Deep learning for financial time series forecasting in A-Trader system

By Jerzy J. Korczak, Marcin Hernes. 2017

40 citations recorded by the source corpus.

The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.

Subfields: Building and Construction, Civil and Structural Engineering, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [180] implemented forex trading (GBP/PLN) using several different input parameters on a multi-agent based trading environment.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[47] USD/GBP, S&P500, FTSE100, oil, gold 2016 Price data AE + CNN SR, % volatility, avg return/trans, rate of return H2O”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “For example, Korczak and Hermes [70] presented financial time-series forecasting utilizing deep learning architectures.”