aszerW
69081297c5
- strategies/shared/factors/momentum.py: MomentumFactor/TrendFactor/ReversalFactor/VolatilityFactor - strategies/shared/signals/selectors.py: TopNSelector/TrendFollower/ReversalTrader - strategies/shared/risk/controls.py: StopLossControl/PositionLimitControl/PremiumControl - strategies/shared/__init__.py: 统一入口导出所有定制组件
215 lines
7.7 KiB
Python
215 lines
7.7 KiB
Python
"""
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定制信号生成器实现
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这些信号生成器继承framework.core.signals.SignalGenerator
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"""
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from framework.signals import SignalGenerator
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Optional, Any
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class TopNSelector(SignalGenerator):
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"""
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Top N选股器（定制实现）
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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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factor_cols = self._get_factor_columns(factor_data)
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if not factor_cols:
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result['signal'] = ''
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return result
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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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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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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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if self.group_by and 'group_info' in factor_data.columns:
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selected = self._grouped_selection(scores, factor_data.loc[date])
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else:
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selected = self._global_top_n(scores)
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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
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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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exclude_cols = ['signal', 'signal_raw', 'group_info', '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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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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sorted_items = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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return [item[0] for item in sorted_items[:self.select_num]]
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def _grouped_selection(self, scores: Dict[str, float], row: pd.Series) -> 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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groups = group_info if isinstance(group_info, dict) else {}
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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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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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"""趋势跟随器（定制实现）"""
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mode = "trend"
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def __init__(self, entry_threshold: float = 0.02, exit_threshold: float = -0.02, select_num: int = 1):
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super().__init__(entry_threshold=entry_threshold, exit_threshold=exit_threshold, select_num=select_num)
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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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result[f'{col}_entry'] = trend_strength > self.entry_threshold
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result[f'{col}_exit'] = trend_strength < self.exit_threshold
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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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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)
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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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"""反转交易器（定制实现）"""
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mode = "reversal"
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def __init__(self, overbought: float = 70, oversold: float = 30, reversal_threshold: float = 0.1):
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super().__init__(overbought=overbought, oversold=oversold, reversal_threshold=reversal_threshold)
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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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result[f'{col}_buy'] = reversal_signal > self.reversal_threshold
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result[f'{col}_sell'] = reversal_signal < -self.reversal_threshold
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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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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)
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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')] |