File size: 6,280 Bytes
590a501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | """Unified strategy backtest runner."""
from __future__ import annotations
import json
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from contextlib import contextmanager
import pandas as pd
from config.settings import load_settings
from data_pipeline.factor_loader import build_signal_from_source
from data_pipeline.init_qlib import init_qlib
from qlib.backtest import backtest_loop, get_strategy_executor
from qlib.contrib.evaluate import risk_analysis
from strategies.registry import resolve_strategy
@dataclass
class StrategyRunResult:
strategy_name: str
report: pd.DataFrame
positions: dict
risk: pd.DataFrame
signal_stats: dict[str, Any] = field(default_factory=dict)
meta: dict[str, Any] = field(default_factory=dict)
def _build_exchange_kwargs(backtest_cfg: dict[str, Any], freq: str = "day") -> dict[str, Any]:
return {
"freq": freq,
"limit_threshold": backtest_cfg.get("limit_threshold", 0.095),
"deal_price": backtest_cfg.get("deal_price", "close"),
"open_cost": backtest_cfg.get("open_cost", 0.0005),
"close_cost": backtest_cfg.get("close_cost", 0.0015),
"min_cost": backtest_cfg.get("min_cost", 5),
}
@contextmanager
def _disable_qlib_benchmark_default():
"""
qlib passes benchmark=None as {} and PortfolioMetrics then defaults to CSI300,
which breaks 30min-only datasets. Treat empty config as no benchmark.
"""
from qlib.backtest.report import PortfolioMetrics
original = PortfolioMetrics._cal_benchmark
@staticmethod
def _cal_benchmark_no_default(benchmark_config, freq):
if not benchmark_config or benchmark_config.get("benchmark") is None:
return None
return original(benchmark_config, freq)
PortfolioMetrics._cal_benchmark = _cal_benchmark_no_default
try:
yield
finally:
PortfolioMetrics._cal_benchmark = original
def _run_qlib_backtest(
*,
start_time: str,
end_time: str,
strategy,
executor_config: dict[str, Any],
account: float | int,
benchmark: str | None,
exchange_kwargs: dict[str, Any],
):
"""Run qlib backtest; disable benchmark when None (qlib defaults empty config to CSI300)."""
with _disable_qlib_benchmark_default():
trade_strategy, trade_executor = get_strategy_executor(
start_time=start_time,
end_time=end_time,
strategy=strategy,
executor=executor_config,
benchmark=benchmark,
account=account,
exchange_kwargs=exchange_kwargs,
)
return backtest_loop(start_time, end_time, trade_strategy, trade_executor)
def run_strategy_backtest(
strategy_name: str,
signal_source: dict[str, Any],
strategy_kwargs: dict[str, Any] | None = None,
start_time: str | None = None,
end_time: str | None = None,
config_path: str | None = None,
) -> StrategyRunResult:
settings = load_settings(config_path)
init_qlib(config_path)
bt = settings.backtest_config
data_freq = settings.backtest_freq
signal = build_signal_from_source(signal_source)
if signal.index.names != ["instrument", "datetime"]:
signal = signal.swaplevel().sort_index()
signal.index.names = ["instrument", "datetime"]
strategy = resolve_strategy(strategy_name, signal, strategy_kwargs)
start_time = start_time or settings.segments.get("test", settings.segments["valid"])[0]
end_time = end_time or settings.segments.get("test", settings.segments["valid"])[1]
executor_config = {
"class": "SimulatorExecutor",
"module_path": "qlib.backtest.executor",
"kwargs": {
"time_per_step": data_freq,
"generate_portfolio_metrics": True,
},
}
benchmark = bt.get("benchmark", settings.benchmark)
if benchmark in (None, "null", "none", ""):
benchmark = None
portfolio_metric_dict, indicator_dict = _run_qlib_backtest(
start_time=start_time,
end_time=end_time,
strategy=strategy,
executor_config=executor_config,
account=bt.get("account", 100_000_000),
benchmark=benchmark,
exchange_kwargs=_build_exchange_kwargs(bt, freq=data_freq),
)
freq_key = next(iter(portfolio_metric_dict.keys()))
report, positions = portfolio_metric_dict[freq_key]
risk = risk_analysis(report["return"]) if "return" in report.columns else pd.DataFrame()
signal_stats = {
"n_obs": int(signal.notna().sum()),
"n_instruments": int(signal.index.get_level_values("instrument").nunique()),
"date_range": [str(signal.index.get_level_values("datetime").min()), str(signal.index.get_level_values("datetime").max())],
}
return StrategyRunResult(
strategy_name=strategy_name,
report=report,
positions=positions,
risk=risk,
signal_stats=signal_stats,
meta={"start_time": start_time, "end_time": end_time, "benchmark": benchmark, "freq": freq_key},
)
def run_strategy_suite(
signal_source: dict[str, Any],
strategy_names: list[str],
start_time: str | None = None,
end_time: str | None = None,
config_path: str | None = None,
) -> dict[str, StrategyRunResult]:
results = {}
for name in strategy_names:
results[name] = run_strategy_backtest(
strategy_name=name,
signal_source=signal_source,
start_time=start_time,
end_time=end_time,
config_path=config_path,
)
return results
def save_backtest_result(result: StrategyRunResult, output_dir: str | Path) -> Path:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
result.report.to_csv(output_dir / f"{result.strategy_name}_report.csv")
if not result.risk.empty:
result.risk.to_csv(output_dir / f"{result.strategy_name}_risk.csv")
summary = {
"strategy": result.strategy_name,
"signal_stats": result.signal_stats,
"meta": result.meta,
}
with open(output_dir / f"{result.strategy_name}_summary.json", "w", encoding="utf-8") as f:
json.dump(summary, f, indent=2, ensure_ascii=False)
return output_dir
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