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b9c68d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | """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 |