Testing Patterns

Test Directory Structure

tests/ ├── conftest.py # Shared fixtures ├── test_elo_ratings.py # Elo implementation tests ├── test_kalshi_markets.py # API integration tests ├── test_data_validation.py # Data quality tests ├── test_dag_parsing.py # Airflow DAG tests └── test_{module}.py # Module-specific tests

Coverage Requirement

Maintain 85%+ code coverage.

Run with coverage

pytest --cov=plugins --cov-report=term-missing

Check specific module

pytest tests/test_nba_elo_rating.py --cov=plugins/nba_elo_rating.py

Common Fixtures (conftest.py)

import pytest from unittest.mock import MagicMock

@pytest.fixture def mock_db(): """Mock database manager.""" db = MagicMock() db.fetch_df.return_value = pd.DataFrame() return db

@pytest.fixture def sample_games(): """Sample game data for testing.""" return [ {"home_team": "Lakers", "away_team": "Celtics", "home_score": 110, "away_score": 105}, {"home_team": "Warriors", "away_team": "Suns", "home_score": 120, "away_score": 115}, ]

Mocking External APIs

from unittest.mock import patch, MagicMock

def test_fetch_markets(): with patch('kalshi_markets.MarketsApi') as mock_api: mock_api.return_value.get_markets.return_value = MagicMock( to_dict=lambda: {"markets": [{"ticker": "NBAWIN-250115-LAKBOS"}]} )

    result = fetch_nba_markets()
    assert len(result) == 1

Testing Elo Predictions

def test_elo_prediction_bounds(): elo = NBAEloRating() prob = elo.predict("Lakers", "Celtics")

assert 0.0 <= prob <= 1.0
assert prob != 0.5  # Should have home advantage

def test_elo_update_conservation(): elo = NBAEloRating() initial_sum = sum(elo.ratings.values()) if elo.ratings else 0

elo.update("Lakers", "Celtics", home_won=True)

# Ratings should be zero-sum
final_sum = sum(elo.ratings.values())
assert abs(final_sum - initial_sum - 3000) < 0.01  # 2 new teams at 1500

DAG Parsing Test

def test_dag_imports(): """Ensure DAG file parses without errors.""" from dags.multi_sport_betting_workflow import dag

assert dag is not None
assert len(dag.tasks) > 0

Data Validation Tests

def test_game_data_completeness(): from data_validation import validate_nba_data

report = validate_nba_data()

assert report.row_count > 1000
assert report.null_percentage < 5.0
assert len(report.missing_teams) == 0

Running Tests

All tests

pytest tests/ -v

Specific test file

pytest tests/test_nba_elo_rating.py -v

With coverage

pytest --cov=plugins --cov-report=html

Fast mode (stop on first failure)

pytest -x

Files to Reference

• tests/conftest.py

• Shared fixtures

• tests/test_nba_elo_rating.py

• Elo test examples

• tests/test_kalshi_markets.py

• API mocking examples