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如何制作wordpress网站地图,网站制作的困难与解决方案,做开发的工资一般多少,美篇相册制作免费下载7.3.8 市场情绪分析测试文件test_sentiment.py实现了对市场情绪分析工具的测试#xff0c;涵盖了CNN恐惧与贪婪指数、加密货币恐惧与贪婪指数以及谷歌趋势数据的获取功能。通过模拟数据请求和返回结果#xff0c;测试了各类情绪指标在不同参数设置#xff08;如获取当前数据…7.3.8 市场情绪分析测试文件test_sentiment.py实现了对市场情绪分析工具的测试涵盖了CNN恐惧与贪婪指数、加密货币恐惧与贪婪指数以及谷歌趋势数据的获取功能。通过模拟数据请求和返回结果测试了各类情绪指标在不同参数设置如获取当前数据、历史数据、指定指标等、异常情况如无效指标、无数据返回下的表现确保情绪分析工具能准确、稳定地提供数据。class TestSentimentTools: 测试情绪分析工具。 patch(investor_agent.server.fetch_fng_data) async def test_cnn_fear_greed_current(self, mock_fetch, mock_fear_greed_data): 测试获取当前CNN恐惧与贪婪指数。 mock_fetch.return_value mock_fear_greed_data result await get_cnn_fear_greed_index(days0) # 当days0时应排除历史数据 assert fear_and_greed in result assert put_call_options in result assert market_volatility_vix in result assert fear_and_greed_historical not in result # 验证模拟函数被调用 mock_fetch.assert_called_once() patch(investor_agent.server.fetch_fng_data) async def test_cnn_fear_greed_historical(self, mock_fetch): 测试获取历史CNN恐惧与贪婪指数。 historical_data { fear_and_greed: {value: 45, description: Neutral}, fear_and_greed_historical: { data: [ {date: 2024-01-01, value: 50}, {date: 2024-01-02, value: 48}, {date: 2024-01-03, value: 45} ] } } mock_fetch.return_value historical_data result await get_cnn_fear_greed_index(days3) assert fear_and_greed_historical in result assert len(result[fear_and_greed_historical][data]) 3 patch(investor_agent.server.fetch_fng_data) async def test_cnn_fear_greed_specific_indicators(self, mock_fetch, mock_fear_greed_data): 测试仅获取特定指标。 mock_fetch.return_value mock_fear_greed_data result await get_cnn_fear_greed_index( days0, indicators[fear_and_greed, market_volatility_vix] ) # 应只包含请求的指标 assert fear_and_greed in result assert market_volatility_vix in result assert put_call_options not in result patch(investor_agent.server.fetch_fng_data) async def test_cnn_fear_greed_invalid_indicators(self, mock_fetch, mock_fear_greed_data): 测试对无效指标的错误处理。 mock_fetch.return_value mock_fear_greed_data with pytest.raises(ValueError, matchInvalid indicators): await get_cnn_fear_greed_index(indicators[invalid_indicator]) patch(investor_agent.server.fetch_fng_data) async def test_cnn_fear_greed_no_data(self, mock_fetch): 测试无数据可用时的错误处理。 mock_fetch.return_value {} # 空字典在if not data检查中应被视为False with pytest.raises(RuntimeError, matchUnable to fetch CNN Fear Greed Index data): await get_cnn_fear_greed_index() patch(investor_agent.yahoo_finance_utils.create_cached_async_client) async def test_crypto_fear_greed_success(self, mock_client, mock_crypto_fng_data): 测试成功获取加密货币恐惧与贪婪指数。 # 模拟异步上下文管理器和HTTP响应 mock_response Mock() mock_response.json.return_value {data: mock_crypto_fng_data} # 同步方法 mock_response.raise_for_status Mock() mock_http_client AsyncMock() mock_http_client.get AsyncMock(return_valuemock_response) mock_client.return_value.__aenter__ AsyncMock(return_valuemock_http_client) result await get_crypto_fear_greed_index(days1) assert result mock_crypto_fng_data mock_http_client.get.assert_called_once_with( https://api.alternative.me/fng/, params{limit: 1} ) patch(investor_agent.yahoo_finance_utils.create_cached_async_client) async def test_crypto_fear_greed_default_days(self, mock_client, mock_crypto_fng_data): 测试使用默认天数参数的加密货币恐惧与贪婪指数。 mock_response Mock() mock_response.json.return_value {data: mock_crypto_fng_data} # 同步方法 mock_response.raise_for_status Mock() mock_http_client AsyncMock() mock_http_client.get AsyncMock(return_valuemock_response) mock_client.return_value.__aenter__ AsyncMock(return_valuemock_http_client) result await get_crypto_fear_greed_index() # 应使用默认的7天 mock_http_client.get.assert_called_once_with( https://api.alternative.me/fng/, params{limit: 7} ) patch(pytrends.request.TrendReq) def test_google_trends_success(self, mock_pytrends): 测试成功获取谷歌趋势数据。 import pandas as pd # 模拟趋势数据 trends_data pd.DataFrame({ AAPL: [75, 80, 85], GOOGL: [60, 65, 70] }) mock_instance mock_pytrends.return_value mock_instance.interest_over_time.return_value trends_data result get_google_trends([AAPL, GOOGL], period_days7) # 应返回平均值 expected {AAPL: 80.0, GOOGL: 65.0} assert result expected mock_instance.build_payload.assert_called_once_with( [AAPL, GOOGL], timeframenow 7-d ) patch(pytrends.request.TrendReq) def test_google_trends_no_data(self, mock_pytrends): 测试谷歌趋势无数据返回的情况。 import pandas as pd mock_instance mock_pytrends.return_value mock_instance.interest_over_time.return_value pd.DataFrame() # 空DataFrame with pytest.raises(ValueError, matchNo data returned from Google Trends): get_google_trends([INVALID], period_days7) patch(pytrends.request.TrendReq) def test_google_trends_single_keyword(self, mock_pytrends): 测试单个关键词的谷歌趋势。 import pandas as pd trends_data pd.DataFrame({ Bitcoin: [45, 50, 55, 60] }) mock_instance mock_pytrends.return_value mock_instance.interest_over_time.return_value trends_data result get_google_trends([Bitcoin], period_days14) assert result {Bitcoin: 52.5} # [45, 50, 55, 60]的平均值 mock_instance.build_payload.assert_called_once_with( [Bitcoin], timeframenow 14-d )7.3.9 个股数据测试文件test_ticker_data.py实现了对get_ticker_data工具的测试该工具用于获取股票代码的相关信息包括核心财务指标、即将到来的收益日期和股息日期、最新新闻文章、最新分析师推荐以及近期分析师评级变动。class TestGetTickerData: 测试get_ticker_data工具重点关注DataFrame边缘情况。 patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_successful_data_retrieval( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info, sample_recommendations_df, sample_news, sample_calendar ): 测试成功获取所有股票数据。 # 设置模拟 mock_info.return_value mock_ticker_info mock_calendar.return_value sample_calendar mock_news.return_value sample_news mock_analyst.side_effect [sample_recommendations_df, sample_recommendations_df] # 先推荐后升级 result get_ticker_data(AAPL, max_news2, max_recommendations2, max_upgrades2) # 验证结构 assert info in result assert calendar in result assert news in result assert recommendations in result assert upgrades_downgrades in result # 验证内容 assert result[info][symbol] AAPL assert len(result[news]) 2 assert data in result[recommendations] assert data in result[upgrades_downgrades] patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_empty_dataframe_handling( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info, empty_dataframe ): 测试对空DataFrame的处理——这是我们修复的bug mock_info.return_value mock_ticker_info mock_calendar.return_value None mock_news.return_value None mock_analyst.return_value empty_dataframe # 两次调用都返回空DataFrame result get_ticker_data(AAPL) # 应该包含info但不包含recommendations或upgrades_downgrades assert info in result assert result[info][symbol] AAPL assert recommendations not in result assert upgrades_downgrades not in result assert calendar not in result assert news not in result # 验证函数在DataFrame布尔值评估时没有崩溃 assert isinstance(result, dict) patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_none_dataframe_handling( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info ): 测试对分析师数据函数返回None的处理。 mock_info.return_value mock_ticker_info mock_calendar.return_value None mock_news.return_value None mock_analyst.return_value None # 两次调用都返回None result get_ticker_data(AAPL) # 应该包含info但不包含recommendations或upgrades_downgrades assert info in result assert result[info][symbol] AAPL assert recommendations not in result assert upgrades_downgrades not in result patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_mixed_dataframe_states( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info, sample_recommendations_df, empty_dataframe ): 测试混合状态部分数据可用部分为空部分为None。 mock_info.return_value mock_ticker_info mock_calendar.return_value None mock_news.return_value None # 第一次调用推荐返回数据第二次调用升级返回空 mock_analyst.side_effect [sample_recommendations_df, empty_dataframe] result get_ticker_data(AAPL) # 应该包含info和recommendations但不包含upgrades_downgrades assert info in result assert recommendations in result assert upgrades_downgrades not in result assert data in result[recommendations] patch(investor_agent.yfinance_utils.get_ticker_info) def test_no_ticker_info_raises_error(self, mock_info): 测试缺少股票信息时是否抛出ValueError。 mock_info.return_value None with pytest.raises(ValueError, matchNo information available for INVALID): get_ticker_data(INVALID) patch(investor_agent.yfinance_utils.get_ticker_info) def test_empty_ticker_info_raises_error(self, mock_info): 测试股票信息为空时是否抛出ValueError。 mock_info.return_value {} with pytest.raises(ValueError, matchNo information available for INVALID): get_ticker_data(INVALID) patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_essential_fields_filtering( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info ): 测试响应中只包含必要字段。 # 向模拟数据添加一些非必要字段 extended_info {** mock_ticker_info, nonEssentialField: should be filtered out} mock_info.return_value extended_info mock_calendar.return_value None mock_news.return_value None mock_analyst.return_value None result get_ticker_data(AAPL) # 验证必要字段存在 essential_fields {symbol, longName, currentPrice, marketCap, trailingPE} for field in essential_fields: if field in mock_ticker_info: # 只检查模拟数据中存在的字段 assert field in result[info], f必要字段 {field} 缺失 # 验证非必要字段被过滤掉 assert nonEssentialField not in result[info] patch(investor_agent.yfinance_utils.get_analyst_data) patch(investor_agent.yfinance_utils.get_news) patch(investor_agent.yfinance_utils.get_calendar) patch(investor_agent.yfinance_utils.get_ticker_info) def test_limits_respected( self, mock_info, mock_calendar, mock_news, mock_analyst, mock_ticker_info, sample_recommendations_df ): 测试最大限制参数是否传递给底层函数。 mock_info.return_value mock_ticker_info mock_calendar.return_value None mock_news.return_value [] mock_analyst.return_value sample_recommendations_df get_ticker_data(AAPL, max_news10, max_recommendations15, max_upgrades20) # 验证限制参数被传递给函数 mock_news.assert_called_with(AAPL, limit10) assert mock_analyst.call_count 2 mock_analyst.assert_any_call(AAPL, recommendations, 15) mock_analyst.assert_any_call(AAPL, upgrades, 20)