C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Technical Analysis and Neural Network Preprocessing

We cannot overstate the importance of preprocessing in developing a forecasting model. There is a large body

of information related to the study of financial market behavior called Technical Analysis. You can use the

mathematical studies defined by Technical Analysis to preprocess your input data to reveal predictive

features. We will present a sampling of Technical Analysis studies that can be used, with formulae and

graphs.

Moving Averages

Moving averages are used very widely to capture the underlying trend of a price move. Moving averages are

simple filters that average data over a moving window. Popular moving averages include 5−, 10−, and 20

period moving averages. The formula is shown below for a simple moving average, SMA:

     SMA

t

 = ( P

t

 + P

t−1

 + ...  P

t−n

)/ n

     where  n = the number of time periods back

        P

−n

= price at n time periods back

An exponential moving average is a weighted moving average that places more weight on the most recent

data. The formula for this indicator, EMA is as follows:

     EMA

t

 = (1 − a)P

t

 + a ( EMA

t−1

)

     where  a = smoothing constant  (typical 0.10)

        P

t

= price at time t