Jordan Platts - Options Trader at Walleye Capital

Jordan Platts

Options Trader at Walleye Capital

Chicago, Illinois, United States

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Summary

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Rockstar

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Top School

Jordan Platts is an options trader and quantitative practitioner with eight years of experience building and trading volatility-focused strategies across equity indices and single-stock options. He blends hands-on trading at firms like IMC and Walleye Capital with a strong quantitative and software background—M.S. in Computational Finance from Carnegie Mellon and developer work in Python and C#—to productionize models and risk dashboards. His open-source contributions to notable Python finance libraries (ffn and bt) show a practical knack for improving performance metrics and backtesting reliability, including Sharpe/Sortino/Calmar computations and algorithmic features like an Or algo. Comfortable across trading floors and engineering teams, he has repeatedly automated workflows and strengthened test suites to make research reproducible and deployable.

8 years of coding experience

3 years of employment as a software developer

Master of Science (M.S.) Computational Finance, Master of Science (M.S.) Computational Finance at Carnegie Mellon University - Tepper School of Business

Bachelor of Science - BS Finance Computer Science, Bachelor of Science - BS Finance Computer Science at University of Central Florida

Github Skills (15)

unit-testing10

financial-analysis10

pandas10

statistics10

backtesting10

python10

statistic10

data-science10

numpy10

backtest10

algorithms8

finance8

testing8

modeling8

statistical-models8

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

pmorissette/ffn

Apr 2018 - Nov 2020

ffn - a financial function library for Python

Role in this project:

Data Scientist

Contributions:29 commits, 19 PRs, 34 pushes in 2 years 7 months

Contributions summary:Jordan primarily contributed to the financial function library by updating and refactoring existing code, specifically focusing on numerical computations and statistical analysis. They implemented improvements to the Sharpe and Sortino ratio calculations, incorporated updates to the performance statistics and added calculations for the Calmar ratio. Furthermore, the user was involved in fixing test cases and general numpy updates within the project.

financialpythonfunction-libraryfinancial-dataquantitative-finance

pmorissette/bt

Apr 2018 - Nov 2020

bt - flexible backtesting for Python

Role in this project:

Back-end Developer & Test Automation Engineer

Contributions:23 commits, 18 PRs, 42 pushes in 2 years 7 months

Contributions summary:Jordan contributed to the core functionality of the `bt` library by merging branches and implementing new features, such as the `Or` algo, and by fixing existing ones related to fractional positions. They also added and updated example files to demonstrate the usage of the library's functionality. Furthermore, the user significantly contributed to the testing suite, adding new test cases to ensure the reliability and correctness of the library's features, like the RunOnDate and Or algos, and improving existing tests.

pythonalgorithmic-tradingbacktesting-trading-strategiespython3quantitative-finance

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