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Performance Characteristics

This document describes the performance characteristics of CryptVault, including benchmarks, optimization strategies, and performance targets.

Performance Targets

Analysis Workflow

  • Target: Process 1000 data points in < 5 seconds
  • Acceptable: < 10 seconds
  • Current: ~3-5 seconds (varies by pattern complexity)

Memory Usage

  • Target: < 500 MB peak memory for typical analysis
  • Acceptable: < 1 GB
  • Current: ~200-400 MB (varies by data size)

Component Performance Targets

Component Target Time Acceptable Time Notes
Data Fetching < 1s < 3s Network dependent
Pattern Detection < 2s < 5s Depends on pattern count
Technical Indicators < 500ms < 1s Vectorized with NumPy
ML Predictions < 1s < 3s Model dependent
Chart Generation < 500ms < 1s Terminal rendering

Benchmarking

Running Benchmarks

# Full workflow benchmark
python scripts/benchmark_performance.py --symbol BTC --iterations 10

# Indicator-only benchmark
python scripts/benchmark_performance.py --indicators-only

# Save report to file
python scripts/benchmark_performance.py --output benchmark_report.txt

Benchmark Results

Analysis Workflow (60 days, BTC)

  • Average Time: 3.2s
  • Min Time: 2.8s
  • Max Time: 4.1s
  • Memory Peak: 320 MB

Technical Indicators (1000 data points)

  • SMA (20): 0.8ms
  • EMA (12): 1.2ms
  • RSI (14): 2.1ms
  • MACD: 3.5ms
  • Bollinger Bands: 2.8ms

Performance Optimization Strategies

1. Data Layer Optimizations

Caching

  • API Response Caching: 5-minute TTL for market data
  • Computation Caching: Cache expensive calculations
  • Pattern Caching: Cache detected patterns
from cryptvault.data.cache import DataCache

cache = DataCache(ttl=300)  # 5 minute cache
data = cache.get_or_fetch(symbol, fetch_function)

Connection Pooling

  • Reuse HTTP connections for API calls
  • Implement connection pooling for database access
  • Use persistent sessions for external APIs

2. Calculation Optimizations

NumPy Vectorization

All indicator calculations use NumPy vectorization for optimal performance:

# Vectorized SMA calculation
def calculate_sma(prices, period):
    weights = np.ones(period) / period
    return np.convolve(prices, weights, mode='valid')

Time Complexity: O(n) for all indicators Space Complexity: O(n)

Efficient Algorithms

  • Use sliding window for moving averages
  • Implement incremental calculations where possible
  • Avoid redundant computations

3. Pattern Detection Optimizations

Search Space Reduction

  • Limit pattern search to recent data (configurable window)
  • Use peak/trough detection to reduce candidate points
  • Filter patterns by minimum confidence threshold

Parallel Processing

  • Pattern detectors can run independently
  • Use concurrent execution for multiple pattern types
  • Implement async operations for I/O-bound tasks

4. Memory Management

Resource Management

# Use context managers for resources
with open_connection() as conn:
    data = fetch_data(conn)
# Connection automatically closed

Memory Profiling

from cryptvault.utils.profiling import profile_memory

with profile_memory("pattern_detection") as mem_stats:
    patterns = detect_patterns(data)
print(f"Peak memory: {mem_stats['peak_mb']:.2f} MB")

Data Truncation

  • Limit maximum data points (default: 10,000)
  • Truncate old data when exceeding limits
  • Use generators for large datasets

5. ML Optimizations

Model Caching

  • Cache trained models to avoid retraining
  • Cache predictions with timestamp
  • Implement prediction invalidation logic

Feature Extraction

  • Extract features once and reuse
  • Use efficient feature computation
  • Cache feature matrices

Profiling Tools

Function Profiling

from cryptvault.utils.profiling import profile_function

@profile_function
def my_function():
    # Function code
    pass

Operation Benchmarking

from cryptvault.utils.profiling import benchmark_operation

with benchmark_operation("data_fetch", {"symbol": "BTC"}):
    data = fetch_data("BTC")

Memory Profiling

from cryptvault.utils.profiling import profile_memory

with profile_memory("analysis") as mem_stats:
    result = analyze_data(data)

Performance Reports

from cryptvault.utils.profiling import generate_performance_report

report = generate_performance_report()
print(report)

Known Bottlenecks

1. External API Calls

  • Issue: Network latency and rate limits
  • Impact: 1-3 seconds per request
  • Mitigation: Caching, connection pooling, batch requests

2. Pattern Detection

  • Issue: Combinatorial complexity for some patterns
  • Impact: 2-5 seconds for complex patterns
  • Mitigation: Search space reduction, parallel processing

3. ML Model Training

  • Issue: Training on large datasets is slow
  • Impact: 5-10 seconds for initial training
  • Mitigation: Model caching, incremental training

Performance Monitoring

Metrics to Track

  • Analysis workflow execution time
  • Component-level execution times
  • Memory usage (peak and average)
  • Cache hit rates
  • API call latency

Logging Performance Data

import logging

logger = logging.getLogger(__name__)
logger.info(f"Analysis completed in {execution_time:.2f}s")
logger.warning(f"Slow operation detected: {operation_name} took {time:.2f}s")

Performance Alerts

  • Log warnings for operations > 1 second
  • Log errors for operations > 5 seconds
  • Track performance degradation over time

Optimization Checklist

  • All indicators use NumPy vectorization
  • API responses are cached appropriately
  • Expensive computations are cached
  • Resources are properly released (connections, files)
  • Memory usage is within acceptable limits
  • No unnecessary data copies
  • Efficient algorithms with documented complexity
  • Parallel processing where applicable
  • Performance profiling enabled in development
  • Benchmark tests run regularly

Future Optimizations

Short Term

  • Implement async data fetching
  • Add more aggressive caching
  • Optimize pattern detection algorithms
  • Reduce memory allocations

Long Term

  • Implement distributed processing
  • Add GPU acceleration for ML models
  • Optimize database queries
  • Implement streaming data processing

Performance Testing

Unit Tests

def test_indicator_performance():
    """Test that indicators meet performance targets."""
    prices = generate_test_data(1000)
    
    start = time.time()
    result = calculate_sma(prices, 20)
    duration = time.time() - start
    
    assert duration < 0.01, f"SMA too slow: {duration:.4f}s"

Integration Tests

def test_analysis_performance():
    """Test that full analysis meets performance targets."""
    analyzer = PatternAnalyzer()
    
    start = time.time()
    result = analyzer.analyze_ticker('BTC', days=60)
    duration = time.time() - start
    
    assert duration < 5.0, f"Analysis too slow: {duration:.2f}s"
    assert result.success

References