## Learn how you can become a Python programmer in just 12 weeks.

We respect your privacy. Unsubscribe at anytime.

# Pandas: Calculate the Moving Average Convergence Divergence (MACD) for a Stock

## What is the Moving Average Convergence Divergence (MACD) Indicator?

Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA.

https://www.investopedia.com/terms/m/macd.asp

That is easy to understand, right?

The good news is that it is easy to calculate using the Pandas DataFrames.

Well, the MACD is a technical indicator that helps to understand if it is a bullish or bearish market. That is, it can help the investor to understand if he should buy or sell the stock.

## Step 1: Get the historic time series stock price data

A great source to get historic stock price data is by using the Pandas-datareader library to collect it.

```import pandas_datareader as pdr
import datetime as dt
start = dt.datetime(2020, 1, 1)
end = dt.datetime.now()
ticker = pdr.get_data_yahoo("AAPL", start, end)['Adj Close']
print(ticker)
```

Here we collect it for Apple (ticker AAPL) from the beginning of the year 2020.

```Date
2020-01-02    298.292145
2020-01-03    295.392120
2020-01-06    297.745880
2020-01-07    296.345581
2020-01-08    301.112640
...
2020-08-05    439.457642
2020-08-06    454.790009
2020-08-07    444.450012
2020-08-10    450.910004
2020-08-11    444.095001
Name: Adj Close, Length: 154, dtype: float64
```

Note that we only keep the Adjusted Close (Adj Close) column to make our calculations.

The Adjusted Close is adjusted for stock splits, dividend payout and other cooperate operations that affect the price (read more on Investopedia.org).

## Step 2: Make the MACD calculations

The formula for MACD = 12-Period EMA − 26-Period EMA (source)

As the description says, we need the Exponential Moving Averages (EMA) for a 12-days and 26-days window.

Luckily, the Pandas DataFrame provides a function ewm(), which together with the mean-function can calculate the Exponential Moving Averages.

```exp1 = ticker.ewm(span=12, adjust=False).mean()
exp2 = ticker.ewm(span=26, adjust=False).mean()
macd = exp1 - exp2
```

But more is needed. We need to make a signal line, which is also defined.

A nine-day EMA of the MACD called the “signal line,” is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals.

https://www.investopedia.com/terms/m/macd.asp

Hence, we end up with the following.

```exp1 = ticker.ewm(span=12, adjust=False).mean()
exp2 = ticker.ewm(span=26, adjust=False).mean()
macd = exp1 - exp2
exp3 = macd.ewm(span=9, adjust=False).mean()
```

## Step 3: Plot the data

We need to plot two y-scales for the plot. One for the MACD and the 9 day EMA of MACD. And one for the actually stock price.

Luckily the Pandas plot method supports having two y-axis.

```macd.plot(label='AAPL MACD', color='g')
ax = exp3.plot(label='Signal Line', color='r')
ticker.plot(ax=ax, secondary_y=True, label='AAPL')
```

As you see, the first two calls to plot use the same axis (the left side) and the final one on ticker, uses the secondary_y (the right side axis).

Then we need to setup labels, legends, and names on axis.

```ax.set_ylabel('MACD')
ax.right_ax.set_ylabel('Price \$')
ax.set_xlabel('Date')
lines = ax.get_lines() + ax.right_ax.get_lines()
ax.legend(lines, [l.get_label() for l in lines], loc='upper left')
```

The variable lines collects the lines plotted on both y-axis and then makes the legend. This is needed, otherwise only the last legend will be visible.

All together it becomes.

```import pandas_datareader as pdr
import datetime as dt
import matplotlib.pyplot as plt
start = dt.datetime(2020, 1, 1)
end = dt.datetime.now()
ticker = pdr.get_data_yahoo("AAPL", start, end)['Adj Close']
exp1 = ticker.ewm(span=12, adjust=False).mean()
exp2 = ticker.ewm(span=26, adjust=False).mean()
macd = exp1 - exp2
exp3 = macd.ewm(span=9, adjust=False).mean()
macd.plot(label='AAPL MACD', color='g')
ax = exp3.plot(label='Signal Line', color='r')
ticker.plot(ax=ax, secondary_y=True, label='AAPL')
ax.set_ylabel('MACD')
ax.right_ax.set_ylabel('Price \$')
ax.set_xlabel('Date')
lines = ax.get_lines() + ax.right_ax.get_lines()
ax.legend(lines, [l.get_label() for l in lines], loc='upper left')
plt.show()
```

Resulting in the graph.

When the signal line (red one) crosses the MACD (green) line, it is time to sell if the green is below and buy if the green is above.

Notice that this is done on historical data and is no guarantee it will work in the future. While the results look pretty promising, it is not wise to make your investments solely on one indicator.

## 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.

## Want to learn more?

Check out my FREE online video course with Python for Finance.

Learn Python for Financial Data Analysis with Pandas (Python library) in this 2 hour free 8-lessons online course.

Also, check this FREE course out.

Learn Python for Finance with Risk and Return with Pandas and NumPy (Python libraries) in this 2.5 hour free 8-lessons online course.

Want to skill up on Python programming?

Learn Python – Python for Beginners – is an 8+ hours full video course for beginners to master Python.

The course is structured with the following resources to improve your learning experience.

• 17 video lessons teaching you everything you need to know to get started with Python.
• 34 Jupyter Notebooks with lesson code and projects.
• A FREE eBook with all the learnings from the lessons.

## Python for Finance: Unlock Financial Freedom and Build Your Dream Life

Discover the key to financial freedom and secure your dream life with Python for Finance!

Say goodbye to financial anxiety and embrace a future filled with confidence and success. If you’re tired of struggling to pay bills and longing for a life of leisure, it’s time to take action.

Imagine breaking free from that dead-end job and opening doors to endless opportunities. With Python for Finance, you can acquire the invaluable skill of financial analysis that will revolutionize your life.

Make informed investment decisions, unlock the secrets of business financial performance, and maximize your money like never before. Gain the knowledge sought after by companies worldwide and become an indispensable asset in today’s competitive market.

Don’t let your dreams slip away. Master Python for Finance and pave your way to a profitable and fulfilling career. Start building the future you deserve today!

Learn pandas, NumPy, Matplotlib for Financial Analysis & learn how to Automate Value Investing.

“Excellent course for anyone trying to learn coding and investing.” – Lorenzo B.

### 4 thoughts on “Pandas: Calculate the Moving Average Convergence Divergence (MACD) for a Stock”

1. how do you export it to excel?

Reply
• Hi Ramses,

you can see how to do it in the video. Also, download the Jupyter Notebook with the code.

But in general you can export a DataFrame by calling to_excel(“filename.xlsx”) on it.

For more details, please see the video.

Rune

Reply
2. Hi,

Can you make this update during the day as the stock price changes? How would you go about doing this?

Reply
• Hi Ronan,
Great question.
The data source we use here is limited to be daily. We are using the Closing price (or Adj Close). Therefore, with this data source, you would only make trades once per day.
If you want to make day-trading you need a data source that provides prices on smaller interval. If you have that, you can use MACD. I would advice not to rely only on MACD.
Cheers, Rune

Reply