# How to compute the Bollinger Bands using Python

• 1 minBollinger Bands are a technical analysis tool that uses moving averages and standard deviations to determine overbought and oversold conditions. Here's how to calculate Bollinger Bands in Python:

- Calculate the moving average: use the
`rolling`

method to calculate the moving average of the close prices. - Calculate the standard deviation: use the
`std`

method to calculate the standard deviation of the close prices. - Calculate the upper band: add the moving average plus two times the standard deviation.
- Calculate the lower band: subtract two times the standard deviation from the moving average.

Here's a sample code for calculating Bollinger Bands in Python:

```
import pandas as pd
import yfinance as yf
prices = yf.download(["AAPL"])["Adj Close"]
def bollinger_bands(prices, window=20, num_of_std=2):
rolling_mean = prices.rolling(window).mean()
rolling_std = prices.rolling(window).std()
upper_band = rolling_mean + (rolling_std * num_of_std)
lower_band = rolling_mean - (rolling_std * num_of_std)
return rolling_mean, upper_band, lower_band
print(bollinger_bands(prices))
```

Replace `prices`

with the data series of close prices. The `window`

argument is the size of the moving average window (defaults to 20), and `num_of_std`

is the number of standard deviations to use in the calculation (defaults to 2).