Recruitment of participants Quantitative Finance & Algorithmic Trading in Python

Quantitative Finance & Algorithmic Trading in Python
Stock Market, Bonds, Markowitz-Portfolio Theory, CAPM, Black-Scholes Model, Value at Risk and Monte-Carlo Simulations

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)

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.

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

• installing Python
• 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
• Macaulay duration
• 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

• derivatives basics
• options (put and call options)
• forward and future contracts
• credit default swaps (CDS)
• interest rate swaps

Section 7 - Random Behavior in Finance

• random behavior
• Wiener processes
• 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
• the greeks

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
• functions
• data structures in Python (lists, arrays, tuples and dictionaries)
• object oriented programming (OOP)
• NumPy

Thanks for joining my course, let's get started!