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# Bollinger Bands vs. Keltner Channels

Nov 10, 2020

###### The trend strategies illustrated below can be downloaded in the Free Strategies section.

Keltner Channels were first introduced by Chester Keltner in his 1960 book, How to Make Money in Commodities, and subsequently updated by Linda Raschke. Similar to Bollinger Bands, they consist of a pair of volatility-based envelopes positioned above and below a middle moving average.

# Keltner Channels Calculations

1. Middle line: Exponential moving average of the typical price (H+L+C/3)
2. Upper line: Middle line + Multiple * Average true range
3. Lower line: Middle line – Multiple * Average true range

Both the moving average and average true range use the same lookback period.

# How Are Keltner Channels Different From Bollinger Bands?

Computationally, the Keltner Channels and Bollinger Bands differ in the following ways:

With its averaging effect, some feel that typical price offers a better representation of price action, compared to using closing prices alone. Together with the exponential moving average, it is plausible that Keltner Channels provide faster and more reliable trend detection.

Average true range also tends to be less volatile than standard deviation, creating bands which are smoother but contain more lag. By default, both indicators use a 20-bar lookback period and a volatility multiple of 2. Let’s plot them on a chart and do a graphical comparison.

The indicators are noticeably different and it is difficult to establish a consistent relationship between them. Due to the increased volatility of the standard deviation, I believe the Bollinger Bands tend to encompass the Keltner Channels when the market is trending, and vice versa when the market is flat.

With their intrinsic differences in volatility measurement, it is likely that these indicators will create different trading profiles. For a practical performance comparison, let’s create similar trend following strategies for each indicator, and optimize each strategy over a broad range of parameters. The indicator which gives better peak and average performance will be declared the winner.

# Trend Strategy Logic

The beauty of these volatility-based channels indicators is their adaptive nature. The moving average is an indication of trend direction, while the channels expand and contract in response to market volatility. For trend following strategies, the moving average can conveniently serve as a trailing stop. These adaptive qualities mean that a complete strategy can be built without introducing many parameters, which usually improves the robustness of the strategy. The Bollinger Band strategy will have the following logic:

Buy when the price closes above the upper Bollinger Band
Close long position when price closes below the moving average

Sell when the price closes below the lower Bollinger Band
Close short position when price closes above the moving average

The Keltner Channels strategy will use the same logic. Each indicator has two key parameters — the lookback period for the moving average and the volatility multiple. The lookback period will be optimized from 20-80, in steps of 1. This should cover most swing and longer-term trend trading applications. The volatility multiple will be optimized form 0.25-3, in steps of 0.25. For the Bollinger Bands, volatility multipliers over 3 produce too few trades for a meaningful comparison.

Both these parameters will be simultaneously optimized for each strategy, giving 732 parameter sets per strategy. Note that simultaneous parameter optimization is not a recommended practice in strategy development because it drastically increases the disk of curve fitting. Nonetheless, this shoud be fine for our research purposes here.

# Markets and Timeframe

For a more comprehensive comparison, the two strategies will be tested on three 4-hourly markets—GBPJPY, AUDJPY, EURAUD. For each parameter set, performance will be averaged across these three markets. The test period will be Jan 2006-Oct 2020.

# Performance Metrics

We will use a simple risk-adjusted return metric, the return/maximum drawdown (Ret/DD) ratio. The average and median Ret/DD will be computed across all 732 parameter sets for each strategy, giving a more robust picture of each indicator’s performance.

# Bollinger Bands vs. Keltner Channels Comparisons

The 3D optimization results for both strategies are shown below. Ret/DD is plotted on the vertical axis, while lookback period and volatility multiple are on the horizontal axes.

The Bollinger Bands strategy seems to perform best with a lookback period of about 30, and a volatility multiple of at least 2.25. The Keltner Channels perfer a 40-bar lookback and a volatility mutiple of about 2.25. Of course, if you trade a different timeframe or market, these optimal parameters could well be different.

Across all 732 parameter sets, the Bollinger Bands produce better average and median Ret/DD values. Let’s take a deeper look at the top 5 parameter sets for each indicator.

The Bollinger Bands strategy spends less time in the market, yet comprehensively outperforms the Keltner Channels profitability-wise. From these tests, it is difficult to discern any edge offered by the Keltner Channels.

Are there any redeeming factors for the Keltner Channels? At least they tend to generate slightly more trades than the Bollinger Bands, improving the reliability of your backtest. Sometimes I consider adding a weaker strategy to my portfolio if it adds diversification. Is the Keltner Channels strategy suffciently uncorrelated? Considering that the two indicators are conceptually similar, you’d probably guess not. Let’s run a quick correlation check to find out.

# How Correlated Are They?

For each strategy, the top parameter set was applied and backtests were done on the three markets (GBPJPY, AUDJPY, EURAUD). A correlation matrix was then constructed, showing the correlation of weekly profit over the backtest period.

The values in the matrix are the Pearson Correlation Coefficient. Looking at the correlation between the Bollinger Bands and Keltner Channels strategies (red box), the average coefficient is 0.37. I consider 0.40 or more to indicate significant correlation; this is a little too close for comfort.

Trading highly-correlated strategies is detrimental to your portfolio’s risk-adjusted returns. Since the Keltner Channels don’t seem to perform that well, I cannot recommend trading them together with the Bollinger Bands.

So how would a portfolio consisting of only the Bollinger Bands strategies perform? The portfolio equity curve from 2006-2020 is illustrated below. Not too shabby for such a simple strategy.

For more information on portfolio correlation measurements, and how to create uncorrelated strategies, head over to my article on portfolio composition.

# Wrapping Up

From the tests above, Bollinger Bands have emerged as the clear winner. Compared to the Keltner Channels, they exhibit better profitability with lower market exposure. If you use a different strategy logic, or use the indicators in a countertrend manner, your test results may well vary. Since both indicators tend to produce correlated strategies, it is not advisable to simultaneously trade both in your portfolio.

Even with its simple logic illustrated above, the Bollinger Bands strategy shows promising performance and should provide a good starting point for further strategy development. Subsequent addition of entry filters can help avoid false breakouts and improve overall risk-adjusted performance.

If you want to backtest the Bollinger Bands and/or Keltner Channel strategies, you can download them from the Free Strategies section.

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###### Have a Question?

Want to develop a portfolio of automated trading strategies?

Supercharge your strategy development with StrategyQuant

Access 14-day FREE trial here!

Get up to USD 300 discount!

Strategies need improvement?

Use QuantAnalyzer’s powerful analysis tools

Try the FREE version here!

Get 20% Discount here!

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