284 lines
14 KiB
Python
284 lines
14 KiB
Python
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import numpy as np
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import pandas as pd
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from typing import Optional, Dict, Any, List, Union
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import talib # <-- 【新增】导入talib库
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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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from src.algo.TrendLine import calculate_latest_trendline_values
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import numpy as np
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import pandas as pd
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from typing import Optional, Dict, Any, List, Union
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import talib
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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 (V8 - O(1) 滚动统计终极版):
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- 对交易量Z-score的计算进行了极致优化，采用增量方式维护滚动窗口的统计量。
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- 每次更新均值和标准差的计算复杂度从 O(N) 降为 O(1)。
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- 这是目前性能最高的实现方式，适用于非常高频的场景。
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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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# --- 所有参数与V7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 = 0.25,
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volume_norm_n: int = 50,
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enable_atr_stop_loss: bool = True,
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atr_period: int = 14,
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atr_multiplier: float = 1.0,
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enable_log: bool = True,
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indicators: Union[Indicator, List[Indicator]] = None,
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):
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super().__init__(context, main_symbol, enable_log)
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# --- 参数赋值 (与V7相同) ---
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# ... (省略) ...
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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.volume_norm_n = volume_norm_n
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self.enable_atr_stop_loss = enable_atr_stop_loss
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self.atr_period = atr_period
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self.atr_multiplier = atr_multiplier
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self.pos_meta: Dict[str, Dict[str, Any]] = {}
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if indicators is None:
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indicators = [Empty(), Empty()]
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self.indicators = indicators
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# --- 霍克斯过程状态 (与V7相同) ---
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self._last_hawkes_unscaled: float = 0.0
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self._hawkes_window: np.ndarray = np.array([], dtype=np.float64)
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self._hawkes_alpha = np.exp(-self.hawkes_kappa)
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# --- 【核心修改】O(1) 滚动统计状态 ---
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# 预分配一个固定长度的数组作为循环缓冲区
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self._volume_window: np.ndarray = np.zeros(self.volume_norm_n, dtype=np.float64)
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self._volume_sum: float = 0.0 # 窗口内元素的和
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self._volume_sum_sq: float = 0.0 # 窗口内元素平方的和
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self._volume_pointer: int = 0 # 指向窗口中最旧元素的指针
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self._is_volume_window_full: bool = False # 窗口是否已填满的标志
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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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# 重置霍克斯状态
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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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# 【核心修改】重置所有滚动统计状态
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self._volume_window.fill(0)
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self._volume_sum = 0.0
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self._volume_sum_sq = 0.0
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self._volume_pointer = 0
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self._is_volume_window_full = False
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# 【核心修改】_initialize_state 和 _update_state_incrementally 被重构
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def _initialize_state(self, initial_volumes: np.ndarray):
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"""
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在策略开始时调用一次，用历史数据填充所有状态。
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这个函数现在也会以增量方式填充滚动统计量。
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"""
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print("首次运行，正在以增量方式初始化所有状态...")
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# 1. 增量填充交易量窗口并计算历史Z-score
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normalized_volumes = []
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for vol in initial_volumes:
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# 调用增量更新函数，该函数会更新窗口、和、平方和
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self._update_volume_stats_incrementally(vol)
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# 计算Z-score
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mean, std = self._get_current_volume_stats()
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z_score = 0.0
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if std > 1e-9:
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z_score = (vol - mean) / std
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normalized_volumes.append(z_score)
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# 2. 使用标准化的交易量历史来初始化霍克斯过程 (逻辑与V7相同)
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print("正在基于标准化的交易量初始化霍克斯过程...")
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alpha = self._hawkes_alpha
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temp_hawkes_history = np.zeros_like(normalized_volumes, dtype=np.float64)
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if len(normalized_volumes) > 0:
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temp_hawkes_history[0] = normalized_volumes[0]
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for i in range(1, len(normalized_volumes)):
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temp_hawkes_history[i] = temp_hawkes_history[i - 1] * alpha + normalized_volumes[i]
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# 3. 记录最后的状态
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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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self._hawkes_window = (temp_hawkes_history * self.hawkes_kappa)[-self.hawkes_lookback:]
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print("状态初始化完成。")
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def _update_volume_stats_incrementally(self, latest_volume: float):
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"""O(1) 增量更新交易量窗口的统计数据"""
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# 获取即将被替换的最旧的元素
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oldest_volume = self._volume_window[self._volume_pointer]
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# 更新和与平方和
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self._volume_sum += latest_volume - oldest_volume
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self._volume_sum_sq += latest_volume ** 2 - oldest_volume ** 2
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# 在循环缓冲区中替换旧值
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self._volume_window[self._volume_pointer] = latest_volume
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# 移动指针
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self._volume_pointer += 1
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if self._volume_pointer >= self.volume_norm_n:
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self._volume_pointer = 0
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self._is_volume_window_full = True # 窗口在指针第一次循环时被填满
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def _get_current_volume_stats(self) -> (float, float):
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"""O(1) 获取当前的均值和标准差"""
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# 在窗口未满时，我们按实际元素数量计算
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n = self.volume_norm_n if self._is_volume_window_full else self._volume_pointer
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if n == 0:
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return 0.0, 0.0
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mean = self._volume_sum / n
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# 为防止浮点误差导致极小的负数，使用 max(0, ...)
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variance = max(0, (self._volume_sum_sq / n) - mean ** 2)
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std = np.sqrt(variance)
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return mean, std
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def _update_state_incrementally(self, latest_volume: float):
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"""【重构】每个Bar上调用的主增量更新函数"""
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# 1. O(1) 更新交易量统计
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self._update_volume_stats_incrementally(latest_volume)
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# 2. O(1) 计算最新Z-score
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mean, std = self._get_current_volume_stats()
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normalized_volume = 0.0
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if std > 1e-9:
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normalized_volume = (latest_volume - mean) / std
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# 3. 更新霍克斯过程 (逻辑与V7相同)
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new_hawkes_unscaled = self._last_hawkes_unscaled * self._hawkes_alpha + normalized_volume
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self._last_hawkes_unscaled = new_hawkes_unscaled
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new_hawkes_scaled = new_hawkes_unscaled * self.hawkes_kappa
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if self._hawkes_window.size < self.hawkes_lookback:
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self._hawkes_window = np.append(self._hawkes_window, new_hawkes_scaled)
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else:
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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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# on_open_bar 逻辑不变，它只负责调用 _update_state_incrementally
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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, self.volume_norm_n + 2,
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self.atr_period + 2)
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if len(bar_history) < min_bars_required:
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return
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# 状态更新 (调用重构后的函数)
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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])
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self._update_state_incrementally(float(bar_history[-1].volume))
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# --- 后续交易逻辑 (与V7完全相同) ---
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# ... (此处省略，代码与V7的 on_open_bar 后半部分完全一样) ...
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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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latest_hawkes_value = self._hawkes_window[-1]
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latest_hawkes_lower = np.quantile(self._hawkes_window, self.hawkes_exit_percent)
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meta = self.pos_meta.get(symbol)
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if meta and pos != 0:
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close_reason = None
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if latest_hawkes_value < latest_hawkes_lower:
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close_reason = f"霍克斯出场信号(强度: {latest_hawkes_value:.4f} < 阈值: {latest_hawkes_lower:.4f})"
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if self.enable_atr_stop_loss and 'stop_loss_price' in meta and meta['stop_loss_price'] is not None:
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last_close = bar_history[-1].close
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stop_loss_price = meta['stop_loss_price']
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if (meta['direction'] == "BUY" and last_close < stop_loss_price) or \
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(meta['direction'] == "SELL" and last_close > stop_loss_price):
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close_reason = f"ATR止损触发(收盘价: {last_close:.2f}, 止损价: {stop_loss_price:.2f})"
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if close_reason:
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self.log(close_reason)
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self.send_market_order("CLOSE_LONG" if meta['direction'] == "BUY" else "CLOSE_SHORT", abs(pos))
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if symbol in self.pos_meta: del self.pos_meta[symbol]
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return
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if pos == 0:
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latest_hawkes_upper = np.quantile(self._hawkes_window, self.hawkes_entry_percent)
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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, last_close = bar_history[-2].close, bar_history[-1].close
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upper_break = last_close > trend_upper and prev_close < trend_upper and self.indicators[0].is_condition_met(*self.get_indicator_tuple())
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lower_break = last_close < trend_lower and prev_close > trend_lower and self.indicators[1].is_condition_met(*self.get_indicator_tuple())
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hawkes_confirm = latest_hawkes_value > latest_hawkes_upper
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if hawkes_confirm and (upper_break or lower_break):
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direction = "BUY"
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if upper_break:
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direction = "SELL" if self.reverse_logic else "BUY"
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elif lower_break:
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direction = "BUY" if self.reverse_logic else "SELL"
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if direction in self.order_direction:
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sl_price = None
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if self.enable_atr_stop_loss:
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atr_val = self._calculate_atr(bar_history[:-1], self.atr_period)
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if atr_val is not None:
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sl_price = open_price - atr_val * self.atr_multiplier if direction == "BUY" else open_price + atr_val * self.atr_multiplier
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self.log(f"ATR({self.atr_period})={atr_val:.4f}, 止损价设置为: {sl_price:.2f}")
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self.log(
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f"开仓信号确认(霍克斯强度: {latest_hawkes_value:.4f} > 阈值: {latest_hawkes_upper:.4f})")
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self.send_open_order(direction, open_price, self.trade_volume, sl_price)
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# ATR计算函数及其他下单函数与V7完全相同
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def _calculate_atr(self, bar_history: List[Bar], period: int) -> Optional[float]:
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if len(bar_history) < period + 1: return None
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highs = np.array([b.high for b in bar_history], dtype=float)
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lows = np.array([b.low for b in bar_history], dtype=float)
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closes = np.array([b.close for b in bar_history], dtype=float)
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atr_values = talib.ATR(highs, lows, closes, timeperiod=period)
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latest_atr = atr_values[-1]
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return latest_atr if not np.isnan(latest_atr) else None
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def send_open_order(self, direction: str, entry_price: float, volume: int, stop_loss_price: Optional[float] = None):
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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] = {
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"direction": direction,
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"volume": volume,
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"entry_price": entry_price,
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"stop_loss_price": stop_loss_price
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}
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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()
|
|||
|
|
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
|
|||
|
|
order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
|
|||
|
|
submitted_time=current_time, offset="CLOSE")
|
|||
|
|
self.send_order(order)
|
|||
|
|
self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
|
|||
|
|
|
|||
|
|
def on_rollover(self, old_symbol: str, new_symbol: str):
|
|||
|
|
super().on_rollover(old_symbol, new_symbol)
|
|||
|
|
self.cancel_all_pending_orders(new_symbol)
|
|||
|
|
self.pos_meta.clear()
|