Financial trading is essentially a search problem. The buy-side agent must find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price. The virtual space where the agents execute their trading actions is called limit-order book. We present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn to an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs. We also give a technological overview of the system and discuss the challenges and potential future work.
Ilija is a Ph.D. candidate in Deep Learning, M.Eng. in Software Engineering for Machine Learning. He is Interested in: Multimodal Deep Learning; Non-convex Optimization; (Visual) Question Answering; Natural Language Processing and Generation. He believes question answering over multimodal data is the next frontier of deep learning. Thus, his research focus is on Visual Question Answering.
As a side project, he created deeplearningtutorials.com. A place to share his experience developing deep learning methods for real-world problems, in a hope to clear up the "dark magic" surrounding the development and application of deep learning models to novel problems. Previously he obtained an M.Sc. in Software Engineering for Machine Learning, by developing an intelligent tool for Urban Data Modeling and Simulation.