Improving N calculation of the RSI financial indicator using neural networks (original) (raw)

Improving trading systems using the RSI financial indicator and neural networks

2010

Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide.

Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network

Expert Clouds and Applications, 2021

Forecasting the future price movements and the market trend with combinations of technical indicators and machine learning techniques has been a broad area of study and it is important to identify those models which produce results with accuracy. Technical analysis of stock movements considers the price and volume of stocks for prediction. Technical indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Bollinger bands, and Moving Averages are used to find out the buy and sell signals along with the chart patterns which determine the price movements and trend of the market. In this article, the various technical indicator signals are considered as inputs and they are trained and tested through machine learning techniques to develop a model that predicts the movements accurately.

Neural Networks as a Decision Maker for Stock Trading: A Technical Analysis Approach

International Journal of Smart Engineering System Design · , 2003

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading pro¢ts than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the pro¢tability of stock trading guided by these neural networks are higher than those guided by their benchmarks.

Algorithmic trading system based on technical indicators in artificial intelligence: A review

1ST INTERNATIONAL POSTGRADUATE CONFERENCE ON OCEAN ENGINEERING TECHNOLOGY AND INFORMATICS 2021 (IPCOETI 2021)

Due to the advanced and hasty development of technology nowadays, researches in relation to Artificial Intelligence (AI) such as Machine Learning (ML), Genetic Algorithm (GA), Neural Network (NN) and Expert System (ES) are progressively introduced. These AI types are also frequently presented in building a new and profitable Algorithmic Trading System (ATS). Apart from Fundamental Analysis (FA), Technical Analysis (TA) based on Technical Indicators (TIs) is one of the most AI used in the development of ATS. However, there is a lack of research despite the available knowledge on TIs in ATS where AI is implemented. Therefore, this study aims to review the ATS current work comprehensively using TIs in AI. A systematic literature review (SLR) has been performed to achieve the objective of this study. This article analyses the rough and systematic literature search process for 17 selected papers from 2010 to 2020. The analysis depicts that in most published studies, GA with multiple TIs is applied. The result of this study leads to a pertinent suggestion that is aimed for further researches on ATS to be conducted by future researchers and traders.

Application Of Neural Network To Technical Analysis Of Stock Market Prediction

This paper presents a neural network model for technical analysis of stock market, and its application to a buying and selling timing prediction system for stock index. When the numbers of learning samples are uneven among categories, the neural network with normal learning has the problem that it tries to improve only the prediction accuracy of most dominant category. In this paper, a learning method is proposed for improving prediction accuracy of other categories, controlling the numbers of learning samples by using information about the importance of each category. Experimental simulation using actual price data is carried out to demonstrate the usefulness of the method.

Application of artificial neural networks to predict the behavior of stocks

Statistical data point to the fact that the vast majority of the world population, even after working for a lifetime, when they retire, do not have significant reserves of financial resources in order to guarantee a good quality of life in the elderly. Bearing in mind that the financial stock market offers a viable opportunity for lifelong capital expansion; Through this work, we sought to develop an innovative technique to allow a simple support based on mathematical models, for support decision making by common people, for buying or selling market stocks. This is because the techniques that support decision-making are relatively complex and not widely mastered by the majority of the Brazilian population. The algorithm was proposed to better perform this task. It was made by one corresponding Artificial Neural Network of the "Multi-Layer Perceptron" type with "Backpropagation". Because this ANN is suitable for learning patterns of historical series, which are usually the object of study of stock price behavior by technical analysis methodologies, that are widely used by the market. Therefore, a comparative study was carried out between the results found using the proposed ANN methodology versus the results obtained from simple technical analysis versus single purchase and sale operations in a period of one year. It was found that the ANN model used guided the achievement of superior results for operations with all the Stocks tested, thus proving to be a promising way to solve problems of this nature; related to the identification of mathematical patterns of historical series of the behavior of stock prices on the São Paulo stock exchange.

Predictive time series analysis of stock prices using neural network classifier

The work pertains to developing financial forecasting systems which can be used for performing an in-depth analysis of the stocks prices, downloading/importing data from the various locations and analyzing that data and producing charts to determine statistical trends. There on it describes to perform a time series predictive analysis of the stocks data that we have and plot the various opening and closing prices of the stocks and then convert it to time series data so that we can proceed and perform a time series predictive analysis thereby predicting the h-days closing prices of a certain stock using the neural networks classification algorithm. The implementation is done using the open source software R & WEKA thereby aiming to reduce the analytics cost for any organization.

NEURAL NETWORKS WITH TECHNICAL INDICATORS IDENTIFY BEST TIMING TO INVEST IN THE SELECTED STOCKS

Selections of stocks that are suitable for investment are always a complex task. The main aim of every investor is to identify a stock that has potential to go up so that the investor can maximize possible returns on investment. After identification of stock the second important point of decision making is the time to make entry in that particular stock so that investor can get returns on investment in short period of time. There are many conventional techniques being used and these include technical and fundamental analysis. The main issue with any approach is the proper weighting of criteria to obtain a list of stocks that are suitable for investments. This paper proposes an improved method for stock picking and finding entry point of investment that stock using a hybrid method consist of self-organizing maps and selected technical indicators. The stocks selected using our method has given 19.1% better returns in a period of one month in comparison to SENSEX index.