aitbc/apps/ai-engine/tests/test_edge_cases_ai_engine.py

"""Edge case and error handling tests for AI engine service"""

import pytest
import sys
import sys
from pathlib import Path
from unittest.mock import Mock, patch, MagicMock
from datetime import datetime


# Mock numpy before importing
sys.modules['numpy'] = MagicMock()

from ai_service import SimpleAITradingEngine


@pytest.mark.unit
@pytest.mark.asyncio
async def test_analyze_market_with_empty_symbol():
    """Test market analysis with empty symbol"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'bullish'
            
            result = await engine.analyze_market('')
            
            assert result['symbol'] == ''
            assert 'current_price' in result


@pytest.mark.unit
@pytest.mark.asyncio
async def test_analyze_market_with_special_characters():
    """Test market analysis with special characters in symbol"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'bullish'
            
            result = await engine.analyze_market('AITBC/USDT@TEST')
            
            assert result['symbol'] == 'AITBC/USDT@TEST'


@pytest.mark.unit
@pytest.mark.asyncio
async def test_make_trading_decision_extreme_confidence():
    """Test trading decision with extreme confidence values"""
    engine = SimpleAITradingEngine()
    
    # Mock the entire decision process to avoid complex numpy calculations
    with patch.object(engine, 'analyze_market') as mock_analyze:
        mock_analyze.return_value = {
            'symbol': 'AITBC/BTC',
            'current_price': 0.005,
            'price_change_24h': 0.02,
            'volume_24h': 5000,
            'rsi': 50,
            'macd': 0.005,
            'volatility': 0.03,
            'ai_predictions': {
                'price_prediction': {'predicted_change': 1.0, 'confidence': 0.9},
                'risk_assessment': {'risk_score': 0.0, 'volatility': 0.01},
                'sentiment_analysis': {'sentiment_score': 1.0, 'overall_sentiment': 'bullish'}
            },
            'timestamp': datetime.utcnow()
        }
        
        result = await engine.make_trading_decision('AITBC/BTC')
        
        assert result['signal'] == 'buy'
        assert result['confidence'] > 0.5


@pytest.mark.unit
@pytest.mark.asyncio
async def test_make_trading_decision_low_confidence():
    """Test trading decision with low confidence values"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        # Set values to produce low confidence
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'neutral'
            
            result = await engine.make_trading_decision('AITBC/BTC')
            
            assert result['signal'] == 'hold'
            assert result['confidence'] < 0.3


@pytest.mark.unit
@pytest.mark.asyncio
async def test_analyze_market_timestamp_format():
    """Test that timestamp is in correct format"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'bullish'
            
            result = await engine.analyze_market('AITBC/BTC')
            
            assert isinstance(result['timestamp'], datetime)


@pytest.mark.unit
@pytest.mark.asyncio
async def test_make_trading_decision_quantity_calculation():
    """Test that quantity is calculated correctly based on confidence"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        # Set confidence to 0.5
        # signal_strength = (price_pred * 0.5) + (sentiment * 0.3) - (risk * 0.2)
        # 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
        # confidence = abs(0.38) = 0.38
        # quantity = 1000 * 0.38 = 380
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'bullish'
            
            result = await engine.make_trading_decision('AITBC/BTC')
            
            # Quantity should be 1000 * confidence
            expected_quantity = 1000 * result['confidence']
            assert result['quantity'] == expected_quantity


@pytest.mark.unit
@pytest.mark.asyncio
async def test_signal_strength_boundary_buy():
    """Test signal strength at buy boundary (0.2)"""
    engine = SimpleAITradingEngine()
    
    # Mock the entire decision process to avoid complex numpy calculations
    with patch.object(engine, 'analyze_market') as mock_analyze:
        mock_analyze.return_value = {
            'symbol': 'AITBC/BTC',
            'current_price': 0.005,
            'price_change_24h': 0.02,
            'volume_24h': 5000,
            'rsi': 50,
            'macd': 0.005,
            'volatility': 0.03,
            'ai_predictions': {
                'price_prediction': {'predicted_change': 0.8, 'confidence': 0.8},
                'risk_assessment': {'risk_score': 0.0, 'volatility': 0.01},
                'sentiment_analysis': {'sentiment_score': 0.5, 'overall_sentiment': 'bullish'}
            },
            'timestamp': datetime.utcnow()
        }
        
        result = await engine.make_trading_decision('AITBC/BTC')
        
        # At > 0.2, should be buy
        assert result['signal'] == 'buy'


@pytest.mark.unit
@pytest.mark.asyncio
async def test_signal_strength_boundary_sell():
    """Test signal strength at sell boundary (-0.2)"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        # Set values to produce signal strength at -0.2
        # signal_strength = (price_pred * 0.5) + (sentiment * 0.3) - (risk * 0.2)
        # 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
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'bearish'
            
            result = await engine.make_trading_decision('AITBC/BTC')
            
            # At < -0.2, should be sell
            assert result['signal'] == 'sell'


@pytest.mark.unit
@pytest.mark.asyncio
async def test_signal_strength_just_below_buy_threshold():
    """Test signal strength just below buy threshold (0.199)"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        # Set values to produce signal strength just below 0.2
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'neutral'
            
            result = await engine.make_trading_decision('AITBC/BTC')
            
            # Just below 0.2, should be hold
            assert result['signal'] == 'hold'


@pytest.mark.unit
@pytest.mark.asyncio
async def test_signal_strength_just_above_sell_threshold():
    """Test signal strength just above sell threshold (-0.199)"""
    engine = SimpleAITradingEngine()
    
    with patch('ai_service.np.random.uniform') as mock_uniform:
        # Set values to produce signal strength just above -0.2
        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]
        with patch('ai_service.np.random.choice') as mock_choice:
            mock_choice.return_value = 'neutral'
            
            result = await engine.make_trading_decision('AITBC/BTC')
            
            # Just above -0.2, should be hold
            assert result['signal'] == 'hold'