1、卡尔曼策略

src/strategies/TrendlineBreakoutStrategy/TrendlineHawkesStrategyFast.py

@@ -1,13 +1,11 @@
 import numpy as np
 import pandas as pd
-from typing import Optional, Dict, Any, List, Union
+from typing import Optional, Dict, Any, List
 
 # 假设这些是你项目中的模块
 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
-from src.algo.TrendLine import calculate_latest_trendline_values
+from src.algo.TrendLine import calculate_latest_trendline_values, calculate_latest_trendline_values_v2
 
 
 class TrendlineHawkesStrategy(Strategy):
@@ -31,7 +29,6 @@ class TrendlineHawkesStrategy(Strategy):
             hawkes_entry_percent: float = 0.95,
             hawkes_exit_percent: float = 0.50,
             enable_log: bool = True,
-            indicators: Union[Indicator, List[Indicator]] = None,
     ):
         super().__init__(context, main_symbol, enable_log)
         # ... 参数赋值与V3完全相同 ...
@@ -45,9 +42,6 @@ class TrendlineHawkesStrategy(Strategy):
         self.hawkes_entry_percent = hawkes_entry_percent
         self.hawkes_exit_percent = hawkes_exit_percent
         self.pos_meta: Dict[str, Dict[str, Any]] = {}
-        if indicators is None:
-            indicators = [Empty(), Empty()]
-        self.indicators = indicators
 
         # --- 【核心修改】状态缓存重构 ---
         # 只缓存上一个时间点的霍克斯强度值 (未缩放)
@@ -146,13 +140,13 @@ class TrendlineHawkesStrategy(Strategy):
         if pos == 0:
             close_prices = np.array([b.close for b in bar_history])
             prices_for_trendline = close_prices[-self.trendline_n - 1:-1]
-            trend_upper, trend_lower = calculate_latest_trendline_values(prices_for_trendline)
+            trend_upper, trend_lower = calculate_latest_trendline_values_v2(prices_for_trendline)
 
             if trend_upper is not None and trend_lower is not None:
                 prev_close = bar_history[-2].close
                 last_close = bar_history[-1].close
-                upper_break_event = last_close > trend_upper and prev_close < trend_upper and self.indicators[0].is_condition_met(*self.get_indicator_tuple())
-                lower_break_event = last_close < trend_lower and prev_close > trend_lower and self.indicators[1].is_condition_met(*self.get_indicator_tuple())
+                upper_break_event = last_close > trend_upper and prev_close < trend_upper
+                lower_break_event = last_close < trend_lower and prev_close > trend_lower
                 hawkes_confirmation = latest_hawkes_value > latest_hawkes_upper
 
                 if hawkes_confirmation and (upper_break_event or lower_break_event):