Quantitative Finance & Algorithmic Trading in Python Quantitative Finance & Algorithmic Trading in Python
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 5 Hours | 662 MB

Stock market, Markowitz-portfolio theory, CAPM, Black-Scholes formula, value at risk, monte carlo simulations, forex

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 !!!

What Will I 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)

Introduction
1 Introduction
2 Course Materials
3 Why to use Python
4 Financial models

Stock Market Basics
5 Present value future value of money
6 Time value of money implementation
7 Stocks shares
8 Commodities
9 Currencies and the FOREX
10 Fundamental terms short and long

Bonds
11 Bonds basics
12 Bond price and interest rate
13 Bond price and maturity
14 Bonds pricing implementation

Modern Portfolio Theory Markowitz-model
15 The main idea – diverzification
16 Mathematical formulation
17 Expected return of the portfolio
18 Expected variance risk of the portfolio
19 Efficient frontier
20 Sharpe ratio
21 Capital allocation line
22 Modern Portfolio Theory implementation – getting data from Yahoo
23 Modern Portfolio Theory implementation – weights
24 Modern Portfolio Theory implementation – mean and variance
25 Modern Portfolio Theory implementation – Monte-Carlo simulation
26 Modern Portfolio Theory implementation – optimization
27 UPDATE order of stocks

Capital Asset Pricing Model CAPM
28 Systematic and unsystematic risk
29 Capital asset pricing model formula
30 The beta value
31 Capital asset pricing model and linear regression
32 Capital asset pricing model implementation I
33 Capital asset pricing model implementation II
34 Capital asset pricing model implementation III

Derivatives Basics
35 Introduction to derivatives
36 Future contracts
37 Interest rate swaps
38 Options basics
39 Call option
40 Put option
41 American and european options

Random Behaviour in Finance
42 Types of analysis
43 Random behaviour of returns
44 Winer-process
45 Stochastic calculus introduction
46 Itos lemma in higher dimensions
47 Brownian-motion implementation

Black-Scholes Model
48 Black-Scholes model introduction – the portfolio
49 Black-Scholes model introduction – dynamic delta hedge
50 Black-Scholes model introduction – no arbitrage principle
51 Solution to Black-Scholes equation
52 The greeks
53 Black-Scholes model implementation I
54 Black-Scholes model implementation II – Monte-Carlo
55 How to make money with Black-Scholes model
56 Long Term Capital Management LTCM

Value At Risk VaR
57 What is Value-at-Risk
58 Value-at-Risk introduction
59 Value at risk implementation I
60 Value at risk implementation II – Monte-Carlo simulation

Machine Leaning in Finance
61 What is machine learning
62 Logistic regression introduction
63 Logistic regression implementation
64 K-nearest neighbor kNN classifier introduction
65 K-nearest neighbor kNN classifier implementation
66 Support vector machine SVM introduction
67 Support vector machine SVM implementation

Long-Term Investing
68 Value investing
69 Efficient market hypothesis

BONUS
70 DISCOUNTS FOR OTHER COURSES