aszerW
2c69c6136a
修正之前的误解，正确实现用户意图： - 短债正常参与动量排序，没有任何特殊处理 - 短债排名 <= select_num → 短债被选中，比短债弱的标的被排除 - 短债排名 > select_num → 短债被排除（有更好的选择） - effective_threshold = min(短债排名, select_num) 验证结果： - 场景1：短债排名3 → 选[NDX, A, 短债]，排名>3排除 - 场景2：短债排名2 → 选[NDX, 短债]，排名>2排除（只持2只） - 场景3：短债排名4 → 选Top3正常，短债被排除
346 lines
13 KiB
Python
346 lines
13 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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- 支持调仓阈值检查（新组合得分需超过当前组合一定比例才调仓）
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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: 分组键名（可选，如'market'）
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- group_mapping: 分组映射字典（可选，{code: group}）
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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，如0表示过滤负分）
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- rebalance_threshold: 调仓阈值（可选，新组合得分需超过当前组合X%才调仓）
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- rebalance_days: 最低调仓周期（可选，持仓至少N天才能调仓）
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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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group_mapping: Optional[Dict[str, str]] = None,
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top_per_group: int = 1,
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min_score: Optional[float] = None,
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rebalance_threshold: float = 0.0,
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rebalance_days: int = 1
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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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group_mapping=group_mapping,
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top_per_group=top_per_group,
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min_score=min_score,
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rebalance_threshold=rebalance_threshold,
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rebalance_days=rebalance_days
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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.group_mapping = group_mapping or {}
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self.top_per_group = top_per_group
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self.min_score = min_score
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self.rebalance_threshold = rebalance_threshold
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self.rebalance_days = rebalance_days
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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（支持调仓周期控制）"""
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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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# Step 1: 每日目标组合（不考虑调仓周期）
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daily_target = []
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for date in factor_data.index:
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row = factor_data.loc[date]
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# 提取得分
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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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# 最小得分过滤（如过滤负分）
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if self.min_score is not None:
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scores = {k: v for k, v in scores.items() if v >= self.min_score}
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# 分组选股或全局选股
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if self.group_mapping:
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selected = self._grouped_selection(scores)
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else:
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selected = self._global_top_n(scores)
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daily_target.append(','.join(selected) if selected else '')
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# Step 2: 逐日生成信号（调仓周期控制）
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signals = self._apply_rebalance_control(daily_target, factor_data)
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result['signal_raw'] = daily_target # 每日目标组合
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result['signal'] = signals
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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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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 _apply_rebalance_control(self, daily_target: List[str], factor_data: pd.DataFrame) -> List[str]:
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"""应用调仓周期控制"""
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signals = []
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current_held = None
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last_rebalance_idx = 0
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for i, target in enumerate(daily_target):
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# 初始持仓为空，等待第一个有效信号
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if current_held is None:
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if not target:
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signals.append('')
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continue
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current_held = target
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last_rebalance_idx = i
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signals.append(current_held)
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continue
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# 检查调仓周期
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days_since = i - last_rebalance_idx
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if days_since < self.rebalance_days:
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# 未达到最低调仓周期，保持当前持仓
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signals.append(current_held)
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continue
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# 检查是否应该调仓
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if target: # 目标信号有效
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should = self._check_rebalance(
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factor_data.iloc[i],
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current_held,
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target,
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self._get_factor_columns(factor_data)
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)
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if should:
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current_held = target
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last_rebalance_idx = i
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else:
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# 目标信号为空（所有标的动量得分低于min_score），清仓
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# 不继续持有负动量标的，转为空仓
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current_held = ''
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last_rebalance_idx = i
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signals.append(current_held)
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return signals
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def _check_rebalance(
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self,
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row: pd.Series,
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current_held: str,
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target: str,
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factor_cols: List[str]
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) -> bool:
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"""检查是否应该调仓（得分阈值检查）"""
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# 提取当前持仓和目标持仓的代码
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old_codes = [c for c in current_held.split(',') if c]
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new_codes = [c for c in target.split(',') if c]
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if not new_codes or not old_codes:
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return True
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if set(new_codes) == set(old_codes):
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return False # 组合完全相同，不调仓
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# 计算新旧组合的总得分
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old_total = sum(float(row.get(col, 0)) for col in factor_cols if col in old_codes)
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new_total = sum(float(row.get(col, 0)) for col in factor_cols if col in new_codes)
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# 新组合得分需超过当前组合一定比例才调仓
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# 即使 threshold=0，也要确保 new_total >= old_total
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if old_total > 0:
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return (new_total / old_total - 1) >= self.rebalance_threshold
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return new_total > 0
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def _grouped_selection(self, scores: Dict[str, float]) -> List[str]:
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"""分组选股：先类内竞争（每大类选Top1），再跨类排序
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设计理念：短债作为"动态过滤阈值"
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1. 短债正常参与动量排序，没有任何特殊处理
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2. 短债排名 <= select_num → 短债被选中，比短债弱的标的被排除
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3. 短债排名 > select_num → 短债被排除（有更好的选择）
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4. 实际持仓数量 = min(短债排名, select_num)，动态调整
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"""
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if not scores:
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return []
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# 建立 group -> (code, score) 的映射
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group_champions = {}
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for code, score in scores.items():
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group = self.group_mapping.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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# 计算全局动量排名
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all_sorted = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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rank_map = {code: rank + 1 for rank, (code, _) in enumerate(all_sorted)}
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# ⭐ 找出短债(BOND大类)的排名位置
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bond_rank = None
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for group, (code, score) in group_champions.items():
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if group == 'BOND':
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bond_rank = rank_map.get(code, len(all_sorted) + 1)
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break
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# ⭐ 确定有效排名阈值
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# 如果短债排名在select_num内，则以短债排名为阈值
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# 如果短债排名超过select_num，则以select_num为阈值
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if bond_rank is not None and bond_rank <= self.select_num:
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# 短债在Top select_num内，以短债排名为阈值
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# 比短债弱的标的（排名 > 短债排名）被排除
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effective_threshold = bond_rank
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else:
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# 短债不在Top select_num内，使用正常select_num阈值
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effective_threshold = self.select_num
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# ⭐ 大类冠军过滤
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valid_champions = []
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for group, (code, score) in group_champions.items():
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rank = rank_map.get(code, len(all_sorted) + 1)
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# 过滤条件：
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# 1. 得分 >= min_score（过滤负动量）
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# 2. 排名 <= effective_threshold（动态阈值）
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if score >= self.min_score and rank <= effective_threshold:
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valid_champions.append((code, score, rank))
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# 对有效冠军按得分排序，选出Top N
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sorted_champions = sorted(valid_champions, key=lambda x: x[1], reverse=True)
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return [code for code, score, rank in sorted_champions[:self.select_num]]
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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')] |