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30-Day Money-Back Guarantee

This course includes:

  • 5 hours on-demand video
  • 5 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Finance & Accounting Investing & Trading Python

Quantitative Finance & Algorithmic Trading in Python

Stock market, Markowitz-portfolio theory, CAPM, Black-Scholes formula, value at risk, monte carlo simulations, FOREX
Rating: 4.3 out of 54.3 (837 ratings)
6,746 students
Created by Holczer Balazs
Last updated 11/2020
English
English [Auto], Indonesian [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Understand stock market fundamentals
  • Understand the Modern Portfolio Theory
  • Understand the CAPM
  • Understand stochastic processes and the famous Black-Scholes mode
  • Understand Monte-Carlo simulations
  • Understand Value-at-Risk (VaR)

Requirements

  • You should have an interest in quantitative finance as well as in mathematics and programming!

Description

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. Markowitz-model is the first step. Then Capital Asset Pricing Model (CAPM). One of the most elegant scientific discoveries in the 20th century is the Black-Scholes model: how to eliminate risk with hedging. Nowadays machine learning techniques are becoming more and more popular. So you will learn about regression, SVM and tree based approaches. 

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

Section 1:

  • installing Python

  • stock market basics

Section 2:

  • what are bonds

  • how to calculate the price of a bond

Section 3:

  • what is modern portfolio theory (Markowitz-model)

  • efficient frontier and capital allocation line

  • sharpe ratio

Section 4:

  • what is capital asset pricing model (CAPM)

  • beta value and market risk

Section 5:

  • derivatives basics

  • options (put and call options)

  • random behaviour

  • stochastic calculus and Ito's lemma

  • brownian motion

  • Black-Scholes model

Section 6:

  • what is value at risk (VaR)

  • Monte-Carlo simulation

Section 7:

  • machine learning in finance

  • how to forecast future stock prices

  • SVM, k-nearest neighbor classifier and logistic regression

Section 8:

  • long term investing (the Warren Buffer way)

  • efficient market hypothesis


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

Who this course is for:

  • Anyone who wants to learn the basics of financial engineering!

Course content

12 sections • 72 lectures • 5h 1m total length

  • Preview01:22
  • Preview03:06
  • Financial models
    03:03
  • IMPORTANT UPDATES
    00:34

  • Present value / future value of money
    05:10
  • Time value of money implementation
    03:02
  • Stocks / shares
    05:10
  • Commodities
    01:18
  • Currencies and the FOREX
    03:56
  • Fundamental terms: short and long
    01:55

  • Bonds basics
    03:09
  • Bond price and interest rate
    03:14
  • Bond price and maturity
    02:06
  • Bonds pricing implementation
    04:29
  • Stock Markets Basics
    4 questions

  • The main idea - diverzification
    05:17
  • Mathematical formulation
    05:00
  • Expected return of the portfolio
    05:27
  • Expected variance (risk) of the portfolio
    04:53
  • UPDATE: valid formula for covariance
    00:08
  • Efficient frontier
    05:33
  • Sharpe ratio
    03:03
  • Capital allocation line
    03:30
  • Modern Portfolio Theory implementation - getting data from Yahoo
    06:08
  • Modern Portfolio Theory implementation - weights
    08:57
  • Modern Portfolio Theory implementation - mean and variance
    04:03
  • Modern Portfolio Theory implementation - Monte-Carlo simulation
    05:52
  • Modern Portfolio Theory implementation - optimization
    08:10
  • UPDATE: order of stocks
    00:10
  • Markowitz Model Quiz
    5 questions

  • Systematic and unsystematic risk
    02:05
  • Capital asset pricing model formula
    03:48
  • The beta value
    04:49
  • Capital asset pricing model and linear regression
    02:40
  • Capital asset pricing model implementation I
    04:07
  • Capital asset pricing model implementation II
    05:17
  • Preview03:45
  • Capital Asset Pricing Model Quiz
    4 questions

  • Introduction to derivatives
    01:45
  • Future contracts
    02:52
  • Interest rate swaps
    02:01
  • Options basics
    02:32
  • Call option
    04:54
  • Put option
    02:45
  • American and european options
    02:18
  • Derivatives Basics Quiz
    5 questions

  • Types of analysis
    05:20
  • Random behaviour of returns
    04:32
  • Winer-process
    05:12
  • Stochastic calculus introduction
    04:20
  • Ito's lemma in higher dimensions
    05:04
  • Brownian-motion implementation
    04:06
  • Random Behaviour Quiz
    2 questions

  • Black-Scholes model introduction - the portfolio
    06:44
  • Black-Scholes model introduction - dynamic delta hedge
    06:09
  • Black-Scholes model introduction - no arbitrage principle
    04:37
  • Solution to Black-Scholes equation
    04:06
  • The greeks
    04:36
  • Black-Scholes model implementation I
    05:39
  • Black-Scholes model implementation II - Monte-Carlo
    09:56
  • How to make money with Black-Scholes model?
    01:56
  • Long Term Capital Management (LTCM)
    06:03
  • Black-Scholes Model Quiz
    4 questions

  • What is Value-at-Risk?
    03:09
  • Value-at-Risk introduction
    07:40
  • Value at risk implementation I
    05:07
  • Preview06:04
  • Value at Risk Quiz
    3 questions

  • What is machine learning?
    06:08
  • Logistic regression introduction
    03:27
  • Logistic regression implementation
    10:20
  • K-nearest neighbor (kNN) classifier introduction
    08:02
  • K-nearest neighbor (kNN) classifier implementation
    03:52
  • UPDATE: kNN classifier (bias and variance)
    00:14
  • Support vector machine (SVM) introduction
    07:13
  • Support vector machine (SVM) implementation
    03:39
  • Machine Learning Quiz
    3 questions

Instructor

Holczer Balazs
Software Engineer
Holczer Balazs
  • 4.5 Instructor Rating
  • 21,756 Reviews
  • 181,788 Students
  • 33 Courses

Hi! 

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.

Take a look at my website if you are interested in these topics!

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