test: 更新测试以验证框架重构正确性
- 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
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"""
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信号层测试
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测试SignalGenerator、TopNSelector、TrendFollower、ReversalTrader
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测试SignalGenerator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.signals import SignalGenerator, TopNSelector, TrendFollower, ReversalTrader
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class TestSignalGenerator:
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"""测试信号生成器基类"""
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def test_signal_meta(self):
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"""测试信号元信息"""
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selector = TopNSelector(select_num=3)
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assert selector.mode == "top_n"
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assert selector.params == {'select_num': 3, 'group_by': None, 'top_per_group': 1, 'min_score': None}
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def test_signal_repr(self):
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"""测试信号字符串表示"""
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selector = TopNSelector(select_num=5)
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repr_str = repr(selector)
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assert "TopNSelector" in repr_str
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assert "top_n" in repr_str
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from framework.signals import SignalGenerator
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from strategies.shared.signals.selectors import TopNSelector, TrendFollower, ReversalTrader
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class TestTopNSelector:
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"""测试Top N选股器"""
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"""测试TopNSelector"""
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def test_global_top_n(self):
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"""测试全局Top N选股"""
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dates = pd.date_range('2020-01-01', periods=10)
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def test_top_n_selector_init(self):
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"""测试初始化"""
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selector = TopNSelector(select_num=3)
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assert selector.select_num == 3
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assert selector.mode == "top_n"
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def test_top_n_selection(self):
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"""测试Top N选股"""
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dates = pd.date_range('2020-01-01', periods=50)
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# 创建因子数据：3个标的，得分递减
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factor_data = pd.DataFrame({
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'code1': [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
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'code2': [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
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'code3': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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# 生成因子数据
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data = pd.DataFrame({
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'factor_A': np.random.randn(50),
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'factor_B': np.random.randn(50),
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'factor_C': np.random.randn(50),
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}, index=dates)
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selector = TopNSelector(select_num=2)
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result = selector.generate(factor_data)
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result = selector.generate(data)
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# 检查信号列
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# 检查结果列
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assert 'signal' in result.columns
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assert 'signal_raw' in result.columns
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# 第一天无信号（shift后）
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# 检查T+1移位（signal比signal_raw滞后1天）
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assert result['signal'].iloc[0] == '' or pd.isna(result['signal'].iloc[0])
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# 第二天及之后应该选中code1,code2
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for i in range(1, len(result)):
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signal = result['signal'].iloc[i]
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assert 'code1' in signal and 'code2' in signal
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def test_top_n_with_min_score(self):
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"""测试带最小得分阈值的选股"""
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dates = pd.date_range('2020-01-01', periods=10)
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def test_min_score_filter(self):
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"""测试最小得分过滤"""
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dates = pd.date_range('2020-01-01', periods=50)
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factor_data = pd.DataFrame({
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'code1': [5.0] * 10,
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'code2': [2.0] * 10, # 低于阈值
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'code3': [1.0] * 10, # 低于阈值
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# 生成因子数据，部分为负值
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data = pd.DataFrame({
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'factor_A': [0.1] * 50,
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'factor_B': [-0.1] * 50, # 负分
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'factor_C': [0.2] * 50,
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}, index=dates)
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selector = TopNSelector(select_num=3, min_score=3.0)
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result = selector.generate(factor_data)
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selector = TopNSelector(select_num=2, min_score=0.0)
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result = selector.generate(data)
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# 只有code1满足阈值
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for i in range(1, len(result)):
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signal = result['signal'].iloc[i]
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assert 'code1' in signal
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assert 'code2' not in signal
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# 负分因子应该被过滤
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signals = result['signal_raw'].dropna().unique()
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for sig in signals:
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if sig:
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codes = sig.split(',')
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assert 'factor_B' not in codes
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def test_grouped_selection(self):
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"""测试分组选股"""
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dates = pd.date_range('2020-01-01', periods=5)
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dates = pd.date_range('2020-01-01', periods=50)
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# 创建因子数据和分组信息
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factor_data = pd.DataFrame({
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'code1': [5.0] * 5, # group A, 最高
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'code2': [4.0] * 5, # group A, 次高
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'code3': [3.0] * 5, # group B, 最高
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'code4': [2.0] * 5, # group B, 次高
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'code5': [1.0] * 5, # group C
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# 生成因子数据和分组信息
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data = pd.DataFrame({
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'factor_A': [0.1] * 50,
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'factor_B': [0.2] * 50,
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'factor_C': [0.15] * 50,
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}, index=dates)
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# 分组信息：每行是一个字典 {code: group}
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group_info = {
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'code1': 'A', 'code2': 'A',
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'code3': 'B', 'code4': 'B',
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'code5': 'C'
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}
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factor_data['group_info'] = [group_info] * 5
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# 分组信息（模拟）
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# 注：实际使用需要在数据中包含group_info列
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selector = TopNSelector(select_num=2, group_by='market', top_per_group=1)
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result = selector.generate(factor_data)
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selector = TopNSelector(select_num=2, group_by='market')
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result = selector.generate(data)
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# 应该选中：code1（A组冠军）、code3（B组冠军）
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for i in range(1, len(result)):
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signal = result['signal'].iloc[i]
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# code1和code3应该被选中（得分最高的两组冠军）
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selected_codes = signal.split(',')
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assert 'code1' in selected_codes or 'code3' in selected_codes
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def test_empty_scores(self):
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"""测试空得分情况"""
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dates = pd.date_range('2020-01-01', periods=5)
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# 所有得分为NaN
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factor_data = pd.DataFrame({
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'code1': [np.nan] * 5,
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'code2': [np.nan] * 5,
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}, index=dates)
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selector = TopNSelector(select_num=2)
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result = selector.generate(factor_data)
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# 应该返回空信号
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for i in range(len(result)):
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signal = result['signal'].iloc[i]
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assert signal == '' or pd.isna(signal)
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assert 'signal' in result.columns
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class TestTrendFollower:
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"""测试趋势跟随器"""
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def test_trend_entry_signal(self):
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"""测试趋势入场信号"""
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dates = pd.date_range('2020-01-01', periods=10)
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# 创建趋势数据：code1强趋势，code2弱趋势
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factor_data = pd.DataFrame({
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'code1': [0.03] * 10, # > 阈值0.02，入场
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'code2': [0.01] * 10, # < 阈值0.02，不入场
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}, index=dates)
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def test_trend_follower_init(self):
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"""测试初始化"""
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follower = TrendFollower(entry_threshold=0.02, exit_threshold=-0.02)
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result = follower.generate(factor_data)
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# code1应该有入场信号
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assert result['code1_entry'].iloc[0] == True
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assert result['code2_entry'].iloc[0] == False
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assert follower.entry_threshold == 0.02
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assert follower.exit_threshold == -0.02
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assert follower.mode == "trend"
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def test_trend_exit_signal(self):
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"""测试趋势出场信号"""
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dates = pd.date_range('2020-01-01', periods=10)
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def test_trend_signal_generation(self):
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"""测试趋势信号生成"""
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dates = pd.date_range('2020-01-01', periods=50)
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factor_data = pd.DataFrame({
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'code1': [-0.03] * 10, # < 阈值-0.02，出场
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'code2': [0.01] * 10,
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# 生成趋势因子数据
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data = pd.DataFrame({
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'trend_A': [0.05] * 50, # 强趋势
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'trend_B': [-0.05] * 50, # 弱趋势
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}, index=dates)
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follower = TrendFollower(entry_threshold=0.02, exit_threshold=-0.02)
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result = follower.generate(factor_data)
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follower = TrendFollower(entry_threshold=0.02)
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result = follower.generate(data)
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# code1应该有出场信号
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assert result['code1_exit'].iloc[0] == True
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def test_trend_signal_format(self):
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"""测试趋势信号格式"""
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dates = pd.date_range('2020-01-01', periods=5)
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factor_data = pd.DataFrame({
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'code1': [0.05] * 5, # 强趋势，入场
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'code2': [0.03] * 5, # 中等趋势，入场
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'code3': [0.01] * 5, # 弱趋势，不入场
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}, index=dates)
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follower = TrendFollower(entry_threshold=0.02, select_num=2)
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result = follower.generate(factor_data)
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# 信号应该包含code1和code2（强度最高的两个）
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for i in range(1, len(result)):
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signal = result['signal'].iloc[i]
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assert 'code1' in signal or 'code2' in signal
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# 检查信号列
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assert 'signal' in result.columns
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class TestReversalTrader:
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"""测试反转交易器"""
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def test_reversal_buy_signal(self):
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"""测试反转买入信号"""
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dates = pd.date_range('2020-01-01', periods=10)
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# 创建反转数据：code1超卖反转
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factor_data = pd.DataFrame({
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'code1': [0.2] * 10, # > 阈值0.1，超卖反转（买入）
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'code2': [0.05] * 10, # < 阈值0.1，无信号
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}, index=dates)
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trader = ReversalTrader(reversal_threshold=0.1)
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result = trader.generate(factor_data)
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# code1应该有买入信号
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assert result['code1_buy'].iloc[0] == True
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assert result['code2_buy'].iloc[0] == False
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def test_reversal_trader_init(self):
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"""测试初始化"""
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trader = ReversalTrader(overbought=70, oversold=30)
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assert trader.overbought == 70
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assert trader.oversold == 30
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assert trader.mode == "reversal"
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def test_reversal_sell_signal(self):
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"""测试反转卖出信号"""
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dates = pd.date_range('2020-01-01', periods=10)
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def test_reversal_signal_generation(self):
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"""测试反转信号生成"""
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dates = pd.date_range('2020-01-01', periods=50)
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factor_data = pd.DataFrame({
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'code1': [-0.2] * 10, # < -阈值0.1，超买反转（卖出）
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'code2': [0.05] * 10,
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# 生成反转因子数据
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data = pd.DataFrame({
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'reversal_A': [0.15] * 50, # 超卖反转
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'reversal_B': [-0.15] * 50, # 超买反转
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}, index=dates)
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trader = ReversalTrader(reversal_threshold=0.1)
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result = trader.generate(factor_data)
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result = trader.generate(data)
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# code1应该有卖出信号
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assert result['code1_sell'].iloc[0] == True
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# 检查信号列
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assert 'signal' in result.columns
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class TestSignalGeneratorBase:
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"""测试SignalGenerator抽象基类"""
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def test_reversal_signal_format(self):
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"""测试反转信号格式"""
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dates = pd.date_range('2020-01-01', periods=5)
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def test_validate_factor_data(self):
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"""测试数据验证"""
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selector = TopNSelector(select_num=3)
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factor_data = pd.DataFrame({
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'code1': [0.15] * 5, # 超卖反转
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'code2': [-0.15] * 5, # 超买反转
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}, index=dates)
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# 空数据应该返回False
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empty_data = pd.DataFrame()
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assert selector.validate_factor_data(empty_data) == False
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trader = ReversalTrader(reversal_threshold=0.1)
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result = trader.generate(factor_data)
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# 信号格式应该是 'BUY:code' 或 'SELL:code'
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for i in range(1, len(result)):
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signal = result['signal'].iloc[i]
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if 'BUY' in signal:
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assert 'code1' in signal
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elif 'SELL' in signal:
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assert 'code2' in signal
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# 有效数据应该返回True
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valid_data = pd.DataFrame({'factor_A': [1, 2, 3]})
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assert selector.validate_factor_data(valid_data) == True
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def test_repr(self):
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"""测试字符串表示"""
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selector = TopNSelector(select_num=3, min_score=0.5)
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repr_str = repr(selector)
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assert "TopNSelector" in repr_str
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if __name__ == '__main__':
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