A 501(c)(3) nonprofit research cohort dedicated to quantitative finance, mathematics, and computation.
The Rindler Institute of Quantitative Education (RIQE) is a selective 501(c)(3) nonprofit. We help students learn quantitative finance and applied math by doing research, not just completing coursework.
We operate as a small research cohort that reads current literature on quantitative market research, reproduces useful methods, and tests ideas through careful data work and paper-traded portfolios.
RIQE is now a 501(c)(3) nonprofit organization. We have received $5,000 in funding and are projected to receive another $3,500 to further our mission.
Funding expands access to data, compute, research materials, and student support for the cohort.
Contact About SupportRIQE is designed to feel like a focused research group: students read, question, implement, and present work together.
Read current papers, translate ideas into code, clean data, and test strategies in a realistic research environment.
We provide financial aid consideration to remove barriers for driven students.
Create code repositories, research notes, and presentations that show how you think through market questions.
Collaborate with peers and guest speakers while shaping projects around the cohort's strongest research questions.
We aim to remove financial barriers while giving students access to the materials needed for serious quantitative research.
Merit-based stipends awarded to participants in recognition of exceptional performance on RIQE projects, research, and presentations.
Funding to cover access to premium data sets, compute credits, papers, or specialized tools needed for quantitative market research.
Support for students presenting their work at meetups, competitions, or academic events aligned with our mission.
RIQE is not a rigid course sequence. Each cohort works from current literature in quantitative market research and builds a shared research agenda around promising questions.
We begin from papers, working notes, and market research topics the cohort can implement. Students build the vocabulary and tools needed for the questions in front of them.
Students reproduce and adapt methods, prototype paper-traded strategies, and evaluate backtests critically instead of treating models as black boxes.
Cohort work moves toward written notes, code, presentations, and project artifacts that explain the question, method, data, and limitations.