200 lines
9.6 KiB
Python
200 lines
9.6 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 (Adaptive Kalman Strategy)
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# =============================================================================
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class AdaptiveKalmanStrategy(Strategy):
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"""
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一个基于市场状态识别与结构化风控的自适应波段策略。
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本策略旨在解决动能策略在“回调中吐出利润、震荡中持续亏损”的核心痛点。
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它通过动态识别市场状态，在有利的环境下交易，并使用结构化的方法持有仓位。
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1. 【状态过滤】: 使用ATR历史分位数构建“波动率状态机”，在低波动率的
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“震荡区”主动休眠，只在高波动率的“趋势区”寻找机会。
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2. 【动能催化】: 沿用卡尔曼滤波器估算内在趋势，当价格以ATR标准化的
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力量“逃逸”出内在趋势时，视为入场信号。
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3. 【结构化持仓】: 抛弃紧跟价格峰值的传统追踪止损，改用基于卡尔曼滤波线
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本身的“结构化止损”，给趋势以充分的“呼吸空间”，旨在持有
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一个完整的波段，避免在健康回调中被过早洗出。
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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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# --- 【信号层】卡尔曼滤波器参数 ---
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kalman_process_noise: float = 0.01,
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kalman_measurement_noise: float = 0.5,
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# --- 【状态过滤】波动率状态机 ---
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atr_period: int = 20,
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atr_lookback: int = 100, # 用于计算ATR分位数的历史窗口
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atr_percentile_threshold: float = 25.0, # ATR必须高于其历史的哪个百分位才认为是“趋势区”
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# --- 【执行与风控】 ---
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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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structural_stop_atr_multiplier: float = 2.5, # 结构化止损的ATR乘数
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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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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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# 状态机与持仓元数据
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self._atr_history: deque = deque(maxlen=self.atr_lookback)
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self.position_meta: Dict[str, Any] = {}
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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("AdaptiveKalmanStrategy Initialized.")
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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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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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# --- 数据预处理与指标计算 ---
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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_close = closes[-1]
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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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# --- 卡尔曼滤波器更新 ---
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if not self.kalman_initialized:
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self.x_hat = current_close
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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 * (current_close - 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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# --- 管理现有持仓 ---
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position_volume = self.get_current_positions().get(self.symbol, 0)
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if position_volume != 0:
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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】检查波动率状态机 ---
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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:
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# self.log(f"Market in Chop Zone. ATR {current_atr:.4f} < Threshold {atr_threshold:.4f}. Standing by.")
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return # 市场处于震荡区，休眠
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# --- 评估新机会 ---
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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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"""采用两阶段止损系统：初始硬止损 + 结构化卡尔曼止损。"""
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meta = self.position_meta.get(self.symbol)
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if not meta: return
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# 阶段一：始终检查初始硬止损
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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"Phase 1: 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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# 阶段二：检查结构化卡尔曼止损
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structural_stop_price = 0
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if volume > 0: # 多头持仓
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structural_stop_price = kalman_price - self.structural_stop_atr_multiplier * current_atr
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# 确保结构止损不会比初始止损更差
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structural_stop_price = max(structural_stop_price, initial_stop_price)
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if current_bar.low <= structural_stop_price:
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self.log(f"Phase 2: Structural Kalman Stop hit for LONG at {structural_stop_price:.4f}")
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self.close_position("CLOSE_LONG", abs(volume))
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elif volume < 0: # 空头持仓
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structural_stop_price = kalman_price + self.structural_stop_atr_multiplier * current_atr
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# 确保结构止损不会比初始止损更差
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structural_stop_price = min(structural_stop_price, initial_stop_price)
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if current_bar.high >= structural_stop_price:
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self.log(f"Phase 2: Structural Kalman Stop hit for SHORT at {structural_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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"""在“趋势区”内，执行基于“动能催化”的入场逻辑。"""
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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 "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 = "SELL"
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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 = "BUY"
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if direction:
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self.log(
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f"Trend Zone Active! Momentum Catalyst Fired. Direction: {direction}. Deviation: {deviation_in_atr:.2f} ATRs.")
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entry_price = current_bar.close
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stop_loss_price = entry_price - self.initial_stop_atr_multiplier * current_atr if direction == "BUY" 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_market_order(direction, self.trade_volume, "OPEN", meta)
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# --- 订单发送与仓位管理辅助函数 ---
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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: del self.position_meta[self.symbol]
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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 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.") |