feat(backtest): add core data types (Signal/Trade/Position/BacktestResult)

- Add Signal dataclass for trading signals with optional price/stop_loss/take_profit
- Add Trade dataclass for executed trades with commission/slippage/pnl/rejected
- Add Position dataclass for position snapshots (long/short/flat)
- Add BacktestResult dataclass with to_dict()/to_json()/summary() methods
- Add comprehensive unit tests (13 test cases, 100% pass)
- All code passes mypy strict, ruff lint+format checks

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

src/easy_tdx/backtest/__init__.py

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+"""easy_tdx.backtest — 向量化策略回测引擎（纯计算，零网络依赖）。"""

src/easy_tdx/backtest/types.py

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+"""回测引擎核心数据类型定义。
+
+使用纯 dataclass + 类型注解，保持 mypy strict 兼容。
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Any, Literal
+
+import pandas as pd
+
+# ── 交易信号 ────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class Signal:
+    """策略产生的交易信号。
+
+    Attributes:
+        datetime: 信号时间（Unix timestamp 毫秒）
+        direction: 交易方向
+        size: 交易数量（0 = 全仓/清仓）
+        price: 限价（None = 市价单）
+        stop_loss: 止损价（None = 不设置）
+        take_profit: 止盈价（None = 不设置）
+    """
+
+    datetime: int
+    direction: Literal["BUY", "SELL"]
+    size: float
+    price: float | None = None
+    stop_loss: float | None = None
+    take_profit: float | None = None
+
+
+# ── 成交记录 ────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class Trade:
+    """已成交记录。
+
+    Attributes:
+        datetime: 成交时间（Unix timestamp 毫秒）
+        direction: 交易方向
+        size: 成交数量
+        price: 成交价格
+        commission: 手续费
+        slippage: 滑点成本
+        pnl: 已实现盈亏（仅平仓时计算）
+        rejected: 是否被拒绝（资金不足/不允许做空等）
+    """
+
+    datetime: int
+    direction: Literal["BUY", "SELL"]
+    size: float
+    price: float
+    commission: float
+    slippage: float
+    pnl: float = 0.0
+    rejected: bool = False
+
+
+# ── 持仓快照 ────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class Position:
+    """持仓快照。
+
+    Attributes:
+        datetime: 快照时间（Unix timestamp 毫秒）
+        size: 持仓数量（正=多头，负=空头，0=空仓）
+        avg_price: 平均持仓成本
+        market_value: 市值
+        unrealized_pnl: 未实现盈亏
+    """
+
+    datetime: int
+    size: float
+    avg_price: float
+    market_value: float
+    unrealized_pnl: float
+
+
+# ── 回测结果 ────────────────────────────────────────────────────────────────
+
+
+@dataclass
+class BacktestResult:
+    """回测完整结果。
+
+    Attributes:
+        performance: 绩效指标字典（总收益率、夏普比率、最大回撤等）
+        equity_curve: 资金曲线 DataFrame（index=datetime, columns=equity/drawdown等）
+        trades: 成交记录 DataFrame
+        positions: 持仓快照 DataFrame
+        config: 配置参数字典
+    """
+
+    performance: dict[str, float]
+    equity_curve: pd.DataFrame
+    trades: pd.DataFrame
+    positions: pd.DataFrame
+    config: dict[str, Any]
+
+    def to_dict(self) -> dict[str, Any]:
+        """将结果转换为 JSON 兼容字典。
+
+        DataFrame 转为 records 列表格式。
+        """
+        return {
+            "performance": self.performance,
+            "equity_curve": self.equity_curve.to_dict(orient="records"),
+            "trades": self.trades.to_dict(orient="records"),
+            "positions": self.positions.to_dict(orient="records"),
+            "config": self.config,
+        }
+
+    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)
+
+    def summary(self) -> None:
+        """打印回测概要（标准输出）。"""
+        print("=== 回测绩效概要 ===")
+        for key, value in self.performance.items():
+            if isinstance(value, float):
+                print(f"{key}: {value:.4f}")
+            else:
+                print(f"{key}: {value}")
+
+        print(f"\n成交记录数: {len(self.trades)}")
+        print(f"持仓快照数: {len(self.positions)}")
+        print(f"资金曲线点数: {len(self.equity_curve)}")

tests/unit/test_backtest_types.py

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+"""回测引擎数据类型单元测试。"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import pandas as pd
+
+from easy_tdx.backtest.types import BacktestResult, Position, Signal, Trade
+
+# ── Signal 测试 ─────────────────────────────────────────────────────────────
+
+
+def test_signal_defaults() -> None:
+    """测试 Signal 的默认值。"""
+    sig = Signal(
+        datetime=1715356800000,  # 2024-06-09 00:00:00 UTC
+        direction="BUY",
+        size=100.0,
+    )
+    assert sig.price is None
+    assert sig.stop_loss is None
+    assert sig.take_profit is None
+
+
+def test_signal_with_all_fields() -> None:
+    """测试 Signal 完整字段。"""
+    sig = Signal(
+        datetime=1715356800000,
+        direction="SELL",
+        size=50.0,
+        price=10.5,
+        stop_loss=11.0,
+        take_profit=10.0,
+    )
+    assert sig.direction == "SELL"
+    assert sig.size == 50.0
+    assert sig.price == 10.5
+    assert sig.stop_loss == 11.0
+    assert sig.take_profit == 10.0
+
+
+def test_direction_literal() -> None:
+    """测试 direction 字面量类型。"""
+    sig_buy = Signal(datetime=0, direction="BUY", size=1.0)
+    sig_sell = Signal(datetime=0, direction="SELL", size=1.0)
+    assert sig_buy.direction == "BUY"
+    assert sig_sell.direction == "SELL"
+
+
+# ── Trade 测试 ──────────────────────────────────────────────────────────────
+
+
+def test_trade_defaults() -> None:
+    """测试 Trade 的默认值。"""
+    trade = Trade(
+        datetime=1715356800000,
+        direction="BUY",
+        size=100.0,
+        price=10.0,
+        commission=0.3,
+        slippage=0.1,
+    )
+    assert trade.pnl == 0.0
+    assert trade.rejected is False
+
+
+def test_trade_with_pnl() -> None:
+    """测试带盈亏的成交记录。"""
+    trade = Trade(
+        datetime=1715356800000,
+        direction="SELL",
+        size=100.0,
+        price=11.0,
+        commission=0.3,
+        slippage=0.1,
+        pnl=50.0,
+    )
+    assert trade.pnl == 50.0
+
+
+def test_trade_rejected() -> None:
+    """测试被拒绝的成交。"""
+    trade = Trade(
+        datetime=1715356800000,
+        direction="BUY",
+        size=1000000.0,  # 超大单模拟资金不足
+        price=10.0,
+        commission=0.0,
+        slippage=0.0,
+        rejected=True,
+    )
+    assert trade.rejected is True
+
+
+# ── Position 测试 ───────────────────────────────────────────────────────────
+
+
+def test_position_long() -> None:
+    """测试多头持仓。"""
+    pos = Position(
+        datetime=1715356800000,
+        size=100.0,
+        avg_price=10.0,
+        market_value=1050.0,
+        unrealized_pnl=50.0,
+    )
+    assert pos.size > 0
+    assert pos.avg_price == 10.0
+    assert pos.market_value == 1050.0
+    assert pos.unrealized_pnl == 50.0
+
+
+def test_position_short() -> None:
+    """测试空头持仓。"""
+    pos = Position(
+        datetime=1715356800000,
+        size=-100.0,
+        avg_price=10.0,
+        market_value=-1050.0,
+        unrealized_pnl=50.0,
+    )
+    assert pos.size < 0
+
+
+def test_position_flat() -> None:
+    """测试空仓。"""
+    pos = Position(
+        datetime=1715356800000,
+        size=0.0,
+        avg_price=0.0,
+        market_value=0.0,
+        unrealized_pnl=0.0,
+    )
+    assert pos.size == 0.0
+
+
+# ── BacktestResult 测试 ─────────────────────────────────────────────────────
+
+
+def test_backtest_result_to_dict() -> None:
+    """测试 BacktestResult.to_dict() 序列化。"""
+    equity_df = pd.DataFrame(
+        {
+            "datetime": [1715356800000, 1715443200000],
+            "equity": [10000.0, 10100.0],
+            "drawdown": [0.0, -0.0099],
+        }
+    )
+    equity_df = equity_df.set_index("datetime")
+
+    trades_df = pd.DataFrame(
+        {
+            "datetime": [1715356800000],
+            "direction": ["BUY"],
+            "size": [100.0],
+            "price": [10.0],
+            "commission": [0.3],
+            "slippage": [0.1],
+            "pnl": [0.0],
+            "rejected": [False],
+        }
+    )
+
+    positions_df = pd.DataFrame(
+        {
+            "datetime": [1715356800000],
+            "size": [100.0],
+            "avg_price": [10.0],
+            "market_value": [1000.0],
+            "unrealized_pnl": [0.0],
+        }
+    )
+
+    result = BacktestResult(
+        performance={"total_return": 0.01, "sharpe_ratio": 1.5},
+        equity_curve=equity_df,
+        trades=trades_df,
+        positions=positions_df,
+        config={"initial_capital": 10000.0},
+    )
+
+    data = result.to_dict()
+
+    assert isinstance(data, dict)
+    assert data["performance"]["total_return"] == 0.01
+    assert isinstance(data["equity_curve"], list)
+    assert len(data["equity_curve"]) == 2
+    assert isinstance(data["trades"], list)
+    assert len(data["trades"]) == 1
+    assert isinstance(data["positions"], list)
+    assert len(data["positions"]) == 1
+    assert data["config"]["initial_capital"] == 10000.0
+
+
+def test_backtest_result_to_json() -> None:
+    """测试 BacktestResult.to_json() 序列化。"""
+    equity_df = pd.DataFrame(
+        {
+            "datetime": [1715356800000],
+            "equity": [10000.0],
+        }
+    )
+    equity_df = equity_df.set_index("datetime")
+
+    result = BacktestResult(
+        performance={"total_return": 0.01},
+        equity_curve=equity_df,
+        trades=pd.DataFrame(),
+        positions=pd.DataFrame(),
+        config={"initial_capital": 10000.0},
+    )
+
+    json_str = result.to_json()
+
+    # 验证是有效 JSON
+    parsed = json.loads(json_str)
+    assert parsed["performance"]["total_return"] == 0.01
+    assert parsed["config"]["initial_capital"] == 10000.0
+    assert parsed["equity_curve"][0]["equity"] == 10000.0
+
+
+def test_backtest_result_empty_dataframes() -> None:
+    """测试空 DataFrame 不崩溃。"""
+    result = BacktestResult(
+        performance={},
+        equity_curve=pd.DataFrame(),
+        trades=pd.DataFrame(),
+        positions=pd.DataFrame(),
+        config={},
+    )
+
+    # to_dict 不崩溃
+    data = result.to_dict()
+    assert data["equity_curve"] == []
+    assert data["trades"] == []
+    assert data["positions"] == []
+
+    # to_json 不崩溃
+    json_str = result.to_json()
+    parsed = json.loads(json_str)
+    assert parsed["equity_curve"] == []
+    assert parsed["trades"] == []
+    assert parsed["positions"] == []
+
+
+def test_backtest_result_summary(capsys: Any) -> None:
+    """测试 summary() 打印输出。"""
+    result = BacktestResult(
+        performance={"total_return": 0.05, "sharpe_ratio": 1.2, "max_drawdown": -0.02},
+        equity_curve=pd.DataFrame(),
+        trades=pd.DataFrame(),
+        positions=pd.DataFrame(),
+        config={"initial_capital": 10000.0},
+    )
+
+    result.summary()
+    captured = capsys.readouterr()
+
+    assert "=== 回测绩效概要 ===" in captured.out
+    assert "total_return: 0.0500" in captured.out
+    assert "sharpe_ratio: 1.2000" in captured.out
+    assert "max_drawdown: -0.0200" in captured.out
+    assert "成交记录数: 0" in captured.out
+    assert "持仓快照数: 0" in captured.out
+    assert "资金曲线点数: 0" in captured.out