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backend/app/backtest/engine.py

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+"""单一回测引擎（fast/strict 两档，同一代码路径、同一撮合逻辑）。
+
+不做"快层 vectorbt + 真层自研"双引擎——用户必然信任更快的那层，
+一旦快层省略 T+1/费用，两套结论背离即反复扯皮"以谁为准"。
+这里用开关控制规则是否注入，引擎只有一份。
+
+数据流：
+  bars -> DataFrame -> strategy.compute(指标) -> 逐 bar 喂 strategy.on_bar(broker)
+       -> broker 产生 fills -> 每根 bar 记录 equity -> metrics
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pandas as pd
+
+from ..domain import Bar
+from .broker import PaperBroker
+from .metrics import compute_metrics
+
+
+@dataclass
+class BacktestConfig:
+    initial_cash: float = 1000000.0
+    fast_mode: bool = False            # True: 关 T+1/费用，交互试探
+    bars_per_year: int = 252           # 日线 252；分钟级另算
+
+
+def run_backtest(bars: list[Bar], strategy, cfg: BacktestConfig | None = None) -> dict:
+    cfg = cfg or BacktestConfig()
+    if not bars:
+        return _empty_result()
+
+    df = pd.DataFrame(
+        [{"ts": b.ts, "open": b.open, "high": b.high, "low": b.low,
+          "close": b.close, "volume": b.volume} for b in bars]
+    ).sort_values("ts").reset_index(drop=True)
+
+    # 指标预计算（由策略持有，单一事实源）
+    ind = strategy.compute(df["close"], df["high"], df["low"])
+    indicator_cols = list(ind.columns)
+    df = pd.concat([df, ind], axis=1)
+
+    broker = PaperBroker(
+        initial_cash=cfg.initial_cash,
+        enable_costs=not cfg.fast_mode,
+        enable_t_plus_1=not cfg.fast_mode,
+    )
+
+    equity_values = []
+    for i in range(len(df)):
+        row = df.iloc[i]
+        strategy.on_bar(i, row, broker)
+        equity_values.append(broker.equity(row["close"]))
+        broker.release_t_plus_1()
+
+    equity = pd.Series(equity_values, index=df["ts"], name="equity")
+    metrics = compute_metrics(equity, cfg.bars_per_year)
+    metrics["num_trades"] = len(broker.fills)
+    metrics["win_rate"] = _win_rate(broker.fills)
+
+    return {
+        "df": df,
+        "indicator_cols": indicator_cols,
+        "fills": broker.fills,
+        "equity": equity,
+        "metrics": metrics,
+        "final_cash": broker.cash,
+        "final_position": broker.position,
+    }
+
+
+def _win_rate(fills) -> float:
+    """FIFO 配对计算卖出胜率。"""
+    from collections import deque
+    buys: deque = deque()
+    wins = 0
+    total_sells = 0
+    for f in fills:
+        if f.side.value == "buy":
+            buys.append((f.price, f.qty))
+        else:  # sell
+            remaining = f.qty
+            total_sells += 1
+            profitable = True
+            while remaining > 0 and buys:
+                buy_price, buy_qty = buys[0]
+                if f.price >= buy_price:
+                    pass
+                else:
+                    profitable = False
+                take = min(remaining, buy_qty)
+                buy_qty -= take
+                remaining -= take
+                if buy_qty <= 0:
+                    buys.popleft()
+                else:
+                    buys[0] = (buy_price, buy_qty)
+            if profitable and f.qty > 0:
+                wins += 1
+    return wins / total_sells if total_sells else 0.0
+
+
+def _empty_result() -> dict:
+    return {
+        "df": pd.DataFrame(),
+        "indicator_cols": [],
+        "fills": [],
+        "equity": pd.Series(dtype=float),
+        "metrics": {"total_return": 0.0, "max_drawdown": 0.0, "sharpe": 0.0,
+                    "volatility": 0.0, "num_trades": 0, "win_rate": 0.0},
+        "final_cash": 0.0,
+        "final_position": 0.0,
+    }