Master the Art of
Quantitative Finance.
A self-paced app to learn, practise, and build projects in quantitative finance—covering Mathematics, Finance, and Programming for aspiring quants.
Explore CurriculumTrusted by students from leading institutions
How it works
A structured three-step approach to mastering quantitative finance—from theory to real-world application.
Step 1
Learn
Master core concepts through structured lessons, video tutorials, and comprehensive study materials.
What's Included
- Stochastic Calculus & Itô's Lemma
- Black-Scholes Model Derivation
- Greeks & Options Sensitivities
- Monte Carlo Simulation Methods
- Mean-Variance Portfolio Theory
- Risk Measures: VaR & CVaR
Step 2
Practise
Reinforce your knowledge with quizzes, problem sets, and interactive exercises designed to test understanding.
What's Included
- Calc 1 and 2 problems
- Quant Interview Brain Teasers
- Probability & Statistics Problems
- Portfolio Optimization Exercises
- Risk Calculation Drills
- Coding Challenges in Python
Step 3
Projects
Apply everything you've learned by building real-world quantitative finance applications from scratch.
What's Included
- Build an Options Pricing Engine
- Construct a Pairs Trading Bot
- Create a Risk Dashboard
- Develop a Backtesting Framework
- Design a Factor Model Portfolio
- Implement a Live Market Data Feed
Table of Contents
Explore our comprehensive curriculum covering everything from the fundamentals to advanced topics in computer science and technology.
1. Probability and Statistics
- • Probability distributions.
- • Expected value and variance.
- • Central limit theorem.
- • Hypothesis testing.
- • Regression analysis.
- • Bayesian inference.
2. Stochastic Calculus
- • Brownian motion.
- • Itô's lemma.
- • Stochastic differential equations.
- • Martingales and filtrations.
- • Girsanov's theorem.
3. Derivatives Pricing
- • Black-Scholes model.
- • The Greeks.
- • Binomial trees.
- • Monte Carlo simulation.
- • Exotic options.
4. Fixed Income
- • Bond pricing and yields.
- • Duration and convexity.
- • Term structure models.
- • Interest rate swaps.
5. Portfolio Theory
- • Mean-variance optimization.
- • CAPM and factor models.
- • Risk parity.
- • Black-Litterman model.
- • Performance attribution.
6. Risk Management
- • Value at Risk (VaR).
- • Expected shortfall.
- • Stress testing.
- • Credit risk models.
7. Algorithmic Trading
- • Market microstructure.
- • Execution algorithms.
- • High-frequency trading.
- • Backtesting strategies.
8. Machine Learning in Finance
- • Feature engineering.
- • Time series forecasting.
- • Reinforcement learning for trading.
Begin your journey to
Quantitative Mastery
Join thousands of students who have transformed their careers through our comprehensive quant curriculum.