Testing Your Smart Money API Integration

Comprehensive testing ensures reliable production deployment. Smart Money API provides sandbox environment, mock data, and testing utilities. This guide covers unit testing, integration testing, load testing, and debugging strategies.

Sandbox Environment

Use sandbox.api.smartmoneyapi.com for testing without consuming production API quota. Sandbox mirrors production API but returns synthetic data. All endpoints work identically in sandbox and production, enabling realistic testing.

Sandbox features: Instant response times (10-50ms), synthetic whale movements, configurable market scenarios, and deterministic data. Perfect for developing bots before production deployment.

Unit Testing

Test individual functions that call Smart Money API. Mock API responses using libraries like unittest.mock (Python) or Jest (JavaScript). Verify correct error handling, data transformation, and edge cases.

PYTHON
import unittest
from unittest.mock import patch, MagicMock
from trading_bot import WhalMonitor

class TestWhaleMonitor(unittest.TestCase):
    @patch('requests.get')
    def test_whale_movement_detection(self, mock_get):
        # Mock API response
        mock_response = MagicMock()
        mock_response.json.return_value = {
            'movements': [
                {
                    'address': '1A1z7agoat...',
                    'btc_amount': 500,
                    'direction': 'to_exchange'
                }
            ]
        }
        mock_get.return_value = mock_response

        # Test detection logic
        monitor = WhalMonitor()
        signal = monitor.check_movements()

        self.assertEqual(signal['type'], 'whale_to_exchange')
        self.assertEqual(signal['amount'], 500)
        mock_get.assert_called_once()

Integration Testing

Test entire workflows calling real sandbox endpoints. Verify data flow from API to storage to trading decisions. Use test fixtures for consistent initial state. Run integration tests in isolation to prevent interference.

Load Testing

Load test your implementation before production. Use Apache JMeter or locust to simulate concurrent users. Test at 2x expected peak load. Verify graceful degradation under overload. Monitor error rates, latency percentiles (p50, p95, p99).

End-to-End Testing

Test complete trading signals from whale detection to position management. Simulate market scenarios (whale accumulation, exchange inflow spikes). Verify correct action execution and signal propagation.

Debugging Strategies

Enable request/response logging in development. Log all API calls with timestamps, response codes, and payloads. Use browser developer tools to inspect WebSocket connections. Monitor API quota usage to catch excessive requests.

Common issues: API key not passed correctly (verify header names and values), authentication failures (ensure valid credentials), rate limiting (check rate limit headers and implement backoff), network timeouts (increase timeout threshold for slow networks).

Continuous Integration

Set up CI/CD pipeline running tests on every commit. Run unit tests quickly (< 5s), integration tests in parallel, then load tests. Fail builds if coverage < 80% or error rates exceed thresholds.