Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

The thesis investigates the usage of Informer architecture for building automated

trading strategies for high frequency Bitcoin data. Two strategies using Informer models with

different loss functions, Quantile loss and Generalized Mean Absolute Directional Loss

(GMADL), are proposed and evaluated against the Buy and Hold benchmark and two benchmark

strategies based on technical indicators. The evaluation is conducted using data of various

frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although

the Informer-based model with Quantile loss did not manage to outperform the benchmark, the

model that uses novel GMADL loss function turned out to be benefiting from higher frequency

data and beat all the other strategies on most of the testing periods. The primary contribution of

this study is the application and assessment of the Quantile and GMADL loss functions with the

Informer model to forecast future returns, subsequently using these forecasts to develop

automated trading strategies. The research provides evidence that employing an Informer model

trained with the GMADL loss function can result in superior trading outcomes compared to the

buy-and-hold approach.