime) families to test whether the generic
|
||||
# families alone dominate the rank dimension.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=tac-qlib/workflows/ablate_generic_only_sp_fields.yaml \
|
||||
# experiment_name=tac-rd-rank-ablate
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
markets: {}
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-rank-ablate"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seed: 42
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-10
|
||||
fit_start_time: 2015-01-03
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2015-01-03, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,141 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# ISOLATION: multi-seed RankIC ensemble, ablate-B generic-only feature set.
|
||||
#
|
||||
# Isolates the ensemble effect on the SP-5d rank signal. Same panel, segments,
|
||||
# history (full backfilled 2016+) and feature set as the exp-9 ablate-B winner
|
||||
# (generic-only sp_* families: jump,har,trend,hurst,signature), but replaces the
|
||||
# single RankICLGBModel with a 5-seed RankICEnsembleLGBModel (42,7,2026,99,123)
|
||||
# that averages per-day predictions.
|
||||
#
|
||||
# Differs from exp-15 (tac-rd-rank-ensemble, mlflow exp 15) ONLY by dropping the
|
||||
# TA subset (rsi_14,roc_10,macd_hist,willr_14,atr_14) and the inter-asset xr_*
|
||||
# features, so any change vs exp-15 is attributable to the feature set alone,
|
||||
# and any change vs exp-9 is attributable to the ensemble + full history alone.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp12_isolation_ensemble.yaml \
|
||||
# experiment_name=tac-rd-rank-ensemble-isolated
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
markets: {}
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///mlruns.db"
|
||||
default_exp_name: "tac-rd-rank-ensemble-isolated"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,141 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
|
||||
#
|
||||
# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
|
||||
# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
|
||||
# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
|
||||
# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
|
||||
# (same pred, liquidity/size/concentration caps) can be compared 1:1.
|
||||
#
|
||||
# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
|
||||
# (tool-level param, not a YAML key); this run records the unconstrained
|
||||
# baseline that the limit A/B is measured against.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
markets: {}
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-risk-limit"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
n_drop: 2
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
Reference in New Issue
Block a user