a while ago i heard about adaptive averages, like jurik, tilson et al. i did not study teh subject in depth so far, but i have a starting point i would like to discuss in principal and in terms of usability.
in principal, i currently use the following concept.
1. take the last eleven close prices and derive the ten percentage changes.
2. calculate the mean percentage change (mpc)
3. take the last percentage change (lpc) and calculate its likelihood if you assume a normal distribution with a mean of the value derived under 2. and a standard deviation of 1. call this P(lpc,mpc,1)
4. calculate (1-P(lpc,mpc,1))
now you have a figure between 0 and 1, which is nearer to 0 if the last percentage change was very near the mean percentage change of the last ten days and which increases towards 1 if the last percentage change is far of the mean of the last ten days.
5. define adaptiveCoefficient as a positive number
6. multiply it by (1-P(lpc,mpc,1)) in order to derive numbers greater than 1 for bigger adaptiveCoefficients. calls this product final adaptive coefficient (fac).
now we have a number that can vary between 0 and the adaptiveCoefficient
6. define E as the exponent for an exponential moving average.
7. calculate a daily simple EMA using E
8. calculate and adaptive EMA by multiplying E with fac, followed by the normal EMA procedure.
it is just a thought that i developed in a couple of minutes in excel. there are several weaknesses, like the use of a normal distribution which flattens out at the tails, while we might like a different feature for this purpose. and the adaptive coefficient is a little strange as well. but i think this must be somehow the basic concept of adaptive averages. if this is true, now my question. do people here have experience with using them for bollinger bands for example? or macd or stuff of that kind?
peace
in principal, i currently use the following concept.
1. take the last eleven close prices and derive the ten percentage changes.
2. calculate the mean percentage change (mpc)
3. take the last percentage change (lpc) and calculate its likelihood if you assume a normal distribution with a mean of the value derived under 2. and a standard deviation of 1. call this P(lpc,mpc,1)
4. calculate (1-P(lpc,mpc,1))
now you have a figure between 0 and 1, which is nearer to 0 if the last percentage change was very near the mean percentage change of the last ten days and which increases towards 1 if the last percentage change is far of the mean of the last ten days.
5. define adaptiveCoefficient as a positive number
6. multiply it by (1-P(lpc,mpc,1)) in order to derive numbers greater than 1 for bigger adaptiveCoefficients. calls this product final adaptive coefficient (fac).
now we have a number that can vary between 0 and the adaptiveCoefficient
6. define E as the exponent for an exponential moving average.
7. calculate a daily simple EMA using E
8. calculate and adaptive EMA by multiplying E with fac, followed by the normal EMA procedure.
it is just a thought that i developed in a couple of minutes in excel. there are several weaknesses, like the use of a normal distribution which flattens out at the tails, while we might like a different feature for this purpose. and the adaptive coefficient is a little strange as well. but i think this must be somehow the basic concept of adaptive averages. if this is true, now my question. do people here have experience with using them for bollinger bands for example? or macd or stuff of that kind?
peace