Performance Issues
Comprehensive guide to identifying, diagnosing, and resolving performance issues in the Vibe Trading platform.
Overview
Performance issues can significantly impact your trading experience and profitability. This guide covers common performance problems, diagnostic tools, optimization strategies, and best practices for maintaining optimal performance.
Common Performance Issues
Slow Order Execution
Symptoms
- Orders taking longer than expected to execute
- Delayed order confirmations
- Timeout errors during order placement
Causes
- Network latency
- Server overload
- Market volatility
- Order size too large
- Insufficient liquidity
Diagnostic Steps
import time
import requests
class OrderPerformanceMonitor:
def __init__(self):
self.order_times = []
def measure_order_execution(self, order_data):
start_time = time.time()
try:
response = requests.post('/api/v1/orders', json=order_data)
end_time = time.time()
execution_time = end_time - start_time
self.order_times.append(execution_time)
return {
'success': True,
'execution_time': execution_time,
'order_id': response.json().get('orderId')
}
except Exception as e:
end_time = time.time()
execution_time = end_time - start_time
return {
'success': False,
'execution_time': execution_time,
'error': str(e)
}
def get_performance_stats(self):
if not self.order_times:
return None
return {
'average_time': sum(self.order_times) / len(self.order_times),
'min_time': min(self.order_times),
'max_time': max(self.order_times),
'total_orders': len(self.order_times)
}
Solutions
-
Optimize Network Connection
- Use wired connection instead of WiFi
- Choose server location closest to you
- Use dedicated trading connection
-
Reduce Order Size
- Split large orders into smaller chunks
- Use limit orders instead of market orders
- Consider order timing
-
Improve Order Parameters
- Use appropriate order types
- Set realistic price limits
- Avoid peak trading hours
High Latency Issues
Symptoms
- Delayed data updates
- Slow WebSocket connections
- Lag in real-time features
Causes
- Network congestion
- Server distance
- Inefficient data processing
- Browser performance issues
Diagnostic Tools
class LatencyMonitor {
constructor() {
this.latencyMeasurements = [];
this.startTime = null;
}
startMeasurement() {
this.startTime = performance.now();
}
endMeasurement() {
if (this.startTime) {
const latency = performance.now() - this.startTime;
this.latencyMeasurements.push(latency);
this.startTime = null;
return latency;
}
return null;
}
getAverageLatency() {
if (this.latencyMeasurements.length === 0) return 0;
return this.latencyMeasurements.reduce((a, b) => a + b) / this.latencyMeasurements.length;
}
getLatencyStats() {
if (this.latencyMeasurements.length === 0) return null;
const sorted = [...this.latencyMeasurements].sort((a, b) => a - b);
return {
average: this.getAverageLatency(),
min: sorted[0],
max: sorted[sorted.length - 1],
median: sorted[Math.floor(sorted.length / 2)],
p95: sorted[Math.floor(sorted.length * 0.95)]
};
}
}
Solutions
-
Network Optimization
- Use CDN for static resources
- Implement connection pooling
- Use compression for data transfer
-
Client-Side Optimization
- Optimize JavaScript code
- Use efficient data structures
- Implement caching strategies
-
Server-Side Optimization
- Use caching layers
- Optimize database queries
- Implement load balancing
Memory Usage Issues
Symptoms
- Browser becomes slow or unresponsive
- High memory consumption
- Memory leaks in long-running sessions
Causes
- Inefficient data structures
- Memory leaks in JavaScript
- Large datasets in memory
- Poor garbage collection
Diagnostic Code
class MemoryMonitor {
constructor() {
this.memorySnapshots = [];
this.intervalId = null;
}
startMonitoring(intervalMs = 5000) {
this.intervalId = setInterval(() => {
this.takeSnapshot();
}, intervalMs);
}
stopMonitoring() {
if (this.intervalId) {
clearInterval(this.intervalId);
this.intervalId = null;
}
}
takeSnapshot() {
if (performance.memory) {
const snapshot = {
timestamp: Date.now(),
usedJSHeapSize: performance.memory.usedJSHeapSize,
totalJSHeapSize: performance.memory.totalJSHeapSize,
jsHeapSizeLimit: performance.memory.jsHeapSizeLimit
};
this.memorySnapshots.push(snapshot);
}
}
getMemoryStats() {
if (this.memorySnapshots.length < 2) return null;
const latest = this.memorySnapshots[this.memorySnapshots.length - 1];
const previous = this.memorySnapshots[this.memorySnapshots.length - 2];
return {
currentUsage: latest.usedJSHeapSize,
usageChange: latest.usedJSHeapSize - previous.usedJSHeapSize,
totalSnapshots: this.memorySnapshots.length
};
}
detectMemoryLeaks() {
if (this.memorySnapshots.length < 10) return false;
const recent = this.memorySnapshots.slice(-10);
const trend = recent.map(s => s.usedJSHeapSize);
// Check if memory usage is consistently increasing
let increasingCount = 0;
for (let i = 1; i < trend.length; i++) {
if (trend[i] > trend[i-1]) {
increasingCount++;
}
}
return increasingCount > 7; // 70% of measurements show increase
}
}
Solutions
-
Code Optimization
- Remove unused variables and functions
- Use efficient data structures
- Implement proper cleanup
-
Data Management
- Limit data retention
- Use pagination for large datasets
- Implement data compression
-
Browser Optimization
- Close unused tabs
- Clear browser cache
- Use hardware acceleration
API Performance Issues
Symptoms
- Slow API responses
- Timeout errors
- Rate limiting errors
Causes
- Inefficient API calls
- Poor request batching
- Inadequate error handling
- Network issues
Performance Optimization
import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
class OptimizedAPIClient:
def __init__(self, base_url, max_concurrent_requests=10):
self.base_url = base_url
self.max_concurrent_requests = max_concurrent_requests
self.semaphore = asyncio.Semaphore(max_concurrent_requests)
self.session = None
async def __aenter__(self):
self.session = aiohttp.ClientSession()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self.session:
await self.session.close()
async def make_request(self, endpoint, method='GET', **kwargs):
async with self.semaphore:
url = f"{self.base_url}{endpoint}"
async with self.session.request(method, url, **kwargs) as response:
return await response.json()
async def batch_requests(self, requests):
"""Execute multiple requests concurrently"""
tasks = []
for request in requests:
task = self.make_request(**request)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
return results
def optimize_requests(self, requests):
"""Optimize request batching and ordering"""
# Group requests by endpoint
grouped_requests = {}
for request in requests:
endpoint = request.get('endpoint', '')
if endpoint not in grouped_requests:
grouped_requests[endpoint] = []
grouped_requests[endpoint].append(request)
# Prioritize critical requests
critical_endpoints = ['/orders', '/account', '/positions']
optimized_requests = []
for endpoint in critical_endpoints:
if endpoint in grouped_requests:
optimized_requests.extend(grouped_requests[endpoint])
# Add remaining requests
for endpoint, requests_list in grouped_requests.items():
if endpoint not in critical_endpoints:
optimized_requests.extend(requests_list)
return optimized_requests
Performance Monitoring
Real-Time Monitoring
Performance Metrics Collection
import time
import psutil
import threading
from collections import deque
class PerformanceMonitor:
def __init__(self, max_samples=1000):
self.max_samples = max_samples
self.metrics = {
'cpu_usage': deque(maxlen=max_samples),
'memory_usage': deque(maxlen=max_samples),
'network_latency': deque(maxlen=max_samples),
'api_response_time': deque(maxlen=max_samples),
'order_execution_time': deque(maxlen=max_samples)
}
self.monitoring = False
self.monitor_thread = None
def start_monitoring(self, interval=1):
self.monitoring = True
self.monitor_thread = threading.Thread(target=self._monitor_loop, args=(interval,))
self.monitor_thread.daemon = True
self.monitor_thread.start()
def stop_monitoring(self):
self.monitoring = False
if self.monitor_thread:
self.monitor_thread.join()
def _monitor_loop(self, interval):
while self.monitoring:
self._collect_metrics()
time.sleep(interval)
def _collect_metrics(self):
# CPU usage
cpu_percent = psutil.cpu_percent()
self.metrics['cpu_usage'].append(cpu_percent)
# Memory usage
memory = psutil.virtual_memory()
self.metrics['memory_usage'].append(memory.percent)
# Network latency (simplified)
latency = self._measure_latency()
self.metrics['network_latency'].append(latency)
def _measure_latency(self):
# Simplified latency measurement
start_time = time.time()
# Simulate network request
time.sleep(0.001) # 1ms simulation
return (time.time() - start_time) * 1000 # Convert to milliseconds
def record_api_response_time(self, response_time):
self.metrics['api_response_time'].append(response_time)
def record_order_execution_time(self, execution_time):
self.metrics['order_execution_time'].append(execution_time)
def get_performance_summary(self):
summary = {}
for metric_name, values in self.metrics.items():
if values:
summary[metric_name] = {
'current': values[-1],
'average': sum(values) / len(values),
'min': min(values),
'max': max(values),
'samples': len(values)
}
return summary
def detect_performance_issues(self):
issues = []
summary = self.get_performance_summary()
# Check CPU usage
if 'cpu_usage' in summary:
if summary['cpu_usage']['current'] > 80:
issues.append('High CPU usage detected')
# Check memory usage
if 'memory_usage' in summary:
if summary['memory_usage']['current'] > 85:
issues.append('High memory usage detected')
# Check API response time
if 'api_response_time' in summary:
if summary['api_response_time']['average'] > 1000: # 1 second
issues.append('Slow API response times')
# Check order execution time
if 'order_execution_time' in summary:
if summary['order_execution_time']['average'] > 500: # 500ms
issues.append('Slow order execution')
return issues
Performance Alerts
Alert System
class PerformanceAlertSystem:
def __init__(self):
self.alert_thresholds = {
'cpu_usage': 80,
'memory_usage': 85,
'api_response_time': 1000,
'order_execution_time': 500,
'network_latency': 100
}
self.alert_history = []
self.alert_callbacks = []
def add_alert_callback(self, callback):
self.alert_callbacks.append(callback)
def check_alerts(self, metrics):
alerts = []
for metric_name, threshold in self.alert_thresholds.items():
if metric_name in metrics:
current_value = metrics[metric_name]['current']
if current_value > threshold:
alert = {
'metric': metric_name,
'value': current_value,
'threshold': threshold,
'timestamp': time.time(),
'severity': self._calculate_severity(current_value, threshold)
}
alerts.append(alert)
if alerts:
self._process_alerts(alerts)
return alerts
def _calculate_severity(self, value, threshold):
ratio = value / threshold
if ratio > 2:
return 'critical'
elif ratio > 1.5:
return 'high'
elif ratio > 1.2:
return 'medium'
else:
return 'low'
def _process_alerts(self, alerts):
for alert in alerts:
self.alert_history.append(alert)
# Call registered callbacks
for callback in self.alert_callbacks:
try:
callback(alert)
except Exception as e:
print(f"Alert callback error: {e}")
def get_alert_summary(self, hours=24):
cutoff_time = time.time() - (hours * 3600)
recent_alerts = [a for a in self.alert_history if a['timestamp'] > cutoff_time]
summary = {
'total_alerts': len(recent_alerts),
'critical_alerts': len([a for a in recent_alerts if a['severity'] == 'critical']),
'high_alerts': len([a for a in recent_alerts if a['severity'] == 'high']),
'medium_alerts': len([a for a in recent_alerts if a['severity'] == 'medium']),
'low_alerts': len([a for a in recent_alerts if a['severity'] == 'low'])
}
return summary
Optimization Strategies
Code Optimization
Efficient Data Processing
import pandas as pd
import numpy as np
from functools import lru_cache
class OptimizedDataProcessor:
def __init__(self):
self.cache = {}
@lru_cache(maxsize=128)
def calculate_technical_indicators(self, price_data_hash, window_size):
"""Calculate technical indicators with caching"""
# Simulate technical indicator calculation
prices = np.array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109])
# Simple moving average
sma = np.convolve(prices, np.ones(window_size)/window_size, mode='valid')
# RSI calculation
delta = np.diff(prices)
gain = np.where(delta > 0, delta, 0)
loss = np.where(delta < 0, -delta, 0)
avg_gain = np.mean(gain)
avg_loss = np.mean(loss)
if avg_loss == 0:
rsi = 100
else:
rs = avg_gain / avg_loss
rsi = 100 - (100 / (1 + rs))
return {
'sma': sma.tolist(),
'rsi': rsi
}
def batch_process_data(self, data_batch):
"""Process multiple data points efficiently"""
results = []
for data_point in data_batch:
# Use vectorized operations where possible
processed = self._vectorized_process(data_point)
results.append(processed)
return results
def _vectorized_process(self, data_point):
"""Use NumPy for vectorized operations"""
if isinstance(data_point, (list, tuple)):
data_array = np.array(data_point)
# Vectorized calculations
mean_val = np.mean(data_array)
std_val = np.std(data_array)
max_val = np.max(data_array)
min_val = np.min(data_array)
return {
'mean': mean_val,
'std': std_val,
'max': max_val,
'min': min_val
}
return data_point
def optimize_memory_usage(self, data):
"""Optimize memory usage for large datasets"""
if isinstance(data, pd.DataFrame):
# Convert to more memory-efficient types
for col in data.columns:
if data[col].dtype == 'object':
# Try to convert to category if possible
if data[col].nunique() / len(data) < 0.5:
data[col] = data[col].astype('category')
elif data[col].dtype == 'float64':
# Convert to float32 if precision allows
data[col] = pd.to_numeric(data[col], downcast='float')
elif data[col].dtype == 'int64':
# Convert to smaller int types if possible
data[col] = pd.to_numeric(data[col], downcast='integer')
return data
Caching Strategies
import redis
import json
from datetime import datetime, timedelta
class PerformanceCache:
def __init__(self, redis_client=None):
self.redis_client = redis_client
self.local_cache = {}
self.cache_ttl = {
'market_data': 60, # 1 minute
'ai_signals': 300, # 5 minutes
'user_data': 3600, # 1 hour
'static_data': 86400 # 24 hours
}
def get(self, key, cache_type='default'):
# Try local cache first
if key in self.local_cache:
cached_item = self.local_cache[key]
if datetime.now() < cached_item['expires']:
return cached_item['data']
else:
del self.local_cache[key]
# Try Redis cache
if self.redis_client:
try:
cached_data = self.redis_client.get(key)
if cached_data:
return json.loads(cached_data)
except Exception as e:
print(f"Redis cache error: {e}")
return None
def set(self, key, data, cache_type='default'):
ttl = self.cache_ttl.get(cache_type, 3600)
expires = datetime.now() + timedelta(seconds=ttl)
# Store in local cache
self.local_cache[key] = {
'data': data,
'expires': expires
}
# Store in Redis cache
if self.redis_client:
try:
self.redis_client.setex(key, ttl, json.dumps(data))
except Exception as e:
print(f"Redis cache error: {e}")
def invalidate(self, key):
# Remove from local cache
if key in self.local_cache:
del self.local_cache[key]
# Remove from Redis cache
if self.redis_client:
try:
self.redis_client.delete(key)
except Exception as e:
print(f"Redis cache error: {e}")
def clear_expired(self):
"""Clear expired items from local cache"""
now = datetime.now()
expired_keys = [
key for key, item in self.local_cache.items()
if now >= item['expires']
]
for key in expired_keys:
del self.local_cache[key]
Network Optimization
Connection Pooling
import aiohttp
import asyncio
from aiohttp import ClientSession, TCPConnector
class OptimizedHTTPClient:
def __init__(self, max_connections=100, max_connections_per_host=30):
self.max_connections = max_connections
self.max_connections_per_host = max_connections_per_host
self.session = None
self.connector = None
async def __aenter__(self):
# Create optimized connector
self.connector = TCPConnector(
limit=self.max_connections,
limit_per_host=self.max_connections_per_host,
keepalive_timeout=30,
enable_cleanup_closed=True
)
# Create session with optimized settings
timeout = aiohttp.ClientTimeout(total=30, connect=10)
self.session = ClientSession(
connector=self.connector,
timeout=timeout,
headers={'Connection': 'keep-alive'}
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self.session:
await self.session.close()
if self.connector:
await self.connector.close()
async def get(self, url, **kwargs):
async with self.session.get(url, **kwargs) as response:
return await response.json()
async def post(self, url, **kwargs):
async with self.session.post(url, **kwargs) as response:
return await response.json()
async def batch_requests(self, requests):
"""Execute multiple requests concurrently"""
tasks = []
for request in requests:
method = request.get('method', 'GET')
url = request.get('url')
kwargs = request.get('kwargs', {})
if method.upper() == 'GET':
task = self.get(url, **kwargs)
elif method.upper() == 'POST':
task = self.post(url, **kwargs)
else:
continue
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
return results
Best Practices
Performance Optimization Checklist
General Optimization
- ✅ Monitor Performance: Implement continuous monitoring
- ✅ Optimize Code: Use efficient algorithms and data structures
- ✅ Implement Caching: Cache frequently accessed data
- ✅ Use Compression: Compress data transfers
- ✅ Minimize Network Calls: Batch requests when possible
- ✅ Optimize Database: Use proper indexing and queries
- ✅ Implement CDN: Use content delivery networks
- ✅ Monitor Memory: Prevent memory leaks
- ✅ Use Async Operations: Implement asynchronous processing
- ✅ Regular Profiling: Profile code for bottlenecks
Trading-Specific Optimization
- ✅ Order Batching: Batch multiple orders efficiently
- ✅ Real-Time Data: Optimize WebSocket connections
- ✅ Signal Processing: Efficient AI signal processing
- ✅ Risk Management: Optimize risk calculations
- ✅ Portfolio Updates: Efficient portfolio tracking
- ✅ Market Data: Optimize market data processing
- ✅ Backtesting: Efficient historical data processing
- ✅ Alert Systems: Optimize notification delivery
- ✅ Data Storage: Efficient data persistence
- ✅ API Rate Limiting: Implement proper rate limiting
Performance Monitoring Guidelines
Key Metrics to Monitor
- Response Time: API and order execution times
- Throughput: Requests per second
- Error Rate: Percentage of failed requests
- Resource Usage: CPU, memory, network usage
- User Experience: Page load times, interaction delays
Alert Thresholds
- Critical: Response time > 5 seconds
- High: Response time > 2 seconds
- Medium: Response time > 1 second
- Low: Response time > 500ms
Regular Maintenance
- Daily: Check performance metrics
- Weekly: Review optimization opportunities
- Monthly: Conduct performance audits
- Quarterly: Update optimization strategies
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