aszerW
7468130450
核心组件： - StrategyBase: 策略抽象基类（含回调钩子） - 类属性（可被配置覆盖） - init_factors(): 初始化因子组合 - init_signal_generator(): 初始化信号生成器 - run(): 运行策略 - RotationStrategy: 轮动策略示例实现 - 动量因子 + TopN选股 - before_entry回调（溢价过滤） - dynamic_stoploss回调（持仓时间动态止损） - ConfigLoader: 配置加载器（YAML支持） - StrategyConfig: 策略配置数据类 特点： - 配置覆盖类属性 - 回调自动注册 - 策略工厂模式 测试覆盖：8个测试全部通过
357 lines
9.6 KiB
Python
357 lines
9.6 KiB
Python
"""
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策略基类与配置层
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核心组件：
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- StrategyBase: 策略抽象基类（含回调钩子）
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- ConfigLoader: 配置加载器
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"""
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import yaml
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import pandas as pd
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from abc import ABC, abstractmethod
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from typing import Dict, Any, Optional, List
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from pathlib import Path
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from dataclasses import dataclass
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from framework.factors import FactorBase, FactorRegistry, FactorCombiner
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from framework.factors.momentum import MomentumFactor
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from framework.signals import SignalGenerator, TopNSelector
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from framework.risk import CallbackHook, Position
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@dataclass
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class StrategyConfig:
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"""策略配置"""
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name: str
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version: int
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factors: List[Dict]
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signal: Dict
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callbacks: Dict
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params: Dict
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class StrategyBase(ABC):
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"""
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策略抽象基类
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融合Freqtrade回调机制 + 模块化因子设计
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类属性（可被配置覆盖）：
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- name: 策略名称
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- version: 接口版本
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- timeframe: K线周期
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- select_num: 选中数量
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- stoploss: 止损比例
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回调钩子（可选实现）：
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- before_entry: 入场前检查
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- after_entry: 入场后处理
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- before_exit: 出场前检查
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- after_exit: 出场后处理
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- dynamic_stoploss: 动态止损
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- custom_exit: 自定义出场条件
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"""
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# 接口版本
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INTERFACE_VERSION = 1
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# 类属性（可被配置覆盖）
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name: str = "base"
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timeframe: str = "1d"
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select_num: int = 3
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stoploss: float = -0.05
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def __init__(self, config: Optional[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: 策略配置（可覆盖类属性）
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"""
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# 配置覆盖类属性
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if config:
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self._apply_config(config)
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# 初始化回调钩子
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self._callbacks = CallbackHook()
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self._register_default_callbacks()
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# 初始化因子和信号生成器
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self._factors = None
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self._signal_gen = None
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def _apply_config(self, config: Dict) -> None:
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"""应用配置"""
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params = config.get('params', {})
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# 覆盖类属性
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for key, value in params.items():
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if hasattr(self, key):
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setattr(self, key, value)
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# 保存完整配置
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self._config = config
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def _register_default_callbacks(self) -> None:
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"""注册默认回调"""
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# 注册入场前回调（溢价过滤）
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if hasattr(self, 'before_entry'):
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self._callbacks.register('before_entry', self.before_entry)
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# 注册动态止损回调
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if hasattr(self, 'dynamic_stoploss'):
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self._callbacks.register('dynamic_stoploss', self.dynamic_stoploss)
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# 注册自定义出场回调
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if hasattr(self, 'custom_exit'):
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self._callbacks.register('custom_exit', self.custom_exit)
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@abstractmethod
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def init_factors(self) -> FactorCombiner:
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"""
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初始化因子组合
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Returns:
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因子组合器
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"""
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pass
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@abstractmethod
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def init_signal_generator(self) -> SignalGenerator:
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"""
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初始化信号生成器
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Returns:
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信号生成器
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"""
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pass
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def run(self, data: pd.DataFrame) -> pd.DataFrame:
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"""
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运行策略
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Args:
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data: 输入数据
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Returns:
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包含信号的DataFrame
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"""
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# 初始化因子和信号生成器
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if self._factors is None:
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self._factors = self.init_factors()
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if self._signal_gen is None:
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self._signal_gen = self.init_signal_generator()
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# 1. 计算因子
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factor_data = self._factors.compute(data)
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# 2. 生成信号
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signals = self._signal_gen.generate(factor_data)
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# 3. 应用回调钩子
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signals = self._apply_callbacks(signals, data)
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return signals
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def _apply_callbacks(self, signals: pd.DataFrame, data: pd.DataFrame) -> pd.DataFrame:
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"""应用回调钩子"""
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# 遍历每行信号
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for date in signals.index:
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signal = signals.loc[date, 'signal']
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if not signal or pd.isna(signal):
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continue
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# 解析信号（逗号分隔的代码）
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codes = signal.split(',')
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# 应用入场前回调
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for code in codes:
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if code not in data.columns:
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continue
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price = data.loc[date, code]
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premium = 0.0 # TODO: 从溢价数据获取
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# 触发回调
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allowed = self._callbacks.trigger(
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'before_entry',
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code,
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price,
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premium=premium,
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history=data
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)
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if not allowed:
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# 移除被拒绝的代码
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codes.remove(code)
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# 更新信号
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signals.loc[date, 'signal'] = ','.join(codes) if codes else ''
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return signals
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# ===== 可选回调方法 =====
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def before_entry(self, code: str, price: float, **kwargs) -> bool:
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"""
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入场前检查
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Args:
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code: 标的代码
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price: 入场价格
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**kwargs: 其他参数（premium, history等）
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Returns:
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是否允许入场
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"""
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# 默认：允许入场
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return True
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def after_entry(self, trade, **kwargs) -> None:
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"""入场后处理"""
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pass
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def before_exit(self, position: Position, **kwargs) -> bool:
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"""出场前检查"""
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return True
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def after_exit(self, trade, **kwargs) -> None:
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"""出场后处理"""
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pass
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def dynamic_stoploss(self, position: Position) -> float:
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"""
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动态止损
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Args:
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position: 持仓信息
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Returns:
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止损比例
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"""
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# 默认：返回固定止损
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return self.stoploss
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def custom_exit(self, position: Position) -> bool:
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"""
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自定义出场条件
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Args:
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position: 持仓信息
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Returns:
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是否触发出场
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"""
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return False
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class ConfigLoader:
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"""
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配置加载器
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支持YAML配置文件加载和验证
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"""
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def __init__(self, config_path: str):
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"""
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初始化配置加载器
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Args:
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config_path: 配置文件路径
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"""
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self._config_path = Path(config_path)
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self._config = None
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def load(self) -> Dict:
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"""加载配置"""
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if not self._config_path.exists():
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raise FileNotFoundError(f"Config file not found: {self._config_path}")
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with open(self._config_path, 'r', encoding='utf-8') as f:
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self._config = yaml.safe_load(f)
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return self._config
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def validate(self) -> bool:
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"""验证配置"""
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if self._config is None:
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self.load()
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# 必须字段
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required_fields = ['strategy', 'factors', 'signal']
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for field in required_fields:
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if field not in self._config:
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raise ValueError(f"Missing required field: {field}")
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return True
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def get_strategy_config(self) -> StrategyConfig:
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"""获取策略配置"""
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if self._config is None:
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self.load()
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return StrategyConfig(
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name=self._config['strategy']['name'],
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version=self._config['strategy'].get('version', 1),
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factors=self._config['factors'],
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signal=self._config['signal'],
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callbacks=self._config.get('callbacks', {}),
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params=self._config.get('params', {})
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)
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@staticmethod
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def from_yaml(yaml_str: str) -> Dict:
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"""从YAML字符串加载"""
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return yaml.safe_load(yaml_str)
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# 示例策略实现
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class RotationStrategy(StrategyBase):
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"""
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ETF轮动策略
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基于动量因子 + Top N选股
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"""
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name = "rotation"
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select_num = 3
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def init_factors(self) -> FactorCombiner:
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"""初始化动量因子"""
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FactorRegistry.clear()
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FactorRegistry.register(MomentumFactor)
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return FactorCombiner([
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FactorRegistry.get('momentum', n_days=25, crash_filter=True)
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])
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def init_signal_generator(self) -> SignalGenerator:
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"""初始化Top N选股器"""
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from framework.signals import TopNSelector
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return TopNSelector(
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select_num=self.select_num,
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min_score=0.0
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)
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def before_entry(self, code: str, price: float, **kwargs) -> bool:
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"""入场前：溢价过滤"""
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premium = kwargs.get('premium', 0)
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# 溢价超过10%拒绝入场
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if premium > 0.10:
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print(f"溢价过高，拒绝入场: {code} (溢价={premium:.2%})")
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return False
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return True
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def dynamic_stoploss(self, position: Position) -> float:
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"""动态止损：根据持仓时间调整"""
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if position.holding_days >= 10:
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return -0.03 # 10天后收紧止损
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elif position.holding_days >= 5:
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return -0.05
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return -0.10 |