## 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

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**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.

## Want to learn more?

This is part of a 2-hour full video course in 8 parts about Technical Analysis with Python.

If you are serious about learning **Python for Finance check out this course**.

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