Overview
Large-scale Automatic Hyperparameter Tuning for Deep Learning Jie Fu, NUS PHD (2017)
Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Book (Quantitative Finance) IIija Ilievski, NUS PHD Candidate
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 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.
