release: v1.14.1 — 高级回测 ExecutionModel 路径 3 个真实数据兼容 Bug 修复

- datetime 类型分歧（致命）：Trade.datetime 转 int 与 PortfolioTracker 的 Timestamp key 失配，TWAP/VWAP/Limit 路径交易全部静默丢失、权益曲线恒定、收益归零
- volume 列名分歧：回测认 volume 而真实行情为 vol，滑点 volume 恒 0 退化百分比模式，VWAP 退化为等权
- date/datetime 列名分歧：日线返回 date 列引擎要 datetime，run() 入口由 date 派生下游无感兼容
新增 3 个回归测试（均红灯验证）。650 单测通过，backtest 模块 ruff + mypy strict 清洁。

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

examples/22_backtest_advanced/backtest_601088_advanced.py

@@ -0,0 +1,138 @@
+"""601088（中国神华）高级回测演示。
+
+对比三档执行精度：
+  1. 无摩擦（baseline）
+  2. 固定百分比滑点 + 即时成交
+  3. 方根市场冲击滑点 + TWAP 拆单执行（高级回测）
+
+并对最高精度档做成本归因分析。
+
+用法: python backtest_601088_demo.py
+"""
+from __future__ import annotations
+
+import sys
+
+# Windows GBK 终端中文输出兜底
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+
+import pandas as pd  # noqa: E402
+
+from easy_tdx import KlineCategory, Market, TdxClient  # noqa: E402
+from easy_tdx.MyTT import MA  # noqa: E402
+from easy_tdx.backtest import BacktestEngine, Strategy  # noqa: E402
+from easy_tdx.backtest.attribution import AttributionAnalyzer  # noqa: E402
+from easy_tdx.backtest.execution import TWAPExecution  # noqa: E402
+from easy_tdx.backtest.slippage import PercentSlippage, SquareRootSlippage  # noqa: E402
+
+CODE = "601088"
+MARKET = Market.SH
+CASH = 1_000_000.0
+
+
+# ── 策略：MA10/MA30 双均线 + 8% 止损 / 18% 止盈 ──────────────────────────────────
+class DualMAStrategy(Strategy):
+    def init(self) -> None:
+        self.ma10 = self.I(MA, self.data.close, 10)
+        self.ma30 = self.I(MA, self.data.close, 30)
+
+    def next(self) -> None:
+        if self._bar_index < 30:
+            return
+        i = self._bar_index
+        price = self.data.close[0]
+        holding = self.position["size"] > 0
+
+        golden = self.ma10[i] > self.ma30[i] and self.ma10[i - 1] <= self.ma30[i - 1]
+        death = self.ma10[i] < self.ma30[i] and self.ma10[i - 1] >= self.ma30[i - 1]
+
+        if golden and not holding:
+            # 金叉开多，带止损止盈
+            self.buy(size=0, stop_loss=price * 0.92, take_profit=price * 1.18)
+        elif death and holding:
+            self.sell(size=0)
+
+
+def fmt_perf(label: str, perf: dict) -> str:
+    return (
+        f"  {label:<20}"
+        f"总收益 {perf['total_return']:>8.2%}  "
+        f"年化 {perf['annual_return']:>8.2%}  "
+        f"最大回撤 {perf['max_drawdown']:>8.2%}  "
+        f"夏普 {perf['sharpe']:>6.3f}"
+    )
+
+
+def main() -> None:
+    # ── 1. 数据 ───────────────────────────────────────────────────────────────
+    client = TdxClient()
+    try:
+        df = client.get_security_bars(MARKET, CODE, KlineCategory.DAY, 0, 800)
+    finally:
+        client.close()
+
+    if len(df) < 60:
+        print(f"数据不足: 仅 {len(df)} 根 K 线，需要网络连接通达信服务器")
+        return
+
+    df = df.dropna(subset=["close"]).reset_index(drop=True)
+    dt_col = "datetime" if "datetime" in df.columns else "date"
+    span = f"{df[dt_col].iloc[0]} ~ {df[dt_col].iloc[-1]}"
+    print(f"=== {CODE} 高级回测 ===")
+    print(f"K 线区间: {span}  共 {len(df)} 根  初始资金: {CASH:,.0f}\n")
+
+    # ── 2. 三档回测对比 ─────────────────────────────────────────────────────────
+    runs: list[tuple[str, dict]] = []
+
+    # (a) 无摩擦基线
+    r_a = BacktestEngine(DualMAStrategy, cash=CASH).run(df)
+    runs.append(("无摩擦基线", r_a.performance))
+
+    # (b) 固定百分比滑点 + 即时成交
+    r_b = BacktestEngine(
+        DualMAStrategy, cash=CASH, slippage_model=PercentSlippage(rate=0.001)
+    ).run(df)
+    runs.append(("固定0.1%滑点", r_b.performance))
+
+    # (c) 高级：方根市场冲击 + TWAP 拆单
+    r_c = BacktestEngine(
+        DualMAStrategy,
+        cash=CASH,
+        slippage_model=SquareRootSlippage(impact_coeff=0.1),
+        execution_model=TWAPExecution(n_bars=3),
+    ).run(df)
+    runs.append(("方根滑点+TWAP", r_c.performance))
+
+    print("── 性能对比 ──")
+    for label, perf in runs:
+        print(fmt_perf(label, perf))
+
+    # ── 3. 摩擦成本（基线 vs 高级）──────────────────────────────────────────────
+    print("\n── 摩擦成本影响 ──")
+    drag = r_a.performance["total_return"] - r_c.performance["total_return"]
+    print(f"  高级档相对无摩擦基线收益折损: {drag:.2%}")
+    print(f"  高级档交易笔数: {len(r_c.trades)}  (含被拒单 {r_c.trades['rejected'].sum()})")
+
+    # ── 4. 高级档成本归因 ───────────────────────────────────────────────────────
+    print("\n── 高级档成本归因 ──")
+    att = AttributionAnalyzer(r_c.trades, r_c.equity_curve)
+    cost = att.cost_attribution()
+    print(f"  区间总收益:           {cost.total_return:>10.2%}")
+    print(f"  总交易成本:           {cost.total_trade_cost:>10.0f} 元")
+    print(f"    佣金:               {cost.commission_cost:>10.0f} 元")
+    print(f"    滑点:               {cost.slippage_cost:>10.0f} 元")
+    print(f"    印花税:             {cost.stamp_tax_cost:>10.0f} 元")
+
+    # ── 5. 交易明细（前 8 笔）──────────────────────────────────────────────────
+    print("\n── 高级档交易明细（前 8 笔）──")
+    cols = ["datetime", "direction", "size", "price", "commission", "slippage"]
+    print(r_c.trades[cols].head(8).to_string(index=False))
+
+    # ── 6. 权益曲线尾段 ─────────────────────────────────────────────────────────
+    print("\n── 高级档权益曲线（尾 5 日）──")
+    print(r_c.equity_curve.tail(5).to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()