Feature selection and engineering
In this section students need to decide which features are helpful in predicting the target
variable – for example, serial correlation, momentum, technical analysis indicators (such as
RSI), and signals from trend-following strategies (such as the moving average crossover).
- Select at least four explanatory variables and perform the necessary transformations
so that they are useful in the model phase. You are encouraged to use more than four
variables. Investigate feature engineering techniques such as PCA and encoding
target variables using one-hot encoding.
- Write a short paragraph about each technique investigated and show an implementation of it in a Jupyter Notebook. Make sure to include references that
indicate where the ideas were sourced.
- At this stage groups should take the opportunity to familiarize themselves with the
cross-validation techniques for forecasting financial time series – for example,
traditional k-fold cross-validation versus walk forward analysis, and Purged K-Fold
CV. Write a short paragraph explaining each technique researched. Research at least
three (they don’t have to be the 3 mentioned here).
The following techniques are covered in Dr Lopéz de Prado’s book (an implementation of the
first and second techniques can be found on Github, and a relevant blog post can be found
- The triple-barrier method (Labeling)
- Fractionally Differentiated Features
The following papers provide insights into using technical analysis for features:
1 Kim, K.J. (2003). ‘Financial Time Series Forecasting Using Support Vector Machines’.
Neurocomputing, 55(1-2), pp.307-319.
2 Patel, J., Shah, S., Thakkar, P. and Kotecha, K. (2015). ‘Predicting Stock Market Index
Using Fusion of Machine Learning Techniques’. Expert Systems with Applications,
3 Patel, J., Shah, S., Thakkar, P. and Kotecha, K. (2015). ‘Predicting Stock And Stock Price
Index Movement Using Trend Deterministic Data Preparation and Machine Learning
Techniques’. Expert Systems with Applications, 42(1), pp.259-268.
4 Kara, Y., Boyacioglu, M.A. and Baykan, Ö.K. (2011). ‘Predicting Direction of Stock Price
Index Movement Using Artificial Neural Networks And Support Vector Machines: The
Sample of The Istanbul Stock Exchange’. Expert systems with Applications, 38(5),
PCA as a technique was covered in Module 2.
There are also many blogs that provide some insights:
4 Robot Wealth