Open-ended deep reinforcement learning for a bitcoin trading bot
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Autor:in (Körperschaft)
Publikationsdatum
2025
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
2025 7th International Symposium on Computational and Business Intelligence (ISCBI)
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
40-45
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
Macau
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Cryptocurrencies have a high volatility but also provide a high potential of profit. This paper discusses the use of deep reinforcement learning in bitcoin trading aiming to optimize profit. Various parameter configurations and sampling modes were explored such as such as different batch sizes (64, 128, 256) and training frequencies (every 64, 128, 256 steps), focusing on hybrid sampling, which combines random and recent experiences from the replay memory. The results demonstrated that hybrid sampling consistently outperformed recent sampling and, under optimal parameters (batch size 256, training every 64 steps), showed significant promise across both the rising market period (01.01.2024 to 15.05.2024) and the declining market period (30.11.2017 to 30.11.2018) which were used as validation sets. However, performance declines were noted over longer periods, likely due to catastrophic forgetting. These findings suggest that while the hybrid sampling approach can enhance a trading bot’s performance, particularly in the short term, its effectiveness diminishes over time.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
2025 7th International Symposium on Computational and Business Intelligence (ISCBI)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
979-8-3315-3378-6
979-8-3315-3377-9
979-8-3315-3377-9
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Lizenz
Zitation
Altermatt, A., Dornberger, R., & Hanne, T. (2025). Open-ended deep reinforcement learning for a bitcoin trading bot. 2025 7th International Symposium on Computational and Business Intelligence (ISCBI), 40–45. https://doi.org/10.1109/ISCBI64586.2025.11015421