Quantitative Finance and Algorithmic Trading in Python – Holczer Balazs

🎓 Learn and Grow with Quantitative Finance and Algorithmic Trading in Python – Holczer Balazs - $139.99 Original price was: $139.99.$29.00Current price is: $29.00.

Discover your true potential with the Quantitative Finance and Algorithmic Trading in Python – Holczer Balazs - $139.99 Original price was: $139.99.$29.00Current price is: $29.00. course. Tailored for individuals at every skill level, this in-depth online program equips you with essential tools and proven strategies to excel in both your personal and professional endeavors.

Salepage link: At HERE. Archive: http://archive.is/wip/dXlQ0

Quantitative Finance & Algorithmic Trading in Python

Markowitz-portfolio theory, CAPM, Black-Scholes formula and Monte-Carlo simulations

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. Hope you will like it!

Course Curriculum

Introduction

• Introduction (1:22)
• Why to use Python
• Financial models (3:03)

Stock Market Basics

• Present value / future value of money (5:10)
• Time value of money implementation (3:02)
• Stocks / shares (5:10)
• Commodities (1:18)
• Currencies and the FOREX (3:56)
• Fundamental terms: short and long (1:55)

Bonds

• Bonds basics (3:09)
• Bond price and interest rate (3:14)
• Bond price and maturity (2:06)
• Bonds pricing implementation (4:29)

Modern Portfolio Theory (Markowitz-model)

• The main idea – diverzification (5:17)
• Mathematical formulation (5:00)
• Expected return of the portfolio (5:27)
• Expected variance (risk) of the portfolio (4:53)
• Efficient frontier (5:33)
• Sharpe ratio (3:03)
• Capital allocation line (3:30)
• Modern Portfolio Theory implementation – getting data from Yahoo (6:08)
• Modern Portfolio Theory implementation – weights
• Modern Portfolio Theory implementation – mean and variance (4:03)
• Modern Portfolio Theory implementation – Monte-Carlo simulation (5:52)
• Modern Portfolio Theory implementation – optimization (8:10)

Capital Asset Pricing Model (CAPM)

• Systematic and unsystematic risk (2:05)
• Capital asset pricing model formula (3:48)
• The beta value (4:49)
• Capital asset pricing model and linear regression (2:40)
• Capital asset pricing model implementation I (4:07)
• Capital asset pricing model implementation II (5:17)
• Capital asset pricing model implementation III (3:45)

Derivatives Basics

• Introduction to derivatives (1:45)
• Future contracts (2:52)
• Interest rate swaps (2:01)
• Options basics (2:32)
• Call option (4:54)
• Put option (2:45)
• American and european options (2:18)

Random Behaviour in Finance

• Types of analysis (5:20)
• Random behaviour of returns (4:32)
• Winer-process (5:12)
• Stochastic calculus introduction (4:20)
• Ito’s lemma in higher dimensions (5:04)
• Brownian-motion implementation (4:06)

Black-Scholes Model

• Black-Scholes model introduction – the portfolio (6:44)
• Black-Scholes model introduction – dynamic delta hedge (6:09)
• Black-Scholes model introduction – no arbitrage principle (4:37)
• Solution to Black-Scholes equation (4:06)
• The greeks (4:36)
• Black-Scholes model implementation I (5:39)
• Black-Scholes model implementation II – Monte-Carlo (9:56)
• How to make money with Black-Scholes model? (1:56)
• Long Term Capital Management (LTCM) (6:03)

Value At Risk (VaR)

• What is Value-at-Risk? (3:09)
• Value-at-Risk introduction (7:40)
• Value at risk implementation I (5:07)
• Value at risk implementation II – Monte-Carlo simulation (6:04)

Machine Learning in Finance

• What is machine learning? (6:08)
• Logistic regression introduction (3:27)
• Logistic regression implementation (10:20)
• K-nearest neighbor (kNN) classifier introduction (8:02)
• K-nearest neighbor (kNN) classifier implementation (3:52)
• Support vector machine (SVM) introduction (7:12)
• Support vector machine (SVM) implementation (3:39)

Long-Term Investing

• Value investing (2:50)
• Efficient market hypothesis

Course Material

• Slides
• Sourcecode

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This course comes with:

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• 📄 Downloadable resources and course materials.
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