SpectralStrategy更新

futures_trading_strategies/MA/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: Indicator = None,
+            model_indicator: Indicator = None,
+            reverse: bool = False,
+    ):
+        super().__init__(context, main_symbol, enable_log)
+        if order_direction is None:
+            order_direction = ['BUY', 'SELL']
+        if indicators is None:
+            indicators = 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.reverse = reverse
+
+        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[-self.spectral_window:]], 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 self.trading:
+            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 * 10:]
+        normalized = (window_data - np.mean(window_data)) / (np.std(window_data) + 1e-8)
+        normalized = normalized[-self.spectral_window:]
+
+        # 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:
+            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 and self.indicators.is_condition_met(*self.get_indicator_tuple()):
+                if self.reverse:
+                    direction = "SELL" if direction == "BUY" else "BUY"
+                self.log(f"Direction={direction}, Open Position")
+                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