📈 Bollinger Band Reversion Strategy with Python – Buy Low, Sell High!

Let's explore an exciting and popular strategy in the world of technical trading – the Bollinger Band Reversion strategy. It’s based on a simple idea:

Buy when the price touches the lower band, and sell when it touches the upper band.

Let’s understand it and implement this strategy using Python!

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🎓 What Are Bollinger Bands?

Bollinger Bands are a technical analysis tool developed by John Bollinger. They consist of:

• Middle Band – A simple moving average (usually 20-day)

• Upper Band – SMA + 2 standard deviations

• Lower Band – SMA - 2 standard deviations

These bands help visualize volatility and overbought/oversold levels.

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💡 Strategy: Buy Low, Sell High

1. Buy when price hits the lower band (oversold)

2. Sell when price hits the upper band (overbought)

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🔧 Let's Code It in Python!

📦 Step 1: Install Required Libraries

pip install pandas yfinance matplotlib

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🧪 Step 2: Python Code for Bollinger Band Reversion Strategy

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Step 1: Download historical data
symbol = "AAPL"  # You can change this to any stock symbol
data = yf.download(symbol, start="2022-01-01", end="2023-12-31")

# Step 2: Calculate Bollinger Bands
data['SMA20'] = data['Close'].rolling(window=20).mean()
data['STD20'] = data['Close'].rolling(window=20).std()

data['UpperBand'] = data['SMA20'] + (2 * data['STD20'])
data['LowerBand'] = data['SMA20'] - (2 * data['STD20'])

# Step 3: Create Buy/Sell signals
#data['Buy'] = data['Close'] < data['LowerBand']
#data['Sell'] = data['Close'] > data['UpperBand']

# Step 4: Plot the strategy
plt.figure(figsize=(14, 7))
plt.plot(data['Close'], label='Close Price', alpha=0.5)
plt.plot(data['UpperBand'], label='Upper Band', linestyle='--', color='red')
plt.plot(data['LowerBand'], label='Lower Band', linestyle='--', color='green')
plt.plot(data['SMA20'], label='SMA 20', color='blue')

# Plot Buy/Sell signals
#plt.plot(data[data['Buy']].index, data['Close'][data['Buy']], '^', color='green', markersize=10, label='Buy Signal')
#plt.plot(data[data['Sell']].index, data['Close'][data['Sell']], 'v', color='red', markersize=10, label='Sell Signal')

plt.title(f'{symbol} Bollinger Band Reversion Strategy')
plt.legend()
plt.grid()
plt.show()

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📊 Example Output:

You'll see a beautiful chart:

• Green arrows = Buy when price is below lower band

• Red arrows = Sell when price is above upper band

• Bands = Indicators of volatility

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🧠 Strategy Summary

• Pros:

  • Works well in sideways markets

  • Helps spot mean reversion opportunities

• Cons:

  • Can give false signals in trending markets

  • Needs confirmation with other indicators (like RSI or volume)

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🚀 Next Steps

Try these enhancements:

• Add RSI filter for confirmation

• Backtest the strategy

• Automate it with live data (using alpaca, kite, etc.)

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🏁 Final Thoughts

The Bollinger Band Reversion Strategy is a classic example of "buy low, sell high" made smarter with math! 📉📈
And with just a few lines of Python code, you can visualize and understand market behavior like a pro.

📌 Disclaimer:

This blog is intended for educational purposes only. It does not constitute financial, investment, or professional advice. The information provided is based on personal research and for learning use only. Please consult with a certified financial advisor or conduct your own research before making any investment decisions.