feat(backtest): add PortfolioTracker with equity curve and drawdown

- Pre-allocate numpy arrays for performance (cash, position, avg_price)
- apply_trades() processes buys/sells with commission and slippage
- equity_curve returns DataFrame with drawdown calculation
- positions returns DataFrame with market value and unrealized PnL
- 12 unit tests covering all scenarios

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

src/easy_tdx/backtest/portfolio.py

@@ -0,0 +1,153 @@
+"""回测引擎持仓追踪器。
+
+负责应用交易记录、计算资金曲线和持仓快照。
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from easy_tdx.backtest.types import Trade
+
+
+class PortfolioTracker:
+    """持仓追踪器。
+
+    预分配 numpy 数组存储每个 bar 的状态，遍历应用交易。
+
+    Attributes:
+        _close: 收盘价数组
+        _datetime: 时间戳数组
+        _n: bar 数量
+        _cash: 每个 bar 的现金
+        _position: 每个 bar 的持仓数量
+        _avg_price: 每个 bar 的平均成本价
+    """
+
+    def __init__(self, df: pd.DataFrame, initial_cash: float = 100000) -> None:
+        """初始化追踪器。
+
+        Args:
+            df: 必须包含 close 和 datetime 列的 DataFrame
+            initial_cash: 初始现金
+        """
+        self._close = df["close"].to_numpy(dtype=float)
+        self._datetime = df["datetime"].to_numpy()
+        self._n = len(df)
+        self._cash = np.full(self._n, initial_cash)
+        self._position = np.zeros(self._n)
+        self._avg_price = np.zeros(self._n)
+        self._initial_cash = initial_cash
+
+    def apply_trades(self, trades: list[Trade]) -> None:
+        """应用交易记录，更新内部状态数组。
+
+        Args:
+            trades: 交易列表
+        """
+        # 构建 datetime → Trade 映射
+        buy_map: dict[int, Trade] = {}
+        sell_map: dict[int, Trade] = {}
+
+        for trade in trades:
+            if trade.rejected:
+                continue
+            if trade.direction == "BUY":
+                buy_map[trade.datetime] = trade
+            else:
+                sell_map[trade.datetime] = trade
+
+        # 遍历每个 bar
+        for i in range(self._n):
+            dt = self._datetime[i]
+
+            # 继承前一个 bar 的状态（除了第一个 bar）
+            if i > 0:
+                self._cash[i] = self._cash[i - 1]
+                self._position[i] = self._position[i - 1]
+                self._avg_price[i] = self._avg_price[i - 1]
+
+            # 处理买入
+            if dt in buy_map:
+                trade = buy_map[dt]
+                cost = trade.size * trade.price + trade.commission + trade.slippage
+                self._cash[i] -= cost
+
+                # 更新均价
+                if self._position[i] > 0:
+                    total_cost = self._position[i] * self._avg_price[i] + trade.size * trade.price
+                    self._position[i] += trade.size
+                    self._avg_price[i] = total_cost / self._position[i]
+                else:
+                    # 新开仓或从空仓开仓
+                    self._position[i] = trade.size
+                    self._avg_price[i] = trade.price
+
+            # 处理卖出
+            if dt in sell_map:
+                trade = sell_map[dt]
+                proceeds = trade.size * trade.price - trade.commission - trade.slippage
+                self._cash[i] += proceeds
+                self._position[i] -= trade.size
+
+                # 清空持仓时归零
+                if self._position[i] <= 0:
+                    self._position[i] = 0.0
+                    self._avg_price[i] = 0.0
+
+    @property
+    def equity_curve(self) -> pd.DataFrame:
+        """计算资金曲线。
+
+        Returns:
+            DataFrame 包含 datetime, cash, position_value, total, drawdown, drawdown_pct
+        """
+        position_value = self._position * self._close
+        total = self._cash + position_value
+        peak = np.maximum.accumulate(total)
+        drawdown = peak - total
+        drawdown_pct = np.divide(
+            drawdown,
+            peak,
+            out=np.zeros_like(drawdown),
+            where=(peak != 0),
+        )
+
+        return pd.DataFrame(
+            {
+                "datetime": self._datetime,
+                "cash": self._cash,
+                "position_value": position_value,
+                "total": total,
+                "drawdown": drawdown,
+                "drawdown_pct": drawdown_pct,
+            }
+        )
+
+    @property
+    def positions(self) -> pd.DataFrame:
+        """计算持仓快照。
+
+        Returns:
+            DataFrame 包含 datetime, size, avg_price, market_value, unrealized_pnl
+        """
+        market_value = self._position * self._close
+        unrealized_pnl = (self._close - self._avg_price) * self._position
+
+        return pd.DataFrame(
+            {
+                "datetime": self._datetime,
+                "size": self._position,
+                "avg_price": self._avg_price,
+                "market_value": market_value,
+                "unrealized_pnl": unrealized_pnl,
+            }
+        )
+
+    @property
+    def initial_cash(self) -> float:
+        """初始现金。"""
+        return self._initial_cash