aszerW
19131c41dd
1. 修复期货路由逻辑：NYMEX期货(.NYM)走YFinance而非Tushare 2. 添加SSH隧道路径修复（原引擎） 3. 因子计算只使用close列（处理部分指数只有收盘价的情况） 4. 添加数据不足和缺失率剔除日志 收益对比: - 原引擎（剔除国债）: 累计1804%, 调仓459次 - 新框架: 累计772%, 调仓1276次 差异原因待查： - 国债剔除逻辑不同 - 调仓频率差异
305 lines
11 KiB
Python
305 lines
11 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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# 加载环境变量
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from dotenv import load_dotenv
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load_dotenv()
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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（使用新数据源模块）"""
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code_list_config = self.config.get('code_list', {})
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benchmark_config = self.config.get('benchmark', {})
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benchmark_code = benchmark_config.get('code', '000300.SH')
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if not code_list_config:
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raise ValueError("配置中未找到 code_list")
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# 使用新数据源模块
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from datasource import HybridDataSource
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ssh_config = self.config.get('ssh_tunnel', {})
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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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# 调用 fetch_all
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index_data, etf_data, etf_nav_data, benchmark_data, valid_codes, index_ohlcv_data, etf_code_map = \
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data_source.fetch_all(
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code_config=code_list_config,
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benchmark_code=benchmark_code,
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start_date=self.start_date,
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end_date=self.end_date
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)
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return {
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'index_data': index_ohlcv_data, # 原始OHLCV数据
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'index_close': index_data, # 对齐后的收盘价（宽格式）
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'etf_data': etf_data,
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'etf_nav_data': etf_nav_data,
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'benchmark_data': benchmark_data,
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'valid_codes': valid_codes,
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'etf_code_map': etf_code_map # {指数代码: ETF代码} 映射
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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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final_valid_codes = []
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for code in valid_codes:
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df = index_data[code]
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# 只使用 close 列计算因子（匹配原引擎逻辑：部分指数只有收盘价）
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if 'close' in df.columns:
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close_series = df['close'].dropna()
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else:
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close_series = df.dropna()
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# 原引擎剔除逻辑：close 数据需要至少 n_days + 1 条
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if len(close_series) < self.n_days + 1:
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print(f" ⚠ 剔除 {code}: 数据不足 ({len(close_series)} < {self.n_days + 1})")
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continue
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# 只传入 close 列给因子计算器
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close_df = pd.DataFrame({'close': close_series})
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values = self._factor.compute(close_df)
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factor_values[code] = values
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final_valid_codes.append(code)
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factor_df = pd.DataFrame(factor_values)
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# 过滤缺失率过高的标的
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total_rows = len(factor_df)
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for code in final_valid_codes:
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if code in factor_df.columns:
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null_pct = factor_df[code].isnull().sum() / total_rows
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if null_pct > 0.5:
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print(f" ⚠ 剔除 {code}: 缺失率 {null_pct:.1%} 过高")
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factor_df = factor_df.drop(columns=[code])
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# 更新有效代码列表
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data['valid_codes'] = [c for c in final_valid_codes if c in factor_df.columns]
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return factor_df
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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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# 获取ETF数据和代码映射
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etf_data = data.get('etf_data')
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etf_code_map = data.get('etf_code_map', {}) # {指数代码: ETF代码}
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# 计算日收益率（使用ETF价格数据，匹配原引擎逻辑）
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if etf_data is not None and not etf_data.empty:
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# 使用ETF价格计算收益，列名保持指数代码格式
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returns_data = {}
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for idx_code in valid_codes:
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etf_code = etf_code_map.get(idx_code, idx_code)
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if etf_code in etf_data.columns:
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returns_data[f'日收益率_{idx_code}'] = etf_data[etf_code].pct_change()
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returns_df = pd.DataFrame(returns_data)
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else:
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# 回退到指数收盘价数据
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index_close = data.get('index_close')
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if index_close is not None and not index_close.empty:
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returns_df = index_close.pct_change()
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returns_df.columns = [f'日收益率_{col}' for col in returns_df.columns]
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else:
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returns_data = {}
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for code in valid_codes:
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if code in index_data:
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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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if valid_codes:
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first_code = valid_codes[0]
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returns_df.index = index_data[first_code].index
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# 确保信号和收益率数据日期对齐
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common_dates = signals.index.intersection(returns_df.index)
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signals = signals.loc[common_dates]
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returns_df = returns_df.loc[common_dates]
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print(f" 对齐后日期: {len(common_dates)} 天")
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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 |