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