refactor(archive): move unused modules to archive/
Archive legacy framework and utility modules that are no longer referenced by the active core (datasource/ and rotation/): - framework/ -> archive/framework/ - framework_v2/ -> archive/framework_v2/ - strategies/ -> archive/strategies/ - config/ -> archive/config/ - visualization/ -> archive/visualization/ - scripts/ -> archive/scripts/ - tests/ -> archive/tests/ - run_rotation.py, run_us_rotation.py -> archive/single_files/ - compare_*.py, test_api_dates.py -> archive/single_files/
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354
archive/strategies/us_rotation/strategy.py
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354
archive/strategies/us_rotation/strategy.py
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"""
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美股轮动策略
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纯美股轮动策略，使用动量因子选股
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特点：
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- 全部使用 yfinance 数据源
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- 美股交易日历
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- 不分组，直接选 Top 5
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- 基准为纳指 NDX
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"""
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import sys
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import yaml
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import time
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple
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# 添加项目根目录
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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from datasource.yfinance_source import YFinanceSource
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from datasource.ssh_tunnel import SSHTunnelManager
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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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from framework.execution import BacktestExecutor
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class USRotationStrategy:
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"""美股轮动策略"""
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def __init__(self, config_path: str = None, config: dict = None):
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"""
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初始化策略
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Args:
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config_path: 配置文件路径
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config: 配置字典（可选）
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"""
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if config_path:
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with open(config_path, 'r', encoding='utf-8') as f:
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self.config = yaml.safe_load(f)
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elif config:
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self.config = config
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else:
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raise ValueError("需要提供 config_path 或 config")
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# 结束日期默认今天
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if not self.config.get('end_date'):
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self.config['end_date'] = datetime.now().strftime('%Y-%m-%d')
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# 应用配置
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self._apply_config()
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# 初始化因子
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self._factor = MomentumFactor(
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n_days=self.n_days,
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weighted=True,
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crash_filter=True
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)
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# 初始化选择器（不分组，直接选 Top N）
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self._selector = TopNSelector(
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select_num=self.select_num,
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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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# 数据源（延迟初始化）
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self._yfinance: Optional[YFinanceSource] = None
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self._tunnel: Optional[SSHTunnelManager] = None
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@classmethod
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def from_yaml(cls, config_path: str) -> 'USRotationStrategy':
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"""从 YAML 文件创建策略实例"""
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return cls(config_path=config_path)
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def _apply_config(self):
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"""应用配置参数"""
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self.select_num = self.config.get('select_num', 5)
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self.n_days = self.config.get('n_days', 250)
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self.rebalance_days = self.config.get('rebalance_days', 1)
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self.rebalance_threshold = self.config.get('rebalance_threshold', 0.0)
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self.trade_cost = self.config.get('trade_cost', 0.001)
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self.start_date = self.config.get('start_date', '2016-01-01')
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self.end_date = self.config['end_date']
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self.use_cache = self.config.get('use_cache', True)
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def _start_tunnel(self) -> bool:
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"""启动 SSH 隧道"""
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ssh_config = self.config.get('ssh_tunnel', {})
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if not ssh_config.get('enabled', False):
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return True
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self._tunnel = SSHTunnelManager(ssh_config)
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return self._tunnel.start()
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def _stop_tunnel(self):
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"""停止 SSH 隧道"""
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if self._tunnel:
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self._tunnel.stop()
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self._tunnel = None
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def _init_yfinance(self):
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"""初始化 YFinance 数据源"""
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if self._yfinance is None:
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self._yfinance = YFinanceSource(use_ssh_tunnel=True)
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def fetch_data(self) -> Dict:
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"""获取数据（全部使用 yfinance）"""
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print("\n" + "=" * 60)
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print("获取美股数据")
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print("=" * 60)
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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', 'NDX')
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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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codes = list(code_list_config.keys())
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print(f"标的池: {len(codes)} 只股票")
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print(f"基准: {benchmark_code}")
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print(f"时间范围: {self.start_date} ~ {self.end_date}")
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# 启动 SSH 隓道
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print("\n启动 SSH 隧道...")
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if not self._start_tunnel():
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raise RuntimeError("SSH 隧道启动失败")
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self._init_yfinance()
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# 获取数据
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all_data: Dict[str, pd.DataFrame] = {}
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valid_codes: List[str] = []
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print("\n获取股票数据...")
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for i, code in enumerate(codes):
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print(f" [{i+1}/{len(codes)}] {code}...", end=" ")
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try:
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df = self._yfinance.fetch(code, self.start_date, self.end_date)
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time.sleep(0.5) # 避免限流
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if df is not None and len(df) >= self.n_days:
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all_data[code] = df
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valid_codes.append(code)
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print(f"✓ {len(df)} 条")
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else:
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print(f"✗ 数据不足")
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except Exception as e:
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print(f"✗ 失败: {e}")
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# 获取基准数据
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print(f"\n获取基准 {benchmark_code}...", end=" ")
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try:
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benchmark_df = self._yfinance.fetch(benchmark_code, self.start_date, self.end_date)
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if benchmark_df is not None and len(benchmark_df) > 0:
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print(f"✓ {len(benchmark_df)} 条")
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else:
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print(f"✗ 基准数据获取失败")
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benchmark_df = None
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except Exception as e:
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print(f"✗ 失败: {e}")
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benchmark_df = None
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# 停止隧道
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self._stop_tunnel()
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print(f"\n数据获取完成: {len(valid_codes)}/{len(codes)} 只有效")
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return {
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'stock_data': all_data,
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'valid_codes': valid_codes,
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'benchmark': benchmark_df,
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'benchmark_code': benchmark_code
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}
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def compute_factors(self, data: Dict) -> pd.DataFrame:
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"""计算动量因子"""
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print("\n" + "=" * 60)
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print("计算动量因子")
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print("=" * 60)
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stock_data = data['stock_data']
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valid_codes = data['valid_codes']
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factor_values: Dict[str, pd.Series] = {}
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for code in valid_codes:
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df = stock_data[code]
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if 'close' not in df.columns:
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continue
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# 数据长度检查
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if len(df) < self.n_days:
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print(f" ⚠ {code}: 数据不足 {self.n_days} 天，跳过")
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continue
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# MomentumFactor.compute 需要DataFrame
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factor_series = self._factor.compute(df)
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if factor_series is not None and len(factor_series) > 0:
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factor_values[code] = factor_series
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# 合成 DataFrame
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factor_df = pd.DataFrame(factor_values)
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print(f"\n因子计算完成: {len(factor_df.columns)} 只标的")
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print(f" 窗口: {self.n_days} 天")
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if len(factor_df) > 0:
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print(f" 日期范围: {factor_df.index.min()} ~ {factor_df.index.max()}")
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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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"""生成轮动信号（不分组，直接选 Top 5）"""
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print("\n" + "=" * 60)
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print("生成轮动信号")
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print("=" * 60)
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# 不分组，直接对因子排序选 Top 5
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# TopNSelector.generate 会自动处理调仓周期和T+1
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signals_df = self._selector.generate(factor_df)
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print(f"\n信号生成完成:")
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print(f" 选股数量: {self.select_num}")
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if 'signal' in signals_df.columns:
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valid_signals = signals_df[signals_df['signal'] != '']
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print(f" 有效信号天数: {len(valid_signals)}")
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return signals_df
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def run_backtest(self, data: Dict = None, save_path: str = None) -> Dict:
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"""运行回测"""
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print("\n" + "=" * 60)
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print("美股动量轮动策略 回测")
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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.fetch_data()
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valid_codes = data['valid_codes']
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# 2. 计算因子
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factor_df = self.compute_factors(data)
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# 3. 生成信号
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signals = self.generate_signals(factor_df)
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# 4. 构建收益数据（BacktestExecutor期望列名格式：日收益率_{code}）
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print("\n构建收益数据...")
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stock_data = data['stock_data']
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# 计算日收益率，列名格式为 '日收益率_{code}'
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returns_data: Dict[str, pd.Series] = {}
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for code in valid_codes:
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if code in stock_data:
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df = stock_data[code]
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if 'close' in df.columns:
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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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# 对齐日期
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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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# 5. 执行回测
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print("\n执行回测...")
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executor = BacktestExecutor(
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initial_capital=100,
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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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# 6. 计算基准收益
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benchmark_df = data['benchmark']
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benchmark_code = data['benchmark_code']
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if benchmark_df is not None and 'close' in benchmark_df.columns:
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benchmark_returns = benchmark_df['close'].pct_change()
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# 对齐日期
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benchmark_returns = benchmark_returns.loc[common_dates]
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# 7. 输出结果
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if hasattr(portfolio, 'backtest_result') and portfolio.backtest_result is not None:
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result = portfolio.backtest_result
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# 策略净值（DataFrame列）
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if '策略净值' in result.columns:
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strategy_nav = result['策略净值'].values
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final_nav = strategy_nav[-1] if len(strategy_nav) > 0 else 100
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total_return = (final_nav - 1) * 100 # 净值归一化起点为1
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else:
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final_nav = 100
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total_return = 0
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# 基准收益
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if benchmark_df is not None and 'close' in benchmark_df.columns:
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benchmark_start = benchmark_df['close'].iloc[0]
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benchmark_end = benchmark_df['close'].iloc[-1]
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benchmark_return = (benchmark_end / benchmark_start - 1) * 100
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else:
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benchmark_return = 0
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print("\n" + "=" * 60)
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print("回测结果")
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print("=" * 60)
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print(f"策略最终净值: {final_nav:.2f}")
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print(f"策略总收益: {total_return:.2f}%")
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print(f"基准 ({benchmark_code}) 收益: {benchmark_return:.2f}%")
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print(f"超额收益: {total_return - benchmark_return:.2f}%")
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print(f"交易成本: {self.trade_cost * 100:.1f}%")
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# 保存结果
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if save_path:
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Path(save_path).parent.mkdir(parents=True, exist_ok=True)
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# 保存净值曲线
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if '策略净值' in result.columns:
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nav_df = pd.DataFrame({
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'date': result.index,
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'strategy_nav': result['策略净值'].values
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})
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if benchmark_df is not None and 'close' in benchmark_df.columns:
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# 重建基准净值
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benchmark_nav = (benchmark_df['close'].pct_change() + 1).cumprod()
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nav_df['benchmark_nav'] = benchmark_nav.reindex(result.index, method='ffill').values
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nav_df.to_csv(f"{save_path}_nav.csv", index=False)
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# 保存信号
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signals.to_csv(f"{save_path}_signals.csv")
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print(f"\n报告保存: {save_path}_*.csv")
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return {
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'final_nav': final_nav,
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'total_return': total_return,
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'benchmark_return': benchmark_return,
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'excess_return': total_return - benchmark_return,
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'signals': signals,
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'result': result
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}
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return {'signals': signals}
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