Quant Insider

Recording for ‘Implementation of Machine Learning in Trading Financial Markets’ Masterclass by Hariom Tatsat 

About the Instructor 

• VP of Quant at Barclay Investment Bank NYC.
• Co-author of "Machine Learning and Data Science Blueprints for Finance." (one of the highly recommended books for Machine Learning for Finance )
• Recognized with the "Indian Achievers Award-2023."
• MFE graduate from UC Berkeley.
• Keynote speaker at prestigious events by NSE and CQF.

MASTERCLASS DETAILS

In the MASTERCLASS, the implementation of different machine-learning models for trading will be taught by building models from scratch.

Supervised Learning: Dive into the fundamentals of supervised learning techniques and discover how to apply them effectively in predicting market movements. Learn how to leverage historical data to train models that can forecast price trends and make informed trading decisions.

Unsupervised Learning: Explore the power of unsupervised learning algorithms in uncovering hidden patterns and structures within financial data. Understand how clustering and dimensionality reduction techniques can aid in identifying market segments and optimizing portfolio diversification strategies.

Reinforcement Learning - Master Adaptive Strategies: Implementing Dynamic Decision-Making Techniques to Navigate Volatile Markets and Maximize Returns. Example: Developing algorithms that adjust trading strategies based on real-time market conditions.

NLP for Sentiment Analysis: Harness the potential of natural language processing (NLP) techniques to analyze market sentiment and news sentiment. Learn how to extract valuable insights from textual data sources such as news articles, social media feeds, and analyst reports to gauge market sentiment and its impact on asset prices.

This Masterclass is for you if you are a working professional in the field of Finance, Investment management/research, a trader, a portfolio manager, or a student who is passionate about making a career in Financial markets.