Hello and welcome! In today's lesson, we will be comparing Simple Moving Average (SMA) and Exponential Moving Average (EMA) using Tesla ($TSLA) stock data. This lesson will help you revise how to handle financial data with Pandas, calculate both SMA and EMA, and create visualizations to understand and compare these technical indicators.

Goal: By the end of this lesson, you will be able to load financial data, compute SMA and EMA, and visualize these indicators to make informed trading decisions.

Lesson Plan:

1. Loading and Preparing the Dataset
2. Calculating and Comparing SMA and EMA
3. Subsetting the Data for Visualization
4. Plotting SMA and EMA for Comparison

Let's start by loading the Tesla dataset and preparing it for analysis. We will convert the 'Date' column to datetime format and set it as the index.

We'll calculate the 20-day Simple Moving Average (SMA) and Exponential Moving Average (EMA) of the closing prices.

To make the visualization clearer, we'll focus on the year 2018.

Finally, we will plot the closing prices along with the SMA and EMA to visualize and compare these indicators using Matplotlib.

The output of the above code will be a visual line graph showing TSLA's closing price, along with the 20-day SMA and EMA values for the year 2018. This graphical representation allows us to visually compare the SMA and EMA against the actual closing prices, thus demonstrating their utility in trend analysis and trading decision-making.

In this lesson, you revised how to load and preprocess financial data, compute both Simple Moving Average (SMA) and Exponential Moving Average (EMA), and visualize the results to compare these technical indicators. Practice exercises will follow, which will solidify your understanding and help you to apply these concepts to make informed trading decisions. The exercises will enhance your skills in handling real-world financial data and in implementing financial analyses effectively.