Designing a Mean Reversion Strategy using Bollinger Bands

In this tutorial, we will explore the concept of mean reversion and how to design a trading strategy using Bollinger Bands in Python. Mean reversion is a popular trading strategy that is based on the assumption that asset prices will tend to revert to their historical average over time. Bollinger Bands, developed by John Bollinger, are a technical analysis tool that can be used to identify potential entry and exit points for mean reversion trading strategies.

Photo by Kostiantyn Li on Unsplash

We will start by understanding the theory behind mean reversion and Bollinger Bands and then we will dive into implementing a mean reversion strategy using Python. We will use the yfinance library to download financial data for a real asset and then we will calculate the Bollinger Bands and implement a trading strategy based on mean reversion.

Let’s get started by importing the necessary libraries and downloading the financial data.

import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt

# Downloading financial data for JPM (JPMorgan Chase & Co.)
ticker = 'JPM'
data = yf.download(ticker, start='2020-01-01', end='2023-11-30')

# Displaying the first few rows of the data
print(data.head())

Now that we have downloaded the financial data, let’s move on to understanding the concept of mean reversion and…