Exploring the Advantages of Transformers for High-Frequency Trading

Fazl Barez, Paul Bilokon, Arthur Gervais and Nikita Lisitsyn

Papers from arXiv.org

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.

Date: 2023-02
New Economics Papers: this item is included in nep-big, nep-mst and nep-pay
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