335 lines
13 KiB
Python
335 lines
13 KiB
Python
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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 timedelta
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from typing import Optional, Any, List
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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 - 双因子自适应版 (Dual-Factor Adaptive)
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优化逻辑：
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不再通过静态参数 reverse 控制方向，而是由两个 Indicator 动态决策：
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1. 计算 STFT 基础方向 (Base Direction)。
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2. 检查 indicator_primary：若满足，则采用 Base Direction (顺势/正向)。
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3. 若不满足，检查 indicator_secondary：若满足，则采用 Reverse Direction (逆势/反转)。
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4. 若都不满足，保持空仓。
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状态追踪：
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self.entry_signal_source 会记录当前持仓是 'PRIMARY' 还是 'SECONDARY'。
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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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# --- STFT 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,
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exit_threshold: float = 0.1,
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slope_threshold: float = 0.0,
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# --- 关键风控参数 (Chandelier Exit) ---
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stop_loss_atr_multiplier: float = 5.0,
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stop_loss_atr_period: int = 14,
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# --- 信号控制指标 (核心优化) ---
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indicator_primary: Indicator = None, # 满足此条件 -> 正向开仓 (reverse=False)
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indicator_secondary: Indicator = None, # 满足此条件 -> 反向开仓 (reverse=True)
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model_indicator: Indicator = None, # 可选：额外的AI模型过滤器
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# --- 其他 ---
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order_direction: Optional[List[str]] = None,
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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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# 信号参数
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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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# 风控参数
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self.sl_atr_multiplier = stop_loss_atr_multiplier
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self.sl_atr_period = stop_loss_atr_period
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self.order_direction = order_direction
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# 初始指标容器 (默认为Empty，即永远返回True或False，视Empty具体实现而定，建议传入具体指标)
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self.indicator_primary = indicator_primary or Empty()
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self.indicator_secondary = indicator_secondary or Empty()
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self.model_indicator = model_indicator or Empty()
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# 计算 STFT 窗口大小
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self.spectral_window = int(self.spectral_window_days * self.bars_per_day)
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if self.spectral_window % 2 != 0:
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self.spectral_window += 1
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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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# --- 持仓状态追踪变量 ---
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self.entry_price = 0.0
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self.pos_highest = 0.0
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self.pos_lowest = 0.0
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# 新增：记录开仓信号来源 ('PRIMARY' or 'SECONDARY')
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self.entry_signal_source = None
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self.log(
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f"SpectralTrend Dual-Adaptive Strategy Initialized.\n"
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f"Window: {self.spectral_window}, ATR Stop: {self.sl_atr_multiplier}x\n"
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f"Primary Ind: {type(self.indicator_primary).__name__}, "
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f"Secondary Ind: {type(self.indicator_secondary).__name__}"
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)
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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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self.cancel_all_pending_orders(self.main_symbol)
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# 1. 数据长度检查
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required_len = max(self.spectral_window, self.sl_atr_period + 5)
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if len(bar_history) < required_len:
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return
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# 2. 计算 ATR
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atr_window = self.sl_atr_period + 10
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highs = np.array([b.high for b in bar_history[-atr_window:]], dtype=float)
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lows = np.array([b.low for b in bar_history[-atr_window:]], dtype=float)
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closes = np.array([b.close for b in bar_history[-atr_window:]], dtype=float)
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try:
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atr_values = talib.ATR(highs, lows, closes, timeperiod=self.sl_atr_period)
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current_atr = atr_values[-1]
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if np.isnan(current_atr): current_atr = 0.0
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except Exception as e:
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self.log(f"ATR Calc Error: {e}")
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current_atr = 0.0
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# 3. 计算 STFT 核心指标
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stft_closes = np.array([b.close for b in bar_history[-self.spectral_window:]], dtype=float)
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trend_strength, trend_slope = self.calculate_market_state(stft_closes)
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# 4. 交易逻辑
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position_volume = self.get_current_positions().get(self.symbol, 0)
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current_high = bar_history[-1].high
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current_low = bar_history[-1].low
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if self.trading:
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if position_volume == 0:
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# 重置所有状态
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self.pos_highest = 0.0
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self.pos_lowest = 0.0
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self.entry_price = 0.0
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self.entry_signal_source = None # 重置信号来源
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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(
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position_volume,
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trend_strength,
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open_price,
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current_atr,
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current_high,
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current_low
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)
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def calculate_market_state(self, prices: np.array) -> (float, float):
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"""
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计算频域能量占比和线性回归斜率
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"""
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if len(prices) < self.spectral_window:
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return 0.0, 0.0
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window_data = prices[-self.spectral_window:]
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mean_val = np.mean(window_data)
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std_val = np.std(window_data)
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if std_val == 0: std_val = 1.0
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normalized = (window_data - mean_val) / (std_val + 1e-8)
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try:
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f, t, Zxx = stft(
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normalized,
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fs=self.bars_per_day,
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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:
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return 0.0, 0.0
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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: return 0.0, 0.0
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current_energy = np.abs(Zxx[:, -1]) ** 2
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low_mask = f < self.low_freq_bound
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high_mask = f > self.high_freq_bound
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low_energy = np.sum(current_energy[low_mask]) if np.any(low_mask) else 0.0
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high_energy = np.sum(current_energy[high_mask]) if np.any(high_mask) else 0.0
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total_energy = low_energy + high_energy + 1e-8
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trend_strength = low_energy / total_energy
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x = np.arange(len(normalized))
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slope, _ = 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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入场逻辑优化：双因子控制
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"""
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# 1. 基础能量阈值检查
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if trend_strength <= self.trend_strength_threshold:
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return
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# 2. 确定 STFT 原始方向 (Raw Direction)
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raw_direction = None
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if trend_slope > self.slope_threshold:
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raw_direction = "BUY"
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elif trend_slope < -self.slope_threshold:
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raw_direction = "SELL"
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if not raw_direction:
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return
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# 3. 双指标分支逻辑 (Dual-Indicator Branching)
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# 获取指标计算所需的参数 (通常是 bar_history 等，依赖基类实现)
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indicator_args = self.get_indicator_tuple()
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final_direction = None
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source_tag = None
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# --- 分支 1: Primary Indicator (优先) ---
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# 如果满足主条件 -> 使用原始方向 (Equivalent to reverse=False)
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if self.indicator_primary.is_condition_met(*indicator_args):
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final_direction = raw_direction
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source_tag = "PRIMARY"
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# --- 分支 2: Secondary Indicator (备选/Else) ---
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# 如果不满足主条件，但满足备选条件 -> 使用反转方向 (Equivalent to reverse=True)
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elif self.indicator_secondary.is_condition_met(*indicator_args):
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final_direction = "SELL" if raw_direction == "BUY" else "BUY"
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source_tag = "SECONDARY"
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# --- 分支 3: 都不满足 ---
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else:
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return # 放弃交易
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# 4. 全局模型过滤 (可选)
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if not self.model_indicator.is_condition_met(*indicator_args):
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return
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# 5. 最终方向检查
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if final_direction not in self.order_direction:
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return
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# 6. 执行开仓
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self.log(
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f"Entry Triggered [{source_tag}]: "
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f"Raw={raw_direction} -> Final={final_direction} | "
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f"Strength={trend_strength:.2f} | Slope={trend_slope:.4f}"
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)
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self.send_limit_order(final_direction, open_price, self.trade_volume, "OPEN")
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# 更新状态
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self.entry_price = open_price
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self.pos_highest = open_price
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self.pos_lowest = open_price
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self.entry_signal_source = source_tag # 保存是由哪一个条件控制的
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def manage_open_position(self, volume: int, trend_strength: float, current_price: float,
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current_atr: float, high_price: float, low_price: float):
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"""
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离场逻辑 (保持不变，但日志中可以体现来源)
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"""
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exit_dir = "CLOSE_LONG" if volume > 0 else "CLOSE_SHORT"
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# 更新极值
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if volume > 0:
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if high_price > self.pos_highest or self.pos_highest == 0:
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self.pos_highest = high_price
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else:
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if (low_price < self.pos_lowest or self.pos_lowest == 0) and low_price > 0:
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self.pos_lowest = low_price
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# 1. 结构性止损 (Chandelier Exit)
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is_stop_triggered = False
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stop_line = 0.0
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if current_atr > 0:
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stop_distance = current_atr * self.sl_atr_multiplier
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if volume > 0:
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stop_line = self.pos_highest - stop_distance
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if current_price <= stop_line:
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is_stop_triggered = True
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else:
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stop_line = self.pos_lowest + stop_distance
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if current_price >= stop_line:
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is_stop_triggered = True
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if is_stop_triggered:
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self.log(
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f"STOP Loss ({self.entry_signal_source}): Price {current_price} hit Chandelier line {stop_line:.2f}")
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self.close_position(exit_dir, abs(volume))
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return
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# 2. 信号衰竭离场
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if trend_strength < self.exit_threshold:
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self.log(
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f"Exit Signal ({self.entry_signal_source}): Energy Faded {trend_strength:.2f} < {self.exit_threshold}")
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self.close_position(exit_dir, abs(volume))
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return
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# --- 交易辅助 ---
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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
|
|||
|
|
)
|
|||
|
|
self.send_order(order)
|
|||
|
|
|
|||
|
|
def send_limit_order(self, direction: str, limit_price: float, volume: int, offset: str):
|
|||
|
|
order_id = f"{self.symbol}_{direction}_LMT_{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)
|