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# Distance Weighted Moving Averages (DWMA and IDWMA)

The distance weighted moving average is another nonlinear filter that provides the basis for further research and exploration. In its traditional form, a distance weighted moving average (DWMA) is designed to be a robust version of a moving average to reduce the impact of outliers. Here is the calculation from the Encyclopedia of Math:

Notice in the example above that “12” is clearly an outlier relative to the other data points and is therefore assigned less weight in the final average. The advantage of this approach to simple winsorization (omitting outliers that are identified from the calculation) is that all of the data is used and no arbitrary threshold needs to be specified. This is especially valuable for multi-dimensional data. By squaring the distance values in the calculation of the DWMA instead of simply taking the absolute value, it is possible to make the average even more insensitive to outliers. Notice that this concept can be also reversed to emphasize outliers or simply larger data points. This can be done by removing the need to invert the distance as a fraction and simply using the distance weights. This can be called an “inverse distance moving average” or IDWMA, and is useful in situations where you want to ignore small moves in time series which can be considered “white noise” and instead make the average more responsive to breakouts. Furthermore, this method may prove more valuable for use in volatility calculations where sensitivity to risk is important. The chart below shows how these different moving averages respond to a fictitious time series with outliers:

Notice that the DWMA is the least sensitive to the price moves and large jumps, while the IDWMA is the most sensitive. Comparatively the SMA response is in between both the DWMA and IDWMA. The key is that neither moving average is superior to one another per se, but rather each is valuable for different applications and can perform better or worse on different time series. With that statement, let’s look at some practical examples. My preference is typically to use returns rather than prices, so in this case we will look at applying the different moving average variations: the DWMA, IDWMA and SMA to two different time series – the S&P500 and Gold. Traders and investors readily acknowledge that the S&P500 is fairly noisy- especially in the short-term. In contrast, Gold tends to be unpredictable using long-term measurements, but large moves tend to be predictable in the short-term. Here is the performance using a 10-day moving average with the different variations from 1995 to present. The rules are long if the average is above zero and cash if it is below (no interest on cash is assumed in this case):

Consistent with anecdotal observation, the DWMA performs the best on the S&P500 by filtering out large noisy or mean-reverting price movements. The IDWMA in contrast performs the worst because it distorts the average by emphasizing these moves. But the pattern is completely different with Gold. In this case the IDWMA benefits from highlighting these large (and apparently useful trend signals), while the DWMA performs the worst. In both cases the SMA has middling performance. One of the disadvantages of a distance weighted moving average is that the calculation ignores the position in time of each data point. An outlier is less relevant if it occurs for example over 60 days ago versus one that occurs today. This aspect can be addressed through clever manipulation of the calculation. However, the main takeaway is that it is possible to use different weighting schemes for a moving average for different time series and achieve potentially superior results. Perhaps an adaptive approach would yield good results. Furthermore, careful thought should go into the appropriate moving average calculation for different types of applications. For example, you may wish to use the DWMA instead of the median to calculate correlations, which can be badly distorted by outliers. Perhaps using a DWMA for performance or trade statistics makes sense as well. As mentioned earlier, using an IDWMA is helpful for volatility-based calculations in many cases. Consider this a very simple tool to add to your quant toolbox.

– By David Varadi of CSSA

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• Marco says:

Really good results, I believe this article will leed to interesting following developments…
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• Ryan says:

Have you looked at John Ehlers and his fractal adaptive moving average?

• Andreas says:

Hi,
is there any mql4 version of this indi?!
Thanks