# Exploring the Advantages of Transformers for High-Frequency Trading

## TL;DR

This paper introduces a novel Transformer-based model called HFformer for high-frequency Bitcoin trading, demonstrating superior performance over LSTM models in backtesting scenarios.

## Contribution

The paper presents HFformer, a hybrid Transformer architecture tailored for high-frequency trading, which outperforms traditional LSTM models in forecasting and profit generation.

## Key findings

- HFformer achieves higher cumulative PnL than LSTM in backtests.
- The model effectively incorporates spiking activations and quantile loss without position encoding.
- Results suggest Transformers are advantageous for high-frequency trading applications.

## Abstract

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.