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High-frequency trading is prevalent, where automated decisions must be madequickly to take advantage of price imbalances and patterns in price action thatforecast near-future movements. While many algorithms have been explored andtested, analytical methods fail to harness the whole nature of the marketenvironment by focusing on a limited domain. With the evergrowing machinelearning field, many large-scale end-to-end studies on raw data have beensuccessfully employed to increase the domain scope for profitable trading butare very difficult to replicate. Combining deep learning on the order bookswith reinforcement learning is one way of breaking down large-scale end-to-endlearning into more manageable and lightweight components for reproducibility,suitable for retail trading.  The following work focuses on forecasting returns across multiple horizonsusing order flow imbalance and training three temporal-difference learningmodels for five financial instruments to provide trading signals. Theinstruments used are two foreign exchange pairs (GBPUSD and EURUSD), twoindices (DE40 and FTSE100), and one commodity (XAUUSD). The performances ofthese 15 agents are evaluated through backtesting simulation, and successfulmodels proceed through to forward testing on a retail trading platform. Theresults prove potential but require further minimal modifications forconsistently profitable trading to fully handle retail trading costs, slippage,and spread fluctuation.

Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading