refactor(framework): 框架只保留抽象接口，具体实现移至strategies/shared
- FactorBase/FactorRegistry/FactorCombiner: 因子抽象接口 - SignalGenerator: 信号生成抽象接口 - RiskControl/Position/CallbackHook: 风控抽象接口 - StrategyBase: 策略抽象基类 - Executor/Portfolio: 执行器抽象接口 - ConfigLoader: 配置加载器 - 删除framework/factors/momentum.py（具体实现）
This commit is contained in:
@@ -1,54 +1,28 @@
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"""
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因子层抽象设计
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因子层抽象接口（通用）
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核心组件：
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- FactorBase: 因子抽象基类
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- FactorRegistry: 因子注册器
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- FactorCombiner: 因子组合器
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只提供抽象基类和注册机制，具体因子实现在strategies/shared/factors/
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional, Any, Type
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import pandas as pd
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import numpy as np
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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@dataclass
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class FactorMeta:
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"""因子元信息"""
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name: str
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category: str # 'momentum', 'trend', 'reversal', 'volatility', 'fundamental'
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params: Dict[str, Any]
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description: str = ""
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class FactorBase(ABC):
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"""
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因子抽象基类
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所有因子必须继承此基类，实现compute方法。
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支持参数配置、数据验证、元信息管理。
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所有因子必须实现compute方法
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"""
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# 类属性（可被配置覆盖）
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name: str = "base"
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category: str = "unknown"
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def __init__(self, **params):
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"""
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初始化因子
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Args:
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**params: 因子参数（如n_days=25, period=14等）
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"""
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"""初始化因子参数"""
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self._params = params
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self._meta = FactorMeta(
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name=self.name,
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category=self.category,
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params=params,
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description=self.__doc__ or ""
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)
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@abstractmethod
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def compute(self, data: pd.DataFrame) -> pd.Series:
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@@ -56,139 +30,84 @@ class FactorBase(ABC):
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计算因子值
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Args:
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data: 包含OHLCV数据的DataFrame
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data: OHLCV数据，必须包含'close'列
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Returns:
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因子值序列（Series）
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因子值序列
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"""
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pass
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@property
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def params(self) -> Dict[str, Any]:
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"""获取因子参数"""
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return self._params
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@property
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def meta(self) -> FactorMeta:
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"""获取因子元信息"""
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return self._meta
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def validate_data(self, data: pd.DataFrame) -> bool:
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"""
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验证数据是否满足计算要求
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"""验证数据是否满足计算要求"""
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if 'close' not in data.columns:
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return False
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Args:
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data: 数据DataFrame
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Returns:
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是否满足要求
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"""
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# 默认验证：数据长度 >= 最小周期
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min_periods = self._params.get('min_periods', 20)
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return len(data) >= min_periods
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def __repr__(self) -> str:
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return f"{self.__class__.__name__}(name={self.name}, params={self._params})"
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params_str = ', '.join([f"{k}={v}" for k, v in self._params.items()])
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return f"{self.__class__.__name__}({params_str})"
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class FactorRegistry:
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"""
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因子注册器
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因子注册器（通用）
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管理所有注册的因子，支持：
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- 注册因子类
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- 获取因子实例
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- 列出可用因子
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- 按类别筛选因子
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管理因子类的注册和获取
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"""
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_factors: Dict[str, type] = {}
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_factors: Dict[str, Type[FactorBase]] = {}
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@classmethod
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def register(cls, factor_class: type) -> None:
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"""
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注册因子类
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Args:
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factor_class: 因子类（必须继承FactorBase）
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"""
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if not isinstance(factor_class, type) or not issubclass(factor_class, FactorBase):
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raise TypeError(f"factor_class must be a subclass of FactorBase")
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# 创建临时实例获取名称
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def register(cls, factor_class: Type[FactorBase]) -> None:
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"""注册因子类"""
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temp_instance = factor_class()
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name = temp_instance.name
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if name in cls._factors:
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print(f"因子已注册，覆盖: {name}")
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cls._factors[name] = factor_class
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print(f"✓ 因子已注册: {name} ({factor_class.__name__})")
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@classmethod
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def get(cls, name: str, **params) -> FactorBase:
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"""
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获取因子实例
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Args:
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name: 因子名称
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**params: 因子参数
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Returns:
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因子实例
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"""
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"""获取因子实例"""
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if name not in cls._factors:
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raise KeyError(f"Factor '{name}' not registered. Available: {cls.list()}")
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raise ValueError(f"因子未注册: {name}")
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factor_class = cls._factors[name]
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return factor_class(**params)
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@classmethod
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def list(cls, category: str = None) -> List[str]:
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"""
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列出可用因子
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Args:
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category: 按类别筛选（可选）
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Returns:
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因子名称列表
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"""
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if category:
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return [
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name for name, factor_class in cls._factors.items()
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if factor_class().category == category
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]
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def list_factors(cls) -> List[str]:
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"""列出所有已注册因子"""
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return list(cls._factors.keys())
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@classmethod
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def list_by_category(cls) -> Dict[str, List[str]]:
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"""
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按类别列出因子
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Returns:
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类别→因子列表字典
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"""
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result = {}
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for name, factor_class in cls._factors.items():
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cat = factor_class().category
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if cat not in result:
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result[cat] = []
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result[cat].append(name)
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return result
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def clear(cls) -> None:
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"""清空注册表"""
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cls._factors = {}
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@classmethod
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def clear(cls) -> None:
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"""清空注册表（用于测试）"""
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cls._factors.clear()
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def get_category(cls, name: str) -> str:
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"""获取因子类别"""
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if name not in cls._factors:
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return "unknown"
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temp_instance = cls._factors[name]()
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return temp_instance.category
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class FactorCombiner:
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"""
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因子组合器
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因子组合器（通用）
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支持多因子加权组合，用于：
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- 多因子策略
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- 因子权重调整
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- 因子结果合并
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支持多因子加权组合
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"""
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SUPPORTED_METHODS = ['weighted_sum', 'rank_average', 'zscore_sum', 'equal_weight']
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def __init__(
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self,
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factors: List[FactorBase],
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@@ -196,87 +115,77 @@ class FactorCombiner:
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method: str = 'weighted_sum'
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):
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"""
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初始化因子组合器
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初始化组合器
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Args:
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factors: 因子实例列表
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weights: 权重列表（默认等权）
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method: 组合方法 ('weighted_sum', 'average', 'max', 'min')
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weights: 因子权重列表（可选）
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method: 组合方法（weighted_sum/rank_average/zscore_sum/equal_weight）
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"""
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if not factors:
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raise ValueError("factors list cannot be empty")
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if method not in self.SUPPORTED_METHODS:
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raise ValueError(f"Unsupported method: {method}")
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self._factors = factors
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self._weights = weights or [1.0 / len(factors)] * len(factors)
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if weights is None:
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self._weights = [1.0 / len(factors)] * len(factors)
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else:
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if len(weights) != len(factors):
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raise ValueError("weights length must match factors length")
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self._weights = weights
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self._method = method
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# 验证权重
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if len(self._weights) != len(factors):
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raise ValueError(f"weights length ({len(self._weights)}) != factors length ({len(factors)})")
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# 归一化权重
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total_weight = sum(self._weights)
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self._weights = [w / total_weight for w in self._weights]
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def compute(self, data: pd.DataFrame) -> pd.DataFrame:
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"""
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计算所有因子并组合
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Args:
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data: 输入数据
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Returns:
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包含各因子值和组合因子值的DataFrame
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DataFrame包含各因子值和combined列
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"""
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result = pd.DataFrame(index=data.index)
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# 计算各因子
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for i, factor in enumerate(self._factors):
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# 验证数据
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if not factor.validate_data(data):
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print(f"⚠ 因子 {factor.name} 数据验证失败，跳过")
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continue
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# 计算因子值
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factor_values = factor.compute(data)
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result[factor.name] = factor_values
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# 加权因子值
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result[f"{factor.name}_weighted"] = factor_values * self._weights[i]
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col_name = f"{factor.name}"
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result[col_name] = factor_values
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# 组合因子值
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weighted_cols = [f"{f.name}_weighted" for f in self._factors if f.name in result.columns]
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if self._method == 'weighted_sum':
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result['combined'] = result[weighted_cols].sum(axis=1)
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elif self._method == 'average':
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factor_cols = [f.name for f in self._factors if f.name in result.columns]
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weighted_cols = [f.name for f in self._factors]
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result['combined'] = result[weighted_cols].apply(
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lambda row: sum(row[col] * self._weights[i] for i, col in enumerate(weighted_cols) if pd.notna(row[col])),
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axis=1
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)
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elif self._method == 'equal_weight':
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factor_cols = [f.name for f in self._factors]
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result['combined'] = result[factor_cols].mean(axis=1)
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elif self._method == 'max':
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factor_cols = [f.name for f in self._factors if f.name in result.columns]
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result['combined'] = result[factor_cols].max(axis=1)
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elif self._method == 'min':
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factor_cols = [f.name for f in self._factors if f.name in result.columns]
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result['combined'] = result[factor_cols].min(axis=1)
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else:
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raise ValueError(f"Unknown method: {self._method}")
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elif self._method == 'rank_average':
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factor_cols = [f.name for f in self._factors]
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ranks = result[factor_cols].rank(axis=1)
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result['combined'] = ranks.mean(axis=1)
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elif self._method == 'zscore_sum':
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factor_cols = [f.name for f in self._factors]
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zscores = result[factor_cols].apply(lambda x: (x - x.mean()) / x.std())
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result['combined'] = zscores.sum(axis=1)
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return result
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@property
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def factors(self) -> List[FactorBase]:
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"""获取因子列表"""
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return self._factors
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@property
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def weights(self) -> List[float]:
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"""获取权重列表"""
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return self._weights
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def set_weights(self, weights: List[float]) -> None:
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"""设置权重"""
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if len(weights) != len(self._factors):
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raise ValueError(f"weights length must equal factors length")
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total = sum(weights)
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self._weights = [w / total for w in weights]
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def get_factor_names(self) -> List[str]:
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"""获取因子名称列表"""
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return [f.name for f in self._factors]
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def __repr__(self) -> str:
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factor_names = [f.name for f in self._factors]
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return f"FactorCombiner(factors={factor_names}, weights={self._weights})"
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return f"FactorCombiner(factors={factor_names}, weights={self._weights}, method={self._method})"
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# 导出抽象接口
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__all__ = ['FactorBase', 'FactorRegistry', 'FactorCombiner']
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@@ -1,312 +0,0 @@
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"""
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动量因子实现
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基于加权线性回归动量的因子
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"""
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import pandas as pd
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import numpy as np
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import math
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from typing import Optional
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from framework.factors import FactorBase
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class MomentumFactor(FactorBase):
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"""
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动量因子
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计算加权线性回归动量得分：
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得分 = 年化收益率 × R²
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参数：
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- n_days: 动量窗口（默认25）
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- weighted: 是否加权（默认True）
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- crash_filter: 是否启用崩盘过滤（默认True）
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"""
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name = "momentum"
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category = "momentum"
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def __init__(
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self,
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n_days: int = 25,
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weighted: bool = True,
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crash_filter: bool = True
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):
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super().__init__(n_days=n_days, weighted=weighted, crash_filter=crash_filter)
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self.n_days = n_days
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self.weighted = weighted
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self.crash_filter = crash_filter
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""计算动量因子值"""
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if 'close' not in data.columns:
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raise ValueError("data must contain 'close' column")
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prices = data['close']
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if self.weighted:
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# 加权动量得分
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factor_values = prices.rolling(self.n_days).apply(
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lambda x: self._weighted_momentum_score(x.values),
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raw=False
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)
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else:
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# 简单动量
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factor_values = prices.pct_change(self.n_days)
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# 应用崩盘过滤
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if self.crash_filter:
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factor_values = self._apply_crash_filter(prices, factor_values)
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return factor_values
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def _weighted_momentum_score(self, prices: np.ndarray) -> float:
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"""计算加权动量得分"""
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if len(prices) < 5:
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return 0.0
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y = np.log(prices)
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x = np.arange(len(y))
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weights = np.linspace(1, 2, len(y))
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# 加权线性回归
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slope, intercept = np.polyfit(x, y, 1, w=weights)
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annualized_returns = math.exp(slope * 250) - 1
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# 加权R²
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y_pred = slope * x + intercept
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ss_res = np.sum(weights * (y - y_pred) ** 2)
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ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
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r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
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return annualized_returns * r2
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def _apply_crash_filter(
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self,
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prices: pd.Series,
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factor_values: pd.Series
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) -> pd.Series:
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"""崩盘过滤：连续3天跌>5%清零"""
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result = factor_values.copy()
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for i in range(3, len(prices)):
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r1 = prices.iloc[i] / prices.iloc[i-1]
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r2 = prices.iloc[i-1] / prices.iloc[i-2]
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r3 = prices.iloc[i-2] / prices.iloc[i-3]
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# 条件1：任一天跌>5%
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con1 = min(r1, r2, r3) < 0.95
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# 条件2：连续下跌且累计跌>5%
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con2 = (r1 < 1) and (r2 < 1) and (r3 < 1) and (prices.iloc[i] / prices.iloc[i-3] < 0.95)
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if con1 or con2:
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result.iloc[i] = 0.0
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return result
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class TrendFactor(FactorBase):
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"""
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趋势因子
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计算趋势强度：
|
||||
- MA交叉偏离度
|
||||
- MACD趋势
|
||||
|
||||
参数：
|
||||
- method: 趋势方法（'ma_cross', 'macd'）
|
||||
- fast: 快线周期（默认5）
|
||||
- slow: 慢线周期（默认20）
|
||||
"""
|
||||
|
||||
name = "trend"
|
||||
category = "trend"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
method: str = 'ma_cross',
|
||||
fast: int = 5,
|
||||
slow: int = 20
|
||||
):
|
||||
super().__init__(method=method, fast=fast, slow=slow)
|
||||
self.method = method
|
||||
self.fast = fast
|
||||
self.slow = slow
|
||||
|
||||
def compute(self, data: pd.DataFrame) -> pd.Series:
|
||||
"""计算趋势因子值"""
|
||||
if 'close' not in data.columns:
|
||||
raise ValueError("data must contain 'close' column")
|
||||
|
||||
prices = data['close']
|
||||
|
||||
if self.method == 'ma_cross':
|
||||
# MA交叉偏离度
|
||||
fast_ma = prices.rolling(self.fast).mean()
|
||||
slow_ma = prices.rolling(self.slow).mean()
|
||||
trend_strength = (fast_ma - slow_ma) / slow_ma
|
||||
return trend_strength
|
||||
|
||||
elif self.method == 'macd':
|
||||
# MACD趋势
|
||||
ema12 = prices.ewm(span=12).mean()
|
||||
ema26 = prices.ewm(span=26).mean()
|
||||
macd = ema12 - ema26
|
||||
signal = macd.ewm(span=9).mean()
|
||||
return macd - signal
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown method: {self.method}")
|
||||
|
||||
|
||||
class ReversalFactor(FactorBase):
|
||||
"""
|
||||
反转因子
|
||||
|
||||
计算超买超卖信号：
|
||||
- RSI偏离度
|
||||
- KDJ
|
||||
|
||||
参数：
|
||||
- method: 反转方法（'rsi', 'kdj'）
|
||||
- period: 周期（默认14）
|
||||
- overbought: 超买阈值（默认70）
|
||||
- oversold: 超卖阈值（默认30）
|
||||
"""
|
||||
|
||||
name = "reversal"
|
||||
category = "reversal"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
method: str = 'rsi',
|
||||
period: int = 14,
|
||||
overbought: float = 70,
|
||||
oversold: float = 30
|
||||
):
|
||||
super().__init__(method=method, period=period, overbought=overbought, oversold=oversold)
|
||||
self.method = method
|
||||
self.period = period
|
||||
self.overbought = overbought
|
||||
self.oversold = oversold
|
||||
|
||||
def compute(self, data: pd.DataFrame) -> pd.Series:
|
||||
"""计算反转因子值"""
|
||||
if 'close' not in data.columns:
|
||||
raise ValueError("data must contain 'close' column")
|
||||
|
||||
prices = data['close']
|
||||
|
||||
if self.method == 'rsi':
|
||||
# RSI反转信号
|
||||
rsi = self._compute_rsi(prices, self.period)
|
||||
|
||||
# 超买超卖偏离度
|
||||
# 超买 → 负值（反转向下信号）
|
||||
# 超卖 → 正值（反转向上信号）
|
||||
reversal_signal = pd.Series(index=prices.index, dtype=float)
|
||||
reversal_signal = np.where(
|
||||
rsi > self.overbought,
|
||||
-(rsi - self.overbought) / (100 - self.overbought), # 超买：负值
|
||||
np.where(
|
||||
rsi < self.oversold,
|
||||
(self.oversold - rsi) / self.oversold, # 超卖：正值
|
||||
0 # 正常区间：0
|
||||
)
|
||||
)
|
||||
return pd.Series(reversal_signal, index=prices.index)
|
||||
|
||||
elif self.method == 'kdj':
|
||||
# KDJ反转信号
|
||||
return self._compute_kdj(data)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown method: {self.method}")
|
||||
|
||||
def _compute_rsi(self, prices: pd.Series, period: int) -> pd.Series:
|
||||
"""计算RSI"""
|
||||
delta = prices.diff()
|
||||
gain = delta.where(delta > 0, 0)
|
||||
loss = (-delta).where(delta < 0, 0)
|
||||
|
||||
avg_gain = gain.rolling(period).mean()
|
||||
avg_loss = loss.rolling(period).mean()
|
||||
|
||||
rs = avg_gain / avg_loss
|
||||
rsi = 100 - (100 / (1 + rs))
|
||||
return rsi
|
||||
|
||||
def _compute_kdj(self, data: pd.DataFrame) -> pd.Series:
|
||||
"""计算KDJ反转信号"""
|
||||
low = data['low']
|
||||
high = data['high']
|
||||
close = data['close']
|
||||
|
||||
# 计算K、D、J
|
||||
low_min = low.rolling(self.period).min()
|
||||
high_max = high.rolling(self.period).max()
|
||||
|
||||
rsv = (close - low_min) / (high_max - low_min) * 100
|
||||
|
||||
k = rsv.ewm(alpha=1/3).mean()
|
||||
d = k.ewm(alpha=1/3).mean()
|
||||
j = 3 * k - 2 * d
|
||||
|
||||
# J值偏离度作为反转信号
|
||||
return j
|
||||
|
||||
|
||||
class VolatilityFactor(FactorBase):
|
||||
"""
|
||||
波动率因子
|
||||
|
||||
计算价格波动率：
|
||||
- ATR
|
||||
- 标准差
|
||||
|
||||
参数：
|
||||
- method: 波动率方法（'atr', 'std'）
|
||||
- period: 周期（默认20）
|
||||
"""
|
||||
|
||||
name = "volatility"
|
||||
category = "volatility"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
method: str = 'std',
|
||||
period: int = 20
|
||||
):
|
||||
super().__init__(method=method, period=period)
|
||||
self.method = method
|
||||
self.period = period
|
||||
|
||||
def compute(self, data: pd.DataFrame) -> pd.Series:
|
||||
"""计算波动率因子值"""
|
||||
if self.method == 'std':
|
||||
# 标准差波动率
|
||||
return data['close'].rolling(self.period).std()
|
||||
|
||||
elif self.method == 'atr':
|
||||
# ATR波动率
|
||||
return self._compute_atr(data)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown method: {self.method}")
|
||||
|
||||
def _compute_atr(self, data: pd.DataFrame) -> pd.Series:
|
||||
"""计算ATR"""
|
||||
high = data['high']
|
||||
low = data['low']
|
||||
close = data['close']
|
||||
|
||||
prev_close = close.shift(1)
|
||||
tr = pd.concat([
|
||||
high - low,
|
||||
(high - prev_close).abs(),
|
||||
(low - prev_close).abs()
|
||||
], axis=1).max(axis=1)
|
||||
|
||||
return tr.rolling(self.period).mean()
|
||||
Reference in New Issue
Block a user