Started in Richmond, Built for Serious Learners
Since 2020, we've been teaching machine learning techniques for quantitative trading to people who want real skills, not surface-level theory.
How This Started
We built Quantizars because the market needed something different. Most online courses about algorithmic trading either skip the math entirely or drown you in academic theory without showing you how anything actually works in practice. We wanted something in between—technical enough to be useful, practical enough to implement.
Our instructors have been working with quantitative models and trading algorithms for years. They've debugged backtests at 2am, optimized models that refused to converge, and dealt with data quality issues that made predictions useless. That experience shapes every lesson we create.
Richmond turned out to be a good home base. The local tech community is growing, and we found people here who wanted to learn about machine learning applications in finance without relocating to major financial centers. We started small, refined our approach based on actual feedback, and built a curriculum that focuses on what works.
What you'll find here are masterclasses that walk through real implementations—data preprocessing pipelines, feature engineering approaches, model validation techniques, and realistic risk management frameworks. The goal is to give you skills you can use, whether you're working on personal trading strategies or building professional systems.
Who Teaches Here
Instructors with hands-on experience in quantitative analysis, algorithm development, and machine learning implementation.
Astrid Koskinen
Lead Instructor - Algorithmic TradingSpent eight years developing and testing systematic trading strategies for institutional clients. Specializes in turning research ideas into production-ready code that actually runs without breaking. Teaches the masterclasses on backtesting frameworks and execution logic.
Viktor Sokolov
Data Science SpecialistBuilt feature engineering pipelines for financial datasets at three different firms. Knows which preprocessing steps matter and which ones waste time. Handles the sessions on data quality, outlier detection, and working with market microstructure noise.
Fionnuala Brennan
Quantitative Analysis ExpertWorked in risk management roles where bad models had real consequences. Focuses on validation techniques, overfitting detection, and realistic performance metrics. Teaches the modules about model evaluation and out-of-sample testing.
What Guides Our Approach
These aren't aspirational statements. They're decisions we make when designing course content and evaluating what works.
Realistic Expectations
Machine learning in trading is difficult. Models fail, markets change, and what worked last year might not work now. We don't promise guaranteed results. We teach you to build robust systems, test them properly, and understand when they're likely to break.
Technical Depth
You'll work with real datasets, write actual code, and debug problems that look easy in theory but get messy in practice. Our masterclasses show you the implementation details—handling missing data, dealing with look-ahead bias, managing execution costs.
Practical Focus
Every technique we teach has been used in production systems. If something sounds theoretically interesting but doesn't work in practice, we tell you why and move on. The curriculum prioritizes methods that have proven useful across different market conditions.
Honest Feedback
When participants submit projects or ask questions, we provide direct technical feedback. If your backtest has methodological problems, we explain what's wrong and how to fix it. The goal is skill development, not encouragement for its own sake.
Continuous Improvement
Markets evolve, libraries get updated, and better techniques emerge. We revise course content based on what's currently useful, remove outdated sections, and add new material when industry practices change. Nothing stays the same just because it worked before.
Local Accessibility
Being based in Richmond means we understand regional schedules and learning patterns. Sessions are designed for people balancing work, family, and education. Materials stay available so you can review them when it fits your timeline.
Ready to Learn Real Techniques?
See what our current masterclasses cover and how the curriculum is structured. Or reach out if you have specific questions about what we teach.