Quantitative Finance & Algorithmic Trading in Python
What you'll learn
- Understand stock market fundamentals
- Understand bonds and bond pricing
- Understand the Modern Portfolio Theory and Markowitz model
- Understand the Capital Asset Pricing Model (CAPM)
- Understand derivatives (futures and options)
- Understand credit derivatives (credit default swaps)
- Understand stochastic processes and the famous Black-Scholes model
- Understand Monte-Carlo simulations
- Understand Value-at-Risk (VaR)
- Understand CDOs and the financial crisis
- Understand interest rate models (Vasicek model)
- You should have an interest in quantitative finance as well as in mathematics and programming!
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.
First of all we have to consider bonds and bond pricing. Markowitz-model is the second step. Then Capital Asset Pricing Model (CAPM). One of the most elegant scientific discoveries in the 20th century is the Black-Scholes model and how to eliminate risk with hedging.
IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!
Section 1 - Introduction
why to use Python programming language
the problem with financial models and historical data
Section 2 - Stock Market Basics
present value and future value of money
stocks and shares
commodities and the FOREX
what are short and long positions?
Section 3 - Bond Theory and Implementation
what are bonds
yields and yield to maturity
bond pricing theory and implementation
Section 4 - Modern Portfolio Theory (Markowitz Model)
what is diverzification in finance?
mean and variance
efficient frontier and the Sharpe ratio
capital allocation line (CAL)
Section 5 - Capital Asset Pricing Model (CAPM)
systematic and unsystematic risks
beta and alpha parameters
linear regression and market risk
why market risk is the only relevant risk?
Section 6 - Derivatives Basics
options (put and call options)
forward and future contracts
credit default swaps (CDS)
interest rate swaps
Section 7 - Random Behavior in Finance
stochastic calculus and Ito's lemma
brownian motion theory and implementation
Section 8 - Black-Scholes Model
Black-Scholes model theory and implementation
Monte-Carlo simulations for option pricing
Section 9 - Value-at-Risk (VaR)
what is value at risk (VaR)
Monte-Carlo simulation to calculate risks
Section 10 - Collateralized Debt Obligation (CDO)
what are CDOs?
the financial crisis in 2008
Section 11 - Interest Rate Models
mean reverting stochastic processes
the Ornstein-Uhlenbeck process
the Vasicek model
using Monte-Carlo simulation to price bonds
Section 12 - Value Investing
long term investing
efficient market hypothesis
APPENDIX - PYTHON CRASH COURSE
basics - variables, strings, loops and logical operators
data structures in Python (lists, arrays, tuples and dictionaries)
object oriented programming (OOP)
Thanks for joining my course, let's get started!
Who this course is for:
- Anyone who wants to learn the basics of financial engineering!
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!