What you'll learn:
- Understand stock market fundamentals
- Understand bonds and bond pricing
- Understand the Modern Portfolio Theory and Markowitz model
- Understand the Capital Asset Pricing Model (CAPM)
- Understand derivatives (futures and options)
- Understand credit derivatives (credit default swaps)
- Understand stochastic processes and the famous Black-Scholes model
- Understand Monte-Carlo simulations
- Understand Value-at-Risk (VaR)
- Understand CDOs and the financial crisis
- Understand interest rate models (Vasicek model)
Master Quantitative Finance with Python
Turn mathematics, statistics, and programming into powerful tools for understanding—and modeling—the financial world.
If you’ve ever wondered how professionals price options, manage portfolio risk, or build financial models used on Wall Street, this course will guide you step by step through the foundations of financial engineering using Python.
In this course, you won’t just learn theory—you’ll implement real financial models in Python, gaining practical skills that quantitative analysts, traders, and financial engineers use every day.
You’ll start with the building blocks of financial markets, including stocks, bonds, and derivatives. From there, we move into the mathematical models that revolutionized modern finance—from portfolio optimization to option pricing.
Along the way, you’ll discover some of the most influential ideas in financial science, including:
Bond pricing and interest rate concepts
Modern Portfolio Theory and the Markowitz model
The Capital Asset Pricing Model (CAPM)
The Black–Scholes model, one of the most elegant breakthroughs in 20th-century finance
Risk management techniques such as Value-at-Risk
Monte Carlo simulations for pricing and risk analysis
You’ll also explore how randomness shapes financial markets through stochastic processes, Brownian motion, and Ito’s calculus, and learn how these ideas are used to model asset prices.
By the end of the course, you’ll understand how quantitative finance works both theoretically and computationally, and you’ll be able to build and implement these models yourself in Python.
Important: This course is designed for learners who are genuinely interested in mathematics, statistics, and analytical thinking. If you enjoy working with numbers, models, and coding, you will find this journey incredibly rewarding.
What You’ll Learn
Section 1 – Introduction
Installing Python
Why Python is one of the most powerful tools in finance
The challenges of financial modeling and historical data
Section 2 – Stock Market Basics
Present value and future value of money
Stocks and equity markets
Commodities and the FOREX market
Long and short positions explained
Section 3 – Bond Theory and Implementation
What bonds are and how they work
Yield and yield to maturity
Macaulay duration
Bond pricing theory and Python implementation
Section 4 – Modern Portfolio Theory (Markowitz Model)
Diversification in finance
Mean–variance optimization
Efficient frontier and Sharpe ratio
Capital Allocation Line (CAL)
Section 5 – Capital Asset Pricing Model (CAPM)
Systematic vs. unsystematic risk
Beta and alpha
Linear regression and market risk
Why market risk is the most relevant risk
Section 6 – Derivatives Basics
Introduction to derivatives
Options: calls and puts
Forward and futures contracts
Mark-to-market mechanism
Credit Default Swaps (CDS)
Interest rate swaps
Section 7 – Random Behavior in Finance
Randomness in financial markets
Wiener processes
Stochastic calculus and Ito’s Lemma
Brownian motion theory and implementation
Section 8 – Black-Scholes Model
Black-Scholes theory and implementation
Monte Carlo simulations for option pricing
The Greeks and risk sensitivities
Section 9 – Value-at-Risk (VaR)
Understanding Value-at-Risk
Monte Carlo simulation for risk estimation
Section 10 – Collateralized Debt Obligations (CDO)
What CDOs are
Lessons from the 2008 financial crisis
Section 11 – Interest Rate Models
Mean-reverting stochastic processes
The Ornstein–Uhlenbeck process
The Vasicek interest rate model
Bond pricing with Monte Carlo simulation
Section 12 – Value Investing
Long-term investing strategies
The Efficient Market Hypothesis
Whether you want to become a quantitative analyst, improve your financial modeling skills, or simply understand the mathematics behind modern finance, this course will give you the tools to do it.
Join now and start building real financial models with Python today.
