feat(backtest): add BacktestEngine with vectorized execution pipeline

- Implement BacktestEngine orchestrator with 4-step pipeline:
  1. Signal generation (Strategy)
  2. Order simulation (OrderSimulator)
  3. Portfolio tracking (PortfolioTracker)
  4. Performance analysis (PerformanceAnalyzer)
- Support both strategy class and instance initialization
- Add PnL calculation for sell trades
- Add JSON serialization with numpy/timestamp support
- Include comprehensive test coverage (12 tests, all passing)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/easy_tdx/backtest/engine.py

@@ -0,0 +1,263 @@
+"""BacktestEngine — orchestrate vectorized execution pipeline.
+
+Coordinates Strategy → OrderSimulator → PortfolioTracker → PerformanceAnalyzer.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from easy_tdx.backtest.orders import OrderSimulator
+from easy_tdx.backtest.performance import PerformanceAnalyzer
+from easy_tdx.backtest.portfolio import PortfolioTracker
+from easy_tdx.backtest.strategy import Strategy
+from easy_tdx.backtest.types import BacktestResult, Signal, Trade
+
+
+class BacktestEngine:
+    """Orchestrate backtest execution pipeline.
+
+    Pipeline:
+        1. Signal generation (Strategy)
+        2. Order simulation (OrderSimulator)
+        3. Portfolio tracking (PortfolioTracker)
+        4. Performance analysis (PerformanceAnalyzer)
+
+    Example:
+        >>> engine = BacktestEngine(MyStrategy, cash=100000)
+        >>> result = engine.run(df)
+    """
+
+    def __init__(
+        self,
+        strategy: type[Strategy] | Strategy,
+        cash: float = 100000.0,
+        commission: float = 0.0003,
+        min_commission: float = 5.0,
+        stamp_tax: float = 0.001,
+        slippage: float = 0.0,
+        execution: str = "next_open",
+        position_mode: str = "full",
+        reject_policy: str = "reduce",
+        benchmark: pd.DataFrame | None = None,
+    ):
+        """Initialize engine.
+
+        Args:
+            strategy: Strategy class or instance
+            cash: Initial cash
+            commission: Commission rate (e.g., 0.0003 = 0.03%)
+            min_commission: Minimum commission per trade
+            stamp_tax: Stamp tax rate (for sells)
+            slippage: Slippage rate
+            execution: Execution mode ('next_open', 'this_close')
+            position_mode: Position mode ('full', 'long_only', 'short_only')
+            reject_policy: Reject policy ('reduce', 'reject')
+            benchmark: Benchmark data for performance comparison
+        """
+        self._strategy_cls = strategy if isinstance(strategy, type) else type(strategy)
+        self._strategy_instance = strategy if isinstance(strategy, Strategy) else None
+
+        self._cash = cash
+        self._commission = commission
+        self._min_commission = min_commission
+        self._stamp_tax = stamp_tax
+        self._slippage = slippage
+        self._execution = execution
+        self._position_mode = position_mode
+        self._reject_policy = reject_policy
+        self._benchmark = benchmark
+
+    def run(self, df: pd.DataFrame, chanlun_result: Any | None = None) -> BacktestResult:
+        """Run backtest.
+
+        Args:
+            df: Price data with OHLCV columns
+            chanlun_result: Optional chanlun analysis result for strategy
+
+        Returns:
+            BacktestResult with performance, equity_curve, trades, positions, config
+        """
+        if len(df) == 0:
+            return self._empty_result()
+
+        # Step 1: Signal generation
+        signals = self._generate_signals(df, chanlun_result)
+
+        # Step 2: Order simulation
+        simulator = OrderSimulator(
+            df,
+            execution=self._execution,
+            position_mode=self._position_mode,
+            reject_policy=self._reject_policy,
+            commission=self._commission,
+            min_commission=self._min_commission,
+            stamp_tax=self._stamp_tax,
+            slippage=self._slippage,
+        )
+        trades = simulator.simulate(
+            signals=signals,
+            cash=self._cash,
+            position=0.0,
+        )
+
+        # Step 3: Portfolio tracking
+        trades = self._compute_pnls(trades)
+        tracker = PortfolioTracker(df, initial_cash=self._cash)
+        tracker.apply_trades(trades)
+
+        # Step 4: Performance analysis
+        trades_df = self._trades_to_df(trades)
+        performance = PerformanceAnalyzer(
+            tracker.equity_curve,
+            trades_df,
+            risk_free_rate=0.03,
+        ).compute()
+
+        # Config snapshot
+        config = {
+            "cash": self._cash,
+            "commission": self._commission,
+            "execution": self._execution,
+            "position_mode": self._position_mode,
+            "reject_policy": self._reject_policy,
+            "future_leak_warning": simulator.future_leak_warning,
+        }
+
+        return BacktestResult(
+            performance=performance,
+            equity_curve=tracker.equity_curve,
+            trades=trades_df,
+            positions=tracker.positions,
+            config=config,
+        )
+
+    def _generate_signals(self, df: pd.DataFrame, chanlun_result: Any | None) -> list[Signal]:
+        """Generate signals from strategy.
+
+        Args:
+            df: Price data
+            chanlun_result: Optional chanlun analysis result
+
+        Returns:
+            List of signals
+        """
+        # Instantiate strategy if needed
+        strat = (
+            self._strategy_instance if self._strategy_instance is not None else self._strategy_cls()
+        )
+
+        # Bind data
+        strat._bind_data(df)
+
+        # Inject chanlun result if provided
+        if chanlun_result is not None:
+            strat._chanlun_result = chanlun_result
+
+        # Call init
+        strat._call_init()
+
+        # Generate signals bar by bar
+        all_signals: list[Signal] = []
+        for i in range(len(df)):
+            strat._set_bar_index(i)
+            strat._call_next()
+            bar_signals = strat._clear_signals()
+            all_signals.extend(bar_signals)
+
+        return all_signals
+
+    def _compute_pnls(self, trades: list[Trade]) -> list[Trade]:
+        """Compute realized PnL for sell trades.
+
+        Args:
+            trades: List of trades
+
+        Returns:
+            Trades with PnL computed
+        """
+        position_cost = 0.0
+        position_size = 0.0
+
+        for trade in trades:
+            if not trade.rejected:
+                if trade.direction == "BUY":
+                    position_cost += trade.size * trade.price + trade.commission
+                    position_size += trade.size
+                    trade.pnl = 0.0
+                elif trade.direction == "SELL":
+                    if position_size > 0:
+                        avg_cost = position_cost / position_size
+                        trade.pnl = (trade.price - avg_cost) * trade.size - trade.commission
+                    else:
+                        trade.pnl = 0.0
+                    position_cost -= avg_cost * trade.size
+                    position_size -= trade.size
+
+        return trades
+
+    def _trades_to_df(self, trades: list[Trade]) -> pd.DataFrame:
+        """Convert trades to DataFrame.
+
+        Args:
+            trades: List of trades
+
+        Returns:
+            DataFrame with trade data
+        """
+        if not trades:
+            return pd.DataFrame(
+                columns=[
+                    "datetime",
+                    "direction",
+                    "size",
+                    "price",
+                    "commission",
+                    "pnl",
+                    "rejected",
+                ]
+            )
+
+        data = [
+            {
+                "datetime": t.datetime,
+                "direction": t.direction,
+                "size": t.size,
+                "price": t.price,
+                "commission": t.commission,
+                "pnl": t.pnl,
+                "rejected": t.rejected,
+            }
+            for t in trades
+        ]
+        return pd.DataFrame(data)
+
+    def _empty_result(self) -> BacktestResult:
+        """Return empty result for empty input.
+
+        Returns:
+            BacktestResult with empty DataFrames
+        """
+        return BacktestResult(
+            performance={},
+            equity_curve=pd.DataFrame(
+                columns=["datetime", "cash", "position_value", "total", "drawdown"]
+            ),
+            trades=pd.DataFrame(
+                columns=[
+                    "datetime",
+                    "direction",
+                    "size",
+                    "price",
+                    "commission",
+                    "pnl",
+                    "rejected",
+                ]
+            ),
+            positions=pd.DataFrame(
+                columns=["datetime", "size", "avg_price", "market_value", "unrealized_pnl"]
+            ),
+            config={},
+        )

src/easy_tdx/backtest/types.py

@@ -121,7 +121,19 @@ class BacktestResult:
 
     def to_json(self) -> str:
         """将结果序列化为 JSON 字符串。"""
-        return json.dumps(self.to_dict(), ensure_ascii=False, indent=2)
+        d = self.to_dict()
+        return json.dumps(d, ensure_ascii=False, indent=2, default=self._json_default)
+
+    @staticmethod
+    def _json_default(obj: Any) -> Any:
+        """JSON serializer for objects not serializable by default json code."""
+        if hasattr(obj, "item"):
+            # numpy types
+            return obj.item()
+        if hasattr(obj, "isoformat"):
+            # datetime/timestamp objects
+            return obj.isoformat()
+        raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
 
     def summary(self) -> None:
         """打印回测概要（标准输出）。"""