Machine Learning for Algorithmic Trading Bots with Python
Course

Machine Learning for Algorithmic Trading Bots with Python

Packt
Updated Jan 07, 2020

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 back testing which is the holy grail of algorithmic trading and is an essential key to successful deployment of reliable algorithms.  


Target Audience 

This course is compiled for data science beginners and professionals who want to shift their career to financial sector. This course assumes a basic knowledge of Python programming such as conditional and looping statements.   The course is self-contained in terms of the concepts, theories, and technologies it requires to build trading bots.   


Business Outcomes 

  • Building high-frequency trading robots 

  • Applying feature engineering on stock market data 

  • Diving deeper into the pros and cons of various financial data structures 

  • Building & evaluating many machine learning models 

  • Implementing backtesting econometrics for trading strategies evaluation 

  • Hacking Ensemble Learning Algorithms in Machine Learning 

  • Featuring a premiere on Ensemble Learning with Bagging & Boosting 

  • Experience-based tutorials and hands-on financial challenges 

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