5f03ded7ff
- Replace all 2,087 uses of datetime.utcnow() across 294 files - Add UTC import to datetime statements where needed - Addresses Python 3.12+ deprecation warning (report item #3)
221 lines
8.5 KiB
Python
221 lines
8.5 KiB
Python
"""Edge case and error handling tests for AI engine service"""
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import pytest
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import sys
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import sys
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from pathlib import Path
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from unittest.mock import Mock, patch, MagicMock
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from datetime import datetime, UTC
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# Mock numpy before importing
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sys.modules['numpy'] = MagicMock()
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from ai_service import SimpleAITradingEngine
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_analyze_market_with_empty_symbol():
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"""Test market analysis with empty symbol"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.5, 0.4]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'bullish'
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result = await engine.analyze_market('')
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assert result['symbol'] == ''
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assert 'current_price' in result
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_analyze_market_with_special_characters():
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"""Test market analysis with special characters in symbol"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.5, 0.4]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'bullish'
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result = await engine.analyze_market('AITBC/USDT@TEST')
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assert result['symbol'] == 'AITBC/USDT@TEST'
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_make_trading_decision_extreme_confidence():
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"""Test trading decision with extreme confidence values"""
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engine = SimpleAITradingEngine()
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# Mock the entire decision process to avoid complex numpy calculations
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with patch.object(engine, 'analyze_market') as mock_analyze:
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mock_analyze.return_value = {
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'symbol': 'AITBC/BTC',
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'current_price': 0.005,
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'price_change_24h': 0.02,
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'volume_24h': 5000,
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'rsi': 50,
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'macd': 0.005,
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'volatility': 0.03,
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'ai_predictions': {
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'price_prediction': {'predicted_change': 1.0, 'confidence': 0.9},
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'risk_assessment': {'risk_score': 0.0, 'volatility': 0.01},
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'sentiment_analysis': {'sentiment_score': 1.0, 'overall_sentiment': 'bullish'}
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},
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'timestamp': datetime.now(datetime.UTC)
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}
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result = await engine.make_trading_decision('AITBC/BTC')
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assert result['signal'] == 'buy'
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assert result['confidence'] > 0.5
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_make_trading_decision_low_confidence():
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"""Test trading decision with low confidence values"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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# Set values to produce low confidence
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.0, 0.4, 0.0, 0.4, 0.4]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'neutral'
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result = await engine.make_trading_decision('AITBC/BTC')
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assert result['signal'] == 'hold'
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assert result['confidence'] < 0.3
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_analyze_market_timestamp_format():
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"""Test that timestamp is in correct format"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.5, 0.4]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'bullish'
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result = await engine.analyze_market('AITBC/BTC')
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assert isinstance(result['timestamp'], datetime)
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_make_trading_decision_quantity_calculation():
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"""Test that quantity is calculated correctly based on confidence"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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# Set confidence to 0.5
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# signal_strength = (price_pred * 0.5) + (sentiment * 0.3) - (risk * 0.2)
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# price_pred=0.5, sentiment=0.5, risk=0.1 => (0.5*0.5) + (0.5*0.3) - (0.1*0.2) = 0.25 + 0.15 - 0.02 = 0.38
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# confidence = abs(0.38) = 0.38
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# quantity = 1000 * 0.38 = 380
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.5, 0.1, 0.5, 0.5, 0.1]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'bullish'
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result = await engine.make_trading_decision('AITBC/BTC')
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# Quantity should be 1000 * confidence
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expected_quantity = 1000 * result['confidence']
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assert result['quantity'] == expected_quantity
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_signal_strength_boundary_buy():
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"""Test signal strength at buy boundary (0.2)"""
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engine = SimpleAITradingEngine()
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# Mock the entire decision process to avoid complex numpy calculations
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with patch.object(engine, 'analyze_market') as mock_analyze:
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mock_analyze.return_value = {
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'symbol': 'AITBC/BTC',
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'current_price': 0.005,
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'price_change_24h': 0.02,
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'volume_24h': 5000,
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'rsi': 50,
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'macd': 0.005,
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'volatility': 0.03,
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'ai_predictions': {
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'price_prediction': {'predicted_change': 0.8, 'confidence': 0.8},
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'risk_assessment': {'risk_score': 0.0, 'volatility': 0.01},
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'sentiment_analysis': {'sentiment_score': 0.5, 'overall_sentiment': 'bullish'}
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},
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'timestamp': datetime.now(datetime.UTC)
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}
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result = await engine.make_trading_decision('AITBC/BTC')
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# At > 0.2, should be buy
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assert result['signal'] == 'buy'
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_signal_strength_boundary_sell():
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"""Test signal strength at sell boundary (-0.2)"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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# Set values to produce signal strength at -0.2
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# signal_strength = (price_pred * 0.5) + (sentiment * 0.3) - (risk * 0.2)
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# To get -0.25: price_pred=-0.5, sentiment=-0.5, risk=0.5 => (-0.5*0.5) + (-0.5*0.3) - (0.5*0.2) = -0.25 - 0.15 - 0.1 = -0.5
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mock_uniform.side_effect = [0.005, -0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, -0.5, 0.5, -0.5, -0.5, 0.5]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'bearish'
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result = await engine.make_trading_decision('AITBC/BTC')
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# At < -0.2, should be sell
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assert result['signal'] == 'sell'
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_signal_strength_just_below_buy_threshold():
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"""Test signal strength just below buy threshold (0.199)"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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# Set values to produce signal strength just below 0.2
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mock_uniform.side_effect = [0.005, 0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, 0.199, 0.4, 0.199, 0.3, 0.0]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'neutral'
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result = await engine.make_trading_decision('AITBC/BTC')
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# Just below 0.2, should be hold
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assert result['signal'] == 'hold'
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@pytest.mark.unit
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@pytest.mark.asyncio
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async def test_signal_strength_just_above_sell_threshold():
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"""Test signal strength just above sell threshold (-0.199)"""
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engine = SimpleAITradingEngine()
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with patch('ai_service.np.random.uniform') as mock_uniform:
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# Set values to produce signal strength just above -0.2
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mock_uniform.side_effect = [0.005, -0.02, 5000, 50, 0.005, 0.03, 0.01, 0.8, 0.6, 0.03, -0.199, 0.4, -0.199, 0.3, 0.0]
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with patch('ai_service.np.random.choice') as mock_choice:
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mock_choice.return_value = 'neutral'
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result = await engine.make_trading_decision('AITBC/BTC')
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# Just above -0.2, should be hold
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assert result['signal'] == 'hold'
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