Designing and evaluating the profitability of linear trading system based on the technical analysis and correctional property

Subject Areas : Financial Mathematics

CharaghAli Bakhtiyari Asl ¹ , Sayyed Mohammad Reza Davoodi ² , Abdolmajid Abdolbaghi Ataabadi ³

1 - PhD student of Financial Engineering, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
2 - Assistant Professor. Department of Management ,Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
3 - management, Industrial Engineering and Management Sciences, shahrood university of technology

Received: 2020-08-07 Accepted : 2021-04-12 Published : 2022-01-01

Keywords: Fuzzy Inference System, PSO, Corrective property, Oscillators,

Abstract :

Traders in the capital market always seek methods to make full use of available information and combine them to find the best buying and selling strategy. The present study uses a linear hybrid system to combine 106 signals from moving averages oscillators and RSI signals in the technical analysis along with two buy and sell bonds. In addition, the system has correctional property and modifies its parameters over time and according to new information. The result of the research on the Tehran Exchange overall index in the period 1380 to 1397 indicates that the system after the optimal training on training data has an average of daily returns of 0/0025, 0/0048 risk, and a daily Sharp ratio of 0/52, which is better than the individual performance of each signal and market performance in daily average return and sharp ratio criterion.

References:

[1] Abbasi, I., Akefi, H., Adibmehr, S., Parameter setting of technical analysis indicators using multi-objective particle swarm optimization and adaptive fuzzy inference system, Journal of Investment Knowledge, 2015, 4(15), P. 111-134. (in Persian).

[2] Fallahpour, S., Golarzi, G., Fatourechian, N., Predicting Stock Price Movement Using Support Vector Machine Based on Genetic Algorithm in Tehran Stock Exchange Market, Financial Research Journal, 2013, 15(2), P.269-288. Doi: 10.22059/jfr.2013.51081

[3] Fallahpour, S., hakimian, H., Evaluating the Performance of a Pairs Trading System in Tehran Stock Exchange, the Cointegration Approach and Sortino Ratio Analysis, 2017, 8(30), P.1-17. (in Persian).

 

[4] Tadi, M., Abkar, M., Motaharinia, V., Evaluation of Pairs Trading Strategy Using Distance Approach at Tehran Stock Exchange, Journal of Investment Knowledge, 7(26), 2018, P.99-112. (in Persian).

[5] Molaee, B., Nikokar, S., Nikokar, F., Khosravani, F., Evaluation of Price Momentum Strategy in Tehran Stock Exchange, Paper presented at the International Conference on Management, Economics and Industrial Engineering, Tehran. 2015.

[6] Nabavi Chashami, S. A., Ayatollah, H., Investigation of MA Index Efficiency in Technical Analysis in Stock Price Forecasting, Journal of Financial Knowledge of Securities Analysis, 2011, 4(10), P.83-106. (in persian)

[7] Nasrolahi, K., Samadi, S., vaez barzani, M., An Appraisal of the Merit of Candlestick Technical Trading Strategies in Tehran Stock Exchange, Journal of Financial Accounting Research, 2013, 5(3), P.59-72. (in persian)

[8] Banga. J., Brorsen. W., Profitability of alternative methods of combining the signals from technical trading systems, Intelligent Systems, 2019, 26, P.32-45

[9] Brown, C., The Composite Index: A Divergence Analysis Study, IFTA Journal, 2018, 15(1), P.25-34.

[10] De Souza, M.J.S., Ramos, D.G.F., Pena, M.G., Examination of the profitability of technical analysis based on moving average strategies in BRICS, Financ Innov, 2018, 4, P.20-235.

[11] Feng, S., Qian. C., Chao. L., An Adaptive Financial Trading System Using Deep Reinforcement Learning with Candlestick Decomposing Features, in IEEE Access, 2020, 8, P.63666-63678

[12] Hirabayashi, A., Aranha, Cl. Iba, H., Optimization of the trading rule in foreign exchange using genetic algorithm, Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, 2009, P.1529-1536. Doi: 10.1145/1569901.1570106.

[13] Ijegwa, A. D., Vincent, O. R., Folorunso, O., Isaac, O. O., A Predictive Stock Market Technical Analysis Using Fuzzy Logic, Computer and information science, 2014,7(3), P.1-17. Doi:10.5539/cis.v7n3p1   

[14] Lim, S., Yanyali, S., Savidge, J., Do Ichimoku Cloud Figures Work and Do They Work Better in Japan? IFTA Journal, 2016, 13(1), P.1-7.

[15] Magda, B., Fayek, Hatem M. El-Boghdad, Sherin M.Omran., Multi-Objective Optimization of technical stock market indicators using GAS, International Journal of Computer Applications, 2013, 68(20).

[16] Murphy. JJ., Technical analysis of financial markets. Prentice Hall Press, Upper Saddle River.1999.

[17] Naranjo, R., Santos, M., Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets, Journal of Intelligent Systems and Computing, 2017, 527(2), P.323-333. Doi:10.1007/978-3-319-47364-2_31.

[18] Shalini. T., Shah. P., Shah. U., Picking Buy-Sell Signals: A Practitioner’s Perspective on Key Technical Indicators for Selected Indian Firms, Studies in Business and Economics, 2019, 14, P.205-219.

[19] Sherbini. A., Time cycle oscilliators, IFTA Journal, 2018, P.66-84. Doi: 10.6084/m9.figshare.12276629 

[20] Silva. T., Li. A., Pamplona. E., Automated Trading System for Stock Index Using LSTM Neural Networks and Risk Management, International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, P.1-8.

[21] Stübinger, J., Bredthauer, J., Statistical arbitrage pairs trading with high-frequency data, International Journal of Economics and Financial Issues, 2017, 7(4).

[22] Theodorus, Z., Dimitrus, K., Short Term Prediction of Foreign Exchange Rates with a Neural-Network Based Ensemble of Financial Technical Indicators, International Journal on Artificial Intelligence Tools, 2013, 22(3), P.220-241. Doi: 10.1142/S0218213013500164.

[23] Volna, E., Kotyrba, M., Jarusek, R., Multi-classifier based on Elliott wave’s recognition. Journal of Computers & Mathematics with Applications, 2013, 66(1), P.213–225. Doi: 10.1016/j.camwa.2013.01.012

[24] Wang. F., Yu. P & Cheung. D., Combining technical trading rules using particle swarm optimization, Expert Systems with Applications, 2014, 41, P.3016-3026. Doi: 10.1016/j.eswa.2013.10.032.

[25] Xucheng, L., Zhihao, P., A Novel Algorithmic Trading Approach Based on Reinforcement Learning, International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Qiqihar, China, 2019, P.394-398, Doi: 10.1109/ICMTMA.2019.00093.

[26] Yang, C., Zhai, J., Tao, G., Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory, Mathematical Problems in Engineering, 2020, 20, P.1-13

[27] Zhou, X. S., Don, M., Can fuzzy logic make technical analysis 20/20, Financial analyst journal, 2004, 60, P.54-73. Doi:10.2469/faj.v60.n4.2637