In this script, we demonstrate the strength of MATLAB’s optimisation capabilities to construct a portfolio driven by smart beta investment techniques. We do not advocate for the use of any particular investment or trading strategy, but rather demonstrate how to import your own data, implement a custom-made optimisation routine, and backtest the strategy: all in one place. We supply the code in the form of this live script for you to get started right away.
Are you looking to make your classes more relevant and engaging? Drones have increased functionality and have decreased in size, making it easier to bring into classrooms. They do not cost a lot and have significant benefits, making teaching and learning so much more fun and enriching. It is a modern dynamic educational model that […]
An essential aspect of the mining process is the froth flotation process. This removes impurities from minerals, such as silica from iron ore, which ultimately determines the quality of the product. In this article we focus on how neural networks can be applied to the mineral extraction process, ensuring products of a higher quality. The […]
When deep learning and image processing come together, they can solve the huge problem of meeting regulations in the financial services industry. Take a look at this video where vacation work students Monaheng and Grace take you through the RegTech app, developed by previous vacation work students Christiaan and Zanele.
Learn about Real-Time Simulation and Testing with this short 4-part video series created by our Vacation Work students Andrew and James. Here we will explore Real-Time Simulations, Rapid Control Prototyping, Hardware-in-the-Loop and Speedgoat Real-Time Target machines.
Watch our two-part video series created by our Vacation Work Students Daniel and Vicky to find out more about how reinforcement learning works and how to implement a practical controller with a reinforcement learning algorithm in MATLAB.
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 […]