Python for Finance: Historical Volatility & Risk-Return Ratios
Today explore historical volatility in python and a method to estimate volatility using the log returns distribution sample variance. We then visualise the historical volatility in terms of the log returns distributions as well as considering a rolling window to plot volatility over time.
In the financial industry, useful measures for decision making are inclusive of both expected returns and volatility. Here we explain and calculate the following risk-return metrics over a rolling time horizon: Sharpe Ratio, Sortino Ratio, M2 Ratio, Max Drawdowns and the Calmar Ratio.
00:00 Intro
01:18 Historical Volatility
07:06 Rolling Window Historical Volatility
08:40 Sharpe Ratio
10:56 Sortino Ratio
13:42 M2 Ratio
16:45 Max Drawdowns
19:50 Calmar Ratio
As a high-level programming language, Python is a great tool for financial data analysis, with quick implementation and well documented API data sources, statistical modules and other frameworks related to the financial industry. We will be using Jupyter Lab as an interactive web browser editor for this series due to ease of use and presenting code in a live notebook is ideal for this tutorial series.
This is the fourth video of many on the topic of Python for Finance. The series will include general techniques used for financial analysis and act as an introduction for more in-depth tutorials that we may explore later (such as time series modelling, building financial dashboards, machine learning ect.).
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