refactor: 删除 SimpleRotationStrategy 简化版
- 删除 simple.py（已被 GlobalRotationStrategy 替代） - 删除 backtest_simple_rotation.py 回测脚本 - 删除 test_simple_rotation.py 测试脚本 - 更新 __init__.py 移除 SimpleRotationStrategy 导出 - 现在只保留 GlobalRotationStrategy 正式版
This commit is contained in:
@@ -2,7 +2,6 @@
|
||||
轮动策略模块
|
||||
"""
|
||||
|
||||
from framework_v2.strategies.rotation.simple import SimpleRotationStrategy
|
||||
from framework_v2.strategies.rotation.rotation import GlobalRotationStrategy
|
||||
|
||||
__all__ = ['SimpleRotationStrategy', 'GlobalRotationStrategy']
|
||||
__all__ = ['GlobalRotationStrategy']
|
||||
|
||||
@@ -1,304 +0,0 @@
|
||||
"""
|
||||
简单轮动策略
|
||||
|
||||
基于动量因子的 ETF 轮动策略
|
||||
- 计算各标的动量得分
|
||||
- 选择 Top-N 标的
|
||||
- 等权分配仓位
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict
|
||||
|
||||
from framework_v2.core.strategy import StrategyBase
|
||||
from framework_v2.config.schemas import StrategyConfig
|
||||
from framework_v2.shared.factors import MomentumFactor
|
||||
|
||||
|
||||
class SimpleRotationStrategy(StrategyBase):
|
||||
"""
|
||||
简单轮动策略
|
||||
|
||||
策略逻辑：
|
||||
1. 计算各标的动量得分（加权线性回归）
|
||||
2. 选择得分最高的 Top-N 标的
|
||||
3. 等权分配仓位
|
||||
|
||||
示例:
|
||||
from framework_v2.config import load_config
|
||||
from framework_v2.strategies.rotation.simple import SimpleRotationStrategy
|
||||
|
||||
config = load_config('rotation_simple.yaml')
|
||||
strategy = SimpleRotationStrategy(config)
|
||||
result = strategy.run()
|
||||
"""
|
||||
|
||||
def __init__(self, config: StrategyConfig):
|
||||
"""
|
||||
初始化策略
|
||||
|
||||
Args:
|
||||
config: 策略配置
|
||||
"""
|
||||
super().__init__(config)
|
||||
|
||||
# 初始化动量因子
|
||||
self.momentum = MomentumFactor(
|
||||
n_days=config.factor.n_days,
|
||||
weighted=(config.factor.type.value == 'weighted_momentum')
|
||||
)
|
||||
|
||||
# 策略参数
|
||||
self.select_num = config.rotation.select_num if config.rotation else 3
|
||||
self.min_score = config.rotation.threshold.fixed_value if config.rotation else 0.0
|
||||
|
||||
def get_codes(self) -> list:
|
||||
"""
|
||||
获取标的列表（信号标的 + 交易标的）
|
||||
|
||||
返回所有需要的数据标的：
|
||||
- signal_source: 用于计算因子和信号
|
||||
- trade_source: 用于计算收益
|
||||
"""
|
||||
codes = set()
|
||||
|
||||
# 添加所有信号标的
|
||||
codes.update(self.config.asset_pools.get_signal_codes())
|
||||
|
||||
# 添加所有交易标的
|
||||
codes.update(self.config.asset_pools.get_trade_codes())
|
||||
|
||||
return list(codes)
|
||||
|
||||
def compute_factors(self, data: Dict[str, pd.DataFrame]) -> Dict[str, pd.Series]:
|
||||
"""
|
||||
计算动量因子（只使用信号标的的数据）
|
||||
|
||||
Args:
|
||||
data: 数据字典 {code: DataFrame}（包含 signal_source 和 trade_source）
|
||||
|
||||
Returns:
|
||||
因子字典 {signal_source: Series}
|
||||
"""
|
||||
factors = {}
|
||||
|
||||
# 只使用信号标的计算因子
|
||||
signal_codes = self.config.asset_pools.get_signal_codes()
|
||||
|
||||
for code in signal_codes:
|
||||
if code not in data:
|
||||
print(f" 警告: {code} 数据不存在，跳过")
|
||||
continue
|
||||
|
||||
try:
|
||||
df = data[code]
|
||||
# 计算动量得分（使用信号标的的数据）
|
||||
factor_values = self.momentum.compute(df)
|
||||
factors[code] = factor_values
|
||||
except Exception as e:
|
||||
print(f" 警告: {code} 因子计算失败 - {e}")
|
||||
continue
|
||||
|
||||
return factors
|
||||
|
||||
def generate_signals(self, factors: Dict[str, pd.Series]) -> pd.DataFrame:
|
||||
"""
|
||||
生成轮动信号
|
||||
|
||||
逻辑：
|
||||
1. 每个交易日选择动量得分最高的 Top-N 标的
|
||||
2. 过滤得分低于阈值的标的
|
||||
|
||||
Args:
|
||||
factors: 因子字典 {code: Series}
|
||||
|
||||
Returns:
|
||||
信号 DataFrame（index=日期, columns=标的, values=1或0）
|
||||
"""
|
||||
if not factors:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 对齐所有因子的日期
|
||||
factor_df = pd.DataFrame(factors)
|
||||
|
||||
# 生成信号
|
||||
signals = pd.DataFrame(index=factor_df.index, columns=factor_df.columns, data=0)
|
||||
|
||||
for date in factor_df.index:
|
||||
# 获取当日因子值
|
||||
scores = factor_df.loc[date].dropna()
|
||||
|
||||
if scores.empty:
|
||||
continue
|
||||
|
||||
# 过滤低分标的
|
||||
if self.min_score > 0:
|
||||
scores = scores[scores >= self.min_score]
|
||||
|
||||
# 选择 Top-N
|
||||
if len(scores) > self.select_num:
|
||||
top_codes = scores.nlargest(self.select_num).index
|
||||
else:
|
||||
top_codes = scores.index
|
||||
|
||||
# 标记信号
|
||||
signals.loc[date, top_codes] = 1
|
||||
|
||||
return signals.astype(int)
|
||||
|
||||
def manage_positions(self, signals: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
仓位管理（等权分配）
|
||||
|
||||
Args:
|
||||
signals: 信号 DataFrame
|
||||
|
||||
Returns:
|
||||
仓位 DataFrame（包含 'weight' 列）
|
||||
"""
|
||||
positions = signals.astype(float).copy()
|
||||
|
||||
# 计算每个日期的权重
|
||||
for date in positions.index:
|
||||
signal_row = positions.loc[date]
|
||||
n_selected = signal_row.sum()
|
||||
|
||||
if n_selected > 0:
|
||||
# 等权分配
|
||||
positions.loc[date] = signal_row / n_selected
|
||||
else:
|
||||
# 空仓
|
||||
positions.loc[date] = 0
|
||||
|
||||
return positions
|
||||
|
||||
def _get_trading_calendar(self) -> pd.DatetimeIndex:
|
||||
"""
|
||||
获取 A 股交易日历
|
||||
|
||||
Returns:
|
||||
A 股交易日历 DatetimeIndex
|
||||
"""
|
||||
from datetime import date
|
||||
|
||||
# 获取回测区间
|
||||
start = self.config.backtest.start_date
|
||||
end = self.config.backtest.end_date
|
||||
if end is None:
|
||||
end = date.today().strftime('%Y-%m-%d')
|
||||
|
||||
# 创建临时数据获取器来获取交易日历
|
||||
if self._data_fetcher is None:
|
||||
self._data_fetcher = self._create_data_fetcher()
|
||||
|
||||
try:
|
||||
# 调用 get_trading_calendar 方法
|
||||
calendar = self._data_fetcher.get_trading_calendar(
|
||||
market='A',
|
||||
start=start,
|
||||
end=end
|
||||
)
|
||||
print(f" [日历] A 股交易日: {len(calendar)} 天 ({calendar[0]} ~ {calendar[-1]})")
|
||||
return calendar
|
||||
except Exception as e:
|
||||
print(f" [警告] 无法获取 A 股交易日历，使用所有日期: {e}")
|
||||
# 降级方案：使用 pandas 生成工作日
|
||||
from pandas.tseries.offsets import BDay
|
||||
start_dt = pd.Timestamp(start)
|
||||
end_dt = pd.Timestamp(end)
|
||||
return pd.date_range(start=start_dt, end=end_dt, freq='B') # 工作日
|
||||
|
||||
def _execute_backtest(self, positions: pd.DataFrame, data: Dict[str, pd.DataFrame]) -> Dict[str, any]:
|
||||
"""
|
||||
执行回测
|
||||
|
||||
核心逻辑：
|
||||
1. 使用 signal_source 计算信号（positions 的 columns 是 signal_source）
|
||||
2. 使用 trade_source 计算收益（通过 signal→trade 映射）
|
||||
3. T+1 执行：今天的信号明天生效
|
||||
4. 过滤非交易日：只保留 A 股交易日
|
||||
|
||||
Args:
|
||||
positions: 仓位 DataFrame（columns=signal_source）
|
||||
data: 数据字典 {code: DataFrame}（包含 signal_source 和 trade_source）
|
||||
|
||||
Returns:
|
||||
回测结果字典
|
||||
"""
|
||||
# 获取信号→交易映射
|
||||
signal_to_trade = self.config.asset_pools.get_signal_to_trade_mapping()
|
||||
|
||||
# 提取交易标的的收盘价
|
||||
close_prices = {}
|
||||
for signal_code, trade_code in signal_to_trade.items():
|
||||
if trade_code in data:
|
||||
# 使用交易标的的数据计算收益
|
||||
close_prices[signal_code] = data[trade_code]['close']
|
||||
else:
|
||||
print(f" 警告: {trade_code} 数据不存在，跳过")
|
||||
|
||||
close_df = pd.DataFrame(close_prices)
|
||||
|
||||
# 计算收益率
|
||||
returns = close_df.pct_change()
|
||||
|
||||
# 获取 A 股交易日历并过滤
|
||||
print("\n [过滤] 获取 A 股交易日历...")
|
||||
trading_calendar = self._get_trading_calendar()
|
||||
|
||||
# 过滤到 A 股交易日
|
||||
original_days = len(returns)
|
||||
returns = returns[returns.index.isin(trading_calendar)]
|
||||
positions = positions[positions.index.isin(trading_calendar)]
|
||||
filtered_days = len(returns)
|
||||
print(f" [过滤] 原始数据: {original_days} 天 -> A 股交易日: {filtered_days} 天 (过滤 {original_days - filtered_days} 天)")
|
||||
|
||||
# 计算策略收益（仓位加权）
|
||||
# 注意：T+1 执行，今天的信号明天生效
|
||||
positions_delayed = positions.shift(1).fillna(0)
|
||||
strategy_returns = (positions_delayed * returns).sum(axis=1)
|
||||
|
||||
# 计算净值曲线
|
||||
equity_curve = (1 + strategy_returns).cumprod()
|
||||
|
||||
# 检查是否有数据
|
||||
if len(equity_curve) == 0:
|
||||
return {
|
||||
'equity_curve': equity_curve,
|
||||
'strategy_returns': strategy_returns,
|
||||
'positions': positions,
|
||||
'metrics': {
|
||||
'total_return': 0,
|
||||
'annual_return': 0,
|
||||
'max_drawdown': 0,
|
||||
'sharpe_ratio': 0,
|
||||
'n_days': 0,
|
||||
}
|
||||
}
|
||||
|
||||
# 计算绩效指标
|
||||
total_return = equity_curve.iloc[-1] / equity_curve.iloc[0] - 1
|
||||
n_days = len(strategy_returns)
|
||||
annual_return = (1 + total_return) ** (252 / n_days) - 1 if n_days > 0 else 0
|
||||
|
||||
# 最大回撤
|
||||
cumulative_max = equity_curve.cummax()
|
||||
drawdown = (equity_curve - cumulative_max) / cumulative_max
|
||||
max_drawdown = drawdown.min()
|
||||
|
||||
# 夏普比率
|
||||
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(252) if strategy_returns.std() > 0 else 0
|
||||
|
||||
return {
|
||||
'equity_curve': equity_curve,
|
||||
'strategy_returns': strategy_returns,
|
||||
'positions': positions,
|
||||
'metrics': {
|
||||
'total_return': total_return,
|
||||
'annual_return': annual_return,
|
||||
'max_drawdown': max_drawdown,
|
||||
'sharpe_ratio': sharpe,
|
||||
'n_days': n_days,
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user