My Custom Bollinger Bands TEST

Understanding Bollinger Bands: A Dynamic Tool for Market Volatility

What Are Bollinger Bands?

Bollinger Bands are one of the most popular technical analysis tools, developed by John Bollinger in the 1980s.
They are designed to measure market volatility and identify potential overbought or oversold conditions.

At their core, Bollinger Bands consist of three lines:

1. Middle Band — A simple moving average (SMA), usually set to 20 periods.
2. Upper Band — The SMA plus two standard deviations.
3. Lower Band — The SMA minus two standard deviations.

“Volatility is not always your enemy; it’s your signal.” — John Bollinger

💡 Quick Callout

How They Work

When the market becomes more volatile, the bands widen.
When the market becomes less volatile, the bands contract.

This dynamic expansion and contraction help traders visualize when prices might be reaching extremes.

Example:

• If the price touches or exceeds the upper band, it may indicate overbought conditions.
• If the price touches or falls below the lower band, it may suggest oversold conditions.

However, these signals should not be used in isolation — always confirm with volume, trend direction, or momentum indicators.

📘 Key Insights

• Bollinger Bands adapt automatically to volatility.
• Price staying near the upper band indicates strong buying pressure.
• Price staying near the lower band shows strong selling pressure.
• Band squeezes often precede large price moves — either upward or downward.

Marked List: Common Bollinger Band Strategies

✅ Bollinger Bounce
When price touches one band and “bounces” back toward the opposite one. Often used in range-bound markets.

✅ Bollinger Squeeze
Occurs when the bands narrow significantly, suggesting a period of low volatility that might lead to a breakout.

✅ Band Breakout
Traders look for the price to close outside the bands, interpreting it as the start of a new trend.

Example in Python

You can easily calculate Bollinger Bands using the pandas library:

import pandas as pd# Assume df contains a 'Close' columndf['SMA'] = df['Close'].rolling(window=20).mean()df['STD'] = df['Close'].rolling(window=20).std()df['UpperBand'] = df['SMA'] + (2 * df['STD'])df['LowerBand'] = df['SMA'] - (2 * df['STD'])

This simple script generates upper and lower Bollinger Bands around your price data.

Final Thoughts

“Markets are like rubber bands — when they stretch too far, they snap back.”

Bollinger Bands are a versatile and adaptive tool.
Whether you’re a day trader or a long-term investor, understanding how prices behave relative to these bands can sharpen your timing, reduce false signals, and boost your confidence in reading the market.