register for the

event

Tuesday, April 16, 2024
9:30 AM ET | 7:00 PM IST | 9:30 PM SGT

About Session

About Session

This comprehensive session explores the scenarios where machine learning seamlessly integrates into momentum trading practices. Delve into various types of momentum trading strategies, understand the necessity and effectiveness of machine learning in this domain, and explore practical applications. We'll walk you through implementing ML- based classifiers and clustering algorithms with practical examples, unlocking the vast potential of machine learning in momentum trading.

Overview

Overview

  • Types of momentum trading approaches
  • A look into traditional and advanced time series momentum trading approaches
  • Need for ML in momentum trading
  • Implementing machine learning for trading time series momentum
  • Improving ML models used for momentum trading
  • Risk management using ML
  • Traditional cross- sectional momentum trading approach
  • Implementing machine learning to trade cross- sectional momentum
  • Interactive Q&A

Pre-Requisites

Pre-Requisites

  • A fundamental understanding of trading terminology.
  • Basic familiarity with machine learning concepts.

Who should attend?

Who should attend?

Traders, Financial Analysts, Quantitative Analysts, Algorithmic Traders, Financial Engineers, Students, Researchers, and anyone interested in the financial markets.

About the Speaker

About the Speaker

Varun holds a Masters degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and algo trading strategist. Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading.

Varun Kumar Pothula

Varun Kumar Pothula

Quantitative Analyst at QuantInsti
https://accounts.quantinsti.com https://blog.quantinsti.com .quantinsti.com Qu@antinsti https://www.quantinsti.com US 1 https://quantra.quantinsti.com/courses https://www.classmarker.com/online-test/