""" 定制信号生成器实现 这些信号生成器继承framework.core.signals.SignalGenerator """ from framework.signals import SignalGenerator import pandas as pd import numpy as np from typing import Dict, List, Optional, Any class TopNSelector(SignalGenerator): """ Top N选股器（定制实现） 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) factor_cols = self._get_factor_columns(factor_data) if not factor_cols: result['signal'] = '' return result signals = [] for date in factor_data.index: row = factor_data.loc[date] scores = {} for col in factor_cols: score = row[col] if pd.notna(score): scores[col] = score if self.min_score: scores = {k: v for k, v in scores.items() if v >= self.min_score} if self.group_by and 'group_info' in factor_data.columns: selected = self._grouped_selection(scores, factor_data.loc[date]) else: selected = self._global_top_n(scores) signals.append(','.join(selected) if selected else '') result['signal'] = signals result['signal_raw'] = signals result['signal'] = result['signal'].shift(1) return result def _get_factor_columns(self, data: pd.DataFrame) -> List[str]: """获取因子列名""" exclude_cols = ['signal', 'signal_raw', 'group_info', 'combined', 'open', 'high', 'low', 'close', 'volume'] return [col for col in data.columns if col not in exclude_cols and not col.endswith('_weighted')] def _global_top_n(self, scores: Dict[str, float]) -> List[str]: """全局Top N选股""" if not scores: return [] sorted_items = sorted(scores.items(), key=lambda x: x[1], reverse=True) return [item[0] for item in sorted_items[:self.select_num]] 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) groups = group_info if isinstance(group_info, dict) else {} 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) 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（定制实现）""" 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] result[f'{col}_entry'] = trend_strength > self.entry_threshold result[f'{col}_exit'] = trend_strength < self.exit_threshold 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)) 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) 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（定制实现）""" 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] result[f'{col}_buy'] = reversal_signal > self.reversal_threshold result[f'{col}_sell'] = reversal_signal < -self.reversal_threshold 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) 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) 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')]