Neural networks training and evaluation tool for open-high-low-close (OHLC) data forecasting.

OHLCFormer provides an easy-to-use API for model prototyping, training, and evaluation to perform open-high-low-close (OHLC) data forecasting tasks.

Getting started

You can find here a list of the official notebooks.

Notebook	Description
Data processing	How to preprocess your data and build a dataset.
Model training	How to set up and train a model on your dataset.
Models evaluation	How to benchmark models with OHLCFormer.
Logging	How to set up and use the logging components.

Model architectures

OHLCFormer currently provides the following architectures:

1. FNet (from Google Research) released with the paper FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
2. BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.