# III. API Integration

## Authentication Protocols

The CADE API implements robust authentication mechanisms utilizing JWT tokens and API keys for secure access control. All requests must be authenticated using bearer token authentication.

### Security Configuration

```python
SECURITY_CONFIG = {
    'jwt': {
        'algorithm': 'HS256',
        'access_token_expire_minutes': 30,
        'refresh_token_expire_days': 7
    },
    'rate_limiting': {
        'enabled': true,
        'strategy': 'sliding_window',
        'requests_per_minute': 60,
        'burst_size': 10
    }
}
```

## Endpoint Specifications

### Core Endpoints

```yaml
GET /api/v1/token/{token_address}/analysis:
    description: Get comprehensive token analysis
    parameters:
        - token_address: Solana token address
        - timeframe: Analysis timeframe (1h, 24h, 7d)
    response:
        risk_score: float
        anomalies: List[AnomalyEvent]
        metrics: Dict[str, float]

POST /api/v1/monitor:
    description: Start token monitoring
    body:
        token_addresses: List[str]
        update_interval: int (seconds)
    response:
        status: str
        monitoring_ids: List[str]

GET /api/v1/anomalies:
    description: Retrieve detected anomalies
    parameters:
        - start_time: datetime
        - end_time: datetime
        - min_risk_score: float
    response:
        anomalies: List[AnomalyEvent]
```

## WebSocket Implementation

The system provides real-time data streaming through WebSocket connections:

```python
WS_CONFIG = {
    'ping_interval': 30,
    'ping_timeout': 10,
    'max_message_size': 1024 * 1024,  # 1MB
    'compression': True,
    'max_connections': 1000
}
```

### Message Format

```json
{
    "type": "anomaly_alert",
    "data": {
        "token_address": "token_address",
        "risk_score": 0.85,
        "anomaly_type": "whale_movement",
        "timestamp": "2024-01-07T12:00:00Z",
        "details": {
            "wallet_address": "wallet_address",
            "amount": 500000,
            "usd_value": 250000
        }
    }
}
```

## Rate Limiting

```python
class RateLimiter:
    def __init__(self, rate_limit: int = 100, window: int = 60):
        self.rate_limit = rate_limit
        self.window = window
        self.requests = defaultdict(list)
        
    async def is_allowed(self, key: str) -> bool:
        """Check if request is allowed under rate limit."""
```

## Error Handling

```yaml
Error Codes:
    400: Bad Request
    401: Unauthorized
    403: Forbidden
    429: Rate Limit Exceeded
    500: Internal Server Error

Error Response Format:
    {
        "error": {
            "code": "error_code",
            "message": "Error description",
            "details": {
                "additional": "error details"
            }
        }
    }
```

## Monitoring Integration

The API exposes metrics through standardized endpoints:

```yaml
Prometheus Metrics:
    - http_request_duration_seconds
    - http_requests_total
    - websocket_connections_active
    - api_error_rate
    - endpoint_latency_histogram

Performance Thresholds:
    Request Latency: <100ms p95
    Error Rate: <1%
    Uptime: 99.9%
    Concurrent Connections: 1000
```

## Deployment Configuration

```yaml
Kubernetes Resources:
    api:
        cpu: 2
        memory: 4Gi
        replicas: 3
    worker:
        cpu: 4
        memory: 8Gi
        replicas: 5
    cache:
        cpu: 2
        memory: 4Gi
        replicas: 3

Infrastructure Requirements:
    Load Balancer: Required
    SSL Termination: Required
    Connection Draining: 30s
    Readiness Probe: /health
```