TY - GEN
T1 - Pattern-Based Trading by Continual Learning of Price and Volume Patterns
AU - Liston, Patrick
AU - Gretton, Charles
AU - Lensky, Artem
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024
Y1 - 2024
N2 - Automating trading decisions has been a pursuit of researchers and practitioners alike for decades. We contribute to the literature focusing on “pattern based” strategies. Dynamic time warping is used to group similar patterns into a representative category, while the method of continual learning augmentation is used to maintain the set of patterns used for decision-making. Thus, we implement a novel approach to pattern-based trading, utilising adaptive memory structures to enable adaptability of agent decision making and overall agent performance. Two new online pattern-based trading agents are introduced and tested on two-sets of historical cryptocurrency data, for the BTCUSDT pair over the periods of 2017–2023 and 2023–2024. We compare our newly formulated agents against an established baseline of rule-based agents, thereby comparing the relative profit generating abilities of a wide range of agents.
AB - Automating trading decisions has been a pursuit of researchers and practitioners alike for decades. We contribute to the literature focusing on “pattern based” strategies. Dynamic time warping is used to group similar patterns into a representative category, while the method of continual learning augmentation is used to maintain the set of patterns used for decision-making. Thus, we implement a novel approach to pattern-based trading, utilising adaptive memory structures to enable adaptability of agent decision making and overall agent performance. Two new online pattern-based trading agents are introduced and tested on two-sets of historical cryptocurrency data, for the BTCUSDT pair over the periods of 2017–2023 and 2023–2024. We compare our newly formulated agents against an established baseline of rule-based agents, thereby comparing the relative profit generating abilities of a wide range of agents.
UR - http://www.scopus.com/inward/record.url?scp=85210872046&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-0348-0_28
DO - 10.1007/978-981-96-0348-0_28
M3 - Conference contribution
AN - SCOPUS:85210872046
SN - 9789819603473
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 381
EP - 393
BT - AI (1)
A2 - Gong, Mingming
A2 - Song, Yiliao
A2 - Koh, Yun Sing
A2 - Xiang, Wei
A2 - Wang, Derui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024
Y2 - 25 November 2024 through 29 November 2024
ER -