重构项目架构，新增 MT5 代理服务

- 重构核心模块：DataProvider 依赖注入、RiskController 门面、信号注册表
- 新增 FastAPI 代理服务 (run/server.py)，支持局域网远程调用 MT5
- 新增 RemoteDataProvider + AttrDict，远端无缝替代 LiveDataProvider
- 新增序列化模块，MT5 对象转 JSON 兼容格式
- 重构入口点至 run/ 包，支持 python -m run.realtime/server/backtest/optimize
- 更新 CLAUDE.md 文档

Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>

strategies/mean_reversion.py

@@ -3,9 +3,10 @@ from .base_strategy import BaseStrategy
 from config import STRATEGY_CONFIG
 
 class MeanReversionStrategy(BaseStrategy):
+    """均值回归策略 — 价格突破布林带后等待回归确认再入场（与 BollingerStrategy 的即时入场区分）"""
+
     def __init__(self, data_provider, symbol, timeframe, period=None, std_dev=None):
         super().__init__(data_provider, symbol, timeframe)
-        # 从配置中获取参数，如果传入参数则使用传入的参数
         config = STRATEGY_CONFIG.get('mean_reversion', {})
         self.period = period if period is not None else config.get('period', 20)
         self.std_dev = std_dev if std_dev is not None else config.get('std_dev', 2.0)
@@ -19,21 +20,33 @@ class MeanReversionStrategy(BaseStrategy):
 
     def generate_signal(self):
         rates = self.data_provider.get_historical_data(self.symbol, self.timeframe, self.period + 5)
-        if rates is None or len(rates) < self.period:
+        if rates is None or len(rates) < self.period + 1:
             return 0
         df = pd.DataFrame(rates)
         df = self._calculate_indicators(df)
 
-        if df['close'].iloc[-1] > df['upper_band'].iloc[-1]:
-            return -1
-        elif df['close'].iloc[-1] < df['lower_band'].iloc[-1]:
+        # 回归确认：价格曾突破边界，现已回归内侧
+        prev_close = df['close'].iloc[-2]
+        prev_lower = df['lower_band'].iloc[-2]
+        prev_upper = df['upper_band'].iloc[-2]
+        curr_close = df['close'].iloc[-1]
+        curr_lower = df['lower_band'].iloc[-1]
+        curr_upper = df['upper_band'].iloc[-1]
+
+        # 买入：上一根K线跌破下轨，当前回升至下轨上方（回归确认）
+        if prev_close < prev_lower and curr_close >= curr_lower:
             return 1
+        # 卖出：上一根K线突破上轨，当前回落至上轨下方（回归确认）
+        elif prev_close > prev_upper and curr_close <= curr_upper:
+            return -1
         return 0
 
     def run_backtest(self, df):
         df = df.copy()
         df = self._calculate_indicators(df)
         signals = pd.Series(0, index=df.index)
-        signals[df['close'] > df['upper_band']] = -1
-        signals[df['close'] < df['lower_band']] = 1
-        return signals
+        # 前一根在轨外 + 当前回归轨内 = 买入
+        signals[(df['close'].shift(1) < df['lower_band'].shift(1)) & (df['close'] >= df['lower_band'])] = 1
+        # 前一根在轨外 + 当前回归轨内 = 卖出
+        signals[(df['close'].shift(1) > df['upper_band'].shift(1)) & (df['close'] <= df['upper_band'])] = -1
+        return signals