Stocks Trading Using Relative Strength Index, Moving Average and Reinforcement Learning Techniques: A Case Study of Apple Stock Index

2019 ◽  
Author(s):  
Vincent Tanoe
Keyword(s):  
Reinforcement Learning ◽  
Moving Average ◽  
Relative Strength ◽  
Stock Index ◽  
Strength Index ◽  
Learning Techniques

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.

Candle charts for financial technical analysis

2019 ◽  
Vol 19 (1) ◽  
pp. 200-209 ◽  
Author(s):  
Mehmet F. Dicle
Keyword(s):  
Moving Average ◽  
Visual Representations ◽  
Technical Analysis ◽  
Relative Strength ◽  
Financial Industry ◽  
Strength Index ◽  
Research And Teaching ◽  
Industry Research ◽  
The Individual

Technical analysis is an important part of financial industry, research, and teaching. The methodology has two parts: i) calculation of the individual tools and ii) visual representations. In this article, I provide a community-contributed command, candlechart, to draw the most common technical analysis charts. My intent is to draw these charts similarly to industry examples. The popular candle price chart is combined with charts for volume, moving-average convergence divergence, relative strength index, and Bollinger bands.

scholarly journals Web-Based Decision Support Systems Application of Stock Recommendation Using Bayesian Methods

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Nina Sevani ◽  
Maria Ariesta
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Relative Strength ◽  
Strength Index ◽  
Web Based ◽  
Maximum Profit ◽  
Stock Recommendation ◽  
Collection Data ◽  
The Right ◽  
Stock Transaction

We propose an application that can support traders by providing recommendation about the right stock transaction. The expected impact from this application is to reduce the risk of loss, even achieve the maximum profit for traders who use this application. Recommendation that resulted by application is based on Bayesian methods calculation and four technical analysis indicators that most commonly used by stock experts, i.e. Bollinger Bands, Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Stochastic Oscillator. Methodology used in this paper consists of data collection, data analysisa, application design, implementation, and testing. From the results of application testing, the accuracy of the application is 87,37%.

scholarly journals Optimization of investment management in Warsaw stock market

2014 ◽  
Vol 15 (2) ◽  
pp. 143-156 ◽  
Author(s):  
Maciej Janowicz ◽  
Arkadiusz Orłowski ◽  
Franciszek Michał Warzyński
Keyword(s):  
Stock Market ◽  
Time Perspective ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Optimal Management ◽  
Brute Force ◽  
Exchange Market ◽  
Strength Index ◽  
Optimal Values

Abstract Application of simple prescriptions of technical analysis on the Warsaw Exchange Market (GPW) has been analyzed using several stocks belonging to WIG20 group as examples. Only long positions have been considered. Three well-known technical-analysis indicators of the market have been investigated: the Donchian channels, the Relative Strength Index, and Moving Average Convergence-Divergence indicator. Optimal values of parameters of those indicators have been found by „brute force“ evaluation of (linear) returns. It has been found that trading based on both Donchian channels and Relative Strength Index easily outperform the „buy and hold“ strategy if supplied with optimal values of parameters. However, those optimal values are by now means universal in the sense that they depend on particular stocks, and are functions of time. The optimal management of capital in the stock market strongly depends on the time perspective of trading. Finally, it has been argued that the criticism of technical analysis which is often delivered by academic quantitative financial science is unjustified as based of false premises.

scholarly journals Effectiveness of Technical Analysis Signals Around the Earning Announcements in Malaysian Stock Market

2018 ◽  
Vol 7 (3.21) ◽  
pp. 109
Author(s):  
Kelvin Lee Yong Ming ◽  
Mohamad Jais
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Kuala Lumpur ◽  
Earning Announcement ◽  
Strength Index ◽  
Corporate Announcements ◽  
Technical Indicator

Technical analysis is an analysis that widely applied by the investor in the stock market. However, various corporate announcements could cause the market to react, and the most significant corporate announcement is the earnings announcement (1). Thus, this study examines the effectiveness of technical analysis signals around the earning announcements dates in Malaysian stock market. In doing so, this study applied and tested four technical indicators, namely Simple Moving Average (SMA), Relative Strength Index (RSI), Stochastic (K line), and Moving Average Convergence/Divergence (MACD) in Malaysian stock market. The sample of this study consisted of 30 largest capitalization companies from the main market of Kuala Lumpur Stock Exchange (KLSE). Meanwhile, the sample period covered from 2nd January 2014 to 31st March 2016. This study found that Moving Average Convergence/Divergence (MACD) significantly produced higher returns as compared to the other technical indicator before the earning announcement dates in financial year 2014 and 2015. The combined indicator of MA-MACD also found to have higher return in financial year 2015. The findings conclude that the technical analysis signals can be used to generate returns before earning announcement dates.  

scholarly journals Technical Financial Analysis Tools for Stata

2017 ◽  
Vol 17 (3) ◽  
pp. 736-747 ◽  
Author(s):  
Mehmet F. Dicle ◽  
John D. Levendis
Keyword(s):  
Financial Analysis ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Analysis Tool ◽  
Investment Strategies ◽  
Moving Averages ◽  
Strength Index ◽  
Analysis Tools

In this article, we provide four financial technical analysis tools: moving averages, Bollinger bands, moving-average convergence divergence, and the relative strength index. The tftools command is used with four subcommands, each referring to a technical analysis tool: bollingerbands, macd, movingaverage, and rsi. We provide examples for each tool. tftools allows researchers to backtest their own investment strategies and will be of interest to investors, researchers, and students of finance.

scholarly journals Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 484-486
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Random Forest Algorithm ◽  
Strength Index ◽  
Training Set ◽  
Trend Prediction ◽  
Unique Role

Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.

Trading rules and excess returns: evidence from Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Massoud Metghalchi ◽  
Nazif Durmaz ◽  
Peggy Cloninger ◽  
Kamvar Farahbod
Keyword(s):  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Multiple Time ◽  
Excess Returns ◽  
Strength Index ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Content Type ◽  
Practical Implications

Purpose This paper aims to investigate popular technical trading rules (TTRs) applied to the FTSE Turkish all-cap and small-cap indexes from September 23, 2003 to August 9, 2019 to determine rules that produce net excess returns over the Buy-and-Hold strategy (B&H). Design/methodology/approach Five TTRs, namely, simple moving average, relative strength index, moving average convergence divergence, momentum, and rate of change, are applied, singly (one indicator) and in combination (two indicators) for multiple time periods. Findings For the small-cap index, some TTRs – including the famous Golden Cross, when the 50-day moving average rises above 200-day moving average – produced net annual excess returns (NAERs) over the B&H strategy, for the entire period and each sub-period, after accounting for risk and transaction costs. Results were mixed for the large-cap index. The results support Cakici and Topyan (2013). Research limitations/implications This study investigates several indicators, but future studies should examine others, especially based on volume and price. Practical implications Investors in the FTSE Turkish small-cap index may use some trading rules to earn NAERs over the B&H strategy. Originality/value This research is important because it addresses a gap in the research by examining numerous TTRs in the Turkish stock market. Studies of TTRs in Turkey are scarce.

Moving Average And Relative Strength Index Indicators In Determining Open And Closed Positions On The Metatrader4 Expert Advisor

2021 ◽  
Author(s):  
Ardimansyah ◽  
Ahyuna ◽  
Asrul Syam ◽  
Salman ◽  
Aminah Dinayati Ghani ◽  
...  
Keyword(s):  
Moving Average ◽  
Relative Strength ◽  
Strength Index

scholarly journals Simulation of Technical Indicators for Better Profits in the Indian Stock Market

2019 ◽  
Vol 8 (3) ◽  
pp. 1612-1619
Keyword(s):  
Stock Market ◽  
Moving Average ◽  
Relative Strength ◽  
Strength Index ◽  
Technical Indicators ◽  
Long Run ◽  
Closing Price ◽  
One Step ◽  
Indian Stock Market

This article designs models and uses simulation to examine optimization of technical indicators in stock market: the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI). Based on sector-wise Nifty 50 group of companies’ daily closing price of the stocks from the year January 2013 to September 2018. This study is to demonstrate how the simulation of technical indicators MACD and RSI helps investor in reducing the trading cycles of investment with better profits in the long run. Results concluded that the experimentation of optimization of technical indicators is one-step forward in making profitable trades as it is evident from the nifty50 stocks. Furthermore, it also proves that both the optimized MACD and RSI outperformed the standard MACD, standard RSI and Buy& Hold strategy.

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