Subject Areas : Financial engineering

Hassan  Kalantari Darunkala ¹ (Department of Accounting, Babol branch, Islamic Azad University, Mazandaran, Iran.)
iman  dadashi ² (Department of Accounting, Babol branch, Islamic Azad University, Mazandaran, Iran.)
hasmidreza  gholamnia roshan ³ (Department of Accounting, Babol branch, Islamic Azad University, Mazandaran, Iran.)
kaveh  Azinfar ⁴ (Department of Accounting, Babol branch, Islamic Azad University, Mazandaran, Iran.)

Received: 2020-02-19 Accepted : 2020-03-08 Published : 2020-09-22

Keywords: Ant Colony, Stock trades, fuzzy candlesticks, fireflies,

Abstract :

recently fuzzy intelligent method was used to dynamically model Japanese candlesticks in order to accurately consider the patterns of a candlestick with uncertain information on such patterns. Since fuzzy logic is expert knowledge, although human specialists can play an important role in regulating the values of membership functions of fuzzy variables but since human knowledge is usually ambiguous, a optimal adjustment is not achieved. Therefore, providing a technique that optimally adjusts the values of membership functions in dynamic candlesticks patterns will play a crucial role in the efficiency of the fuzzy trading system. One of the most commonly used optimization methods is the meta-heuristic methods, most of the meta-heuristics have a structure similar to the particle swarm optimization method. meta-heuristic methods such as fireflies and ant colony are more powerful than particle swarm optimization due to their efficient exploitation capabilities. In this paper, fireflies and ant colony are used to adjust and optimize the membership functions of fuzzy membership function of fuzzy candlesticks variables in order to trading analysis and stock price forecasting in the stock trading system. The results of applying the proposed method to iranian stock companies indicate the high accuracy of the proposed method.

References:

 

[1]           R. Naranjo and M. Santos, "Fuzzy candlesticks forecasting using pattern recognition for stock markets," in International Joint Conference SOCO’16-CISIS’16-ICEUTE’16, 2016: Springer, pp. 323-333.

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[7]           K. Lee and G. Jo, "Expert system for predicting stock market timing using a candlestick chart," Expert systems with applications, vol. 16, no. 4, pp. 357-364, 1999.

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[1] R. Naranjo and M. Santos, "Fuzzy candlesticks forecasting using pattern recognition for stock markets," in International Joint Conference SOCO'16-CISIS'16-ICEUTE'16, 2016: Springer, pp. 323-333.

[2] S. Fallahpour and J. Alipour Rikende, "Prediction of stock index using wavelet neural networks in Tehran Stock Exchange," Financial Management Strategy, vol. 2, no. 4, pp. 15-31, 2014.

[2] S. Thammakesorn and O. Sornil, "Generating Trading Strategies Based on Candlestick Chart Pattern Characteristics," in Journal of Physics: Conference Series, 2019, vol. 1195, no. 1: IOP Publishing, p. 012008.

[3] K. Michell and W. Kristjanpoller, "Generating trading rules on US Stock Market using strongly typed genetic programming," Soft Computing, pp. 1-18, 2019.

[4] S. Chopra, D. Yadav, and A. Chopra, "Artificial Neural Networks Based Indian Stock Market Price Prediction: Before and After Demonetization," J Swarm Intel Evol Comput, vol. 8, no. 174, p. 2, 2019.

[5] C. Dong and F. Wan, "A fuzzy approach to stock market timing," in 2009 7th International Conference on Information, Communications and Signal Processing (ICICS), 2009: IEEE, pp. 1-4.

[6] R. Arévalo, J. García, F. Guijarro, and A. Peris, "A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting," Expert Systems with Applications, vol. 81, pp. 177-192, 2017.

[7] K. Lee and G. Jo, "Expert system for predicting stock market timing using a candlestick chart," Expert systems with applications, vol. 16, no. 4, pp. 357-364, 1999.

[8] V. Vella and W. L. Ng, "Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic," Expert Systems with Applications, vol. 55, pp. 70-86, 2016.

 [9] R. Naranjo, J. Arroyo, and M. Santos, "Fuzzy modeling of stock trading with fuzzy candlesticks," Expert Systems with Applications, vol. 93, pp. 15-27, 2018.