Corporate Pension Fund Live Tearsheet Review & Winners Announcement Webinar
In this webinar, Dr. Thomas Wiecki and Dr. Rene Zhang will be announcing the winners and reviewing the winning tearsheets that were submitted to our Third-Party Corporate Pension Challenge.
You can find the original challenge post here: https://www.quantopian.com/posts/$10k-third-party-challenge-design-a-factor-for-a-large-us-corporate-pension
About the speakers:
Thomas Wiecki is the VP of Data Science at Quantopian Inc. Thomas did his Ph.D. at Brown University building computational models of the brain. He is the co-author of the popular probabilistic programming package PyMC3.
Rene Zhang is the Director of Data Science at Quantopian. She holds a Ph.D. in applied mathematics from Tufts University specializing in tensor algebra, numerical analysis, and image processing. She joined Quantopian in 2016 and worked on building Quantopian's risk model, feature factory, and statistical selection of trading strategies.
Learn more by subscribing to our YouTube channel to access all of our videos.
As always, if there are any topics you would like us to focus on for future videos, please comment below or send us a quick note at info@quantopian.com.
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.
Видео Corporate Pension Fund Live Tearsheet Review & Winners Announcement Webinar канала Quantopian
You can find the original challenge post here: https://www.quantopian.com/posts/$10k-third-party-challenge-design-a-factor-for-a-large-us-corporate-pension
About the speakers:
Thomas Wiecki is the VP of Data Science at Quantopian Inc. Thomas did his Ph.D. at Brown University building computational models of the brain. He is the co-author of the popular probabilistic programming package PyMC3.
Rene Zhang is the Director of Data Science at Quantopian. She holds a Ph.D. in applied mathematics from Tufts University specializing in tensor algebra, numerical analysis, and image processing. She joined Quantopian in 2016 and worked on building Quantopian's risk model, feature factory, and statistical selection of trading strategies.
Learn more by subscribing to our YouTube channel to access all of our videos.
As always, if there are any topics you would like us to focus on for future videos, please comment below or send us a quick note at info@quantopian.com.
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.
Видео Corporate Pension Fund Live Tearsheet Review & Winners Announcement Webinar канала Quantopian
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