## Visualize the trend with pandas rolling statistics

In today’s issue, I’m going to show you how to apply rolling statistics to stock prices with pandas.

“Rolling” a statistic applies a calculation to a chunk of data, slides (or rolls) the chunk forward, and does it again. It’s how all technical analysis calculations are done.

Today I’ll show you two examples: z-score and minimum return.

Learning how to apply rolling statistics unlocks the power of pandas:

- Identify outliers
- Visualize trends
- Apply predictive measures

Unfortunately, most people are lost when it comes to rolling statistics.

But you’re in luck!

I’m going to show you how to do it. Step by step.

## Step 1: Get the data

I’m using Jupyter Notebook. I want to plot my charts inline, so I call *%matplotlib inline* first.

We’ll start by importing the libraries we need.

%matplotlib inline import yfinance as yf

Let’s get some data.

data = yf.download("NFLX", start="2020-01-01", end="2022-06-30")

We’ll use yfinance to get stock data – in this case, Netflix. You can use any stock and any price range you want.

## Step 2: Define the function for z-score

The z-score is the number of standard deviations a value is away from it’s mean. It’s a great way to summarize where a value lies on a distribution.

For example, if you’re 189 cm tall, the z-score of your height might be 2.5. That means you are 2.5 standard deviations away from the mean height of everyone in the distribution.

The math is simple:

(value – average value) / standard deviation of values

Here’s what it looks like in Python.

def z_score(chunk): return (chunk[-1] - chunk.mean()) / chunk.std()

This function accepts a chunk of data. Then it takes the last value from the chunk, subtracts the mean (average), and divides by the standard deviation.

The [-1] means “take the last value from the chunk”.

## Step 3: Create the rolling statistic

Creating the rolling z-score is one line of code using pandas.

rolled = data.Close.rolling(window=30).apply(z_score)

We use the closing price and apply the rolling function to it. The job of *rolling* is to take 30 rows of data and apply the *z_score* function to those rows. Then move forward one row, and do it again.

Now we can plot the z-score over time.

rolled.plot()

And as a histogram.

rolled.hist(bins=20)

You can see a large negative z-score of -4.4. This is a -4.4 standard deviation move in the stock price! It corresponds to the -35% drop in NFLX on 20 April 2022.

**Step 4: Rolling minimum return**

Let’s take a look at the largest percentage drop over a rolling 30-day period.

min_pct_change = ( data .Close .pct_change() .rolling(window=30) .min() )

Here, we calculate the daily percentage change on the closing price. Then we apply the *min* function to the rolling window of data.

Here’s the plot.

min_pct_change.plot()

And the histogram.

min_pct_change.hist(bins=20)

You can see Netflix has had a couple of pretty bad days!