Technical analysis is the study of prices. Proponents of the discipline argue that history has a tendency to repeat and a study of market action in the past can be a useful tool for forecasting the likely direction of price trends in the future.
Technicians use price charts and various indicators to develop their forecasts. Price charts are studied to determine if there are any significant visual cues that can be used to identify potential trends. This is a subjective form of analysis because each analyst will bring unique biases to the chart.
Despite the subjective nature of chart analysis, there have been some attempts in recent years to program patterns and test their effectiveness. The results have been mixed with some researchers finding a few patterns are useful while many patterns do not hold up to scrutiny.
Advocates of technical analysis can argue that pattern analysis is subjective, and that any programming effort will also be subjective. This could be true. But, this argument does not hold up for indicators. An indicator is defined with a formula and can be readily tested.
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An Encyclopedic Effort to Test Indicators
While testing can be done in an objective manner, it is important to remember that the tests need to be consistent if the results are used to compare different indicators. Ideally, the tests would follow the same general rules for entry and exit costs and other parameters.
While this sounds simple enough, few analysts have published results of a large number of tests. One who has is Robert W. Colby who compiled descriptions of hundreds of technical indicators in his 2003 book, The Encyclopedia of Technical Market Indicators.
Results of some tests are readily available in an article called, 126 Comparably Measured Technical Market Indicators Ranked by Annual Relative Advantage.
Comparably measured means the same data set was used for each indicator. The same transaction costs were used for each test. In fact, the same programmer devised the testing strategy and that ensures a level of comparability that is rarely found in technical analysis.
These tests answer several important questions. Of course, traders will want to know what works. But, traders also have access to information about which indicators do not work. Traders can also learn about the specific status of their favorite indicator.
What Works Well
Colby helpfully provides a table with his results. The results shown below are ranked by annual relative advantage.
Annual Relative Advantage is the most effective way to quickly rank indicators for those interested in maximizing profit. As Colby explains, “Annual Relative Advantage is the most effective way to quickly rank indicators for those interested in maximizing profit. “
Annual Relative Advantage is the returns compared to a buy and hold strategy divided by the number of years in the test. This ratio makes indicators measured over different time intervals more comparable and provides a baseline for traders to determine how an indicator performs.
An indicator may be profitable, but it also might not outperform a buy and hold strategy. A trader looking solely at returns could be deceived into thinking profits mean the strategy is worth following. The fact that it underperforms buy and hold could mean it is not worth the expense of following.
Annual Relative Advantage is one of the few metrics that directly compares an indicator to the buy and hold returns that should be the baseline for active traders.
The table below summarizes the top 5 indicators, based on these tests.
At the top of the ranking is a very simple indicator, an exponential moving average (EMA) measured over five days. Buy signals occur when the closing price is above the EMA and sell signals are given when the close is below the EMA.
This strategy assumes that the trader takes a short position when the indicator is on a short signal. This is an active strategy with the average trade lasting less than 6 days.
The weighted moving average, or WMA, assigns a different weighting factor to each day’s data. The most recent closing in the 6 day WMA counts 6 times as much as the oldest piece of data. Again, the strategy uses short selling and would be short term with the average trade open just 6 days.
Both indicators catch major trends and will have frequent losing trades. But, in the long run, a short term moving average can be a profitable strategy.
The other three indicators in the top five require more work to calculate and the indicators are not readily available. The top two are relatively easy to find and almost all traders should be able to follow strategies relying on those moving averages,
What Doesn’t Work Well
On the other hand, there are a number of indicators that fail to beat a buy and hold strategy. These indicators carry a negative value in the Annual Relative Advantage. As Colby explains, these indicators “would not be appropriate indicators for those seeking profit maximization.”
The bottom portion of the table is reproduced below.
While some of these are more obscure indicators like the Qstick or Klinger Oscillator, some are more familiar names like the RSI, Key Reversal Days and Bollinger Bands.
Here, we see the value of an indicator even if it underperforms buy and hold. Bollinger Bands would have a win rate of 88.6% for long trades, ignoring the short positions which are included in the results shown above.
The rules for the Bollinger Bands would be to use a 10 day simple moving average and buy when the closing prices falls below the lower Band which is set at 2 standard deviations. The trade would be closed when the closing price breaks the upper Band. Trades lasted an average of 86 days.
An example of this strategy is shown in the next chart.
It will not catch all trends but has a high probability of capturing winning trades.
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