Quantifying / Qualifying market structure as a way ofpredicting optimal MA values - eg using Fractal Efficiency

[mark.keenan@xxxxxxxxxxxxxx]

This is a stupid question I know - but was just curious if anyone had done
any work of this?

Assume a Simple Trading System Based on a single moving average on a daily
bar  chart- for example

Trades are entered on a change in slope of the average on a DAILY bar chart
- for example if the slope of the 100 day moving average turns negative the
market is sold.

Assume the following premises:

Results are determined by optimization.

      Certain moving average lengths will work better on some markets than
on others. (eg 100 day moving average works well on Crude Oil)

      Certain moving average lengths will work better at different times on
the same market. (eg 10 day moving average worked well on the FTSE two
years ago, now the 40 day     moving average works better.

Possible Conclusion:

For a given market structure (for lack of a better word) there is an
optimal moving average length. Factors that may contribute to structure
are:


Volatility
Cycle/ wave  activity (Ehlers indicators etc)
ATR
Fractal Efficiency
Seasonality
etc etc

Has anyone done any work (I believe Merrill Lynch commissioned a study
using option volatilities to predict Linear regression values) on this
area:

Essentially Do you believe it is possible to arrive at the following
conclusion, using fractal efficiency  say as an example:

if the FE is > 80% use a 10 day average, if the FE  is between 30% and 40%
use  20 day and so on.

Obviously Fractal efficiency could be anything and is just an idea.

Mark




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