"""单元测试：多策略资金分仓组合回测引擎（MultiStrategyEngine）。 覆盖： - 基本多策略回测（2~3 个策略，各跑各的 df，合并曲线） - 资金均分（1/N） - individual_results 的 key 格式 "{label}@{symbol}" - 合并净值曲线列结构 + 日期并集对齐 - 空策略列表兜底 - 同标的不同策略可区分 """ from __future__ import annotations import numpy as np import pandas as pd import pytest from easy_tdx.backtest.multi_strategy_engine import ( MultiStrategyEngine, StrategySlot, ) from easy_tdx.backtest.strategy import Strategy class SimpleBuyStrategy(Strategy): """简单策略：bar 5 买入，bar 30 卖出。""" def init(self) -> None: pass def next(self) -> None: if self._bar_index == 5 and self.position["size"] == 0: self.buy(size=0) elif self._bar_index == 30 and self.position["size"] > 0: self.sell(size=0) class HoldStrategy(Strategy): """从不交易的策略（净值曲线恒等于初始资金）。""" def init(self) -> None: pass def next(self) -> None: pass def _make_df(n: int = 100, seed: int = 42, start: str = "2024-01-01") -> pd.DataFrame: """生成随机 OHLCV DataFrame（与 test_portfolio_engine 同构造方式）。""" rng = np.random.default_rng(seed) close = 100.0 + np.cumsum(rng.normal(0, 1, n)) high = close + rng.uniform(0, 1, n) low = close - rng.uniform(0, 1, n) open_ = low + rng.uniform(0, high - low, n) vol = rng.integers(1_000_000, 10_000_000, n).astype(float) return pd.DataFrame( { "datetime": pd.date_range(start, periods=n, freq="D"), "open": open_, "high": high, "low": low, "close": close, "vol": vol, "amount": vol * close, } ) class TestMultiStrategyEngine: def test_basic_run_two_strategies(self) -> None: """两个策略各跑各的 df，应产出合并结果。""" slots = [ StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)), StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)), ] engine = MultiStrategyEngine(slots, total_cash=1_000_000) result = engine.run() # individual_results 的 key 形如 "{label}@{symbol}" assert set(result.individual_results.keys()) == { "双均线@SH:601088", "RSI@SZ:000001", } # 整体绩效含基本字段 assert "total_return" in result.total_performance assert result.total_performance["total_stocks"] == 2 assert result.total_performance["total_cash"] == 1_000_000 def test_total_performance_has_full_metrics(self) -> None: """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。""" slots = [ StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)), StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)), ] perf = MultiStrategyEngine(slots, total_cash=1_000_000).run().total_performance # 关键指标都应在（来自 PerformanceAnalyzer） for key in [ "total_return", "annual_return", "sharpe", "sortino", "calmar", "max_drawdown", "max_dd_duration", "volatility", "total_trades", "win_trades", "lose_trades", "win_rate", "profit_factor", "avg_win", "avg_loss", "max_win", "max_loss", ]: assert key in perf, f"缺少指标 {key}" # max_drawdown 用正值约定（与单标的一致），介于 0~1 assert 0 <= perf["max_drawdown"] <= 1 # 合并净值曲线的 drawdown 也应是正值 result = MultiStrategyEngine(slots, total_cash=1_000_000).run() assert (result.combined_equity["drawdown"] >= 0).all() def test_max_drawdown_relative_to_peak_not_initial(self) -> None: """最大回撤必须相对「当时峰值」而非「初始资金」。 回归 v1.17.11/v1.17.12 的 bug：drawdown_pct 分母误用 initial（固定初始值）， 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%）。 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 ≈ 33%（旧逻辑会算成 200%，超出 1.0）。 """ # 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。 # 用从不交易的 HoldStrategy，使 total ≈ initial_cash（曲线不随 close 变）…… # 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 # 的 drawdown_pct 计算逻辑：构造两段净值的合成 df 喂给 _build_combined_equity。 from easy_tdx.backtest.types import BacktestResult # 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） dates = pd.date_range("2024-01-01", periods=100, freq="D") up = np.linspace(1.0, 6.0, 50) # 0→50: 1→6 down = np.linspace(6.0, 4.0, 50) # 50→100: 6→4 totals = np.concatenate([up, down]) # 峰值 6.0 在第 50 根，谷底 4.0 在末尾 ec = pd.DataFrame( { "datetime": dates, "total": totals * 100_000, # 缩放到资金量级 "drawdown": np.zeros(100), "drawdown_pct": np.zeros(100), } ) # 造一个空 trades/positions 的 BacktestResult 占位 empty_df = pd.DataFrame() fake = BacktestResult( performance={"total_return": 3.0}, equity_curve=ec, trades=empty_df, positions=empty_df, config={}, ) engine = MultiStrategyEngine.__new__(MultiStrategyEngine) combined = engine._build_combined_equity( # noqa: SLF001 — 直接测内部算法 {"A@SZ:000001": fake}, {"A@SZ:000001": 100_000.0} ) # 真实最大回撤（相对峰值）：峰值 600000，谷底 400000，回撤 = 200000/600000 ≈ 33.3% dd_pct = combined["drawdown_pct"].to_numpy() max_dd = float(np.max(dd_pct)) assert 0.30 <= max_dd <= 0.36, f"max_drawdown 应≈33%，实际 {max_dd:.4f}" # 旧 bug（除以 initial=100000）会算成 200%（200000/100000），必然 >1 assert max_dd <= 1.0, "drawdown_pct 相对峰值，绝不可能超过 100%" def test_capital_split_equal(self) -> None: """资金按策略数均分：每个槽位 1/N。""" slots = [ StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)), StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), _make_df(50, seed=2)), StrategySlot("C", "SZ:000002", SimpleBuyStrategy(), _make_df(50, seed=3)), ] engine = MultiStrategyEngine(slots, total_cash=900_000) allocs = engine._compute_allocations() # noqa: SLF001 — 测试内部均分逻辑 assert len(allocs) == 3 assert all(v == 300_000 for v in allocs.values()) # equity_allocation 是占比，各 1/3 result = engine.run() assert all(abs(v - 1 / 3) < 1e-9 for v in result.equity_allocation.values()) def test_combined_equity_has_expected_columns(self) -> None: """合并净值曲线应有 datetime/total/drawdown/drawdown_pct 列。""" slots = [ StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(60, seed=7)), ] engine = MultiStrategyEngine(slots, total_cash=500_000) result = engine.run() cols = set(result.combined_equity.columns) assert {"datetime", "total", "drawdown", "drawdown_pct"} <= cols assert len(result.combined_equity) > 0 def test_combined_equity_aligns_disjoint_dates(self) -> None: """两个策略日期范围不同时，合并曲线应按并集对齐（ffill）。""" # 策略 A 跑 2024-01 起 60 根，策略 B 跑 2024-03 起 60 根 df_a = _make_df(60, seed=1, start="2024-01-01") df_b = _make_df(60, seed=2, start="2024-03-01") slots = [ StrategySlot("A", "SH:601088", SimpleBuyStrategy(), df_a), StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), df_b), ] engine = MultiStrategyEngine(slots, total_cash=1_000_000) result = engine.run() # 合并曲线长度应至少覆盖两个范围的最晚结束日（并集） assert len(result.combined_equity) >= 60 def test_total_return_capital_weighted_with_disjoint_dates(self) -> None: """晚起步槽位建仓前应按初始资金趴账（合并曲线首值=总投入资金）。 回归：旧实现对日期并集的前导缺口填 0——B 槽位起步前贡献 0 而非其 分得的 50 万，合并曲线首值 = 50 万 < 总资金 100 万，total_return 被虚增。 正确口径：前导缺口用每列首个有效值（=初始资金）回填（bfill）。 """ df_a = _make_df(60, seed=1, start="2024-01-01") df_b = _make_df(60, seed=2, start="2024-03-01") slots = [ StrategySlot("A", "SH:601088", SimpleBuyStrategy(), df_a), StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), df_b), ] result = MultiStrategyEngine(slots, total_cash=1_000_000).run() # 合并曲线首值 = 总投入资金（旧实现 = 500000，缺晚起步槽位的资金） assert result.combined_equity["total"].iloc[0] == pytest.approx(1_000_000.0) # total_return == 各槽位资金加权真实收益 weighted = sum( 0.5 * res.performance.get("total_return", 0.0) for res in result.individual_results.values() ) assert result.total_performance["total_return"] == pytest.approx(weighted, abs=1e-9) def test_empty_strategies_returns_empty_result(self) -> None: """空策略列表应返回空结果，不抛异常。""" engine = MultiStrategyEngine([], total_cash=1_000_000) result = engine.run() assert result.individual_results == {} assert result.total_performance["total_return"] == 0.0 # combined_equity 为带表头的空 DataFrame assert len(result.combined_equity) == 0 assert set(result.combined_equity.columns) == { "datetime", "total", "drawdown", "drawdown_pct", } def test_same_symbol_different_strategies_distinguished(self) -> None: """同标的不同策略应能区分（key 含 label）。""" df = _make_df(60, seed=5) slots = [ StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), df.copy()), StrategySlot("RSI", "SH:601088", HoldStrategy(), df.copy()), ] engine = MultiStrategyEngine(slots, total_cash=1_000_000) result = engine.run() # 两个 key 不同，都带同一 symbol assert "双均线@SH:601088" in result.individual_results assert "RSI@SH:601088" in result.individual_results def test_hold_strategy_keeps_initial_capital(self) -> None: """从不交易的策略，其净值曲线末值应等于初始分得资金。""" slots = [ StrategySlot("Hold", "SH:601088", HoldStrategy(), _make_df(40, seed=1)), ] engine = MultiStrategyEngine(slots, total_cash=1_000_000) result = engine.run() ec = result.individual_results["Hold@SH:601088"].equity_curve # 不交易 → 末值 ≈ 初始资金 1_000_000（单策略拿全部） assert abs(ec["total"].iloc[-1] - 1_000_000) < 1.0 def test_to_dict_serializable(self) -> None: """to_dict 应产出 JSON 兼容结构（含 individual_results / combined_equity）。""" slots = [ StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)), ] result = MultiStrategyEngine(slots, total_cash=500_000).run() d = result.to_dict() assert "total_performance" in d assert "individual_results" in d assert "combined_equity" in d assert isinstance(d["individual_results"]["A@SH:601088"], dict)