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)
186 lines
6.6 KiB
Python
186 lines
6.6 KiB
Python
"""Integration 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 datetime import datetime, UTC
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from unittest.mock import Mock, patch, MagicMock
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from fastapi.testclient import TestClient
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# Mock numpy before importing
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sys.modules['numpy'] = MagicMock()
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from ai_service import app, ai_engine
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@pytest.mark.integration
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def test_analyze_market_endpoint():
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"""Test /api/ai/analyze endpoint"""
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client = TestClient(app)
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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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response = client.post("/api/ai/analyze", json={"symbol": "AITBC/BTC", "analysis_type": "full"})
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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assert 'analysis' in data
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assert data['analysis']['symbol'] == 'AITBC/BTC'
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@pytest.mark.integration
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def test_execute_ai_trade_endpoint():
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"""Test /api/ai/trade endpoint"""
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client = TestClient(app)
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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, 0.5, 0.3, 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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response = client.post("/api/ai/trade", json={"symbol": "AITBC/BTC", "strategy": "ai_enhanced"})
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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assert 'decision' in data
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assert data['decision']['symbol'] == 'AITBC/BTC'
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assert 'signal' in data['decision']
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@pytest.mark.integration
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def test_predict_market_endpoint():
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"""Test /api/ai/predict/{symbol} endpoint"""
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client = TestClient(app)
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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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response = client.get("/api/ai/predict/AITBC-BTC")
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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assert 'predictions' in data
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assert 'price' in data['predictions']
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assert 'risk' in data['predictions']
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assert 'sentiment' in data['predictions']
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@pytest.mark.integration
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def test_get_ai_dashboard_endpoint():
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"""Test /api/ai/dashboard endpoint"""
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client = TestClient(app)
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# The dashboard endpoint calls analyze_market and make_trading_decision multiple times
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# Mock the entire ai_engine methods to avoid complex numpy mocking
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with patch.object(ai_engine, 'analyze_market') as mock_analyze, \
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patch.object(ai_engine, 'make_trading_decision') as mock_decision:
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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': 0.01, 'confidence': 0.8},
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'risk_assessment': {'risk_score': 0.5, 'volatility': 0.03},
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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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mock_decision.return_value = {
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'symbol': 'AITBC/BTC',
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'signal': 'buy',
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'confidence': 0.5,
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'quantity': 500,
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'price': 0.005,
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'reasoning': 'Test reasoning',
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'timestamp': datetime.now(datetime.UTC)
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}
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response = client.get("/api/ai/dashboard")
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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assert 'dashboard' in data
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assert 'market_overview' in data['dashboard']
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assert 'symbol_analysis' in data['dashboard']
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assert len(data['dashboard']['symbol_analysis']) == 3
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@pytest.mark.integration
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def test_get_ai_status_endpoint():
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"""Test /api/ai/status endpoint"""
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client = TestClient(app)
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response = client.get("/api/ai/status")
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'active'
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assert data['models_loaded'] is True
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assert 'services' in data
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assert 'capabilities' in data
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assert 'trading_engine' in data['services']
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assert 'market_analysis' in data['services']
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@pytest.mark.integration
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def test_health_check_endpoint():
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"""Test /api/health endpoint"""
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client = TestClient(app)
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response = client.get("/api/health")
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'ok'
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@pytest.mark.integration
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def test_analyze_market_with_default_strategy():
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"""Test analyze endpoint with default strategy"""
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client = TestClient(app)
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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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response = client.post("/api/ai/analyze", json={"symbol": "AITBC/ETH"})
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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@pytest.mark.integration
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def test_trade_endpoint_with_default_strategy():
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"""Test trade endpoint with default strategy"""
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client = TestClient(app)
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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, 0.5, 0.3, 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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response = client.post("/api/ai/trade", json={"symbol": "AITBC/USDT"})
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assert response.status_code == 200
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data = response.json()
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assert data['status'] == 'success'
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