1 code implementation • 5 Jan 2024 • Zinuo You, Pengju Zhang, Jin Zheng, John Cartlidge
Stock trend classification remains a fundamental yet challenging task, owing to the intricate time-evolving dynamics between and within stocks.
1 code implementation • 3 Jan 2024 • Zinuo You, Zijian Shi, Hongbo Bo, John Cartlidge, Li Zhang, Yan Ge
Moreover, the ablation study and sensitivity study further illustrate the effectiveness of the proposed method in modeling the time-evolving inter-stock and intra-stock dynamics.
no code implementations • 28 Feb 2023 • Zijian Shi, John Cartlidge
We show that the stylised facts remain and we demonstrate order flow impact and financial herding behaviours that are in accordance with empirical observations of real markets.
no code implementations • 1 Nov 2022 • Hugo Alcaraz-Herrera, John Cartlidge
We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.
no code implementations • 4 Aug 2022 • Bingde Liu, John Cartlidge
We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions.
no code implementations • 6 Aug 2021 • Hugo Alcaraz-Herrera, John Cartlidge
We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms.
no code implementations • 1 Jul 2021 • Zijian Shi, John Cartlidge
The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies.
no code implementations • 2 Mar 2021 • Zijian Shi, Yu Chen, John Cartlidge
In an order-driven financial market, the price of a financial asset is discovered through the interaction of orders - requests to buy or sell at a particular price - that are posted to the public limit order book (LOB).
no code implementations • 5 Dec 2019 • Henry Hanifan, John Cartlidge
In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better.