Pattern Detection Tools
Python packages, MT5 methods, and code examples for automated candlestick pattern recognition.
Python Pattern Detection Libraries
TA-Lib
The most comprehensive technical analysis library with 60+ candlestick pattern recognition functions. Industry standard for algorithmic trading.
pip install TA-Lib61 patternsKey Features
- Industry standard used by professionals worldwide
- Blazing fast C-based engine for real-time detection
- Comprehensive coverage of 61 candlestick patterns
- Well documented with extensive community support
- Battle-tested in production trading systems
Code Example
pandas-ta
Modern pandas-native technical analysis library with intuitive candlestick pattern recognition built for Python data workflows.
pip install pandas-ta30+ patternsKey Features
- Pure Python — install anywhere with zero dependencies
- Seamless pandas DataFrame integration
- Detect all patterns at once with a single function call
- Actively maintained with regular updates
- Clean, Pythonic API design
Code Example
candlestick-patterns
Lightweight Python library specifically designed for candlestick pattern recognition with minimal overhead.
pip install candlestick-patterns20+ patternsKey Features
- Lightweight with minimal memory footprint
- Simple, intuitive API for quick integration
- Pure Python — no external dependencies required
- Focused specifically on pattern detection
- Easy to extend with custom patterns
Code Example
MT5 / MQL5 Pattern Detection
Hammer Pattern Detection
MQL5 function to detect hammer candlestick patterns programmatically in MetaTrader 5.
Engulfing Pattern Detection
Detect bullish and bearish engulfing patterns in MQL5 for automated trading.
Morning/Evening Star Detection
Detect three-candle reversal patterns for high-probability trade entries.
Pattern Scanner EA Framework
Complete Expert Advisor framework for scanning multiple candlestick patterns simultaneously.
📋 TA-Lib Pattern Functions Reference
Complete list of candlestick pattern functions available in TA-Lib:
💡 Pattern Detection Best Practices
For Python / Backtesting
- Use TA-Lib for production — fastest and most reliable
- Combine multiple patterns for stronger signals
- Filter patterns by trend direction
- Add support/resistance context
- Backtest before live trading
For MT5 Expert Advisors
- Always check bar 1 (completed candle), not bar 0
- Use tolerances for pattern detection
- Add trend filters (moving averages)
- Consider volume confirmation
- Test on Strategy Tester first