aszerW
f5e6202eee
核心组件： - SignalGenerator: 信号生成器抽象基类 - TopNSelector: Top N选股器（轮动策略） - 支持分组选股（先类内竞争，再跨类排序） - 支持最小得分阈值过滤 - TrendFollower: 趋势跟随器（趋势策略） - 入场阈值/出场阈值控制 - ReversalTrader: 反转交易器（反转策略） - 超买超卖信号生成 特点： - T+1执行机制（信号shift向后移位） - 向量化计算，避免前视偏差 测试覆盖：10个测试全部通过
353 lines
11 KiB
Python
353 lines
11 KiB
Python
"""
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信号层抽象设计
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核心组件：
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- SignalGenerator: 信号生成器抽象基类
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- TopNSelector: Top N选股器（轮动策略）
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- TrendFollower: 趋势跟随器（趋势策略）
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- ReversalTrader: 反转交易器（反转策略）
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"""
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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 SignalMeta:
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"""信号元信息"""
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mode: str # 'top_n', 'trend', 'reversal'
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select_num: int
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description: str = ""
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class SignalGenerator(ABC):
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"""
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信号生成器抽象基类
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所有信号生成器必须继承此基类，实现generate方法。
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支持不同策略类型的信号生成逻辑。
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"""
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# 类属性（可被配置覆盖）
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mode: str = "base"
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def __init__(self, **params):
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"""
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初始化信号生成器
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Args:
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**params: 信号参数
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"""
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self._params = params
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self._meta = SignalMeta(
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mode=self.mode,
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select_num=params.get('select_num', 1),
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description=self.__doc__ or ""
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)
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@abstractmethod
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def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
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"""
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生成交易信号
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Args:
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factor_data: 包含因子值的DataFrame
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Returns:
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包含信号列的DataFrame
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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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"""获取信号参数"""
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return self._params
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@property
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def meta(self) -> SignalMeta:
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"""获取信号元信息"""
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return self._meta
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def __repr__(self) -> str:
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return f"{self.__class__.__name__}(mode={self.mode}, params={self._params})"
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class TopNSelector(SignalGenerator):
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"""
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Top N选股器
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用于轮动策略：
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- 按因子值排序，选出Top N标的
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- 支持分组选股（先类内竞争，再跨类排序）
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参数：
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- select_num: 选中数量（默认3）
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- group_by: 分组列名（可选，如'market'）
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- top_per_group: 每组选中数量（默认1）
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- min_score: 最小得分阈值（可选）
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"""
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mode = "top_n"
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def __init__(
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self,
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select_num: int = 3,
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group_by: Optional[str] = None,
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top_per_group: int = 1,
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min_score: Optional[float] = None
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):
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super().__init__(
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select_num=select_num,
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group_by=group_by,
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top_per_group=top_per_group,
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min_score=min_score
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)
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self.select_num = select_num
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self.group_by = group_by
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self.top_per_group = top_per_group
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self.min_score = min_score
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def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
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"""生成Top N选股信号"""
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result = pd.DataFrame(index=factor_data.index)
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# 获取因子列（排除非因子列）
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factor_cols = self._get_factor_columns(factor_data)
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if not factor_cols:
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print("⚠ 未找到因子列")
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result['signal'] = ''
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return result
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# 每日选股
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signals = []
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for date in factor_data.index:
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row = factor_data.loc[date]
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# 提取当日因子值
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scores = {}
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for col in factor_cols:
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score = row[col]
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if pd.notna(score):
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scores[col] = score
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# 应用最小得分过滤
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if self.min_score:
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scores = {k: v for k, v in scores.items() if v >= self.min_score}
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# 选股逻辑
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if self.group_by and 'group_info' in factor_data.columns:
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# 分组选股：先类内竞争，再跨类排序
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selected = self._grouped_selection(scores, factor_data.loc[date])
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else:
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# 全局Top N
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selected = self._global_top_n(scores)
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# 信号格式：逗号分隔的代码列表
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signals.append(','.join(selected) if selected else '')
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result['signal'] = signals
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result['signal_raw'] = signals # 原始信号（未shift）
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# T+1执行：信号向后移位1天
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result['signal'] = result['signal'].shift(1)
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return result
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def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
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"""获取因子列名"""
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# 排除已知非因子列
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exclude_cols = ['signal', 'signal_raw', 'group_info', 'combined', 'open', 'high', 'low', 'close', 'volume']
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factor_cols = [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')]
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return factor_cols
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def _global_top_n(self, scores: Dict[str, float]) -> List[str]:
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"""全局Top N选股"""
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if not scores:
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return []
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# 按得分排序
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sorted_items = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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# 选Top N
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selected = [item[0] for item in sorted_items[:self.select_num]]
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return selected
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def _grouped_selection(
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self,
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scores: Dict[str, float],
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row: pd.Series
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) -> List[str]:
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"""分组选股：先类内竞争，再跨类排序"""
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if 'group_info' not in row.index:
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return self._global_top_n(scores)
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group_info = row['group_info']
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if pd.isna(group_info):
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return self._global_top_n(scores)
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# 解析分组信息：{code: group}
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groups = group_info if isinstance(group_info, dict) else {}
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# 类内竞争：每组选Top1
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group_champions = {}
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for code, score in scores.items():
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group = groups.get(code, 'default')
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if group not in group_champions or score > group_champions[group][1]:
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group_champions[group] = (code, score)
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# 跨类排序：从冠军中选Top N
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champions_scores = {code: score for code, score in group_champions.values()}
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return self._global_top_n(champions_scores)
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class TrendFollower(SignalGenerator):
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"""
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趋势跟随器
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用于趋势跟踪策略：
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- 趋势强度 > 入场阈值 → 入场信号
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- 趋势强度 < 出场阈值 → 出场信号
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参数：
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- entry_threshold: 入场阈值（默认0.02）
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- exit_threshold: 出场阈值（默认-0.02）
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- select_num: 最大持仓数量（默认1）
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"""
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mode = "trend"
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def __init__(
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self,
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entry_threshold: float = 0.02,
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exit_threshold: float = -0.02,
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select_num: int = 1
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):
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super().__init__(
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entry_threshold=entry_threshold,
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exit_threshold=exit_threshold,
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select_num=select_num
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)
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self.entry_threshold = entry_threshold
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self.exit_threshold = exit_threshold
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self.select_num = select_num
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def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
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"""生成趋势跟随信号"""
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result = pd.DataFrame(index=factor_data.index)
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factor_cols = self._get_factor_columns(factor_data)
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for col in factor_cols:
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trend_strength = factor_data[col]
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# 入场信号：趋势强度 > 阈值
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result[f'{col}_entry'] = trend_strength > self.entry_threshold
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# 出场信号：趋势强度 < 阈值
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result[f'{col}_exit'] = trend_strength < self.exit_threshold
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# 综合信号：入场强度最高的Top N
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signals = []
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for date in result.index:
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entry_signals = []
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for col in factor_cols:
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if result.loc[date, f'{col}_entry']:
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score = factor_data.loc[date, col]
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if pd.notna(score):
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entry_signals.append((col, score))
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# 按强度排序，选Top N
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entry_signals.sort(key=lambda x: x[1], reverse=True)
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selected = [item[0] for item in entry_signals[:self.select_num]]
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signals.append(','.join(selected) if selected else '')
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result['signal'] = signals
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result['signal'] = result['signal'].shift(1) # T+1执行
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return result
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def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
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"""获取因子列名"""
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exclude_cols = ['signal', 'signal_raw', 'combined', 'open', 'high', 'low', 'close', 'volume']
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return [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')]
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class ReversalTrader(SignalGenerator):
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"""
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反转交易器
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用于反转策略：
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- 超买区域（RSI>70） → 反转向下信号（卖出）
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- 超卖区域（RSI<30） → 反转向上信号（买入）
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参数：
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- overbought: 超买阈值（默认70）
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- oversold: 超卖阈值（默认30）
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- reversal_threshold: 反转信号强度阈值（默认0.1）
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"""
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mode = "reversal"
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def __init__(
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self,
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overbought: float = 70,
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oversold: float = 30,
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reversal_threshold: float = 0.1
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):
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super().__init__(
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overbought=overbought,
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oversold=oversold,
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reversal_threshold=reversal_threshold
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)
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self.overbought = overbought
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self.oversold = oversold
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self.reversal_threshold = reversal_threshold
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def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
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"""生成反转交易信号"""
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result = pd.DataFrame(index=factor_data.index)
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factor_cols = self._get_factor_columns(factor_data)
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for col in factor_cols:
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reversal_signal = factor_data[col]
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# 买入信号：反转信号 > 阈值（正值，超卖反转）
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result[f'{col}_buy'] = reversal_signal > self.reversal_threshold
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# 卖出信号：反转信号 < -阈值（负值，超买反转）
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result[f'{col}_sell'] = reversal_signal < -self.reversal_threshold
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# 综合信号
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signals = []
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for date in result.index:
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buy_signals = []
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sell_signals = []
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for col in factor_cols:
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if result.loc[date, f'{col}_buy']:
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buy_signals.append(col)
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if result.loc[date, f'{col}_sell']:
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sell_signals.append(col)
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# 信号格式：'BUY:code1,code2' 或 'SELL:code1' 或 ''
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if buy_signals:
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signals.append(f"BUY:{','.join(buy_signals)}")
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elif sell_signals:
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signals.append(f"SELL:{','.join(sell_signals)}")
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else:
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signals.append('')
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result['signal'] = signals
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result['signal'] = result['signal'].shift(1) # T+1执行
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return result
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def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
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"""获取因子列名"""
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exclude_cols = ['signal', 'signal_raw', 'combined', 'open', 'high', 'low', 'close', 'volume']
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return [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')] |