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Stocks & Commodities V. 23:3 (38-41): Adding Volume To The Move-Adjusted Moving Average by Stephen Bisse
Stocks & Commodities V. 23:3 (38-41): Adding Volume To The Move-Adjusted Moving Average by Stephen Bisse
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Stocks & Commodities V. 23:3 (38-41): Adding Volume To The Move-Adjusted Moving Average by Stephen Bisse
In this second article of the series, we look at how adding volume can help identify large moves in one direction.
In Visiting MOMA, my previous Technical Analysis of STOCKS & COMMODITIES article, a simple moving average (SMA) was adjusted by the relative magnitude of the change between closes to create a move-adjusted moving average (MOMA). The reasoning behind this adjustment was that moving averages on their own can, by definition, never say anything about the future direction of a time series, only give a view of where a time series has been. This holds true regardless of the lookback period used or any weightings applied to the datapoints, be it a linear weighting such as in a weighted moving average (WMA) or an exponential moving average (EMA), which uses an exponent to determine the rate at which the significance of older datapoint decays. It is no coincidence that moving averages often form the basis of trend-following trading systems.
FUTURE DIRECTION
Logically, the only way that a moving average can act as a predictor of the prices is if additional information is incorporated into the calculation. This additional information has to be some sort of leading indicator for the time series in question.
Two well-known variations on moving averages already introduce additional information into the calculation: the volatility index dynamic average (VIDYA) developed by Tushar Chande and the volumeadjusted moving average (VAMA) developed by Richard Arms. The VIDYA uses a volatility index for weighting the datapoints, while the VAMA weights the datapoints in the lookback period based on their corresponding relative volume.
In this second article of the series, we look at how adding volume can help identify large moves in one direction.
In Visiting MOMA, my previous Technical Analysis of STOCKS & COMMODITIES article, a simple moving average (SMA) was adjusted by the relative magnitude of the change between closes to create a move-adjusted moving average (MOMA). The reasoning behind this adjustment was that moving averages on their own can, by definition, never say anything about the future direction of a time series, only give a view of where a time series has been. This holds true regardless of the lookback period used or any weightings applied to the datapoints, be it a linear weighting such as in a weighted moving average (WMA) or an exponential moving average (EMA), which uses an exponent to determine the rate at which the significance of older datapoint decays. It is no coincidence that moving averages often form the basis of trend-following trading systems.
FUTURE DIRECTION
Logically, the only way that a moving average can act as a predictor of the prices is if additional information is incorporated into the calculation. This additional information has to be some sort of leading indicator for the time series in question.
Two well-known variations on moving averages already introduce additional information into the calculation: the volatility index dynamic average (VIDYA) developed by Tushar Chande and the volumeadjusted moving average (VAMA) developed by Richard Arms. The VIDYA uses a volatility index for weighting the datapoints, while the VAMA weights the datapoints in the lookback period based on their corresponding relative volume.
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