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1-inch piece of Kombu sea vegetable 3 tablespoons + 8 cups filtered water 3 shallots, chopped 1 2 cup chopped onion 1 medium stalk celery, chopped 1 cup chopped carrot 1 2 teaspoons sea salt 1 cup split peas 1 2 teaspoon mustard seed 3 4 teaspoon cardamom seed 1 4 teaspoon allspice seed 1 4 teaspoon ground cumin 4 cups cubed buttercup squash 1 stalk lemon grass, peeled and minced Freshly ground black pepper to taste Sesame oil to taste Lightly wipe the Kombu with a clean damp cloth and let it soak in a little water to reconstitute. Set the Kombu aside. In a large, heavy soup kettle gently saut in two to three tablespoons of filtered water, the shallots, onion, celery, and carrot for about fifteen minutes. Season moderately with several pinches of salt and cook until tender.

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In the equations, aj represents the moving average at the i-th bar, si the i-th bar or data point of the original time series, m the period of the moving average, and c (normally set to 2 / (m + I)) is a coefficient that determines the effective period for the exponential moving average. The equations show that the moving averages differ in how the data points are weighted. In a simple moving average, all data points receive equal weight or emphasis. Exponential moving averages give more weight to recent points, with the weights decreasing exponentially with distance into the past. The front-weighted triangular moving average weighs the more recent points more heavily, but the weights decline in a linear fashion with time; TradeStation calls this a weighted moving average, a popular misnomer. Adaptive moving averages were developed to obtain a speedier response. The goal was to have the moving average adapt to current market behavior, much as Dolby noise reduction adapts to the level of sound in an audio signal: Smoothing increases when the market exhibits mostly noise and little movement (more noise attenuation during quiet periods), and smoothing declines (response quickens) during periods of more significant market activity (less noise suppression during loud

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passages) There are several adaptive moving averages One that seems to work well was developed by Mark Jurik (wwwjurikrescom) Another was VIDYA (Variable Index Dynamic Moving Average) developed by Chande A recursive algorithm for the exponential moving average is as follows: For each bar, a coefficient (c) that determines the effective length (m) of the moving average is multiplied by the bar now being brought into the average and, to the result, is added 10 - c multiplied by the existing value of the moving average, yielding an updated value The coefficient c is set to 20/(10 + m) where m is the desired length or period.

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Chande (1992) modified this algorithm by changing the coefficient (c) from a fixed number to a number determined by current market volatility, the market s loudness, as measured by the standard deviation of the prices over some number of past bars Because the standard deviation can vary greatly between markets, and the measure of volatility needs to be relative, Chande divided the observed standard deviation on any bar by the average of the standard deviations over all bars on the S&P 500 For each bar, he recomputed the coefficient c in the recursive algorithm as 20 / (10 + m) multiplied by the relative volatility, thus creating a moving average with a length that dynamically responds to changes in market activity.