Deployment of a Smart Trading System for Intelligent Stock Trading


  • I. Ali Department of Mathematics, University of Hafr Al-Batin, 31991, Saudi Arabia
  • S.Z. Mahfooz Department of Computer Sc. & Eng., University of Hafr Al-Batin, 31991, Saudi Arabia
  • N.Q. Mehmood Department of Computer Science & Information Technology, University of Lahore, Chenab Campus, Pakistan
  • M.N. Mehmood GIFT Business School, GIFT University, Gujranwala, Pakistan


In this article we evaluate the deployment of a smart trading system that exploits the features of different technical indicators for intelligent stock trading. Depending on their behaviors, these indicators help in trading under various market conditions. Our smart trading system uses a unified trading strategy that selects five indicators from three well-known categories referred as leading, lagging, and volatility indicators. The trading system looks for common trend signals from at least three indicators within a certain period of time. Collectively generated signals from the technical indicators are used to train a neural network model. The trained neural network model is then used to produce buy and sell signals for trading in stocks. The system is efficient and convenient to use for both individual traders and fund managers. We tested the model on actual data collected from Saudi Stock Exchange and New York Stock Exchange. The performance of the model was checked in terms of percentage returns. The results of the proposed trading model were compared with the benchmark trading strategy. The deployed smart trading system is efficient to produce significant returns over the longer and shorter timeframes.


A.W. Lo, H. Mamaysky, and J. Wang, “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and empirical implementation,” The Journal of Finance, vol. 55, no. 4, pp. 1705–1765, Aug., 2000.

L. Koskinen, “Statistical models and methods for Financial Markets by Tze Leung Lai, Haipeng Xing,” International Statistical Review, vol. 77, no. 1, pp. 154–155, Apr., 2009.

Y. Chen and X. Wang, “A hybrid stock trading system using genetic network programming and mean conditional value-at-risk,” European Journal of Operational Research, vol. 240, no. 3, pp. 861–871, Feb., 2015.

B.I. Jacobs, K.N. Levy, and H.M. Markowitz, “Financial market simulation,” The Journal of Portfolio Management, vol. 30, no. 5, pp. 142–152, Jan., 2004.

V. Tanoe, “Stocks trading using relative strength index, moving average and Reinforcement Learning Techniques: A case study of apple stock index,” SSRN Electronic Journal, Dec., 2019.

J.K. Hutson, “TRIX-triple exponential smoothing oscillator,” Technical Analysis of Stocks and Commodities, pp.105-108, Jul., 1983.

J.W. Wilder, New Concepts in Technical Trading Systems. Winston-Salem, NC: Hunter Pub., 1978.

A. Antonio Agudelo Aguirre, R. Alfredo Rojas Medina, and N. Darío Duque Méndez, “Machine learning applied in the stock market through the moving average convergence divergence (MACD) indicator,” Investment Management and Financial Innovations, vol. 17, no. 4, pp. 44–60, Dec., 2020.

D. Bao and Z. Yang, “Intelligent stock trading system by turning point confirming and probabilistic reasoning,” Expert Systems with Applications, vol. 34, no. 1, pp. 620–627, Jan., 2008.

J.L. Wu, L.C. Yu, and P.C. Chang, “An intelligent stock trading system using comprehensive features,” Applied Soft Computing, vol. 23, pp. 39–50, Oct., 2014.

S. Banik, N. Sharma, M. Mangla, S.N. Mohanty, and S.S., “LSTM based decision support system for Swing Trading in Stock Market,” Knowledge-Based Systems, vol. 239, p. 107994, Mar., 2022.

S. Argade, P. Chothe, A. Gawande, S. Joshi, and A. Birajdar, “Machine learning in stock market prediction: A Review,” Available at SSRN Electronic Journal 4128716, Jun., 2022.

W. Wang and K.K. Mishra, “A novel stock trading prediction and recommendation system,” Multimedia Tools and Applications, vol. 77, no. 4, pp. 4203–4215, 2017.

G.S. Atsalakis and K.P. Valavanis, “Surveying stock market forecasting techniques – part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941, 2009.

T. Chavarnakul, D. Enke, “A hybrid stock trading system for intelligent technical analysis-based equivolume charting,” Neurocomputing, vol. 72, no. 16-18, pp. 3517-3528, 2009.

X. Lin, Z. Yang, and Y. Song, “Intelligent stock trading system based on improved technical analysis and Echo State Network,” Expert Systems with Applications, vol. 38, no. 9, pp. 11347–11354, 2011.

S. Duz Tan and O. Tas, “Social media sentiment in international stock returns and trading activity,” Journal of Behavioral Finance, vol. 22, no. 2, pp. 221–234, 2020.

D. Shah, H. Isah, and F. Zulkernine, “Predicting the effects of news sentiments on the stock market,” 2018 IEEE International Conference on Big Data (Big Data), pp. 4705-4708, Dec., 2018.

S.M. Huang, Y.C. Hung, and D.C. Yen, “A study on decision factors in adopting an online stock trading system by brokers in Taiwan,” Decision Support Systems, vol. 40, no. 2, pp. 315–328, 2005.

A.C. Briza and P.C. Naval, “Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data,” Applied Soft Computing, vol. 11, no. 1, pp. 1191–1201, 2011.

O.B. Sezer, A.M. Ozbayoglu, and E. Dogdu, “An artificial neural network-based stock trading system using technical analysis and Big Data Framework,” Proceedings of the SouthEast Conference, 2017.

T. Han, Q. Peng, Z. Zhu, Y. Shen, H. Huang, and N.N. Abid, “A pattern representation of stock time series based on DTW,” Physica A: Statistical Mechanics and its Applications, vol. 550, p. 124161, 2020.

M. Vogl, P.G. Rötzel, “Chaoticity versus stochasticity in financial markets: Are daily S&P 500 return dynamics chaotic?”. Communications in Nonlinear Science and Numerical Simulation, vol. 108, 106218, May., 2022.

S.Z. Mahfooz, I. Ali, and M.N. Khan, “Improving stock trend prediction using LSTM neural network trained on a complex trading strategy,” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 7, pp. 4361–4371, 2022.

M. Obthong, N. Tantisantiwong, W. Jeamwatthanachai, and G. Wills, “A survey on machine learning for stock price prediction: Algorithms and techniques,” Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business, pp. 63-71, May., 2020.

A.S. Girsang, F. Lioexander, D. Tanjung, “Stock price prediction using lstm and search economics optimization,” IAENG International Journal of Computer Science, 47(4), 758-764, 2020.

A. Moghar and M. Hamiche, “Stock market prediction using LSTM recurrent neural network,” Procedia Computer Science, vol. 170, pp. 1168–1173, 2020.

X. Yan, W. Wang, M. Chang. "Research on financial assets transaction prediction model based on LSTM neural network," Neural Computing and Applications, vol. 33, no. 1, pp. 257-270, 2021.

S. Dami and M. Esterabi, “Predicting stock returns of Tehran Exchange using LSTM neural network and feature engineering technique,” Multimedia Tools and Applications, vol. 80, no. 13, pp. 19947–19970, 2021.

Yahoo Finance [Online]. Available:

Saudi Exchange [Online]. Available:




How to Cite

Ali, I., Mahfooz, S., Mehmood, N., & Mehmood, M. (2022). Deployment of a Smart Trading System for Intelligent Stock Trading. The Nucleus, 60(1-2), 1–8. Retrieved from