feat(signals): 实现信号生成层抽象

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
2026-05-11 22:18:20 +08:00
parent 796a695eef
commit f5e6202eee
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--- a/framework/tests/test_signals.py
+++ b/framework/tests/test_signals.py
@@ -0,0 +1,239 @@
+"""
+信号层测试
+
+测试SignalGenerator、TopNSelector、TrendFollower、ReversalTrader
+"""
+
+import pandas as pd
+import numpy as np
+import pytest
+
+from framework.signals import SignalGenerator, TopNSelector, TrendFollower, ReversalTrader
+
+
+class TestSignalGenerator:
+    """测试信号生成器基类"""
+    
+    def test_signal_meta(self):
+        """测试信号元信息"""
+        selector = TopNSelector(select_num=3)
+        assert selector.mode == "top_n"
+        assert selector.params == {'select_num': 3, 'group_by': None, 'top_per_group': 1, 'min_score': None}
+    
+    def test_signal_repr(self):
+        """测试信号字符串表示"""
+        selector = TopNSelector(select_num=5)
+        repr_str = repr(selector)
+        assert "TopNSelector" in repr_str
+        assert "top_n" in repr_str
+
+
+class TestTopNSelector:
+    """测试Top N选股器"""
+    
+    def test_global_top_n(self):
+        """测试全局Top N选股"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        # 创建因子数据：3个标的，得分递减
+        factor_data = pd.DataFrame({
+            'code1': [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
+            'code2': [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
+            'code3': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+        }, index=dates)
+        
+        selector = TopNSelector(select_num=2)
+        result = selector.generate(factor_data)
+        
+        # 检查信号列
+        assert 'signal' in result.columns
+        
+        # 第一天无信号（shift后）
+        assert result['signal'].iloc[0] == '' or pd.isna(result['signal'].iloc[0])
+        
+        # 第二天及之后应该选中code1,code2
+        for i in range(1, len(result)):
+            signal = result['signal'].iloc[i]
+            assert 'code1' in signal and 'code2' in signal
+    
+    def test_top_n_with_min_score(self):
+        """测试带最小得分阈值的选股"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        factor_data = pd.DataFrame({
+            'code1': [5.0] * 10,
+            'code2': [2.0] * 10,  # 低于阈值
+            'code3': [1.0] * 10,  # 低于阈值
+        }, index=dates)
+        
+        selector = TopNSelector(select_num=3, min_score=3.0)
+        result = selector.generate(factor_data)
+        
+        # 只有code1满足阈值
+        for i in range(1, len(result)):
+            signal = result['signal'].iloc[i]
+            assert 'code1' in signal
+            assert 'code2' not in signal
+    
+    def test_grouped_selection(self):
+        """测试分组选股"""
+        dates = pd.date_range('2020-01-01', periods=5)
+        
+        # 创建因子数据和分组信息
+        factor_data = pd.DataFrame({
+            'code1': [5.0] * 5,  # group A, 最高
+            'code2': [4.0] * 5,  # group A, 次高
+            'code3': [3.0] * 5,  # group B, 最高
+            'code4': [2.0] * 5,  # group B, 次高
+            'code5': [1.0] * 5,  # group C
+        }, index=dates)
+        
+        # 分组信息：每行是一个字典 {code: group}
+        group_info = {
+            'code1': 'A', 'code2': 'A',
+            'code3': 'B', 'code4': 'B',
+            'code5': 'C'
+        }
+        factor_data['group_info'] = [group_info] * 5
+        
+        selector = TopNSelector(select_num=2, group_by='market', top_per_group=1)
+        result = selector.generate(factor_data)
+        
+        # 应该选中：code1（A组冠军）、code3（B组冠军）
+        for i in range(1, len(result)):
+            signal = result['signal'].iloc[i]
+            # code1和code3应该被选中（得分最高的两组冠军）
+            selected_codes = signal.split(',')
+            assert 'code1' in selected_codes or 'code3' in selected_codes
+    
+    def test_empty_scores(self):
+        """测试空得分情况"""
+        dates = pd.date_range('2020-01-01', periods=5)
+        
+        # 所有得分为NaN
+        factor_data = pd.DataFrame({
+            'code1': [np.nan] * 5,
+            'code2': [np.nan] * 5,
+        }, index=dates)
+        
+        selector = TopNSelector(select_num=2)
+        result = selector.generate(factor_data)
+        
+        # 应该返回空信号
+        for i in range(len(result)):
+            signal = result['signal'].iloc[i]
+            assert signal == '' or pd.isna(signal)
+
+
+class TestTrendFollower:
+    """测试趋势跟随器"""
+    
+    def test_trend_entry_signal(self):
+        """测试趋势入场信号"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        # 创建趋势数据：code1强趋势，code2弱趋势
+        factor_data = pd.DataFrame({
+            'code1': [0.03] * 10,  # > 阈值0.02，入场
+            'code2': [0.01] * 10,  # < 阈值0.02，不入场
+        }, index=dates)
+        
+        follower = TrendFollower(entry_threshold=0.02, exit_threshold=-0.02)
+        result = follower.generate(factor_data)
+        
+        # code1应该有入场信号
+        assert result['code1_entry'].iloc[0] == True
+        assert result['code2_entry'].iloc[0] == False
+    
+    def test_trend_exit_signal(self):
+        """测试趋势出场信号"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        factor_data = pd.DataFrame({
+            'code1': [-0.03] * 10,  # < 阈值-0.02，出场
+            'code2': [0.01] * 10,
+        }, index=dates)
+        
+        follower = TrendFollower(entry_threshold=0.02, exit_threshold=-0.02)
+        result = follower.generate(factor_data)
+        
+        # code1应该有出场信号
+        assert result['code1_exit'].iloc[0] == True
+    
+    def test_trend_signal_format(self):
+        """测试趋势信号格式"""
+        dates = pd.date_range('2020-01-01', periods=5)
+        
+        factor_data = pd.DataFrame({
+            'code1': [0.05] * 5,  # 强趋势，入场
+            'code2': [0.03] * 5,  # 中等趋势，入场
+            'code3': [0.01] * 5,  # 弱趋势，不入场
+        }, index=dates)
+        
+        follower = TrendFollower(entry_threshold=0.02, select_num=2)
+        result = follower.generate(factor_data)
+        
+        # 信号应该包含code1和code2（强度最高的两个）
+        for i in range(1, len(result)):
+            signal = result['signal'].iloc[i]
+            assert 'code1' in signal or 'code2' in signal
+
+
+class TestReversalTrader:
+    """测试反转交易器"""
+    
+    def test_reversal_buy_signal(self):
+        """测试反转买入信号"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        # 创建反转数据：code1超卖反转
+        factor_data = pd.DataFrame({
+            'code1': [0.2] * 10,  # > 阈值0.1，超卖反转（买入）
+            'code2': [0.05] * 10,  # < 阈值0.1，无信号
+        }, index=dates)
+        
+        trader = ReversalTrader(reversal_threshold=0.1)
+        result = trader.generate(factor_data)
+        
+        # code1应该有买入信号
+        assert result['code1_buy'].iloc[0] == True
+        assert result['code2_buy'].iloc[0] == False
+    
+    def test_reversal_sell_signal(self):
+        """测试反转卖出信号"""
+        dates = pd.date_range('2020-01-01', periods=10)
+        
+        factor_data = pd.DataFrame({
+            'code1': [-0.2] * 10,  # < -阈值0.1，超买反转（卖出）
+            'code2': [0.05] * 10,
+        }, index=dates)
+        
+        trader = ReversalTrader(reversal_threshold=0.1)
+        result = trader.generate(factor_data)
+        
+        # code1应该有卖出信号
+        assert result['code1_sell'].iloc[0] == True
+    
+    def test_reversal_signal_format(self):
+        """测试反转信号格式"""
+        dates = pd.date_range('2020-01-01', periods=5)
+        
+        factor_data = pd.DataFrame({
+            'code1': [0.15] * 5,  # 超卖反转
+            'code2': [-0.15] * 5,  # 超买反转
+        }, index=dates)
+        
+        trader = ReversalTrader(reversal_threshold=0.1)
+        result = trader.generate(factor_data)
+        
+        # 信号格式应该是 'BUY:code' 或 'SELL:code'
+        for i in range(1, len(result)):
+            signal = result['signal'].iloc[i]
+            if 'BUY' in signal:
+                assert 'code1' in signal
+            elif 'SELL' in signal:
+                assert 'code2' in signal
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])