How Convex Optimization is Used in Finance w/ Scott Sanderson

In our latest video, “Quantopian presents: How to Apply Convex Optimization in Finance”, Scott Sanderson gives an overview of the talk he gave at QuantCon 2018, “Convex Optimization in Finance”. Scott explains what optimization algorithms are, how they can be used in quantitative finance, and what problems and issues can occur when trying to use optimization algorithms. He also talks about how convex optimization enables efficient solutions to problems like portfolio construction. View Scott’s QuantCon Talk here:    • Convex Optimization for Finance  . Scott will be speaking at JupyterCon on August 24th, 2018: oreil.ly/2oeY6A3. About the Speaker Scott Sanderson, Senior Software Engineer, Quantopian Scott Sanderson is a senior software engineer at Quantopian, where he is responsible for the design and implementation of Quantopian’s backtesting and research APIs. Outside of work, Scott is a contributor to several open source projects in the Python data science ecosystem, and he is a regular conference speaker on topics in numerical programming. To learn more about Quantopian, visit www.quantopian.com/. As always, if there are any topics you would like us to focus on for future videos, please send us a quick note at [email protected]. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

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