Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Basic Equations for Quantitative Finance
New
Rating: 4.0 out of 5(3 ratings)
98 students

Basic Equations for Quantitative Finance

Basic Mathematics for Quantitative Finance Complete Beginner's Guide
Created byEbi Mindset
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Build a solid mathematical foundation for quantitative finance.
  • Apply calculus to financial modeling and optimization.
  • Master linear algebra for portfolio analysis and machine learning.
  • Understand probability, statistics, and stochastic processes.

Course content

2 sections • 11 lectures • 1h 34m total length

  • Linear Algebra Vectors for Quant Finance29:48

    Learn linear algebra concepts of vectors and dimensions to determine portfolio weightage and returns, converting allocations to decimals and computing total return via a weighted sum.

  • Essential Probability Formulas for Quant Finance11:30

    Explore essential probability formulas for quantitative finance, using favorable outcomes over total outcomes to illustrate how probability underpins normal, advanced, and AI-based equations in trading strategies.

  • Why Every Quant Needs Statistics4:11
  • Standard Brownian Motion7:39
  • What is Geometric Brownian Motion? Explained Simply6:23

    Explore geometric Brownian motion, the growth of price with random noise, described by dS_t = μ S_t dt + standard deviation S_t dW_t, used in risk management algorithms.

  • What is the Ornstein-Uhlenbeck Process? Explained Simply5:35
  • What is HPR? How to Calculate Holding Period Return Easily14:30

    Compute the holding period return (hpr) by adding end value and income (dividends), dividing by the beginning value, and subtracting 1 to express the percentage.

Requirements

  • Basic high-school mathematics
  • No programming experience required
  • No finance background required
  • A willingness to learn and practice

Description

Have you ever wondered how quantitative analysts (Quants), hedge funds, investment banks, and algorithmic traders use mathematics to make data-driven financial decisions?

This course is designed to build the mathematical foundation required for quantitative finance in a simple, structured, and practical way. Whether you're a beginner or an aspiring quantitative analyst, you'll learn the essential mathematical concepts used in financial modeling, algorithmic trading, portfolio management, risk analysis, derivatives pricing, and machine learning.

Unlike traditional mathematics courses, every topic is explained with financial applications and real-world examples, helping you understand not just the theory but also where it is used in modern finance.

What you'll learn

• Build a strong mathematical foundation for quantitative finance

• Understand algebra, functions, and logarithms

• Learn differential and integral calculus

• Master vectors, matrices, eigenvalues, and linear algebra

• Understand probability and probability distributions

• Learn descriptive and inferential statistics

• Study regression analysis and predictive modeling

• Explore time series analysis for financial markets

• Understand Brownian Motion and stochastic processes

• Learn optimization techniques used in portfolio management

• Apply numerical methods and Monte Carlo simulation

• Understand financial mathematics including present value, future value, discounting, and bond pricing

• Learn the mathematical foundations behind machine learning

• Explore advanced quantitative finance concepts including Black-Scholes, CAPM, Modern Portfolio Theory, Value at Risk (VaR), and stochastic differential equations

This course is ideal for

• Beginners interested in quantitative finance

• Students studying finance, economics, mathematics, or engineering

• Algorithmic traders

• Stock market enthusiasts

• Data scientists entering finance

• Financial analysts

• Quantitative researchers

• Machine learning engineers working in finance

Who this course is for:

  • Beginners in Quantitative Finance
  • Finance Students
  • Mathematics Students
  • Algorithmic Traders
  • Stock Market Investors
  • Financial Analysts

Report abuse