1、vp策略
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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
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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_v2
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class DualModeTrendlineHawkesStrategy(Strategy):
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"""
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趋势线与霍克斯过程双模式策略 (V5 - 趋势/回归自适应版):
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- 支持两套独立的参数配置，分别对应趋势跟踪和均值回归逻辑。
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- 开平仓条件共享，但交易方向相反。
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- 内置冲突解决机制，用于处理两种模式同时发出开仓信号的情况。
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- 保持了V4版本高效的增量计算特性。
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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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trade_volume: int = 1,
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# 【核心修改】使用字典来配置两种模式
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trend_params: Dict[str, Any] = None,
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reversion_params: Dict[str, Any] = None,
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# 【新增】模式启用开关
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enabled_modes: Optional[List[str]] = None,
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# 【新增】信号冲突解决方案: 'TREND_PRIORITY', 'REVERSION_PRIORITY', 'NONE'
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conflict_resolution: str = 'TREND_PRIORITY',
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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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self.main_symbol = main_symbol
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self.trade_volume = trade_volume
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# --- 【核心修改】参数结构化 ---
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# 提供默认参数，防止用户未提供
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default_params = {
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"order_direction": ["BUY", "SELL"],
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"trendline_n": 50,
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"hawkes_kappa": 0.1,
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"hawkes_lookback": 50,
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"hawkes_entry_percent": 0.95,
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"hawkes_exit_percent": 0.50,
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}
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self.trend_params = default_params.copy()
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if trend_params:
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self.trend_params.update(trend_params)
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self.reversion_params = default_params.copy()
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if reversion_params:
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self.reversion_params.update(reversion_params)
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self.enabled_modes = enabled_modes or ['TREND', 'REVERSION']
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self.conflict_resolution = conflict_resolution
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self.pos_meta: Dict[str, Dict[str, Any]] = {}
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# --- 【核心修改】为每个模式维护独立的状态 ---
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# 趋势模式状态
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self._trend_last_hawkes_unscaled: float = 0.0
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self._trend_hawkes_window: np.ndarray = np.array([], dtype=np.float64)
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self._trend_hawkes_alpha = np.exp(-self.trend_params['hawkes_kappa'])
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# 回归模式状态
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self._reversion_last_hawkes_unscaled: float = 0.0
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self._reversion_hawkes_window: np.ndarray = np.array([], dtype=np.float64)
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self._reversion_hawkes_alpha = np.exp(-self.reversion_params['hawkes_kappa'])
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print("DualModeTrendlineHawkesStrategy initialized.")
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print(f"Enabled modes: {self.enabled_modes}")
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print(f"Conflict resolution: {self.conflict_resolution}")
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# --- 辅助函数，用于状态管理 (可复用) ---
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def _initialize_hawkes_state(self, params: Dict, initial_volumes: np.ndarray) -> (float, np.ndarray):
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"""根据给定参数和历史成交量，初始化霍克斯状态。"""
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print(f"Initializing Hawkes state with lookback {params['hawkes_lookback']}...")
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alpha = np.exp(-params['hawkes_kappa'])
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kappa = params['hawkes_kappa']
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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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last_hawkes_unscaled = temp_hawkes_history[-1] if len(temp_hawkes_history) > 0 else 0.0
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hawkes_window = (temp_hawkes_history * kappa)[-params['hawkes_lookback']:]
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return last_hawkes_unscaled, hawkes_window
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def _update_hawkes_state_incrementally(self, params: Dict, latest_volume: float, last_unscaled: float,
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window: np.ndarray) -> (float, np.ndarray):
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"""根据给定参数，增量更新霍克斯状态。"""
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alpha = np.exp(-params['hawkes_kappa'])
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kappa = params['hawkes_kappa']
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new_hawkes_unscaled = last_unscaled * alpha + (latest_volume if not np.isnan(latest_volume) else 0.0)
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new_hawkes_scaled = new_hawkes_unscaled * kappa
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new_window = np.roll(window, -1)
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new_window[-1] = new_hawkes_scaled
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return new_hawkes_unscaled, new_window
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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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# 重置所有状态
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self._trend_last_hawkes_unscaled = 0.0
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self._trend_hawkes_window = np.array([], dtype=np.float64)
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self._reversion_last_hawkes_unscaled = 0.0
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self._reversion_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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# 确保有足够的数据来初始化两个模式
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min_bars_required = max(
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self.trend_params['trendline_n'] + 2, self.trend_params['hawkes_lookback'] + 2,
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self.reversion_params['trendline_n'] + 2, self.reversion_params['hawkes_lookback'] + 2
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)
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if len(bar_history) < min_bars_required:
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return
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# --- 状态初始化与更新 ---
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# 首次运行时，为两个启用的模式初始化状态
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if self._trend_hawkes_window.size == 0 and 'TREND' in self.enabled_modes:
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initial_volumes = np.array([b.volume for b in bar_history], dtype=float)
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self._trend_last_hawkes_unscaled, self._trend_hawkes_window = self._initialize_hawkes_state(
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self.trend_params, initial_volumes[:-1]
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)
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if self._reversion_hawkes_window.size == 0 and 'REVERSION' in self.enabled_modes:
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initial_volumes = np.array([b.volume for b in bar_history], dtype=float)
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self._reversion_last_hawkes_unscaled, self._reversion_hawkes_window = self._initialize_hawkes_state(
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self.reversion_params, initial_volumes[:-1]
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)
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# 增量更新两个模式的状态
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latest_volume = float(bar_history[-1].volume)
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if 'TREND' in self.enabled_modes:
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self._trend_last_hawkes_unscaled, self._trend_hawkes_window = self._update_hawkes_state_incrementally(
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self.trend_params, latest_volume, self._trend_last_hawkes_unscaled, self._trend_hawkes_window
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)
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if 'REVERSION' in self.enabled_modes:
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self._reversion_last_hawkes_unscaled, self._reversion_hawkes_window = self._update_hawkes_state_incrementally(
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self.reversion_params, latest_volume, self._reversion_last_hawkes_unscaled,
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self._reversion_hawkes_window
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)
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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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# --- 1. 平仓逻辑 ---
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meta = self.pos_meta.get(symbol)
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if meta and pos != 0:
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strategy_mode = meta.get('strategy_mode')
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params_to_use = self.trend_params if strategy_mode == 'TREND' else self.reversion_params
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window_to_use = self._trend_hawkes_window if strategy_mode == 'TREND' else self._reversion_hawkes_window
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if window_to_use.size > 0:
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latest_hawkes_value = window_to_use[-1]
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latest_hawkes_lower = np.quantile(window_to_use, params_to_use['hawkes_exit_percent'])
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if latest_hawkes_value < latest_hawkes_lower:
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self.log(f"[{strategy_mode}模式] 霍克斯出场信号触发，平仓。")
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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 ---
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if pos == 0:
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trend_signal = None
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reversion_signal = None
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# 分别计算两个模式的信号
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if 'TREND' in self.enabled_modes:
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trend_signal = self._calculate_entry_signal(
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'TREND', bar_history, self.trend_params, self._trend_hawkes_window
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)
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if 'REVERSION' in self.enabled_modes:
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reversion_signal = self._calculate_entry_signal(
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'REVERSION', bar_history, self.reversion_params, self._reversion_hawkes_window
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)
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final_direction = None
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winning_mode = None
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# --- 信号冲突解决 ---
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if trend_signal and reversion_signal:
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self.log(f"信号冲突：趋势模式 ({trend_signal}) vs 回归模式 ({reversion_signal})")
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if self.conflict_resolution == 'TREND_PRIORITY':
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final_direction = trend_signal
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winning_mode = 'TREND'
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elif self.conflict_resolution == 'REVERSION_PRIORITY':
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final_direction = reversion_signal
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winning_mode = 'REVERSION'
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else: # 'NONE'
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self.log("冲突解决策略为'NONE'，本次不开仓。")
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elif trend_signal:
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final_direction = trend_signal
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winning_mode = 'TREND'
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elif reversion_signal:
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final_direction = reversion_signal
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winning_mode = 'REVERSION'
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# 执行最终决策
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if final_direction and winning_mode:
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params_to_use = self.trend_params if winning_mode == 'TREND' else self.reversion_params
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if final_direction in params_to_use['order_direction']:
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self.log(f"[{winning_mode}模式] 开仓信号确认: {final_direction}")
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self.send_open_order(final_direction, open_price, self.trade_volume, winning_mode)
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def _calculate_entry_signal(self, mode: str, bar_history: List[Bar], params: Dict, hawkes_window: np.ndarray) -> \
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Optional[str]:
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"""计算单个模式的入场信号，返回 'BUY', 'SELL' 或 None。"""
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if hawkes_window.size == 0:
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return None
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# 霍克斯确认
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latest_hawkes_value = hawkes_window[-1]
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latest_hawkes_upper = np.quantile(hawkes_window, params['hawkes_entry_percent'])
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hawkes_confirmation = latest_hawkes_value > latest_hawkes_upper
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if not hawkes_confirmation:
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return None
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# 趋势线突破事件
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close_prices = np.array([b.close for b in bar_history])
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prices_for_trendline = close_prices[-params['trendline_n'] - 1:-1]
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trend_upper, trend_lower = calculate_latest_trendline_values_v2(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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if upper_break_event:
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# 趋势模式：向上突破 -> 买入
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# 回归模式：向上突破 -> 卖出 (认为是假突破，价格将回归)
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return "BUY" if mode == 'TREND' else "SELL"
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elif lower_break_event:
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# 趋势模式：向下突破 -> 卖出
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# 回归模式：向下突破 -> 买入 (认为是超卖，价格将反弹)
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return "SELL" if mode == 'TREND' else "BUY"
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return None
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def send_open_order(self, direction: str, entry_price: float, volume: int, strategy_mode: str):
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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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# 【核心修改】记录仓位属于哪个模式
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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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"strategy_mode": strategy_mode
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}
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self.log(f"发送开仓订单 ({strategy_mode}): {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()
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