1、vp策略-v2
This commit is contained in:
@@ -236,12 +236,12 @@ if __name__ == "__main__":
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# 这种方式适合获取相对较短或中等长度的历史K线数据。
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df_if_backtest_daily = collect_and_save_tqsdk_data_stream(
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symbol="KQ.m@DCE.v",
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symbol="KQ.m@CZCE.CF",
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# symbol='SHFE.rb2510',
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# symbol='KQ.i@SHFE.bu',
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freq="min15",
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start_date_str="2021-01-01",
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end_date_str="2025-09-20",
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end_date_str="2025-10-20",
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mode="backtest", # 指定为回测模式
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tq_user=TQ_USER_NAME,
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tq_pwd=TQ_PASSWORD,
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@@ -120,7 +120,7 @@ class ResultAnalyzer:
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) # 明确标题
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print("图表绘制完成。")
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def analyze_indicators(self):
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def analyze_indicators(self, profit_offset: float = 0.0) -> None:
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"""
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分析所有平仓交易的指标值与实现盈亏的关系，并绘制累积盈亏曲线图。
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图表将展示指标值区间与对应累积盈亏的关系，帮助找出具有概率优势的指标区间。
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@@ -156,7 +156,7 @@ class ResultAnalyzer:
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and np.isnan(trade.indicator_dict[indicator_name])
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):
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indi_values.append(trade.indicator_dict[indicator_name])
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pnls.append(trade.realized_pnl)
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pnls.append(trade.realized_pnl - profit_offset)
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if not indi_values:
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print(f"指标 '{indicator_name}' 没有对应的有效平仓交易数据。跳过绘图。")
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@@ -3,108 +3,103 @@
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from datetime import datetime
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from typing import Optional, Any, Dict, TYPE_CHECKING
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# 使用 TYPE_CHECKING 避免循环导入，只在类型检查时导入 BacktestEngine
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if TYPE_CHECKING:
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from .backtest_engine import BacktestEngine
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from .execution_simulator import ExecutionSimulator
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from .data_manager import DataManager
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from .core_data import Bar, Order # 确保导入 Order
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from .state_repo import StateRepository
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from .execution_simulator import ExecutionSimulator
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from .data_manager import DataManager
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from .core_data import Bar, Order
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class BacktestContext:
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"""
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回测上下文，用于连接策略与数据管理器、模拟器。
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回测上下文，用于连接策略与数据管理器、模拟器和状态持久化。
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策略通过此上下文与回测引擎进行交互。
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"""
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def __init__(self, data_manager: 'DataManager', simulator: 'ExecutionSimulator'):
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def __init__(self,
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data_manager: 'DataManager',
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simulator: 'ExecutionSimulator',
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state_repository: 'StateRepository'): # MODIFIED: 新增参数
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"""
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初始化回测上下文。
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Args:
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data_manager (DataManager): 数据管理器实例。
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simulator (ExecutionSimulator): 交易模拟器实例。
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state_repository (StateRepository): 状态管理仓储实例，用于持久化策略状态。
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"""
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self._data_manager = data_manager
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self._simulator = simulator
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self._state_repository = state_repository # NEW: 存储状态仓储实例
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self._current_bar: Optional['Bar'] = None
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self._engine: Optional['BacktestEngine'] = None # 添加对引擎的引用
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self._engine = None
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# --- 新增：状态管理功能 ---
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def save_state(self, state: Dict[str, Any]) -> None:
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"""
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保存策略的当前状态。
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策略应在适当的时机（例如，每日结束、策略关闭时）调用此方法
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来持久化其内部变量。
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Args:
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state (Dict[str, Any]): 包含策略状态的字典。
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"""
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self._state_repository.save(state)
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def load_state(self) -> Dict[str, Any]:
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"""
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加载策略的历史状态。
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策略应在初始化时调用此方法来恢复之前的运行状态。
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如果不存在历史状态，将返回一个空字典。
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Returns:
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Dict[str, Any]: 包含策略历史状态的字典。
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"""
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return self._state_repository.load()
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# --- 现有功能保持不变 ---
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def set_current_bar(self, bar: 'Bar'):
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"""
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设置当前正在处理的 K 线数据。
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由 BacktestEngine 调用。
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"""
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self._current_bar = bar
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def get_current_bar(self) -> Optional['Bar']:
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"""
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获取当前正在处理的 K 线数据。
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策略可以通过此方法获取最新 K 线。
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"""
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return self._current_bar
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def get_current_time(self) -> datetime:
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"""
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获取当前模拟时间。
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"""
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return self._simulator.get_current_time()
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def get_current_positions(self) -> Dict[str, int]:
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"""
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获取当前所有持仓。
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"""
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return self._simulator.get_current_positions()
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def get_pending_orders(self) -> Dict[str, 'Order']:
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"""
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获取当前所有待处理（未成交）订单。
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"""
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return self._simulator.get_pending_orders()
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def get_account_cash(self) -> float:
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"""
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获取当前可用现金。
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"""
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return self._simulator.cash
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def get_average_position_price(self, symbol: str) -> Optional[float]:
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"""
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获取指定合约的平均持仓成本。
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"""
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return self._simulator.get_average_position_price(symbol)
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def send_order(self, order: 'Order') -> Optional['Order']:
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"""
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策略通过此方法发送订单到模拟器。
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"""
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return self._simulator.send_order_to_pending(order)
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def cancel_order(self, order_id: str) -> bool:
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"""
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策略通过此方法取消指定ID的订单。
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"""
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return self._simulator.cancel_order(order_id)
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def set_engine(self, engine: 'BacktestEngine'):
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"""
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设置对 BacktestEngine 实例的引用。
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由 BacktestEngine 在初始化时调用，用于允许 Context 访问 Engine 的状态。
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"""
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self._engine = engine
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def get_bar_history(self):
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return self._engine.get_bar_history()
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def get_price_history(self, key: str):
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return self._engine.get_price_history(key)
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@property
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def is_rollover_bar(self) -> bool:
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"""
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属性：判断当前 K 线是否为换月 K 线（即新合约的第一根 K 线）。
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用于在换月时禁止策略开仓。
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"""
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if self._engine:
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return self._engine.is_rollover_bar
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# 如果没有设置引擎引用，默认不认为是换月 K 线
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# 这通常发生在测试 Context 本身时，或 Engine 初始化不完整的情况。
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return False
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return False
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@@ -5,12 +5,14 @@ import numpy as np
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import pandas as pd
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from src.indicators.base_indicators import Indicator
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from .common_utils import generate_strategy_identifier
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# 导入所有需要协调的模块
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from .core_data import Bar, Order, Trade, PortfolioSnapshot
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from .data_manager import DataManager
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from .execution_simulator import ExecutionSimulator
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from .backtest_context import BacktestContext
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from .state_repo import MemoryStateRepository
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from .strategies.base_strategy import Strategy
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class BacktestEngine:
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@@ -50,7 +52,8 @@ class BacktestEngine:
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commission_rate=commission_rate
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)
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# 传入引擎自身给 context，以便 context 可以获取引擎的状态（如 is_rollover_bar）
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self.context = BacktestContext(self.data_manager, self.simulator)
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identifier = generate_strategy_identifier(strategy_class, strategy_params)
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self.context = BacktestContext(self.data_manager, self.simulator, MemoryStateRepository(identifier))
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self.context.set_engine(self) # 建立 Context 到 Engine 的引用
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# self.current_segment_symbol = current_segment_symbol # 此行移除或作为内部变量动态管理
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@@ -212,3 +212,93 @@ def is_bar_pre_close_period(
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# 6. 判断当前系统时间是否在这个窗口内
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# 窗口定义为 [pre_close_window_start_time, final_bar_end_time)，即包含开始时间，不包含结束时间
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return pre_close_window_start_time <= current_system_time < final_bar_end_time
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import hashlib
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import json
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from typing import Type, Any, Dict, Union
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# --- 辅助编码器，用于处理参数中的 “类” 对象 (保持不变) ---
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class StrategyParamEncoder(json.JSONEncoder):
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def default(self, o: Any) -> Any:
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if isinstance(o, type):
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return f"{o.__module__}.{o.__name__}"
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return super().default(o)
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# --- 新增：递归净化函数，用于移除实例 ---
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def _clean_params_for_hashing(data: Any) -> Any:
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"""
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递归地“净化”参数数据，将所有非基本类型、非类的实例对象替换为 None。
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这确保了只有可序列化的配置值会影响最终的哈希结果。
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Args:
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data: 输入的数据，可以是字典、列表、或任何值。
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Returns:
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一个净化后的版本，其中所有实例对象都被替换为 None。
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"""
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# 1. 基本情况：如果数据是基本类型或一个“类”，直接返回
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if isinstance(data, (str, int, float, bool, type(None), type)):
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return data
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# 2. 递归情况：处理字典
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if isinstance(data, dict):
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# 遍历字典，对每个值进行递归净化
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return {key: _clean_params_for_hashing(value) for key, value in data.items()}
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# 3. 递归情况：处理列表和元组
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if isinstance(data, (list, tuple)):
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# 遍历序列，对每个元素进行递归净化
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return type(data)([_clean_params_for_hashing(item) for item in data])
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# 4. 最终情况：如果代码运行到这里，说明 `data` 是一个我们不希望
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# 纳入哈希计算的实例对象。我们用 None 替换它。
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return None
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def generate_strategy_identifier(
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strategy_class: Type,
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parameters: Dict[str, Any]
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) -> str:
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"""
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为策略实例生成一个唯一的、确定性的标识符 (忽略实例版)。
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此版本会主动忽略参数中的实例对象，在计算哈希前将它们替换为 None。
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这允许策略接收已实例化的组件，同时确保标识符的唯一性仅由
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可配置的、可序列化的参数决定。
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Args:
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strategy_class (Type): 策略的类本身。
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parameters (Dict[str, Any]): 用于初始化该策略的参数字典。
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Returns:
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str: 一个唯一的、适合用作文件名或Redis键的标识符。
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"""
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# 1. 获取模块路径和类名
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module_path = strategy_class.__module__
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class_name = strategy_class.__name__
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# 2. [核心修改] 首先，净化参数字典，移除实例对象
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cleaned_parameters = _clean_params_for_hashing(parameters)
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# 3. 对净化后的参数进行稳定序列化
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# 这里仍然需要自定义的Encoder来处理参数中合法的“类”对象。
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try:
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param_string = json.dumps(
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cleaned_parameters,
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sort_keys=True,
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separators=(',', ':'),
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cls=StrategyParamEncoder
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)
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except TypeError as e:
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# 理论上，经过净化后，这里不应该再出现TypeError，但作为保护性代码保留
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raise TypeError(
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f"净化后的策略 '{class_name}' 参数依然无法序列化。请检查参数结构。原始错误: {e}"
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)
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# 4. 计算参数字符串的哈希值
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param_hash = hashlib.md5(param_string.encode('utf-8')).hexdigest()
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# 5. 组合成最终的标识符
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identifier = f"{module_path}.{class_name}_{param_hash}"
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return identifier
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134
src/state_repo.py
Normal file
134
src/state_repo.py
Normal file
@@ -0,0 +1,134 @@
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from abc import ABC, abstractmethod
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from typing import Any, Dict
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class StateRepository(ABC):
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"""
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状态仓储的抽象基类 (优化版)。
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每个仓储实例都与一个唯一标识符绑定，专门负责该标识符对应状态的持久化。
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这种设计简化了API，并使其职责更加单一。
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"""
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def __init__(self, identifier: str):
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"""
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:param identifier: 此仓储实例绑定的唯一状态标识符。
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"""
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self.identifier = identifier
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@abstractmethod
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def save(self, state: Dict[str, Any]) -> None:
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"""
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持久化当前的状态。
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:param state: 需要保存的状态数据。
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"""
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pass
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@abstractmethod
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def load(self) -> Dict[str, Any]:
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"""
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加载历史状态。
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:return: 返回保存的状态字典。若无历史状态，则返回空字典。
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"""
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pass
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class MemoryStateRepository(StateRepository):
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"""
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一个完全基于内存的状态仓储。
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状态仅在程序的生命周期内存在。适用于回测和单元测试。
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"""
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# 使用一个类级别的字典来模拟全局存储，以便在同一进程中多次创建同名实例时能找回状态
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_global_storage: Dict[str, Dict[str, Any]] = {}
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def __init__(self, identifier: str):
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super().__init__(identifier)
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# 确保该标识符在全局存储中有个位置
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if self.identifier not in MemoryStateRepository._global_storage:
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MemoryStateRepository._global_storage[self.identifier] = {}
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print(f"内存仓储已初始化，管理ID: '{self.identifier}'")
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def save(self, state: Dict[str, Any]) -> None:
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# 使用 .copy() 存储副本，防止外部修改影响内部状态
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MemoryStateRepository._global_storage[self.identifier] = state.copy()
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def load(self) -> Dict[str, Any]:
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# 返回副本，防止调用方意外修改内部状态
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state = MemoryStateRepository._global_storage.get(self.identifier, {}).copy()
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return state
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import json
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import os
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class JsonFileStateRepository(StateRepository):
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"""
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使用JSON文件作为后端的状态仓储 (简化版)。
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本实现假设在任何时间点，最多只有一个进程会写入与本实例关联的
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特定文件。因此，它采用直接写入文件的方式，没有包含处理并发
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写入的原子操作逻辑。
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"""
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def __init__(self, identifier: str, storage_path: str = './states'):
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super().__init__(identifier)
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self.storage_path = storage_path
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self._file_path = os.path.join(self.storage_path, f"{self.identifier}.json")
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if not os.path.exists(self.storage_path):
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os.makedirs(self.storage_path)
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print(f"JSON文件仓储(简化版)已初始化，将管理文件: '{self._file_path}'")
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def save(self, state: Dict[str, Any]) -> None:
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"""
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将状态直接写入到本实例绑定的文件中。
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注意：此操作不是原子的。
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"""
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try:
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with open(self._file_path, 'w', encoding='utf-8') as f:
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json.dump(state, f, indent=4, ensure_ascii=False)
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print(f"(JSON) 状态已直接保存至 '{self._file_path}'")
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except (IOError, TypeError) as e:
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print(f"错误：无法将状态写入文件 '{self._file_path}'。错误: {e}")
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def load(self) -> Dict[str, Any]:
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"""
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从本实例绑定的文件中加载状态。
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"""
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if not os.path.exists(self._file_path):
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return {}
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try:
|
||||
with open(self._file_path, 'r', encoding='utf-8') as f:
|
||||
state = json.load(f)
|
||||
print(f"(JSON) 从 '{self._file_path}' 加载了状态。")
|
||||
return state
|
||||
except (IOError, json.JSONDecodeError) as e:
|
||||
print(f"错误：无法从 '{self._file_path}' 加载状态。文件可能已损坏。错误: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
import redis
|
||||
class RedisStateRepository(StateRepository):
|
||||
DEFAULT_EXPIRATION_SECONDS = 7 * 24 * 60 * 60
|
||||
|
||||
def __init__(self, identifier: str, host='localhost', port=6379, db=0, expiration_sec: int = DEFAULT_EXPIRATION_SECONDS):
|
||||
super().__init__(identifier)
|
||||
try:
|
||||
self.redis_client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
|
||||
self.redis_client.ping()
|
||||
self.expiration_seconds = expiration_sec
|
||||
print(f"Redis仓储已连接，将管理Key: '{self.identifier}'")
|
||||
except redis.exceptions.ConnectionError as e:
|
||||
raise ConnectionError(f"无法连接到Redis服务器 at {host}:{port}。") from e
|
||||
|
||||
def save(self, state: Dict[str, Any]) -> None:
|
||||
serialized_state = json.dumps(state)
|
||||
self.redis_client.set(self.identifier, serialized_state, ex=self.expiration_seconds)
|
||||
print(f"(Redis) Key '{self.identifier}' 的状态已保存。")
|
||||
|
||||
def load(self) -> Dict[str, Any]:
|
||||
serialized_state = self.redis_client.get(self.identifier)
|
||||
if serialized_state is None:
|
||||
return {}
|
||||
return json.loads(serialized_state)
|
||||
File diff suppressed because one or more lines are too long
@@ -276,13 +276,3 @@ class ValueMigrationStrategy(Strategy):
|
||||
)
|
||||
self.send_order(order)
|
||||
|
||||
|
||||
def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
|
||||
# ... (与之前版本相同) ...
|
||||
order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
|
||||
self.order_id_counter += 1
|
||||
order = Order(
|
||||
id=order_id, symbol=self.symbol, direction=direction, volume=volume,
|
||||
price_type="MARKET", submitted_time=self.get_current_time(), offset=offset
|
||||
)
|
||||
self.send_order(order)
|
||||
|
||||
315
src/strategies/ValueMigrationStrategy/ValueMigrationStrategy2.py
Normal file
315
src/strategies/ValueMigrationStrategy/ValueMigrationStrategy2.py
Normal file
@@ -0,0 +1,315 @@
|
||||
# =====================================================================================
|
||||
# 以下是新增的 ValueMigrationStrategy 策略代码
|
||||
# =====================================================================================
|
||||
from collections import deque
|
||||
from datetime import timedelta, time
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import List, Any, Optional, Dict
|
||||
|
||||
import talib
|
||||
|
||||
from src.core_data import Bar, Order
|
||||
from src.strategies.ValueMigrationStrategy.data_class import ProfileStats, calculate_profile_from_bars
|
||||
from src.strategies.base_strategy import Strategy
|
||||
|
||||
|
||||
# = ===================================================================
|
||||
# 全局辅助函数 (Global Helper Functions)
|
||||
# 将这些函数放在文件顶部，以便所有策略类都能调用
|
||||
# =====================================================================
|
||||
|
||||
def compute_price_volume_distribution(bars: List[Bar], tick_size: float) -> Optional[pd.Series]:
|
||||
"""
|
||||
[全局函数] 从K线数据中计算出原始的价格-成交量分布。
|
||||
"""
|
||||
if not bars:
|
||||
return None
|
||||
|
||||
data = []
|
||||
# 为了性能，我们只处理有限数量的bars，防止内存问题
|
||||
# 在实际应用中，更高效的实现是必要的
|
||||
for bar in bars[-500:]: # 添加一个安全限制
|
||||
price_range = np.arange(bar.low, bar.high + tick_size, tick_size)
|
||||
if len(price_range) == 0 or bar.volume == 0: continue
|
||||
|
||||
# 将成交量近似分布到K线覆盖的每个tick上
|
||||
volume_per_tick = bar.volume / len(price_range)
|
||||
for price in price_range:
|
||||
data.append({'price': price, 'volume': volume_per_tick})
|
||||
|
||||
if not data:
|
||||
return None
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
if df.empty:
|
||||
return None
|
||||
|
||||
return df.groupby('price')['volume'].sum().sort_index()
|
||||
|
||||
|
||||
# 确保在文件顶部导入
|
||||
from scipy.signal import find_peaks
|
||||
|
||||
|
||||
def find_hvns_with_distance(price_volume_dist: pd.Series, distance_in_ticks: int) -> List[float]:
|
||||
"""
|
||||
[全局函数] 使用峰值查找算法，根据峰值间的最小距离来识别HVNs。
|
||||
|
||||
Args:
|
||||
price_volume_dist: 价格-成交量分布序列。
|
||||
distance_in_ticks: 两个HVN之间必须间隔的最小tick数量。
|
||||
|
||||
Returns:
|
||||
一个包含所有被识别出的HVN价格的列表。
|
||||
"""
|
||||
if price_volume_dist.empty or len(price_volume_dist) < 3:
|
||||
return []
|
||||
|
||||
# distance参数确保找到的峰值之间至少相隔N个点
|
||||
peaks_indices, _ = find_peaks(price_volume_dist.values, distance=distance_in_ticks)
|
||||
|
||||
if len(peaks_indices) == 0:
|
||||
return [price_volume_dist.idxmax()] # 默认返回POC
|
||||
|
||||
hvn_prices = price_volume_dist.index[peaks_indices].tolist()
|
||||
return hvn_prices
|
||||
|
||||
|
||||
def find_hvns_strict(price_volume_dist: pd.Series, window_radius: int) -> List[float]:
|
||||
"""
|
||||
[全局函数] 使用严格的“滚动窗口最大值”定义来识别HVNs。
|
||||
一个点是HVN，当且仅当它的成交量大于其左右各 `window_radius` 个点的成交量。
|
||||
|
||||
Args:
|
||||
price_volume_dist: 价格-成交量分布序列。
|
||||
window_radius: 定义了检查窗口的半径 (即您所说的 N)。
|
||||
|
||||
Returns:
|
||||
一个包含所有被识别出的HVN价格的列表。
|
||||
"""
|
||||
if price_volume_dist.empty or window_radius == 0:
|
||||
return [price_volume_dist.idxmax()] if not price_volume_dist.empty else []
|
||||
|
||||
# 1. 确保价格序列是连续的，用0填充缺失的ticks
|
||||
full_price_range = np.arange(price_volume_dist.index.min(),
|
||||
price_volume_dist.index.max() + price_volume_dist.index.to_series().diff().min(),
|
||||
price_volume_dist.index.to_series().diff().min())
|
||||
continuous_dist = price_volume_dist.reindex(full_price_range, fill_value=0)
|
||||
|
||||
# 2. 计算滚动窗口最大值
|
||||
window_size = 2 * window_radius + 1
|
||||
rolling_max = continuous_dist.rolling(window=window_size, center=True).max()
|
||||
|
||||
# 3. 找到那些自身成交量就等于其窗口最大值的点
|
||||
is_hvn = (continuous_dist == rolling_max) & (continuous_dist > 0)
|
||||
hvn_prices = continuous_dist[is_hvn].index.tolist()
|
||||
|
||||
# 4. 处理平顶山：如果连续多个点都是HVN，只保留中间那个
|
||||
if not hvn_prices:
|
||||
return [price_volume_dist.idxmax()] # 如果找不到，返回POC
|
||||
|
||||
final_hvns = []
|
||||
i = 0
|
||||
while i < len(hvn_prices):
|
||||
# 找到一个连续HVN块
|
||||
j = i
|
||||
while j + 1 < len(hvn_prices) and (hvn_prices[j + 1] - hvn_prices[j]) < (
|
||||
2 * price_volume_dist.index.to_series().diff().min()):
|
||||
j += 1
|
||||
|
||||
# 取这个连续块的中间点
|
||||
middle_index = i + (j - i) // 2
|
||||
final_hvns.append(hvn_prices[middle_index])
|
||||
|
||||
i = j + 1
|
||||
|
||||
return final_hvns
|
||||
|
||||
|
||||
# 确保在文件顶部导入
|
||||
from scipy.signal import find_peaks
|
||||
|
||||
|
||||
# =====================================================================================
|
||||
# 以下是V2版本的、简化了状态管理的 HVNPullbackStrategy 代码
|
||||
# =====================================================================================
|
||||
|
||||
class ValueMigrationStrategy(Strategy):
|
||||
"""
|
||||
一个基于动态HVN突破后回测的量化交易策略。(V2: 简化状态管理)
|
||||
|
||||
V2版本简化了内部状态管理，移除了基于order_id的复杂元数据传递，
|
||||
使用更直接、更健壮的单一状态变量来处理挂单的止盈止损参数，
|
||||
完美适配“单次单持仓”的策略逻辑。
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
context: Any,
|
||||
main_symbol: str,
|
||||
enable_log: bool,
|
||||
trade_volume: int,
|
||||
tick_size: float = 1,
|
||||
profile_period: int = 100,
|
||||
recalc_interval: int = 4,
|
||||
hvn_distance_ticks: int = 20,
|
||||
entry_offset_atr: float = 0.0,
|
||||
stop_loss_atr: float = 1.0,
|
||||
take_profit_atr: float = 2.0,
|
||||
atr_period: int = 14,
|
||||
order_direction=None,
|
||||
indicators=[None, None],
|
||||
):
|
||||
super().__init__(context, main_symbol, enable_log)
|
||||
if order_direction is None:
|
||||
order_direction = ['BUY', 'SELL']
|
||||
|
||||
self.trade_volume = trade_volume
|
||||
self.tick_size = tick_size
|
||||
self.profile_period = profile_period
|
||||
self.recalc_interval = recalc_interval
|
||||
self.hvn_distance_ticks = hvn_distance_ticks
|
||||
self.entry_offset_atr = entry_offset_atr
|
||||
self.stop_loss_atr = stop_loss_atr
|
||||
self.take_profit_atr = take_profit_atr
|
||||
self.atr_period = atr_period
|
||||
|
||||
self.order_direction = order_direction
|
||||
self.indicator_long = indicators[0]
|
||||
self.indicator_short = indicators[1]
|
||||
|
||||
self.main_symbol = main_symbol
|
||||
self.order_id_counter = 0
|
||||
|
||||
self._bar_counter = 0
|
||||
self._cached_hvns: List[float] = []
|
||||
|
||||
# --- V2: 简化的状态管理 ---
|
||||
self._pending_sl_price: Optional[float] = None
|
||||
self._pending_tp_price: Optional[float] = None
|
||||
|
||||
def on_open_bar(self, open_price: float, symbol: str):
|
||||
self.symbol = symbol
|
||||
self._bar_counter += 1
|
||||
bar_history = self.get_bar_history()
|
||||
|
||||
required_len = max(self.profile_period, self.atr_period) + 1
|
||||
if len(bar_history) < required_len:
|
||||
return
|
||||
|
||||
# --- 1. 取消所有挂单并重置挂单状态 ---
|
||||
self.cancel_all_pending_orders(self.symbol)
|
||||
# self._pending_sl_price = None
|
||||
# self._pending_tp_price = None
|
||||
|
||||
# --- 2. 管理现有持仓 ---
|
||||
position_volume = self.get_current_positions().get(self.symbol, 0)
|
||||
if position_volume != 0:
|
||||
self.manage_open_position(position_volume, open_price)
|
||||
return
|
||||
|
||||
# --- 3. 周期性地计算HVNs ---
|
||||
if self._bar_counter % self.recalc_interval == 1:
|
||||
profile_bars = bar_history[-self.profile_period:]
|
||||
dist = compute_price_volume_distribution(profile_bars, self.tick_size)
|
||||
if dist is not None and not dist.empty:
|
||||
# self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks)
|
||||
self._cached_hvns = find_hvns_strict(dist, self.hvn_distance_ticks)
|
||||
self.log(f"New HVNs identified at: {[f'{p:.2f}' for p in self._cached_hvns]}")
|
||||
|
||||
if not self._cached_hvns: return
|
||||
|
||||
# --- 4. 评估新机会 (挂单逻辑) ---
|
||||
self.evaluate_entry_signal(bar_history)
|
||||
|
||||
def manage_open_position(self, volume: int, current_price: float):
|
||||
"""主动管理已开仓位的止盈止损。"""
|
||||
|
||||
# # [V2 关键逻辑]: 检测是否为新持仓
|
||||
# # 如果这是一个新持仓，并且我们有预设的止盈止损，就将其存入
|
||||
# if self._pending_sl_price is not None and self._pending_tp_price is not None:
|
||||
# meta = {'sl_price': self._pending_sl_price, 'tp_price': self._pending_tp_price}
|
||||
# self.position_meta = meta
|
||||
# self.log(f"新持仓确认。已设置TP/SL: {meta}")
|
||||
# else:
|
||||
# # 这种情况理论上不应发生，但作为保护
|
||||
# self.log("Error: New position detected but no pending TP/SL values found.")
|
||||
# self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume))
|
||||
# return
|
||||
|
||||
# [常规逻辑]: 检查止盈止损
|
||||
sl_price = self._pending_sl_price
|
||||
tp_price = self._pending_tp_price
|
||||
|
||||
if volume > 0: # 多头
|
||||
if current_price <= sl_price or current_price >= tp_price:
|
||||
action = "止损" if current_price <= sl_price else "止盈"
|
||||
self.log(f"多头{action}触发 at {current_price:.2f}")
|
||||
self.close_position("CLOSE_LONG", abs(volume))
|
||||
elif volume < 0: # 空头
|
||||
if current_price >= sl_price or current_price <= tp_price:
|
||||
action = "止损" if current_price >= sl_price else "止盈"
|
||||
self.log(f"空头{action}触发 at {current_price:.2f}")
|
||||
self.close_position("CLOSE_SHORT", abs(volume))
|
||||
|
||||
def evaluate_entry_signal(self, bar_history: List[Bar]):
|
||||
prev_close = bar_history[-2].close
|
||||
current_close = bar_history[-1].close
|
||||
|
||||
highs = np.array([b.high for b in bar_history], dtype=float)
|
||||
lows = np.array([b.low for b in bar_history], dtype=float)
|
||||
closes = np.array([b.close for b in bar_history], dtype=float)
|
||||
current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
|
||||
if current_atr < self.tick_size: return
|
||||
|
||||
for hvn in sorted(self._cached_hvns):
|
||||
# (为了简洁，买卖逻辑合并)
|
||||
direction = None
|
||||
if "BUY" in self.order_direction and (prev_close < hvn < current_close):
|
||||
direction = "BUY"
|
||||
pass_filter = self.indicator_long is None or self.indicator_long.is_condition_met(
|
||||
*self.get_indicator_tuple())
|
||||
elif "SELL" in self.order_direction and (prev_close > hvn > current_close):
|
||||
direction = "SELL"
|
||||
pass_filter = self.indicator_short is None or self.indicator_short.is_condition_met(
|
||||
*self.get_indicator_tuple())
|
||||
else:
|
||||
continue # 没有触发穿越
|
||||
|
||||
if direction and pass_filter:
|
||||
offset = self.entry_offset_atr * current_atr
|
||||
limit_price = hvn + offset if direction == "BUY" else hvn - offset
|
||||
|
||||
self.log(f"价格穿越HVN({hvn:.2f}). 在 {limit_price:.2f} 挂限价{direction}单。")
|
||||
self.send_hvn_limit_order(direction, limit_price, current_atr)
|
||||
return # 每次只挂一个单
|
||||
|
||||
def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
|
||||
# [V2 关键逻辑]: 直接更新实例变量
|
||||
self._pending_sl_price = limit_price - self.stop_loss_atr * entry_atr if direction == "BUY" else limit_price + self.stop_loss_atr * entry_atr
|
||||
self._pending_tp_price = limit_price + self.take_profit_atr * entry_atr if direction == "BUY" else limit_price - self.take_profit_atr * entry_atr
|
||||
|
||||
order_id = f"{self.symbol}_{direction}_LIMIT_{self.order_id_counter}"
|
||||
self.order_id_counter += 1
|
||||
|
||||
order = Order(
|
||||
id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume,
|
||||
price_type="LIMIT", limit_price=limit_price, submitted_time=self.get_current_time(),
|
||||
offset="OPEN"
|
||||
)
|
||||
self.send_order(order)
|
||||
|
||||
def close_position(self, direction: str, volume: int):
|
||||
self.send_market_order(direction, volume)
|
||||
|
||||
|
||||
def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
|
||||
order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
|
||||
self.order_id_counter += 1
|
||||
order = Order(
|
||||
id=order_id, symbol=self.symbol, direction=direction, volume=volume,
|
||||
price_type="MARKET", submitted_time=self.get_current_time(), offset=offset
|
||||
)
|
||||
self.send_order(order)
|
||||
317
src/strategies/ValueMigrationStrategy/ValueMigrationStrategy3.py
Normal file
317
src/strategies/ValueMigrationStrategy/ValueMigrationStrategy3.py
Normal file
@@ -0,0 +1,317 @@
|
||||
# =====================================================================================
|
||||
# 以下是新增的 ValueMigrationStrategy 策略代码
|
||||
# =====================================================================================
|
||||
from collections import deque
|
||||
from datetime import timedelta, time
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import List, Any, Optional, Dict
|
||||
|
||||
import talib
|
||||
|
||||
from src.core_data import Bar, Order
|
||||
from src.strategies.ValueMigrationStrategy.data_class import ProfileStats, calculate_profile_from_bars
|
||||
from src.strategies.base_strategy import Strategy
|
||||
|
||||
|
||||
# = ===================================================================
|
||||
# 全局辅助函数 (Global Helper Functions)
|
||||
# 将这些函数放在文件顶部，以便所有策略类都能调用
|
||||
# =====================================================================
|
||||
|
||||
def compute_price_volume_distribution(bars: List[Bar], tick_size: float) -> Optional[pd.Series]:
|
||||
"""
|
||||
[全局函数] 从K线数据中计算出原始的价格-成交量分布。
|
||||
"""
|
||||
if not bars:
|
||||
return None
|
||||
|
||||
data = []
|
||||
# 为了性能，我们只处理有限数量的bars，防止内存问题
|
||||
# 在实际应用中，更高效的实现是必要的
|
||||
for bar in bars[-500:]: # 添加一个安全限制
|
||||
price_range = np.arange(bar.low, bar.high + tick_size, tick_size)
|
||||
if len(price_range) == 0 or bar.volume == 0: continue
|
||||
|
||||
# 将成交量近似分布到K线覆盖的每个tick上
|
||||
volume_per_tick = bar.volume / len(price_range)
|
||||
for price in price_range:
|
||||
data.append({'price': price, 'volume': volume_per_tick})
|
||||
|
||||
if not data:
|
||||
return None
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
if df.empty:
|
||||
return None
|
||||
|
||||
return df.groupby('price')['volume'].sum().sort_index()
|
||||
|
||||
|
||||
# 确保在文件顶部导入
|
||||
from scipy.signal import find_peaks
|
||||
|
||||
|
||||
def find_hvns_with_distance(price_volume_dist: pd.Series, distance_in_ticks: int) -> List[float]:
|
||||
"""
|
||||
[全局函数] 使用峰值查找算法，根据峰值间的最小距离来识别HVNs。
|
||||
|
||||
Args:
|
||||
price_volume_dist: 价格-成交量分布序列。
|
||||
distance_in_ticks: 两个HVN之间必须间隔的最小tick数量。
|
||||
|
||||
Returns:
|
||||
一个包含所有被识别出的HVN价格的列表。
|
||||
"""
|
||||
if price_volume_dist.empty or len(price_volume_dist) < 3:
|
||||
return []
|
||||
|
||||
# distance参数确保找到的峰值之间至少相隔N个点
|
||||
peaks_indices, _ = find_peaks(price_volume_dist.values, distance=distance_in_ticks)
|
||||
|
||||
if len(peaks_indices) == 0:
|
||||
return [price_volume_dist.idxmax()] # 默认返回POC
|
||||
|
||||
hvn_prices = price_volume_dist.index[peaks_indices].tolist()
|
||||
return hvn_prices
|
||||
|
||||
|
||||
# 确保在文件顶部导入
|
||||
from scipy.signal import find_peaks
|
||||
|
||||
|
||||
# =====================================================================================
|
||||
# 以下是V2版本的、简化了状态管理的 HVNPullbackStrategy 代码
|
||||
# =====================================================================================
|
||||
|
||||
# 引入必要的类型，确保代码清晰
|
||||
from typing import Any, Dict, Optional, List
|
||||
import numpy as np
|
||||
import talib
|
||||
|
||||
|
||||
class ValueMigrationStrategy(Strategy):
|
||||
"""
|
||||
一个基于动态HVN突破后回测的量化交易策略。(V3: 集成上下文状态管理)
|
||||
|
||||
V3版本完全集成BacktestContext的状态管理功能，实现了策略重启后的状态恢复。
|
||||
- 状态被简化为两个核心变量：_pending_sl_price 和 _pending_tp_price。
|
||||
- 在策略初始化时安全地加载状态，并兼容空状态或旧版状态。
|
||||
- 在下单或平仓时立即持久化状态，确保数据一致性。
|
||||
- 增加了逻辑检查，处理重启后可能出现的状态与实际持仓不一致的问题。
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
context: Any, # 通常会是 BacktestContext
|
||||
main_symbol: str,
|
||||
enable_log: bool,
|
||||
trade_volume: int,
|
||||
tick_size: float = 1,
|
||||
profile_period: int = 100,
|
||||
recalc_interval: int = 4,
|
||||
hvn_distance_ticks: int = 1,
|
||||
entry_offset_atr: float = 0.0,
|
||||
stop_loss_atr: float = 1.0,
|
||||
take_profit_atr: float = 1.0,
|
||||
atr_period: int = 14,
|
||||
order_direction=None,
|
||||
indicators=[None, None],
|
||||
):
|
||||
super().__init__(context, main_symbol, enable_log)
|
||||
# --- 参数初始化 (保持不变) ---
|
||||
if order_direction is None:
|
||||
order_direction = ['BUY', 'SELL']
|
||||
self.trade_volume = trade_volume
|
||||
self.tick_size = tick_size
|
||||
self.profile_period = profile_period
|
||||
self.recalc_interval = recalc_interval
|
||||
self.hvn_distance_ticks = hvn_distance_ticks
|
||||
self.entry_offset_atr = entry_offset_atr
|
||||
self.stop_loss_atr = stop_loss_atr
|
||||
self.take_profit_atr = take_profit_atr
|
||||
self.atr_period = atr_period
|
||||
self.order_direction = order_direction
|
||||
self.indicator_long = indicators[0]
|
||||
self.indicator_short = indicators[1]
|
||||
self.main_symbol = main_symbol
|
||||
self.order_id_counter = 0
|
||||
self._bar_counter = 0
|
||||
self._cached_hvns: List[float] = []
|
||||
|
||||
# --- 新增: 初始化时加载状态 ---
|
||||
self._pending_sl_price: Optional[float] = None
|
||||
self._pending_tp_price: Optional[float] = None
|
||||
self._load_state_from_context()
|
||||
|
||||
def _get_state_dict(self) -> Dict[str, Any]:
|
||||
"""一个辅助函数，用于生成当前需要保存的状态字典。"""
|
||||
return {
|
||||
"_pending_sl_price": self._pending_sl_price,
|
||||
"_pending_tp_price": self._pending_tp_price,
|
||||
}
|
||||
|
||||
def _load_state_from_context(self):
|
||||
"""
|
||||
[新增] 从上下文中加载状态，并进行健壮性处理。
|
||||
"""
|
||||
loaded_state = self.context.load_state()
|
||||
if not loaded_state:
|
||||
self.log("未找到历史状态，进行全新初始化。")
|
||||
return
|
||||
|
||||
# 使用 .get() 方法安全地读取，即使key不存在或state为空也不会报错。
|
||||
# 这完美解决了“读取的state的key不一样”的问题。
|
||||
self._pending_sl_price = loaded_state.get("_pending_sl_price")
|
||||
self._pending_tp_price = loaded_state.get("_pending_tp_price")
|
||||
|
||||
if self._pending_sl_price is not None:
|
||||
self.log(f"成功从上下文加载状态: SL={self._pending_sl_price}, TP={self._pending_tp_price}")
|
||||
else:
|
||||
self.log("加载的状态为空或格式不兼容，视为全新初始化。")
|
||||
|
||||
def on_open_bar(self, open_price: float, symbol: str):
|
||||
self.symbol = symbol
|
||||
self._bar_counter += 1
|
||||
bar_history = self.get_bar_history()
|
||||
|
||||
required_len = max(self.profile_period, self.atr_period) + 1
|
||||
if len(bar_history) < required_len:
|
||||
return
|
||||
|
||||
# 取消所有挂单，这符合原逻辑，确保每根bar都是新的开始
|
||||
self.cancel_all_pending_orders(self.symbol)
|
||||
|
||||
position_volume = self.get_current_positions().get(self.symbol, 0)
|
||||
|
||||
# --- 新增: 状态一致性检查 ---
|
||||
# 场景：策略重启后，加载了之前的止盈止损状态，但发现实际上并没有持仓
|
||||
# (可能因为上次平仓后、清空状态前程序就关闭了)。
|
||||
# 这种情况下，状态是无效的“幽灵状态”，必须清除。
|
||||
if position_volume == 0 and self._pending_sl_price is not None:
|
||||
self.log("检测到状态与实际持仓不符 (有状态但无持仓)，重置本地状态。")
|
||||
self._pending_sl_price = None
|
||||
self._pending_tp_price = None
|
||||
self.context.save_state(self._get_state_dict()) # 立即同步清除后的状态
|
||||
|
||||
# --- 1. 管理现有持仓 (如果存在) ---
|
||||
if position_volume != 0:
|
||||
self.manage_open_position(position_volume, open_price)
|
||||
return
|
||||
|
||||
|
||||
# 周期性地计算HVNs
|
||||
if self._bar_counter % self.recalc_interval == 1:
|
||||
profile_bars = bar_history[-self.profile_period:]
|
||||
dist = compute_price_volume_distribution(profile_bars, self.tick_size)
|
||||
if dist is not None and not dist.empty:
|
||||
self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks)
|
||||
self.log(f"识别到新的高价值节点: {[f'{p:.2f}' for p in self._cached_hvns]}")
|
||||
|
||||
if not self._cached_hvns: return
|
||||
|
||||
# 评估新机会 (挂单逻辑)
|
||||
self.evaluate_entry_signal(bar_history)
|
||||
|
||||
def manage_open_position(self, volume: int, current_price: float):
|
||||
"""
|
||||
[修改] 主动管理已开仓位的止盈止损。
|
||||
不再使用 position_meta，直接依赖实例变量。
|
||||
"""
|
||||
# [关键安全检查]: 如果有持仓，但却没有止盈止损状态，这是一个危险的信号。
|
||||
# 可能是状态文件损坏或逻辑错误。为控制风险，应立即平仓。
|
||||
if self._pending_sl_price is None or self._pending_tp_price is None:
|
||||
self.log("风险警告：存在持仓但无有效的止盈止损价格，立即市价平仓！")
|
||||
self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume))
|
||||
return
|
||||
|
||||
sl_price = self._pending_sl_price
|
||||
tp_price = self._pending_tp_price
|
||||
|
||||
# 止盈止损逻辑 (保持不变)
|
||||
if volume > 0: # 多头
|
||||
if current_price <= sl_price or current_price >= tp_price:
|
||||
action = "止损" if current_price <= sl_price else "止盈"
|
||||
self.log(f"多头{action}触发于 {current_price:.2f} (SL: {sl_price}, TP: {tp_price})")
|
||||
self.close_position("CLOSE_LONG", abs(volume))
|
||||
elif volume < 0: # 空头
|
||||
if current_price >= sl_price or current_price <= tp_price:
|
||||
action = "止损" if current_price >= sl_price else "止盈"
|
||||
self.log(f"空头{action}触发于 {current_price:.2f} (SL: {sl_price}, TP: {tp_price})")
|
||||
self.close_position("CLOSE_SHORT", abs(volume))
|
||||
|
||||
def evaluate_entry_signal(self, bar_history: List[Bar]):
|
||||
# [修改] 在挂单前，先重置旧的挂单状态，虽然on_open_bar开头也做了，但这里更保险
|
||||
self._pending_sl_price = None
|
||||
self._pending_tp_price = None
|
||||
|
||||
# ... 原有挂单信号计算逻辑保持不变 ...
|
||||
prev_close = bar_history[-2].close
|
||||
current_close = bar_history[-1].close
|
||||
highs = np.array([b.high for b in bar_history], dtype=float)
|
||||
lows = np.array([b.low for b in bar_history], dtype=float)
|
||||
closes = np.array([b.close for b in bar_history], dtype=float)
|
||||
current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
|
||||
if current_atr < self.tick_size: return
|
||||
|
||||
for hvn in sorted(self._cached_hvns):
|
||||
if "BUY" in self.order_direction and (prev_close < hvn < current_close):
|
||||
direction = "BUY"
|
||||
pass_filter = self.indicator_long is None or self.indicator_long.is_condition_met(
|
||||
*self.get_indicator_tuple())
|
||||
elif "SELL" in self.order_direction and (prev_close > hvn > current_close):
|
||||
direction = "SELL"
|
||||
pass_filter = self.indicator_short is None or self.indicator_short.is_condition_met(
|
||||
*self.get_indicator_tuple())
|
||||
else:
|
||||
continue
|
||||
|
||||
if direction and pass_filter:
|
||||
offset = self.entry_offset_atr * current_atr
|
||||
limit_price = hvn + offset if direction == "BUY" else hvn - offset
|
||||
self.log(f"价格穿越HVN({hvn:.2f})。在 {limit_price:.2f} 挂限价{direction}单。")
|
||||
self.send_hvn_limit_order(direction, limit_price, current_atr)
|
||||
return
|
||||
|
||||
def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
|
||||
# 1. 设置实例的止盈止损状态
|
||||
self._pending_sl_price = limit_price - self.stop_loss_atr * entry_atr if direction == "BUY" else limit_price + self.stop_loss_atr * entry_atr
|
||||
self._pending_tp_price = limit_price + self.take_profit_atr * entry_atr if direction == "BUY" else limit_price - self.take_profit_atr * entry_atr
|
||||
|
||||
# 2. [新增] 状态已更新，立即通过上下文持久化
|
||||
self.context.save_state(self._get_state_dict())
|
||||
self.log(f"状态已更新并保存: SL={self._pending_sl_price}, TP={self._pending_tp_price}")
|
||||
|
||||
# 3. 发送订单
|
||||
order_id = f"{self.symbol}_{direction}_LIMIT_{self.order_id_counter}"
|
||||
self.order_id_counter += 1
|
||||
order = Order(
|
||||
id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume,
|
||||
price_type="LIMIT", limit_price=limit_price, submitted_time=self.get_current_time(),
|
||||
offset="OPEN"
|
||||
)
|
||||
self.send_order(order)
|
||||
|
||||
def close_position(self, direction: str, volume: int):
|
||||
"""[修改] 平仓时，必须清空状态并立即保存。"""
|
||||
# 1. 发送平仓市价单
|
||||
self.send_market_order(direction, volume)
|
||||
|
||||
# 2. 清空本地的止盈止损状态
|
||||
self._pending_sl_price = None
|
||||
self._pending_tp_price = None
|
||||
|
||||
# 3. [新增] 状态已清空，立即通过上下文持久化这个“空状态”
|
||||
self.context.save_state(self._get_state_dict())
|
||||
self.log("持仓已平，相关的止盈止损状态已清空并保存。")
|
||||
|
||||
def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
|
||||
# ... 此辅助函数保持不变 ...
|
||||
order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
|
||||
self.order_id_counter += 1
|
||||
order = Order(
|
||||
id=order_id, symbol=self.symbol, direction=direction, volume=volume,
|
||||
price_type="MARKET", submitted_time=self.get_current_time(), offset=offset
|
||||
)
|
||||
self.send_order(order)
|
||||
Reference in New Issue
Block a user