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import pytest
import pandas as pd
import numpy as np
import tempfile
import os
from unittest.mock import patch, MagicMock
from agentic_ai_system.orchestrator import run, run_backtest, run_live_trading
from agentic_ai_system.main import load_config
class TestIntegration:
"""Integration tests for the entire trading system"""
@pytest.fixture
def config(self):
"""Sample configuration for integration testing"""
return {
'data_source': {
'type': 'synthetic',
'path': 'data/synthetic_market_data_test.csv'
},
'trading': {
'symbol': 'AAPL',
'timeframe': '1min',
'capital': 100000
},
'risk': {
'max_position': 100,
'max_drawdown': 0.05
},
'execution': {
'broker_api': 'paper',
'order_size': 10,
'delay_ms': 10, # Fast for testing
'success_rate': 1.0 # Always succeed for testing
},
'synthetic_data': {
'base_price': 150.0,
'volatility': 0.02,
'trend': 0.001,
'noise_level': 0.005,
'data_path': 'data/synthetic_market_data_test.csv'
},
'logging': {
'log_level': 'INFO',
'log_dir': 'logs',
'enable_console': True,
'enable_file': True
}
}
def test_full_workflow(self, config):
"""Test the complete trading workflow"""
result = run(config)
# Check result structure
assert isinstance(result, dict)
assert 'success' in result
assert 'data_loaded' in result
assert 'signal_generated' in result
assert 'order_executed' in result
assert 'execution_time' in result
assert 'errors' in result
# Check that data was loaded
assert result['data_loaded'] == True
# Check that signal was generated
assert result['signal_generated'] == True
# Check execution time is reasonable
assert result['execution_time'] > 0
assert result['execution_time'] < 60 # Should complete within 60 seconds
def test_backtest_workflow(self, config):
"""Test the backtest workflow"""
result = run_backtest(config, '2024-01-01', '2024-01-02')
# Check result structure
assert isinstance(result, dict)
assert 'success' in result
if result['success']:
assert 'start_date' in result
assert 'end_date' in result
assert 'initial_capital' in result
assert 'final_value' in result
assert 'total_return' in result
assert 'total_trades' in result
assert 'trades' in result
assert 'positions' in result
# Check that backtest completed
assert result['initial_capital'] == config['trading']['capital']
assert result['final_value'] >= 0
assert isinstance(result['total_return'], float)
assert result['total_trades'] >= 0
assert isinstance(result['trades'], list)
assert isinstance(result['positions'], dict)
def test_live_trading_workflow(self, config):
"""Test the live trading workflow (short duration)"""
# Test with very short duration to avoid long test times
result = run_live_trading(config, duration_minutes=1)
# Check result structure
assert isinstance(result, dict)
assert 'success' in result
if result['success']:
assert 'duration_minutes' in result
assert 'total_trades' in result
assert 'trades' in result
assert 'start_time' in result
assert 'end_time' in result
# Check that live trading completed
assert result['duration_minutes'] == 1
assert result['total_trades'] >= 0
assert isinstance(result['trades'], list)
def test_workflow_with_csv_data(self, config):
"""Test workflow with CSV data source"""
# Create temporary CSV file
with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as tmp_file:
# Generate sample data with correct column names
dates = pd.date_range(start='2024-01-01', periods=100, freq='1min')
data = []
for i, date in enumerate(dates):
base_price = 150.0 + (i * 0.1)
data.append({
'date': date,
'open': base_price + np.random.normal(0, 1),
'high': base_price + abs(np.random.normal(0, 2)),
'low': base_price - abs(np.random.normal(0, 2)),
'close': base_price + np.random.normal(0, 1),
'volume': np.random.randint(1000, 100000)
})
df = pd.DataFrame(data)
df.to_csv(tmp_file.name, index=False)
config['data_source']['type'] = 'csv'
config['data_source']['path'] = tmp_file.name
try:
result = run(config)
assert result['success'] == True
assert result['data_loaded'] == True
assert result['signal_generated'] == True
finally:
os.unlink(tmp_file.name)
def test_workflow_error_handling(self, config):
"""Test workflow error handling"""
# Test with invalid configuration
invalid_config = config.copy()
invalid_config['data_source']['type'] = 'invalid_type'
result = run(invalid_config)
assert result['success'] == False
assert len(result['errors']) > 0
def test_backtest_with_different_periods(self, config):
"""Test backtest with different time periods"""
# Test short period
short_result = run_backtest(config, '2024-01-01', '2024-01-01')
assert isinstance(short_result, dict)
# Test longer period
long_result = run_backtest(config, '2024-01-01', '2024-01-07')
assert isinstance(long_result, dict)
# Both should be valid results (success or failure)
assert 'success' in short_result
assert 'success' in long_result
def test_system_with_different_symbols(self, config):
"""Test system with different trading symbols"""
symbols = ['AAPL', 'GOOGL', 'MSFT', 'TSLA']
for symbol in symbols:
test_config = config.copy()
test_config['trading']['symbol'] = symbol
result = run(test_config)
assert result['success'] == True
assert result['data_loaded'] == True
assert result['signal_generated'] == True
def test_system_with_different_capital_amounts(self, config):
"""Test system with different capital amounts"""
capital_amounts = [10000, 50000, 100000, 500000]
for capital in capital_amounts:
test_config = config.copy()
test_config['trading']['capital'] = capital
result = run(test_config)
assert result['success'] == True
assert result['data_loaded'] == True
assert result['signal_generated'] == True
def test_execution_failure_simulation(self, config):
"""Test system behavior with execution failures"""
# Set success rate to 0 to simulate all failures
test_config = config.copy()
test_config['execution']['success_rate'] = 0.0
result = run(test_config)
# System should still complete workflow
assert result['success'] == True
assert result['data_loaded'] == True
assert result['signal_generated'] == True
# If a non-hold order was executed, it should fail with success_rate = 0.0
# But if only hold signals were generated, no orders would be executed
if result['order_executed'] and result.get('execution_result', {}).get('action') != 'hold':
assert result['execution_result']['success'] == False
def test_data_validation_integration(self, config):
"""Test data validation integration"""
# Create invalid data
with tempfile.NamedTemporaryFile(mode='w', suffix='.csv', delete=False) as tmp_file:
invalid_data = pd.DataFrame({
'date': pd.date_range('2024-01-01', periods=10, freq='1min'),
'open': [150] * 10,
'high': [145] * 10, # Invalid: high < open
'low': [145] * 10,
'close': [152] * 10,
'volume': [1000] * 10
})
invalid_data.to_csv(tmp_file.name, index=False)
config['data_source']['type'] = 'csv'
config['data_source']['path'] = tmp_file.name
try:
result = run(config)
# System should still work (fallback to synthetic data)
assert result['success'] == True
finally:
os.unlink(tmp_file.name)
def test_performance_metrics(self, config):
"""Test that performance metrics are calculated correctly"""
result = run_backtest(config, '2024-01-01', '2024-01-03')
if result['success']:
# Check that return is calculated correctly
initial_capital = result['initial_capital']
final_value = result['final_value']
calculated_return = (final_value - initial_capital) / initial_capital
assert abs(result['total_return'] - calculated_return) < 0.001
# Check that trade count is reasonable
assert result['total_trades'] >= 0
def test_config_loading(self):
"""Test configuration loading functionality"""
# Test with valid config
with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as tmp_file:
config_content = """
data_source:
type: 'synthetic'
path: 'data/market_data.csv'
trading:
symbol: 'AAPL'
timeframe: '1min'
capital: 100000
risk:
max_position: 100
max_drawdown: 0.05
execution:
broker_api: 'paper'
order_size: 10
"""
tmp_file.write(config_content)
tmp_file.flush()
try:
config = load_config(tmp_file.name)
assert config['data_source']['type'] == 'synthetic'
assert config['trading']['symbol'] == 'AAPL'
assert config['trading']['capital'] == 100000
finally:
os.unlink(tmp_file.name)
def test_system_scalability(self, config):
"""Test system scalability with larger datasets"""
# Test with larger synthetic dataset
test_config = config.copy()
test_config['synthetic_data']['base_price'] = 200.0
test_config['synthetic_data']['volatility'] = 0.03
result = run(test_config)
assert result['success'] == True
assert result['data_loaded'] == True
assert result['signal_generated'] == True
# Check execution time is reasonable
assert result['execution_time'] < 30 # Should complete within 30 seconds |