Learning Track · 7 Modules · 27 Lessons · 23 Exercises · 21h
The Quantitative trading curriculum
A backtest is a hypothesis about market mechanics that has to survive execution friction. Build a limit order book, quantitative time series signals, an event-driven backtester, options pricing models, portfolio risk systems, and statistical validation that resists overfitting.
Prerequisites & Preparation
Prerequisites: Python 3.10+, basic probability and statistics
Recommended: Introduction to programming
Scope: Engineers building limit order books, time-series signals, event-driven backtesting, and portfolio risk systems.
Track Architecture
- [foundations] Foundations: Microstructure, order books, and time series math.
- [strategies] Systematic strategies: Event-driven backtesting, statistical arbitrage, and execution friction.
- [risk] Derivatives and risk: Options pricing, the Greeks, and portfolio tail risk.
- [validation] Validation and research: Data structures, path labeling, and deflated performance metrics.
Foundations
Microstructure, order books, and time series math.
Systematic strategies
Event-driven backtesting, statistical arbitrage, and execution friction.
Derivatives and risk
Options pricing, the Greeks, and portfolio tail risk.
Validation and research
Data structures, path labeling, and deflated performance metrics.