Machine Learning for Algorithmic Trading
From the series: Machine Learning in Finance
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
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.
Recorded: 31 Oct 2018
