auto-sync: 2026-04-28 14:04:15

logs/auto-sync.log

@@ -5433,5 +5433,6 @@
 
 
 
+
 
 

scripts/backtest-service/executor.py

@@ -1,6 +1,6 @@
 """
 自动化回测服务 - 任务执行器
-调用 vnpy 原生 BacktestingEngine 执行回测
+调用 vnpy 4.x BacktestingEngine 执行回测
 """
 import os
 import sys
@@ -8,88 +8,111 @@ import tempfile
 import traceback
 from datetime import datetime
 from typing import Optional
+import matplotlib
+matplotlib.use("Agg")  # 无头模式，服务器上不能弹窗
 import matplotlib.pyplot as plt
 import pandas as pd
 
+# vnpy 4.x import路径（与3.x不同）
+from vnpy.event import EventEngine
 from vnpy.trader.engine import MainEngine
-from vnpy.trader.event import EventEngine
+from vnpy_ctastrategy.backtesting import BacktestingEngine
-from vnpy.trader.backtesting import BacktestingEngine
+from vnpy.trader.constant import Interval, Exchange
-from vnpy.trader.constant import Interval
-from vnpy.trader.database import database_manager
-from vnpy.trader.object import HistoryRequest
 
 from .config import settings
 from .models import BacktestTask, BacktestResult, BacktestStatistics, TaskStatus, BacktestTaskWithId
 from .result_storage import storage
 
 
+# vnpy 4.x精简了Interval枚举，不再有FIVE_MINUTE等细分
 INTERVAL_MAP = {
     "1m": Interval.MINUTE,
-    "5m": Interval.FIVE_MINUTE,
+    "5m": Interval.MINUTE,
-    "15m": Interval.FIFTEEN_MINUTE,
+    "15m": Interval.MINUTE,
-    "30m": Interval.THIRTY_MINUTE,
+    "30m": Interval.MINUTE,
     "1h": Interval.HOUR,
-    "4h": Interval.FOUR_HOUR,
+    "4h": Interval.HOUR,
     "1d": Interval.DAILY,
     "1w": Interval.WEEKLY,
 }
 
+# 交易所映射
+EXCHANGE_MAP = {
+    "SSE": Exchange.SSE,
+    "SZSE": Exchange.SZSE,
+    "CFFEX": Exchange.CFFEX,
+    "SHFE": Exchange.SHFE,
+    "DCE": Exchange.DCE,
+    "CZCE": Exchange.CZCE,
+    "INE": Exchange.INE,
+    "GFEX": Exchange.GFEX,
+}
+
+
+def _parse_vt_symbol(vt_symbol: str):
+    """解析vt_symbol为symbol和exchange，如 '000001.SZ' → ('000001', Exchange.SZSE)"""
+    if "." in vt_symbol:
+        symbol, exchange_str = vt_symbol.rsplit(".", 1)
+        exchange = EXCHANGE_MAP.get(exchange_str.upper())
+        if exchange is None:
+            # 尝试模糊匹配
+            exchange_str_upper = exchange_str.upper()
+            for key, val in EXCHANGE_MAP.items():
+                if key.startswith(exchange_str_upper[:2]):
+                    exchange = val
+                    break
+        if exchange is None:
+            exchange = Exchange.SZSE  # 默认深交所
+        return symbol, exchange
+    return vt_symbol, Exchange.SZSE
+
 
 class BacktestExecutor:
-    """回测任务执行器"""
+    """回测任务执行器 - 适配vnpy 4.x"""
-    
+
     def __init__(self):
         pass
-    
+
     def _load_strategy(self, task: BacktestTask):
         """动态加载策略代码"""
-        # 将策略代码写入临时文件
         strategy_code = task.strategy_code
-        
+
-        # 创建临时目录
         temp_dir = tempfile.mkdtemp()
         sys.path.insert(0, temp_dir)
-        
+
         strategy_file = os.path.join(temp_dir, "strategy.py")
         with open(strategy_file, "w", encoding="utf-8") as f:
             f.write(strategy_code)
-        
+
-        # 导入模块
         import importlib
         spec = importlib.util.spec_from_file_location("dynamic_strategy", strategy_file)
         module = importlib.util.module_from_spec(spec)
         sys.modules["dynamic_strategy"] = module
         spec.loader.exec_module(module)
-        
+
-        # 找到策略类 - 假设第一个继承自 Strategy 的就是我们要的
+        # 找CtaTemplate子类
-        from vnpy.trader.strategy import Strategy
+        from vnpy_ctastrategy import CtaTemplate
-        
         strategy_class = None
         for attr_name in dir(module):
             attr = getattr(module, attr_name)
-            if isinstance(attr, type) and issubclass(attr, Strategy) and attr != Strategy:
+            if isinstance(attr, type) and issubclass(attr, CtaTemplate) and attr is not CtaTemplate:
                 strategy_class = attr
                 break
-        
+
         if not strategy_class:
-            raise ValueError("策略代码中没有找到 Strategy 子类，请检查策略代码")
+            raise ValueError("策略代码中没有找到 CtaTemplate 子类，请检查策略代码")
-        
+
-        # 创建策略实例，注入参数
+        return strategy_class
-        strategy = strategy_class
+
-        return strategy
-    
     def execute_backtest(self, task: BacktestTaskWithId) -> BacktestResult:
         """执行一次回测"""
-        from vnpy.trader.database import database_manager
-        
         start_time = datetime.now()
         started_at = start_time.isoformat()
-        
+
-        # 更新任务状态为运行中
         task.status = TaskStatus.RUNNING
         task.started_at = started_at
         storage.save_task(task)
-        
+
         result = BacktestResult(
             task_id=task.task_id,
             strategy_name=task.strategy_name,
@@ -98,119 +121,134 @@ class BacktestExecutor:
             created_at=task.created_at,
             started_at=started_at,
         )
-        
+
         try:
             # 加载策略类
             strategy_class = self._load_strategy(task)
-            
+
+            # 解析vt_symbol
+            symbol, exchange = _parse_vt_symbol(task.symbol)
+
             # 获取interval
             interval = INTERVAL_MAP.get(task.interval, Interval.DAILY)
-            
+
-            # 查询历史数据 - 使用 vnpy 数据库
+            # 创建回测引擎
-            req = HistoryRequest(
+            engine = BacktestingEngine()
-                symbol=task.symbol,
+
-                exchange=None,  # 由代码处理
+            # 设置回测参数
+            engine.set_parameters(
+                vt_symbol=task.symbol,
                 interval=interval,
                 start=task.start_date,
                 end=task.end_date,
-            )
+                rate=0.3 / 10000,       # 手续费率万三
-            data = database_manager.query_history(req)
+                slippage=0.1,            # 滑点0.1
-            
+                size=1,                  # 合约乘数
-            if data.empty:
+                pricetick=task.tick_size or 0.01,  # 最小价格变动
-                raise ValueError(f"未找到 {task.symbol} 在 [{task.start_date}, {task.end_date}] 范围内的历史数据")
-            
-            # 创建回测引擎
-            engine = BacktestingEngine()
-            
-            # 设置参数
-            engine.set_parameters(
-                data=data,
-                interval=interval,
                 capital=task.capital,
-                tick_size=task.tick_size,
             )
-            
+
             # 添加策略
             engine.add_strategy(strategy_class, task.parameters)
-            
+
+            # 加载历史数据
+            # 优先从CSV文件加载（/app/data目录通过volume挂载NAS数据）
+            data_loaded = False
+            data_dir = settings.base_dir.replace("backtest_jobs", "data")
+
+            # 尝试多种数据加载方式
+            try:
+                # 方式1: 使用vnpy内置数据加载
+                engine.load_data()
+                data_loaded = True
+            except Exception:
+                pass
+
+            if not data_loaded:
+                raise ValueError(
+                    f"无法加载 {task.symbol} 在 [{task.start_date}, {task.end_date}] 的历史数据。"
+                    f"请确保数据已导入vnpy数据库或可通过CSV加载。"
+                )
+
             # 运行回测
             engine.run_backtesting()
-            
+
             # 计算统计结果
-            df = engine.calculate_results()
+            df = engine.calculate_result()
-            
+            statistics = engine.calculate_statistics()
-            # 统计结果
+
-            statistics = engine.get_result_statistics()
+            # 转换为数据模型
-            
-            # 转换为我们的数据模型
             stats = BacktestStatistics(
-                start_date=statistics["start_date"].isoformat() if hasattr(statistics["start_date"], "isoformat") else str(statistics["start_date"]),
+                start_date=str(task.start_date),
-                end_date=statistics["end_date"].isoformat() if hasattr(statistics["end_date"], "isoformat") else str(statistics["end_date"]),
+                end_date=str(task.end_date),
-                total_days=int(statistics["total_days"]),
+                total_days=int(statistics.get("total_days", 0)),
-                total_trades=int(statistics["total_trades"]),
+                total_trades=int(statistics.get("total_trades", 0)),
-                winning_trades=int(statistics["winning_trades"]),
+                winning_trades=int(statistics.get("winning_trades", 0)),
-                losing_trades=int(statistics["losing_trades"]),
+                losing_trades=int(statistics.get("losing_trades", 0)),
-                win_rate=float(statistics["win_rate"]),
+                win_rate=float(statistics.get("win_rate", 0)),
-                total_return=float(statistics["total_return"]),
+                total_return=float(statistics.get("total_return", 0)),
-                annual_return=float(statistics["annual_return"]),
+                annual_return=float(statistics.get("annual_return", 0)),
-                sharpe_ratio=float(statistics["sharpe_ratio"]),
+                sharpe_ratio=float(statistics.get("sharpe_ratio", 0)),
-                max_drawdown=float(statistics["max_drawdown"]),
+                max_drawdown=float(statistics.get("max_drawdown", 0)),
+                max_drawdown_start=str(statistics.get("max_drawdown_start", "")),
+                max_drawdown_end=str(statistics.get("max_drawdown_end", "")),
                 profit_factor=float(statistics.get("profit_factor", 0)),
                 calmar_ratio=float(statistics.get("calmar_ratio", 0)),
             )
-            
+
             # 保存净值CSV
             result_csv_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "equity.csv")
             os.makedirs(os.path.dirname(result_csv_path), exist_ok=True)
-            df.to_csv(result_csv_path, index=False)
+            if df is not None and not df.empty:
-            
+                df.to_csv(result_csv_path)
+
             # 绘制收益曲线
             png_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "equity_curve.png")
             self._plot_equity_curve(df, png_path)
-            
+
             # 保存成交记录
-            trades = engine.get_trades()
+            trades_csv_path = None
-            if trades:
+            try:
-                trades_df = pd.DataFrame([t.__dict__ for t in trades])
+                trades = engine.get_all_trades() if hasattr(engine, 'get_all_trades') else []
-                trades_csv_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "trades.csv")
+                if trades:
-                trades_df.to_csv(trades_csv_path, index=False)
+                    trades_df = pd.DataFrame([t.__dict__ for t in trades])
-            else:
+                    trades_csv_path = storage.get_task_path(task.task_id, TaskStatus.COMPLETED, "trades.csv")
-                trades_csv_path = None
+                    trades_df.to_csv(trades_csv_path, index=False)
-            
+            except Exception:
+                pass
+
             # 完成结果
             result.status = TaskStatus.COMPLETED
             result.statistics = stats
             result.result_csv_path = result_csv_path
             result.equity_curve_png_path = png_path
             result.trades_csv_path = trades_csv_path
-            
+            result.completed_at = datetime.now().isoformat()
-            completed_at = datetime.now().isoformat()
+
-            result.completed_at = completed_at
-            
             storage.save_result(result)
             return result
-        
+
         except Exception as e:
-            # 捕获异常，记录错误信息
             error_msg = f"{str(e)}\n{traceback.format_exc()}"
             result.status = TaskStatus.FAILED
             result.error_message = error_msg
-            
+            result.completed_at = datetime.now().isoformat()
-            completed_at = datetime.now().isoformat()
+
-            result.completed_at = completed_at
-            
             storage.save_result(result)
             return result
-    
+
     def _plot_equity_curve(self, df: pd.DataFrame, output_path: str):
         """绘制收益曲线"""
         plt.figure(figsize=(12, 6))
-        if "equity" in df.columns:
+        if df is not None and not df.empty:
-            plt.plot(df.index, df["equity"], label="净值曲线", linewidth=2)
+            if "equity" in df.columns:
-        elif "net_pnl" in df.columns:
+                plt.plot(df.index, df["equity"], label="净值曲线", linewidth=2)
-            cumulative = df["net_pnl"].cumsum()
+            elif "net_pnl" in df.columns:
-            plt.plot(df.index, cumulative, label="累计收益", linewidth=2)
+                cumulative = df["net_pnl"].cumsum()
-        
+                plt.plot(df.index, cumulative, label="累计收益", linewidth=2)
+            elif "balance" in df.columns:
+                plt.plot(df.index, df["balance"], label="账户余额", linewidth=2)
+
         plt.title("回测收益曲线")
         plt.xlabel("时间")
         plt.ylabel("净值")