Matplotlib Tutorial 21 - adding more indicator data to our charts

TL;DR

This tutorial demonstrates how to create functions to generate additional data for plotting in Matplotlib, including a moving average and high minus low values.

Transcript

hello everybody and welcome to part 21 of our matplotlib tutorial series in this video we're gonna do is create a couple of functions that will give us some more data to plot so we don't really have anything on that axis 1 or axis 3 so we're gonna write a couple of quick functions to generate some sort of useful data to place on those on those axes... Read More

Key Insights

• 📈 Moving averages are a popular tool for analyzing trends in data, especially in stock trading.
• 😒 The moving average function in this tutorial uses numpy's repeat and convolve functions for efficient calculation.
• 😘 Calculating the difference between high and low values can provide insights into market volatility.
• 👂 List mapping is a useful technique for applying a function to each pair of values in a list.
• 📔 Customizing the appearance and behavior of plotted data will be covered in future tutorials.
• ❓ The purpose of this tutorial is to generate sample data for Matplotlib, not to provide in-depth financial analysis.
• 😷 The tutorial encourages viewers to ask questions and seek further understanding of the concepts covered.

Questions & Answers

Q: What is the purpose of a moving average?

A moving average is used to calculate trends in data by taking the average of a specified number of data points. It is commonly used in stock analysis to identify buying and selling opportunities.

Q: How is the moving average function implemented?

The moving average function takes two parameters: values (the data points) and window (the number of data points to consider for the average). It uses numpy's repeat and convolve functions to calculate the moving average.

Q: What is the purpose of calculating the difference between high and low values?

Calculating the difference between high and low values provides information about the volatility of the data. It can help identify periods of high or low market activity.

Q: How is the high minus low function implemented?

The high minus low function takes two parameters: highs and lows (the corresponding high and low values). It uses list mapping to apply the high minus low calculation to each pair of values, returning a list of the differences.

Summary & Key Takeaways

• The video tutorial focuses on creating functions to generate data for axes 1 and 3 in Matplotlib.

• The first function created is for calculating a moving average, which is useful for analyzing trends in data.

• The second function calculates the difference between high and low values, providing additional insights for analysis.