Real-Time Stock Market Forecasting using Ensemble Deep Learning and Rainbow DQN

7 Pages Posted: 28 Apr 2020

See all articles by Raj Shah

Raj Shah

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT)

Ashutosh Tambe

University of Mumbai - Department of Information Technology

Tej Bhatt

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT)

Uday Rote

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT)

Date Written: April 8, 2020

Abstract

After years of study by researchers and finance experts on stock market prediction, there is no definite method that seems to predict stock price both accurately and is long- lasting at the same time. This is due to the uncertain behavior of stock movement and numerous parameters that take part in deciding market performance. Lots of techniques have been examined for stock market price prediction. Also, various comparative studies have been done to find the best techniques which can help traders make decisions. This paper proposes the use of ensemble techniques like Rainbow DQN, LSTM, and GRU for real time stock market forecasting and indicating buy/sell signals. The results of these techniques used were closely examined keeping a check on overfitting. The performance of the techniques was evaluated using accuracy, return of investment. Few well-known indicators like MACD and RSI were also used for verifying results. Tests were run on NIFTY50, Microsoft and Google with real-time data structured in 1-minute intervals.

Keywords: Rainbow Deep Q Network, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI)

Suggested Citation

Shah, Raj and Tambe, Ashutosh and Bhatt, Tej and Rote, Uday, Real-Time Stock Market Forecasting using Ensemble Deep Learning and Rainbow DQN (April 8, 2020). Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST) 2020, Available at SSRN: https://ssrn.com/abstract=3586788 or http://dx.doi.org/10.2139/ssrn.3586788

Raj Shah

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT)

Vidyavihar
Mumbai, Maharashtra 400077
India

Ashutosh Tambe (Contact Author)

University of Mumbai - Department of Information Technology ( email )

Somaiya Ayurvihar Complex
Eastern Express Highway
Mumbai, 400022
India

Tej Bhatt

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT) ( email )

Somaiya Ayurvihar Complex
Eastern Express Highway
Mumbai, MA Maharashtra 400022
India

Uday Rote

University of Mumbai - K. J. Somaiya Institute of Engineering and Information Technology (KJSIEIT) ( email )

Somaiya Ayurvihar Complex
Eastern Express Highway
Mumbai, MA Maharashtra 400022
India

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