Dr hab. Robert Ślepaczuk, prof. ucz. oraz Filip Stefaniuk z wykładem - 27.01 br.

Prelekcja poświęcona będzie zagadnieniu “Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data”, a odbędzie się 27 stycznia (poniedziałek) o godz. 18:30.

W seminarium można uczestniczyć stacjonarnie – na WNE UW (w s. B002) oraz zdalnie (na platformie Zoom – wówczas poprosimy o zalogowanie możliwie do godz. 18:25).

[Link do spotkania: https://uw-edu-pl.zoom.us/j/93886973103?pwd=2S2L5mY29vEPl3VGAasjTQlB1iwMHu.1

Identyfikator spotkania: 938 8697 3103,

Kod dostępu: 716724]

Zachęcamy do zapoznania się z abstraktem planowanego wykładu [ENG]:

The paper 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.