188 lines
8.9 KiB
Python
188 lines
8.9 KiB
Python
import numpy as np
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import pandas as pd
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from typing import Optional, Dict, Any, List
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# 假设这些是你项目中的模块
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from src.core_data import Bar, Order
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from src.strategies.base_strategy import Strategy
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from src.algo.TrendLine import calculate_latest_trendline_values
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class TrendlineHawkesStrategy(Strategy):
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"""
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趋势线与霍克斯过程双重确认策略 (V4 - 终极性能版):
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- 霍克斯过程和滚动分位数都实现为高效的有状态增量计算。
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- 使用固定长度的Numpy数组作为滚动窗口，避免Pandas.rolling的开销和不一致性。
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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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# ... 参数与V3完全相同 ...
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trade_volume: int = 1,
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order_direction: Optional[List[str]] = None,
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reverse_logic: bool = False,
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trendline_n: int = 50,
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hawkes_kappa: float = 0.1,
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hawkes_lookback: int = 50,
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hawkes_entry_percent: float = 0.95,
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hawkes_exit_percent: float = 0.50,
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enable_log: bool = True,
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):
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super().__init__(context, main_symbol, enable_log)
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# ... 参数赋值与V3完全相同 ...
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self.main_symbol = main_symbol
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self.trade_volume = trade_volume
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self.order_direction = order_direction or ["BUY", "SELL"]
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self.reverse_logic = reverse_logic
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self.trendline_n = trendline_n
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self.hawkes_kappa = hawkes_kappa
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self.hawkes_lookback = hawkes_lookback
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self.hawkes_entry_percent = hawkes_entry_percent
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self.hawkes_exit_percent = hawkes_exit_percent
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self.pos_meta: Dict[str, Dict[str, Any]] = {}
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# --- 【核心修改】状态缓存重构 ---
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# 只缓存上一个时间点的霍克斯强度值 (未缩放)
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self._last_hawkes_unscaled: float = 0.0
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# 只维护一个固定长度的滚动窗口，用于计算分位数
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self._hawkes_window: np.ndarray = np.array([], dtype=np.float64)
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# 衰减因子
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self._hawkes_alpha = np.exp(-self.hawkes_kappa)
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# ... 日志与V3相同 ...
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def _initialize_state(self, initial_volumes: np.ndarray):
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"""
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仅在策略开始时调用一次，用于填充初始的滚动窗口。
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"""
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print("首次运行，正在初始化霍克斯状态和滚动窗口...")
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alpha = self._hawkes_alpha
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kappa = self.hawkes_kappa
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# 完整计算一次历史强度，只为填充窗口
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temp_hawkes_history = np.zeros_like(initial_volumes, dtype=np.float64)
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if len(initial_volumes) > 0:
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temp_hawkes_history[0] = initial_volumes[0] if not np.isnan(initial_volumes[0]) else 0.0
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for i in range(1, len(initial_volumes)):
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temp_hawkes_history[i] = temp_hawkes_history[i - 1] * alpha + (
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initial_volumes[i] if not np.isnan(initial_volumes[i]) else 0.0)
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# 记录最后一个点的强度值，作为下一次增量计算的起点
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self._last_hawkes_unscaled = temp_hawkes_history[-1] if len(temp_hawkes_history) > 0 else 0.0
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# 用历史强度值的最后 hawkes_lookback 个点来填充滚动窗口
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self._hawkes_window = (temp_hawkes_history * kappa)[-self.hawkes_lookback:]
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print("状态初始化完成。")
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def _update_state_incrementally(self, latest_volume: float):
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"""
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【增量计算】在每个新的Bar上调用，更新强度值和滚动窗口。
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"""
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# 1. 计算最新的霍克斯强度值 (未缩放)
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new_hawkes_unscaled = self._last_hawkes_unscaled * self._hawkes_alpha + (
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latest_volume if not np.isnan(latest_volume) else 0.0)
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# 2. 更新上一个点的状态，为下一次计算做准备
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self._last_hawkes_unscaled = new_hawkes_unscaled
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# 3. 将新的缩放后的强度值推入滚动窗口
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new_hawkes_scaled = new_hawkes_unscaled * self.hawkes_kappa
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# np.roll 会高效地将数组元素移动，然后我们将新值放在第一个位置
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# 这比 append + delete 的效率高得多
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self._hawkes_window = np.roll(self._hawkes_window, -1)
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self._hawkes_window[-1] = new_hawkes_scaled
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def on_init(self):
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super().on_init()
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self.pos_meta.clear()
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# 重置状态
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self._last_hawkes_unscaled = 0.0
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self._hawkes_window = np.array([], dtype=np.float64)
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def on_open_bar(self, open_price: float, symbol: str):
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bar_history = self.get_bar_history()
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min_bars_required = max(self.trendline_n + 2, self.hawkes_lookback + 2)
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if len(bar_history) < min_bars_required:
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return
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# --- 【核心修改】霍克斯过程的状态更新 ---
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# 检查是否是第一次运行
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if self._hawkes_window.size == 0:
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initial_volumes = np.array([b.volume for b in bar_history], dtype=float)
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self._initialize_state(initial_volumes[:-1]) # 用到上一根bar为止的数据初始化
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# 增量更新当前bar的状态
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self._update_state_incrementally(float(bar_history[-1].volume))
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# --- 后续逻辑使用更新后的状态进行计算 ---
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self.cancel_all_pending_orders(symbol)
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pos = self.get_current_positions().get(symbol, 0)
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# 【核心修改】直接在固定长度的窗口上计算分位数
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# 这比pandas.rolling快几个数量级，且结果稳定
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latest_hawkes_value = self._hawkes_window[-1]
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latest_hawkes_upper = np.quantile(self._hawkes_window, self.hawkes_entry_percent)
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latest_hawkes_lower = np.quantile(self._hawkes_window, self.hawkes_exit_percent)
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# 1. 平仓逻辑 (完全不变)
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meta = self.pos_meta.get(symbol)
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if meta and pos != 0:
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if latest_hawkes_value < latest_hawkes_lower:
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self.log(f"霍克斯出场信号...") # 日志简化
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self.send_market_order("CLOSE_LONG" if meta['direction'] == "BUY" else "CLOSE_SHORT", abs(pos))
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del self.pos_meta[symbol]
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return
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# 2. 开仓逻辑 (完全不变)
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if pos == 0:
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close_prices = np.array([b.close for b in bar_history])
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prices_for_trendline = close_prices[-self.trendline_n - 1:-1]
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trend_upper, trend_lower = calculate_latest_trendline_values(prices_for_trendline)
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if trend_upper is not None and trend_lower is not None:
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prev_close = bar_history[-2].close
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last_close = bar_history[-1].close
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upper_break_event = last_close > trend_upper and prev_close < trend_upper
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lower_break_event = last_close < trend_lower and prev_close > trend_lower
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hawkes_confirmation = latest_hawkes_value > latest_hawkes_upper
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if hawkes_confirmation and (upper_break_event or lower_break_event):
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trade_direction = None
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if upper_break_event:
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trade_direction = "SELL" if self.reverse_logic else "BUY"
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elif lower_break_event:
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trade_direction = "BUY" if self.reverse_logic else "SELL"
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if trade_direction and trade_direction in self.order_direction:
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self.log(f"开仓信号确认...") # 日志简化
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self.send_open_order(trade_direction, open_price, self.trade_volume)
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# send_open_order, send_market_order, on_rollover 等方法保持不变
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# ... (代码省略，与之前版本相同) ...
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# send_open_order, send_market_order, on_rollover 等方法保持不变
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def send_open_order(self, direction: str, entry_price: float, volume: int):
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current_time = self.get_current_time()
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order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
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order_direction = "BUY" if direction == "BUY" else "SELL"
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order = Order(id=order_id, symbol=self.symbol, direction=order_direction, volume=volume, price_type="MARKET",
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submitted_time=current_time, offset="OPEN")
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self.send_order(order)
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self.pos_meta[self.symbol] = {"direction": direction, "volume": volume, "entry_price": entry_price}
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self.log(f"发送开仓订单: {direction} {volume}手 @ Market Price (执行价约 {entry_price:.2f})")
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def send_market_order(self, direction: str, volume: int):
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current_time = self.get_current_time()
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order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
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order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
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submitted_time=current_time, offset="CLOSE")
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self.send_order(order)
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self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
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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.cancel_all_pending_orders(new_symbol)
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self.pos_meta.clear() |