A Prediction Model Using the Price Cyclicality Function Optimized for Algorithmic Trading in Financial Market
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A Prediction Model Using the Price Cyclicality Function Optimized for Algorithmic Trading in Financial Market

Authors: Cristian Păuna


After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: Algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643984

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[1] R.D. Zota, L. Ciovica, Designing software solutions using business processes, 7th International Conference on Globalization and Higher Education in Economics and Business Administration, GEBA 2013, DOI: 10.1016/S2212-5671(15)00125-2
[2] V.D. Păvăloaia, Methodological Approaches to Computer Modeling Possibilities in Financial Analysis, Scientific Annals of Economics and Business, January 2012, ISSN: 2501-3165, DOI: 10.2478/v10316-012-0026-5, Available at: https://www.researchgate. net/publication/267202159
[3] C. Păuna, Automated Trading Software. Design and Integration in Business Intelligence Systems. Bucharest, Romania: Database Systems Journal, vol. IX/2018, ISSN: 2069-3230. Academy of Economic Studies. Available at: http://dbjournal.ro/archive /29/29_3.pdf
[4] C. Păuna, I. Lungu, Price Cyclicality Model for Financial Markets. Reliable Limit Conditions for Algorithmic Trading, Journal of Studies and Researches of Economic Calculation and Economic Cybernetics, 4/2018, ISSN: 0585-7511. Academy of Economic Studies DOI: 10.24818/18423264/ Available at: http://ecocyb.ase.ro
[5] C. Păuna, A Price Prediction Model for Algorithmic Trading, under final review at Romanian Journal for Information Science and Technology, ISSN: 1453-8245. Romanian Academy.
[6] Cox, D.R. Sir, Prediction by Exponentially Weighted Moving Averages and Related Methods, 1961, Journal of the royal Statistical Society, Series B, Vol. 23, No. 2, pp. 414-422.
[7] C. Reinsch, Smoothing by Spline functions, Numerische Mathematik, Volume 10, Issue 3, ISSN 0945-3245, 1967, DOI https://doi.org/ 10.1007/BF02162161. pp. 177-183.
[8] C. Berbente, S. Mitran, S. Zancu, Metode Numerice. Bucharest, Romania: Editura Tehnica, 1997, ISBN 973-31-1135-X, pp. 15-20.
[9] Börse, Frankfurt, Frankfurt Stock Exchange Deutscher Aktienindex DAX30 Components, 2018. Available on: http://www.boerse-frankfurt.de/index/dax
[10] J.W. Wilder, Jr., New Concepts in Technical Trading Systems. Greensboro, NC Trend Research, 1978. ISBN 978-0-89459-027-6.
[11] C. Păuna, Capital and risk management for automated trading systems, Iași, Romania: Proceedings of the 17th International Conference on Information in Economy, May 2018, Alexandru Ioan Cuza Academy. Available at: https://pauna.biz/ideas
[12] Meta Trader 4, 2018. Available at: https://www.metatrader4.com
[13] Meta Quotes Language 4, 2018 Available at: https://www. metatrader4.com/en/automated-trading/mql4-programming
[14] C. Păuna, TheDaxTrader. Automated trading system, 2010. Online software presentation. Available at: https://pauna.biz/ thedaxtrader
[15] C. Păuna, The Quality Trading Coefficient. General Formula to Qualify a Trade and a Trading Methodology, Bucharest, Romania: Economic Informatics Journal, vol. 22, no.3/2018. ISSN: 1453-1305, Academy of Economic Studies. DOI: 10.12948/issn14531305/ 22.3.2018.09. Available at: http://revistaie. ase.ro/content/87/09%20-%20pauna.pdf
[16] K. Lien, Day Trading & Swing Trading the Currency Market – Technical and Fundamental strategies to Profit from Market Moves, John Wiley & Sons, 2009, pp. 138-140
[17] C. Păuna, Reliable Signals Based on Fisher Transform for Algorithmic Trading, Timișoara, Romania: Timisoara Journal of Economic and Business, Volume 11, Issue 1/2018. ISSN: 2286-0991 DOI: 10.2478/tjeb-2018-0006 Available at: https://www.tjeb.ro
[18] C. Păuna, A Different Approach for the Turtle Strategy in Algorithmic Trading, under final review for INEKA 2019 at University of Verona, Italy. Available at: https://pauna.biz/ideas
[19] L. Connors, C. Alvarez, Short Term Trading Strategies That Work – A Quantified Guide to Trading Stocks and ETFs, TradingMarkets Publishing Group, 2009. pp. 53-74