Parameterized Trade on the Futures Market on the WIG20

Open access


Research background: Market participants have been trying to forecast future price movements and create tools to facilitate making the right investment decisions since the beginning of the operation of stock exchanges. As a result, there are an increasing number of methods, tools, strategies and models to make the decision process which is becoming extremely complicated.

Purpose: to maximize the simplification of trade rules and to check whether it is possible to parameterize transactions based on the length of price movements in order that the system built in this way would generate profits.

Research methodology: empirical research was conducted on data from the period between 20/01/1998 and 29/06/2018 covering listing futures contracts for the WIG20. First, the length of the price movements was determined according to the closing rate, then the frequency of individual lengths of the price movements was determined so transaction parameters were fixed. Next, the parameters were optimized and the rates of return from the tested options were examined.

Result: It is possible to parameterize transactions based on the length of price movements and to create a simple investment strategy which generates profits. In the audited period, the optimal length of traffic was 25 points with a simultaneous use of a profit/loss ratio of 1 : 1, 1 : 2 or 1 : 3.

Novelty: an original investment strategy based on the parameterization of transactions that is based on length of price movement and profit/loss ratio.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Biondo A.E. Pluchino A. Rapisarda A. Helbing D. (2013). Are Random Trading Strategies More Successful than Technical Ones? Montoya ARH PLoS ONE8 (7) e68344. DOI: 10.1371/journal.pone.0068344.

  • Biondo A.E. Pluchino A. Rapisarda A. (2014). Micro and Macro Benefits of Random Investments in Financial Markets. Contemporary Physics55 (4) 318–334.

  • Biondo A.E. Pluchino A. Rapisarda A. (2013). The beneficial role of random strategies in social and financial systems. Journal of Statistical Physics3–4 (151) 607–622.

  • Chong T.T-L. Ng W.K. Liew V.K.-S. (2014). Revisiting the Performance of MACD and RSI Oscillators. Journal of Risk and Financial Management7 1–12. DOI: 10.3390/jrfm7010001.

  • Cohen G. Cabiri E. (2015). Can technical oscillators outperform the buy and hold strategy? Applied Economics47 (30) 3189–3197. DOI: 10.1080/00036846.2015.1013609.

  • Fama E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance25 383–417.

  • Gold S. (2015). The Viability of Six Popular Technical Analysis Trading Rules in Determining Effective Buy and Sell Signals: MACD AROON RSI SO OBV and ADL. Journal of Applied Financial Research2 8–29.

  • Grossman S.J. Stiglitz J.E. (1980). On the impossibility of informationally efficient markets. American Economic Review70 393–408.

  • Heiberger R.H. (2015). Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor’s 100. PLoS ONE10 (8) e0135311. DOI: 10.1371/journal. pone.0135311.

  • Hejase A.J. Srour R.M. Hejase H. J. Younis J. (2017). Technical Analysis: Exploring MACD in the Lebanese Stock Market. Journal of Research in Business Economics and Management8 (4) 1493–1502. Retrieved from:

  • Murphy J.J. (1999). Analiza techniczna rynków finansowych. Warszawa: WIG-Press.

  • Nor S.M. Wickremasinghe G. (2014). The profitability of MACD and RSI trading rules in the Australian stock market. Investment Management and Financial Innovations11 (4) 194–199. Retrieved from:

  • Odean T. (1998). Are investors reluctant to realize their losses? Journal of Finance53 (5) 1775–1798. DOI: 10.1111/0022-1082.00072.

  • Park Ch. Irwin S.H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys21 (4) 786–826.

  • Rosillo R. de la Fuente D. Brugos J.A.L. (2013). Technical analysis and the Spanish stock exchange: testing the RSI MACD momentum and stochastic rules using Spanish market companies. Applied Economics45 1541–1550.

  • Satinover J.B. Sornette D. (2007). Illusion of control in Time-Horizon Minority and Parrondo Games. The European Physical Journal B60 (3) 369–384.

  • Subramanian V. Balakrishnan K.P. (2014). Efficacy of Refined MACD Indicators: Evidence from Indian Stock Markets. The IUP Journal of Applied Finance20 (1) 76–91.

  • Szyszka A. (2009). Finanse behawioralne. Nowe podejście do inwestowania na rynku kapita-łowym. Poznań: Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu.

  • Tharavanij P. Siraprapasiri V. Rajchamaha K. (2015). Performance of technical trading rules: evidence from Southeast Asian stock markets. SpringerPlus4 (552). DOI: 10.1186/s40064-015-1334-7.

  • Wang J. Kim J. (2018). Predicting Stock Price Trend Using MACD Optimized by Historical Volatility. Mathematical Problems in Engineering. Article ID 9280590 12 pages. DOI: 10.1155/2018/9280590.

  • Yu H. Nartea G.V. Gan C. Yao L.J. (2013). Predictive ability and profitability of simple technical trading rules: recent evidence from Southeast Asian stock markets. International Review of Economics & Finance25 (c) 356–371. DOI: 10.1016/j.iref.2012.07.016.

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 82 82 14
PDF Downloads 58 58 12