History
Bollinger Bands were invented in the early 1980s by John Bollinger, a technical analyst and trader. Bollinger wished to create a tool that would assist traders in determining whether an asset was overbought or oversold.
He noticed that asset prices tended to stay within a narrow range, which he could define using a moving average and standard deviation. Bollinger Bands quickly became popular among traders and are now one of the most widely used technical analysis tools in the financial markets.
Definition
Bollinger Bands are a technical analysis tool comprised of three lines: a simple moving average (SMA) in the middle, an upper band two standard deviations above the SMA, and a lower band two standard deviations below the SMA. Based on the asset’s trading history, the upper and lower bands serve as a relative definition of high and low prices.
When the price of an asset approaches the upper band, it is considered overbought; when it approaches the lower band, it is considered oversold.
Most common types
| Type | Short Definition | Usage |
|---|---|---|
| Standard Bollinger Bands | The original Bollinger Bands created by John Bollinger in the 1980s. | Widely used by traders to identify potential overbought or oversold levels of an asset. |
| %B Bollinger Bands | Measures where the price is in relation to the bands using percentage. | Used to identify potential buy or sell signals based on the percentage of the price that is within the bands. |
| Squeeze Bollinger Bands | Identifies periods of low volatility and potential breakouts. | Used to identify potential entry or exit points during periods of low volatility. |
| Double Bollinger Bands | Uses two sets of Bollinger Bands with different standard deviations to identify trend and momentum. | Used to identify potential trend reversals and to confirm the strength of a trend. |
Formula
The formula for Bollinger Bands is as follows:
Upper Band = SMA + (k * Standard Deviation)
Lower Band = SMA – (k * Standard Deviation)
Where:
SMA = Simple Moving Average
k = The number of standard deviations to use (typically 2)
Standard Deviation = A measure of the volatility of the asset’s price
Calculation example:
Assume we want to calculate Bollinger Bands for the Luis1k stock using a 20-period simple moving average (SMA) and two standard deviations above and below the SMA.
A financial data provider can provide us with the following information:
| Day | Closing Price |
|---|---|
| 1 | 50.00 |
| 2 | 52.50 |
| 3 | 49.75 |
| 4 | 48.25 |
| 5 | 51.00 |
| 6 | 52.75 |
| 7 | 53.00 |
| 8 | 51.50 |
| 9 | 50.25 |
| 10 | 49.75 |
| 11 | 51.25 |
| 12 | 49.50 |
| 13 | 51.75 |
| 14 | 50.00 |
| 15 | 52.25 |
| 16 | 52.00 |
| 17 | 50.50 |
| 18 | 48.25 |
| 19 | 49.00 |
| 20 | 50.25 |
| 21 | 51.50 |
| 22 | 52.00 |
| 23 | 53.25 |
| 24 | 54.75 |
| 25 | 56.25 |
Step 1: Determine the 20-day Simple Moving Average (SMA):
- 20-day SMA = (50.5 + 49.75 + 48.25 + 51 + 52.75 + 53 + 51.5 + 50.25 + 49.75 + 51.25 + 49.5 + 50 + 52.25 + 52 + 50.5 + 48.25 + 49 + 50.25 + 51.5 + 50.75) / 20 = 50.75
Step 2: Calculate the Standard Deviation
- Standard deviation = sqrt[((50.5 – 50.75)^2 + (49.75 – 50.75)^2 + (48.25 – 50.75)^2 + (51 – 50.75)^2 + (52.75 – 50.75)^2 + (53 – 50.75)^2 + (51.5 – 50.75)^2 + (50.25 – 50.75)^2 + (49.75 – 50.75)^2 + (51.25 – 50.75)^2 + (49.5 – 50.75)^2 + (50 – 50.75)^2 + (52.25 – 50.75)^2 + (52 – 50.75)^2 + (50.5 – 50.75)^2 + (48.25 – 50.75)^2 + (49 – 50.75)^2 + (50.25 – 50.75)^2 + (51.5 – 50.75)^2 + (50.75 – 50.75)^2] / 19) = 1.66
Step 3: Calculating Upper and Lower Band:
- Upper Band = 50.75 + (2 * 1.66) = 54.08
- Lower Band = 50.75 – (2 * 1.66) = 47.42
Step 4: Final values for Day 20 are:
- 20-day SMA = 50.75
- Standard deviation = 1.66
- Upper Band = 54.08
Step 5: Do the same for the Days 21-25
Step 6: Final results for Days 20-25
| Day | High | Low | Closing | SMA | SD | Upper Band | Lower Band |
|---|---|---|---|---|---|---|---|
| 20 | 51.8 | 48.5 | 50.25 | 50.75 | 1.66 | 54.08 | 47.42 |
| 21 | 52.5 | 46.5 | 51.5 | 51.15 | 1.49 | 54.13 | 48.17 |
| 22 | 53 | 48.5 | 52 | 51.30 | 1.30 | 53.89 | 48.71 |
| 23 | 54.2 | 50.2 | 53.25 | 51.65 | 1.53 | 54.71 | 48.59 |
| 24 | 55.2 | 53 | 54.75 | 52.05 | 2.04 | 56.13 | 47.97 |
| 25 | 57.5 | 54 | 56.25 | 52.60 | 2.97 | 58.54 | 46.66 |
Step 7: Draw chart and interpret:
Based on the information provided, it appears that the Luis1k stock is currently not oversold. The closing price for Day 25 is higher than the 20-day SMA, which indicates that the stock price is trending upwards.
Interpretation guidelines
•Oversold : The stock is considered oversold if it touches or falls below the lower Bollinger Band.
•Undervalued: If the stock price remains below the lower Bollinger Band for an extended period of time, it may indicate that the stock is undervalued and represents an excellent buy opportunity.
Usecases
1. Identifiying selling conditions: Overbought and oversold conditions can be identified using Bollinger Bands. When the price of a security moves towards the upper band, it may indicate that the security has become overbought and is due for a pullback.
When the price moves towards the lower band, it may indicate that the security has been oversold and is due for a rebound.
2. Measuring volatility: Bollinger Bands can be used to calculate a security’s volatility. When the bands are narrow, the security may be experiencing low volatility, whereas when the bands are wide, the security may be experiencing high volatility.
3. Trading Breakouts: Traders can use Bollinger Bands to help them identify potential breakout opportunities. When the price of a security breaks above the upper band, it may indicate that the security is experiencing a bullish breakout, whereas when the price breaks below the lower band, it may indicate a bearish breakout.
Pros and Cons
Pros
• Volatility measurement: Bollinger Bands are an effective tool for estimating market volatility. The bands can help traders identify periods of high and low volatility, which can help them make better trading decisions.
• Confirmation of trends: Bollinger Bands can also be used to confirm trends. If the price is consistently trading above the middle band and the upper band is sloping upwards, the security may be in an uptrend.
In contrast, if the price is consistently trading below the middle band and the lower band is sloping downwards, the security may be in a downtrend.
• Overbought and oversold conditions: Bollinger Bands can help traders identify potential overbought and oversold conditions. When the price of a security moves towards the upper band, it may indicate that the security has become overbought and is due for a pullback.
When the price moves towards the lower band, it may indicate that the security has been oversold and is due for a rebound.
Cons
• False signals: Bollinger Bands are not always accurate and can produce false signals. For instance, the price may touch the upper or lower band, but the security may continue to move in the same direction, potentially resulting in losses.
Bollinger Bands may not be appropriate for all securities: Bollinger Bands may not be appropriate for all securities. Securities with low liquidity or high volatility, for example, may not be suitable for Bollinger Bands.
• Lagging indicator: Bollinger Bands are a lagging indicator, which means they are based on historical data and may not accurately reflect current market conditions.
To get a more accurate picture of the market, traders may need to use other indicators in addition to Bollinger Bands.
Is it a necessary measurement?
Simpler metrics can be used to assess market conditions and identify potential buying or selling opportunities. Moving averages (MA), for example, are a popular technical indicator that can help identify trends in price movements.
SMAs are calculated by taking the average closing price of a stock over a specified time period, such as 50 or 200 days.
The Relative Strength Index (RSI) is another simple metric for determining the strength of a stock’s price action. The RSI measures the magnitude of recent price changes to determine whether a stock is overbought or oversold.
While Bollinger Bands can provide valuable information about market volatility and potential trading opportunities, they are not always required for profitable trading. Simpler indicators, such as moving averages and the RSI, can help traders and investors analyze market conditions and make informed decisions.
Bollinger Band in the future
The availability and analysis of large amounts of data is one area where technology may have an impact. As market data becomes more abundant, it may become possible to incorporate more complex and sophisticated algorithms into Bollinger Band calculations.
Machine learning algorithms, for example, could be used to identify patterns and trends in market data and, based on these insights, adjust Bollinger Band parameters in real-time.
Integration of Bollinger Bands with other advanced analytical tools, such as artificial intelligence and predictive analytics, is another potential area of development.
Traders and investors may be able to gain a more comprehensive understanding of market conditions and make more accurate predictions about future price movements by combining multiple techniques and approaches.
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