Machine Learning for Algorithmic Trading Bots with Python [Video]
Have you ever wondered how the Stock Market, Forex, Cryptocurrency and Online Trading works? Have you ever wanted to become a rich trader having your computers work and make money for you while you’re away for a trip in the Maldives? Ever wanted to land a decent job in a brokerage, bank, or any other prestigious financial institution?We have compiled this course for you in order to seize your moment and land your dream job in financial sector. This course covers the advances in the techniques developed for algorithmic trading and financial analysis based on the recent breakthroughs in machine learning. We leverage the classic techniques widely used and applied by financial data scientists to equip you with the necessary concepts and modern tools to reach a common ground with financial professionals and conquer your next interview.By the end of the course, you will gain a solid understanding of financial terminology and methodology and a hands-on experience in designing and building financial machine learning models. You will be able to evaluate and validate different algorithmic trading strategies. We have a dedicated section to backtesting which is the holy grail of algorithmic trading and is an essential key to successful deployment of reliable algorithms.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-PythonStyle and Approach
Algorithmic trading in practise is a very complex process and it requires data engineering, strategies design, and models evaluation. This course covers every single step in the process from a practical point of view with vivid explanation of the theory behind. The concepts and theories are explained with the aid of illustrations, diagrams and charts whenever possible to make it easier to grasp. The coding parts are explained line by line with clear reasoning why everything is done the way it is.
|Course Length||4 hours 50 minutes|
|Date Of Publication||28 Feb 2019|