Quantitative Finance & Algorithmic Trading in Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Why to use Python?

Lecture 3: Financial models

Chapter 2: Environment Setup

Lecture 1: Installing Python

Lecture 2: Installing PyCharm

Chapter 3: Stock Market Basics

Lecture 1: Present value and future value of money

Lecture 2: Time value of money implementation

Lecture 3: Stocks and shares

Lecture 4: Commodities

Lecture 5: Currencies and the FOREX

Lecture 6: Short and long positions

Chapter 4: Bonds Theory

Lecture 1: What are bonds?

Lecture 2: Yields and yield to maturity

Lecture 3: Yields and yield to maturity

Lecture 4: Interest rates and bonds

Lecture 5: Macaulay duration

Lecture 6: Risks with bonds

Lecture 7: Stocks and bonds

Chapter 5: Bonds Implementation

Lecture 1: Bonds pricing implementation I

Lecture 2: Bonds pricing implementation II

Lecture 3: Exercise – continuous model for discounting

Lecture 4: Solution – continuous model for discounting

Chapter 6: Modern Portfolio Theory (Markowitz-Model)

Lecture 1: What are mean, variance and correlation?

Lecture 2: The main idea – diverzification

Lecture 3: Mathematical formulation

Lecture 4: Expected return of the portfolio

Lecture 5: Expected variance (risk) of the portfolio

Lecture 6: Efficient frontier

Lecture 7: Sharpe ratio

Lecture 8: Capital allocation line

Chapter 7: Markowitz-Model Implementation

Lecture 1: Markowitz model implementation I

Lecture 2: Markowitz model implementation II

Lecture 3: Markowitz model implementation III

Lecture 4: Markowitz model implementation IV

Lecture 5: Markowitz model implementation V

Chapter 8: Capital Asset Pricing Model (CAPM) Theory

Lecture 1: Systematic and unsystematic risk

Lecture 2: Capital asset pricing model formula

Lecture 3: The beta value

Lecture 4: What is linear regression?

Lecture 5: Capital asset pricing model and linear regression

Chapter 9: Capital Asset Pricing Model (CAPM) Implementation

Lecture 1: Capital asset pricing model implementation I

Lecture 2: Capital asset pricing model implementation II

Lecture 3: Capital asset pricing model implementation III

Lecture 4: Exercise – normal distribution of returns

Lecture 5: Solution – normal distribution of returns

Chapter 10: Derivatives Basics

Lecture 1: Introduction to derivatives

Lecture 2: Forward and future contracts

Lecture 3: Swaps and interest rate swaps

Lecture 4: Credit default swap (CDS)

Lecture 5: Options basics

Lecture 6: Call option

Lecture 7: Put option

Lecture 8: American and european options

Chapter 11: Random Behavior in Finance

Lecture 1: Types of analysis

Lecture 2: Random behavior of returns

Lecture 3: Wiener-processes and random walks

Lecture 4: Wiener-process implementation

Lecture 5: Stochastic calculus introduction

Lecture 6: Ito's lemma in higher dimensions

Lecture 7: Solving the geometric random walk equation

Lecture 8: Geometric brownian motion implementation

Chapter 12: Black-Scholes Model

Lecture 1: Black-Scholes model introduction – the portfolio

Lecture 2: Black-Scholes model introduction – dynamic delta hedge

Lecture 3: Black-Scholes model introduction – no arbitrage principle

Lecture 4: Solution to Black-Scholes equation

Lecture 5: The greeks

Lecture 6: How to make money with Black-Scholes model?

Lecture 7: Long Term Capital Management (LTCM)

Chapter 13: Black-Scholes Model Implementation

Lecture 1: Black-Scholes model implementation

Lecture 2: What is Monte-Carlo simulation?

Lecture 3: Predicting stock prices with Monte-Carlo simulation

Lecture 4: Black-Scholes model implementation with Monte-Carlo simulation I

Lecture 5: Black-Scholes model implementation with Monte-Carlo simulation II

Lecture 6: Black-Scholes model implementation with Monte-Carlo simulation III

Chapter 14: Value at Risk (VaR)

Lecture 1: What is Value-at-Risk?

Lecture 2: Value-at-Risk introduction

Lecture 3: Value at risk implementation

Lecture 4: Value at risk implementation with Monte-Carlo simulation I

Lecture 5: Value at risk implementation with Monte-Carlo simulation II