Python for Finance
Course descriptionPython for Finance is the crossing point where programming in Python blends with financial theory. Together, they give you the know-how to apply that theory into practice and real-life scenarios. In a world where individuals and companies are aiming to become more and more autonomous, your ability to combine programming skills with financial data will allow you to create independent analyses. And that competency will give you an edge over your competitors or in your personal investments. To prepare you for these multi-faceted challenges, this course provides the relevant topics in financial theory and their hands-on application in Python.
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.
Calculating and Comparing Rates of Return in Python
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.
Meаsuring Investment Risk
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.
Markowitz Portfolio Optimization
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.
The Capital Asset Pricing Model
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!
Multivariate Regression Analysis
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.
Monte Carlo Simulations as a Decision-Making Tool
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.