TY - GEN
T1 - Pattern-Based Trading by Continual Learning of Price and Volume Patterns
AU - Liston, Patrick
AU - Gretton, Charles
AU - Lensky, Artem
N1 - © 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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 - https://www.scopus.com/pages/publications/85210872046
U2 - 10.1007/978-981-96-0348-0_28
DO - 10.1007/978-981-96-0348-0_28
M3 - Conference Paper
AN - SCOPUS:85210872046
SN - 9789819603473
VL - Part 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 381
EP - 393
BT - AI 2024
A2 - Gong, Mingming
A2 - Song, Yiliao
A2 - Koh, Yun Sing
A2 - Xiang, Wei
A2 - Wang, Derui
PB - Springer Science+Business Media B.V.
T2 - 37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024
Y2 - 25 November 2024 through 29 November 2024
ER -