Advanced 20 weeks 4 courses
Advanced Quant Trading Strategies
Master machine learning for trading, adapt strategies across timeframes, and understand market microstructure. Build production-ready systems with proper execution and risk management.
Who Is This For?
- Experienced quants looking to add ML to their toolkit
- Systematic traders scaling to higher frequencies
- Developers building production trading systems
Prerequisites
- All Intermediate roadmap courses
- Basic Quant Strategies
- Strong programming skills
At a Glance
4
Total Courses
46
Practice Exercises
~20
Weeks to Complete
What You'll Learn
Train and deploy machine learning models for trading
Translate ML predictions into executable trading signals
Adapt strategies across daily, hourly, and tick-level timeframes
Understand market microstructure and execution optimization
Build production-grade systems with proper monitoring and controls
Structured Learning Path
1
Machine Learning
Learn core supervised learning workflows, feature engineering, validation, and model diagnostics.
Learning Outcome
Train and validate ML models correctly on financial data.
5 weeks 5 chapters 12 exercises
2
Machine Learning Strategies
Translate ML predictions into tradable portfolios with position sizing, costs, and robustness checks.
Learning Outcome
Convert predictions into executable and risk-aware strategy logic.
5 weeks 5 chapters 12 exercises
3
Frequency Scaling
Adapt ideas from daily to intraday horizons while handling noise, latency assumptions, and execution impact.
Learning Outcome
Adapt strategy design across multiple trading frequencies.
4 weeks 4 chapters 10 exercises
4
Market Microstructure
Study order books, spread dynamics, adverse selection, and execution tactics in real trading environments.
Learning Outcome
Understand how market mechanics affect fills, costs, and edge decay.
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.