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"""Configuration for end-to-end tests.
This file contains pytest fixtures and configuration for end-to-end tests.
"""
import logging
import os
import pandas as pd
import pytest
from src.folio.portfolio import process_portfolio_data
# Set up logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
@pytest.fixture(scope="class")
def portfolio_data():
"""Load portfolio data from CSV file.
Returns:
pandas.DataFrame: The loaded data, or None if no data is available.
"""
# Define paths to test data
private_test_path = "private-data/test/test-portfolio.csv"
sample_path = "sample-data/sample-portfolio.csv"
# Try to load private test data first
if os.path.exists(private_test_path):
logger.info(f"Loading test data from {private_test_path}")
return pd.read_csv(private_test_path)
# Fall back to sample data
if os.path.exists(sample_path):
logger.info(f"Loading test data from {sample_path}")
return pd.read_csv(sample_path)
# No data available
logger.warning("No test data available")
return None
@pytest.fixture(scope="class")
def processed_portfolio(portfolio_data):
"""Process portfolio data using the same functions as the UI.
Args:
portfolio_data: The portfolio data loaded from CSV.
Returns:
tuple: A tuple containing (groups, summary, summary_dict).
"""
if portfolio_data is None:
pytest.skip("No portfolio data available for testing")
logger.info("Processing portfolio data...")
result = process_portfolio_data(portfolio_data)
# Check the structure of the result
if isinstance(result, tuple):
if len(result) == 3:
# Newer version: (groups, summary, cash_like_positions)
groups, summary, cash_like_positions = result
elif len(result) == 2:
# Possible alternative: (groups, cash_like_positions)
groups, cash_like_positions = result
from src.folio.portfolio import calculate_portfolio_summary
summary = calculate_portfolio_summary(groups, cash_like_positions, 0.0)
else:
# If result is not a tuple, it's likely just the groups
groups = result
from src.folio.portfolio import calculate_portfolio_summary
summary = calculate_portfolio_summary(groups, [], 0.0)
cash_like_positions = []
# Ensure we have a valid summary object
if not hasattr(summary, "to_dict"):
logger.error("Error: summary object does not have to_dict method")
logger.error(f"Type of summary: {type(summary)}")
# Create a minimal summary for testing
# Import here to avoid circular imports
from src.folio.data_model import ExposureBreakdown, PortfolioSummary
empty_exposure = ExposureBreakdown()
summary = PortfolioSummary(
net_market_exposure=0.0,
portfolio_beta=0.0,
long_exposure=empty_exposure,
short_exposure=empty_exposure,
options_exposure=empty_exposure,
)
# Convert summary to dictionary for use in tests
summary_dict = summary.to_dict()
return groups, summary, summary_dict
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