Where math and finance meet.
The modern approach to markets.
As a boutique quantitative research firm, our strength lies in our ability to curate proprietary datasets that help us navigate markets.We deploy rigorous theoretical frameworks in conjunction with machine learning models to identify and capture structural inefficiencies within option markets.
A "data first" methodology.
| Underlying | Date | Feature_0 | Feature_1 | Target |
|---|---|---|---|---|
| AAPL | 2025-01-01 | 0.88 | 0.32 | 1 |
| MSFT | 2025-01-01 | 0.75 | 0.28 | 1 |
| CBOE | 2025-01-01 | 0.40 | 0.71 | 0 |
Contact
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Our Process
At Vega Research Group, we employ a systematic and data-driven approach to derivative markets.We make use of high-quality datasets and rigorous backtesting frameworks to thoroughly vet our ideas and strategies. This data undergoes meticulous cleaning, validation, and preprocessing, ensuring its accuracy and reliability.Utilizing the latest advancements in machine learning and statistical methods, our models capture inefficiencies and nuances in the options volatility landscape. By incorporating features such as dispersion metrics, volatility indices, and macroeconomic indicators, we build comprehensive predictive models designed to forecast market behavior with precision.
Our Team
Earl Charles – Founder
Our founder, Earl Charles, is deeply passionate about few things other than quantitative finance.He began his career through internships on the derivatives strategy team at the Chicago Board Options Exchange (CBOE) and Envestnet's Quantitative Research Group.Once familiar with applying institutional-grade processes to markets, he began independently modeling and capturing inefficiencies within derivative markets.
Careers
Interested in joining our team?
We are currently hiring for the following positions:• Quantitative Researcher Intern (Atlanta, GA)DescriptionAs a quantitative researcher, you will work closely with our team to develop robust backtests, machine learning models, and trading systems.ResponsibilitiesIn this role, you will:– Make extensive use of pandas, NumPy, scikit-learn
and other Python libraries conducive to data science.– Become intimate with several financial data providers and APIs.– Learn how to build proper and robust models, backtests, and trading strategies.What we offer– Opportunity to work on actionable, real-world quantitative trading projects.– Potential for equity ownership and full-time employment.Preferred Qualifications– Enthusiasm for financial markets and quantitative finance.– Working knowledge of derivative markets, particularly options.– Intermediate Python programming ability.
If it seems like a match, please upload your resume below!Don't see a specific role, but still want to join us? Upload your resume and let us know where you think you'll fit!