109 lines
5.0 KiB
Python
109 lines
5.0 KiB
Python
import multiprocessing
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from typing import Tuple, Dict, Any, Optional
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from src.analysis.result_analyzer import ResultAnalyzer
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from src.backtest_engine import BacktestEngine
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from src.data_manager import DataManager
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# --- 单个回测任务函数 ---
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# 这个函数将在每个独立的进程中运行，因此它必须是自包含的
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def run_single_backtest(
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combination: Tuple[float, float], # 传入当前参数组合
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common_config: Dict[str, Any] # 传入公共配置 (如数据路径, 初始资金等)
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) -> Optional[Dict[str, Any]]:
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"""
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运行单个参数组合的回测任务。
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此函数将在一个独立的进程中执行。
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"""
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p1_value, p2_value = combination
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# 从 common_config 中获取必要的配置
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symbol = common_config['symbol']
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data_path = common_config['data_path']
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initial_capital = common_config['initial_capital']
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slippage_rate = common_config['slippage_rate']
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commission_rate = common_config['commission_rate']
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start_time = common_config['start_time']
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end_time = common_config['end_time']
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roll_over_mode = common_config['roll_over_mode']
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# bar_duration_seconds = common_config['bar_duration_seconds'] # 如果DataManager需要，可以再传
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param1_name = common_config['param1_name']
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param2_name = common_config['param2_name']
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# 每个进程内部独立初始化 DataManager 和 BacktestEngine
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# 确保每个进程有自己的数据副本和模拟状态
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data_manager = DataManager(
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file_path=data_path,
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symbol=symbol,
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# bar_duration_seconds=bar_duration_seconds, # 如果DataManager需要，根据数据文件路径推断或者额外参数传入
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# start_date=start_time.date(), # DataManager 现在通过 file_path 和 symbol 处理数据
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# end_date=end_time.date(),
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)
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# data_manager.load_data() # DataManager 内部加载数据
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strategy_parameters = {
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'main_symbol': common_config['main_symbol'],
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'trade_volume': 1,
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param1_name: p1_value, # 15分钟扫荡K线下影线占其总范围的最小比例。
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param2_name: p2_value, # 15分钟限价单的入场点位于扫荡K线低点到收盘价的斐波那契回撤比例。
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'order_direction': common_config['order_direction'],
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'enable_log': False, # 建议在调试和测试时开启日志
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}
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# strategy_parameters['spectral_window_days'] = 2
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strategy_parameters['low_freq_days'] = strategy_parameters['spectral_window_days']
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strategy_parameters['high_freq_days'] = int(strategy_parameters['spectral_window_days'] / 2)
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strategy_parameters['exit_threshold'] = max(strategy_parameters['trend_strength_threshold'] - 0.3, 0)
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# 打印当前进程正在处理的组合信息
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# 注意：多进程打印会交错显示
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print(f"--- 正在运行组合: {strategy_parameters} (PID: {multiprocessing.current_process().pid}) ---")
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try:
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# 初始化回测引擎
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engine = BacktestEngine(
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data_manager=data_manager,
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strategy_class=common_config['strategy'],
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strategy_params=strategy_parameters,
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initial_capital=initial_capital,
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slippage_rate=slippage_rate,
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commission_rate=commission_rate,
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roll_over_mode=True, # 保持换月模式
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start_time=common_config['start_time'],
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end_time=common_config['end_time']
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)
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# 运行回测，传入时间范围
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engine.run_backtest()
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# 获取回测结果并分析
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results = engine.get_backtest_results()
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portfolio_snapshots = results["portfolio_snapshots"]
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trade_history = results["trade_history"]
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bars = results["all_bars"]
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initial_capital_result = results["initial_capital"]
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if portfolio_snapshots:
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analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result)
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# analyzer.generate_report()
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# analyzer.plot_performance()
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metrics = analyzer.calculate_all_metrics()
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# 将当前组合的参数和性能指标存储起来
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result_entry = {**strategy_parameters, **metrics}
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return result_entry
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else:
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print(
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f" 组合 {strategy_parameters} 没有生成投资组合快照，无法进行结果分析。(PID: {multiprocessing.current_process().pid})")
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# 返回一个包含参数和默认0值的结果，以便追踪失败组合
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return {**strategy_parameters, "total_return": 0.0, "annualized_return": 0.0, "sharpe_ratio": 0.0,
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"max_drawdown": 0.0, "error": "No portfolio snapshots"}
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except Exception as e:
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import traceback
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error_trace = traceback.format_exc()
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print(
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f" 组合 {strategy_parameters} 运行失败: {e}\n{error_trace} (PID: {multiprocessing.current_process().pid})")
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# 返回错误信息，以便后续处理
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return {**strategy_parameters, "error": str(e), "traceback": error_trace}
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