Article.EikonAPI.Python.CustomisableRelativeStrengthIndices

In this article, we build a data-frame to output an instrument's Daily (i) Close Prices, (ii) ln (logarithme naturel / natural logarithm) Return, (iii) Close Price's 10, 30 and 60 Day Moving Averages, (iv) Close Price's ln Return 10, 30 and 60 Day Moving Average, (v) Annualised Standard Deviation (i.e.: volatility) of the 10, 30 and 60 Day Rolling Window (Natural Log) Returns (based on CLOSE prices), and (vi) Relative Strength Index (RSI). When it comes to the RSI data, a great article to read about its implementation can be found here (thank you Umer and Jason!). You may want to read into the installation of the TA-Lib library used to compute RSI data here. You may also want to read into how the way TA-lib calculates RSI; as per its GitHub documentation, it uses a Wilder Smoothing method to compute Moving Averages. Others may use different such methods (e.g.: Exponential Moving Average or Simple Moving Average). More information on Refinitiv Workspace's Chart App RSI can be found here and here. Previous RSI Python functions do not provide great amounts of customisation; in this article, we will create a Python function that does just that.