Optimal Portfolio Allocation Using Machine Learning

A quant fund manager + A HFT prop desk founder + A quant teacher = a session worth watching On 9 April, we hosted Kelvin Foo, Dr Gaurav Raizada, and Vivek Krishnamoorthy for a workshop on Algorithmic Trading & Options Risk Management. Watch the recording: www.quantinsti.com/articles/algorithmic-trading-python-ai-options-risk-management-webinar/ . . - Kickstart Your Algorithmic Trading Career Today! Join the Executive Programme in Algorithmic Trading (EPAT®) and unlock your potential with: ✅ Hands-on Learning ✅ Expert mentorship ✅ Lifetime placement assistance Your Algorithmic Trading Journey begins here! 👉 Learn More & Enroll: https://www.quantinsti.com/epat ___________ Free Quantitative Trading Learning Track: 34 Hours of In-Depth Training in Python, Machine Learning, and Trading Strategies for Beginners. Enroll Now: https://bit.ly/3TsrgJh _________ This session aims to teach you about the methods of Optimal Portfolio Allocation Using Machine Learning. Learn how to use algorithms that leverage machine learning at its core to make the capital allocation choice. Presented by a Vivin Thomas, VP, Quantitative Research, Equities (EDG) Modelling, JPMorgan Chase & Co. ******** 👉 Learn more such concepts of Algorithmic trading from Dr. Thomas Starke through - Join EPAT - https://bit.ly/3Sb87vr 👉 Complete Recording and associated files: https://blog.quantinsti.com/algo-trading-epat-projects-15-june-2021/ 👉 Project link: https://blog.quantinsti.com/long-only-low-frequency-asset-allocation-algorithms-project-vivin-thomas/ ******** ▶️ Learn Algorithmic Trading - Tutorials: https://www.youtube.com/playlist?list=PLD7IrLyN7uvK473JGNGZZbjNPvNgI5Gmd ▶️ Quantitative Trading Strategies | Playlist: https://www.youtube.com/playlist?list=PLD7IrLyN7uvJWp1J5pbH4c0_wSjpE4lg3 🔔 Subscribe to our channel for more Algorithmic Trading tutorials and tips! 👍 Like this video and share it with your fellow traders. 💬 Drop your questions and comments below. We'd love to hear from you --------------------------------------------------------------------- About the Session: 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. --------------------------------------------------------------------- Additional Reading: Introduction To Portfolio Management https://blog.quantinsti.com/introduction-portfolio-management/ Portfolio Optimization Methods https://blog.quantinsti.com/portfolio-optimization-methods/ --------------------------------------------------------------------- About the Speaker: 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 is also a proud recipient of the EPAT Certificate of Excellence. --------------------------------------------------------------------- Most useful links: * Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B * Visit us at: https://www.quantinsti.com/ * Algo trading Blogs: https://blog.quantinsti.com/ --------------------------------------------------------------------- Chapters: 00:00 Welcome note and introduction 01:15 Speaker and project background 07:25 Presentation outline 09:44 Motivation 14:44 Preparation 21:35 Specifications 28:43 Results 32:54 Tweaks to the learning model 37:47 Limitations 40:34 Q&A 49:55 Thank you note #portfoliomanagement #MachineLearning #AlgorithmicTrading #quantinsti