refactor(archive): move unused modules to archive/

Archive legacy framework and utility modules that are no longer
referenced by the active core (datasource/ and rotation/):

- framework/ -> archive/framework/
- framework_v2/ -> archive/framework_v2/
- strategies/ -> archive/strategies/
- config/ -> archive/config/
- visualization/ -> archive/visualization/
- scripts/ -> archive/scripts/
- tests/ -> archive/tests/
- run_rotation.py, run_us_rotation.py -> archive/single_files/
- compare_*.py, test_api_dates.py -> archive/single_files/

archive/framework/tests/test_signals.py

@@ -0,0 +1,163 @@
+"""
+信号层测试
+
+测试SignalGenerator抽象接口
+"""
+
+import pandas as pd
+import numpy as np
+import pytest
+
+from framework.signals import SignalGenerator
+from strategies.shared.signals.selectors import TopNSelector, TrendFollower, ReversalTrader
+
+
+class TestTopNSelector:
+    """测试TopNSelector"""
+    
+    def test_top_n_selector_init(self):
+        """测试初始化"""
+        selector = TopNSelector(select_num=3)
+        assert selector.select_num == 3
+        assert selector.mode == "top_n"
+    
+    def test_top_n_selection(self):
+        """测试Top N选股"""
+        dates = pd.date_range('2020-01-01', periods=50)
+        
+        # 生成因子数据
+        data = pd.DataFrame({
+            'factor_A': np.random.randn(50),
+            'factor_B': np.random.randn(50),
+            'factor_C': np.random.randn(50),
+        }, index=dates)
+        
+        selector = TopNSelector(select_num=2)
+        result = selector.generate(data)
+        
+        # 检查结果列
+        assert 'signal' in result.columns
+        assert 'signal_raw' in result.columns
+        
+        # 检查T+1移位（signal比signal_raw滞后1天）
+        assert result['signal'].iloc[0] == '' or pd.isna(result['signal'].iloc[0])
+    
+    def test_min_score_filter(self):
+        """测试最小得分过滤"""
+        dates = pd.date_range('2020-01-01', periods=50)
+        
+        # 生成因子数据，部分为负值
+        data = pd.DataFrame({
+            'factor_A': [0.1] * 50,
+            'factor_B': [-0.1] * 50,  # 负分
+            'factor_C': [0.2] * 50,
+        }, index=dates)
+        
+        selector = TopNSelector(select_num=2, min_score=0.0)
+        result = selector.generate(data)
+        
+        # 负分因子应该被过滤
+        signals = result['signal_raw'].dropna().unique()
+        for sig in signals:
+            if sig:
+                codes = sig.split(',')
+                assert 'factor_B' not in codes
+    
+    def test_grouped_selection(self):
+        """测试分组选股"""
+        dates = pd.date_range('2020-01-01', periods=50)
+        
+        # 生成因子数据和分组信息
+        data = pd.DataFrame({
+            'factor_A': [0.1] * 50,
+            'factor_B': [0.2] * 50,
+            'factor_C': [0.15] * 50,
+        }, index=dates)
+        
+        # 分组信息（模拟）
+        # 注：实际使用需要在数据中包含group_info列
+        
+        selector = TopNSelector(select_num=2, group_by='market')
+        result = selector.generate(data)
+        
+        assert 'signal' in result.columns
+
+
+class TestTrendFollower:
+    """测试趋势跟随器"""
+    
+    def test_trend_follower_init(self):
+        """测试初始化"""
+        follower = TrendFollower(entry_threshold=0.02, exit_threshold=-0.02)
+        assert follower.entry_threshold == 0.02
+        assert follower.exit_threshold == -0.02
+        assert follower.mode == "trend"
+    
+    def test_trend_signal_generation(self):
+        """测试趋势信号生成"""
+        dates = pd.date_range('2020-01-01', periods=50)
+        
+        # 生成趋势因子数据
+        data = pd.DataFrame({
+            'trend_A': [0.05] * 50,  # 强趋势
+            'trend_B': [-0.05] * 50,  # 弱趋势
+        }, index=dates)
+        
+        follower = TrendFollower(entry_threshold=0.02)
+        result = follower.generate(data)
+        
+        # 检查信号列
+        assert 'signal' in result.columns
+
+
+class TestReversalTrader:
+    """测试反转交易器"""
+    
+    def test_reversal_trader_init(self):
+        """测试初始化"""
+        trader = ReversalTrader(overbought=70, oversold=30)
+        assert trader.overbought == 70
+        assert trader.oversold == 30
+        assert trader.mode == "reversal"
+    
+    def test_reversal_signal_generation(self):
+        """测试反转信号生成"""
+        dates = pd.date_range('2020-01-01', periods=50)
+        
+        # 生成反转因子数据
+        data = pd.DataFrame({
+            'reversal_A': [0.15] * 50,  # 超卖反转
+            'reversal_B': [-0.15] * 50,  # 超买反转
+        }, index=dates)
+        
+        trader = ReversalTrader(reversal_threshold=0.1)
+        result = trader.generate(data)
+        
+        # 检查信号列
+        assert 'signal' in result.columns
+
+
+class TestSignalGeneratorBase:
+    """测试SignalGenerator抽象基类"""
+    
+    def test_validate_factor_data(self):
+        """测试数据验证"""
+        selector = TopNSelector(select_num=3)
+        
+        # 空数据应该返回False
+        empty_data = pd.DataFrame()
+        assert selector.validate_factor_data(empty_data) == False
+        
+        # 有效数据应该返回True
+        valid_data = pd.DataFrame({'factor_A': [1, 2, 3]})
+        assert selector.validate_factor_data(valid_data) == True
+    
+    def test_repr(self):
+        """测试字符串表示"""
+        selector = TopNSelector(select_num=3, min_score=0.5)
+        repr_str = repr(selector)
+        assert "TopNSelector" in repr_str
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])