Where trading floors were once fuelled by research, experience, and gut instinct, investment decisions in the fickle financial markets are now increasingly coming from maths experts, and the computer programmes they release. Algorithms, formulas, and Quant trading is taking over, so why not get up to speed and learn how to apply quant trading techniques and better navigate the investment market. This short course is underpinned with theory, with an emphasis on doing, so you will finish with a host of skills that you can take with you onto the trading floor.
What Will I Learn?
This course focuses on machine learning and the ability to make computers learn without being specifically programmed to do so. You will study a variety of advanced machine learning techniques, and how they can be used in Quant trading to help with quantitative analysis. You’ll learn more about Python libraries, and how to use them to build financial models that can inform investment decisions.
Why Invest In This Course?
This exciting yet affordable course is ideal for anyone that wants a healthy dose of theory, with more of an emphasis on doing, and practical application. If you’re already in the business but haven’t encountered machine learning techniques, or you’re interesting in learning new skills in developing quant trading models, then this is the course for you. With this course some knowledge of python is necessary to enable you to run source coding, and you will also benefit from some basic knowledge of machine learning classification.
KEY LEARNING POINTS
Learn how to professionally play the financial markets and make wiser investment decisions by integrating machine learning into your strategies.
Gain an understanding of machine learning theory, and how ML techniques apply to quant trading.
Study a course with a focus on ‘doing’ and practical application of machine learning techniques.
Learn how to develop sophisticated quant trading models.
Look at how to set-up historical price databases in MySQL and write many lines of python code.
Use python libraries to build financial models that will improve your investment decision making.
Get to grips with Sharpe ratios and use them to compare and evaluate strategies.
Look at different ways of developing quant trading models, and how you can use random forests and k-nearest neighbour techniques to help construct them.
Learn how to use gradient boosted trees, and further your knowledge to get the best performance from them.
Apply all you’ve learned to build an end to end application, working through data collection and preparation and on to model selection.
ADVANTAGES OF THIS COURSE
Study wherever you have internet access and a suitable device.
You can learn at your own pace, and revisit material throughout the course.
Ideal for anyone interested in machine learning, or quant traders who have not used these techniques before.
Suitable for a range of professionals, including analytics or big data professionals along with quant traders seeking hands-on learning.
A great option to learn more about Python’s capabilities and real life applications.