Volatility indicator: Bollinger Bands

Financial Trading in Python

Chelsea Yang

Data Science Instructor

What are Bollinger Bands?

Developed by John Bollinger
- "Bollinger on Bollinger Bands"

Measure price volatility
Composed of three lines:
- Middle band: n-period simple moving average
- Upper band: k-standard deviations above the middle band
- Lower band: k-standard deviations below the middle band

Bollinger Bands implications

The wider the bands, the more volatile the asset prices.

Measure whether a price is too high or too low on a relative basis:
- Relatively high: price close to the upper band
- Relatively low: price close to the lower band

Implementing Bollinger Bands in Python

# Define the Bollinger Bands
upper, mid, lower = talib.BBANDS(stock_data['Close'],
                                 nbdevup=2,
                                 nbdevdn=2,
                                 timeperiod=20)

Plotting Bollinger Bands

import matplotlib.pyplot as plt

# Plot the Bollinger Bands 
plt.plot(stock_data['Close'], label='Price')
plt.plot(upper, label="Upper band")
plt.plot(mid, label='Middle band')
plt.plot(lower, label='Lower band')


# Customize and show the plot
plt.title('Bollinger Bands')
plt.legend()
plt.show()

Bollinger Bands plot

Let's practice!

Financial Trading in Python