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Pattern to build a robust trend indicator for automated trading
Published in Noura Metawa, Mohamed Elhoseny, Aboul Ella Hassanien, M. Kabir Hassan, Expert Systems in Finance, 2019
Khalid Abouloula, Ali Ou-Yassine, Salah-ddine Krit
Forecasting market trends is a fresh field in the scientific research community using computing methods. For many years, traders have been hoping for some reliable decision-making tools that will help them to predict the market by relying on many commercial products and academic research models that have not been enough to achieve their goals. Some popular choices are genetic algorithms [4], support vector machines [5], and artificial neural networks [6]. They are used to analyze past financial data as far back as 20 years ago to try divining the market direction. The technical analysis is the study of the evolution of a market, mainly on the basis of graphs, in order to predict future trends. It is based on price evolution is the result of everything the rest, the variation of the course curves follow major and minor trends and the appearance of a frank trend encourages operators to act in the same direction as the trend.
Exchange Rate Modeling and Decision Support
Published in Yi Chen, Yun Li, Computational Intelligence Assisted Design, 2018
For trading decisions, technical analysis is sometimes utilized to assist traders in making buying and selling decisions, therein attempting to properly utilize available time-series data. This paper provides an evolutionary alternative to exchange rate determination, which differs from conventional econometrical methods; we also would like to apply this proposed technique to other trading data sets in practice.
Blended computation of machine learning with the recurrent neural network for intra-day stock market movement prediction using a multi-level classifier
Published in International Journal of Computers and Applications, 2021
Krishna Kumar, Md. Tanwir Uddin Haider
Analysis of the market is based on the studies of numerous market attributes and features that effect on the prices of fiscal assets. The technical and fundamental analyses are the two ways commonly used to analyze market behavior and to forecast the stock price. The former studies conclude that all the fundamentals that affect market directions are instantly incorporated in the stock price [1,2]. Also, fundamental analysis is mainly used for the long-term prediction spectrum. This strategy allows the researcher to work on an intra-day prediction by avoiding the analysis of all those subjective economic factors [3]. Technical indicators are mathematical calculations based on the market data such as stock price and volume to perform the technical analysis over the data to identify the market behavior like trend and momentum for future prediction [4]. Predictive ability of an intelligent system totally depends on the way they interpret the dataset and the skills to adapt to the changes required to make a smart analysis. The quality model to improve their predictive capability over the traditional model mainly focuses on feature extraction. Smart feature extraction with the most relevant feature as the training data always leads toward the best system development with high accuracy. Furthermore, many researchers in their studies have claimed and verified that the feature selection process is the key factor to the success of precise forecasting model building [5].
Automated trading systems statistical and machine learning methods and hardware implementation: a survey
Published in Enterprise Information Systems, 2019
Boming Huang, Yuxiang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou
Automated trading system is a kind of decision-making systems based on the massive amounts of enterprise information. The dependability and performance of such a system is highly related to the efficiency of big data analysis and modelling. Predicting price movements in stock, commodity and other derivative markets has always been a challenging task that draws great interest from researchers and investors and is affected by myriads of factors ranging from macroeconomics to the participant’s sentiment. Models have been built to solve the problem of future price predictions to guide investments, and these models can be classified as two types: fundamental analysis and technical analysis. Fundamental analysis is concerned with the intrinsic value of corporate stocks (Lev and Thiagarajan 1993) and considers the past performance and future expectation of a specific corporation as well as the political climate and economic environment. Technical analysis tends to neglect fundamental information and concentrates simply on exploiting past market data, especially price and volume data. The technical analysis method assumes that history will repeat itself; therefore, the main principle is to transform the data into certain technical indicators, chart patterns or other statistical features and then generate specific trading rules for purchases or sales, including time and price. The academic finance community indicates that the efficiency of technical analysis (Lo, Mamaysky, and Wang 2000) is low compared with that of fundamental analysis. Controversies are still ongoing, and diverse opinions have been reached (Park and Irwin 2007). Regardless, technical analysis has been adopted by most of the automated trading systems in practical use. For convenience, we consider all exchange data-oriented strategies to be under the umbrella term ‘technical analysis’.