## What will we cover?

In this tutorial we will calculate and visualize the **MACD** for a stock price.

## Step 1: Retrieve stock prices into a DataFrame (Pandas)

Let’s get started. You can get the CSV file from here or get your own from **Yahoo! Finance**.

```
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
data = pd.read_csv("AAPL.csv", index_col=0, parse_dates=True)
```

## Step 2: Calculate the MACD indicator with Pandas DataFrame

First we want to calcite the **MACD**.

The calculation (12-26-9 MACD (default)) is defined as follows.

- MACD=12-Period EMA − 26-Period EMA
- Singal line 9-Perioed EMA of MACD

Where EMA is the Exponential Moving Average we learned about in the last lesson.

```
exp1 = data['Close'].ewm(span=12, adjust=False).mean()exp2 = data['Close'].ewm(span=26, adjust=False).mean()data['MACD'] = exp1 - exp2data['Signal line'] = data['MACD'].ewm(span=9, adjust=False).mean()
```

Now that was simple, right?

## Step 3: Visualize the MACD with matplotlib

To visualize it you can use the following with **Matplotlib**.

```
fig, ax = plt.subplots()
data[['MACD', 'Signal line']].plot(ax=ax)
data['Close'].plot(ax=ax, alpha=0.25, secondary_y=True)
```

Resulting in an output similar to this one.

## 12% Investment Solution

Would you like to get 12% in return of your investments?

D. A. Carter promises and shows how his simple investment strategy will deliver that in the book **The 12% Solution**. The book shows how to test this statement by using backtesting.

**Did Carter find a strategy that will consistently beat the market?**

Actually, it is not that hard to use Python to validate his calculations. But we can do better than that. If you want to work smarter than traditional investors then continue to read here.

## Next Steps?

Want to learn more?

This is part of the **FREE** online course on my page. No signup required and 2 hours of free video content with code and **Jupyter** **Notebooks** available on **GitHub**.

Follow the link and read more.