Portfolio Optimization using Deep Reinforcement Learning

Nov 2019

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Portfolio Optimization using Deep Reinforcement Learning

Submitted Aug 19, 2018

Section: Full talk Technical level: Advanced Session type: Demo

What is Portfolio Management? What is Deep Learning? How does one apply deep learning to the complex problem of portfolio management? What is the intuitive interpretation of this application? What lies under the black box?

In this talk the speaker would answer these questions and demonstrate the usage of a deep machine learning model for portfolio management of a basket of cryptocurrencies using reinforcement learning. She will also present backtested results of how this advanced approach compare to the traditional approaches and what are reasons for the outperformance.

Outline

The speaker would start of by introducing the problem of portfolio management followed by explaining deep reinforcement learning.

She would then introduce the problem at hand - applying deep RL for Portfolio Management on a basket of cryptocurrencies and the rationale behind choosing this particular problem.

She will show what lies under the hood in this problem by explaining the framework and the methodology.

She will end by presenting the results and their interpretation.

Speaker bio

Sonam Srivastava is a Senior Quantitative Researcher at qplum, an Investment Management startup, focussed on the application of data science in Systematic Investment. She was previously a Senior Associate at HSBC Global Banking and Markets. She has more than 7 years of experience working in Algorithmic Trading, Systematic Risk Trading and Equity Index Structuring. She has worked at the forefront of advanced algorithmic applications in Trading and has a deep belief that advanced machine learning when used correctly can provide a great edge in the Financial Markets.
She holds a Masters in Financial Engineering from Worldquant University and an Bachelors in Technology from IIT Kanpur.