fix(backtest): A股适配层—定寸/做空拦截/真实费用/口径统一(Phase1+2)

审计发现包装层系统性失真(2 CRITICAL+7 HIGH)，vnpy底座可信但A股场景未适配：
- C1 定寸: engine.size=N(满仓手数)，策略volume=1手=N股，开平对称(pos归零)
- C2 做空拦截: SHORT+OPEN拒单，long-only，SHORT+CLOSE平多允许
- H3 A股费用: AShareDailyResult重算(佣金保底5元/印花税卖方/过户费沪市)
- H4 收益口径: simple return从balance算(不再用vnpy log return喂empyrical)
- H5+口径: benchmark ffill对齐不缩样本; sizing_shares_per_lot暴露
- H7 退化检测: 零成交/空数据标degenerate不静默done
- H8 task_id: optimize/factor用uuid4(原id()内存地址)
- 静默except改warning

验证: 容器内真实vnpy DoubleMa 600000 2022-2024, total_return 1e-6→42.3%,
end_balance 100万→142万, SHORT+OPEN成交0笔, N=7800股/手.
22 backtest测试全绿(含集成测试), API健康200.

sanguo_backtest/cta_engine.py

@@ -2,6 +2,7 @@
 import sys
 import os
 import math
+import logging
 import traceback
 import uuid
 from datetime import datetime
@@ -66,9 +67,25 @@ def guess_exchange(symbol: str) -> Exchange:
     return Exchange("SSE")
 
 
-def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end: str, cfg, db_path: str, benchmark: str = "hs300", task_id: str | None = None) -> BacktestResult:
+def run_cta_backtest(
+    strategy_class,
+    symbol: str,
+    params: dict,
+    start: str,
+    end: str,
+    cfg,
+    db_path: str,
+    benchmark: str = "hs300",
+    task_id: str | None = None,
+    capital: float = 1_000_000,
+    position_pct: float = 0.95,
+    commission_rate: float = 0.00025,
+    min_commission: float = 5.0,
+    stamp_duty_rate: float = 0.0005,
+    transfer_fee_rate: float = 0.00001,
+) -> BacktestResult:
     """
-    Run CTA strategy backtest using vnpy_ctastrategy BacktestingEngine.
+    Run CTA strategy backtest using AShareBacktestingEngine (vnpy 子类化).
 
     Args:
         strategy_class: CTA strategy class to backtest
@@ -81,6 +98,12 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
         benchmark: Benchmark code (hs300/zz500)
         task_id: Optional task ID from runner (reused as the persisted task_id so
             runner-id == DB task_id; if omitted a fresh uuid is generated).
+        capital: Starting capital (元)
+        position_pct: 仓位占比 0~1（定寸用：shares = floor(capital*pct/price/100)*100）
+        commission_rate: A 股佣金率双边（默认万 2.5）
+        min_commission: 单笔最低佣金（默认 5 元）
+        stamp_duty_rate: 印花税率卖方（默认 0.0005）
+        transfer_fee_rate: 过户费率沪市（默认 0.00001）
 
     Returns:
         BacktestResult: Result object with backtest statistics and status
@@ -90,18 +113,19 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
         task_id = f"cta_{uuid.uuid4().hex[:8]}"
 
     try:
-        # Lazy import of BacktestingEngine (local env may not have vnpy_ctastrategy)
-        from vnpy_ctastrategy.backtesting import BacktestingEngine
+        # Lazy import of AShareBacktestingEngine (subclass of vnpy BacktestingEngine)
+        from sanguo_backtest.ashare_engine import AShareBacktestingEngine
 
         # Build vt_symbol for A-shares
-        vt_symbol = f"{symbol}.{guess_exchange(symbol).value}"
+        _exchange = guess_exchange(symbol)
+        vt_symbol = f"{symbol}.{_exchange.value}"
 
         # Convert date strings to datetime objects
         start_dt = datetime.strptime(start, "%Y-%m-%d")
         end_dt = datetime.strptime(end, "%Y-%m-%d") if end else None
 
-        # Create and configure backtesting engine
-        engine = BacktestingEngine()
+        # Create and configure A-share backtesting engine
+        engine = AShareBacktestingEngine()
 
         # Set parameters with A-share specific values
         engine.set_parameters(
@@ -109,13 +133,21 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             interval="d",  # Interval.DAILY.value — vnpy enum uses "d" not "1d"
             start=start_dt,
             end=end_dt,
-            rate=0.001,  # Commission rate (0.1% for A-shares)
+            rate=commission_rate,  # 佣金率（AShareDailyResult 用自身 commission_rate，此处仅保持一致）
             slippage=0,  # No slippage for simplicity
             size=1,  # Contract size (1 for stocks)
             pricetick=0.01,  # Minimum price tick (0.01 yuan for A-shares)
-            capital=1_000_000  # Starting capital — 0 causes instant liquidation on first trade
+            capital=capital,  # Starting capital
         )
 
+        # A 股适配参数（定寸 + 费用）
+        engine.position_pct = position_pct
+        engine.commission_rate = commission_rate
+        engine.min_commission = min_commission
+        engine.stamp_duty_rate = stamp_duty_rate
+        engine.transfer_fee_rate = transfer_fee_rate
+        engine.is_sse = (_exchange.value == "SSE")
+
         # Add strategy
         engine.add_strategy(strategy_class, params)
 
@@ -130,8 +162,8 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             _dcfg = load_config(find_config_path())
             SETTINGS["database.name"] = "sqlite"
             SETTINGS["database.database"] = _dcfg.data_paths["vnpy_db"]
-        except Exception:
-            pass
+        except Exception as e:
+            logging.warning("vnpy 数据库配置加载失败（回测可能无法加载历史数据）: %s", e)
 
         # Capture engine run output (load/run/stats) to per-task log file, so the
         # result page 日志 tab has real content. Tee stdout inside the worker process
@@ -150,6 +182,22 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             # Load historical data
             engine.load_data()
 
+            # C1 定寸：size = N 股/手。load_data 后取首根 bar close 算满仓手数。
+            # vnpy 的 turnover/PnL/commission 自动 ×size，策略 volume 保持 1 手=N 股=满仓，
+            # 所有策略（DoubleMa/BollChannel/DualThrust）无需改。
+            _sizing_shares_per_lot = 0
+            if engine.history_data:
+                _first_close = engine.history_data[0].close_price
+                if _first_close > 0:
+                    _sizing_shares_per_lot = int(
+                        math.floor(capital * position_pct / _first_close / 100) * 100
+                    )
+                    engine.size = _sizing_shares_per_lot
+                    logging.info(
+                        "定寸: N=%s 股/手 (capital=%s pct=%s first_close=%s)",
+                        _sizing_shares_per_lot, capital, position_pct, _first_close,
+                    )
+
             # Run backtesting
             engine.run_backtesting()
 
@@ -169,6 +217,20 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             for k, v in raw_stats.items()
         }
 
+        # H7 degenerate 检测：零成交或空数据不静默 done
+        _degenerate_reason = None
+        trades_dict_check = engine.trades if isinstance(engine.trades, dict) else {}
+        if not trades_dict_check:
+            _degenerate_reason = "零成交记录（策略未触发任何交易信号）"
+        elif daily_df is None or (hasattr(daily_df, "empty") and daily_df.empty):
+            _degenerate_reason = "日度盈亏数据为空"
+        if _degenerate_reason:
+            statistics["degenerate_reason"] = _degenerate_reason
+            logging.warning("回测退化: %s (symbol=%s strategy=%s)", _degenerate_reason, symbol, strategy_class.__name__)
+
+        # C1: 暴露定寸参数到结果（集成测试 + 前端可查）
+        statistics["sizing_shares_per_lot"] = _sizing_shares_per_lot
+
         # Calculate relative metrics against benchmark (Task 3)
         # Ensure daily_df index is datetime for compute_metrics
         if daily_df is not None and not daily_df.empty:
@@ -193,15 +255,8 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
                     benchmark_returns = bench_df["close"].pct_change().dropna()
                     benchmark_returns.index = pd.to_datetime(bench_df["date"].iloc[1:])
 
-                    # vnpy daily_df must have "return" column for compute_metrics
-                    # If not present, calculate from balance
-                    if "return" not in daily_df.columns:
-                        if "balance" in daily_df.columns:
-                            daily_df["return"] = daily_df["balance"].pct_change().fillna(0)
-                        elif "net_pnl" in daily_df.columns:
-                            daily_df["return"] = (daily_df["net_pnl"] / 1_000_000).fillna(0)
-                        else:
-                            daily_df["return"] = 0.0
+                    # H4: compute_metrics 内部从 daily_df["balance"] 自算 simple return，
+                    # 不再依赖 vnpy 的 log return 列（删原三路 fallback）
 
                     # Compute relative metrics
                     metrics_result = compute_metrics(daily_df, benchmark_returns)
@@ -228,8 +283,7 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
 
             except Exception as metrics_error:
                 # Log but don't fail backtest if metrics calculation fails
-                import logging
-                logging.warning(f"Failed to compute relative metrics: {metrics_error}")
+                logging.warning("相对指标计算失败（回测结果不受影响）: %s", metrics_error)
 
         # Build equity curve DataFrame (S1.2): use the daily_df returned by
         # calculate_result (index=date, has a 'balance' column). get_all_daily_results
@@ -238,7 +292,7 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             if "balance" in daily_df.columns:
                 _bal = daily_df["balance"].astype(float)
             elif "net_pnl" in daily_df.columns:
-                _bal = daily_df["net_pnl"].astype(float).cumsum() + 1_000_000
+                _bal = daily_df["net_pnl"].astype(float).cumsum() + capital
             else:
                 _bal = None
             equity_df = pd.DataFrame({
@@ -262,11 +316,11 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
             for t in trades_dict.values()
         ])
 
-        # Build result object
+        # Build result object — H7: degenerate 时标记状态（不静默 done）
         result = BacktestResult(
             task_id=task_id,
             type="cta",
-            status="done",
+            status="degenerate" if _degenerate_reason else "done",
             strategy=strategy_class.__name__,
             symbol=symbol,
             params=params,