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Deep Reinforcement Learning for Financial Trading Using Price Trailing

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
KustantajaIEEE
Sivut3067-3071
Sivumäärä5
ISBN (elektroninen)9781479981311
DOI - pysyväislinkit
TilaJulkaistu - 1 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia
Kesto: 12 toukokuuta 201917 toukokuuta 2019

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
MaaIso-Britannia
KaupunkiBrighton
Ajanjakso12/05/1917/05/19

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.

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