This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model, called \textbf{HFformer}, is then introduced for time series forecasting which incorporates a Transformer encoder, linear decoder, spiking activations, and quantile loss function, and does not use position encoding. Furthermore, possible high-frequency trading strategies for use with the HFformer model are discussed, including trade sizing, trading signal aggregation, and minimal trading threshold. Ultimately, the performance of the HFformer and Long Short-Term Memory models are assessed and results indicate that the HFformer achieves a higher cumulative PnL than the LSTM when trading with multiple signals during backtesting.
@article{arxiv.2302.13850,
title = {Exploring the Advantages of Transformers for High-Frequency Trading},
author = {Fazl Barez and Paul Bilokon and Arthur Gervais and Nikita Lisitsyn},
journal= {arXiv preprint arXiv:2302.13850},
year = {2023}
}