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