Deep Reinforcement Learning for Financial Trading Using Price Trailing
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings |
| Kustantaja | IEEE |
| Sivut | 3067-3071 |
| Sivumäärä | 5 |
| ISBN (elektroninen) | 9781479981311 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 1 toukokuuta 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia Kesto: 12 toukokuuta 2019 → 17 toukokuuta 2019 |
Conference
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing |
|---|---|
| Maa | Iso-Britannia |
| Kaupunki | Brighton |
| Ajanjakso | 12/05/19 → 17/05/19 |
Tiivistelmä
Developing accurate financial analysis tools can be useful both for speculative trading, as well as for analyzing the behavior of markets and promptly responding to unstable conditions ensuring the smooth operation of the financial markets. This led to the development of various methods for analyzing and forecasting the behaviour of financial assets, ranging from traditional quantitative finance to more modern machine learning approaches. However, the volatile and unstable behavior of financial markets forbids the accurate prediction of future prices, reducing the performance of these approaches. In contrast, in this paper we propose a novel price trailing method that goes beyond traditional price forecasting by reformulating trading as a control problem, effectively overcoming the aforementioned limitations. The proposed method leads to developing robust agents that can withstand large amounts of noise, while still capturing the price trends and allowing for taking profitable decisions.