Zelin Chen is a principal quantitative researcher with eight years’ experience translating econometrics and machine learning into commercial insight for the energy sector. Trained at the University of Melbourne in Applied Econometrics, he combines deep statistical modelling (ARIMA, GARCH, Kalman, Bayesian) and ML (ANNs, SVMs, bandits) with production-ready programming in R, Python and Matlab. At Aurora Energy Research he progressed from analyst to principal, leading research teams and shaping product-facing analytics that inform market strategy. He has practical experience building algorithmic trading tools (genetic-programming FOREX strategy on GitHub) and has a track record of communicating complex models clearly to diverse stakeholders. A former tutor in quantitative finance and contributor to internal ML strategy projects, he blends academic rigor with hands-on delivery and an appetite for generative design and automation.
8 years of coding experience
3 years of employment as a software developer
Australian National University
Japanese, Japanese at Osaka University
High School Affiliated to Nanjing Normal University
It is my research interest and also my fulfilment to my Master of Applied Econometrics research project. This program helps you to combine available technical trading indicators into a tree-structured complex trading rules and genetic programming helps you to evolve those trading rules into better ones.
Contributions:8 releases, 81 commits, 4 PRs in 7 months
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