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f70aa1d3d1
使用新框架的因子、信号、执行器： - FactorRegistry + MomentumFactor（因子层） - TopNSelector（信号层，支持分散化选股） - BacktestExecutor（执行层，完整回测） 暂时复用归档的HybridDataSource获取数据 执行方式： python run_rotation.py python run_rotation.py --config config/strategies/rotation.yaml
304 lines
8.6 KiB
Python
304 lines
8.6 KiB
Python
#!/usr/bin/env python3
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"""
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ETF轮动策略回测入口（新框架）
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用法:
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python run_rotation.py
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python run_rotation.py --config config/strategies/rotation.yaml
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"""
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import sys
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import time
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import yaml
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import argparse
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from pathlib import Path
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from datetime import datetime
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# 添加项目根目录到路径
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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def load_config(config_path: str) -> dict:
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"""加载配置文件"""
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with open(config_path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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def get_data_from_archive(code_list: list, config: dict) -> dict:
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"""
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从归档的HybridDataSource获取数据
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暂时复用旧数据源，后续迁移到新框架
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"""
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print("\n" + "=" * 60)
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print("获取数据...")
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print("=" * 60)
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# 使用归档的HybridDataSource
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from archive.legacy_core.core.datasource.hybrid_source import HybridDataSource
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ssh_config = config.get('ssh_tunnel', {})
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if ssh_config.get('enabled'):
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ssh_config = {
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'host': ssh_config.get('host'),
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'port': ssh_config.get('port', 22),
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'username': ssh_config.get('username', 'root'),
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'key_path': ssh_config.get('key_path', 'hk_ecs.pem'),
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'local_port': ssh_config.get('local_port', 1080)
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}
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else:
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ssh_config = None
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data_source = HybridDataSource(
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ssh_config=ssh_config,
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use_cache=config.get('use_cache', True)
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)
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start_date = config.get('start_date', '2019-01-01')
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end_date = config.get('end_date', datetime.now().strftime('%Y-%m-%d'))
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# 获取指数数据
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print(f" 回测区间: {start_date} ~ {end_date}")
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print(f" 候选标的: {len(code_list)} 只")
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index_data = {}
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etf_data = {}
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# 获取数据
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all_data = data_source.fetch_batch(code_list, start_date, end_date)
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# 分离指数和ETF数据
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code_list_config = config.get('code_list', {})
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for code, df in all_data.items():
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if df is not None and not df.empty:
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index_data[code] = df
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# 获取对应的ETF数据
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if code in code_list_config:
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etf_code = code_list_config[code].get('etf')
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if etf_code:
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etf_df = data_source.fetch(etf_code, start_date, end_date)
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if etf_df is not None and not etf_df.empty:
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etf_data[etf_code] = etf_df
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print(f" 指数数据: {len(index_data)} 只")
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print(f" ETF数据: {len(etf_data)} 只")
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return {
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'index_data': index_data,
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'etf_data': etf_data,
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'valid_codes': list(index_data.keys())
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}
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def run_backtest(config: dict, data: dict) -> dict:
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"""
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使用新框架运行回测
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因子 → 信号 → 执行
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"""
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print("\n" + "=" * 60)
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print("计算因子...")
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print("=" * 60)
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from framework import FactorRegistry, FactorCombiner, BacktestExecutor
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from strategies.shared.factors.momentum import MomentumFactor
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from strategies.shared.signals.selectors import TopNSelector
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# 清空注册表
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FactorRegistry.clear()
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FactorRegistry.register(MomentumFactor)
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# 初始化因子
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n_days = config.get('n_days', 25)
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factor = FactorRegistry.get('momentum', n_days=n_days, crash_filter=True)
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combiner = FactorCombiner([factor])
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print(f" 因子类型: momentum (weighted)")
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print(f" 窗口天数: {n_days}")
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print(f" 崩盘过滤: True")
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# 计算因子值
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index_data = data['index_data']
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valid_codes = data['valid_codes']
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factor_values = {}
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for code in valid_codes:
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df = index_data[code]
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if len(df) >= n_days:
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values = factor.compute(df)
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factor_values[code] = values
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print(f" 计算完成: {len(factor_values)} 只")
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# 生成信号
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print("\n" + "=" * 60)
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print("生成信号...")
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print("=" * 60)
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select_num = config.get('select_num', 3)
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rebalance_days = config.get('rebalance_days', 1)
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rebalance_threshold = config.get('rebalance_threshold', 0.0)
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# 构建分组映射（分散化选股）
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code_list_config = config.get('code_list', {})
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group_mapping = {}
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for code, cfg in code_list_config.items():
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if isinstance(cfg, dict):
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group_mapping[code] = cfg.get('market', 'default')
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selector = TopNSelector(
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select_num=select_num,
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group_mapping=group_mapping,
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min_score=0.0,
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rebalance_days=rebalance_days,
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rebalance_threshold=rebalance_threshold
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)
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print(f" 选股数量: {select_num}")
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print(f" 分组选股: {len(set(group_mapping.values()))} 个大类")
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print(f" 调仓周期: {rebalance_days} 天")
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print(f" 调仓阈值: {rebalance_threshold:.2%}")
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# 合并因子数据为DataFrame
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factor_df = pd.DataFrame(factor_values)
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# 生成信号
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signals = selector.generate(factor_df)
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print(f" 信号日期: {len(signals)} 天")
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# 计算日收益率数据
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print("\n" + "=" * 60)
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print("执行回测...")
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print("=" * 60)
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# 准备收益率数据
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returns_data = {}
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for code in valid_codes:
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df = index_data[code]
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returns_data[f'日收益率_{code}'] = df['close'].pct_change()
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returns_df = pd.DataFrame(returns_data)
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returns_df.index = index_data[valid_codes[0]].index
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trade_cost = config.get('trade_cost', 0.001)
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executor = BacktestExecutor(
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initial_capital=100000,
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trade_cost=trade_cost,
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select_num=select_num
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)
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print(f" 初始资金: 100,000")
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print(f" 交易成本: {trade_cost:.2%}")
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portfolio = executor.execute(signals, returns_df)
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if hasattr(portfolio, 'backtest_result'):
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result = portfolio.backtest_result
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# 计算绩效
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final_nav = result['策略净值'].iloc[-1]
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total_return = (final_nav - 1) * 100
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print(f"\n回测结果:")
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print(f" 最终净值: {final_nav:.4f}")
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print(f" 累计收益: {total_return:.2f}%")
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return {
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'signals': signals,
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'result': result,
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'portfolio': portfolio,
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'total_return': total_return
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}
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return {'signals': signals, 'result': None}
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def generate_report(backtest_result: dict, config: dict, data: dict, save_path: str):
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"""生成报告"""
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print("\n" + "=" * 60)
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print("生成报告...")
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print("=" * 60)
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import pandas as pd
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result = backtest_result.get('result')
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if result is None:
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print(" 无回测结果，跳过报告生成")
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return
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# 保存净值曲线
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nav_df = result[['策略净值']].copy()
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nav_df.to_csv(f"{save_path}_nav.csv")
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# 保存信号记录
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signals = backtest_result.get('signals')
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if signals is not None:
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signals.to_csv(f"{save_path}_signals.csv")
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# 简单统计
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total_return = backtest_result.get('total_return', 0)
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print(f" 报告保存至: {save_path}_*.csv")
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print(f" 累计收益: {total_return:.2f}%")
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# TODO: 使用 visualization/report_generator 生成完整HTML报告
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def main():
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parser = argparse.ArgumentParser(description="ETF轮动策略回测（新框架）")
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parser.add_argument(
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"--config",
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type=str,
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default="config/strategies/rotation.yaml",
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help="配置文件路径",
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)
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parser.add_argument(
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"--save-path",
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type=str,
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default="results/rotation",
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help="报告保存路径前缀",
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)
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args = parser.parse_args()
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start_time = time.time()
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print("=" * 60)
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print(" ETF轮动策略 回测系统（新框架）")
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print("=" * 60)
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# 加载配置
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config = load_config(args.config)
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# 设置结束日期
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if not config.get('end_date'):
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config['end_date'] = datetime.now().strftime('%Y-%m-%d')
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# 获取代码列表
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code_list_config = config.get('code_list', {})
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code_list = list(code_list_config.keys())
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print(f"\n配置文件: {args.config}")
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print(f"候选标的: {len(code_list)} 只")
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# 获取数据
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data = get_data_from_archive(code_list, config)
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# 运行回测
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backtest_result = run_backtest(config, data)
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# 生成报告
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generate_report(backtest_result, config, data, args.save_path)
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elapsed = time.time() - start_time
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print(f"\n总耗时: {elapsed:.1f}秒")
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return backtest_result
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if __name__ == "__main__":
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import pandas as pd # 确保pd在全局可用
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main() |