Timely and accurate access to data is a staple in the world of financial analysis. See how Vacation Work students Grace and Monaheng access equity data via a web app originally developed by Application Engineer, Verushen Coopoo.
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.
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.
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?
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 […]
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.
The video presents an overview of how MATLAB can add value to the Finance Industry as an all-in-one platform that supports the entire computational finance workflow.