Applied Finance in Python

I had earlier wrote about doing some data courses on DataCamp and what a whirlwind of a journey it has been in the past couple of months! I’ve since learnt how to use data extracted from Yahoo! Finance and compute risk metrics such as Value at Risk (VaR) and maximum drawdown. There’s also putting some of the portfolio optimization theories into practice – calculating Sharpe ratios, and even looking for the efficient frontier. Now I won’t say that I’m an expert on all this yet, but I’m readier to be involved in applying quantitative finance concepts.

Here’s a simple few lines of code for a DataCamp project in which I computed the Sharpe ratios of Amazon and Facebook against the S&P500 in 2016 (because that’s the dataset provided).

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The next project involves using a Machine Learning technique known as logistic regression to predict whether credit card loan applications will be approved or rejected. No specific finance concept, but it’s definitely a tool commonly used in banks.

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I will definitely be playing around with Python a lot more from here on. Hit me up if you have some ideas!

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