Python for Finance

Curriculum

• Useful Tools Free

  11 Lessons 41 Min

  In this course, we assume that you have already gone through our Introduction to Python course. However, you can add more layers to what you have learnt there. In this section, we will add more Python programming skills to your arsenal, explaining OOP, importing modules, in particular - the pandas-datareader module, as it allows us to download financial data and more.

  Course Introduction Free Must-Have Packages for Finance and Data Science Free Working with Arrays Free Generating Random Numbers Free Important Note on Using Online Financial Data Sources Free Using Financial Data in Python Free Importing and Organizing Data in Python - Part I Free Importing and Organizing Data in Python - Part II Free Importing and Organizing Data in Python - Part III Free Changing the Index of Your Time-Series Data Free Restarting the Jupyter Kernel Free
• Calculating and Comparing Rates of Return in Python Free

  11 Lessons 45 Min

  As an investor, you would like to be able to compare the performance of the stocks in your portfolio. One of the most important measures that will allow you to do that is the rate of return of the stock. This section will explain the relevant theory in detail and will provide you with the tools to do that yourself using Python.

  Considering Both Risk and Return Free What Are We Going to See Next Free Calculating a Security's Rate of Return Free Calculating a Security's Rate of Return in Python - Simple Returns - Part I Free Calculating a Security's Rate of Return in Python - Simple Returns - Part II Free Calculating a Security's Rate of Return in Python - Logarithmic Returns Free What Is a Portfolio of Securities and How to Calculate Its Rate of Return Free Using 'Loc' and 'Iloc' - Notes Free Calculating the Rate of Return of a Portfolio of Securities Free Popular Stock Indices Free Calculating the Rate of Return of Indices Free
• Meаsuring Investment Risk

  10 Lessons 41 Min

  Achieving a high rate of return of your stocks doesn’t come at no cost. Every investment is associated with a certain level of risk, and an investor must be well aware of it before putting their money in a certain basket. This section will teach you how to understand and measure such risk using Python.

  How Do We Measure a Security's Risk Calculating a Security's Risk in Python The Benefits of Portfolio Diversification Calculating the Covariance Between Securities Measuring the Correlation between Stocks Calculating Covariance and Correlation Considering the Risk of Multiple Securities in a Portfolio Calculating Portfolio Risk Understanding Systematic vs. Idiosyncratic Risk Calculating Diversifiable and Non-diversifiable Risk of a Portfolio
• Using Regressions for Financial Analysis

  4 Lessons 22 Min

  Understanding rates of return and risk is not all there is about finance. Working with regression analysis is a must, and you will see that Python only helps you to be quicker and more precise when doing such estimations.

  The Fundamentals of Simple Regression Analysis Running a Regression in Python Are All Regressions Created Equal? Learning How to Distinguish Good Regressions Computing Alpha, Beta, and R Squared in Python
• Markowitz Portfolio Optimization

  4 Lessons 20 Min

  One of the main pillars of modern finance is the Markowitz Portfolio Theory. It relates to building a portfolio optimization model, which is quite a complex task mathematically. However, you can see once more how Python can make such a challenge manageable, so long as we stick to theory and are careful at each step while coding.

  Markowitz Portfolio Theory - One of the main Pillars of Modern Finance Obtaining the Efficient Frontier in Python - Part I Obtaining the Efficient Frontier in Python - Part II Obtaining the Efficient Frontier in Python - Part III
• The Capital Asset Pricing Model

  8 Lessons 26 Min

  As good or bad your portfolio may be, it is not self-sufficient. It is always part of a bigger picture – the market. That’s why you’d always want to compare the performance of your portfolio to the market. The Capital Asset Pricing Model (CAPM), the Beta of a stock, the Sharpe ratio and other measures will come in handy… and will be applied to real data with Python!

  The Intuition behind the Capital Asset Pricing Model (CAPM) Understanding and Calculating a Security's Beta Calculating the Beta of a Stock The CAPM Formula Calculating the Expected Return of a Stock (CAPM) Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice Obtaining the Sharpe Ratio in Python Measuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing
• Multivariate Regression Analysis

  2 Lessons 12 Min

  While in Section 4 we deal with simple regression analysis, here we will take this technique to the next level. As you can guess, multivariate regression analysis is more advanced, but is also more interesting, as it allows you to deal with more complex financial problems.

  Multivariate Regression Analysis - a Valuable Tool for Finance Practitioners Running a Multivariate Regression in Python
• Monte Carlo Simulations as a Decision-Making Tool

  15 Lessons 58 Min

  You can’t consider yourself a full-fledged investor if you don’t know how to use Monte Carlo Simulations. All tools you’ve learnt so far in the course will be essential to your ability to embrace the advantages of this technique for visualizing the potential outcomes of financial operations and improving your estimation of the risk associated with them.

  The Essence of Monte Carlo Simulations What is a Normal Distribution? - Note Monte Carlo Applied in a Corporate Finance Context Monte Carlo: Predicting Gross Profit - Part I Monte Carlo: Predicting Gross Profit - Part II Forecasting Stock Prices with a Monte Carlo Simulation Another Way to Calculate Simple and Log Returns - Note Monte Carlo: Forecasting Stock Prices - Part I Monte Carlo: Forecasting Stock Prices - Part II Monte Carlo: Forecasting Stock Prices - Part III An Introduction to Derivative Contracts The Black-Scholes Formula for Option Pricing Monte Carlo: Black-Scholes-Merton Monte Carlo: Euler Discretization - Part I Monte Carlo: Euler Discretization - Part II