262 lines
13 KiB
Python
262 lines
13 KiB
Python
import numpy as np
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import pandas as pd
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import talib
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from collections import deque
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from typing import Optional, Any, List, Dict
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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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# =============================================================================
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# 策略实现 (Dual-Mode Kalman Strategy)
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# =============================================================================
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class DualModeKalmanStrategy(Strategy):
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"""
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一个内置两种相反交易逻辑（趋势跟踪 vs. 均值回归）的自适应策略。
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本策略旨在通过一个核心参数 `strategy_mode`，在两种市场范式间切换，
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以适应不同品种的内在“性格”。
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人格1: 'TREND' (趋势模式)
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- 哲学: 价格的强力突破会引发自我强化的趋势。
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- 入场: 价格向上/下“逃逸”卡尔曼线 -> 做多/做空。
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- 出场: 使用结构化卡尔曼止损，让利润奔跑。
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人格2: 'REVERSION' (均值回归模式)
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- 哲学: 价格的极端偏离是不可持续的，终将回归均值。
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- 入场: 价格向上/下极端偏离（超买/超卖） -> 做空/做多。
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- 出场: 当价格回归至卡爾曼線時获利了结。
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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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# --- 【核心人格切换】 ---
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strategy_mode: str = 'TREND', # 可选: 'TREND' 或 'REVERSION'
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# --- 【通用参数】 ---
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kalman_process_noise: float = 0.01,
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kalman_measurement_noise: float = 0.5,
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atr_period: int = 20,
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atr_lookback: int = 100,
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atr_percentile_threshold: float = 25.0,
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entry_threshold_atr: float = 2.5,
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initial_stop_atr_multiplier: float = 2.0,
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# --- 【趋势模式专用】 ---
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structural_stop_atr_multiplier: float = 2.5,
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order_direction: Optional[List[str]] = None,
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indicators: Optional[List[Indicator]] = 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: order_direction = ['BUY', 'SELL']
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# --- 参数验证与赋值 ---
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if strategy_mode.upper() not in ['TREND', 'REVERSION']:
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raise ValueError(f"strategy_mode must be 'TREND' or 'REVERSION', but got {strategy_mode}")
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self.strategy_mode = strategy_mode.upper()
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self.trade_volume = trade_volume
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self.atr_period = atr_period
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self.atr_lookback = atr_lookback
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self.atr_percentile_threshold = atr_percentile_threshold
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self.entry_threshold_atr = entry_threshold_atr
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self.initial_stop_atr_multiplier = initial_stop_atr_multiplier
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self.structural_stop_atr_multiplier = structural_stop_atr_multiplier
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self.order_direction = order_direction
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# 卡尔曼滤波器状态
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self.Q = kalman_process_noise
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self.R = kalman_measurement_noise
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self.P = 1.0
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self.x_hat = 0.0
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self.kalman_initialized = False
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self._atr_history: deque = deque(maxlen=self.atr_lookback)
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self.position_meta: Dict[str, Any] = self.context.load_state()
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self.main_symbol = main_symbol
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self.order_id_counter = 0
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if indicators is None: indicators = [Empty(), Empty()]
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self.indicators = indicators
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self.log(f"DualModeKalmanStrategy Initialized with Personality: [{self.strategy_mode}]")
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def on_init(self):
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super().on_init()
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self.cancel_all_pending_orders(self.main_symbol)
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self.position_meta = self.context.load_state()
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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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if len(bar_history) < max(self.atr_period, self.atr_lookback) + 2: return
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self.cancel_all_pending_orders(symbol)
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# --- 通用数据计算 ---
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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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current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
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self._atr_history.append(current_atr)
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if current_atr <= 0 or len(self._atr_history) < self.atr_lookback: return
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if not self.kalman_initialized: self.x_hat = closes[-1]
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self.kalman_initialized = True
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x_hat_minus = self.x_hat
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P_minus = self.P + self.Q
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K = P_minus / (P_minus + self.R)
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self.x_hat = x_hat_minus + K * (closes[-1] - x_hat_minus)
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self.P = (1 - K) * P_minus
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kalman_price = self.x_hat
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# --- 分模式管理持仓 ---
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position_volume = self.get_current_positions().get(self.symbol, 0)
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meta = self.position_meta.get(symbol)
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if position_volume != 0 and not meta:
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self.log(f"警告：检测到实际持仓({position_volume})与策略状态(无记录)不一致！"
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f"可能由状态加载失败导致。将强制平仓以同步状态。", level='WARNING')
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direction_to_close = "CLOSE_LONG" if position_volume > 0 else "CLOSE_SHORT"
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self.send_market_order(direction_to_close, abs(position_volume), 'CLOSE')
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return
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if position_volume == 0 and meta:
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self.log(f"信息：检测到策略状态({meta.get('direction')})与实际持仓(0)不一致，meta:{meta}。"
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f"可能是外部平仓导致。正在清理过时状态。", level='INFO')
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new_pos_meta = {k: v for k, v in self.position_meta.items() if k != symbol}
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self.position_meta = new_pos_meta
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self.save_state(new_pos_meta)
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if not self.trading:
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return
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if position_volume != 0:
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self.log(f'manage_open_position')
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self.manage_open_position(position_volume, bar_history[-1], current_atr, kalman_price)
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return
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# --- 状态过滤 (对两种模式都适用) ---
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atr_threshold = np.percentile(list(self._atr_history), self.atr_percentile_threshold)
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if current_atr < atr_threshold: return
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# --- 分模式评估新机会 ---
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self.log(f'evaluate_entry_signal')
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self.evaluate_entry_signal(bar_history[-1], kalman_price, current_atr)
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def manage_open_position(self, volume: int, current_bar: Bar, current_atr: float, kalman_price: float):
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meta = self.position_meta.get(self.symbol)
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if not meta: return
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# --- 通用逻辑：首先检查初始硬止损 ---
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initial_stop_price = meta['initial_stop_price']
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if (volume > 0 and current_bar.low <= initial_stop_price) or \
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(volume < 0 and current_bar.high >= initial_stop_price):
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self.log(f"Initial Stop Loss hit at {initial_stop_price:.4f}")
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self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume))
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return
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# --- 分模式出场逻辑 ---
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if self.strategy_mode == 'TREND':
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# 【趋势模式】: 使用结构化卡尔曼止损
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stop_price = 0
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if volume > 0:
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stop_price = max(kalman_price - self.structural_stop_atr_multiplier * current_atr, initial_stop_price)
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self.log(f'stop_price: {stop_price:.4f}, kalman_price: {kalman_price:.4f}')
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if current_bar.low <= stop_price:
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self.log(f"TREND Mode: Structural Stop hit for LONG at {stop_price:.4f}")
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self.close_position("CLOSE_LONG", abs(volume))
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else: # volume < 0
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stop_price = min(kalman_price + self.structural_stop_atr_multiplier * current_atr, initial_stop_price)
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self.log(f'stop_price: {stop_price:.4f}, kalman_price: {kalman_price:.4f}')
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if current_bar.high >= stop_price:
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self.log(f"TREND Mode: Structural Stop hit for SHORT at {stop_price:.4f}")
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self.close_position("CLOSE_SHORT", abs(volume))
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elif self.strategy_mode == 'REVERSION':
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# 【回归模式】: 检查是否触及均值作为止盈
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if volume > 0:
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stop_price = max(kalman_price - self.structural_stop_atr_multiplier * current_atr, initial_stop_price)
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self.log(f'stop_price: {stop_price:.4f}, kalman_price: {kalman_price:.4f}')
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if current_bar.low <= stop_price:
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self.log(f"TREND Mode: Structural Stop hit for LONG at {stop_price:.4f}")
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self.close_position("CLOSE_LONG", abs(volume))
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else: # volume < 0
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stop_price = min(kalman_price + self.structural_stop_atr_multiplier * current_atr, initial_stop_price)
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self.log(f'stop_price: {stop_price:.4f}, kalman_price: {kalman_price:.4f}')
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if current_bar.high >= stop_price:
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self.log(f"TREND Mode: Structural Stop hit for SHORT at {stop_price:.4f}")
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self.close_position("CLOSE_SHORT", abs(volume))
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def evaluate_entry_signal(self, current_bar: Bar, kalman_price: float, current_atr: float):
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deviation = current_bar.close - kalman_price
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deviation_in_atr = deviation / current_atr
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direction = None
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if self.strategy_mode == 'TREND':
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# 【趋势模式】入场逻辑
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if "BUY" in self.order_direction and deviation_in_atr > self.entry_threshold_atr and self.indicators[
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0].is_condition_met(*self.get_indicator_tuple()):
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direction = "BUY"
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elif "SELL" in self.order_direction and deviation_in_atr < -self.entry_threshold_atr and self.indicators[
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1].is_condition_met(*self.get_indicator_tuple()):
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direction = "SELL"
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elif self.strategy_mode == 'REVERSION':
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# 【回归模式】入场逻辑 (完全相反)
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if "SELL" in self.order_direction and deviation_in_atr > self.entry_threshold_atr and self.indicators[
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1].is_condition_met(*self.get_indicator_tuple()):
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direction = "SELL" # 价格超买 -> 做空
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elif "BUY" in self.order_direction and deviation_in_atr < -self.entry_threshold_atr and self.indicators[
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0].is_condition_met(*self.get_indicator_tuple()):
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direction = "BUY" # 价格超卖 -> 做多
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if direction:
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self.log(
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f"{self.strategy_mode} Mode: Catalyst Fired. Direction: {direction}. Deviation: {deviation_in_atr:.2f} ATRs.")
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entry_price = current_bar.close + (1 if direction == "BUY" else -1)
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stop_loss_price = entry_price - self.initial_stop_atr_multiplier * current_atr if direction in ["BUY",
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"CLOSE_SHORT"] else entry_price + self.initial_stop_atr_multiplier * current_atr
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meta = {'entry_price': entry_price, 'initial_stop_price': stop_loss_price}
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self.send_limit_order(entry_price, direction, self.trade_volume, "OPEN", meta)
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self.save_state(self.position_meta)
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# ... (辅助函数 close_position, send_market_order, on_rollover 保持不变) ...
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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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if self.symbol in self.position_meta:
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self.position_meta = {}
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self.save_state(self.position_meta)
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def send_market_order(self, direction: str, volume: int, offset: str, meta: Optional[Dict] = None):
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if offset == "OPEN" and meta: self.position_meta[self.symbol] = meta
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order_id = f"{self.symbol}_{direction}_MARKET_{self.order_id_counter}"
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self.order_id_counter += 1
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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=self.get_current_time(), offset=offset)
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self.send_order(order)
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def send_limit_order(self, limit_price: float, direction: str, volume: int, offset: str,
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meta: Optional[Dict] = None):
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if offset == "OPEN" and meta: self.position_meta[self.symbol] = meta
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order_id = f"{self.symbol}_{direction}_MARKET_{self.order_id_counter}"
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self.order_id_counter += 1
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order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="LIMIT",
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submitted_time=self.get_current_time(), offset=offset, limit_price=limit_price)
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self.send_order(order)
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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.position_meta = {}
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self.kalman_initialized = False
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self._atr_history.clear()
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self.log("Rollover detected. All strategy states have been reset.")
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