List of Articles Trading system

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Developing a Stock Technical Trading System Integrating MLP Neural Network with Evolutionary Algorithms
  Alireza Saranj Ahmadreza Ghasemi Asghar Eram Reza Tehrani
  Stock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms More

  Stock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms (i.e., GA, ACOR, and PSO). In order to select the final variables as the selected features, a return comparison of each indicator ratings was used based on tradings. Finally, the performance of each model is tested in comparison with the buy and hold strategy. The results show that the evolutionary learning algorithms significantly outperform the benchmark models in terms of the average return and the hybrid MLP_PSO model outperforms others. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Parameter setting of technical analysis indicators using multi-objective particle swarm optimization and adaptive fuzzy inference system
  Ibrahim Abbasi Hossein Akefi Shahaboddin Adibmehr
  In this paper, we propose automatic stock trading system which combines technical analysis and adaptive neural fuzzy inference system to predict the stock price trend to increase return of investment. In this trading system, at first the optimal value of technical indic More

  In this paper, we propose automatic stock trading system which combines technical analysis and adaptive neural fuzzy inference system to predict the stock price trend to increase return of investment. In this trading system, at first the optimal value of technical indicator's parameters is determined by using multi-objective particle swarm optimization and according to these parameters; technical indicators are calculated to predict stock price changes with the help of adaptive neural fuzzy inference system. We have chosen eight different stocks from Tehran stock exchange to test our trading system for two months. A computational experience is carried out in order to analyze the proposed algorithm and the obtained results are compared with usual conventional methods which have been proposed in previous researches. The computational results show our proposed method performs better than other previous methods and obtains superior results. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Performance Evaluation of the Technical Analysis Indicators in Comparison with the Buy and Hold Strategy in Tehran Stock Exchange Indices
  Ebrahim Abbasi Mohammad Ebrahim Samavi Emad Koosha
  10.22034/amfa.2020.1893194.1376
  Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange
  Hamidreza Haddadian Morteza Baky Haskuee Gholamreza Zomorodian
  10.22034/amfa.2020.1894049.1380
  Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - The Interpretation of "Public Body" in the Multilateral Trading System (WTO): A Case Study of China and the United States
  Seyed Mohamad Hassan Razavi Zahra Biniaz
  10.30495/alr.2024.1996383.2637
  Background and purpose: The conflicting perspectives of the United States and China in relation to the choice of the optimal economic model have resulted in international economic lawfare, which can be defined as resorting to law as an instrument to win the transnationa More

  Background and purpose: The conflicting perspectives of the United States and China in relation to the choice of the optimal economic model have resulted in international economic lawfare, which can be defined as resorting to law as an instrument to win the transnational US-China trade war. One of the prime areas of lawfare between the United States and China in the trade war is the question of whether China’s State-Owned Enterprises (SOEs) should be deemed as "public bodies.” This affects the applicability of WTO’s multilateral disciplines to government subsidies to China’s SOEs. The main question of the present research is the analysis of the defining criteria of the term “public body” in the interpretation of the WTO’s Dispute Settlement Body (DSB). Method: The present study was carried out using a descriptive-analytical method. Findings and results: Three criteria for interpreting the term “public body” are discernable: a) The US favors the “government control” criteria, b) China defines its criteria based on the government's responsibility towards "government organs,” and c) the dispute settlement body has found that the criteria of “government function” based on “exercise of government authority" to be appropriate. The interpretation of the term “public body” offered by the Dispute Settlement Body has achieved a balance between the perspectives of the United States and China. Although China has complied with the Dispute Settlement Body’s interpretation in developing its national legislation, the conflict between the two economic powers has not subsided. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - Smart operating system based on technical parameters optimized with firefly algorithm
  Fatemeh Asiaei Taheri Gholamreza zomorodian Mirfeiz Fallahshams
  The main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done clo More

  The main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  7 - Optimization of technical indicators’ parameters for intraday data using optics – inspired optimization (OIO): a case study of Tehran stock exchange
  Mohammad Ali Rastegar Farah Ashuri
  In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) al More

  In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) algorithm. In the proposed model the technical indicators’ optimized parameters are obtained using both COIO and genetic algorithms with the aim of maximization of modified Sharpe ratio. The presented paper uses stock intra-day prices as input data and considers the transaction costs. The designed strategy is compared against several other approaches including: using the indicators’ default parameters, buy and hold strategy and optimization using genetic algorithm, for both daily and intra-day prices and due to a greater modified Sharpe ratio for the proposed model, its superiority is shown in all cases. Moreover, in a comparison based on end- of- period returns, it is shown that without considering the transaction costs the results of the intra-day data beats the results of the daily data while no superiority is observed when considering the transaction costs. So reducing the transaction costs is recommended to motivate traders to trade on an intra-day basis. Manuscript profile