81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
# -*- coding: utf-8 -*-
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"""网格回撤止盈状态机。
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该模块只负责记录每个持仓的最高盈利网格，并判断当前盈亏率是否从
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峰值网格回撤。它不包含下单逻辑，由主策略和 Upmax 根据返回的状态决定是否卖出。
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"""
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from dataclasses import dataclass
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from enum import Enum
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import math
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import threading
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class GridState(str, Enum):
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"""单次盈亏率观察后的网格状态。"""
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ARMED = "armed" # 首次记录该持仓的峰值网格
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RAISED = "raised" # 盈利继续上升，峰值网格已抬高
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RETREAT = "retreat" # 从峰值网格回撤，应由调用方执行止盈
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STEADY = "steady" # 仍处于当前峰值网格，继续持有
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@dataclass(frozen=True)
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class GridObservation:
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"""一次网格观察的不可变结果。"""
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state: GridState
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current_grid: int # 当前盈亏率所处的网格
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peak_grid: int # 该持仓自观察以来的最高网格
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class GridTrailingTracker:
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"""按持仓键隔离、线程安全的峰值网格跟踪器。"""
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def __init__(self, step: float = 1.0):
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"""
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Args:
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step: 单个网格的盈亏率跨度（百分点），必须大于 0。
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"""
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if step <= 0:
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raise ValueError("grid step must be positive")
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self._step = step
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# key 由调用方组成“账户 + 股票代码”，防止多账户状态串扰。
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self._peaks: dict[str, int] = {}
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# 主策略和回调线程可能并发访问，所有峰值读写均在同一把锁内。
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self._lock = threading.Lock()
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def observe(self, position_key: str, pnl_rate: float) -> GridObservation:
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"""记录当前盈亏率，并返回相对于历史峰值的状态。"""
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# floor 保证负数盈亏率也按完整网格向下归档。
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current_grid = math.floor(pnl_rate / self._step)
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with self._lock:
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peak_grid = self._peaks.get(position_key)
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# 第一次看到该持仓：建立基准，不触发止盈。
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if peak_grid is None:
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self._peaks[position_key] = current_grid
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return GridObservation(GridState.ARMED, current_grid, current_grid)
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# 进入更高网格：更新峰值，继续持有。
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if current_grid > peak_grid:
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self._peaks[position_key] = current_grid
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return GridObservation(GridState.RAISED, current_grid, current_grid)
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# 跌破峰值网格：报告回撤，但保留峰值直到卖出成功后 clear。
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if current_grid < peak_grid:
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return GridObservation(GridState.RETREAT, current_grid, peak_grid)
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return GridObservation(GridState.STEADY, current_grid, peak_grid)
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def clear(self, position_key: str) -> None:
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"""持仓卖出成功后删除峰值，使下次建仓从新状态开始。"""
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with self._lock:
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self._peaks.pop(position_key, None)
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def retain(self, position_keys) -> None:
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"""删除已不在券商持仓中的峰值，避免同代码重新开仓继承旧状态。"""
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active = set(position_keys)
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with self._lock:
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self._peaks = {key: value for key, value in self._peaks.items() if key in active}
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