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Justin Gu
239ff46ac8
用户反馈：3 个策略各自最大回撤仅 45.53%/40.16%/16.89%，组合却显示 83.76%。 根因：_build_combined_equity 的 drawdown_pct 分母误用 initial（固定初始资金）， 净值大涨后（总收益 545%，峰值≈6.45×初始）绝对回撤额除以小初始值被等比放大。 改为 drawdown / peak（相对当时峰值，0~1），与单标的 PortfolioTracker 一致。 连带修复卡玛比率（年化/最大回撤）。其余指标经核对均正确。加回归守卫。
259 lines
11 KiB
Python
259 lines
11 KiB
Python
"""单元测试：多策略资金分仓组合回测引擎（MultiStrategyEngine）。
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覆盖：
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- 基本多策略回测（2~3 个策略，各跑各的 df，合并曲线）
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- 资金均分（1/N）
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- individual_results 的 key 格式 "{label}@{symbol}"
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- 合并净值曲线列结构 + 日期并集对齐
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- 空策略列表兜底
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- 同标的不同策略可区分
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"""
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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.backtest.multi_strategy_engine import (
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MultiStrategyEngine,
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StrategySlot,
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)
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from easy_tdx.backtest.strategy import Strategy
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class SimpleBuyStrategy(Strategy):
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"""简单策略：bar 5 买入，bar 30 卖出。"""
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def init(self) -> None:
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pass
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def next(self) -> None:
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if self._bar_index == 5 and self.position["size"] == 0:
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self.buy(size=0)
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elif self._bar_index == 30 and self.position["size"] > 0:
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self.sell(size=0)
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class HoldStrategy(Strategy):
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"""从不交易的策略（净值曲线恒等于初始资金）。"""
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def init(self) -> None:
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pass
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def next(self) -> None:
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pass
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def _make_df(n: int = 100, seed: int = 42, start: str = "2024-01-01") -> pd.DataFrame:
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"""生成随机 OHLCV DataFrame（与 test_portfolio_engine 同构造方式）。"""
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rng = np.random.default_rng(seed)
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close = 100.0 + np.cumsum(rng.normal(0, 1, n))
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high = close + rng.uniform(0, 1, n)
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low = close - rng.uniform(0, 1, n)
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open_ = low + rng.uniform(0, high - low, n)
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vol = rng.integers(1_000_000, 10_000_000, n).astype(float)
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return pd.DataFrame(
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{
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"datetime": pd.date_range(start, periods=n, freq="D"),
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"vol": vol,
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"amount": vol * close,
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}
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)
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class TestMultiStrategyEngine:
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def test_basic_run_two_strategies(self) -> None:
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"""两个策略各跑各的 df，应产出合并结果。"""
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slots = [
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StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)),
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StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)),
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]
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engine = MultiStrategyEngine(slots, total_cash=1_000_000)
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result = engine.run()
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# individual_results 的 key 形如 "{label}@{symbol}"
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assert set(result.individual_results.keys()) == {
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"双均线@SH:601088",
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"RSI@SZ:000001",
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}
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# 整体绩效含基本字段
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assert "total_return" in result.total_performance
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assert result.total_performance["total_stocks"] == 2
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assert result.total_performance["total_cash"] == 1_000_000
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def test_total_performance_has_full_metrics(self) -> None:
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"""组合整体绩效应含完整 19 项指标（夏普/回撤/胜率/盈亏比等），与单标的同口径。"""
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slots = [
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StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)),
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StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)),
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]
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perf = MultiStrategyEngine(slots, total_cash=1_000_000).run().total_performance
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# 关键指标都应在（来自 PerformanceAnalyzer）
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for key in [
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"total_return",
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"annual_return",
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"sharpe",
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"sortino",
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"calmar",
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"max_drawdown",
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"max_dd_duration",
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"volatility",
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"total_trades",
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"win_trades",
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"lose_trades",
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"win_rate",
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"profit_factor",
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"avg_win",
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"avg_loss",
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"max_win",
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"max_loss",
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]:
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assert key in perf, f"缺少指标 {key}"
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# max_drawdown 用正值约定（与单标的一致），介于 0~1
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assert 0 <= perf["max_drawdown"] <= 1
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# 合并净值曲线的 drawdown 也应是正值
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result = MultiStrategyEngine(slots, total_cash=1_000_000).run()
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assert (result.combined_equity["drawdown"] >= 0).all()
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def test_max_drawdown_relative_to_peak_not_initial(self) -> None:
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"""最大回撤必须相对「当时峰值」而非「初始资金」。
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回归 v1.17.11/v1.17.12 的 bug：drawdown_pct 分母误用 initial（固定初始值），
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导致净值大涨后回撤被严重放大（如峰值 6x 初始时，真实 45% 回撤被算成 290%）。
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构造一个大涨后回撤的场景：净值为 1→6→4（即从峰值回撤 33%），验证 max_drawdown
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≈ 33%（旧逻辑会算成 200%，超出 1.0）。
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"""
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# 构造单标的净值序列：前 50 根 close 线性涨到 6 倍，后 50 根跌到 4 倍。
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# 用从不交易的 HoldStrategy，使 total ≈ initial_cash（曲线不随 close 变）……
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# 不行——HoldStrategy 净值恒为初始资金，无法制造涨跌。改用直接断言合并曲线
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# 的 drawdown_pct 计算逻辑：构造两段净值的合成 df 喂给 _build_combined_equity。
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from easy_tdx.backtest.types import BacktestResult
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# 两根等长净值曲线：均从 1.0 涨到 6.0 再跌到 4.0（各 50 根，峰值在第 50 根）
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dates = pd.date_range("2024-01-01", periods=100, freq="D")
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up = np.linspace(1.0, 6.0, 50) # 0→50: 1→6
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down = np.linspace(6.0, 4.0, 50) # 50→100: 6→4
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totals = np.concatenate([up, down]) # 峰值 6.0 在第 50 根，谷底 4.0 在末尾
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ec = pd.DataFrame(
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{
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"datetime": dates,
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"total": totals * 100_000, # 缩放到资金量级
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"drawdown": np.zeros(100),
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"drawdown_pct": np.zeros(100),
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}
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)
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# 造一个空 trades/positions 的 BacktestResult 占位
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empty_df = pd.DataFrame()
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fake = BacktestResult(
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performance={"total_return": 3.0},
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equity_curve=ec,
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trades=empty_df,
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positions=empty_df,
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config={},
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)
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engine = MultiStrategyEngine.__new__(MultiStrategyEngine)
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combined = engine._build_combined_equity( # noqa: SLF001 — 直接测内部算法
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{"A@SZ:000001": fake}, {"A@SZ:000001": 100_000.0}
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)
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# 真实最大回撤（相对峰值）：峰值 600000，谷底 400000，回撤 = 200000/600000 ≈ 33.3%
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dd_pct = combined["drawdown_pct"].to_numpy()
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max_dd = float(np.max(dd_pct))
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assert 0.30 <= max_dd <= 0.36, f"max_drawdown 应≈33%，实际 {max_dd:.4f}"
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# 旧 bug（除以 initial=100000）会算成 200%（200000/100000），必然 >1
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assert max_dd <= 1.0, "drawdown_pct 相对峰值，绝不可能超过 100%"
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def test_capital_split_equal(self) -> None:
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"""资金按策略数均分：每个槽位 1/N。"""
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slots = [
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StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)),
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StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), _make_df(50, seed=2)),
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StrategySlot("C", "SZ:000002", SimpleBuyStrategy(), _make_df(50, seed=3)),
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]
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engine = MultiStrategyEngine(slots, total_cash=900_000)
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allocs = engine._compute_allocations() # noqa: SLF001 — 测试内部均分逻辑
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assert len(allocs) == 3
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assert all(v == 300_000 for v in allocs.values())
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# equity_allocation 是占比，各 1/3
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result = engine.run()
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assert all(abs(v - 1 / 3) < 1e-9 for v in result.equity_allocation.values())
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def test_combined_equity_has_expected_columns(self) -> None:
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"""合并净值曲线应有 datetime/total/drawdown/drawdown_pct 列。"""
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slots = [
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StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(60, seed=7)),
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]
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engine = MultiStrategyEngine(slots, total_cash=500_000)
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result = engine.run()
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cols = set(result.combined_equity.columns)
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assert {"datetime", "total", "drawdown", "drawdown_pct"} <= cols
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assert len(result.combined_equity) > 0
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def test_combined_equity_aligns_disjoint_dates(self) -> None:
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"""两个策略日期范围不同时，合并曲线应按并集对齐（ffill）。"""
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# 策略 A 跑 2024-01 起 60 根，策略 B 跑 2024-03 起 60 根
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df_a = _make_df(60, seed=1, start="2024-01-01")
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df_b = _make_df(60, seed=2, start="2024-03-01")
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slots = [
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StrategySlot("A", "SH:601088", SimpleBuyStrategy(), df_a),
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StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), df_b),
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]
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engine = MultiStrategyEngine(slots, total_cash=1_000_000)
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result = engine.run()
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# 合并曲线长度应至少覆盖两个范围的最晚结束日（并集）
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assert len(result.combined_equity) >= 60
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def test_empty_strategies_returns_empty_result(self) -> None:
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"""空策略列表应返回空结果，不抛异常。"""
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engine = MultiStrategyEngine([], total_cash=1_000_000)
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result = engine.run()
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assert result.individual_results == {}
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assert result.total_performance["total_return"] == 0.0
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# combined_equity 为带表头的空 DataFrame
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assert len(result.combined_equity) == 0
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assert set(result.combined_equity.columns) == {
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"datetime",
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"total",
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"drawdown",
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"drawdown_pct",
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}
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def test_same_symbol_different_strategies_distinguished(self) -> None:
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"""同标的不同策略应能区分（key 含 label）。"""
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df = _make_df(60, seed=5)
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slots = [
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StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), df.copy()),
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StrategySlot("RSI", "SH:601088", HoldStrategy(), df.copy()),
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]
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engine = MultiStrategyEngine(slots, total_cash=1_000_000)
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result = engine.run()
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# 两个 key 不同，都带同一 symbol
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assert "双均线@SH:601088" in result.individual_results
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assert "RSI@SH:601088" in result.individual_results
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def test_hold_strategy_keeps_initial_capital(self) -> None:
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"""从不交易的策略，其净值曲线末值应等于初始分得资金。"""
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slots = [
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StrategySlot("Hold", "SH:601088", HoldStrategy(), _make_df(40, seed=1)),
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]
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engine = MultiStrategyEngine(slots, total_cash=1_000_000)
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result = engine.run()
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ec = result.individual_results["Hold@SH:601088"].equity_curve
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# 不交易 → 末值 ≈ 初始资金 1_000_000（单策略拿全部）
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assert abs(ec["total"].iloc[-1] - 1_000_000) < 1.0
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def test_to_dict_serializable(self) -> None:
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"""to_dict 应产出 JSON 兼容结构（含 individual_results / combined_equity）。"""
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slots = [
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StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)),
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]
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result = MultiStrategyEngine(slots, total_cash=500_000).run()
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d = result.to_dict()
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assert "total_performance" in d
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assert "individual_results" in d
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assert "combined_equity" in d
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assert isinstance(d["individual_results"]["A@SH:601088"], dict)
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