"""Pytest configuration and fixtures for AlphaVantage MCP Server tests."""
import os
from datetime import datetime, timezone
from unittest.mock import MagicMock, patch
import pandas as pd
import pytest
@pytest.fixture(autouse=True)
def set_api_key(monkeypatch):
"""Set a dummy API key for all tests."""
monkeypatch.setenv("ALPHA_VANTAGE_API_KEY", "test_api_key")
@pytest.fixture
def mock_company_overview_data():
"""Sample company overview data as returned by AlphaVantage."""
return pd.DataFrame([{
"Name": "Apple Inc",
"Description": "Apple Inc. designs, manufactures, and markets smartphones.",
"Sector": "Technology",
"Industry": "Consumer Electronics",
"MarketCapitalization": "3000000000000",
"PERatio": "28.5",
"PEGRatio": "2.1",
"BookValue": "4.25",
"DividendPerShare": "0.96",
"DividendYield": "0.005",
"EPS": "6.15",
"RevenuePerShareTTM": "24.32",
"ProfitMargin": "0.25",
"OperatingMarginTTM": "0.30",
"ReturnOnAssetsTTM": "0.20",
"ReturnOnEquityTTM": "1.47",
"RevenueTTM": "394000000000",
"GrossProfitTTM": "170000000000",
"DilutedEPSTTM": "6.13",
"QuarterlyEarningsGrowthYOY": "0.05",
"QuarterlyRevenueGrowthYOY": "0.02",
"AnalystTargetPrice": "200.00",
"TrailingPE": "28.5",
"ForwardPE": "25.0",
"PriceToSalesRatioTTM": "7.5",
"PriceToBookRatio": "45.0",
"EVToRevenue": "7.8",
"EVToEBITDA": "22.5",
"Beta": "1.25",
"52WeekHigh": "199.62",
"52WeekLow": "124.17",
"50DayMovingAverage": "178.50",
"200DayMovingAverage": "165.20",
}])
@pytest.fixture
def mock_income_statement_data():
"""Sample income statement data."""
return pd.DataFrame({
"totalRevenue": ["394000000000", "383000000000"],
"grossProfit": ["170000000000", "165000000000"],
"operatingIncome": ["115000000000", "110000000000"],
"netIncome": ["97000000000", "95000000000"],
}, index=["2023-09-30", "2022-09-30"])
@pytest.fixture
def mock_balance_sheet_data():
"""Sample balance sheet data."""
return pd.DataFrame({
"totalAssets": ["352000000000", "340000000000"],
"totalLiabilities": ["290000000000", "280000000000"],
"totalShareholderEquity": ["62000000000", "60000000000"],
}, index=["2023-09-30", "2022-09-30"])
@pytest.fixture
def mock_cash_flow_data():
"""Sample cash flow data."""
return pd.DataFrame({
"operatingCashflow": ["110000000000", "105000000000"],
"capitalExpenditures": ["-10000000000", "-9500000000"],
"freeCashFlow": ["100000000000", "95500000000"],
}, index=["2023-09-30", "2022-09-30"])
@pytest.fixture
def mock_earnings_data():
"""Sample earnings data."""
return pd.DataFrame({
"reportedEPS": ["1.53", "1.46", "1.40", "1.52"],
"estimatedEPS": ["1.50", "1.45", "1.38", "1.50"],
"surprise": ["0.03", "0.01", "0.02", "0.02"],
}, index=["2023-09-30", "2023-06-30", "2023-03-31", "2022-12-31"])
@pytest.fixture
def mock_daily_prices_data():
"""Sample daily price data."""
dates = pd.date_range(end=datetime.now(), periods=5, freq="D")
return pd.DataFrame({
"1. open": [175.0, 176.5, 177.0, 178.5, 179.0],
"2. high": [178.0, 179.0, 180.5, 181.0, 182.0],
"3. low": [174.0, 175.5, 176.0, 177.5, 178.0],
"4. close": [177.5, 178.0, 179.5, 180.0, 181.5],
"5. volume": [50000000, 48000000, 52000000, 49000000, 51000000],
}, index=dates)
@pytest.fixture
def mock_intraday_prices_data():
"""Sample intraday price data."""
dates = pd.date_range(end=datetime.now(), periods=5, freq="5min")
return pd.DataFrame({
"1. open": [180.0, 180.5, 181.0, 180.75, 181.25],
"2. high": [180.5, 181.0, 181.5, 181.25, 181.75],
"3. low": [179.75, 180.25, 180.75, 180.5, 181.0],
"4. close": [180.25, 180.75, 181.25, 181.0, 181.5],
"5. volume": [100000, 95000, 110000, 98000, 105000],
}, index=dates)
@pytest.fixture
def mock_rsi_data():
"""Sample RSI indicator data."""
dates = pd.date_range(end=datetime.now(), periods=5, freq="D")
return pd.DataFrame({
"RSI": [65.5, 68.2, 72.1, 70.5, 67.8],
}, index=dates)
@pytest.fixture
def mock_macd_data():
"""Sample MACD indicator data."""
dates = pd.date_range(end=datetime.now(), periods=5, freq="D")
return pd.DataFrame({
"MACD": [1.25, 1.35, 1.45, 1.40, 1.50],
"MACD_Signal": [1.15, 1.22, 1.30, 1.35, 1.40],
"MACD_Hist": [0.10, 0.13, 0.15, 0.05, 0.10],
}, index=dates)
@pytest.fixture
def mock_bbands_data():
"""Sample Bollinger Bands indicator data."""
dates = pd.date_range(end=datetime.now(), periods=5, freq="D")
return pd.DataFrame({
"Real Upper Band": [185.0, 186.0, 187.0, 186.5, 187.5],
"Real Middle Band": [180.0, 181.0, 182.0, 181.5, 182.5],
"Real Lower Band": [175.0, 176.0, 177.0, 176.5, 177.5],
}, index=dates)
@pytest.fixture
def mock_news_response():
"""Sample news API response."""
return {
"feed": [
{
"title": "Apple Reports Strong Q4 Earnings",
"url": "https://example.com/apple-q4",
"time_published": "20231101T120000",
"summary": "Apple Inc. reported better than expected earnings.",
"source": "Financial Times",
"overall_sentiment_score": 0.75,
"overall_sentiment_label": "Bullish",
"topics": [{"topic": "Earnings"}, {"topic": "Technology"}],
"ticker_sentiment": [{"ticker": "AAPL"}],
},
{
"title": "Tech Sector Rally Continues",
"url": "https://example.com/tech-rally",
"time_published": "20231101T100000",
"summary": "Technology stocks continue their upward momentum.",
"source": "Bloomberg",
"overall_sentiment_score": 0.60,
"overall_sentiment_label": "Somewhat-Bullish",
"topics": [{"topic": "Technology"}],
"ticker_sentiment": [{"ticker": "AAPL"}, {"ticker": "MSFT"}],
},
]
}
@pytest.fixture
def mock_fundamental_data(
mock_company_overview_data,
mock_income_statement_data,
mock_balance_sheet_data,
mock_cash_flow_data,
mock_earnings_data,
):
"""Create a mock FundamentalData client."""
mock = MagicMock()
mock.get_company_overview.return_value = (mock_company_overview_data, None)
mock.get_income_statement_annual.return_value = (mock_income_statement_data, None)
mock.get_balance_sheet_annual.return_value = (mock_balance_sheet_data, None)
mock.get_cash_flow_annual.return_value = (mock_cash_flow_data, None)
mock.get_earnings_quarterly.return_value = (mock_earnings_data, None)
mock.get_earnings_annual.return_value = (mock_earnings_data, None)
return mock
@pytest.fixture
def mock_time_series(mock_daily_prices_data, mock_intraday_prices_data):
"""Create a mock TimeSeries client."""
mock = MagicMock()
mock.get_daily.return_value = (mock_daily_prices_data, None)
mock.get_intraday.return_value = (mock_intraday_prices_data, None)
return mock
@pytest.fixture
def mock_tech_indicators(mock_rsi_data, mock_macd_data, mock_bbands_data):
"""Create a mock TechIndicators client."""
mock = MagicMock()
mock.get_rsi.return_value = (mock_rsi_data, None)
mock.get_macd.return_value = (mock_macd_data, None)
mock.get_bbands.return_value = (mock_bbands_data, None)
mock.get_sma.return_value = (mock_rsi_data, None) # Reuse RSI structure
mock.get_ema.return_value = (mock_rsi_data, None)
mock.get_stoch.return_value = (mock_macd_data, None) # Reuse MACD structure
mock.get_adx.return_value = (mock_rsi_data, None)
mock.get_willr.return_value = (mock_rsi_data, None)
return mock
@pytest.fixture
def server(mock_fundamental_data, mock_time_series, mock_tech_indicators):
"""Create a server instance with mocked clients."""
with patch("alphavantage_mcp.server.FundamentalData", return_value=mock_fundamental_data), \
patch("alphavantage_mcp.server.TechIndicators", return_value=mock_tech_indicators), \
patch("alphavantage_mcp.server.TimeSeries", return_value=mock_time_series):
from alphavantage_mcp.server import AlphaVantageMCPServer
return AlphaVantageMCPServer()
