Implementation of TA indicators in Python

I am sure you remember that any TA indicator uses a certain period as a parameter. This period means a number of data points that we take into consideration. To calculate an indicator on every bar, we start from the oldest one (the leftmost on the chart) and then move one by one, updating our dataset with each new bar.

Since we are talking about an absolutely essential thing that lies in the foundation of all TA, let me be very detailed here – probably too detailed – but I want to leave no place for ambiguity or misunderstanding in the following concepts and code samples.

Let’s start with the core concept of time series processing: the sliding window.

Sliding windows

Let’s go back to the example of a random walk (around bars and movies) that we considered in the previous section. The entire dataset, or historical data, consists of 10 data points:

S1 = {0.7, 2, 1.5, 0.3, 2.6, 1.1, 1.8, 0.45, 3.1, 2...