How to Create Moving Averages in Python

This is part 2 in the forecasting stock market prices series in python. Part 1 dealt with getting the daily stock market trading data. In this video we will deal with creating 3 different moving averages based upon the Tesla daily closing price data we obtained from video 1.

As always, I will show you every piece of code you need to accomplish this from the libraries (Pandas, Numpy and Seaborn) and even spacing of loading in a specific library that tends to error out if you load it with all the other libraries. You will iterate through 3 different moving averages (10 day, 30 day and 60 day). We will place them in an array and then create 3 new columns with these moving averages built around the mean.

Once we have done that we will then do a custom, professional looking plot that shows all 3 moving averages plotted against the closing price. This is so that we can quickly see the volatility of the data. Even though Tesla has quite a spike up in 2020. the data shows great fit throughout.

In the next video we will look at the distribution of the data and plot this to see if it is normally distributed. And in the final video we will use LSTM forecasting to end up at a very accurate forecast that is easily tested and can be applied to most other company data for predicting future stock prices.

I hope you found this video interesting and helpful.

Please take a moment to subscribe, like and share!

Thanks again and God bless!