Quantitative Finance & Algorithmic Trading in Python

Stock Market, Bonds, Markowitz-Portfolio Theory, CAPM, Black-Scholes Model, Value at Risk 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.

What you’ll learn

  • Understand the Modern Portfolio Theory and Markowitz model.
  • Understand stock market fundamentals.
  • Understand derivatives (futures and options).
  • Understand stochastic processes and the famous Black-Scholes model.
  • Understand Value-at-Risk (VaR).
  • Understand Value-at-Risk (VaR).
  • Understand bonds and bond pricing.
  • Understand the Capital Asset Pricing Model (CAPM).
  • Understand credit derivatives (credit default swaps).
  • Understand Monte-Carlo simulations.

Course Content

  • A trader’s guide to quantitative trading –> 5 lectures • 19min.
  • Algorithmic trading with Python: How to get started –> 5 lectures • 35min.
  • Applying quantitative finance to algorithmic trading –> 9 lectures • 1hr 25min.

Quantitative Finance & Algorithmic Trading in Python

Requirements

  • 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

  • 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