aszerW
893a75a27f
重构 RotationStrategy:
- 添加 from_yaml() 从配置创建实例
- 添加 get_data() 获取数据
- 添加 compute_factors() 计算因子
- 添加 generate_signals() 生成信号
- 添加 run_backtest() 完整回测流程
简化 run_rotation.py:
- 从 264 行简化为 9 行
- 只做策略调用入口
执行方式:
python run_rotation.py --config config/strategies/rotation.yaml
python run_rotation.py --save-path results/my_rotation
代码方式:
strategy = RotationStrategy.from_yaml('config/strategies/rotation.yaml')
result = strategy.run_backtest()
246 lines
8.3 KiB
Python
246 lines
8.3 KiB
Python
"""
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轮动策略完整实现
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整合数据获取、因子计算、信号生成、回测执行
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"""
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import pandas as pd
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import yaml
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from datetime import datetime
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from pathlib import Path
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from framework.factors import FactorRegistry, FactorCombiner
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from framework.signals import SignalGenerator
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from framework.execution import BacktestExecutor
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from framework.risk import CallbackHook, Position
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from framework.strategy import StrategyBase
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# 导入定制组件
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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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class RotationStrategy(StrategyBase):
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"""
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ETF轮动策略（完整实现）
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基于动量因子 + Top N选股 + 分散化
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使用方式:
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from strategies.rotation.strategy import RotationStrategy
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strategy = RotationStrategy.from_yaml('config/strategies/rotation.yaml')
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result = strategy.run_backtest()
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"""
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name = "rotation"
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select_num = 3
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stoploss = -0.05
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n_days = 25
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rebalance_days = 1
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rebalance_threshold = 0.0
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trade_cost = 0.001
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def __init__(self, config: dict = None):
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"""初始化策略"""
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# 应用配置
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if config:
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self._apply_config(config)
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self.config = config
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else:
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self.config = {}
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# 初始化因子
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FactorRegistry.clear()
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FactorRegistry.register(MomentumFactor)
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self._factor = FactorRegistry.get(
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'momentum',
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n_days=self.n_days,
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crash_filter=True
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)
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# 构建分组映射（分散化选股）
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self._group_mapping = self._build_group_mapping()
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# 初始化信号生成器
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self._selector = TopNSelector(
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select_num=self.select_num,
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group_mapping=self._group_mapping,
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min_score=0.0,
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rebalance_days=self.rebalance_days,
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rebalance_threshold=self.rebalance_threshold
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)
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@classmethod
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def from_yaml(cls, config_path: str) -> 'RotationStrategy':
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"""从YAML配置创建策略实例"""
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with open(config_path, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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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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return cls(config)
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def _apply_config(self, config: dict) -> None:
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"""应用配置参数"""
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self.select_num = config.get('select_num', self.select_num)
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self.n_days = config.get('n_days', self.n_days)
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self.rebalance_days = config.get('rebalance_days', self.rebalance_days)
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self.rebalance_threshold = config.get('rebalance_threshold', self.rebalance_threshold)
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self.trade_cost = config.get('trade_cost', self.trade_cost)
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self.start_date = config.get('start_date', '2019-01-01')
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self.end_date = config.get('end_date', datetime.now().strftime('%Y-%m-%d'))
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def _build_group_mapping(self) -> dict:
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"""构建分组映射（分散化选股）"""
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group_mapping = {}
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code_list_config = self.config.get('code_list', {})
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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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return group_mapping
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def get_data(self) -> dict:
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"""获取数据（复用归档的数据源）"""
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code_list_config = self.config.get('code_list', {})
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code_list = list(code_list_config.keys())
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if not code_list:
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raise ValueError("配置中未找到 code_list")
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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 = self.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=self.config.get('use_cache', True)
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)
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# 获取数据
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index_data = {}
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all_data = data_source.fetch_batch(code_list, self.start_date, self.end_date)
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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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return {
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'index_data': index_data,
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'valid_codes': list(index_data.keys())
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}
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def compute_factors(self, data: dict) -> pd.DataFrame:
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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) >= self.n_days:
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values = self._factor.compute(df)
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factor_values[code] = values
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return pd.DataFrame(factor_values)
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def generate_signals(self, factor_df: pd.DataFrame) -> pd.DataFrame:
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"""生成信号"""
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return self._selector.generate(factor_df)
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def run_backtest(self, data: dict = None, save_path: str = None) -> dict:
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"""
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完整回测流程
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Args:
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data: 可选，如不提供则自动获取
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save_path: 报告保存路径
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Returns:
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回测结果字典
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"""
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print("\n" + "=" * 60)
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print(" ETF轮动策略 回测系统")
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print("=" * 60)
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# 1. 获取数据
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if data is None:
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data = self.get_data()
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valid_codes = data['valid_codes']
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index_data = data['index_data']
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print(f"\n候选标的: {len(valid_codes)} 只")
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print(f"回测区间: {self.start_date} ~ {self.end_date}")
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# 2. 计算因子
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print("\n计算因子...")
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factor_df = self.compute_factors(data)
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print(f" 因子类型: momentum (weighted)\n 窗口天数: {self.n_days}\n 计算完成: {len(factor_df.columns)} 只")
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# 3. 生成信号
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print("\n生成信号...")
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signals = self.generate_signals(factor_df)
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print(f" 选股数量: {self.select_num}\n 分组选股: {len(set(self._group_mapping.values()))} 个大类\n 信号日期: {len(signals)} 天")
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# 4. 执行回测
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print("\n执行回测...")
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returns_data = {}
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first_code = valid_codes[0]
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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[first_code].index
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executor = BacktestExecutor(
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initial_capital=100000,
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trade_cost=self.trade_cost,
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select_num=self.select_num
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)
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portfolio = executor.execute(signals, returns_df)
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# 5. 输出结果
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if hasattr(portfolio, 'backtest_result'):
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result = portfolio.backtest_result
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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("\n回测结果:")
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print(f" 最终净值: {final_nav:.4f}\n 累计收益: {total_return:.2f}%")
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# 保存报告
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if save_path:
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result[['策略净值']].to_csv(f"{save_path}_nav.csv")
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signals.to_csv(f"{save_path}_signals.csv")
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print(f" 报告保存: {save_path}_*.csv")
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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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# 保留抽象方法实现
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def init_factors(self) -> FactorCombiner:
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return FactorCombiner([self._factor])
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def init_signal_generator(self) -> SignalGenerator:
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return self._selector |