Upcoming Webinar

Machine Learning for Algorithmic Trading

Date:
August 22, 2017
Session 1:
9:00 a.m. U.S. EDT/ 2:00 p.m. BST/ 3:00 p.m. CEST
Session 2:
2:00 p.m. U.S. EDT/ 7:00 p.m. GMT/ 8:00 p.m. CEST

Overview

In this webinar we will use regression and machine learning techniques in MATLAB to train and test an algorithmic trading strategy on a liquid currency pair. Using real life data, we will explore how to manage time-stamped data, create a series of derived features, then build predictive models for short term FX returns.

We will then show how to backtest this strategy historically, while taking into account trading costs in the strategy and the machine learning modelling process.

Highlights

  • Handling data using the timetable object
  • Linear regression modelling
  • Machine Learning techniques for Supervised Learning
  • Backtesting strategy performance historically

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenter

Dan Owen is Industry Manager for Financial Applications for the APAC region. Dan has worked at MathWorks for over 12 years in Consulting and as an Applications Engineer, always focusing on Financial Services. He has also worked as a Director of Systematic Trading at Dresdner Kleinwort and within a Quant Technology group at Fidelity International. He holds a BSc and a PhD in Applied Mathematics from the University of Birmingham in the United Kingdom.

Product Focus

  • MATLAB®
  • Statistics and Machine Learning Toolbox
  • Financial Toolbox
  • Trading Toolbox