Performance Optimization Guide¶

Last Updated: December 2025
Status: Performance Best Practices

This guide covers performance optimization, scaling considerations, and performance tuning for the Trading System.

Overview¶

Performance is critical for trading systems, where milliseconds can matter. This guide provides optimization strategies, profiling techniques, and scaling considerations.

Database Performance¶

Indexing Strategy¶

1. Primary Indexes:
2. Ensure primary keys are indexed
3. Index foreign keys

4. Index frequently queried columns

5. Query Optimization:

   -- Add indexes for common queries
   CREATE INDEX idx_market_data_symbol_timestamp 
   ON data_ingestion.market_data(symbol, timestamp);
   
   CREATE INDEX idx_market_data_date 
   ON data_ingestion.market_data(date);
   

6. Composite Indexes:

7. Use composite indexes for multi-column queries
8. Order columns by selectivity
9. Monitor index usage

Query Optimization¶

1. Efficient Queries:
2. Use EXPLAIN ANALYZE to understand query plans
3. Avoid N+1 query problems

4. Use eager loading when appropriate

5. Batch Operations:

   # Use bulk inserts instead of individual inserts
   session.bulk_insert_mappings(MarketData, data_list)
   

6. Pagination:

7. Always paginate large result sets
8. Use cursor-based pagination for large datasets
9. Limit result sets appropriately

Connection Pooling¶

1. Pool Configuration:

   # Configure connection pool
   engine = create_engine(
       DATABASE_URL,
       pool_size=10,
       max_overflow=20,
       pool_pre_ping=True
   )
   

2. Pool Monitoring:

3. Monitor pool usage
4. Adjust pool size based on load
5. Use connection pooling appropriately

Data Ingestion Performance¶

Batch Processing¶

1. Batch Sizes:
2. Optimize batch sizes for your use case
3. Balance between memory and API limits

4. Test different batch sizes

5. Parallel Processing:

   # Use async for parallel requests
   async def load_multiple_symbols(symbols):
       tasks = [load_symbol(s) for s in symbols]
       await asyncio.gather(*tasks)
   

Rate Limiting¶

1. Respect API Limits:
2. Implement proper rate limiting
3. Use exponential backoff

4. Cache responses when possible

5. Efficient API Usage:

6. Batch API requests when possible
7. Use incremental updates
8. Avoid redundant requests

Caching Strategy¶

1. Redis Caching:
2. Cache frequently accessed data
3. Set appropriate TTL values

4. Use cache invalidation strategies

5. Application Caching:

   # Cache expensive computations
   @lru_cache(maxsize=128)
   def expensive_calculation(symbol):
       # Expensive operation
       pass
   

API Performance¶

FastAPI Optimization¶

1. Async Operations:
2. Use async/await for I/O operations
3. Leverage FastAPI's async capabilities

4. Avoid blocking operations

5. Response Caching:

   @router.get("/api/data/{symbol}")
   @cache(expire=300)  # Cache for 5 minutes
   async def get_data(symbol: str):
       return await fetch_data(symbol)
   

6. Response Compression:

7. Enable gzip compression
8. Compress large responses
9. Use appropriate content types

Database Query Optimization¶

1. Selective Queries:

   # Only select needed columns
   session.query(MarketData.close, MarketData.volume)\
          .filter(MarketData.symbol == symbol)\
          .all()
   

2. Query Result Caching:

3. Cache query results in Redis
4. Invalidate cache on updates
5. Use appropriate cache keys

Streamlit Performance¶

Page Load Optimization¶

1. Lazy Loading:
2. Load data on demand
3. Use pagination for large tables

4. Defer heavy computations

5. Session State:

6. Cache data in session state
7. Avoid reloading unchanged data
8. Clear unused session state

Chart Performance¶

1. Plotly Optimization:
2. Limit data points in charts
3. Use downsampling for long time series

4. Optimize chart configurations

5. Data Aggregation:

   # Aggregate data before plotting
   df_daily = df.resample('D').agg({
       'close': 'last',
       'volume': 'sum'
   })
   

Computational Performance¶

Indicator Calculation¶

1. Vectorization:
2. Use pandas/numpy vectorized operations
3. Avoid Python loops when possible

4. Leverage NumPy for calculations

5. Batch Processing:

   # Calculate indicators for multiple symbols
   symbols_batch = symbols[:100]
   indicators = calculate_indicators_batch(symbols_batch)
   

Memory Management¶

1. Memory Efficiency:
2. Use appropriate data types
3. Release memory when done

4. Monitor memory usage

5. Data Chunking:

   # Process data in chunks
   for chunk in pd.read_csv(file, chunksize=10000):
       process_chunk(chunk)
   

Profiling & Monitoring¶

Performance Profiling¶

1. Python Profiling:

   # Use cProfile for profiling
   import cProfile
   cProfile.run('your_function()')
   

2. Database Profiling:

3. Use PostgreSQL EXPLAIN ANALYZE
4. Monitor slow queries
5. Set up query logging

Monitoring Tools¶

1. Application Monitoring:
2. Monitor API response times
3. Track database query times

4. Watch memory usage

5. Database Monitoring:

6. Monitor connection pool usage
7. Track slow queries
8. Watch table sizes

Scaling Considerations¶

Vertical Scaling¶

1. Resource Allocation:
2. Increase database memory
3. Add more CPU cores

4. Upgrade hardware as needed

5. Configuration Tuning:

6. Tune PostgreSQL settings
7. Optimize connection pools
8. Configure worker processes

Horizontal Scaling¶

1. Service Scaling:
2. Scale services independently
3. Use load balancing

4. Implement service discovery

5. Database Scaling:

6. Consider read replicas
7. Implement partitioning
8. Use connection pooling

Prefect Workflow Scaling¶

1. Worker Configuration:
2. Configure worker pools appropriately
3. Scale workers based on load

4. Use appropriate resources

5. Flow Optimization:

6. Optimize flow execution
7. Use parallel tasks
8. Minimize dependencies

Best Practices Summary¶

Database Performance ✅¶

• ✅ Use appropriate indexes
• ✅ Optimize queries with EXPLAIN
• ✅ Use connection pooling
• ✅ Batch database operations
• ✅ Paginate large result sets
• ✅ Monitor slow queries

API Performance ✅¶

• ✅ Use async operations
• ✅ Implement response caching
• ✅ Enable compression
• ✅ Optimize database queries
• ✅ Use selective queries
• ✅ Monitor response times

Data Processing ✅¶

• ✅ Use batch processing
• ✅ Implement parallel processing
• ✅ Cache frequently accessed data
• ✅ Use vectorized operations
• ✅ Optimize memory usage
• ✅ Profile performance bottlenecks

Monitoring ✅¶

• ✅ Profile application code
• ✅ Monitor database performance
• ✅ Track API response times
• ✅ Watch resource usage
• ✅ Set up alerts
• ✅ Review logs regularly

Performance Testing¶

Load Testing¶

1. Tools:
2. Use locust for API load testing
3. Test database under load

4. Monitor system resources

5. Metrics:

6. Response times
7. Throughput
8. Error rates
9. Resource usage

Benchmarking¶

1. Baseline Metrics:
2. Establish performance baselines
3. Track improvements

4. Compare against targets

5. Regular Testing:

6. Run performance tests regularly
7. Test after major changes
8. Monitor regression

Troubleshooting Performance Issues¶

Common Issues¶

1. Slow Queries:
2. Check query plans
3. Add missing indexes

4. Optimize query logic

5. High Memory Usage:

6. Identify memory leaks
7. Optimize data structures

8. Use generators

9. API Slowdowns:

10. Check database queries
11. Review caching strategy
12. Monitor external APIs

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Remember: Performance optimization is an iterative process. Profile first, optimize based on data, and measure improvements.