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The Trouble with Trend Reversal Indicators

Many of us use various trend reversal indicators to time our trades. Our desire is to determine when prices have changed direction so that we can ride the new trend. Why doesn't it always work out?

The first reason, of course, is that unforeseen events often drive prices in unexpected directions. That is something we can't change and it often makes all of us technical traders crazy. On the other hand, sometimes an unforeseen event is a prelude to a new trend. A stock spikes up on a what seems to be a one-time piece of good fortune and soon falls back. Does it start making its way back up or does it resume a previous down trend?

The conflict within trend reversal indicators is that, though they can definitely tell when prices change direction, they suffer from two problems. One, they often can't determine how significant that move in prices actually will be. Two, they are often lagging indicators. As such, they can be late in providing a signal, sometimes leading the investor to miss a significant portion of a price move.

The most common trend reversal indicators are Moving Average Crossovers and Moving Average Convergence/Divergence (MACD).

Moving Average Crossovers

Moving Average Crossovers work by showing when a faster moving average crosses a slower moving average. A bullish trend, for example, is revealed when the faster moving average crosses above a slower moving average. When the faster moving average crosses below the slower moving average a bearish trend has been identified.

These indicators can be tuned to be more or less responsive. Given that moving averages are lagging indicators, the stronger the filtering (the more days that go into calculating an interval of the average) the slower the indicator will respond to price movements. For example, many traders use the 20-day and 50-day moving averages to look for crossovers. In this case, the 20-day is the faster moving average and the 50-day is the slower moving average. For longer term trends, you might use the 50-day and 200-day moving averages.


To calculate the MACD, moving averages are turned into a momentum oscillator by subtracting the longer moving average from the shorter moving average. The resulting plot forms a line that oscillates above and below zero, without any upper or lower limits. The most popular formula for the "standard" MACD is the difference between a security's 26-day and 12-day Exponential Moving Averages (EMAs).

How MACD is Displayed

Usually, a 9-day EMA of MACD is plotted along side to act as a trigger line. A bullish crossover occurs when MACD moves above its 9-day EMA, and a bearish crossover occurs when MACD moves below its 9-day EMA. The histogram represents the difference between MACD and its 9-day EMA. The histogram is positive when MACD is above its 9-day EMA and negative when MACD is below its 9-day EMA. (Click here to see an example illustrating both moving average crossovers and MACD. The vertical blue bars are the MACD histogram.)

How to use MACD

MACD provides three methods of interpretation.
  • The trend of the MACD signal itself - if the signal is going up when the stock is going down, you should be on the lookout for a reversal in price from bearish to bullish
  • Moving Average Crossover - as described above, if the MACD crosses above the 9-day EMA, a bullish move is predicted and vice versa.
  • Centerline Crossover - this is exhibited when the MACD and the histogram cross the zero line. It is bullish when they are going in a positive direction and bearish when going in a negative direction.
As you can see, MACD builds upon the foundation provided by the Moving Average Crossover method and clearly provides more information for the technical analyst.

The Trouble

Unfortunately, both of these systems suffer from the fact that they are lagging indicators. The trading signal is often just too late and the investor misses out. Even though it tries to anticipate trend reversals, the MACD histogram is even worse as it is a lagging indicator of a lagging indicator (for you calculus buffs out there, you can think of it as a second derivative). To reduce the lagging effect you can, of course, reduce the amount of filtering. This will increase responsiveness to rapid price movements but will have the unfortunate effect of generating more false signals and whipsaws. The technical analyst will need to adjust his filter parameters to suit his style of trading and the past action of the stock being analyzed.

A Different Approach

The TradeRadar software was developed as an attempt to avoid some of the drawbacks of the systems described above while still providing the benefits; ie, the ability to reliably identify trend reversals. TradeRadar starts out by identifying the maximum or minimum points on a price chart. It then applies a formula to price action around the maximum or minimum to determine if a trend reversal is taking place.

Indicators that identify local maximums or minimums are pretty much always correct about picking out when a stock has peaked or bottomed within the window of observation. Where it gets complicated is determining whether the trend is truly reversing. In order to identify a reversal, it turns out we need to apply our old friend the filter. The filtering takes into account what price action is occurring AFTER the absolute high or low. Every chart will have an absolute high or low point but not every chart will flash a BUY or SELL signal.

In an attempt to create a responsive but reliable trading signal, several unusual concepts were applied.

  • A transfer function is applied to the price data. This is how the peaks around local maxima or minima in the price data are generated. A peak in the signal data can only be generated when prices change direction. (Click here to see an example of a peak generated as a BUY signal in the lower chart in the image)
  • The filtering is applied to the signal data, not to the underlying price. This filtering has the effect of shifting the peak slightly to the right of the actual maxima or minima. It also has the important effect of helping to determine whether the signal is reliable. It shows whether price action after the max or min is strong enough to indicate a serious change in trend direction by smoothing out variations in the signal.
As in the indicators based on moving averages, the amount of filtering can be adjusted to vary the responsiveness of the signal. The TradeRadar signal also provides a sensitivity control which has the effect of exaggerating or suppressing the strength of the signal.

A better mousetrap?

So, at the end of the day, is the TradeRadar signal superior to the Moving Average Crossover or MACD systems? Well, we're getting some decent results but the jury is still out. There appears to be a need to develop a familiarity with how to set the filter and sensitivity controls for most reliable performance. And there is a definite need to develop a feel for how to interpret the auxiliary indicators generated by the signal software. These auxiliary indicators include Signal Strength, Signal/Noise Ratio and Kurtosis (an indication of how "peaked" the signal peak's shape is).

I invite readers to visit the TradeRadar site or blog and take a look at the investment results thus far. If you would like to experiment with the TradeRadar signal I encourage you to download the software or, if you're running Microsoft Internet Explorer, try the online version.


Anonymous said…
thanks !! very helpful post!

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