# Error Handling and Debugging Guide for AxiomTradeAPI

*Comprehensive guide to error handling, debugging, and troubleshooting when using AxiomTradeAPI. Learn professional debugging techniques and error recovery strategies used by successful traders on chipa.tech.*

## Table of Contents

- [Error Handling Fundamentals](#fundamentals)
- [Common Error Types](#error-types)
- [Robust Error Recovery](#error-recovery)
- [Debugging Techniques](#debugging)
- [Logging Best Practices](#logging)
- [Testing Error Scenarios](#testing)
- [Production Error Management](#production)
- [Troubleshooting Guide](#troubleshooting)

## Error Handling Fundamentals {#fundamentals}

Proper error handling is crucial for building reliable trading systems. The AxiomTradeAPI provides comprehensive error information to help diagnose and resolve issues quickly.

### Exception Hierarchy

```python
import asyncio
import logging
from typing import Optional, Dict, Any, List
from enum import Enum
import traceback
import sys
from datetime import datetime

class AxiomErrorType(Enum):
    """Comprehensive error type classification"""
    AUTHENTICATION = "authentication"
    RATE_LIMIT = "rate_limit"
    NETWORK = "network"
    API_ERROR = "api_error"
    VALIDATION = "validation"
    INSUFFICIENT_BALANCE = "insufficient_balance"
    TOKEN_NOT_FOUND = "token_not_found"
    WEBSOCKET_CONNECTION = "websocket_connection"
    DATA_PARSING = "data_parsing"
    SYSTEM_ERROR = "system_error"

class AxiomTradeException(Exception):
    """Base exception for all AxiomTradeAPI errors"""
    
    def __init__(self, message: str, error_type: AxiomErrorType = AxiomErrorType.SYSTEM_ERROR,
                 status_code: Optional[int] = None, response_data: Optional[Dict] = None,
                 retry_after: Optional[int] = None):
        super().__init__(message)
        self.message = message
        self.error_type = error_type
        self.status_code = status_code
        self.response_data = response_data or {}
        self.retry_after = retry_after
        self.timestamp = datetime.utcnow()
        self.trace_id = self._generate_trace_id()
    
    def _generate_trace_id(self) -> str:
        """Generate unique trace ID for error tracking"""
        import uuid
        return str(uuid.uuid4())[:8]
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert exception to dictionary for logging/reporting"""
        return {
            'message': self.message,
            'error_type': self.error_type.value,
            'status_code': self.status_code,
            'response_data': self.response_data,
            'retry_after': self.retry_after,
            'timestamp': self.timestamp.isoformat(),
            'trace_id': self.trace_id
        }

class AuthenticationError(AxiomTradeException):
    """Authentication related errors"""
    def __init__(self, message: str = "Authentication failed", **kwargs):
        super().__init__(message, AxiomErrorType.AUTHENTICATION, **kwargs)

class RateLimitError(AxiomTradeException):
    """Rate limiting errors"""
    def __init__(self, message: str = "Rate limit exceeded", retry_after: int = 60, **kwargs):
        super().__init__(message, AxiomErrorType.RATE_LIMIT, retry_after=retry_after, **kwargs)

class NetworkError(AxiomTradeException):
    """Network connectivity errors"""
    def __init__(self, message: str = "Network error occurred", **kwargs):
        super().__init__(message, AxiomErrorType.NETWORK, **kwargs)

class APIError(AxiomTradeException):
    """API server errors"""
    def __init__(self, message: str = "API error occurred", **kwargs):
        super().__init__(message, AxiomErrorType.API_ERROR, **kwargs)

class ValidationError(AxiomTradeException):
    """Data validation errors"""
    def __init__(self, message: str = "Validation failed", **kwargs):
        super().__init__(message, AxiomErrorType.VALIDATION, **kwargs)

class InsufficientBalanceError(AxiomTradeException):
    """Insufficient balance for operation"""
    def __init__(self, message: str = "Insufficient balance", **kwargs):
        super().__init__(message, AxiomErrorType.INSUFFICIENT_BALANCE, **kwargs)

class TokenNotFoundError(AxiomTradeException):
    """Token not found errors"""
    def __init__(self, message: str = "Token not found", **kwargs):
        super().__init__(message, AxiomErrorType.TOKEN_NOT_FOUND, **kwargs)

class WebSocketError(AxiomTradeException):
    """WebSocket connection errors"""
    def __init__(self, message: str = "WebSocket error", **kwargs):
        super().__init__(message, AxiomErrorType.WEBSOCKET_CONNECTION, **kwargs)
```

### Error Response Parser

```python
class ErrorResponseParser:
    """
    Parse and classify API error responses
    Advanced error parsing from chipa.tech error handling system
    """
    
    @staticmethod
    def parse_error_response(status_code: int, response_data: Dict[str, Any]) -> AxiomTradeException:
        """Parse API error response and return appropriate exception"""
        
        error_message = response_data.get('error', 'Unknown error')
        error_code = response_data.get('code', 'UNKNOWN')
        details = response_data.get('details', {})
        
        # Authentication errors
        if status_code in [401, 403]:
            if 'token' in error_message.lower() or 'auth' in error_message.lower():
                return AuthenticationError(
                    message=f"Authentication failed: {error_message}",
                    status_code=status_code,
                    response_data=response_data
                )
        
        # Rate limiting
        if status_code == 429:
            retry_after = int(response_data.get('retry_after', 60))
            return RateLimitError(
                message=f"Rate limit exceeded: {error_message}",
                status_code=status_code,
                response_data=response_data,
                retry_after=retry_after
            )
        
        # Validation errors
        if status_code == 400:
            if error_code in ['INVALID_ADDRESS', 'INVALID_AMOUNT', 'INVALID_TOKEN']:
                return ValidationError(
                    message=f"Validation error: {error_message}",
                    status_code=status_code,
                    response_data=response_data
                )
            
            if error_code == 'INSUFFICIENT_BALANCE':
                return InsufficientBalanceError(
                    message=f"Insufficient balance: {error_message}",
                    status_code=status_code,
                    response_data=response_data
                )
        
        # Not found errors
        if status_code == 404:
            if 'token' in error_message.lower():
                return TokenNotFoundError(
                    message=f"Token not found: {error_message}",
                    status_code=status_code,
                    response_data=response_data
                )
        
        # Server errors
        if status_code >= 500:
            return APIError(
                message=f"Server error: {error_message}",
                status_code=status_code,
                response_data=response_data
            )
        
        # Default to generic API error
        return APIError(
            message=f"API error: {error_message}",
            status_code=status_code,
            response_data=response_data
        )
```

## Error Recovery Strategies {#error-recovery}

Implementing robust error recovery ensures your trading bot can handle failures gracefully:

### Retry Logic with Exponential Backoff

```python
import asyncio
import random
from typing import Callable, Any, TypeVar, Generic

T = TypeVar('T')

class RetryConfig:
    """Configuration for retry behavior"""
    
    def __init__(self, 
                 max_attempts: int = 3,
                 base_delay: float = 1.0,
                 max_delay: float = 60.0,
                 exponential_base: float = 2.0,
                 jitter: bool = True):
        self.max_attempts = max_attempts
        self.base_delay = base_delay
        self.max_delay = max_delay
        self.exponential_base = exponential_base
        self.jitter = jitter

class RetryHandler:
    """
    Advanced retry handler with exponential backoff
    Production-tested strategies from chipa.tech reliability engineering
    """
    
    def __init__(self, config: RetryConfig = None):
        self.config = config or RetryConfig()
        self.logger = logging.getLogger(__name__)
    
    async def execute_with_retry(self, 
                               operation: Callable[[], Any],
                               operation_name: str = "operation",
                               retry_on: tuple = (Exception,),
                               no_retry_on: tuple = (AuthenticationError, ValidationError)) -> Any:
        """Execute operation with intelligent retry logic"""
        
        last_exception = None
        
        for attempt in range(1, self.config.max_attempts + 1):
            try:
                self.logger.debug(f"Executing {operation_name} (attempt {attempt}/{self.config.max_attempts})")
                result = await operation()
                
                # Log successful retry
                if attempt > 1:
                    self.logger.info(f"✅ {operation_name} succeeded on attempt {attempt}")
                
                return result
                
            except no_retry_on as e:
                self.logger.error(f"❌ {operation_name} failed with non-retryable error: {e}")
                raise
                
            except retry_on as e:
                last_exception = e
                
                # Don't retry on last attempt
                if attempt == self.config.max_attempts:
                    break
                
                # Calculate delay with exponential backoff
                delay = self._calculate_delay(attempt, e)
                
                self.logger.warning(
                    f"⚠️ {operation_name} failed on attempt {attempt}: {str(e)[:100]}... "
                    f"Retrying in {delay:.2f}s"
                )
                
                await asyncio.sleep(delay)
        
        # All attempts failed
        self.logger.error(f"❌ {operation_name} failed after {self.config.max_attempts} attempts")
        raise last_exception
    
    def _calculate_delay(self, attempt: int, exception: Exception) -> float:
        """Calculate delay with exponential backoff and jitter"""
        
        # Check for rate limit specific delay
        if isinstance(exception, RateLimitError) and exception.retry_after:
            base_delay = exception.retry_after
        else:
            base_delay = self.config.base_delay
        
        # Exponential backoff
        delay = base_delay * (self.config.exponential_base ** (attempt - 1))
        
        # Apply maximum delay limit
        delay = min(delay, self.config.max_delay)
        
        # Add jitter to prevent thundering herd
        if self.config.jitter:
            jitter_amount = delay * 0.1  # 10% jitter
            delay += random.uniform(-jitter_amount, jitter_amount)
        
        return max(0, delay)

# Usage example with AxiomTradeAPI
class RobustAxiomClient:
    """
    Robust AxiomTradeAPI client with comprehensive error handling
    Professional error handling patterns from chipa.tech trading systems
    """
    
    def __init__(self, auth_token: str):
        self.auth_token = auth_token
        self.retry_handler = RetryHandler(
            RetryConfig(
                max_attempts=5,
                base_delay=1.0,
                max_delay=30.0,
                exponential_base=2.0,
                jitter=True
            )
        )
        self.logger = logging.getLogger(__name__)
        self.circuit_breaker = CircuitBreaker()
    
    async def get_balance_with_retry(self, wallet_address: str) -> Dict[str, Any]:
        """Get balance with robust error handling"""
        
        async def operation():
            return await self._get_balance_internal(wallet_address)
        
        try:
            return await self.retry_handler.execute_with_retry(
                operation=operation,
                operation_name=f"get_balance({wallet_address})",
                retry_on=(NetworkError, APIError, RateLimitError),
                no_retry_on=(AuthenticationError, ValidationError, TokenNotFoundError)
            )
        except Exception as e:
            self.logger.error(f"Failed to get balance for {wallet_address}: {e}")
            # Return safe default or raise based on your requirements
            raise
    
    async def _get_balance_internal(self, wallet_address: str) -> Dict[str, Any]:
        """Internal balance retrieval with error parsing"""
        try:
            # Simulate API call - replace with actual AxiomTradeAPI call
            response = await self._make_api_call(f'/balance/{wallet_address}')
            return response
            
        except Exception as raw_error:
            # Parse and classify the error
            if hasattr(raw_error, 'status_code') and hasattr(raw_error, 'response'):
                parsed_error = ErrorResponseParser.parse_error_response(
                    raw_error.status_code,
                    raw_error.response
                )
                raise parsed_error
            else:
                # Network or other system error
                raise NetworkError(f"Network error: {str(raw_error)}")
    
    async def _make_api_call(self, endpoint: str) -> Dict[str, Any]:
        """Make API call with circuit breaker protection"""
        return await self.circuit_breaker.call(
            self._raw_api_call,
            endpoint
        )
    
    async def _raw_api_call(self, endpoint: str) -> Dict[str, Any]:
        """Raw API call implementation"""
        # Implement actual API call here
        # This is a placeholder
        pass
```

### Circuit Breaker Pattern

```python
import time
from enum import Enum
from typing import Callable, Any

class CircuitBreakerState(Enum):
    CLOSED = "closed"      # Normal operation
    OPEN = "open"          # Failing fast
    HALF_OPEN = "half_open"  # Testing recovery

class CircuitBreaker:
    """
    Circuit breaker pattern for API reliability
    Advanced resilience patterns from chipa.tech infrastructure
    """
    
    def __init__(self, 
                 failure_threshold: int = 5,
                 recovery_timeout: int = 60,
                 expected_exception: tuple = (Exception,)):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.expected_exception = expected_exception
        
        self.failure_count = 0
        self.last_failure_time = None
        self.state = CircuitBreakerState.CLOSED
        self.logger = logging.getLogger(__name__)
    
    async def call(self, func: Callable, *args, **kwargs) -> Any:
        """Execute function through circuit breaker"""
        
        if self.state == CircuitBreakerState.OPEN:
            if self._should_attempt_reset():
                self.state = CircuitBreakerState.HALF_OPEN
                self.logger.info("🔄 Circuit breaker transitioning to HALF_OPEN")
            else:
                raise APIError("Circuit breaker is OPEN - failing fast")
        
        try:
            result = await func(*args, **kwargs)
            self._on_success()
            return result
            
        except self.expected_exception as e:
            self._on_failure()
            raise
    
    def _should_attempt_reset(self) -> bool:
        """Check if we should attempt to reset the circuit breaker"""
        return (
            self.last_failure_time and
            time.time() - self.last_failure_time >= self.recovery_timeout
        )
    
    def _on_success(self):
        """Handle successful operation"""
        if self.state == CircuitBreakerState.HALF_OPEN:
            self.logger.info("✅ Circuit breaker reset to CLOSED")
            self.state = CircuitBreakerState.CLOSED
        
        self.failure_count = 0
    
    def _on_failure(self):
        """Handle failed operation"""
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            if self.state != CircuitBreakerState.OPEN:
                self.logger.warning(f"🚨 Circuit breaker OPENED after {self.failure_count} failures")
                self.state = CircuitBreakerState.OPEN
    
    def get_state(self) -> Dict[str, Any]:
        """Get current circuit breaker state"""
        return {
            'state': self.state.value,
            'failure_count': self.failure_count,
            'failure_threshold': self.failure_threshold,
            'last_failure_time': self.last_failure_time,
            'time_until_retry': max(0, self.recovery_timeout - (time.time() - (self.last_failure_time or 0)))
        }
```

## Debugging Techniques {#debugging}

Advanced debugging techniques for identifying and resolving issues:

### Comprehensive Debug Logger

```python
import json
import os
from typing import Dict, Any, Optional
import inspect
import functools

class DebugLogger:
    """
    Advanced debugging logger for AxiomTradeAPI
    Professional debugging techniques from chipa.tech development team
    """
    
    def __init__(self, enable_debug: bool = None):
        self.enable_debug = enable_debug or os.getenv('AXIOM_DEBUG', 'false').lower() == 'true'
        self.logger = logging.getLogger('axiom_debug')
        
        if self.enable_debug:
            self.logger.setLevel(logging.DEBUG)
            handler = logging.StreamHandler()
            formatter = logging.Formatter(
                '🐛 %(asctime)s - %(name)s - %(levelname)s - %(message)s'
            )
            handler.setFormatter(formatter)
            self.logger.addHandler(handler)
    
    def debug_api_call(self, func_name: str = None):
        """Decorator for debugging API calls"""
        def decorator(func):
            @functools.wraps(func)
            async def async_wrapper(*args, **kwargs):
                if not self.enable_debug:
                    return await func(*args, **kwargs)
                
                call_name = func_name or func.__name__
                call_id = self._generate_call_id()
                
                # Log entry
                self.logger.debug(f"🚀 [{call_id}] Starting {call_name}")
                self.logger.debug(f"📝 [{call_id}] Args: {self._safe_repr(args)}")
                self.logger.debug(f"📝 [{call_id}] Kwargs: {self._safe_repr(kwargs)}")
                
                start_time = time.time()
                
                try:
                    result = await func(*args, **kwargs)
                    execution_time = time.time() - start_time
                    
                    self.logger.debug(f"✅ [{call_id}] Completed {call_name} in {execution_time:.3f}s")
                    self.logger.debug(f"📤 [{call_id}] Result: {self._safe_repr(result)}")
                    
                    return result
                    
                except Exception as e:
                    execution_time = time.time() - start_time
                    self.logger.debug(f"❌ [{call_id}] Failed {call_name} in {execution_time:.3f}s")
                    self.logger.debug(f"🔥 [{call_id}] Error: {str(e)}")
                    self.logger.debug(f"📚 [{call_id}] Traceback: {traceback.format_exc()}")
                    raise
            
            @functools.wraps(func)
            def sync_wrapper(*args, **kwargs):
                if not self.enable_debug:
                    return func(*args, **kwargs)
                
                call_name = func_name or func.__name__
                call_id = self._generate_call_id()
                
                self.logger.debug(f"🚀 [{call_id}] Starting {call_name}")
                start_time = time.time()
                
                try:
                    result = func(*args, **kwargs)
                    execution_time = time.time() - start_time
                    self.logger.debug(f"✅ [{call_id}] Completed {call_name} in {execution_time:.3f}s")
                    return result
                except Exception as e:
                    execution_time = time.time() - start_time
                    self.logger.debug(f"❌ [{call_id}] Failed {call_name} in {execution_time:.3f}s: {e}")
                    raise
            
            return async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper
        return decorator
    
    def debug_websocket_message(self, message: Dict[str, Any], direction: str = "received"):
        """Debug WebSocket messages"""
        if not self.enable_debug:
            return
        
        direction_emoji = "📥" if direction == "received" else "📤"
        self.logger.debug(f"{direction_emoji} WebSocket {direction}: {self._safe_repr(message)}")
    
    def debug_state_change(self, component: str, old_state: Any, new_state: Any):
        """Debug state changes"""
        if not self.enable_debug:
            return
        
        self.logger.debug(f"🔄 {component} state change: {old_state} -> {new_state}")
    
    def _generate_call_id(self) -> str:
        """Generate unique call ID for tracking"""
        import uuid
        return str(uuid.uuid4())[:8]
    
    def _safe_repr(self, obj: Any, max_length: int = 500) -> str:
        """Safe representation of objects for logging"""
        try:
            if isinstance(obj, dict):
                # Hide sensitive information
                safe_obj = {}
                for key, value in obj.items():
                    if any(sensitive in key.lower() for sensitive in ['token', 'key', 'secret', 'password']):
                        safe_obj[key] = "[HIDDEN]"
                    else:
                        safe_obj[key] = value
                repr_str = json.dumps(safe_obj, indent=2, default=str)
            else:
                repr_str = repr(obj)
            
            if len(repr_str) > max_length:
                repr_str = repr_str[:max_length] + "... [TRUNCATED]"
            
            return repr_str
        except Exception:
            return f"<Unable to represent {type(obj).__name__}>"

# Usage with AxiomTradeAPI
class DebuggableAxiomClient:
    """AxiomTradeAPI client with comprehensive debugging"""
    
    def __init__(self, auth_token: str, enable_debug: bool = False):
        self.auth_token = auth_token
        self.debug_logger = DebugLogger(enable_debug)
        self.client_id = self._generate_client_id()
        
    def _generate_client_id(self) -> str:
        import uuid
        return str(uuid.uuid4())[:8]
    
    @property
    def debug_api_call(self):
        return self.debug_logger.debug_api_call
    
    @debug_api_call("get_balance")
    async def get_balance(self, wallet_address: str) -> Dict[str, Any]:
        """Get balance with debugging"""
        # Validate input
        if not wallet_address:
            raise ValidationError("Wallet address is required")
        
        if len(wallet_address) < 32:  # Basic Solana address validation
            raise ValidationError("Invalid wallet address format")
        
        # Make API call
        return await self._make_api_request(f'/balance/{wallet_address}')
    
    @debug_api_call("websocket_connect")
    async def connect_websocket(self) -> None:
        """Connect to WebSocket with debugging"""
        self.debug_logger.debug_state_change("WebSocket", "disconnected", "connecting")
        
        try:
            # WebSocket connection logic here
            self.debug_logger.debug_state_change("WebSocket", "connecting", "connected")
        except Exception as e:
            self.debug_logger.debug_state_change("WebSocket", "connecting", "failed")
            raise WebSocketError(f"Failed to connect WebSocket: {e}")
    
    async def _make_api_request(self, endpoint: str) -> Dict[str, Any]:
        """Make API request with debug logging"""
        # This would contain actual API request logic
        pass
```

## Advanced Error Analysis {#analysis}

### Error Pattern Detection

```python
from collections import defaultdict, Counter
from datetime import datetime, timedelta
import statistics

class ErrorAnalyzer:
    """
    Advanced error pattern analysis
    Error intelligence from chipa.tech monitoring systems
    """
    
    def __init__(self, retention_hours: int = 24):
        self.retention_hours = retention_hours
        self.errors = []
        self.error_patterns = defaultdict(list)
        self.error_stats = defaultdict(int)
    
    def record_error(self, error: AxiomTradeException, context: Dict[str, Any] = None):
        """Record error for analysis"""
        error_record = {
            'timestamp': datetime.utcnow(),
            'error_type': error.error_type.value,
            'message': error.message,
            'status_code': error.status_code,
            'trace_id': error.trace_id,
            'context': context or {}
        }
        
        self.errors.append(error_record)
        self.error_stats[error.error_type.value] += 1
        
        # Clean old errors
        self._cleanup_old_errors()
        
        # Analyze patterns
        self._analyze_error_patterns()
    
    def _cleanup_old_errors(self):
        """Remove errors older than retention period"""
        cutoff_time = datetime.utcnow() - timedelta(hours=self.retention_hours)
        self.errors = [e for e in self.errors if e['timestamp'] > cutoff_time]
    
    def _analyze_error_patterns(self):
        """Analyze error patterns and trends"""
        if len(self.errors) < 10:  # Need minimum errors for analysis
            return
        
        # Group errors by time windows
        time_windows = defaultdict(list)
        for error in self.errors:
            # Group by 5-minute windows
            window = error['timestamp'].replace(minute=(error['timestamp'].minute // 5) * 5, second=0, microsecond=0)
            time_windows[window].append(error)
        
        # Detect error spikes
        error_counts = [len(errors) for errors in time_windows.values()]
        if len(error_counts) > 1:
            mean_errors = statistics.mean(error_counts)
            std_errors = statistics.stdev(error_counts) if len(error_counts) > 1 else 0
            
            for window, errors in time_windows.items():
                if len(errors) > mean_errors + (2 * std_errors):  # 2 standard deviations
                    self._alert_error_spike(window, errors)
    
    def _alert_error_spike(self, window: datetime, errors: List[Dict]):
        """Alert on error spike detection"""
        error_types = Counter(e['error_type'] for e in errors)
        
        print(f"🚨 ERROR SPIKE DETECTED at {window}")
        print(f"   Total errors: {len(errors)}")
        print(f"   Error types: {dict(error_types)}")
        
        # Additional analysis could trigger alerts to monitoring systems
    
    def get_error_summary(self) -> Dict[str, Any]:
        """Get comprehensive error summary"""
        if not self.errors:
            return {'status': 'No errors recorded'}
        
        recent_errors = [e for e in self.errors if e['timestamp'] > datetime.utcnow() - timedelta(hours=1)]
        
        error_types = Counter(e['error_type'] for e in self.errors)
        recent_error_types = Counter(e['error_type'] for e in recent_errors)
        
        # Calculate error rate trends
        hourly_counts = defaultdict(int)
        for error in self.errors:
            hour = error['timestamp'].replace(minute=0, second=0, microsecond=0)
            hourly_counts[hour] += 1
        
        return {
            'total_errors': len(self.errors),
            'recent_errors_1h': len(recent_errors),
            'error_types': dict(error_types),
            'recent_error_types': dict(recent_error_types),
            'hourly_error_trend': dict(hourly_counts),
            'most_common_error': error_types.most_common(1)[0] if error_types else None,
            'error_rate_per_hour': len(self.errors) / max(1, self.retention_hours)
        }
    
    def get_troubleshooting_suggestions(self) -> List[str]:
        """Get troubleshooting suggestions based on error patterns"""
        suggestions = []
        error_types = Counter(e['error_type'] for e in self.errors)
        
        # Authentication errors
        if error_types.get('authentication', 0) > 5:
            suggestions.append("🔐 Multiple authentication errors detected. Check your API token validity.")
            suggestions.append("🔄 Consider implementing token refresh mechanism.")
        
        # Rate limiting
        if error_types.get('rate_limit', 0) > 3:
            suggestions.append("⏱️ Rate limiting detected. Implement exponential backoff.")
            suggestions.append("🚀 Consider upgrading to higher rate limit tier.")
        
        # Network errors
        if error_types.get('network', 0) > 10:
            suggestions.append("🌐 High network error rate. Check internet connectivity.")
            suggestions.append("🔄 Implement connection pooling and retry logic.")
        
        # WebSocket errors
        if error_types.get('websocket_connection', 0) > 5:
            suggestions.append("🔌 WebSocket connection issues. Check firewall settings.")
            suggestions.append("💔 Implement WebSocket reconnection logic.")
        
        return suggestions
```

## Production Error Management {#production}

### Centralized Error Handling

```python
import os
import json
import aiofiles
from typing import Dict, Any, Optional
import httpx

class ProductionErrorHandler:
    """
    Production-grade error handling and reporting
    Enterprise error management from chipa.tech production systems
    """
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.error_analyzer = ErrorAnalyzer()
        self.logger = logging.getLogger('production_errors')
        
        # External monitoring integration
        self.webhook_url = config.get('webhook_url')
        self.slack_webhook = config.get('slack_webhook')
        self.enable_file_logging = config.get('enable_file_logging', True)
        
        if self.enable_file_logging:
            self._setup_file_logging()
    
    def _setup_file_logging(self):
        """Setup file logging for errors"""
        os.makedirs('logs', exist_ok=True)
        
        file_handler = logging.FileHandler('logs/errors.log')
        file_handler.setLevel(logging.ERROR)
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        file_handler.setFormatter(formatter)
        self.logger.addHandler(file_handler)
    
    async def handle_error(self, error: Exception, context: Dict[str, Any] = None):
        """Centralized error handling"""
        
        # Convert to AxiomTradeException if needed
        if not isinstance(error, AxiomTradeException):
            axiom_error = AxiomTradeException(
                message=str(error),
                error_type=AxiomErrorType.SYSTEM_ERROR
            )
        else:
            axiom_error = error
        
        # Record for analysis
        self.error_analyzer.record_error(axiom_error, context)
        
        # Log error
        self.logger.error(f"Error occurred: {axiom_error.to_dict()}")
        
        # Determine severity
        severity = self._determine_severity(axiom_error)
        
        # Handle based on severity
        if severity == 'critical':
            await self._handle_critical_error(axiom_error, context)
        elif severity == 'high':
            await self._handle_high_severity_error(axiom_error, context)
        elif severity == 'medium':
            await self._handle_medium_severity_error(axiom_error, context)
        
        # Send to external monitoring if configured
        if self.webhook_url:
            await self._send_to_webhook(axiom_error, context, severity)
        
        if self.slack_webhook:
            await self._send_to_slack(axiom_error, context, severity)
    
    def _determine_severity(self, error: AxiomTradeException) -> str:
        """Determine error severity"""
        
        critical_errors = [AxiomErrorType.AUTHENTICATION, AxiomErrorType.SYSTEM_ERROR]
        high_errors = [AxiomErrorType.API_ERROR, AxiomErrorType.WEBSOCKET_CONNECTION]
        medium_errors = [AxiomErrorType.RATE_LIMIT, AxiomErrorType.NETWORK]
        
        if error.error_type in critical_errors:
            return 'critical'
        elif error.error_type in high_errors:
            return 'high'
        elif error.error_type in medium_errors:
            return 'medium'
        else:
            return 'low'
    
    async def _handle_critical_error(self, error: AxiomTradeException, context: Dict[str, Any]):
        """Handle critical errors"""
        print(f"🚨 CRITICAL ERROR: {error.message}")
        
        # Save error details
        await self._save_error_details(error, context)
        
        # Could trigger emergency procedures here
        # e.g., stop trading, send alerts, etc.
    
    async def _handle_high_severity_error(self, error: AxiomTradeException, context: Dict[str, Any]):
        """Handle high severity errors"""
        print(f"⚠️ HIGH SEVERITY ERROR: {error.message}")
    
    async def _handle_medium_severity_error(self, error: AxiomTradeException, context: Dict[str, Any]):
        """Handle medium severity errors"""
        print(f"⚡ MEDIUM SEVERITY ERROR: {error.message}")
    
    async def _save_error_details(self, error: AxiomTradeException, context: Dict[str, Any]):
        """Save detailed error information"""
        error_details = {
            'error': error.to_dict(),
            'context': context,
            'system_info': {
                'python_version': sys.version,
                'platform': sys.platform,
                'timestamp': datetime.utcnow().isoformat()
            }
        }
        
        filename = f"logs/error_detail_{error.trace_id}.json"
        async with aiofiles.open(filename, 'w') as f:
            await f.write(json.dumps(error_details, indent=2, default=str))
    
    async def _send_to_webhook(self, error: AxiomTradeException, context: Dict[str, Any], severity: str):
        """Send error to external webhook"""
        try:
            payload = {
                'severity': severity,
                'error': error.to_dict(),
                'context': context,
                'timestamp': datetime.utcnow().isoformat()
            }
            
            async with httpx.AsyncClient() as client:
                response = await client.post(self.webhook_url, json=payload, timeout=10)
                response.raise_for_status()
                
        except Exception as e:
            self.logger.error(f"Failed to send error to webhook: {e}")
    
    async def _send_to_slack(self, error: AxiomTradeException, context: Dict[str, Any], severity: str):
        """Send error alert to Slack"""
        try:
            severity_emoji = {
                'critical': '🚨',
                'high': '⚠️',
                'medium': '⚡',
                'low': 'ℹ️'
            }
            
            message = {
                'text': f"{severity_emoji.get(severity, 'ℹ️')} AxiomTradeAPI Error - {severity.upper()}",
                'attachments': [
                    {
                        'color': 'danger' if severity == 'critical' else 'warning',
                        'fields': [
                            {'title': 'Error Type', 'value': error.error_type.value, 'short': True},
                            {'title': 'Message', 'value': error.message[:200], 'short': False},
                            {'title': 'Trace ID', 'value': error.trace_id, 'short': True},
                            {'title': 'Timestamp', 'value': error.timestamp.isoformat(), 'short': True}
                        ]
                    }
                ]
            }
            
            async with httpx.AsyncClient() as client:
                response = await client.post(self.slack_webhook, json=message, timeout=10)
                response.raise_for_status()
                
        except Exception as e:
            self.logger.error(f"Failed to send error to Slack: {e}")
    
    def get_health_status(self) -> Dict[str, Any]:
        """Get system health status based on error patterns"""
        error_summary = self.error_analyzer.get_error_summary()
        
        # Determine overall health
        recent_errors = error_summary.get('recent_errors_1h', 0)
        error_rate = error_summary.get('error_rate_per_hour', 0)
        
        if recent_errors > 50 or error_rate > 20:
            health_status = 'unhealthy'
        elif recent_errors > 20 or error_rate > 10:
            health_status = 'degraded'
        else:
            health_status = 'healthy'
        
        return {
            'status': health_status,
            'error_summary': error_summary,
            'troubleshooting_suggestions': self.error_analyzer.get_troubleshooting_suggestions()
        }
```

## Troubleshooting Guide {#troubleshooting}

Common issues and their solutions:

### Quick Troubleshooting Checklist

```
🔍 AxiomTradeAPI Troubleshooting Checklist
==========================================

🔐 Authentication Issues:
□ Verify API token is valid and not expired
□ Check token permissions and scopes
□ Ensure proper authorization headers
□ Test with minimal API call first

🌐 Network Connectivity:
□ Check internet connection stability
□ Verify API endpoints are accessible
□ Test with curl or similar tool
□ Check firewall and proxy settings

⚡ Performance Issues:
□ Monitor API response times
□ Check for rate limiting
□ Verify connection pooling is enabled
□ Review concurrent request limits

🔌 WebSocket Problems:
□ Test WebSocket connectivity separately
□ Check for proxy/firewall blocking WebSockets
□ Verify authentication for WebSocket connection
□ Implement reconnection logic

💾 Memory Issues:
□ Monitor memory usage over time
□ Check for memory leaks in long-running processes
□ Implement proper cleanup procedures
□ Use memory profiling tools

📊 Data Issues:
□ Validate input data format
□ Check API response format changes
□ Verify token addresses are correct
□ Handle missing or null data gracefully
```

### Error Code Reference

```python
ERROR_CODES = {
    # Authentication Errors (401, 403)
    'INVALID_TOKEN': {
        'description': 'API token is invalid or expired',
        'solution': 'Generate a new API token from your dashboard',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/authentication'
    },
    'INSUFFICIENT_PERMISSIONS': {
        'description': 'Token lacks required permissions',
        'solution': 'Update token permissions in your account settings',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/permissions'
    },
    
    # Rate Limiting (429)
    'RATE_LIMIT_EXCEEDED': {
        'description': 'Too many requests in time window',
        'solution': 'Implement exponential backoff and respect rate limits',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/rate-limits'
    },
    
    # Validation Errors (400)
    'INVALID_WALLET_ADDRESS': {
        'description': 'Wallet address format is invalid',
        'solution': 'Verify Solana address format (44 characters, base58)',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/wallet-addresses'
    },
    'INVALID_TOKEN_ADDRESS': {
        'description': 'Token address format is invalid',
        'solution': 'Check token address on Solana explorer',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/token-addresses'
    },
    'INSUFFICIENT_BALANCE': {
        'description': 'Wallet has insufficient balance for operation',
        'solution': 'Check wallet balance and add funds if needed',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/balance-management'
    },
    
    # Not Found Errors (404)
    'TOKEN_NOT_FOUND': {
        'description': 'Specified token does not exist',
        'solution': 'Verify token address on Solana blockchain',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/token-lookup'
    },
    'WALLET_NOT_FOUND': {
        'description': 'Wallet address not found or has no activity',
        'solution': 'Check wallet address and ensure it exists on-chain',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/wallet-lookup'
    },
    
    # Server Errors (500+)
    'INTERNAL_SERVER_ERROR': {
        'description': 'Internal server error occurred',
        'solution': 'Retry request with exponential backoff',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs/error-handling'
    },
    'SERVICE_UNAVAILABLE': {
        'description': 'Service temporarily unavailable',
        'solution': 'Check service status and retry later',
        'documentation': 'https://chipa.tech/status'
    }
}

def get_error_help(error_code: str) -> Dict[str, str]:
    """Get help information for specific error code"""
    return ERROR_CODES.get(error_code, {
        'description': 'Unknown error code',
        'solution': 'Check API documentation for details',
        'documentation': 'https://chipa.tech/axiomtradeapi-docs'
    })
```

## Best Practices Summary

### Error Handling Principles

1. **Fail Fast**: Detect and handle errors as early as possible
2. **Graceful Degradation**: Provide fallback behavior when possible
3. **Comprehensive Logging**: Log all errors with sufficient context
4. **User-Friendly Messages**: Provide clear, actionable error messages
5. **Monitoring Integration**: Connect errors to monitoring systems

### Production Readiness Checklist

```
✅ Production Error Handling Checklist
=====================================

🔧 Error Classification:
□ All error types properly classified
□ Custom exceptions for business logic errors
□ Appropriate error codes and messages

🔄 Retry Logic:
□ Exponential backoff implemented
□ Maximum retry limits set
□ Non-retryable errors identified

🚨 Monitoring:
□ Error tracking and alerting configured
□ Health check endpoints implemented
□ Performance metrics collected

📝 Logging:
□ Structured logging format
□ Appropriate log levels
□ Sensitive data sanitization

🧪 Testing:
□ Error scenarios covered in tests
□ Chaos engineering practices
□ Load testing with error injection
```

## Community Support

For additional help with error handling and debugging:

- 🛠️ **Technical Support**: [chipa.tech/support](https://chipa.tech/support)
- 📚 **Documentation**: [chipa.tech/axiomtradeapi-docs](https://chipa.tech/axiomtradeapi-docs)
- 💬 **Discord Community**: [chipa.tech/discord](https://chipa.tech/discord)
- 🐛 **Bug Reports**: [chipa.tech/bug-reports](https://chipa.tech/bug-reports)
- 📈 **Status Page**: [chipa.tech/status](https://chipa.tech/status)

## Conclusion

Effective error handling is crucial for building reliable trading systems. This guide provides comprehensive strategies for handling errors, debugging issues, and maintaining production stability with AxiomTradeAPI.

Key takeaways:
- Implement comprehensive error classification and handling
- Use retry logic with exponential backoff
- Set up proper monitoring and alerting
- Test error scenarios thoroughly
- Connect with the [chipa.tech community](https://chipa.tech) for support

Remember that good error handling is not just about catching exceptions—it's about providing a robust, reliable experience for your users and maintaining system stability under all conditions.

---

*This error handling guide represents production-tested practices from successful trading systems. For the latest updates and community discussions, visit [chipa.tech](https://chipa.tech).*
