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Machine Learning Based Optimal Portfolio Allocation | Algo Trading Projects

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Long-only, Low frequency, Asset-allocation Algorithms [EPAT Project]


In continuation with our EPATian project webinars depicting the applications of quantitative and algorithmic trading concepts in real-world markets scenarios, this webinar will cover an interesting project presentation on “Machine Learning Based Optimal Portfolio Allocation” by Vivin Thomas.

About the Presentation

Machine Learning Based Optimal Portfolio Allocation

Focus on algorithms that leverage machine learning at its core to make the capital allocation choice. Come up with a low-frequency strategy that can optimally allocate its prevailing capital amongst a pre-selected set of underliers (basket assets) at regular intervals. And with this process, create Long-only, low frequency, asset-allocation algorithms. Benchmark these against a vanilla allocation strategy which only depends on empirical momentum indicators for its decision making.

About the Presenter

Vivin Thomas (An Experienced Quant)

Vivin Thomas is a Quant in the financial services industry and is based out of Mumbai, India. He has a cumulative professional experience of 9 years in quantitative finance, covering derivatives pricing and risk. He has grown across multiple roles and organizations, notably holding the position of Vice President with two globally reputable investment banks in recent years. Vivin possesses a Bachelor’s and Masters in Engineering from one of the premier institutions in India, IIT Madras. Vivin has also completed the QuantInsti's algo trading course and is the proud recipient of the EPAT Certificate of Excellence.

Thie event was conducted on:
Tuesday, June 15, 2021
9:30 AM ET | 7:00 PM IST | 9:30 PM SGT

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