Feature Engineering and Feature Importance in Machine Learning for Financial Markets

Background knowledge for Feature Analysis in Finance

Technical Indicators

• I studied over 80 technical indicators.

  • 1. Technical Indicators - Volume.ipynb
  • 2. Technical Indicators - Volatility.ipynb
  • 3. Technical Indicators - Trend.ipynb
  • 4. Technical Indicators - Momentum.ipynb
  • 5. Using technical indicators in Meta-labeling.ipynb

• Technical Indicators Binary Matrix

  • for input

Old ones

• T.I. Analysis (old version)
  • TI Analysis
• Is TA better than simple market data?
  • TI vs. Simple

Feature Importance

• Which one is important? with MDI
  • Correlation with the PC (principle component) of highest MDI

Feature Engineering (.. in progress)

• Deep Autoencoder
• CNN architecture
• FinEmbedding

Data

• High Frequency Cryptos Prices
• Daily Stock Prices

Other example

• ML trading rule
• Simple trading strategies using technical indicators

References

• De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
  • Chapter 8 Feature Importance
• Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in Finance (Vol. 1170). Berlin and Heidelberg: Springer International Publishing.
  • Chapter 5. Interpretability
  • Chapter 8. 6. Autoencoders
• Jansen, S. (2018). Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python. Packt Publishing Ltd.
  • Chapter 4: Financial Feature Engineering
• Python library ta (https://github.com/bukosabino/ta)