新增实盘策略：ITrendStrategy(SA)
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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.strategies.base_strategy import Strategy
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import numpy as np
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import talib
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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.strategies.base_strategy import Strategy
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class KalmanTrendFollower(Strategy):
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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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# --- 显著降低灵敏度，过滤日内杂波 ---
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fast_sensitivity: float = 0.05, # 调低：让快线也变得稳重
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slow_sensitivity: float = 0.005, # 极低：慢线只代表大趋势方向
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lookback_variance: int = 60, # 增加窗口，计算更稳定的市场噪声
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# --- 趋势跟踪参数 ---
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atr_period: int = 23,
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entry_threshold: float = 0.5, # 差值需超过0.5倍ATR才考虑入场
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trailing_stop_multiplier: float = 4.0, # 关键：4倍ATR跟踪止损，给趋势留足空间
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structural_stop_multiplier: float = 1.0, # 价格破位慢线多少ATR才出场
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indicator: Indicator = None,
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):
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super().__init__(context, main_symbol, enable_log)
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self.trade_volume = trade_volume
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# 参数初始化
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self.fast_sensitivity = fast_sensitivity
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self.slow_sensitivity = slow_sensitivity
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self.lookback_variance = lookback_variance
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self.atr_period = atr_period
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self.entry_threshold = entry_threshold
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self.trailing_stop_multiplier = trailing_stop_multiplier
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self.structural_stop_multiplier = structural_stop_multiplier
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self.indicator = indicator
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# 状态变量
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self.kf_fast = {'x': 0.0, 'P': 1.0}
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self.kf_slow = {'x': 0.0, 'P': 1.0}
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self.kalman_initialized = False
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self.position_meta: Dict[str, Any] = {}
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self.order_id_counter = 0
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def _update_kalman(self, state: dict, measurement: float, Q: float, R: float) -> float:
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p_minus = state['P'] + Q
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k_gain = p_minus / (p_minus + R)
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state['x'] = state['x'] + k_gain * (measurement - state['x'])
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state['P'] = (1 - k_gain) * p_minus
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return state['x']
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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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if len(bar_history) < 100: return
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self.cancel_all_pending_orders()
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closes = np.array([b.close for b in bar_history], dtype=float)
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last_price = closes[-1]
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# 1. 动态计算卡尔曼参数
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# 增加基础噪声 R，使曲线更平滑
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rolling_var = np.var(closes[-self.lookback_variance:])
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r_base = rolling_var if rolling_var > 0 else 1.0
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# 计算快慢线
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if not self.kalman_initialized:
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self.kf_fast['x'] = self.kf_slow['x'] = last_price
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self.kalman_initialized = True
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return
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fast_line = self._update_kalman(self.kf_fast, last_price, r_base * self.fast_sensitivity, r_base)
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slow_line = self._update_kalman(self.kf_slow, last_price, r_base * self.slow_sensitivity, r_base * 5.0)
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# 2. 计算 ATR 和 趋势指标
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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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atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
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diff = fast_line - slow_line
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diff_in_atr = diff / atr if atr > 0 else 0
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# 3. 仓位管理逻辑
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pos = self.get_current_positions().get(symbol, 0)
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if pos == 0 and (self.indicator is None or self.indicator.is_condition_met(*self.get_indicator_tuple())):
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# --- 入场逻辑：必须形成明显的发散 ---
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if diff_in_atr > self.entry_threshold:
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self.open_trade(symbol, "BUY", open_price, atr, slow_line)
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elif diff_in_atr < -self.entry_threshold:
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self.open_trade(symbol, "SELL", open_price, atr, slow_line)
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else:
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# --- 出场逻辑：保护肥尾收益 ---
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self.manage_exit(symbol, pos, last_price, atr, slow_line)
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def open_trade(self, symbol, direction, price, atr, slow_line):
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# 记录入场时的最高/最低价，用于动态跟踪止损
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meta = {
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'entry_price': price,
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'extreme_price': price, # 记录持仓期间到达过的最高(多头)或最低(空头)
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'direction': direction,
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'initial_atr': atr
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}
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self.send_limit_order(symbol, direction, price, self.trade_volume, "OPEN", meta)
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self.log(f"TREND ENTRY {direction}: Price={price}, ATR={atr:.2f}")
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def manage_exit(self, symbol, pos, price, atr, slow_line):
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meta = self.position_meta.get(symbol)
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if not meta: return
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is_long = pos > 0
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should_close = False
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# 更新持仓期间的极端价格（用于计算吊灯止损）
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if is_long:
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meta['extreme_price'] = max(meta['extreme_price'], price)
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# 吊灯止损位：最高价回落 N 倍 ATR
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chandelier_stop = meta['extreme_price'] - self.trailing_stop_multiplier * atr
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# 结构止损位：跌破慢速趋势线一定距离
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structural_stop = slow_line - self.structural_stop_multiplier * atr
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# 综合取较严的价格作为保护，但不轻易离场
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if price < max(chandelier_stop, structural_stop):
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should_close = True
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exit_reason = "Trailing/Structural Break"
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else:
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meta['extreme_price'] = min(meta['extreme_price'], price)
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chandelier_stop = meta['extreme_price'] + self.trailing_stop_multiplier * atr
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structural_stop = slow_line + self.structural_stop_multiplier * atr
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if price > min(chandelier_stop, structural_stop):
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should_close = True
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exit_reason = "Trailing/Structural Break"
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if should_close:
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direction = "CLOSE_LONG" if is_long else "CLOSE_SHORT"
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self.log(f"EXIT {direction}: Price={price}, Reason={exit_reason}")
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self.close_position(symbol, direction, abs(pos))
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# (底层 send_market_order / close_position 同前，注意更新 state 时保留 meta['extreme_price'])
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# --- 底层封装 ---
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def send_market_order(self, symbol, direction, volume, offset, meta=None):
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if offset == "OPEN": self.position_meta[symbol] = meta
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order_id = f"{symbol}_{direction}_{self.order_id_counter}"
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self.order_id_counter += 1
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order = Order(id=order_id, symbol=symbol, direction=direction,
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volume=volume, price_type="MARKET", offset=offset)
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self.send_order(order)
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self.save_state(self.position_meta)
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def send_limit_order(self, symbol, direction, price, volume, offset, meta=None):
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if offset == "OPEN": self.position_meta[symbol] = meta
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order_id = f"{symbol}_{direction}_{self.order_id_counter}"
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self.order_id_counter += 1
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order = Order(id=order_id, symbol=symbol, direction=direction,
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volume=volume, price_type="LIMIT", offset=offset, limit_price=price)
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self.send_order(order)
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self.save_state(self.position_meta)
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def close_position(self, symbol, direction, volume):
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order_id = f"{symbol}_{direction}_{self.order_id_counter}"
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self.order_id_counter += 1
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order = Order(id=order_id, symbol=symbol, direction=direction,
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volume=volume, price_type="MARKET", offset="CLOSE")
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self.send_order(order)
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self.position_meta.pop(symbol, None)
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self.save_state(self.position_meta)
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