In a previous example, it was shown how MATLAB can be used to backtest a simple trading strategy in 8 lines of code, where the trading strategy was developed based on the Relative Strength (RS) index. In this article, I take Kawee’s work a step further and investigate if we can optimise the buy and sell RS index values to maximise our returns. The buy and sell signals are originally chosen at an RS Index of 40 and 70 respectively. Over a six-year period, this gave approximately 18% return-on-equity. Can this be improved?
Machine learning has made its mark! From medical diagnostic tools, speech recognition (think Siri and Alexa) to movie recommendations and predictive maintenance; machine learning techniques are being used to make critical business and life decisions.
Accessing and processing of data can be done entirely within MATLAB, no matter where it might be stored, including SQL/NoSQL databases, Spark™, and/or Hadoop®. Find out how you can tackle Big Data with MATLAB
In this article, you will note the ease with which you can train and validate a neural network, and then see how this is applied by means of a trading strategy. By simple manipulations of historical data, we can leverage the flexibility of deep learning in MATLAB to generate future predictions of time series data, with reasonable precision.
Pairs trading is a popular trading strategy. However, it can be shrouded in statistics. By leveraging the strengths of MATLAB’s machine learning and statistics capabilities, you too can formulate your own pairs trading strategies. In this example, we examine 300 stocks from the S&P 500 and group them into clusters of stocks such that each […]
What do smart homes, self-driving cars, intelligent factory condition monitoring and fault diagnosis have in common? They all need data This data often exists in several different formats The data changes with time The first step in any Machine Learning workflow involves accessing the data you want to work with. If you have worked with […]
In MATLAB; row, column and linear indexing use integer indices. While most MATLAB users are familiar with normal numerical indexing into vectors, matrices etc. MATLAB also provides a very efficient technique called logical indexing, where you use logical variables to index into data, extracting information that satisfies a condition. In this article, you will learn […]
AI For Risk Management: Classifying Credit Card Default
In this article, we explore machine learning models using MATLAB which aim to classify whether or not somebody will default on their credit card payment in the next 30 days. We develop models which are instinctive to understand and able to be quickly prototyped and improved. We provide a live script for you to get started immediately with model development.
Learn how easy Machine Learning in MATLAB is for FOREX Algorithmic Trading. Read how supervised machine learning can be used to identify trading signals. Download the code and get started creating your own models right away.
To celebrate Valentine’s Day, Application Engineers Verushen and Boitumelo created our very own heart GIF using MATLAB. Find out how you can create your own.
How do you write code for a dynamic process making sure that all states are accurately retained for future time steps? Keeping track of states in a dynamic process is complicated as the outputs are determined by past states and the current input. This means that the code must keep track of past values and […]
This 10-minute webinar uses MathWorks tools to showcase a method for a common design environment that allows engineers from different disciplines to collaborate effectively to streamline the design process. The common framework offered by Model-Based Design is an efficient design process that enables faster development and testing to bring products to market sooner.