feat: multi-stock portfolio backtest engine

- Add PortfolioBacktestEngine for shared-capital multi-stock backtesting
- Support equal allocation mode (total_cash / N per stock)
- Individual BacktestEngine per stock with allocated capital
- Aggregate performance via capital-weighted returns
- Add StockData, PortfolioResult data classes
- Add 4 tests: basic run, equal allocation, empty stocks, serialization

tests/unit/test_portfolio_engine.py

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+"""单元测试：多标的组合回测引擎."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from easy_tdx.backtest.portfolio_engine import (
+    PortfolioBacktestEngine,
+    StockData,
+)
+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)
+
+
+def _make_df(n: int = 100, seed: int = 42) -> pd.DataFrame:
+    """生成随机 OHLCV DataFrame."""
+    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(1000000, 10000000, n).astype(float)
+
+    return pd.DataFrame(
+        {
+            "datetime": pd.date_range("2024-01-01", periods=n, freq="D"),
+            "open": open_,
+            "high": high,
+            "low": low,
+            "close": close,
+            "vol": vol,
+            "amount": vol * close,
+        }
+    )
+
+
+class TestPortfolioBacktest:
+    """测试组合回测引擎."""
+
+    def test_basic_portfolio_run(self) -> None:
+        """基本组合回测应正常完成."""
+        stocks = [
+            StockData("000001", "SZ", _make_df(100, seed=42)),
+            StockData("600000", "SH", _make_df(100, seed=99)),
+        ]
+
+        engine = PortfolioBacktestEngine(
+            strategy_cls=SimpleBuyStrategy,
+            stocks=stocks,
+            total_cash=200000,
+        )
+        result = engine.run()
+
+        assert result.total_performance is not None
+        assert "total_return" in result.total_performance
+        assert len(result.individual_results) == 2
+        assert result.total_performance["total_stocks"] == 2
+
+    def test_equal_allocation(self) -> None:
+        """均等分配：每只标的资金应为总资金/标的数."""
+        stocks = [
+            StockData("000001", "SZ", _make_df(100, seed=42)),
+            StockData("000002", "SZ", _make_df(100, seed=99)),
+        ]
+
+        engine = PortfolioBacktestEngine(
+            strategy_cls=SimpleBuyStrategy,
+            stocks=stocks,
+            total_cash=100000,
+            allocation="equal",
+        )
+        result = engine.run()
+
+        # 每只标的分配 50000
+        assert result.equity_allocation["SZ000001"] == 0.5
+        assert result.equity_allocation["SZ000002"] == 0.5
+
+    def test_empty_stocks(self) -> None:
+        """空标的列表应返回零绩效."""
+        engine = PortfolioBacktestEngine(
+            strategy_cls=SimpleBuyStrategy,
+            stocks=[],
+            total_cash=100000,
+        )
+        result = engine.run()
+
+        assert result.total_performance["total_return"] == 0.0
+        assert len(result.individual_results) == 0
+
+    def test_to_dict_serializable(self) -> None:
+        """结果应可序列化为字典."""
+        stocks = [StockData("000001", "SZ", _make_df(100))]
+        engine = PortfolioBacktestEngine(
+            strategy_cls=SimpleBuyStrategy,
+            stocks=stocks,
+            total_cash=100000,
+        )
+        result = engine.run()
+        d = result.to_dict()
+
+        assert "total_performance" in d
+        assert "individual_results" in d
+        assert "equity_allocation" in d