Trading signals based on Fisher transform for algorithmic trading
DOI:
https://doi.org/10.2478/tjeb-2018-0006Keywords:
Financial markets, Fisher transform, Algorithmic trading, High frequency trading, Automated trading systemsAbstract
Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades are made automatically today by computers using algorithmic trading procedures. For this particular field, special aspects must be met in order to automate the trading process. On this paper it will be presented one of these mathematical models used in automated trading systems, a method based on the price Fisher transform. It will be presented a general form of this method, the functional parameters and the way to optimize them in order to reduce the risk. It will be also revealed a method to build reliable trading signals with the Fischer function. Three different trading signal types will be explained together with the significance of the functional parameters in the price field. A code sample will be included in this paper to prove the simplicity of this method. Real results obtained with the Fisher trading signals will be also presented, compared and analyzed in order to show how this method can be implemented in algorithmic trading.
References
Börse. (2018). Frankfurt Stock Exchange Deutsche Aktienindex DAX30 Components. Retrieved from http://www.boerse-frankfurt.de/index/dax
Connors, L., & Alvarez, C. (2009). Short Term Trading Strategies That Work. A Quantified Guide to Trading Stocks and ETFs. New Jersey, US: TradingMarkets Publishing Group.
Ehlers, J.F. (2002). Using the Fisher Transform. Stocks & Commodities, V(20:11), 40-42.
Ehlers, J.F. (2004). Cybernetic Analysis for Stocks and Futures. Cutting-Edge DSP Technology to Improve Your Trading. US: Wiley, ISBN: 9780471463078.
Funke, N., & Goldstein, A. (1996). Financial Market Volatility. Intereconomics Journal, 31(5). DOI: https://doi.org/10.1007/BF02927152.
Lien, K. (2009). Day Trading & Swing Trading the Currency Market. Technical and Fundamental strategies to Profit from Market Moves, US: John Wiley & Sons.
Nuti, G., Mirghaemi, M., Traleaven, P., & Yingsaeree, C. (2011). Algorithmic trading. Computer, 44(11), 61 – 69. DOI: 10.1109/MC.2011.31
Păuna C. (2010). TheDaxTrader automated trading system online presentation. Retrieved from https://pauna.biz/thedaxtrader
Păuna, C. (2018). Capital and Risk Management for Automated Trading Systems. In Proceedings of the 17th International Conference on Informatics in Economy. Iași, Romania. Retrieved from https://pauna.biz/ideas
Păuna, C., & Lungu, I. (2018). Price Cyclicality Model for Financial Markets. Reliable Limit Conditions for Algorithmic Trading, Bucharest, Romania: Studii și Cercetări de Calcul Economic și Cibernetică Economică, ECOCIB. ISSN: 0585-7511. Volume 4/2018.
Ward, S. (2010). High Performance Trading. 35 Practical Strategies and Techniques to Enhance Your Trading Psychology and performance, UK: Hariman House, ISBN: 978-1-905641-61-1.
Wilder, J.W., Jr. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research. ISBN 978-0-89459-027-6.
Downloads
Published
Issue
Section
License

(CC BY-NC-ND 3.0) (Since 2014)