Jeffrey Varner holds a Bachelor of Science degree (Chemistry), a Masters and a Ph.D. degree in Chemical Engineering, from Purdue University. Prof. Varner’s graduate thesis work at Purdue was done under the direction of Prof. D. Ramkrishna in the area of modeling and analysis of metabolic networks. Following Purdue, Prof. Varner was a postdoctoral researcher in the Department of Biology at the ETH-Zurich where he studied signal transduction mechanisms involved in cell-death under Prof. Jay Bailey. After the ETH, Prof. Varner was a Scientist in the Oncology business unit of Genencor International Inc, Palo Alto, CA. While at Genencor, Prof. Varner was involved in the discovery of novel targets in human cancers, and was a project team member for preclinical, phase-I and II studies of protein therapeutics for the treatment of colorectal cancer and Chronic Lymphocytic Leukemia (CLL). Prof. Varner left Genencor at the end of 2005 to join the faculty of the Chemical and Biomolecular Engineering department at Cornell University. At Cornell, the Varner lab is developing physiochemical modeling tools to rationally reprogram human signal transduction architectures.
Course Overview
Machine learning and artificial intelligence are revolutionizing many fields, including quantitative finance and financial decision making. These technologies offer the possibility of developing advanced approaches to model market behavior and predict optimal trade decisions using various investment tools.
In this course, you will discover how to use the Julia programming language for quantitative financial decision making. You will be introduced to tools like Markov models, Markov decision processes, reinforcement learning, and Q-learning. To apply this knowledge, you will get firsthand experience with the process of building a trading bot. By the end of the course, you will be able to model and analyze investment decision making and develop automated trading systems using these tools.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Quantitative Modeling of Fixed Income Debt Securities
- Equity Asset Pricing Using Stochastic Models
- Analysis of Equity Derivatives at Expiration
- Analysis of Equity Derivatives Before Expiration
- Optimizing Portfolio Allocation
Key Course Takeaways
- Build an agent-based simulation of investor sentiment using hidden Markov models
- Build a binary Bernoulli bandit risk-aware ticker picker
- Build a trading bot
How It Works
Course Author
Who Should Enroll
- Quantitative analysts
- Finance professionals looking to upskill in data modeling
- Engineers looking to transition into finance
- Research scientists
- Computer scientists
- Personal investors
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