Intelligent Stock Trading Strategy Based on Aroon Indicator

Authors

  •   Arjun Sing Saud Assistant Professor, Central Department of Computer Science and IT, Tribhuvan University Kathmandu, Bagmati - 44613
  •   Subarna Shakya Professor, Department of Electronics and Computer Engineering, IOE, Tribhuvan University, Lalitpur, Bagmati - 44700
  •   Bindu Neupane Lecturer, Nagarjuna College of IT, Tribhuvan University, Lalitpur, Bagmati - 44700

DOI:

https://doi.org/10.17010/ijrcm/2022/v9i1/170401

Keywords:

Intelligent stock trading

, Aroon indicator, trading signals, algorithmic stock trading.

Paper Submission Date

, October 21, 2021, Paper sent back for Revision, February 9, 2022, Paper Acceptance Date, February 25, 2022.

Abstract

The study of stock trading signal forecasting has piqued the interest of machine learning and technical analysis specialists. One of the popular tools for anticipating buy and sell signals is the Aroon indicator, but it is not utilized by machine learning researchers to predict stock trading signals. This study proposed an intelligent stock trading strategy based on the association between Aroon indicators. The performance of the proposed stock trading strategy was compared to that of a classical Aroon indicator based trading strategy in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of gain/loss trades. In terms of all three measures, it was discovered that the intelligent trading strategy outperformed the classical trading method. The intelligent trading method generated 3.91% to 40.07% greater ARR than the classical strategy, and it did so with a positive SR for all 10 stocks studied. In addition, the intelligent approach executed a higher percentage of profitable transactions than the traditional strategy. Thus, it was established that the proposed intelligent trading method is a better and safer trading technique than the classical strategy.


Downloads

Download data is not yet available.

Downloads

Published

2022-06-30

How to Cite

Saud, A. S., Shakya, S., & Neupane, B. (2022). Intelligent Stock Trading Strategy Based on Aroon Indicator. Indian Journal of Research in Capital Markets, 9(1), 18–29. https://doi.org/10.17010/ijrcm/2022/v9i1/170401

Issue

Volume 9, Issue 1, January-March 2022

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932–5941. https://doi.org/10.1016/j.eswa.2008.07.006

Bombay Stock Exchange. (n.d.). S&P BSE SENSEX stock prices. https://www.bseindia.com/markets/equity/EQReports/StockPrcHistori.aspx

Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340–355. https://doi.org/10.1016/j.eswa.2017.02.044

Chiang, W.-C., Enke, D., Wu, T., & Wang, R. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195–207. https://doi.org/10.1016/j.eswa.2016.04.025

Cho, K., Merriënboer, B. V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734. https://doi.org/10.3115/v1/d14-1179

Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307–1317. https://doi.org/10.1007/s13042-019-01041-1

Gao, T., & Chai, Y. (2018). Improving stock closing price prediction using recurrent neural network and technical indicators. Neural Computation, 30(10), 2833–2854. https://doi.org/10.1162/neco_a_01124

Gudelek, M. U., Boluk, S. A., & Ozbayoglu, A. M. (2017). A deep learning based stock trading model with 2-D CNN trend detection (Accession No. 17560625). In, Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285188

Jabbarzadeh, A., Shavvalpour, S., Khanjarpanah, H., & Dourvash, D. (2016). A multiple-criteria approach for forecasting stock price direction: Nonlinear probability models with application in S&P 500 index. International Journal of Applied Engineering Research, 11(6), 3870–3878.

Kim, T., & Kim, H. Y. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PLOS One, 14(2), Article 0212320. https://doi.org/10.1371/journal.pone.0212320

Lien Minh, D., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access, 6, 55392–55404. https://doi.org/10.1109/ACCESS.2018.2868970

Long, W., Lu, Z., & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163–173. https://doi.org/10.1016/j.knosys.2018.10.034

Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168–1173. https://doi.org/10.1016/j.procs.2020.03.049

Nada, P., & Igor, J. (2016). Comparative analysis between the fundamental and technical analysis of stocks. Journal of Process Management and New Technologies, 4(2),26–31. https://doi.org/10.5937/JPMNT1602026P

Nepal Stock Exchange Ltd. (n.d.). Stock prices. http://nepalstock.com.np/stockWisePrices

Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53 (4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. https://doi.org/10.1016/j.eswa.2014.07.040

Rodríguez-González, A., García-Crespo, Ã., Colomo-Palacios, R., Guldrís Iglesias, F., & Gómez-Berbís, J. M. (2011). CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Expert Systems with Applications, 38(9), 11489–11500. https://doi.org/10.1016/j.eswa.2011.03.023

Sarangi, P., Sinha, D., Sinha, S., & Dubey, M. (2019). Financial modeling using ANN technologies: Result analysis with different network architectures and parameters. Indian Journal of Research in Capital Markets, 6(1), 21–33. https://doi.org/10.17010/ ijrcm/2019/v6/i1/144039

Saud, A. S., & Shakya, S. (2019). Analysis of gradient descent optimization techniques with gated recurrent unit for stock price prediction: A case study on banking sector of nepal stock exchange. Journal of Institute of Science and Technology, 24(2), 17–21. https://doi.org/10.3126/jist.v24i2.27247

Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26. https://doi.org/10.3390/ijfs7020026

Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science, 131, 895–903. https://doi.org/10.1016/j.procs.2018.04.298

Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264, 71–88. https://doi.org/10.1016/j.neucom.2016.11.095

Stoean, C., Paja, W., Stoean, R., & Sandita, A. (2019). Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLoS ONE, 14(10), Article 0223593. https://doi.org/10.1371/journal.pone.0223593

TA Library in Python. (n.d.). Documentation. https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Tilakaratne, C. D., Mammadov, M. A., & Morris, S. A. (2009). Modified neural network algorithms for predicting trading signals of stock market indices. Advances in Decision Sciences, Article id 125308. https://doi.org/10.1155/2009/125308

Trading Strategy Guides. (n.d.). Aroon indicator trading strategy. https://tradingstrategyguides.com/aroon-indicator/

Weiss, G., Goldberg, Y., & Yahav, E. (2018). On the practical computational power of finite precision RNNs for language recognition. In, Proceedings of 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 2, 740–745. https://doi.org/10.18653/v1/p18-2117

Yang, H., Liu, X.-Y., Zhong, S., & Walid, A. (2020). Deep reinforcement learning for automated stock trading: An ensemble strategy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3690996