Best Quantitative Finance Courses

Find the best online Quantitative Finance Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Quantitative Finance Courses.

Financial Derivatives: A Quantitative Finance View

The financial engineering of forwards, futures, swaps, and options, with Python tools for fixed income and options

Created by Cameron Connell - Mathematician, Statistician, and Quantitative Financier


Students: 9301, Price: $109.99

Students: 9301, Price:  Paid

Student Testimonials:

  • This course offers an unreal value. Very rich content! This beats any financial course I've taken at my university. Looking forward to completing this course and using some of these skills in my career.--Steven

  • Cameron is an outstanding teacher. Thank you very much for making the most important and difficult Finance concepts so easy to understand. Looking forward to the further courses.--Gevorg

  • I got (am getting) some intuition about quant finance, not just leaning facts without really understanding the concepts.

    Cameron gives nice detailed answers to students questions.--Rich

Interested in a lucrative and rewarding position in quantitative finance?  Are you a quantitative professional working in finance or a technical field and want to bridge the gap and become a full on quant?  Then read on.

The role of a quantitative analyst in an investment bank, hedge fund, or financial company is an attractive career option for many quantitatively skilled professionals working in finance or other fields like data science, technology or engineering.  If this describes you, what you need to move to the next level is a gateway to the quantitative finance knowledge required for this role that builds on the technical foundations you have already mastered.

This course is designed to be exactly such a gateway into the quant world.  If you succeed in this course you will become a master of quantitative finance and the financial engineering of the most influential class of financial products that exist on markets today: derivatives.

About the instructor:

This course was created by a mathematician and financial quant holding a Ph.D. from the Courant Institute of Mathematical Sciences at NYU, and who earned his quant chops on Wall Street after an accomplished career as a theoretical materials scientist.

The focus of the course is thus very much on the practical skills someone working in the trenches in the real world of finance needs to have.  But since the course author also has 10 years of college teaching experience, it is taught with an eye to sound course structure and sensitivity to the concerns of students.

What you will learn:

Many finance students and professionals find derivatives the most challenging subject in their field.  But if you have a background in quantitative fields like statistics or computer science this course will show you that these most daunting of financial products are completely accessible to you.

Even if you are completely new to the world of finance, after completing this course you will have a deep mastery of the fundamental derivative structures traded on markets today: forwards, futures, swaps, and options.  But since this course is presented by a practitioner you will also learn how derivatives are actually used in the real world, as tools for both speculation and risk management.

The world of finance and markets is fast-paced and exciting, but can also be very intimidating.  In the heat of the moment, the markets are volatile and unpredictable, positions go south in unanticipated ways, you have traders yelling at you, you have computer software failing, you're relying on data you can't trust.  Keeping your head above water in this environment can be well nigh impossible.

You need a conceptual framework that allows you to keep above the fray and keep your wits about you.  In this course, my primary purpose is to convey that conceptual framework to my students.  The same conceptual framework that allowed me to survive and thrive in the pits of Wall Street during the dark days of the financial crisis.

Concerned that you may not have the required background to succeed in this course?  As long as you meet the formal prerequisites you need not be.  A quantitatively strong business background is more than enough to meet these requirements.  Any decent course in statistics and the basics of calculus is enough.  In truth, high school mathematics is all that is needed for 80-90% of the course material.  The most important requirement is simply to think analytically and logically.

Here is a sampling of some of the main topics that we'll cover on your journey into the quant profession:

  • Interest rate fundamentals

  • Periodic and continuous compounding

  • Discounted cash flow analysis

  • Bond analysis

  • The fundamentals of equity, currency, and commodity assets

  • Portfolio modelling

  • Long and short positions

  • The principle of arbitrage

  • The Law of One Price

  • Forwards, futures, and swaps

  • Risk management principles

  • Futures hedging

  • Stochastic processes

  • Time series concepts

  • The real statistics of asset prices: volatility clustering and autocorrelation

  • Fat-tailed distribution and their importance for financial assets

  • Brownian motion

  • The log-normal model of asset prices

  • Options

  • Put-call parity

  • The binomial model of option pricing

  • The Black-Scholes theory and formula

  • Option greeks: delta, gamma, and vega

  • Dynamic hedging

  • Volatility trading

  • Implied volatility

Includes Python tools

Python based tools are now included for computations with bonds, yield curves, and options.  All software that is part of this course is released under a permissive MIT license, so students are free to take these tools with them and use them in their future careers, include them in their own projects, whether open source or proprietary, anything you want!

So Sign Up Now!

Accelerate your finance career by taking this course, and advancing into quantitative finance.  With 23 hours of lectures and supplemental course materials including 10 problem sets and solutions, the course content is equivalent to a full semester college course, available for a fraction of that price, not to mention a 30 day money back guarantee.  You can't go wrong!

Quantitative Finance & Algorithmic Trading in Python

Stock Market, Bonds, Markowitz-Portfolio Theory, CAPM, Black-Scholes Model, Value at Risk and Monte-Carlo Simulations

Created by Holczer Balazs - Software Engineer


Students: 7680, Price: $109.99

Students: 7680, Price:  Paid

This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main.

First of all we have to consider bonds and bond pricing. Markowitz-model is the second step. Then Capital Asset Pricing Model (CAPM). One of the most elegant scientific discoveries in the 20th century is the Black-Scholes model and how to eliminate risk with hedging.

IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!

Section 1 - Introduction

  • installing Python

  • why to use Python programming language

  • the problem with financial models and historical data

Section 2 - Stock Market Basics

  • present value and future value of money

  • stocks and shares

  • commodities and the FOREX

  • what are short and long positions?

Section 3 - Bond Theory and Implementation

  • what are bonds

  • yields and yield to maturity

  • Macaulay duration

  • bond pricing theory and implementation

Section 4 - Modern Portfolio Theory (Markowitz Model)

  • what is diverzification in finance?

  • mean and variance

  • efficient frontier and the Sharpe ratio

  • capital allocation line (CAL)

Section 5 - Capital Asset Pricing Model (CAPM)

  • systematic and unsystematic risks

  • beta and alpha parameters

  • linear regression and market risk

  • why market risk is the only relevant risk?

Section 6 - Derivatives Basics

  • derivatives basics

  • options (put and call options)

  • forward and future contracts

  • credit default swaps (CDS)

  • interest rate swaps

Section 7 - Random Behavior in Finance

  • random behavior

  • Wiener processes

  • stochastic calculus and Ito's lemma

  • brownian motion theory and implementation

Section 8 - Black-Scholes Model

  • Black-Scholes model theory and implementation

  • Monte-Carlo simulations for option pricing

  • the greeks

Section 9 - Value-at-Risk (VaR)

  • what is value at risk (VaR)

  • Monte-Carlo simulation to calculate risks

Section 10 - Collateralized Debt Obligation (CDO)

  • what are CDOs?

  • the financial crisis in 2008

Section 11 - Interest Rate Models

  • mean reverting stochastic processes

  • the Ornstein-Uhlenbeck process

  • the Vasicek model

  • using Monte-Carlo simulation to price bonds

Section 12 - Value Investing

  • long term investing

  • efficient market hypothesis


  • basics - variables, strings, loops and logical operators

  • functions

  • data structures in Python (lists, arrays, tuples and dictionaries)

  • object oriented programming (OOP)

  • NumPy

Thanks for joining my course, let's get started!

Applied Machine Learning with R (Trading Use Case) – 2020

Learn the complete quantitative finance workflow and use machine learning algorithms in R to develop trading strategies

Created by The Trading Whisperer - Data scientist, trader and chairman of an investment club


Students: 337, Price: $99.99

Students: 337, Price:  Paid

The course is designed to fully immerse you into the complete quantitative trading/finance workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero using R. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

Course elements:

  • Learn about trading and the quantitative trading workflow. Develop a solid understand of what is required to do quantitative trading analysis and the advantages and disadvantages.

  • Learn how to write simple and complex codes in r with some r refresher lecture. Learn how to use the quantmod package to access/load free market data from yahoo finance and other sources.

  • Learn how to download futures data from NinjaTrader. Load the data in R and do data preparation and visualization.

  • Explore various trading ideas/hypothesis on the web, and learn how to generate original trading ideas.

  • Learn and understand what machine learning is and get a good grip of the type of machine learning algorithms available to solve different type of problems ( namely classification and regression problems).

  • Code along while learning about feature engineering, write algorithms for training and testing support vector machine, naïve bayes and random forest models and use these to predict the next price direction of crude oil futures. Realize that these strategies can be used for other trading instruments/products.

  • Compare the model performance and do portfolio selection by only selecting the non correlated models.


This course is for educational purpose and does not constitute trading or investment advice. All content, teaching material and codes are presented with sharing and learning purpose and with no guarantee of exactness or completeness.

No past performance is indicative of future performance and the trading strategies presented here are based on hypothetical and historical backtesting. Trading futures, forex and options involves the risk of loss. Please consider carefully if trading is appropriate to your financial situation. Only risk capital you can afford to lose, and the risk of loss being substantial, you should consider carefully the inherent risks.