新增实盘策略：ITrendStrategy(SA)

futures_trading_strategies/hc/KalmanTrendFollower/KalmanMeanReversion.py

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+import numpy as np
+import talib
+from typing import Optional, Any, List, Dict
+from src.core_data import Bar, Order
+from src.strategies.base_strategy import Strategy
+
+
+class KalmanMeanReversion(Strategy):
+    """
+    改进版卡尔曼均值回归策略
+    1. 以 Slow Line 为止盈目标 (真正的均值回归)
+    2. 严格的 ATR 止损 (防止趋势爆发)
+    3. 时间衰减退出机制
+    4. 趋势斜率过滤
+    """
+
+    def __init__(
+            self,
+            context: Any,
+            main_symbol: str,
+            enable_log: bool,
+            trade_volume: int,
+            fast_sensitivity: float = 0.05,  # 快线灵敏度
+            slow_sensitivity: float = 0.01,  # 慢线灵敏度 (更平滑作为均值)
+            lookback_variance: int = 60,
+            atr_period: int = 20,
+            entry_threshold: float = 1.2,  # 偏离多少个ATR入场
+            stop_loss_multiplier: float = 2.0,  # 止损倍数
+            max_hold_bars: int = 30,  # 最大持仓时间
+            indicator: Any = None,
+    ):
+        super().__init__(context, main_symbol, enable_log)
+        self.trade_volume = trade_volume
+        self.fast_sensitivity = fast_sensitivity
+        self.slow_sensitivity = slow_sensitivity
+        self.lookback_variance = lookback_variance
+        self.atr_period = atr_period
+        self.entry_threshold = entry_threshold
+        self.stop_loss_multiplier = stop_loss_multiplier
+        self.max_hold_bars = max_hold_bars
+
+        self.indicator = indicator
+
+        # 状态变量
+        self.kf_fast = {'x': 0.0, 'P': 1.0}
+        self.kf_slow = {'x': 0.0, 'P': 1.0}
+        self.kalman_initialized = False
+
+        # 记录持仓信息
+        self.entry_price = 0.0
+        self.entry_bar_count = 0
+        self.order_id_counter = 0
+
+    def _update_kalman(self, state: dict, measurement: float, Q: float, R: float) -> float:
+        """递归卡尔曼滤波更新"""
+        p_minus = state['P'] + Q
+        k_gain = p_minus / (p_minus + R)
+        state['x'] = state['x'] + k_gain * (measurement - state['x'])
+        state['P'] = (1 - k_gain) * p_minus
+        return state['x']
+
+    def on_open_bar(self, open_price: float, symbol: str):
+        bar_history = self.get_bar_history()
+        if len(bar_history) < 100:
+            return
+
+        self.cancel_all_pending_orders()
+        closes = np.array([b.close for b in bar_history], dtype=float)
+        last_price = closes[-1]
+
+        # 1. 计算自适应噪声 (基于滚动方差)
+        rolling_var = np.var(closes[-self.lookback_variance:])
+        r_base = rolling_var if rolling_var > 0 else 1.0
+
+        # 2. 初始化或更新卡尔曼滤波器
+        if not self.kalman_initialized:
+            self.kf_fast['x'] = self.kf_slow['x'] = last_price
+            self.kalman_initialized = True
+            return
+
+        # 快线追踪价格，慢线代表平衡位置
+        fast_line = self._update_kalman(self.kf_fast, last_price, r_base * self.fast_sensitivity, r_base)
+        slow_line = self._update_kalman(self.kf_slow, last_price, r_base * self.slow_sensitivity, r_base * 10.0)
+
+        # 3. 计算 ATR 和 偏离度
+        highs = np.array([b.high for b in bar_history], dtype=float)
+        lows = np.array([b.low for b in bar_history], dtype=float)
+        atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
+
+        if atr <= 0: return
+
+        # 计算价格偏离慢线的程度 (Z-Score 的变体)
+        diff_in_atr = (last_price - slow_line) / atr
+
+        # 4. 仓位逻辑
+        pos = self.get_current_positions().get(symbol, 0)
+
+        if pos == 0:
+            # --- 入场逻辑 ---
+            # 只有在外部指标允许且偏离度足够大时入场
+            can_trade = self.indicator is None or self.indicator.is_condition_met(*self.get_indicator_tuple())
+
+            if can_trade:
+                if diff_in_atr > self.entry_threshold:
+                    # 超买，做空
+                    self.execute_order(symbol, "SELL", "OPEN", last_price)
+                elif diff_in_atr < -self.entry_threshold:
+                    # 超卖，做多
+                    self.execute_order(symbol, "BUY", "OPEN", last_price)
+        else:
+            # --- 出场逻辑 ---
+            self.entry_bar_count += 1
+            is_long = pos > 0
+            should_close = False
+            exit_reason = ""
+
+            # A. 均值回归止盈：触碰或穿过慢线
+            if is_long and last_price >= slow_line:
+                should_close = True
+                exit_reason = "Take Profit: Reached Mean"
+            elif not is_long and last_price <= slow_line:
+                should_close = True
+                exit_reason = "Take Profit: Reached Mean"
+
+            # B. 严谨止损：背离程度进一步扩大
+            elif is_long and last_price < self.entry_price - self.stop_loss_multiplier * atr:
+                should_close = True
+                exit_reason = "Stop Loss: Deviation Too Large"
+            elif not is_long and last_price > self.entry_price + self.stop_loss_multiplier * atr:
+                should_close = True
+                exit_reason = "Stop Loss: Deviation Too Large"
+
+            # C. 时间退出：久攻不下，由于均值回归的时效性，超时即走
+            elif self.entry_bar_count >= self.max_hold_bars:
+                should_close = True
+                exit_reason = "Time Exit: Holding Too Long"
+
+            if should_close:
+                direction = "CLOSE_LONG" if is_long else "CLOSE_SHORT"
+                self.log(f"EXIT {direction}: Price={last_price}, Reason={exit_reason}")
+                self.execute_order(symbol, direction, "CLOSE", last_price)
+
+    def execute_order(self, symbol, direction, offset, price):
+        """执行订单并更新状态"""
+        if offset == "OPEN":
+            self.entry_price = price
+            self.entry_bar_count = 0
+
+        order_id = f"{symbol}_{direction}_{self.order_id_counter}"
+        self.order_id_counter += 1
+
+        order = Order(
+            id=order_id,
+            symbol=symbol,
+            direction=direction,
+            volume=self.trade_volume,
+            price_type="MARKET",
+            offset=offset
+        )
+        self.send_order(order)