Intermediate 16 weeks 4 courses
Intermediate Quant Trading Strategies
Deepen your quantitative toolkit with probability theory, calculus, and optimization. Implement momentum, mean reversion, and factor-based strategies with robust validation frameworks.
Who Is This For?
- Traders with basic programming and statistics knowledge
- Data analysts transitioning to quantitative trading
- Beginners who completed the foundational roadmap
Prerequisites
- Python Fundamentals
- Pandas for Data Analysis
- Statistics Fundamentals
At a Glance
4
Total Courses
43
Practice Exercises
~16
Weeks to Complete
What You'll Learn
Validate trading signals using rigorous statistical methods
Apply probability theory to quantify uncertainty in trading
Use calculus and optimization for strategy parameter tuning
Implement classic quant strategies: momentum, mean reversion, and factor models
Build robust backtesting frameworks with proper bias controls
Structured Learning Path
1
Statistical Edges
Turn statistical concepts into practical strategy ideas, edge validation, and robust backtesting workflows.
Learning Outcome
Identify and validate repeatable alpha candidates.
4 weeks 5 chapters 12 exercises
2
Probability for Trading
Master conditional probability, Bayes intuition, and expected value for decision-making under uncertainty.
Learning Outcome
Quantify uncertainty and make probability-driven decisions.
3 weeks 4 chapters 10 exercises
3
Calculus for Quants
Cover derivatives, gradients, optimization basics, and continuous-time intuition used in quant modeling.
Learning Outcome
Understand optimization mechanics behind modern quant models.
3 weeks 4 chapters 9 exercises
4
Basic Quant Strategies
Implement momentum, mean reversion, and simple factor ideas with clean backtests and risk controls.
Learning Outcome
Build and compare baseline strategies with realistic assumptions.
6 weeks 5 chapters 12 exercises
Ready to Start Your Journey?
Enroll in this data.roadmap to track your progress through the structured learning path.