tests/unit/test_advanced_market_processing.py

"""Unit tests for AdvancedMarketProcessor module."""

import pytest
from unittest.mock import Mock, patch, MagicMock
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Import the module under test
try:
    from src.core.advanced_market_processing import AdvancedMarketProcessor
except ImportError:
    pytest.skip("AdvancedMarketProcessor not available", allow_module_level=True)


class TestAdvancedMarketProcessor:
    """Test cases for AdvancedMarketProcessor."""
    
    @pytest.fixture
    def processor(self):
        """Create AdvancedMarketProcessor instance for testing."""
        return AdvancedMarketProcessor()
    
    @pytest.fixture
    def sample_price_data(self):
        """Sample price data for testing."""
        dates = pd.date_range(start='2024-01-01', periods=10, freq='D')
        return pd.DataFrame({
            'Open': np.random.uniform(100, 110, 10),
            'High': np.random.uniform(110, 120, 10),
            'Low': np.random.uniform(90, 100, 10),
            'Close': np.random.uniform(100, 110, 10),
            'Volume': np.random.randint(1000000, 5000000, 10)
        }, index=dates)
    
    def test_processor_initialization(self, processor):
        """Test processor initialization."""
        assert processor is not None
        assert hasattr(processor, 'process_data')
        assert hasattr(processor, 'analyze_trends')
    
    def test_process_data_with_valid_input(self, processor, sample_price_data):
        """Test process_data with valid input."""
        result = processor.process_data(sample_price_data)
        
        assert result is not None
        assert isinstance(result, dict)
        # Add more specific assertions based on expected output
    
    def test_process_data_with_empty_input(self, processor):
        """Test process_data with empty input."""
        empty_df = pd.DataFrame()
        
        with pytest.raises((ValueError, KeyError)):
            processor.process_data(empty_df)
    
    def test_process_data_with_invalid_columns(self, processor):
        """Test process_data with invalid column names."""
        invalid_df = pd.DataFrame({
            'invalid_col1': [1, 2, 3],
            'invalid_col2': [4, 5, 6]
        })
        
        with pytest.raises((KeyError, ValueError)):
            processor.process_data(invalid_df)
    
    @patch('src.core.advanced_market_processing.ta')
    def test_technical_indicators_calculation(self, mock_ta, processor, sample_price_data):
        """Test technical indicators calculation."""
        # Mock technical analysis library
        mock_ta.trend.sma_indicator.return_value = pd.Series([105] * 10)
        mock_ta.momentum.rsi.return_value = pd.Series([50] * 10)
        
        result = processor.process_data(sample_price_data)
        
        # Verify technical analysis functions were called
        assert mock_ta.trend.sma_indicator.called or mock_ta.momentum.rsi.called
    
    def test_analyze_trends_bullish(self, processor):
        """Test trend analysis for bullish market."""
        # Create bullish trend data
        bullish_data = {
            'price_change': 5.0,
            'volume_trend': 'increasing',
            'rsi': 65,
            'macd': 2.5
        }
        
        with patch.object(processor, 'process_data', return_value=bullish_data):
            result = processor.analyze_trends()
            
            assert 'trend' in result
            # Add assertions based on expected trend analysis logic
    
    def test_analyze_trends_bearish(self, processor):
        """Test trend analysis for bearish market."""
        # Create bearish trend data
        bearish_data = {
            'price_change': -5.0,
            'volume_trend': 'decreasing',
            'rsi': 35,
            'macd': -2.5
        }
        
        with patch.object(processor, 'process_data', return_value=bearish_data):
            result = processor.analyze_trends()
            
            assert 'trend' in result
            # Add assertions based on expected trend analysis logic
    
    @pytest.mark.parametrize("rsi_value,expected_signal", [
        (80, 'overbought'),
        (20, 'oversold'),
        (50, 'neutral')
    ])
    def test_rsi_signal_interpretation(self, processor, rsi_value, expected_signal):
        """Test RSI signal interpretation."""
        # Mock method that interprets RSI values
        with patch.object(processor, '_interpret_rsi') as mock_interpret:
            mock_interpret.return_value = expected_signal
            
            result = processor._interpret_rsi(rsi_value)
            assert result == expected_signal
            mock_interpret.assert_called_once_with(rsi_value)
    
    def test_volume_analysis(self, processor, sample_price_data):
        """Test volume analysis functionality."""
        # Test volume trend analysis
        with patch.object(processor, '_analyze_volume') as mock_volume:
            mock_volume.return_value = {'trend': 'increasing', 'strength': 'high'}
            
            result = processor._analyze_volume(sample_price_data['Volume'])
            
            assert 'trend' in result
            assert 'strength' in result
    
    def test_price_volatility_calculation(self, processor, sample_price_data):
        """Test price volatility calculation."""
        volatility = processor._calculate_volatility(sample_price_data['Close'])
        
        assert isinstance(volatility, (float, np.float64))
        assert volatility >= 0
    
    def test_support_resistance_levels(self, processor, sample_price_data):
        """Test support and resistance level identification."""
        levels = processor._find_support_resistance(sample_price_data)
        
        assert isinstance(levels, dict)
        assert 'support' in levels
        assert 'resistance' in levels
    
    def test_error_handling_with_nan_values(self, processor):
        """Test error handling with NaN values in data."""
        nan_data = pd.DataFrame({
            'Open': [100, np.nan, 102],
            'High': [105, 107, np.nan],
            'Low': [95, 96, 97],
            'Close': [102, 104, np.nan],
            'Volume': [1000000, 1100000, 1200000]
        })
        
        # Should handle NaN values gracefully
        try:
            result = processor.process_data(nan_data)
            # Verify result is still valid despite NaN values
            assert result is not None
        except ValueError as e:
            # Or should raise appropriate error
            assert "NaN" in str(e) or "missing" in str(e).lower()
    
    def test_concurrent_processing(self, processor, sample_price_data):
        """Test concurrent data processing."""
        import threading
        import time
        
        results = []
        
        def process_data_thread():
            result = processor.process_data(sample_price_data)
            results.append(result)
        
        # Create multiple threads
        threads = []
        for _ in range(3):
            thread = threading.Thread(target=process_data_thread)
            threads.append(thread)
            thread.start()
        
        # Wait for all threads to complete
        for thread in threads:
            thread.join()
        
        # Verify all threads completed successfully
        assert len(results) == 3
        for result in results:
            assert result is not None
    
    @pytest.mark.slow
    def test_large_dataset_processing(self, processor):
        """Test processing of large datasets."""
        # Create large dataset
        large_dates = pd.date_range(start='2020-01-01', end='2024-01-01', freq='D')
        large_data = pd.DataFrame({
            'Open': np.random.uniform(100, 110, len(large_dates)),
            'High': np.random.uniform(110, 120, len(large_dates)),
            'Low': np.random.uniform(90, 100, len(large_dates)),
            'Close': np.random.uniform(100, 110, len(large_dates)),
            'Volume': np.random.randint(1000000, 5000000, len(large_dates))
        }, index=large_dates)
        
        start_time = time.time()
        result = processor.process_data(large_data)
        processing_time = time.time() - start_time
        
        assert result is not None
        # Verify processing time is reasonable (adjust threshold as needed)
        assert processing_time < 30  # seconds
    
    def test_memory_usage(self, processor, sample_price_data):
        """Test memory usage during processing."""
        import psutil
        import os
        
        process = psutil.Process(os.getpid())
        initial_memory = process.memory_info().rss
        
        # Process data multiple times
        for _ in range(10):
            processor.process_data(sample_price_data)
        
        final_memory = process.memory_info().rss
        memory_increase = final_memory - initial_memory
        
        # Memory increase should be reasonable (adjust threshold as needed)
        assert memory_increase < 100 * 1024 * 1024  # 100MB
    
    def test_configuration_options(self, processor):
        """Test processor configuration options."""
        # Test with different configuration
        config = {
            'window_size': 20,
            'smoothing_factor': 0.1,
            'volatility_threshold': 0.02
        }
        
        processor_with_config = AdvancedMarketProcessor(config=config)
        assert processor_with_config is not None
        
        # Verify configuration is applied
        if hasattr(processor_with_config, 'config'):
            assert processor_with_config.config['window_size'] == 20