Financial time series prediction

And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series.

Why?

Machine learning on financial time series require a fundamentally different approach than used in other ML domains. The data is non-stationary, where the patterns frequently change, and it's extremely important to not to leak out-of-sample data into the training set objective evaluation is important.

There are very few open-source end-to-end machine learning pipelines that can be effectively used to train and evaluate ML models on financial time series. Among them areqlib, AlphaPy.

This repo is different to them in a couple of angles:

• Feature extraction and selection is an important, pre-built step in the pipeline. Training models on
• Training and evaluation is done in a walk-forward manner. We argue that that one or two train/test split is not adoquate to evaluate an ML model's performance in a non-stationary, regime changing environment. The walk-forward methodology enables us to evaluate the model's performance on almost the whole time series.
• The walk-forward training/evaluation methodology enables "online" (ever-changing) models, that adapt to the market environment. You can specify how frequently would you like to re-train the models.

This project is inspired partially by Marcos Lopez de Prado's Advances in Financial Machine Learning and The Alpha Scientist's blogposts.

Installation

Use the conda environment file attached!:)

Pipeline components

• Feature extraction
• Dimensionality reduction
• Feature selection
• Training Level-1 models
• Training Level-2 (Ensemble) model
• Evaluation of models
• Cross-sectional portfolio construction [IN PROGRESS]