College Programs
Bring practical market, Python, quantitative research and AI skills to your campus through workshops, competitions, projects and training designed with your faculty.
Why it matters
Connect classroom theory with how markets run today
Students learn finance, statistics and programming in separate courses. Trading technology is where those subjects meet. Quantzivo programs give students a structured way to combine them on realistic market data problems.
- Practical exposure to market data, Python tooling and research workflows
- Formats that fit the calendar, from single workshops to longer projects
- Content drawn from Academy courses, adapted for the group's level
- Risk-aware teaching that never promises trading returns
Program options
Six ways Quantzivo can work with your campus
These are the formats we can offer. Each one is scoped with the institution, and they can be combined.
Campus workshops
Hands-on sessions that introduce markets, Python for market data or backtesting fundamentals to a student group.
- Topic matched to the audience's level
- Live coding on historical data
- Follow-up material for self-study
Coding competitions
Problem-based challenges where teams build data tools or strategy backtests and are judged on method, not on simulated profit.
- Problem sets designed around real data issues
- Rubrics that reward rigor and clarity
- Judging focused on bias and robustness
Quant research projects
Guided projects in which students investigate a market question, test it carefully and report findings and limitations.
- Factor, volatility or sentiment studies
- Research plan and experiment log
- Written report with peer review
Student training
Multi-session training built from Academy courses for students preparing for quant, data or fintech roles.
- Content drawn from the 50-course Academy catalog
- Exercises, projects and assessments
- Certificates of completion
Faculty collaboration
Opportunities to work with faculty on case studies, lab exercises or project briefs that complement existing coursework.
- Lab exercises on market data
- Project briefs for existing courses
- Guest sessions on trading technology
Educational partnerships
For institutions that want an ongoing relationship, we are open to discussing longer-term educational partnerships.
- Recurring programs across semesters
- Shared goals agreed up front
- Regular review of what is working
Who we work with
Built for departments, clubs and career teams
Computer science & engineering
Apply programming, data structures and systems thinking to financial data.
Finance, economics & business
Pair theory with the code and data used to test it in practice.
Data science & statistics
Work on time-series validation, feature engineering and model evaluation.
Student clubs & career cells
Run workshops and competitions that build skills students can show employers.
How a program comes together
From first conversation to delivery
Conversation
You tell us about your students, departments and goals. We explain which formats could fit.
Scope
Together we agree on the audience, topics, level, format and what success looks like.
Prepare
We adapt material, exercises and setup guidance, and share them for review before delivery.
Deliver & review
The program runs, then we review feedback with you and agree on any next steps.
What students take away
Skills and work they can show
Programs are designed so students leave with more than notes: code, analysis and written reasoning they produced themselves.
Working Python projects on historical market data- An understanding of backtest bias, costs and overfitting
- Experience presenting findings and their limitations
Every program is scoped individually. Formats, content and any certificates are agreed with each institution. Quantzivo certificates recognize completion and are not degrees or professional qualifications.
For institutions
Ask us directlyCollege program questions
Costs depend on the format and scope, so they are discussed once we understand what your institution needs. There is no fixed package price.
Not necessarily. Introductory workshops assume no background, while research projects and competitions suit students with some Python and statistics. Level is agreed during scoping.
That is what faculty collaboration is for. Lab exercises, project briefs or guest sessions can be designed to complement what is already taught.
No. Competitions use historical data and simulated results, and entries are judged on method, rigor and clarity rather than on simulated returns.
For colleges and universities
Let's explore what would work for your campus
Share your department, student groups and goals. We will follow up to discuss which program formats could fit and how to scope them.