Practical Market Indicators for Algorithmic Stock Market Trading: Machine Learning Techniques and Grid Strategy
In this paper, market indicators from three different approaches for algorithmic trading are analysed (moving average convergence divergence (MACD) crossovers, machine learning (ML) label-based indicators, and grid investing strategy). Market indicators are used by traders in the stock market, to define entry and exit points of a trade. These indicators are also useful to compare different trading strategies. We take a practical stand for the approaches mentioned above, where the same data feed from the exchange is preprocessed to remove redundant or anomalous content. Furthermore, use of correlation data between different stocks is analysed. (i) MACD crossovers are dealt in two dimensions of variability, the dimensions being frequency of trades and length of trading intervals. (ii) The outputs of different algorithms are passed through a voting classifier to get the best possible accuracy in the ML label-based approach. Precision/Recall analysis is done to qualify the algorithms for skewed data. (iii) Finally, a grid-based trading strategy is analysed. We conclude with a trading strategy, proposed using results of indicators based on the three approaches.
KeywordsMACD Machine learning Grid trading Precision Recall Market indicators
- 1.Koopman, S. J., Jungbacker, B., & Uspensky, E. H. (2004). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. SSRN Electronic Journal.Google Scholar
- 3.The S&P 500 Index, Wikipedia, (Online). Available: https://en.wikipedia.org/wiki/S%26P_500_Index. Accessed 10 March 2018.
- 4.Zhang, P., & Su, W. (2012). Statistical inference on recall, precision and average precision under random selection. In Proceedings of the 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.Google Scholar
- 5.Almeida, R. D., Reynoso-Meza, G., & Steiner, M. T. A. (2016). Multi-objective optimization approach to stock market technical indicators. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC).Google Scholar
- 6.Troiano, L., Villa, E. M., & Loia, V. (2018). Replicating a trading strategy by means of LSTM for financial industry applications. IEEE Transactions on Industrial Informatics, 1–1.Google Scholar
- 7.Wu, M., & Diao, X. (2015). Technical analysis of three stock oscillators testing MACD, RSI and KDJ rules in SH & SZ stock markets. In Proceedings of the 2015 4th International Conference on Computer Science and Network Technology (ICCSNT).Google Scholar
- 8.Kamble, R. A. (2017). Short and long-term stock trend prediction using decision tree. In Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS).Google Scholar
- 9.Staff, I. (2018). Moving average convergence divergence—MACD, Investopedia, 09 May 2018 (Online). Available: https://www.investopedia.com/terms/m/macd.asp. Accessed 10 May 2018.
- 10.Pring, M. J. (2014). Study guide for technical analysis explained. New York: McGraw-Hill.Google Scholar
- 11.Li, Y., Wu, J., & Bu, H. (2016). When quantitative trading meets machine learning: A pilot survey. In Proceedings of the 2016 13th International Conference on Service Systems and Service Management (ICSSSM).Google Scholar
- 12.Ruta, D. (2014). Automated trading with machine learning on big data. In Proceedings of the 2014 IEEE International Congress on Big Data, 2014.Google Scholar
- 15.Sadewa, C., & Harlili. (2017). Exploration and analysis of some online machine learning on GBP/USD trading simulation. In Proceedings of the 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA).Google Scholar
- 16.Wang, G. (2008). A survey on training algorithms for support vector machine classifiers. In Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management-Volume 01, ser. NCM ’08 (Vol. 1, pp. 123–128).Google Scholar
- 18.True-false-positive-negative, Google Developers, (Online). Available: https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative.
- 19.Han, J., & Pei, J. (2011). Data mining: Concepts and techniques, Elsevier, pp. 402/740.Google Scholar
- 20.Zhang, P., & Su, W. (2012). Statistical inference on recall, precision and average precision under random selection. In Proceedings of the 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.Google Scholar
- 21.Forex grid trading strategy explained, Admiral markets (United Kingdom), 2018. (Online). Available: https://admiralmarkets.com/education/articles/forex-strategy/forex-grid-trading-strategy-explained. Accessed 1 March 2018.