**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)

**Table of Contents**

**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

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