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排查发现用户反馈的"回测统计数据缺失/异常"并非服务器连接问题， 而是回测引擎与组合优化器自身的代码缺陷： #23: 首根 bar 访问 close[-1] 崩溃 _SeriesAccessor 负向越界改返回 NaN（不抛 IndexError）； BacktestEngine 新增 warmup_bars 参数跳过指标预热期。 #25-A: FactorWeightedOptimizer 权重坍缩 n_stocks=2 且得分接近时，减最小值把低分标的权重压到 ~6e-8， 等于单股满仓、n_stocks 被无视，进而出现持仓1只/-99.98%回撤。 新增 _apply_weight_floor 权重下限保证入选标的都有实质权重。 #25-B: RebalanceEngine total_trades 统计错误 total_trades = len(equity_curve)（天数）改为 len(trades_df)（真实笔数）。 #22: 绩效别名键 + 数据异常诊断 performance dict 新增 sharpe_ratio/start_cash/end_value 别名键； 资金曲线异常时返回 diagnostic_warning 而非静默全 0，CLI 显示提示。 文档：README 加入回测手册导航；backtest_usage.md 补充 warmup 说明。 测试：新增 6 个回归测试，更新 3 个；932 passed。
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
"""Test RebalanceEngine."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from easy_tdx.portfolio.optimizer import EqualWeightOptimizer, FactorWeightedOptimizer
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from easy_tdx.portfolio.rebalance import RebalanceEngine
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def _make_market(n_stocks: int = 10, n_days: int = 120, seed: int = 42) -> dict[str, pd.DataFrame]:
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rng = np.random.default_rng(seed)
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data = {}
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for i in range(n_stocks):
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close = 10.0 + np.cumsum(rng.normal(0.01, 0.5, n_days))
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close = np.maximum(close, 1.0)
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data[f"{i:06d}"] = pd.DataFrame(
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{
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"datetime": pd.date_range("2024-01-01", periods=n_days, freq="D"),
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"open": close,
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"high": close + 0.3,
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"low": close - 0.3,
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"close": close,
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"vol": rng.integers(1e5, 1e7, n_days).astype(float),
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"amount": close * 1e6,
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}
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)
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return data
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class TestRebalanceEngine:
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def test_basic_run(self):
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engine = RebalanceEngine(
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optimizer=EqualWeightOptimizer(),
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factor_name="momentum_20d",
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n_stocks=5,
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rebalance_freq="M",
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cash=1_000_000,
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)
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result = engine.run(_make_market(), start_date=20240101, end_date=20240430)
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assert len(result.states) > 0
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assert len(result.rebalance_dates) > 0
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assert len(result.equity_curve) > 0
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assert "total_return" in result.performance
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def test_with_factor_weighted(self):
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engine = RebalanceEngine(
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optimizer=FactorWeightedOptimizer(),
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factor_name="momentum_20d",
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n_stocks=5,
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)
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result = engine.run(_make_market(), start_date=20240101, end_date=20240430)
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assert len(result.states) > 0
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def test_empty_data(self):
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result = RebalanceEngine(optimizer=EqualWeightOptimizer()).run({})
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assert result.performance["total_return"] == 0.0
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def test_equity_curve_dates_sorted(self):
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result = RebalanceEngine(
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optimizer=EqualWeightOptimizer(),
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rebalance_freq="M",
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).run(_make_market(), start_date=20240101, end_date=20240430)
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dates = result.equity_curve["datetime"].tolist()
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assert dates == sorted(dates)
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def test_trades_recorded(self):
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engine = RebalanceEngine(
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optimizer=EqualWeightOptimizer(),
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n_stocks=3,
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rebalance_freq="M",
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)
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result = engine.run(_make_market(), start_date=20240101, end_date=20240430)
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assert len(result.trades) > 0
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assert "BUY" in result.trades["direction"].values
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def test_total_trades_matches_trade_rows(self):
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"""issue #25: performance['total_trades'] 应等于真实交易笔数，而非天数。"""
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engine = RebalanceEngine(
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optimizer=EqualWeightOptimizer(),
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n_stocks=3,
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rebalance_freq="M",
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)
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result = engine.run(_make_market(), start_date=20240101, end_date=20240430)
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assert result.performance["total_trades"] == len(result.trades)
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# 修复前 total_trades == len(equity_curve)（天数），明显大于交易笔数
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assert result.performance["total_trades"] != len(result.equity_curve)
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