255 lines
10 KiB
Python
255 lines
10 KiB
Python
import numpy as np
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import talib
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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
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from src.strategies.base_strategy import Strategy
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class SpectralTrendStrategy(Strategy):
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"""
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频域能量相变策略 - 极简回归版
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核心哲学:
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1. 频域 (STFT): 负责"判势" —— 现在的市场是震荡(噪音主导)还是趋势(低频主导)？
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2. 时域 (Regression): 负责"定向" —— 这个低频趋势是向上的还是向下的？
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这种组合避免了频域相位计算的复杂性和不稳定性，回归了量化的本质。
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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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# --- 市场参数 ---
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bars_per_day: int = 23,
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# --- 策略参数 ---
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spectral_window_days: float = 2.0,
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low_freq_days: float = 2.0,
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high_freq_days: float = 1.0,
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trend_strength_threshold: float = 0.2, # 强度阈值
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exit_threshold: float = 0.1, # 退出阈值
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slope_threshold: float = 0.0, # 斜率阈值 (0.05表示每根K线移动0.05个标准差)
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max_hold_days: int = 10,
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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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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.slope_threshold = slope_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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self.model_indicator = model_indicator or Empty()
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self.indicators = indicators or Empty()
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self.reverse = reverse
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# 计算窗口大小
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self.spectral_window = int(self.spectral_window_days * self.bars_per_day)
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# 确保偶数 (STFT偏好)
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if self.spectral_window % 2 != 0:
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self.spectral_window += 1
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# 频率边界
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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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self.order_id_counter = 0
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self.entry_time = None
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self.position_direction = None
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self.log(f"SpectralTrendStrategy (Regression) Init. Window: {self.spectral_window} bars")
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def on_open_bar(self, open_price: float, symbol: str):
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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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if len(bar_history) < self.spectral_window + 5:
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return
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# 强制平仓检查
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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.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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# 获取数据并归一化
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closes = np.array([b.close for b in bar_history[-self.spectral_window:]], dtype=float)
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# 计算核心指标
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trend_strength, trend_slope = self.calculate_market_state(closes)
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position_volume = self.get_current_positions().get(self.symbol, 0)
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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, trend_slope)
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else:
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self.manage_open_position(position_volume, trend_strength, trend_slope)
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def calculate_market_state(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:]
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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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# --- 3. 时域分析 (Regression) - 只负责"方向" ---
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# 使用最小二乘法拟合一条直线 y = kx + b
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# x 是时间序列 [0, 1, 2...], y 是归一化价格
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# slope 代表：每经过一根K线，价格变化多少个标准差
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x = np.arange(len(normalized))
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slope, intercept = np.polyfit(x, normalized, 1)
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return trend_strength, slope
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def evaluate_entry_signal(self, open_price: float, trend_strength: float, trend_slope: float):
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"""
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入场逻辑：
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当频域告诉我们"有趋势"(Strength高)，且时域告诉我们"方向明确"(Slope陡峭)时入场。
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"""
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# 1. 滤除噪音震荡 (STFT关卡)
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if trend_strength > self.trend_strength_threshold:
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direction = None
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# 2. 确认方向 (回归关卡)
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# slope > 0.05 意味着趋势向上且有一定力度
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if "BUY" in self.order_direction and trend_slope > self.slope_threshold:
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direction = "BUY"
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# slope < -0.05 意味着趋势向下且有一定力度
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elif "SELL" in self.order_direction and trend_slope < -self.slope_threshold:
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direction = "SELL"
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if direction:
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# 辅助指标过滤
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if not self.indicators.is_condition_met(*self.get_indicator_tuple()):
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return
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# 反向逻辑
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direction = direction
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if not self.model_indicator.is_condition_met(*self.get_indicator_tuple()):
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direction = "SELL" if direction == "BUY" else "BUY"
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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"Signal: {direction} | Strength={trend_strength:.2f} | Slope={trend_slope:.4f}")
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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, trend_slope: float):
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"""
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离场逻辑：
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仅依赖频域能量。只要低频能量依然主导，说明趋势（无论方向）未被破坏。
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一旦能量降到 exit_threshold 以下，说明市场进入混乱/震荡，离场观望。
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"""
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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"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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# --- 交易辅助 ---
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def close_all_positions(self):
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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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dir = "CLOSE_LONG" if positions[self.symbol] > 0 else "CLOSE_SHORT"
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self.close_position(dir, abs(positions[self.symbol]))
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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}_MKT_{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, symbol=self.symbol, direction=direction, volume=volume,
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price_type="MARKET", submitted_time=self.get_current_time(), 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}_LMT_{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, symbol=self.symbol, direction=direction, volume=volume,
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price_type="LIMIT", submitted_time=self.get_current_time(), offset=offset,
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limit_price=limit_price
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)
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self.send_order(order) |