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/
2026-06-03 23:41:46 +08:00
parent d700bc1dfd
commit c905230a40
98 changed files with 0 additions and 714 deletions
--- a/archive/framework/tests/test_factors.py
+++ b/archive/framework/tests/test_factors.py
@@ -0,0 +1,288 @@
+"""
+因子层测试
+
+测试FactorBase、FactorRegistry、FactorCombiner抽象接口
+"""
+
+import pandas as pd
+import numpy as np
+import pytest
+
+from framework.factors import FactorBase, FactorRegistry, FactorCombiner
+from strategies.shared.factors.momentum import MomentumFactor, TrendFactor, ReversalFactor, VolatilityFactor
+
+
+class TestFactorBase:
+    """测试FactorBase抽象基类"""
+    
+    def test_factor_meta(self):
+        """测试因子元信息"""
+        factor = MomentumFactor(n_days=25)
+        assert factor.name == "momentum"
+        assert factor.category == "momentum"
+    
+    def test_factor_repr(self):
+        """测试因子字符串表示"""
+        factor = MomentumFactor(n_days=30)
+        repr_str = repr(factor)
+        assert "MomentumFactor" in repr_str
+    
+    def test_validate_data(self):
+        """测试数据验证"""
+        factor = MomentumFactor(n_days=25)
+        
+        # 数据充足
+        data = pd.DataFrame({
+            'close': np.random.randn(100).cumsum() + 100
+        })
+        assert factor.validate_data(data) == True
+        
+        # 数据不足
+        short_data = pd.DataFrame({
+            'close': np.random.randn(10).cumsum() + 100
+        })
+        assert factor.validate_data(short_data) == False
+
+
+class TestFactorRegistry:
+    """测试因子注册器"""
+    
+    def setup_method(self):
+        """每个测试前清空注册表"""
+        FactorRegistry.clear()
+    
+    def test_register_factor(self):
+        """测试因子注册"""
+        FactorRegistry.register(MomentumFactor)
+        assert 'momentum' in FactorRegistry.list_factors()
+    
+    def test_get_factor(self):
+        """测试获取因子实例"""
+        FactorRegistry.register(MomentumFactor)
+        factor = FactorRegistry.get('momentum', n_days=30)
+        assert isinstance(factor, MomentumFactor)
+        assert factor.n_days == 30
+    
+    def test_get_unknown_factor(self):
+        """测试获取未注册因子"""
+        with pytest.raises(ValueError):
+            FactorRegistry.get('unknown_factor')
+    
+    def test_get_category(self):
+        """测试获取因子类别"""
+        FactorRegistry.register(MomentumFactor)
+        category = FactorRegistry.get_category('momentum')
+        assert category == 'momentum'
+
+
+class TestFactorCombiner:
+    """测试因子组合器"""
+    
+    def setup_method(self):
+        """每个测试前清空注册表"""
+        FactorRegistry.clear()
+    
+    def test_combiner_init(self):
+        """测试组合器初始化"""
+        factors = [
+            MomentumFactor(n_days=25),
+            TrendFactor(method='ma_cross')
+        ]
+        combiner = FactorCombiner(factors, weights=[0.7, 0.3])
+        
+        assert len(combiner.get_factor_names()) == 2
+    
+    def test_combiner_equal_weights(self):
+        """测试等权组合"""
+        factors = [
+            MomentumFactor(n_days=25),
+            TrendFactor()
+        ]
+        combiner = FactorCombiner(factors)  # 默认等权
+        
+        # 权重应该归一化
+        assert sum(combiner._weights) == 1.0
+    
+    def test_combiner_compute(self):
+        """测试因子组合计算"""
+        # 生成测试数据
+        dates = pd.date_range('2020-01-01', periods=100)
+        data = pd.DataFrame({
+            'close': np.random.randn(100).cumsum() + 100,
+            'high': np.random.randn(100).cumsum() + 105,
+            'low': np.random.randn(100).cumsum() + 95
+        }, index=dates)
+        
+        factors = [
+            MomentumFactor(n_days=20),
+            TrendFactor(fast=5, slow=10)
+        ]
+        combiner = FactorCombiner(factors, weights=[0.6, 0.4])
+        
+        result = combiner.compute(data)
+        
+        # 检查结果列
+        assert 'momentum' in result.columns
+        assert 'trend' in result.columns
+        assert 'combined' in result.columns
+    
+    def test_combiner_method_rank_average(self):
+        """测试rank_average组合方法"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        data = pd.DataFrame({
+            'close': np.random.randn(100).cumsum() + 100
+        }, index=dates)
+        
+        factors = [
+            MomentumFactor(n_days=20),
+            TrendFactor()
+        ]
+        combiner = FactorCombiner(factors, method='rank_average')
+        
+        result = combiner.compute(data)
+        
+        # combined应该是排名平均值
+        assert 'combined' in result.columns
+
+
+class TestMomentumFactor:
+    """测试动量因子"""
+    
+    def test_momentum_compute(self):
+        """测试动量因子计算"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        
+        # 生成上升趋势数据
+        prices = 100 + np.arange(100) * 0.5
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = MomentumFactor(n_days=25, weighted=True)
+        values = factor.compute(data)
+        
+        # 上升趋势应该有正的动量得分
+        assert values.iloc[-1] > 0
+    
+    def test_crash_filter(self):
+        """测试崩盘过滤"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        
+        # 生成正常数据，然后在末尾添加崩盘
+        prices = 100 + np.random.randn(100).cumsum()
+        prices[-3:] = prices[-4] * np.array([0.96, 0.93, 0.90])  # 连续大跌
+        
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = MomentumFactor(n_days=25, crash_filter=True)
+        values = factor.compute(data)
+        
+        # 崩盘后动量得分应该被清零
+        assert values.iloc[-1] == 0.0
+    
+    def test_simple_momentum(self):
+        """测试简单动量（无加权，无崩盘过滤）"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        prices = 100 + np.random.randn(100).cumsum()
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = MomentumFactor(n_days=25, weighted=False, crash_filter=False)
+        values = factor.compute(data)
+        
+        # 简单动量应该是N日涨幅
+        expected = data['close'].pct_change(25)
+        # 验证长度一致
+        assert len(values) == len(expected)
+
+
+class TestTrendFactor:
+    """测试趋势因子"""
+    
+    def test_ma_cross(self):
+        """测试MA交叉趋势"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        
+        # 生成上升趋势
+        prices = 100 + np.arange(100) * 0.5
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = TrendFactor(method='ma_cross', fast=5, slow=20)
+        values = factor.compute(data)
+        
+        # 上升趋势应该有正的趋势强度
+        assert values.iloc[-1] > 0
+    
+    def test_macd(self):
+        """测试MACD趋势"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        prices = 100 + np.random.randn(100).cumsum()
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = TrendFactor(method='macd')
+        values = factor.compute(data)
+        
+        # 检查计算结果
+        assert len(values) == len(data)
+
+
+class TestReversalFactor:
+    """测试反转因子"""
+    
+    def test_rsi_reversal(self):
+        """测试RSI反转信号"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        
+        # 生成超买数据（持续上涨）
+        prices = 100 + np.arange(100) * 1.0
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = ReversalFactor(method='rsi', period=14, overbought=70)
+        values = factor.compute(data)
+        
+        # RSI超过70应该产生负值（反转向下信号）
+        assert values.iloc[-1] < 0
+    
+    def test_rsi_oversold(self):
+        """测试RSI超卖信号"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        
+        # 生成超卖数据（持续下跌）
+        prices = 100 - np.arange(100) * 1.0
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = ReversalFactor(method='rsi', period=14, oversold=30)
+        values = factor.compute(data)
+        
+        # RSI低于30应该产生正值（反转向上信号）
+        assert values.iloc[-1] > 0
+
+
+class TestVolatilityFactor:
+    """测试波动率因子"""
+    
+    def test_std_volatility(self):
+        """测试标准差波动率"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        prices = 100 + np.random.randn(100).cumsum()
+        data = pd.DataFrame({'close': prices}, index=dates)
+        
+        factor = VolatilityFactor(method='std', period=20)
+        values = factor.compute(data)
+        
+        assert len(values) == len(data)
+    
+    def test_atr_volatility(self):
+        """测试ATR波动率"""
+        dates = pd.date_range('2020-01-01', periods=100)
+        data = pd.DataFrame({
+            'close': np.random.randn(100).cumsum() + 100,
+            'high': np.random.randn(100).cumsum() + 105,
+            'low': np.random.randn(100).cumsum() + 95
+        }, index=dates)
+        
+        factor = VolatilityFactor(method='atr', period=20)
+        values = factor.compute(data)
+        
+        assert len(values) == len(data)
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])