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Mark Aron Szulyovszky
34 lines
2.0 KiB
Markdown
34 lines
2.0 KiB
Markdown
# Financial time series prediction
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And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series.
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## Why?
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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.
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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 are[qlib](https://github.com/microsoft/qlib), [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy).
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This repo is different to them in a couple of angles:
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- Feature extraction and selection is an important, pre-built step in the pipeline. Training models on
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- Training and evaluation is done in a [walk-forward manner](https://en.wikipedia.org/wiki/Walk_forward_optimization). 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.
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- 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.
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This project is inspired partially by [Marcos Lopez de Prado's Advances in Financial Machine Learning](https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086) and [The Alpha Scientist's blogposts](https://alphascientist.com/).
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## Installation
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Use the conda environment file attached!:)
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## Pipeline components
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- Feature extraction
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- Dimensionality reduction
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- Feature selection
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- Training Level-1 models
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- Training Level-2 (Ensemble) model
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- Evaluation of models
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- Cross-sectional portfolio construction [IN PROGRESS] |