Macquarie University
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Deep learning of algorithmic trading strategies & limit order book dynamics

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posted on 2022-03-28, 23:25 authored by Justin Bercich
The transcendental advancements over the past decade in automated trading systems and theoretical models of deep learning have led to the rise of machines as the primordial arbiter of decision making in financial markets. Consequently, markets organised around limit order books (LOB) have come to be dominated by algorithmic trading strategies based on objective rules derived form the market microstructure. Traders are increasingly capable of wielding deep learning tools to extract complex abstract hierarchical representations from the plethora of high-frequency financial data available. Despite these manifest changes to the trader ecosystem driven by significant investment in automated technology and algorithms, there remain only fractured frameworks through which regulators, practitioners and academics view this new complex and intricate trader environment. The objective of this thesis is to develop deep learning models capable of defining algorithmic traders in this new environment by the type of strategy the firm is conducting, analysing the impact of these strategies on UL equity market quality and modelling the nexus between algorithmic trading strategies and LOB dynamics.


Table of Contents

1. Introduction -- 2. Market microstructure & algorithmic trading -- 3. Modelling algorithmic trading using deep neural networks -- 4. Algorithmic trading strategy identification & market quality impact using unsupervised learning & axiomatic feature attribution -- 5. Predicting limit order book dynamics using deep recurrent reinforcement learning -- 6. Conclusion -- Appendix -- Bibliography.


Empirical thesis. Bibliography: pages 320-331

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Business and Economics, Macquarie Graduate School of Management

Department, Centre or School

Macquarie Graduate School of Management

Year of Award


Principal Supervisor

Andrew Lepone


Copyright Justin Bercich 2018. Copyright disclaimer:




1 online resource (x, 331 pages) diagrams, graphs, tables

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