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.

docs/design/backtest-ashare-adapter-plan.md

@@ -0,0 +1,90 @@
+# 回测引擎 A 股适配层 — Phase 1+2 实施计划
+
+> 起因：审计发现包装层在"A 股股票回测"场景系统性失真（2 个 CRITICAL + 7 个 HIGH）。
+> 见 `memory/backtest-engine-soundness.md`（待写）+ 审计 agent 报告。
+> vnpy 源码（项目实际 import 份）：容器内 site-packages；参考副本 `~/.openclaw/knowledge_base/vnpy_ctastrategy/`。
+> 约束：**vnpy_v4.4.0 源码零修改**，全部用子类化/包装在外层实现。Mac 无 vnpy_ctastrategy，测试在 NAS 容器内跑。
+
+## 目标（Phase 1+2）
+
+回测结果**诚实**（不虚构做空盈亏、不 1 股空转）且**准确**（A 股真实费用、收益/年化口径一致）。
+
+---
+
+## Phase 1 — 诚实（3 项）
+
+### C1 定寸：按资金 + 价格算手数
+- 机制：策略传 volume 是"满仓单位数"（vnpy 模板默认 1 = 1 个满仓），包装层换算成实际股数。
+- 公式（每次下单按当时 price 重算）：`shares_per_unit = floor(capital * position_pct / price / 100) * 100`（按手取整 100 股）；`actual_volume = volume * shares_per_unit`。
+- 落点：`AShareBacktestingEngine.send_order` 覆写，重算 volume 再 super。
+
+### C2 做空拦截：long-only
+- 机制：SSE/SZSE 标的，`direction==SHORT and offset==OPEN` 直接拒单（返回 []，warning 日志）。允许 SHORT+CLOSE（平多）。
+- 落点：`AShareBacktestingEngine.send_order` 覆写开头判断。
+- T+1：日 bar 策略层面影响小（信号收盘、次日执行），Phase 3 再处理。
+
+### H9 真实集成测试
+- 新增 `tests/backtest/test_integration_ashare.py`，`@pytest.mark.integration`，容器内跑真 vnpy：DoubleMa 600000 2024-01~2024-06，断言：
+  - `end_balance != capital`（非空转）
+  - 全部 trade `direction != SHORT or offset == CLOSE`（做空拦截）
+  - 存在 `trade.volume > 100`（定寸生效，满仓手数）
+  - `total_return` 绝对值 > 1e-3（非噪声）
+
+---
+
+## Phase 2 — 准确（4 项）
+
+### H3 A 股费用模型
+- `AShareDailyResult(DailyResult)` 覆写 `calculate_pnl`，每笔 trade：
+  - `turnover = volume * size * price`（size=1）
+  - `commission += max(turnover * rate, min_commission)`（双边，rate 默认 0.00025 万 2.5，min_commission 默认 5 元）
+  - `stamp_duty += turnover * stamp_duty_rate if direction==SHORT else 0`（卖方 0.0005）
+  - `transfer_fee += turnover * transfer_fee_rate if 沪市 else 0`（0.00001）
+  - `net_pnl = total_pnl - commission - stamp_duty - transfer_fee - slippage`
+  - 把 stamp_duty/transfer_fee 存为实例属性（持久化可见）。
+- 落点：`AShareBacktestingEngine` 覆写 DailyResult 创建处用 `AShareDailyResult`（agent 读源码定位 `self.daily_results[date]` 创建点，可能需覆写 run_backtesting 里的工厂或设类属性）。
+
+### H4 log→simple return
+- vnpy `df["return"]` 是 `np.log(...)`（backtesting.py:353）。empyrical 期望 simple return。
+- 修：`compute_metrics` 入参改为从 `daily_df["balance"]` 自算 `s = balance.pct_change().fillna(0)`，不再依赖 vnpy 的 log 列。删 cta_engine:198-204 的三路 fallback。
+
+### H5 年化统一 252
+- empyrical `period='daily'` 内部 252，已对。确认 statistics 最终用的是 empyrical 那套 scalars（cta_engine:210 `statistics.update(metrics_result.scalars)` 覆盖 vnpy 键）。前端展示字段映射到 empyrical scalars。
+
+### 口径统一（MEDIUM 顺带）
+- benchmark 对齐：`metrics.py:32` `dropna()` → `benchmark.reindex(daily_df.index).ffill().fillna(0)`，不丢策略日期。
+- equity 单一源：`/equity-curve`（绝对 balance）与 `/benchmark-curve`（相对）尺度对齐——统一改相对净值 `balance/capital`，benchmark 用 `cum_returns`，两图同尺度。
+
+---
+
+## 顺带修（成本几乎为零，同文件）
+
+- H8 `runner.py:66,93`：`id(grid)`/`id(factor_names)` → `uuid4().hex[:8]`。
+- H7 `cta_engine`：零成交/空数据 → `status="degenerate"` + `statistics["degenerate_reason"]`，不静默 done。
+- 静默吞错改 warning：`strategy_registry.py` import except、`cta_engine:127-134` config except、`:229-232` metrics except —— 加 `logging.warning` + 失败时 statistics 塞错误字段。
+
+---
+
+## schemas/routes 改动（最小）
+
+- `CtaBacktestRequest` 加 `capital: float = 1_000_000`、`position_pct: float = 0.95`（+ 可选 commission/stamp_duty/transfer_fee/min_commission，给默认值，前端先不暴露）。
+- `routes.py` run 端点透传 capital/position_pct → `run_cta_backtest`。
+- `run_cta_backtest` 签名加这些参数，传给 `AShareBacktestingEngine`。
+
+---
+
+## 文件清单
+
+| 文件 | 动作 |
+|------|------|
+| `sanguo_backtest/ashare_engine.py` | **新**：AShareDailyResult + AShareBacktestingEngine |
+| `sanguo_backtest/metrics.py` | 改：simple return、ffill 对齐、单一 equity |
+| `sanguo_backtest/cta_engine.py` | 改：换 AShare 引擎、传参、删 fallback、degenerate 检测、静默吞改 warning |
+| `sanguo_api/schemas.py` | 改：加 capital/position_pct |
+| `sanguo_api/routes.py` | 改：透传参数 |
+| `sanguo_orchestrator/runner.py` | 改：task_id uuid |
+| `sanguo_backtest/strategy_registry.py` | 改：except 加 warning |
+| `tests/backtest/test_integration_ashare.py` | **新**：真实集成测试 |
+
+## 不做（Phase 3 候选）
+T+1、组合回测（需 vnpy_portfoliostrategy）、optimize parent 分组、SQLite WAL、MockExchange 重构、滑点/年化可配置化、rolling alpha/beta 口径。

sanguo_api/routes.py

@@ -76,7 +76,9 @@ async def submit_cta(req: CtaBacktestRequest):
         start=req.start,
         end=req.end,
         cfg=None,
-        benchmark=req.benchmark
+        benchmark=req.benchmark,
+        capital=req.capital,
+        position_pct=req.position_pct
     )
     return {"task_id": tid}
 

sanguo_api/schemas.py

@@ -12,6 +12,13 @@ class CtaBacktestRequest(BaseModel):
     start: str
     end: str
     benchmark: str = "hs300"
+    capital: float = 1_000_000
+    position_pct: float = 0.95
+    # A 股费用参数（可选，前端先不暴露，给默认值）
+    commission_rate: float = 0.00025  # 万 2.5
+    min_commission: float = 5.0  # 最低 5 元
+    stamp_duty_rate: float = 0.0005  # 卖方 0.05%
+    transfer_fee_rate: float = 0.00001  # 沪市 0.001%
 
 
 class OptimizeRequest(BaseModel):

sanguo_api/strategy_registry.py

@@ -7,8 +7,11 @@ Task S1.3.
 from __future__ import annotations
 
 import importlib
+import logging
 import pkgutil
 
+logger = logging.getLogger(__name__)
+
 # Fallback strategy names (when vnpy_ctastrategy import fails).
 STRATEGY_NAMES: list[str] = ["DoubleMaStrategy", "BollChannelStrategy", "AtrRsiStrategy"]
 
@@ -25,10 +28,11 @@ def _load_strategy_classes() -> dict[str, type]:
                     obj = getattr(m, attr)
                     if isinstance(obj, type) and attr.endswith("Strategy") and hasattr(obj, "parameters"):
                         classes[attr] = obj
-            except Exception:
+            except Exception as e:
+                logger.warning("导入策略模块 %s 失败: %s", name, e)
                 continue
-    except Exception:
-        pass
+    except Exception as e:
+        logger.warning("加载 vnpy_ctastrategy 策略列表失败（降级为静态列表）: %s", e)
     return classes
 
 

sanguo_backtest/ashare_engine.py

@@ -0,0 +1,176 @@
+"""A 股适配层：子类化 vnpy_ctastrategy BacktestingEngine + DailyResult。
+
+vnpy 源码零修改，全部覆写在本文件。
+
+- AShareDailyResult：A 股费用模型（佣金保底、印花税卖方、过户费沪市）
+- AShareBacktestingEngine：
+  - send_order 覆写 → 做空拦截（SHORT+OPEN 拒单）+ 定寸重算 volume
+  - update_daily_close 覆写 → 工厂换 AShareDailyResult（父类在 :647 实例化 DailyResult）
+"""
+from __future__ import annotations
+
+import logging
+
+from vnpy_ctastrategy.backtesting import BacktestingEngine, DailyResult
+from vnpy.trader.constant import Direction, Offset
+
+logger = logging.getLogger(__name__)
+
+
+class AShareDailyResult(DailyResult):
+    """A 股日度盈亏：佣金双边保底 + 印花税卖方 + 过户费沪市。
+
+    父类 calculate_pnl 用 turnover*rate 算佣金（单边商），A 股实际：
+    - 佣金 = max(turnover * commission_rate, min_commission)，买卖都收
+    - 印花税 = turnover * stamp_duty_rate，卖方 only（direction==SHORT）
+    - 过户费 = turnover * transfer_fee_rate，沪市 only
+    """
+
+    def __init__(
+        self,
+        date,
+        close_price: float,
+        commission_rate: float = 0.00025,
+        min_commission: float = 5.0,
+        stamp_duty_rate: float = 0.0005,
+        transfer_fee_rate: float = 0.00001,
+        is_sse: bool = False,
+    ) -> None:
+        super().__init__(date, close_price)
+        self.commission_rate: float = commission_rate
+        self.min_commission: float = min_commission
+        self.stamp_duty_rate: float = stamp_duty_rate
+        self.transfer_fee_rate: float = transfer_fee_rate
+        self.is_sse: bool = is_sse
+        # 费用明细（持久化可见——父类 calculate_result 会遍历 __dict__ 落列）
+        self.stamp_duty: float = 0.0
+        self.transfer_fee: float = 0.0
+
+    def calculate_pnl(
+        self,
+        pre_close: float,
+        start_pos: float,
+        size: float,
+        rate: float,
+        slippage: float,
+    ) -> None:
+        """覆写父类：用 A 股费用模型替换 commission = turnover * rate。
+
+        签名与父类一致（calculate_result 传 self.rate/self.slippage/self.size），
+        但 rate 参数被忽略——佣金由 self.commission_rate + self.min_commission 决定。
+        """
+        # 首日无 pre_close 时用 1 防除零（与父类逻辑一致）
+        self.pre_close = pre_close if pre_close else 1
+
+        self.start_pos = start_pos
+        self.end_pos = start_pos
+
+        self.holding_pnl = self.start_pos * (self.close_price - self.pre_close) * size
+
+        self.trade_count = len(self.trades)
+        for trade in self.trades:
+            if trade.direction == Direction.LONG:
+                pos_change = trade.volume
+            else:
+                pos_change = -trade.volume
+
+            self.end_pos += pos_change
+
+            turnover: float = trade.volume * size * trade.price
+            self.trading_pnl += pos_change * (self.close_price - trade.price) * size
+            self.slippage += trade.volume * size * slippage
+
+            self.turnover += turnover
+            # A 股佣金：双边，最低 min_commission 元
+            self.commission += max(turnover * self.commission_rate, self.min_commission)
+
+            # 印花税：卖方 only（SHORT = 卖出）
+            if trade.direction == Direction.SHORT:
+                self.stamp_duty += turnover * self.stamp_duty_rate
+
+            # 过户费：沪市 only
+            if self.is_sse:
+                self.transfer_fee += turnover * self.transfer_fee_rate
+
+        # net_pnl 扣除全部费用
+        self.total_pnl = self.trading_pnl + self.holding_pnl
+        self.net_pnl = (
+            self.total_pnl
+            - self.commission
+            - self.slippage
+            - self.stamp_duty
+            - self.transfer_fee
+        )
+
+
+class AShareBacktestingEngine(BacktestingEngine):
+    """A 股回测引擎：long-only 拦截 + A 股费用。
+
+    覆写点：
+    1. send_order → 拦截 SHORT+OPEN（A股不可做空）
+    2. update_daily_close → 工厂换 AShareDailyResult（父类 :647 实例化 DailyResult）
+
+    定寸（C1）通过 engine.size = N 实现，不在此处处理：
+    cta_engine 在 load_data 后取首根 bar close 算 N = floor(capital*pct/close/100)*100，
+    设置 engine.size = N。vnpy 的 turnover/PnL 自动 ×size，策略 volume 保持 1 手=N 股=满仓。
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        # A 股费用参数（默认值，cta_engine 可覆盖）
+        self.commission_rate: float = 0.00025  # 万 2.5
+        self.min_commission: float = 5.0  # 最低 5 元
+        self.stamp_duty_rate: float = 0.0005  # 卖方 0.05%
+        self.transfer_fee_rate: float = 0.00001  # 沪市 0.001%
+        self.is_sse: bool = False
+
+    def send_order(
+        self,
+        strategy,
+        direction: Direction,
+        offset: Offset,
+        price: float,
+        volume: float,
+        stop: bool,
+        lock: bool,
+        net: bool,
+    ) -> list:
+        """覆写父类 send_order：做空拦截（C2）。
+
+        C2 做空拦截：SSE/SZSE 标的不可做空，SHORT+OPEN 直接拒单。
+        SHORT+CLOSE（平多）允许。
+        volume 不动——定寸由 engine.size = N 实现（见类文档）。
+        """
+        if direction == Direction.SHORT and offset == Offset.OPEN:
+            logger.warning(
+                "A股不支持做空，拒单: direction=%s offset=%s price=%s volume=%s",
+                direction, offset, price, volume,
+            )
+            return []
+
+        return super().send_order(
+            strategy, direction, offset, price, volume, stop, lock, net
+        )
+
+    def update_daily_close(self, price: float) -> None:
+        """覆写父类工厂方法：用 AShareDailyResult 替换 DailyResult。
+
+        父类原实现（backtesting.py:639-647）：
+            daily_result = self.daily_results.get(d)
+            if daily_result: daily_result.close_price = price
+            else: self.daily_results[d] = DailyResult(d, price)
+        """
+        d = self.datetime.date()
+        daily_result = self.daily_results.get(d, None)
+        if daily_result:
+            daily_result.close_price = price
+        else:
+            self.daily_results[d] = AShareDailyResult(
+                d,
+                price,
+                commission_rate=self.commission_rate,
+                min_commission=self.min_commission,
+                stamp_duty_rate=self.stamp_duty_rate,
+                transfer_fee_rate=self.transfer_fee_rate,
+                is_sse=self.is_sse,
+            )

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,

sanguo_backtest/metrics.py

@@ -23,13 +23,24 @@ def compute_metrics(
 ) -> MetricsResult:
     """对 vnpy daily_df + 基准日收益计算聚宽级指标。
 
-    daily_df: vnpy calculate_result() 产出，须含 "return" 列（日收益率），index 为日期。
-    benchmark_returns: 基准日收益率 Series，index 对齐 daily_df。
+    H4: 从 daily_df["balance"] 自算 simple return（vnpy df["return"] 是 log return，
+    empyrical 期望 simple return，直接用会失真）。
+    H5: period='daily' → empyrical 内部 252 交易日年化，口径统一。
+    benchmark 对齐：reindex 到策略交易日 + ffill，不丢策略日期（原 dropna 会丢停牌日）。
+
+    daily_df: vnpy calculate_result() 产出，须含 "balance" 列（账户余额），index 为日期。
+    benchmark_returns: 基准日收益率 Series，index 为日期（不必与 daily_df 对齐）。
     period: 年化周期（整数，默认252交易日），empyrical 内部使用 'daily'
     """
-    strat = daily_df["return"].astype(float)
-    # 对齐
-    aligned = pd.concat([strat.rename("s"), benchmark_returns.rename("b")], axis=1).dropna()
+    # H4: simple return from balance（pct_change 首项 NaN → 0）
+    if "balance" not in daily_df.columns:
+        raise ValueError("daily_df 缺少 balance 列，无法计算 simple return")
+    strat = daily_df["balance"].astype(float).pct_change().fillna(0)
+
+    # benchmark ffill 对齐：reindex 到策略交易日，前向填充，不丢策略日期
+    b = benchmark_returns.reindex(daily_df.index).ffill().fillna(0)
+
+    aligned = pd.concat([strat.rename("s"), b.rename("b")], axis=1)
     s, b = aligned["s"], aligned["b"]
 
     scalars = {

sanguo_orchestrator/runner.py

@@ -31,7 +31,8 @@ class Orchestrator:
             await self._on_stage(task_id, stage)
 
     async def submit_cta(self, strategy_class, symbol: str, params: dict,
-                       start: str, end: str, cfg, benchmark: str = "hs300") -> str:
+                       start: str, end: str, cfg, benchmark: str = "hs300",
+                       capital: float = 1_000_000, position_pct: float = 0.95) -> str:
         """Submit a CTA backtesting task asynchronously"""
         # Stable uuid up front → reused as the persisted DB task_id, so runner-id ==
         # DB task_id (durable across restarts; previously used id(params) memory addr).
@@ -44,14 +45,17 @@ class Orchestrator:
             start=start,
             end=end,
             cfg=cfg,
-            benchmark=benchmark
+            benchmark=benchmark,
+            capital=capital,
+            position_pct=position_pct,
         )
         await self._notify_stage(task_id, "排队中")
 
         spec = self._pending[task_id]
         fut: Future = self.pool.submit_work(
             task_id, _cta_worker, spec["strategy_class"], spec["symbol"],
-            spec["params"], spec["start"], spec["end"], spec["cfg"], spec["benchmark"], self.db_path, task_id
+            spec["params"], spec["start"], spec["end"], spec["cfg"], spec["benchmark"],
+            self.db_path, task_id, spec["capital"], spec["position_pct"]
         )
 
         task = self.pool.get_task(task_id)
@@ -63,7 +67,7 @@ class Orchestrator:
     async def submit_optimize(self, strategy_class, symbol: str, grid: dict,
                            start: str, end: str, cfg) -> str:
         """Submit a CTA optimization task asynchronously"""
-        task_id = f"opt_{symbol}_{id(grid)}"
+        task_id = f"opt_{uuid.uuid4().hex[:8]}"
         self.pool.submit(task_id, "optimize")
         self._pending[task_id] = dict(
             strategy_class=strategy_class,
@@ -90,7 +94,7 @@ class Orchestrator:
     async def submit_factor(self, symbols: list, factor_names: list,
                           start: str, end: str, cfg, output_dir: str) -> str:
         """Submit a factor analysis task asynchronously"""
-        task_id = f"factor_{id(factor_names)}"
+        task_id = f"factor_{uuid.uuid4().hex[:8]}"
         self.pool.submit(task_id, "factor")
         self._pending[task_id] = dict(
             symbols=symbols,
@@ -168,10 +172,10 @@ class Orchestrator:
 
 
 # Module-level worker functions (must be top-level for ProcessPoolExecutor pickle)
-def _cta_worker(strategy_class, symbol: str, params: dict, start: str, end: str, cfg, benchmark: str, db_path: str, task_id: str) -> any:
+def _cta_worker(strategy_class, symbol: str, params: dict, start: str, end: str, cfg, benchmark: str, db_path: str, task_id: str, capital: float = 1_000_000, position_pct: float = 0.95) -> any:
     """Worker for CTA backtest (lazy import, spawn-friendly)"""
     from sanguo_backtest.cta_engine import run_cta_backtest
-    return run_cta_backtest(strategy_class, symbol, params, start, end, cfg, db_path, benchmark=benchmark, task_id=task_id)
+    return run_cta_backtest(strategy_class, symbol, params, start, end, cfg, db_path, benchmark=benchmark, task_id=task_id, capital=capital, position_pct=position_pct)
 
 
 def _opt_worker(strategy_class, symbol: str, grid: dict, start: str, end: str, cfg, db_path: str) -> any:

tests/backtest/test_cta_engine.py

@@ -22,7 +22,11 @@ _MOCK_FACTORIES = {
 }
 _SAVED_MODULES = {}
 for _name, _factory in _MOCK_FACTORIES.items():
-    if importlib.util.find_spec(_name) is None:
+    try:
+        _found = importlib.util.find_spec(_name)
+    except ModuleNotFoundError:
+        _found = None
+    if _found is None:
         _SAVED_MODULES[_name] = sys.modules.get(_name)
         sys.modules[_name] = _factory()
 
@@ -42,7 +46,7 @@ def _restore_modules_after():
     metrics from the module cache (they were imported while mocks were active) so they
     re-import fresh with real dependencies."""
     yield
-    for cached in ("sanguo_backtest.cta_engine", "sanguo_backtest.metrics"):
+    for cached in ("sanguo_backtest.cta_engine", "sanguo_backtest.metrics", "sanguo_backtest.ashare_engine"):
         sys.modules.pop(cached, None)
     for k, orig in _SAVED_MODULES.items():
         if orig is None:
@@ -63,7 +67,10 @@ class TestRunCtaBacktest:
         # Mock BacktestingEngine — calculate_result() returns daily_df (DataFrame),
         # calculate_statistics(df) returns the stats dict (vnpy API, matches cta_engine)
         mock_engine = MagicMock()
-        mock_engine.calculate_result.return_value = MagicMock(name="daily_df")
+        mock_engine.calculate_result.return_value = pd.DataFrame(
+            {"balance": [1_000_000.0, 1_010_000.0], "net_pnl": [0.0, 10000.0]},
+            index=pd.date_range("2024-01-01", periods=2, freq="D"),
+        )
         mock_engine.calculate_statistics.return_value = {
             "total_return": 0.15,
             "sharpe_ratio": 1.2,
@@ -78,11 +85,13 @@ class TestRunCtaBacktest:
         # Mock config
         mock_cfg = Mock()
 
-        # Create mock module with BacktestingEngine
-        mock_module = MagicMock()
-        mock_module.BacktestingEngine = Mock(return_value=mock_engine)
+        # Create mock for AShareBacktestingEngine
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
+        mock_engine.trades = {"t1": MagicMock()}  # 非空 trades 避免 degenerate
+        mock_engine.history_data = []  # 空 history → 定寸跳过（mock 无真实 bar）
 
-        with patch.dict("sys.modules", {"vnpy_ctastrategy.backtesting": mock_module}):
+        with patch.dict("sys.modules", {"sanguo_backtest.ashare_engine": mock_ashare}):
             result = run_cta_backtest(
                 strategy_class=mock_strategy_class,
                 symbol="600000",
@@ -101,12 +110,12 @@ class TestRunCtaBacktest:
         assert result.params == {"window": 20}
         assert result.start == "2024-01-01"
         assert result.end == "2024-03-31"
-        assert result.statistics == {
-            "total_return": 0.15,
-            "sharpe_ratio": 1.2,
-            "max_drawdown": -0.08,
-            "win_rate": 0.55
-        }
+        # statistics 是 dict——vnpy calculate_statistics 的原始键会被 compute_metrics
+        # 的 scalars 覆盖/扩充（容器内 config 加载成功时 metrics 会跑，覆盖 mock 的 0.15；
+        # Mac 无 config 时 metrics 跳过，保留 mock 值）。具体数值随环境，真实值由
+        # test_integration_ashare 验证；此处只验结构 + mock 特有字段。
+        assert isinstance(result.statistics, dict)
+        assert result.statistics.get("sizing_shares_per_lot") == 0
         assert result.error_msg is None
 
         # Verify BacktestingEngine methods were called
@@ -130,11 +139,11 @@ class TestRunCtaBacktest:
         # Mock config
         mock_cfg = Mock()
 
-        # Create mock module with BacktestingEngine
-        mock_module = MagicMock()
-        mock_module.BacktestingEngine = Mock(return_value=mock_engine)
+        # Create mock for AShareBacktestingEngine
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
 
-        with patch.dict("sys.modules", {"vnpy_ctastrategy.backtesting": mock_module}):
+        with patch.dict("sys.modules", {"sanguo_backtest.ashare_engine": mock_ashare}):
             result = run_cta_backtest(
                 strategy_class=mock_strategy_class,
                 symbol="000001",
@@ -165,11 +174,11 @@ class TestRunCtaBacktest:
 
         mock_cfg = Mock()
 
-        # Create mock module with BacktestingEngine
-        mock_module = MagicMock()
-        mock_module.BacktestingEngine = Mock(return_value=mock_engine)
+        # Create mock for AShareBacktestingEngine
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
 
-        with patch.dict("sys.modules", {"vnpy_ctastrategy.backtesting": mock_module}):
+        with patch.dict("sys.modules", {"sanguo_backtest.ashare_engine": mock_ashare}):
             result1 = run_cta_backtest(
                 strategy_class=mock_strategy_class,
                 symbol="600000",
@@ -243,16 +252,18 @@ class TestRunCtaBacktest:
             "drawdown": pd.Series([0.0, -0.01, -0.02])
         }
 
-        # Create mock module with BacktestingEngine
-        mock_module = MagicMock()
-        mock_module.BacktestingEngine = Mock(return_value=mock_engine)
+        # Create mock for AShareBacktestingEngine
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
+        mock_engine.trades = {"t1": MagicMock()}  # 非空 trades 避免 degenerate
+        mock_engine.history_data = []  # 空 history → 定寸跳过（mock 无真实 bar）
 
         # Mock tzlocal and vnpy modules to avoid import errors
         mock_tzlocal = MagicMock()
         mock_tzlocal.get_localzone_name = Mock(return_value="UTC")
 
         with patch.dict("sys.modules", {
-            "vnpy_ctastrategy.backtesting": mock_module,
+            "sanguo_backtest.ashare_engine": mock_ashare,
             "tzlocal": mock_tzlocal,
             "vnpy.trader.setting": MagicMock()
         }):
@@ -320,15 +331,17 @@ class TestRunCtaBacktest:
         mock_metrics_result.scalars = {"alpha": 0.05}
         mock_metrics_result.series = {}
 
-        mock_module = MagicMock()
-        mock_module.BacktestingEngine = Mock(return_value=mock_engine)
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
+        mock_engine.trades = {"t1": MagicMock()}  # 非空 trades 避免 degenerate
+        mock_engine.history_data = []  # 空 history → 定寸跳过（mock 无真实 bar）
 
         # Mock tzlocal and vnpy modules to avoid import errors
         mock_tzlocal = MagicMock()
         mock_tzlocal.get_localzone_name = Mock(return_value="UTC")
 
         with patch.dict("sys.modules", {
-            "vnpy_ctastrategy.backtesting": mock_module,
+            "sanguo_backtest.ashare_engine": mock_ashare,
             "tzlocal": mock_tzlocal,
             "vnpy.trader.setting": MagicMock()
         }):

tests/backtest/test_integration_ashare.py

@@ -0,0 +1,83 @@
+"""A股适配层真实集成测试（容器内跑，Mac 本机无法运行）。
+
+需要 vnpy_ctastrategy + quant_trading.db + A 股 K 线数据。
+用法：容器内 `pytest tests/backtest/test_integration_ashare.py -m integration`
+
+验证 Phase 1+2 四项核心断言：
+- C1 定寸：成交金额 ≈ 满仓量级（size=N 方案，volume=1 手=N 股）
+- C2 做空拦截：无 SHORT+OPEN 成交
+- H3 真实费用：end_balance != capital（非空转，费用+盈亏反映在余额）
+- 非噪声：|total_return| > 1e-3
+"""
+import pytest
+
+# 容器内才有 vnpy_ctastrategy；Mac 本机自动 skip（不中断 pytest 全量跑）
+pytest.importorskip("vnpy_ctastrategy")
+
+pytestmark = [pytest.mark.integration]
+
+
+def test_double_ma_600000_2022_2024():
+    """DoubleMa 600000 2022-2024 真实回测验证。
+
+    标记 integration → 仅容器内跑（需 vnpy + quant_trading.db + A 股日线数据）。
+    用 3 年窗口确保 ArrayManager(100) 充分暖机 + 产生足够多 MA 交叉信号
+    （2024H1 窗口太短，仅 111 根 bar 暖机后信号窗口不足，会误判退化）。
+    """
+    from vnpy_ctastrategy.strategies.double_ma_strategy import DoubleMaStrategy
+    from sanguo_backtest.cta_engine import run_cta_backtest
+
+    capital = 1_000_000
+    position_pct = 0.95
+
+    result = run_cta_backtest(
+        strategy_class=DoubleMaStrategy,
+        symbol="600000",
+        params={"fast_window": 5, "slow_window": 10},
+        start="2022-01-01",
+        end="2024-12-31",
+        cfg=None,  # cta_engine 内部 load_config
+        db_path="/tmp/test_integration_ashare.db",
+        benchmark="hs300",
+        capital=capital,
+        position_pct=position_pct,
+    )
+
+    # 基本成功检查
+    assert result.status in ("done", "degenerate"), f"回测失败: {result.error_msg}"
+    assert result.status == "done", f"回测退化（不应退化）: {result.statistics.get('degenerate_reason')}"
+
+    stats = result.statistics
+    trades = result.trades
+
+    # H3: end_balance != capital（非空转——有费用+盈亏）
+    end_balance = stats.get("end_balance")
+    assert end_balance is not None, "statistics 缺 end_balance"
+    assert abs(end_balance - 1_000_000) > 1.0, f"end_balance={end_balance} 与 capital 几乎相同（空转）"
+
+    # C2: 做空拦截——无 SHORT+OPEN
+    if trades is not None and not trades.empty:
+        short_opens = trades[
+            (trades["direction"].str.contains("SHORT"))
+            & (trades["offset"].str.contains("OPEN"))
+        ]
+        assert len(short_opens) == 0, f"存在 SHORT+OPEN 成交（做空未拦截）: {short_opens}"
+
+    # C1: 定寸生效——成交金额 ≈ 满仓量级（size=N 方案：volume=1 手，turnover=1*N*price）
+    sizing_shares_per_lot = stats.get("sizing_shares_per_lot", 0)
+    if trades is not None and not trades.empty and sizing_shares_per_lot > 0:
+        first_trade = trades.iloc[0]
+        turnover = first_trade["volume"] * sizing_shares_per_lot * first_trade["price"]
+        assert turnover > capital * position_pct * 0.5, (
+            f"首笔成交金额={turnover:.0f} 未达满仓量级 "
+            f"(capital={capital} pct={position_pct} N={sizing_shares_per_lot})"
+        )
+
+    # 非噪声：|total_return| > 1e-3
+    total_return = stats.get("total_return")
+    if total_return is not None:
+        assert abs(total_return) > 1e-3, f"|total_return|={abs(total_return)} <= 1e-3（噪声）"
+
+    # H3: 费用可见——statistics 含 stamp_duty 或 commission > 0
+    total_commission = stats.get("total_commission", 0)
+    assert total_commission > 0, f"total_commission={total_commission}（费用未计入）"

tests/backtest/test_metrics.py

@@ -1,3 +1,8 @@
+"""metrics.py 纯函数单测。
+
+H4: compute_metrics 从 daily_df["balance"] 自算 simple return（不再用 vnpy log return 列），
+所以测试需构造含 "balance" 列的 daily_df。
+"""
 import sys, os
 _VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0"))
 sys.path.insert(0, _VNPY_SRC)
@@ -7,20 +12,36 @@ import numpy as np
 import empyrical
 from sanguo_backtest.metrics import compute_metrics, MetricsResult, BENCHMARK_SYMBOL
 
-def _make_daily(returns):
+
+def _make_daily_balance(returns: np.ndarray) -> pd.DataFrame:
+    """从收益率数组构造含 balance 列的 daily_df。
+
+    compute_metrics 内部算 simple return = balance.pct_change().fillna(0)，
+    所以实际传入 metrics 的 strat = [0, r0, r1, ...]（首项 NaN→0）。
+    """
     idx = pd.date_range("2024-01-01", periods=len(returns), freq="B")
-    return pd.DataFrame({"return": returns}, index=idx)
+    balance = (1 + pd.Series(returns, index=idx)).cumprod()
+    return pd.DataFrame({"balance": balance}, index=idx)
+
+
+def _actual_strat(returns: np.ndarray) -> pd.Series:
+    """compute_metrics 从 balance 推导出的实际 strat 序列（首项=0）。"""
+    idx = pd.date_range("2024-01-01", periods=len(returns), freq="B")
+    balance = (1 + pd.Series(returns, index=idx)).cumprod()
+    return balance.pct_change().fillna(0)
+
 
 def test_compute_metrics_scalars_match_empyrical():
     np.random.seed(42)
-    strat = pd.Series(np.random.normal(0.001, 0.02, 100),
+    raw_returns = np.random.normal(0.001, 0.02, 100)
+    strat = _actual_strat(raw_returns)
+    bench = pd.Series(np.random.normal(0.0005, 0.015, 100),
                       index=pd.date_range("2024-01-01", periods=100, freq="B"))
-    bench = pd.Series(np.random.normal(0.0005, 0.015, 100), index=strat.index)
-    daily_df = pd.DataFrame({"return": strat.values}, index=strat.index)
+    daily_df = _make_daily_balance(raw_returns)
 
     res = compute_metrics(daily_df, bench)
     assert isinstance(res, MetricsResult)
-    # 标量口径与 empyrical 直接计算一致
+    # 标量口径与 empyrical 直接计算一致（用推导出的 strat）
     assert abs(res.scalars["alpha"] - empyrical.alpha(strat, bench)) < 1e-9
     assert abs(res.scalars["beta"] - empyrical.beta(strat, bench)) < 1e-9
     assert abs(res.scalars["sharpe_ratio"] - empyrical.sharpe_ratio(strat)) < 1e-9
@@ -28,26 +49,54 @@ def test_compute_metrics_scalars_match_empyrical():
     assert abs(res.scalars["max_drawdown"] - empyrical.max_drawdown(strat)) < 1e-9
     assert abs(res.scalars["annual_volatility"] - empyrical.annual_volatility(strat)) < 1e-9
 
+
 def test_compute_metrics_has_all_required_scalars():
-    strat = pd.Series([0.01, -0.005, 0.02, 0.0],
+    daily_df = _make_daily_balance([0.01, -0.005, 0.02, 0.0])
+    bench = pd.Series([0.005, 0.001, 0.01, -0.002],
                       index=pd.date_range("2024-01-01", periods=4, freq="B"))
-    bench = pd.Series([0.005, 0.001, 0.01, -0.002], index=strat.index)
-    res = compute_metrics(pd.DataFrame({"return": strat.values}, index=strat.index), bench)
-    required = {"total_return","annual_return","alpha","beta","sharpe_ratio",
-                "sortino_ratio","information_ratio","annual_volatility","max_drawdown",
-                "benchmark_return","benchmark_volatility"}
+    res = compute_metrics(daily_df, bench)
+    required = {"total_return", "annual_return", "alpha", "beta", "sharpe_ratio",
+                "sortino_ratio", "information_ratio", "annual_volatility", "max_drawdown",
+                "benchmark_return", "benchmark_volatility"}
     assert required.issubset(res.scalars.keys())
 
+
 def test_compute_metrics_series_keys_and_length():
-    strat = pd.Series(np.random.normal(0, 0.01, 50),
+    raw_returns = np.random.normal(0, 0.01, 50)
+    daily_df = _make_daily_balance(raw_returns)
+    bench = pd.Series(np.random.normal(0, 0.01, 50),
                       index=pd.date_range("2024-01-01", periods=50, freq="B"))
-    bench = pd.Series(np.random.normal(0, 0.01, 50), index=strat.index)
-    res = compute_metrics(pd.DataFrame({"return": strat.values}, index=strat.index), bench)
-    for key in ["equity_curve","benchmark_curve","alpha","beta","drawdown"]:
+    res = compute_metrics(daily_df, bench)
+    for key in ["equity_curve", "benchmark_curve", "alpha", "beta", "drawdown"]:
         assert key in res.series
         assert len(res.series[key]) == 50
     assert res.series["drawdown"].max() <= 1e-9  # 回撤 <= 0
 
+
 def test_benchmark_symbol_map():
     assert BENCHMARK_SYMBOL["hs300"] == "sh000300"
     assert BENCHMARK_SYMBOL["zz500"] == "sz000905"
+
+
+def test_compute_metrics_ffill_aligns_benchmark():
+    """H5: benchmark 有缺失日期时 ffill 对齐，不丢策略交易日。"""
+    raw_returns = np.random.normal(0, 0.01, 10)
+    daily_df = _make_daily_balance(raw_returns)
+    # benchmark 只有部分日期（模拟停盘日缺失）
+    partial_dates = daily_df.index[::2]  # 隔日取一个
+    bench = pd.Series([0.001] * len(partial_dates), index=partial_dates)
+    res = compute_metrics(daily_df, bench)
+    # 不应崩溃，且 strat 长度 = daily_df 行数（没有被 dropna 削短）
+    assert len(res.series["equity_curve"]) == 10
+
+
+def test_compute_metrics_raises_without_balance():
+    """缺少 balance 列时应抛 ValueError。"""
+    idx = pd.date_range("2024-01-01", periods=5, freq="B")
+    daily_df = pd.DataFrame({"net_pnl": [1, 2, 3, 4, 5]}, index=idx)
+    bench = pd.Series([0.01] * 5, index=idx)
+    try:
+        compute_metrics(daily_df, bench)
+        assert False, "应抛 ValueError"
+    except ValueError as e:
+        assert "balance" in str(e)