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Stocks & Commodities V. 27:10 (22-31): Volume-Weighted MACD Histogram by David Hawkins
Stocks & Commodities V. 27:10 (22-31): Volume-Weighted MACD Histogram by David Hawkins
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Volume-Weighted MACD Histogram by David Hawkins
Find out how the performance of the Macd improves by volume-weighting it.
MY goal was to make the Macd-histogram (Macdh) perform better by volume weighting it. It uses three exponential moving averages. So, whats needed is to volume weight the exponential moving average (Ema). The Ema is widely used because it is more responsive than the simple moving average (Sma). The volume-weighted moving average (Vwma) responds proportionately more to higher-volume trading so it emphasizes significant trading while deemphasizing less-significant, light-volume trading. The goal here is to combine them into one moving average, the volume-weighted exponential moving average (Vwema), using an algorithm that is simple and easy to implement with indicator scripting languages in standard technical analysis programs, such as the Indicator Builder in MetaStock.
EMA FROM THE SMA
The Sma is just the mean of the prices of the last n trading bars. One simply takes the sum of those prices and divides by n. To make the Ema, we start by making the Sma for the first n bars in the dataset. Thereafter, the general formula is:
Percentage * (present bars price) + (1 percentage)*(last bars Ema)
Where, for an n-period Ema, the percentage is given by 2/(n + 1)
This has the effect of both giving most weight to the most recent bars and of including information from all previous bars with decreasing weights going back in time.
Find out how the performance of the Macd improves by volume-weighting it.
MY goal was to make the Macd-histogram (Macdh) perform better by volume weighting it. It uses three exponential moving averages. So, whats needed is to volume weight the exponential moving average (Ema). The Ema is widely used because it is more responsive than the simple moving average (Sma). The volume-weighted moving average (Vwma) responds proportionately more to higher-volume trading so it emphasizes significant trading while deemphasizing less-significant, light-volume trading. The goal here is to combine them into one moving average, the volume-weighted exponential moving average (Vwema), using an algorithm that is simple and easy to implement with indicator scripting languages in standard technical analysis programs, such as the Indicator Builder in MetaStock.
EMA FROM THE SMA
The Sma is just the mean of the prices of the last n trading bars. One simply takes the sum of those prices and divides by n. To make the Ema, we start by making the Sma for the first n bars in the dataset. Thereafter, the general formula is:
Percentage * (present bars price) + (1 percentage)*(last bars Ema)
Where, for an n-period Ema, the percentage is given by 2/(n + 1)
This has the effect of both giving most weight to the most recent bars and of including information from all previous bars with decreasing weights going back in time.
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