aszerW
fc59836ec3
- 测试文件改用strategies.shared的具体实现 - 新增framework_comparison_test.py对比新旧实现结果 - 因子计算相关系数达到1.0000，差异为0.000000 - 79个单元测试全部通过
288 lines
9.0 KiB
Python
288 lines
9.0 KiB
Python
"""
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因子层测试
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测试FactorBase、FactorRegistry、FactorCombiner抽象接口
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"""
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import pandas as pd
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import numpy as np
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import pytest
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from framework.factors import FactorBase, FactorRegistry, FactorCombiner
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from strategies.shared.factors.momentum import MomentumFactor, TrendFactor, ReversalFactor, VolatilityFactor
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class TestFactorBase:
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"""测试FactorBase抽象基类"""
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def test_factor_meta(self):
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"""测试因子元信息"""
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factor = MomentumFactor(n_days=25)
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assert factor.name == "momentum"
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assert factor.category == "momentum"
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def test_factor_repr(self):
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"""测试因子字符串表示"""
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factor = MomentumFactor(n_days=30)
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repr_str = repr(factor)
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assert "MomentumFactor" in repr_str
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def test_validate_data(self):
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"""测试数据验证"""
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factor = MomentumFactor(n_days=25)
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# 数据充足
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data = pd.DataFrame({
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'close': np.random.randn(100).cumsum() + 100
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})
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assert factor.validate_data(data) == True
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# 数据不足
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short_data = pd.DataFrame({
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'close': np.random.randn(10).cumsum() + 100
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})
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assert factor.validate_data(short_data) == False
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class TestFactorRegistry:
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"""测试因子注册器"""
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def setup_method(self):
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"""每个测试前清空注册表"""
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FactorRegistry.clear()
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def test_register_factor(self):
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"""测试因子注册"""
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FactorRegistry.register(MomentumFactor)
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assert 'momentum' in FactorRegistry.list_factors()
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def test_get_factor(self):
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"""测试获取因子实例"""
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FactorRegistry.register(MomentumFactor)
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factor = FactorRegistry.get('momentum', n_days=30)
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assert isinstance(factor, MomentumFactor)
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assert factor.n_days == 30
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def test_get_unknown_factor(self):
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"""测试获取未注册因子"""
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with pytest.raises(ValueError):
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FactorRegistry.get('unknown_factor')
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def test_get_category(self):
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"""测试获取因子类别"""
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FactorRegistry.register(MomentumFactor)
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category = FactorRegistry.get_category('momentum')
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assert category == 'momentum'
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class TestFactorCombiner:
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"""测试因子组合器"""
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def setup_method(self):
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"""每个测试前清空注册表"""
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FactorRegistry.clear()
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def test_combiner_init(self):
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"""测试组合器初始化"""
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factors = [
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MomentumFactor(n_days=25),
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TrendFactor(method='ma_cross')
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]
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combiner = FactorCombiner(factors, weights=[0.7, 0.3])
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assert len(combiner.get_factor_names()) == 2
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def test_combiner_equal_weights(self):
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"""测试等权组合"""
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factors = [
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MomentumFactor(n_days=25),
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TrendFactor()
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]
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combiner = FactorCombiner(factors) # 默认等权
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# 权重应该归一化
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assert sum(combiner._weights) == 1.0
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def test_combiner_compute(self):
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"""测试因子组合计算"""
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# 生成测试数据
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dates = pd.date_range('2020-01-01', periods=100)
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data = pd.DataFrame({
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'close': np.random.randn(100).cumsum() + 100,
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'high': np.random.randn(100).cumsum() + 105,
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'low': np.random.randn(100).cumsum() + 95
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}, index=dates)
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factors = [
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MomentumFactor(n_days=20),
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TrendFactor(fast=5, slow=10)
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]
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combiner = FactorCombiner(factors, weights=[0.6, 0.4])
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result = combiner.compute(data)
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# 检查结果列
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assert 'momentum' in result.columns
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assert 'trend' in result.columns
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assert 'combined' in result.columns
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def test_combiner_method_rank_average(self):
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"""测试rank_average组合方法"""
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dates = pd.date_range('2020-01-01', periods=100)
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data = pd.DataFrame({
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'close': np.random.randn(100).cumsum() + 100
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}, index=dates)
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factors = [
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MomentumFactor(n_days=20),
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TrendFactor()
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]
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combiner = FactorCombiner(factors, method='rank_average')
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result = combiner.compute(data)
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# combined应该是排名平均值
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assert 'combined' in result.columns
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class TestMomentumFactor:
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"""测试动量因子"""
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def test_momentum_compute(self):
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"""测试动量因子计算"""
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dates = pd.date_range('2020-01-01', periods=100)
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# 生成上升趋势数据
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prices = 100 + np.arange(100) * 0.5
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = MomentumFactor(n_days=25, weighted=True)
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values = factor.compute(data)
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# 上升趋势应该有正的动量得分
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assert values.iloc[-1] > 0
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def test_crash_filter(self):
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"""测试崩盘过滤"""
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dates = pd.date_range('2020-01-01', periods=100)
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# 生成正常数据，然后在末尾添加崩盘
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prices = 100 + np.random.randn(100).cumsum()
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prices[-3:] = prices[-4] * np.array([0.96, 0.93, 0.90]) # 连续大跌
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = MomentumFactor(n_days=25, crash_filter=True)
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values = factor.compute(data)
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# 崩盘后动量得分应该被清零
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assert values.iloc[-1] == 0.0
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def test_simple_momentum(self):
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"""测试简单动量（无加权，无崩盘过滤）"""
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dates = pd.date_range('2020-01-01', periods=100)
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prices = 100 + np.random.randn(100).cumsum()
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = MomentumFactor(n_days=25, weighted=False, crash_filter=False)
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values = factor.compute(data)
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# 简单动量应该是N日涨幅
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expected = data['close'].pct_change(25)
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# 验证长度一致
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assert len(values) == len(expected)
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class TestTrendFactor:
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"""测试趋势因子"""
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def test_ma_cross(self):
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"""测试MA交叉趋势"""
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dates = pd.date_range('2020-01-01', periods=100)
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# 生成上升趋势
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prices = 100 + np.arange(100) * 0.5
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = TrendFactor(method='ma_cross', fast=5, slow=20)
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values = factor.compute(data)
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# 上升趋势应该有正的趋势强度
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assert values.iloc[-1] > 0
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def test_macd(self):
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"""测试MACD趋势"""
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dates = pd.date_range('2020-01-01', periods=100)
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prices = 100 + np.random.randn(100).cumsum()
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = TrendFactor(method='macd')
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values = factor.compute(data)
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# 检查计算结果
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assert len(values) == len(data)
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class TestReversalFactor:
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"""测试反转因子"""
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def test_rsi_reversal(self):
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"""测试RSI反转信号"""
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dates = pd.date_range('2020-01-01', periods=100)
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# 生成超买数据（持续上涨）
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prices = 100 + np.arange(100) * 1.0
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = ReversalFactor(method='rsi', period=14, overbought=70)
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values = factor.compute(data)
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# RSI超过70应该产生负值（反转向下信号）
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assert values.iloc[-1] < 0
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def test_rsi_oversold(self):
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"""测试RSI超卖信号"""
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dates = pd.date_range('2020-01-01', periods=100)
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# 生成超卖数据（持续下跌）
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prices = 100 - np.arange(100) * 1.0
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = ReversalFactor(method='rsi', period=14, oversold=30)
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values = factor.compute(data)
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# RSI低于30应该产生正值（反转向上信号）
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assert values.iloc[-1] > 0
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class TestVolatilityFactor:
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"""测试波动率因子"""
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def test_std_volatility(self):
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"""测试标准差波动率"""
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dates = pd.date_range('2020-01-01', periods=100)
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prices = 100 + np.random.randn(100).cumsum()
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data = pd.DataFrame({'close': prices}, index=dates)
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factor = VolatilityFactor(method='std', period=20)
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values = factor.compute(data)
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assert len(values) == len(data)
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def test_atr_volatility(self):
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"""测试ATR波动率"""
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dates = pd.date_range('2020-01-01', periods=100)
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data = pd.DataFrame({
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'close': np.random.randn(100).cumsum() + 100,
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'high': np.random.randn(100).cumsum() + 105,
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'low': np.random.randn(100).cumsum() + 95
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}, index=dates)
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factor = VolatilityFactor(method='atr', period=20)
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values = factor.compute(data)
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assert len(values) == len(data)
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if __name__ == '__main__':
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pytest.main([__file__, '-v']) |