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feat(signals): 实现信号生成层抽象

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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
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aszerW
2026-05-11 22:18:20 +08:00
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"""
信号层抽象设计
核心组件：
- SignalGenerator: 信号生成器抽象基类
- TopNSelector: Top N选股器（轮动策略）
- TrendFollower: 趋势跟随器（趋势策略）
- ReversalTrader: 反转交易器（反转策略）
"""
import pandas as pd
import numpy as np
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
@dataclass
class SignalMeta:
"""信号元信息"""
mode: str # 'top_n', 'trend', 'reversal'
select_num: int
description: str = ""
class SignalGenerator(ABC):
"""
信号生成器抽象基类
所有信号生成器必须继承此基类，实现generate方法。
支持不同策略类型的信号生成逻辑。
"""
# 类属性（可被配置覆盖）
mode: str = "base"
def __init__(self, **params):
"""
初始化信号生成器
Args:
**params: 信号参数
"""
self._params = params
self._meta = SignalMeta(
mode=self.mode,
select_num=params.get('select_num', 1),
description=self.__doc__ or ""
)
@abstractmethod
def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
"""
生成交易信号
Args:
factor_data: 包含因子值的DataFrame
Returns:
包含信号列的DataFrame
"""
pass
@property
def params(self) -> Dict[str, Any]:
"""获取信号参数"""
return self._params
@property
def meta(self) -> SignalMeta:
"""获取信号元信息"""
return self._meta
def __repr__(self) -> str:
return f"{self.__class__.__name__}(mode={self.mode}, params={self._params})"
class TopNSelector(SignalGenerator):
"""
Top N选股器
用于轮动策略：
- 按因子值排序，选出Top N标的
- 支持分组选股（先类内竞争，再跨类排序）
参数：
- select_num: 选中数量（默认3）
- group_by: 分组列名（可选，如'market'）
- top_per_group: 每组选中数量（默认1）
- min_score: 最小得分阈值（可选）
"""
mode = "top_n"
def __init__(
self,
select_num: int = 3,
group_by: Optional[str] = None,
top_per_group: int = 1,
min_score: Optional[float] = None
):
super().__init__(
select_num=select_num,
group_by=group_by,
top_per_group=top_per_group,
min_score=min_score
)
self.select_num = select_num
self.group_by = group_by
self.top_per_group = top_per_group
self.min_score = min_score
def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
"""生成Top N选股信号"""
result = pd.DataFrame(index=factor_data.index)
# 获取因子列（排除非因子列）
factor_cols = self._get_factor_columns(factor_data)
if not factor_cols:
print("⚠ 未找到因子列")
result['signal'] = ''
return result
# 每日选股
signals = []
for date in factor_data.index:
row = factor_data.loc[date]
# 提取当日因子值
scores = {}
for col in factor_cols:
score = row[col]
if pd.notna(score):
scores[col] = score
# 应用最小得分过滤
if self.min_score:
scores = {k: v for k, v in scores.items() if v >= self.min_score}
# 选股逻辑
if self.group_by and 'group_info' in factor_data.columns:
# 分组选股：先类内竞争，再跨类排序
selected = self._grouped_selection(scores, factor_data.loc[date])
else:
# 全局Top N
selected = self._global_top_n(scores)
# 信号格式：逗号分隔的代码列表
signals.append(','.join(selected) if selected else '')
result['signal'] = signals
result['signal_raw'] = signals # 原始信号（未shift）
# T+1执行：信号向后移位1天
result['signal'] = result['signal'].shift(1)
return result
def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
"""获取因子列名"""
# 排除已知非因子列
exclude_cols = ['signal', 'signal_raw', 'group_info', 'combined', 'open', 'high', 'low', 'close', 'volume']
factor_cols = [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')]
return factor_cols
def _global_top_n(self, scores: Dict[str, float]) -> List[str]:
"""全局Top N选股"""
if not scores:
return []
# 按得分排序
sorted_items = sorted(scores.items(), key=lambda x: x[1], reverse=True)
# 选Top N
selected = [item[0] for item in sorted_items[:self.select_num]]
return selected
def _grouped_selection(
self,
scores: Dict[str, float],
row: pd.Series
) -> List[str]:
"""分组选股：先类内竞争，再跨类排序"""
if 'group_info' not in row.index:
return self._global_top_n(scores)
group_info = row['group_info']
if pd.isna(group_info):
return self._global_top_n(scores)
# 解析分组信息：{code: group}
groups = group_info if isinstance(group_info, dict) else {}
# 类内竞争：每组选Top1
group_champions = {}
for code, score in scores.items():
group = groups.get(code, 'default')
if group not in group_champions or score > group_champions[group][1]:
group_champions[group] = (code, score)
# 跨类排序：从冠军中选Top N
champions_scores = {code: score for code, score in group_champions.values()}
return self._global_top_n(champions_scores)
class TrendFollower(SignalGenerator):
"""
趋势跟随器
用于趋势跟踪策略：
- 趋势强度 > 入场阈值 → 入场信号
- 趋势强度 < 出场阈值 → 出场信号
参数：
- entry_threshold: 入场阈值（默认0.02）
- exit_threshold: 出场阈值（默认-0.02）
- select_num: 最大持仓数量（默认1）
"""
mode = "trend"
def __init__(
self,
entry_threshold: float = 0.02,
exit_threshold: float = -0.02,
select_num: int = 1
):
super().__init__(
entry_threshold=entry_threshold,
exit_threshold=exit_threshold,
select_num=select_num
)
self.entry_threshold = entry_threshold
self.exit_threshold = exit_threshold
self.select_num = select_num
def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
"""生成趋势跟随信号"""
result = pd.DataFrame(index=factor_data.index)
factor_cols = self._get_factor_columns(factor_data)
for col in factor_cols:
trend_strength = factor_data[col]
# 入场信号：趋势强度 > 阈值
result[f'{col}_entry'] = trend_strength > self.entry_threshold
# 出场信号：趋势强度 < 阈值
result[f'{col}_exit'] = trend_strength < self.exit_threshold
# 综合信号：入场强度最高的Top N
signals = []
for date in result.index:
entry_signals = []
for col in factor_cols:
if result.loc[date, f'{col}_entry']:
score = factor_data.loc[date, col]
if pd.notna(score):
entry_signals.append((col, score))
# 按强度排序，选Top N
entry_signals.sort(key=lambda x: x[1], reverse=True)
selected = [item[0] for item in entry_signals[:self.select_num]]
signals.append(','.join(selected) if selected else '')
result['signal'] = signals
result['signal'] = result['signal'].shift(1) # T+1执行
return result
def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
"""获取因子列名"""
exclude_cols = ['signal', 'signal_raw', 'combined', 'open', 'high', 'low', 'close', 'volume']
return [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')]
class ReversalTrader(SignalGenerator):
"""
反转交易器
用于反转策略：
- 超买区域（RSI>70） → 反转向下信号（卖出）
- 超卖区域（RSI<30） → 反转向上信号（买入）
参数：
- overbought: 超买阈值（默认70）
- oversold: 超卖阈值（默认30）
- reversal_threshold: 反转信号强度阈值（默认0.1）
"""
mode = "reversal"
def __init__(
self,
overbought: float = 70,
oversold: float = 30,
reversal_threshold: float = 0.1
):
super().__init__(
overbought=overbought,
oversold=oversold,
reversal_threshold=reversal_threshold
)
self.overbought = overbought
self.oversold = oversold
self.reversal_threshold = reversal_threshold
def generate(self, factor_data: pd.DataFrame) -> pd.DataFrame:
"""生成反转交易信号"""
result = pd.DataFrame(index=factor_data.index)
factor_cols = self._get_factor_columns(factor_data)
for col in factor_cols:
reversal_signal = factor_data[col]
# 买入信号：反转信号 > 阈值（正值，超卖反转）
result[f'{col}_buy'] = reversal_signal > self.reversal_threshold
# 卖出信号：反转信号 < -阈值（负值，超买反转）
result[f'{col}_sell'] = reversal_signal < -self.reversal_threshold
# 综合信号
signals = []
for date in result.index:
buy_signals = []
sell_signals = []
for col in factor_cols:
if result.loc[date, f'{col}_buy']:
buy_signals.append(col)
if result.loc[date, f'{col}_sell']:
sell_signals.append(col)
# 信号格式：'BUY:code1,code2' 或 'SELL:code1' 或 ''
if buy_signals:
signals.append(f"BUY:{','.join(buy_signals)}")
elif sell_signals:
signals.append(f"SELL:{','.join(sell_signals)}")
else:
signals.append('')
result['signal'] = signals
result['signal'] = result['signal'].shift(1) # T+1执行
return result
def _get_factor_columns(self, data: pd.DataFrame) -> List[str]:
"""获取因子列名"""
exclude_cols = ['signal', 'signal_raw', 'combined', 'open', 'high', 'low', 'close', 'volume']
return [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')]