222 lines
9.3 KiB
Python
222 lines
9.3 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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class DualModeKalmanStrategy(Strategy):
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"""
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基于卡尔曼因子对称性的双模自适应策略
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因子定义: Deviation = (Current_Close - Kalman_Price) / ATR
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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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strategy_mode: str = 'TREND', # 'TREND' 或 'REVERSION'
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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 = 23,
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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, # 入场偏离倍数
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stop_loss_atr: float = 2.0, # 保护性硬止损倍数
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trend_trailing_atr: float = 2.5, # 趋势模式下卡尔曼轨道的宽度
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order_direction=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:
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order_direction = ['BUY', 'SELL']
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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.stop_loss_atr = stop_loss_atr
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self.trend_trailing_atr = entry_threshold_atr
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# 卡尔曼状态
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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.order_id_counter = 0
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self.order_direction = order_direction
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if indicators is None:
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self.indicators = [Empty(), Empty()]
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else:
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self.indicators = indicators
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self.log(f"Initialized [{self.strategy_mode}] Mode with Kalman Symmetry.")
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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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# 1. 计算核心指标
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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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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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# 2. 更新卡尔曼滤波器
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if not self.kalman_initialized:
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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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# 3. 计算对称性因子：偏离度 (Deviation in ATR)
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deviation_in_atr = (closes[-1] - kalman_price) / current_atr
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# 4. 状态校验与持仓管理
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position_volume = self.get_current_positions().get(self.symbol, 0)
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if self.trading:
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if position_volume != 0:
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self.manage_logic(position_volume, bar_history[-1], current_atr, kalman_price, deviation_in_atr)
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else:
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# 波动率过滤：只在波动率处于自身中高水平时入场
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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.evaluate_entry_signal(bar_history[-1], deviation_in_atr, current_atr, open_price)
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def manage_logic(self, volume: int, current_bar: Bar, current_atr: float, kalman_price: float, dev: float):
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"""
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基于对称因子的出场逻辑
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"""
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meta = self.position_meta.get(self.symbol)
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if not meta: return
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# A. 保护性硬止损 (防止跳空或极端行情)
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initial_stop = meta.get('initial_stop_price', 0)
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if (volume > 0 and current_bar.low <= initial_stop) or (volume < 0 and current_bar.high >= initial_stop):
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self.log(f"Hard Stop Hit. Price: {current_bar.close}")
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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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# B. 对称因子出场逻辑
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if self.strategy_mode == 'TREND':
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if volume > 0:
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trend_floor = kalman_price - self.trend_trailing_atr * current_atr
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if dev < 0:
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self.log(f"TREND: Structural Floor Hit at {trend_floor:.4f}")
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self.close_position("CLOSE_LONG", abs(volume))
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else:
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trend_ceiling = kalman_price + self.trend_trailing_atr * current_atr
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if dev > 0:
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self.log(f"TREND: Structural Ceiling Hit at {trend_ceiling:.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：出场基于“均值修复成功”或“偏离失控止损”
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# 1. 目标达成：回归到均值附近 (止盈)
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if volume > 0:
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trend_floor = kalman_price - self.trend_trailing_atr * current_atr
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if dev > 0:
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self.log(f"TREND: Structural Floor Hit at {trend_floor:.4f}")
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self.close_position("CLOSE_LONG", abs(volume))
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else:
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trend_ceiling = kalman_price + self.trend_trailing_atr * current_atr
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if dev < 0:
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self.log(f"TREND: Structural Ceiling Hit at {trend_ceiling:.4f}")
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self.close_position("CLOSE_SHORT", abs(volume))
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def evaluate_entry_signal(self, current_bar: Bar, dev: float, current_atr: float, open_price: float):
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"""
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基于对称因子的入场逻辑
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"""
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direction = None
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if self.strategy_mode == 'TREND':
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# 趋势：顺着偏离方向入场
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if dev > self.entry_threshold_atr and self.indicators[0].is_condition_met(*self.get_indicator_tuple()):
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direction = "BUY"
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elif dev < -self.entry_threshold_atr and self.indicators[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：逆着偏离方向入场
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if dev > self.entry_threshold_atr and self.indicators[1].is_condition_met(*self.get_indicator_tuple()):
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direction = "SELL" # 超买做空
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elif dev < -self.entry_threshold_atr and self.indicators[0].is_condition_met(*self.get_indicator_tuple()):
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direction = "BUY" # 超卖做多
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if direction:
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# 使用最小变动单位修正价格（此处假设最小变动为0.5，实盘应从context获取）
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tick_size = 1
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entry_price = open_price
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# 设置保护性硬止损
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stop_offset = self.stop_loss_atr * current_atr
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stop_price = entry_price - stop_offset if direction == "BUY" else entry_price + stop_offset
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meta = {'entry_price': entry_price, 'initial_stop_price': stop_price}
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self.log(f"Entry Signal: {self.strategy_mode} | {direction} | Dev: {dev:.2f}")
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self.send_custom_order(entry_price, direction, self.trade_volume, "OPEN", meta)
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# --- 辅助函数 ---
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def send_custom_order(self, price: float, direction: str, volume: int, offset: str, meta: Dict):
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self.position_meta[self.symbol] = meta
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order_type = "LIMIT"
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order_id = f"{self.symbol}_{direction}_{order_type}_{self.order_id_counter}"
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self.order_id_counter += 1
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order = Order(
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id=order_id, symbol=self.symbol, direction=direction,
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volume=volume, price_type=order_type,
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submitted_time=self.get_current_time(), offset=offset, limit_price=price
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)
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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, direction: str, volume: int):
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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(
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id=order_id, symbol=self.symbol, direction=direction,
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volume=volume, price_type="MARKET",
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submitted_time=self.get_current_time(), offset="CLOSE"
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)
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self.send_order(order)
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self.position_meta.pop(self.symbol, None)
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self.save_state(self.position_meta)
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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: States Reset.")
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