#!/usr/bin/env python3 """ ETF轮动策略回测入口（新框架） 用法: python run_rotation.py python run_rotation.py --config config/strategies/rotation.yaml """ import sys import time import yaml import argparse from pathlib import Path from datetime import datetime # 添加项目根目录到路径 project_root = Path(__file__).parent sys.path.insert(0, str(project_root)) def load_config(config_path: str) -> dict: """加载配置文件""" with open(config_path, "r", encoding="utf-8") as f: return yaml.safe_load(f) def get_data_from_archive(code_list: list, config: dict) -> dict: """ 从归档的HybridDataSource获取数据 暂时复用旧数据源，后续迁移到新框架 """ print("\n" + "=" * 60) print("获取数据...") print("=" * 60) # 使用归档的HybridDataSource from archive.legacy_core.core.datasource.hybrid_source import HybridDataSource ssh_config = config.get('ssh_tunnel', {}) if ssh_config.get('enabled'): ssh_config = { 'host': ssh_config.get('host'), 'port': ssh_config.get('port', 22), 'username': ssh_config.get('username', 'root'), 'key_path': ssh_config.get('key_path', 'hk_ecs.pem'), 'local_port': ssh_config.get('local_port', 1080) } else: ssh_config = None data_source = HybridDataSource( ssh_config=ssh_config, use_cache=config.get('use_cache', True) ) start_date = config.get('start_date', '2019-01-01') end_date = config.get('end_date', datetime.now().strftime('%Y-%m-%d')) # 获取指数数据 print(f" 回测区间: {start_date} ~ {end_date}") print(f" 候选标的: {len(code_list)} 只") index_data = {} etf_data = {} # 获取数据 all_data = data_source.fetch_batch(code_list, start_date, end_date) # 分离指数和ETF数据 code_list_config = config.get('code_list', {}) for code, df in all_data.items(): if df is not None and not df.empty: index_data[code] = df # 获取对应的ETF数据 if code in code_list_config: etf_code = code_list_config[code].get('etf') if etf_code: etf_df = data_source.fetch(etf_code, start_date, end_date) if etf_df is not None and not etf_df.empty: etf_data[etf_code] = etf_df print(f" 指数数据: {len(index_data)} 只") print(f" ETF数据: {len(etf_data)} 只") return { 'index_data': index_data, 'etf_data': etf_data, 'valid_codes': list(index_data.keys()) } def run_backtest(config: dict, data: dict) -> dict: """ 使用新框架运行回测 因子 → 信号 → 执行 """ print("\n" + "=" * 60) print("计算因子...") print("=" * 60) from framework import FactorRegistry, FactorCombiner, BacktestExecutor from strategies.shared.factors.momentum import MomentumFactor from strategies.shared.signals.selectors import TopNSelector # 清空注册表 FactorRegistry.clear() FactorRegistry.register(MomentumFactor) # 初始化因子 n_days = config.get('n_days', 25) factor = FactorRegistry.get('momentum', n_days=n_days, crash_filter=True) combiner = FactorCombiner([factor]) print(f" 因子类型: momentum (weighted)") print(f" 窗口天数: {n_days}") print(f" 崩盘过滤: True") # 计算因子值 index_data = data['index_data'] valid_codes = data['valid_codes'] factor_values = {} for code in valid_codes: df = index_data[code] if len(df) >= n_days: values = factor.compute(df) factor_values[code] = values print(f" 计算完成: {len(factor_values)} 只") # 生成信号 print("\n" + "=" * 60) print("生成信号...") print("=" * 60) select_num = config.get('select_num', 3) rebalance_days = config.get('rebalance_days', 1) rebalance_threshold = config.get('rebalance_threshold', 0.0) # 构建分组映射（分散化选股） code_list_config = config.get('code_list', {}) group_mapping = {} for code, cfg in code_list_config.items(): if isinstance(cfg, dict): group_mapping[code] = cfg.get('market', 'default') selector = TopNSelector( select_num=select_num, group_mapping=group_mapping, min_score=0.0, rebalance_days=rebalance_days, rebalance_threshold=rebalance_threshold ) print(f" 选股数量: {select_num}") print(f" 分组选股: {len(set(group_mapping.values()))} 个大类") print(f" 调仓周期: {rebalance_days} 天") print(f" 调仓阈值: {rebalance_threshold:.2%}") # 合并因子数据为DataFrame factor_df = pd.DataFrame(factor_values) # 生成信号 signals = selector.generate(factor_df) print(f" 信号日期: {len(signals)} 天") # 计算日收益率数据 print("\n" + "=" * 60) print("执行回测...") print("=" * 60) # 准备收益率数据 returns_data = {} for code in valid_codes: df = index_data[code] returns_data[f'日收益率_{code}'] = df['close'].pct_change() returns_df = pd.DataFrame(returns_data) returns_df.index = index_data[valid_codes[0]].index trade_cost = config.get('trade_cost', 0.001) executor = BacktestExecutor( initial_capital=100000, trade_cost=trade_cost, select_num=select_num ) print(f" 初始资金: 100,000") print(f" 交易成本: {trade_cost:.2%}") portfolio = executor.execute(signals, returns_df) if hasattr(portfolio, 'backtest_result'): result = portfolio.backtest_result # 计算绩效 final_nav = result['策略净值'].iloc[-1] total_return = (final_nav - 1) * 100 print(f"\n回测结果:") print(f" 最终净值: {final_nav:.4f}") print(f" 累计收益: {total_return:.2f}%") return { 'signals': signals, 'result': result, 'portfolio': portfolio, 'total_return': total_return } return {'signals': signals, 'result': None} def generate_report(backtest_result: dict, config: dict, data: dict, save_path: str): """生成报告""" print("\n" + "=" * 60) print("生成报告...") print("=" * 60) import pandas as pd result = backtest_result.get('result') if result is None: print(" 无回测结果，跳过报告生成") return # 保存净值曲线 nav_df = result[['策略净值']].copy() nav_df.to_csv(f"{save_path}_nav.csv") # 保存信号记录 signals = backtest_result.get('signals') if signals is not None: signals.to_csv(f"{save_path}_signals.csv") # 简单统计 total_return = backtest_result.get('total_return', 0) print(f" 报告保存至: {save_path}_*.csv") print(f" 累计收益: {total_return:.2f}%") # TODO: 使用 visualization/report_generator 生成完整HTML报告 def main(): parser = argparse.ArgumentParser(description="ETF轮动策略回测（新框架）") parser.add_argument( "--config", type=str, default="config/strategies/rotation.yaml", help="配置文件路径", ) parser.add_argument( "--save-path", type=str, default="results/rotation", help="报告保存路径前缀", ) args = parser.parse_args() start_time = time.time() print("=" * 60) print(" ETF轮动策略 回测系统（新框架）") print("=" * 60) # 加载配置 config = load_config(args.config) # 设置结束日期 if not config.get('end_date'): config['end_date'] = datetime.now().strftime('%Y-%m-%d') # 获取代码列表 code_list_config = config.get('code_list', {}) code_list = list(code_list_config.keys()) print(f"\n配置文件: {args.config}") print(f"候选标的: {len(code_list)} 只") # 获取数据 data = get_data_from_archive(code_list, config) # 运行回测 backtest_result = run_backtest(config, data) # 生成报告 generate_report(backtest_result, config, data, args.save_path) elapsed = time.time() - start_time print(f"\n总耗时: {elapsed:.1f}秒") return backtest_result if __name__ == "__main__": import pandas as pd # 确保pd在全局可用 main()