1、新增傅里叶策略

2、新增策略管理、策略重启功能

futures_trading_strategies/SA/Spectral/SpectralTrendStrategy.py

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+import numpy as np
+from scipy.signal import stft
+from datetime import datetime, timedelta
+from typing import Optional, Any, List, Dict
+
+from src.core_data import Bar, Order
+from src.indicators.base_indicators import Indicator
+from src.indicators.indicators import Empty, NormalizedATR, AtrVolatility, ZScoreATR
+from src.strategies.base_strategy import Strategy
+
+
+# =============================================================================
+#  策略实现 (SpectralTrendStrategy)
+# =============================================================================
+
+class SpectralTrendStrategy(Strategy):
+    """
+    频域能量相变策略 - 捕获肥尾趋势
+
+    核心哲学:
+    1. 显式傅里叶变换: 直接分离低频(趋势)、高频(噪音)能量
+    2. 相变临界点: 仅当低频能量占比 > 阈值时入场
+    3. 低频交易: 每月仅2-5次信号，持仓数日捕获肥尾
+    4. 完全参数化: 无硬编码，适配任何市场时间结构
+
+    参数说明:
+    - bars_per_day: 市场每日K线数量 (e.g., 23 for 15min US markets)
+    - low_freq_days: 低频定义下限 (天), 默认2.0
+    - high_freq_days: 高频定义上限 (天), 默认1.0
+    """
+
+    def __init__(
+            self,
+            context: Any,
+            main_symbol: str,
+            enable_log: bool,
+            trade_volume: int,
+            # --- 【市场结构参数】 ---
+            bars_per_day: int = 23,  # 关键: 适配23根/天的市场
+            # --- 【频域核心参数】 ---
+            spectral_window_days: float = 2.0,  # STFT窗口大小(天)
+            low_freq_days: float = 2.0,  # 低频下限(天)
+            high_freq_days: float = 1.0,  # 高频上限(天)
+            trend_strength_threshold: float = 0.1,  # 相变临界值
+            exit_threshold: float = 0.4,  # 退出阈值
+            # --- 【持仓管理】 ---
+            max_hold_days: int = 10,  # 最大持仓天数
+            # --- 其他 ---
+            order_direction: Optional[List[str]] = None,
+            indicators: Optional[List[Indicator]] = None,
+            model_indicator: Indicator = None,
+    ):
+        super().__init__(context, main_symbol, enable_log)
+        if order_direction is None:
+            order_direction = ['BUY', 'SELL']
+        if indicators is None:
+            indicators = [Empty(), Empty()]  # 保持兼容性
+
+        # --- 参数赋值 (完全参数化) ---
+        self.trade_volume = trade_volume
+        self.bars_per_day = bars_per_day
+        self.spectral_window_days = spectral_window_days
+        self.low_freq_days = low_freq_days
+        self.high_freq_days = high_freq_days
+        self.trend_strength_threshold = trend_strength_threshold
+        self.exit_threshold = exit_threshold
+        self.max_hold_days = max_hold_days
+        self.order_direction = order_direction
+        if model_indicator is None:
+            model_indicator = Empty()
+        self.model_indicator = model_indicator
+
+        # --- 动态计算参数 ---
+        self.spectral_window = int(self.spectral_window_days * self.bars_per_day)
+        # 确保窗口大小为偶数 (STFT要求)
+        self.spectral_window = self.spectral_window if self.spectral_window % 2 == 0 else self.spectral_window + 1
+
+        # 频率边界 (cycles/day)
+        self.low_freq_bound = 1.0 / self.low_freq_days if self.low_freq_days > 0 else float('inf')
+        self.high_freq_bound = 1.0 / self.high_freq_days if self.high_freq_days > 0 else 0.0
+
+        # --- 内部状态变量 ---
+        self.main_symbol = main_symbol
+        self.order_id_counter = 0
+        self.indicators = indicators
+        self.entry_time = None  # 入场时间
+        self.position_direction = None  # 'LONG' or 'SHORT'
+        self.last_trend_strength = 0.0
+        self.last_dominant_freq = 0.0  # 主导周期(天)
+
+        self.log(f"SpectralTrendStrategy Initialized (bars/day={bars_per_day}, window={self.spectral_window} bars)")
+
+    def on_open_bar(self, open_price: float, symbol: str):
+        """每根K线开盘时被调用"""
+        self.symbol = symbol
+        bar_history = self.get_bar_history()
+        current_time = self.get_current_time()
+
+        self.cancel_all_pending_orders(self.main_symbol)
+
+        # 需要足够的数据 (STFT窗口 + 缓冲)
+        if len(bar_history) < self.spectral_window + 10:
+            if self.enable_log and len(bar_history) % 50 == 0:
+                self.log(f"Waiting for  {len(bar_history)}/{self.spectral_window + 10} bars")
+            return
+
+        position_volume = self.get_current_positions().get(self.symbol, 0)
+
+        # 获取历史价格 (使用完整历史)
+        closes = np.array([b.close for b in bar_history], dtype=float)
+
+        # 【核心】计算频域趋势强度 (显式傅里叶)
+        trend_strength, dominant_freq = self.calculate_trend_strength(closes)
+        self.last_trend_strength = trend_strength
+        self.last_dominant_freq = dominant_freq
+
+        # 检查最大持仓时间 (防止极端事件)
+        if self.entry_time and (current_time - self.entry_time) >= timedelta(days=self.max_hold_days):
+            self.log(f"Max hold time reached ({self.max_hold_days} days). Forcing exit.")
+            self.close_all_positions()
+            self.entry_time = None
+            self.position_direction = None
+            return
+
+        # 核心逻辑：相变入场/退出
+        if position_volume == 0:
+            self.evaluate_entry_signal(open_price, trend_strength, dominant_freq)
+        else:
+            self.manage_open_position(position_volume, trend_strength, dominant_freq)
+
+    def calculate_trend_strength(self, prices: np.array) -> (float, float):
+        """
+        【显式傅里叶】计算低频能量占比 (完全参数化)
+
+        步骤:
+        1. 价格归一化 (窗口内)
+        2. 短时傅里叶变换 (STFT) - 采样率=bars_per_day
+        3. 动态计算频段边界 (基于bars_per_day)
+        4. 趋势强度 = 低频能量 / (低频+高频能量)
+        """
+        # 1. 验证数据长度
+        if len(prices) < self.spectral_window:
+            return 0.0, 0.0
+
+        # 2. 价格归一化 (仅使用窗口内数据)
+        window_data = prices[-self.spectral_window:]
+        normalized = (window_data - np.mean(window_data)) / (np.std(window_data) + 1e-8)
+
+        # 3. STFT (采样率=bars_per_day)
+        try:
+            # fs: 每天的样本数 (bars_per_day)
+            f, t, Zxx = stft(
+                normalized,
+                fs=self.bars_per_day,  # 关键: 适配市场结构
+                nperseg=self.spectral_window,
+                noverlap=max(0, self.spectral_window // 2),
+                boundary=None,
+                padded=False
+            )
+        except Exception as e:
+            self.log(f"STFT calculation error: {str(e)}")
+            return 0.0, 0.0
+
+        # 4. 过滤无效频率 (STFT返回频率范围: 0 到 fs/2)
+        valid_mask = (f >= 0) & (f <= self.bars_per_day / 2)
+        f = f[valid_mask]
+        Zxx = Zxx[valid_mask, :]
+
+        if Zxx.size == 0 or Zxx.shape[1] == 0:
+            return 0.0, 0.0
+
+        # 5. 计算最新时间点的能量
+        current_energy = np.abs(Zxx[:, -1]) ** 2
+
+        # 6. 动态频段定义 (cycles/day)
+        # 低频: 周期 > low_freq_days → 频率 < 1/low_freq_days
+        low_freq_mask = f < self.low_freq_bound
+        # 高频: 周期 < high_freq_days → 频率 > 1/high_freq_days
+        high_freq_mask = f > self.high_freq_bound
+
+        # 7. 能量计算
+        low_energy = np.sum(current_energy[low_freq_mask]) if np.any(low_freq_mask) else 0.0
+        high_energy = np.sum(current_energy[high_freq_mask]) if np.any(high_freq_mask) else 0.0
+        total_energy = low_energy + high_energy + 1e-8  # 防除零
+
+        # 8. 趋势强度 = 低频能量占比
+        trend_strength = low_energy / total_energy
+
+        # 9. 计算主导趋势周期 (天)
+        dominant_freq = 0.0
+        if np.any(low_freq_mask) and low_energy > 0:
+            # 找到低频段最大能量对应的频率
+            low_energies = current_energy[low_freq_mask]
+            max_idx = np.argmax(low_energies)
+            dominant_freq = 1.0 / (f[low_freq_mask][max_idx] + 1e-8)  # 转换为周期(天)
+
+        return trend_strength, dominant_freq
+
+    def evaluate_entry_signal(self, open_price: float, trend_strength: float, dominant_freq: float):
+        """评估相变入场信号"""
+        # 仅当趋势强度跨越临界点且有明确周期时入场
+        self.log(
+            f"Strength={trend_strength:.2f}")
+        if (trend_strength > self.trend_strength_threshold
+                and self.model_indicator.is_condition_met(*self.get_indicator_tuple())):
+            direction = None
+
+            indicator = self.model_indicator
+
+            # 做多信号: 价格在窗口均值上方
+            closes = np.array([b.close for b in self.get_bar_history()[-self.spectral_window:]], dtype=float)
+            if "BUY" in self.order_direction and np.mean(closes[-5:]) > np.mean(closes):
+                direction = "BUY" if indicator.is_condition_met(*self.get_indicator_tuple()) else "SELL"
+            # 做空信号: 价格在窗口均值下方
+            elif "SELL" in self.order_direction and np.mean(closes[-5:]) < np.mean(closes):
+                direction = "SELL" if indicator.is_condition_met(*self.get_indicator_tuple()) else "BUY"
+
+            if direction:
+                self.send_limit_order(direction, open_price, self.trade_volume, "OPEN")
+                self.entry_time = self.get_current_time()
+                self.position_direction = "LONG" if direction == "BUY" else "SHORT"
+
+    def manage_open_position(self, volume: int, trend_strength: float, dominant_freq: float):
+        """管理持仓：仅当相变逆转时退出"""
+        # 相变逆转条件: 趋势强度 < 退出阈值
+        if trend_strength < self.exit_threshold:
+            direction = "CLOSE_LONG" if volume > 0 else "CLOSE_SHORT"
+            self.log(f"Phase Transition Exit: {direction} | Strength={trend_strength:.2f} < {self.exit_threshold}")
+            self.close_position(direction, abs(volume))
+            self.entry_time = None
+            self.position_direction = None
+
+    # --- 辅助函数区 ---
+    def close_all_positions(self):
+        """强制平仓所有头寸"""
+        positions = self.get_current_positions()
+        if self.symbol in positions and positions[self.symbol] != 0:
+            direction = "CLOSE_LONG" if positions[self.symbol] > 0 else "CLOSE_SHORT"
+            self.close_position(direction, abs(positions[self.symbol]))
+            self.log(f"Forced exit of {abs(positions[self.symbol])} contracts")
+
+    def close_position(self, direction: str, volume: int):
+        self.send_market_order(direction, volume, offset="CLOSE")
+
+    def send_market_order(self, direction: str, volume: int, offset: str):
+        order_id = f"{self.symbol}_{direction}_MARKET_{self.order_id_counter}"
+        self.order_id_counter += 1
+        order = Order(
+            id=order_id,
+            symbol=self.symbol,
+            direction=direction,
+            volume=volume,
+            price_type="MARKET",
+            submitted_time=self.get_current_time(),
+            offset=offset
+        )
+        self.send_order(order)
+
+    def send_limit_order(self, direction: str, limit_price: float, volume: int, offset: str):
+        order_id = f"{self.symbol}_{direction}_MARKET_{self.order_id_counter}"
+        self.order_id_counter += 1
+        order = Order(
+            id=order_id,
+            symbol=self.symbol,
+            direction=direction,
+            volume=volume,
+            price_type="LIMIT",
+            submitted_time=self.get_current_time(),
+            offset=offset,
+            limit_price=limit_price
+        )
+        self.send_order(order)
+
+    def on_init(self):
+        super().on_init()
+        self.cancel_all_pending_orders(self.main_symbol)
+        self.log("Strategy initialized. Waiting for phase transition signals...")
+
+    def on_rollover(self, old_symbol: str, new_symbol: str):
+        super().on_rollover(old_symbol, new_symbol)
+        self.log(f"Rollover from {old_symbol} to {new_symbol}. Resetting position state.")
+        self.entry_time = None
+        self.position_direction = None
+        self.last_trend_strength = 0.0

futures_trading_strategies/SA/Spectral/SpectralTrendStrategy2.py

@@ -0,0 +1,377 @@
+import numpy as np
+from scipy.signal import stft
+from datetime import datetime, timedelta
+from typing import Optional, Any, List, Dict
+
+from src.core_data import Bar, Order
+from src.indicators.base_indicators import Indicator
+from src.indicators.indicators import Empty
+from src.strategies.base_strategy import Strategy
+
+
+# =============================================================================
+#  策略实现 (VolatilityAdaptiveSpectralStrategy)
+# =============================================================================
+
+class SpectralTrendStrategy(Strategy):
+    """
+    波动率自适应频域趋势策略
+
+    核心哲学:
+    1. 显式傅里叶变换: 分离低频(趋势)、高频(噪音)能量
+    2. 波动率条件信号: 根据波动率环境动态调整交易方向
+       - 低波动环境: 趋势策略 (高趋势强度 → 延续)
+       - 高波动环境: 反转策略 (高趋势强度 → 反转)
+    3. 无硬编码参数: 所有阈值通过配置参数设定
+    4. 严格无未来函数: 所有计算使用历史数据
+
+    参数说明:
+    - bars_per_day: 市场每日K线数量
+    - volatility_lookback: 波动率计算窗口(天)
+    - low_vol_threshold: 低波动环境阈值(0-1)
+    - high_vol_threshold: 高波动环境阈值(0-1)
+    """
+
+    def __init__(
+            self,
+            context: Any,
+            main_symbol: str,
+            enable_log: bool,
+            trade_volume: int,
+            # --- 【市场结构参数】 ---
+            bars_per_day: int = 23,  # 适配23根/天的市场
+            # --- 【频域核心参数】 ---
+            spectral_window_days: float = 2.0,  # STFT窗口大小(天)
+            low_freq_days: float = 2.0,  # 低频下限(天)
+            high_freq_days: float = 1.0,  # 高频上限(天)
+            trend_strength_threshold: float = 0.8,  # 趋势强度阈值
+            exit_threshold: float = 0.5,  # 退出阈值
+            # --- 【波动率参数】 ---
+            volatility_lookback_days: float = 5.0,  # 波动率计算窗口(天)
+            low_vol_threshold: float = 0.3,  # 低波动环境阈值(0-1)
+            high_vol_threshold: float = 0.7,  # 高波动环境阈值(0-1)
+            # --- 【持仓管理】 ---
+            max_hold_days: int = 10,  # 最大持仓天数
+            # --- 其他 ---
+            order_direction: Optional[List[str]] = None,
+            indicators: Optional[List[Indicator]] = None,
+    ):
+        super().__init__(context, main_symbol, enable_log)
+        if order_direction is None:
+            order_direction = ['BUY', 'SELL']
+        if indicators is None:
+            indicators = [Empty(), Empty()]  # 保持兼容性
+
+        # --- 参数赋值 (完全参数化) ---
+        self.trade_volume = trade_volume
+        self.bars_per_day = bars_per_day
+        self.spectral_window_days = spectral_window_days
+        self.low_freq_days = low_freq_days
+        self.high_freq_days = high_freq_days
+        self.trend_strength_threshold = trend_strength_threshold
+        self.exit_threshold = exit_threshold
+        self.volatility_lookback_days = volatility_lookback_days
+        self.low_vol_threshold = low_vol_threshold
+        self.high_vol_threshold = high_vol_threshold
+        self.max_hold_days = max_hold_days
+        self.order_direction = order_direction
+
+        # --- 动态计算参数 ---
+        self.spectral_window = int(self.spectral_window_days * self.bars_per_day)
+        self.spectral_window = self.spectral_window if self.spectral_window % 2 == 0 else self.spectral_window + 1
+        self.volatility_window = int(self.volatility_lookback_days * self.bars_per_day)
+
+        # 频率边界 (cycles/day)
+        self.low_freq_bound = 1.0 / self.low_freq_days if self.low_freq_days > 0 else float('inf')
+        self.high_freq_bound = 1.0 / self.high_freq_days if self.high_freq_days > 0 else 0.0
+
+        # --- 内部状态变量 ---
+        self.main_symbol = main_symbol
+        self.order_id_counter = 0
+        self.indicators = indicators
+        self.entry_time = None  # 入场时间
+        self.position_direction = None  # 'LONG' or 'SHORT'
+        self.last_trend_strength = 0.0
+        self.last_dominant_freq = 0.0  # 主导周期(天)
+        self.last_volatility = 0.0  # 标准化波动率(0-1)
+        self.volatility_history = []  # 存储历史波动率
+
+        self.log(f"VolatilityAdaptiveSpectralStrategy Initialized (bars/day={bars_per_day}, "
+                 f"window={self.spectral_window} bars, vol_window={self.volatility_window} bars)")
+
+    def on_open_bar(self, open_price: float, symbol: str):
+        """每根K线开盘时被调用"""
+        self.symbol = symbol
+        bar_history = self.get_bar_history()
+        current_time = self.get_current_time()
+
+        # 需要足够的数据 (最大窗口 + 缓冲)
+        min_required = max(self.spectral_window, self.volatility_window) + 10
+        if len(bar_history) < min_required:
+            if self.enable_log and len(bar_history) % 50 == 0:
+                self.log(f"Waiting for {len(bar_history)}/{min_required} bars")
+            return
+
+        position_volume = self.get_current_positions().get(self.symbol, 0)
+
+        # 获取必要历史价格 (仅取所需部分)
+        recent_bars = bar_history[-(max(self.spectral_window, self.volatility_window) + 5):]
+        closes = np.array([b.close for b in recent_bars], dtype=np.float32)
+        highs = np.array([b.high for b in recent_bars], dtype=np.float32)
+        lows = np.array([b.low for b in recent_bars], dtype=np.float32)
+
+        # 【核心】计算频域趋势强度 (显式傅里叶)
+        trend_strength, dominant_freq = self.calculate_trend_strength(closes)
+        self.last_trend_strength = trend_strength
+        self.last_dominant_freq = dominant_freq
+
+        # 【核心】计算标准化波动率 (0-1范围)
+        volatility = self.calculate_normalized_volatility(highs, lows, closes)
+        self.last_volatility = volatility
+
+        # 检查最大持仓时间 (防止极端事件)
+        if self.entry_time and (current_time - self.entry_time) >= timedelta(days=self.max_hold_days):
+            self.log(f"Max hold time reached ({self.max_hold_days} days). Forcing exit.")
+            self.close_all_positions()
+            self.entry_time = None
+            self.position_direction = None
+            return
+
+        # 核心逻辑：相变入场/退出
+        if position_volume == 0:
+            self.evaluate_entry_signal(open_price, trend_strength, dominant_freq, volatility, recent_bars)
+        else:
+            self.manage_open_position(position_volume, trend_strength, volatility)
+
+    def calculate_trend_strength(self, closes: np.array) -> (float, float):
+        """
+        【显式傅里叶】计算低频能量占比 (完全参数化)
+        """
+        if len(closes) < self.spectral_window:
+            return 0.0, 0.0
+
+        # 仅使用窗口内数据
+        window_data = closes[-self.spectral_window:]
+        window_mean = np.mean(window_data)
+        window_std = np.std(window_data)
+        if window_std < 1e-8:
+            return 0.0, 0.0
+
+        normalized = (window_data - window_mean) / window_std
+
+        try:
+            f, t, Zxx = stft(
+                normalized,
+                fs=self.bars_per_day,
+                nperseg=self.spectral_window,
+                noverlap=max(0, self.spectral_window // 2),
+                boundary=None,
+                padded=False
+            )
+        except Exception as e:
+            self.log(f"STFT calculation error: {str(e)}")
+            return 0.0, 0.0
+
+        # 过滤无效频率
+        max_freq = self.bars_per_day / 2
+        valid_mask = (f >= 0) & (f <= max_freq)
+        if not np.any(valid_mask):
+            return 0.0, 0.0
+
+        f = f[valid_mask]
+        Zxx = Zxx[valid_mask, :]
+
+        if Zxx.size == 0 or Zxx.shape[1] == 0:
+            return 0.0, 0.0
+
+        # 计算最新时间点的能量
+        current_energy = np.abs(Zxx[:, -1]) ** 2
+
+        # 动态频段定义
+        low_freq_mask = f < self.low_freq_bound
+        high_freq_mask = f > self.high_freq_bound
+
+        # 能量计算
+        low_energy = np.sum(current_energy[low_freq_mask]) if np.any(low_freq_mask) else 0.0
+        high_energy = np.sum(current_energy[high_freq_mask]) if np.any(high_freq_mask) else 0.0
+        total_energy = low_energy + high_energy + 1e-8
+
+        # 趋势强度 = 低频能量占比
+        trend_strength = low_energy / total_energy
+
+        # 计算主导趋势周期 (天)
+        dominant_freq = 0.0
+        if np.any(low_freq_mask) and low_energy > 0:
+            low_energies = current_energy[low_freq_mask]
+            max_idx = np.argmax(low_energies)
+            dominant_freq = 1.0 / (f[low_freq_mask][max_idx] + 1e-8)
+
+        return float(trend_strength), float(dominant_freq)
+
+    def calculate_normalized_volatility(self, highs: np.array, lows: np.array, closes: np.array) -> float:
+        """
+        计算标准化波动率 (0-1范围)
+
+        步骤:
+        1. 计算ATR (真实波幅)
+        2. 标准化ATR (除以价格)
+        3. 归一化到0-1范围 (基于历史波动率)
+        """
+        if len(closes) < self.volatility_window + 1:
+            return 0.5  # 默认中性值
+
+        # 1. 计算真实波幅 (TR)
+        tr1 = highs[-self.volatility_window - 1:] - lows[-self.volatility_window - 1:]
+        tr2 = np.abs(highs[-self.volatility_window - 1:] - np.roll(closes, 1)[-self.volatility_window - 1:])
+        tr3 = np.abs(lows[-self.volatility_window - 1:] - np.roll(closes, 1)[-self.volatility_window - 1:])
+        tr = np.maximum(tr1, np.maximum(tr2, tr3))
+
+        # 2. 计算ATR
+        atr = np.mean(tr[-self.volatility_window:])
+
+        # 3. 标准化ATR (除以当前价格)
+        current_price = closes[-1]
+        normalized_atr = atr / current_price if current_price > 0 else 0.0
+
+        # 4. 归一化到0-1范围 (基于历史波动率)
+        self.volatility_history.append(normalized_atr)
+        if len(self.volatility_history) > 1000:  # 保留1000个历史值
+            self.volatility_history.pop(0)
+
+        if len(self.volatility_history) < 50:  # 需要足够历史数据
+            return 0.5
+
+        # 使用历史50-95百分位进行归一化
+        low_percentile = np.percentile(self.volatility_history, 50)
+        high_percentile = np.percentile(self.volatility_history, 95)
+
+        if high_percentile - low_percentile < 1e-8:
+            return 0.5
+
+        # 归一化到0-1范围
+        normalized_vol = (normalized_atr - low_percentile) / (high_percentile - low_percentile + 1e-8)
+        normalized_vol = max(0.0, min(1.0, normalized_vol))  # 限制在0-1范围内
+
+        return normalized_vol
+
+    def evaluate_entry_signal(self, open_price: float, trend_strength: float, dominant_freq: float,
+                              volatility: float, recent_bars: List[Bar]):
+        """评估波动率条件入场信号"""
+        # 仅当趋势强度跨越临界点且有明确周期时入场
+        if trend_strength > self.trend_strength_threshold:
+            direction = None
+            trade_type = ""
+
+            # 计算价格位置 (短期vs长期均值)
+            window_closes = np.array([b.close for b in recent_bars[-self.spectral_window:]], dtype=np.float32)
+            short_avg = np.mean(window_closes[-5:])
+            long_avg = np.mean(window_closes)
+
+            # 添加统计显著性过滤
+            if abs(short_avg - long_avg) < 0.0005 * long_avg:
+                return
+
+            # 【核心】根据波动率环境决定交易逻辑
+            if volatility < self.low_vol_threshold:
+                # 低波动环境: 趋势策略
+                trade_type = "TREND"
+                if "BUY" in self.order_direction and short_avg > long_avg:
+                    direction = "BUY"
+                elif "SELL" in self.order_direction and short_avg < long_avg:
+                    direction = "SELL"
+
+            elif volatility > self.high_vol_threshold:
+                # 高波动环境: 反转策略
+                trade_type = "REVERSAL"
+                if "BUY" in self.order_direction and short_avg < long_avg:
+                    direction = "BUY"  # 价格低于均值，预期回归
+                elif "SELL" in self.order_direction and short_avg > long_avg:
+                    direction = "SELL"  # 价格高于均值，预期反转
+
+            else:
+                # 中波动环境: 谨慎策略 (需要更强信号)
+                trade_type = "CAUTIOUS"
+                if trend_strength > 0.9 and "BUY" in self.order_direction and short_avg > long_avg:
+                    direction = "BUY"
+                elif trend_strength > 0.9 and "SELL" in self.order_direction and short_avg < long_avg:
+                    direction = "SELL"
+
+            if direction:
+                self.log(
+                    f"Entry: {direction} | Type={trade_type} | Strength={trend_strength:.2f} | "
+                    f"Volatility={volatility:.2f} | ShortAvg={short_avg:.4f} vs LongAvg={long_avg:.4f}"
+                )
+                self.send_market_order(direction, self.trade_volume, "OPEN")
+                self.entry_time = self.get_current_time()
+                self.position_direction = "LONG" if direction == "BUY" else "SHORT"
+
+    def manage_open_position(self, volume: int, trend_strength: float, volatility: float):
+        """管理持仓：波动率条件退出"""
+        # 退出条件1: 趋势强度 < 退出阈值
+        if trend_strength < self.exit_threshold:
+            direction = "CLOSE_LONG" if volume > 0 else "CLOSE_SHORT"
+            self.log(f"Exit (Strength): {direction} | Strength={trend_strength:.2f} < {self.exit_threshold}")
+            self.close_position(direction, abs(volume))
+            self.entry_time = None
+            self.position_direction = None
+            return
+
+        # 退出条件2: 波动率环境突变 (从低波动变为高波动，或反之)
+        if self.position_direction == "LONG" and volatility > self.high_vol_threshold * 1.2:
+            # 多头仓位在波动率突增时退出
+            self.log(
+                f"Exit (Volatility Spike): CLOSE_LONG | Volatility={volatility:.2f} > {self.high_vol_threshold * 1.2:.2f}")
+            self.close_position("CLOSE_LONG", abs(volume))
+            self.entry_time = None
+            self.position_direction = None
+        elif self.position_direction == "SHORT" and volatility > self.high_vol_threshold * 1.2:
+            # 空头仓位在波动率突增时退出
+            self.log(
+                f"Exit (Volatility Spike): CLOSE_SHORT | Volatility={volatility:.2f} > {self.high_vol_threshold * 1.2:.2f}")
+            self.close_position("CLOSE_SHORT", abs(volume))
+            self.entry_time = None
+            self.position_direction = None
+
+    # --- 辅助函数区 ---
+    def close_all_positions(self):
+        """强制平仓所有头寸"""
+        positions = self.get_current_positions()
+        if not positions or self.symbol not in positions or positions[self.symbol] == 0:
+            return
+
+        direction = "CLOSE_LONG" if positions[self.symbol] > 0 else "CLOSE_SHORT"
+        self.close_position(direction, abs(positions[self.symbol]))
+        if self.enable_log:
+            self.log(f"Closed {abs(positions[self.symbol])} contracts")
+
+    def close_position(self, direction: str, volume: int):
+        self.send_market_order(direction, volume, offset="CLOSE")
+
+    def send_market_order(self, direction: str, volume: int, offset: str):
+        order_id = f"{self.symbol}_{direction[-6:]}_{self.order_id_counter}"
+        self.order_id_counter += 1
+        order = Order(
+            id=order_id,
+            symbol=self.symbol,
+            direction=direction,
+            volume=volume,
+            price_type="MARKET",
+            submitted_time=self.get_current_time(),
+            offset=offset
+        )
+        self.send_order(order)
+
+    def on_init(self):
+        super().on_init()
+        self.cancel_all_pending_orders(self.main_symbol)
+        if self.enable_log:
+            self.log("Strategy initialized. Waiting for volatility-adaptive signals...")
+
+    def on_rollover(self, old_symbol: str, new_symbol: str):
+        super().on_rollover(old_symbol, new_symbol)
+        if self.enable_log:
+            self.log(f"Rollover: {old_symbol} -> {new_symbol}. Resetting state.")
+        self.entry_time = None
+        self.position_direction = None
+        self.last_trend_strength = 0.0
+        self.volatility_history = []  # 重置波动率历史

futures_trading_strategies/SA/Spectral/utils.py

@@ -0,0 +1,103 @@
+import multiprocessing
+from typing import Tuple, Dict, Any, Optional
+
+from src.analysis.result_analyzer import ResultAnalyzer
+from src.backtest_engine import BacktestEngine
+from src.data_manager import DataManager
+
+
+# --- 单个回测任务函数 ---
+# 这个函数将在每个独立的进程中运行，因此它必须是自包含的
+def run_single_backtest(
+        combination: Tuple[float, float],  # 传入当前参数组合
+        common_config: Dict[str, Any]  # 传入公共配置 (如数据路径, 初始资金等)
+) -> Optional[Dict[str, Any]]:
+    """
+    运行单个参数组合的回测任务。
+    此函数将在一个独立的进程中执行。
+    """
+    p1_value, p2_value = combination
+
+    # 从 common_config 中获取必要的配置
+    symbol = common_config['symbol']
+    data_path = common_config['data_path']
+    initial_capital = common_config['initial_capital']
+    slippage_rate = common_config['slippage_rate']
+    commission_rate = common_config['commission_rate']
+    start_time = common_config['start_time']
+    end_time = common_config['end_time']
+    roll_over_mode = common_config['roll_over_mode']
+    # bar_duration_seconds = common_config['bar_duration_seconds'] # 如果DataManager需要，可以再传
+    param1_name = common_config['param1_name']
+    param2_name = common_config['param2_name']
+
+    # 每个进程内部独立初始化 DataManager 和 BacktestEngine
+    # 确保每个进程有自己的数据副本和模拟状态
+    data_manager = DataManager(
+        file_path=data_path,
+        symbol=symbol,
+        # bar_duration_seconds=bar_duration_seconds, # 如果DataManager需要，根据数据文件路径推断或者额外参数传入
+        # start_date=start_time.date(), # DataManager 现在通过 file_path 和 symbol 处理数据
+        # end_date=end_time.date(),
+    )
+    # data_manager.load_data() # DataManager 内部加载数据
+
+    strategy_parameters = {
+        'main_symbol': common_config['main_symbol'],
+        'trade_volume': 1,
+        param1_name: p1_value,  # 15分钟扫荡K线下影线占其总范围的最小比例。
+        param2_name: p2_value,  # 15分钟限价单的入场点位于扫荡K线低点到收盘价的斐波那契回撤比例。
+        'order_direction': common_config['order_direction'],
+        'enable_log': False,  # 建议在调试和测试时开启日志
+    }
+    # 打印当前进程正在处理的组合信息
+    # 注意：多进程打印会交错显示
+    print(f"--- 正在运行组合: {strategy_parameters} (PID: {multiprocessing.current_process().pid}) ---")
+
+    try:
+        # 初始化回测引擎
+        engine = BacktestEngine(
+            data_manager=data_manager,
+            strategy_class=common_config['strategy'],
+            strategy_params=strategy_parameters,
+            initial_capital=initial_capital,
+            slippage_rate=slippage_rate,
+            commission_rate=commission_rate,
+            roll_over_mode=True,  # 保持换月模式
+            start_time=common_config['start_time'],
+            end_time=common_config['end_time']
+        )
+        # 运行回测，传入时间范围
+        engine.run_backtest()
+
+        # 获取回测结果并分析
+        results = engine.get_backtest_results()
+        portfolio_snapshots = results["portfolio_snapshots"]
+        trade_history = results["trade_history"]
+        bars = results["all_bars"]
+        initial_capital_result = results["initial_capital"]
+
+        if portfolio_snapshots:
+            analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result)
+
+            # analyzer.generate_report()
+            # analyzer.plot_performance()
+            metrics = analyzer.calculate_all_metrics()
+
+            # 将当前组合的参数和性能指标存储起来
+            result_entry = {**strategy_parameters, **metrics}
+            return result_entry
+        else:
+            print(
+                f"  组合 {strategy_parameters} 没有生成投资组合快照，无法进行结果分析。(PID: {multiprocessing.current_process().pid})")
+            # 返回一个包含参数和默认0值的结果，以便追踪失败组合
+            return {**strategy_parameters, "total_return": 0.0, "annualized_return": 0.0, "sharpe_ratio": 0.0,
+                    "max_drawdown": 0.0, "error": "No portfolio snapshots"}
+    except Exception as e:
+        import traceback
+        error_trace = traceback.format_exc()
+        print(
+            f"  组合 {strategy_parameters} 运行失败: {e}\n{error_trace} (PID: {multiprocessing.current_process().pid})")
+        # 返回错误信息，以便后续处理
+        return {**strategy_parameters, "error": str(e), "traceback": error_trace}
+