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Article

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

STMicroelectronics S.r.l.–ADG Central R&D Group, 95121 Catania, Italy
Appl. Sci. 2019, 9(20), 4460; https://doi.org/10.3390/app9204460
Received: 13 September 2019 / Revised: 15 October 2019 / Accepted: 17 October 2019 / Published: 21 October 2019
High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability. View Full-Text
Keywords: STM32; financial; deep learning; LSTM; reinforcement learning STM32; financial; deep learning; LSTM; reinforcement learning
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MDPI and ACS Style

Rundo, F. Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems. Appl. Sci. 2019, 9, 4460. https://doi.org/10.3390/app9204460

AMA Style

Rundo F. Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems. Applied Sciences. 2019; 9(20):4460. https://doi.org/10.3390/app9204460

Chicago/Turabian Style

Rundo, Francesco. 2019. "Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems" Applied Sciences 9, no. 20: 4460. https://doi.org/10.3390/app9204460

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