Quantitative Finance & Algorithmic Trading in Python
4.3 (743 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,059 students enrolled

Quantitative Finance & Algorithmic Trading in Python

Stock market, Markowitz-portfolio theory, CAPM, Black-Scholes formula, value at risk, monte carlo simulations, FOREX
4.3 (743 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,059 students enrolled
Created by Holczer Balazs
Last updated 11/2019
English
English [Auto], Indonesian [Auto], 3 more
  • Polish [Auto]
  • Romanian [Auto]
  • Thai [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 5 hours on-demand video
  • 7 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 74 lectures 05:02:28
+ Stock Market Basics
6 lectures 20:31
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
4 lectures 12:58
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
+ Modern Portfolio Theory (Markowitz-model)
14 lectures 01:06:12
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
+ Capital Asset Pricing Model (CAPM)
7 lectures 26:31
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
Capital Asset Pricing Model Quiz
4 questions
+ Derivatives Basics
7 lectures 19:07
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
+ Random Behaviour in Finance
6 lectures 28:34
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
9 lectures 49:46
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
+ Value At Risk (VaR)
4 lectures 22:00
What is Value-at-Risk?
03:09
Value-at-Risk introduction
07:40
Value at risk implementation I
05:07
Value at Risk Quiz
3 questions
+ Machine Leaning in Finance
8 lectures 42:55
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