C++ Neural Networks and Fuzzy Logic: Preface

Herrick Payoff Index

This indicator makes use of other market data that is available besides price information. It uses the volume of

the security, which, for a stock, is the number of shares traded for a stock during a particular interval. It also

uses the open interest, which is the value of the total number of open trades at a particular time. For a

commodity future, this is the number of open short and long positions. This study attempts to measure the

flow of money in and out of a market. The formula for this is as follows (note that a tick is the smallest

permissible move in a given market) :

     Let MP = mean price over a particular interval

     OI = the larger of yesterday’s or today’s open interest

then

     K =[ (MP

t

 − MP

t−1

 ) x dollar value of 1 tick move x volume ]

       x [1 +/−  2/OI]

     HPI

t

 = HPI

t−1

  + [ 0.1 x (K −      HPI

t−1

 )] / 100,000

MACD

The MACD (moving average convergence divergence) indicator is the difference between two moving

averages, and it tells you when short−term overbought or oversold conditions exist in the market. The formula

is as follows:

     Let OSC = EMA1 − EMA2,

     where  EMA1 is for one smoothing constant and time period, for example

     0.15 and 12   weeks

          EMA2 is for another smoothing constant and time period,

           for example

     0.075 and 26   weeks

then

    MACD

t

 = MACD

t−1

  + K x ( OSC

t

 − MACD

t−1

 )

    where K is a smoothing constant, for example, 0.2

C++ Neural Networks and Fuzzy Logic:Preface

Percentage R

318

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The final formula effectively does another exponential smoothing on the difference of the two moving

averages, for example, over a 9−week period.

“Stochastics”

This indicator has absolutely nothing to do with stochastic processes. The reason for the name is a mystery,

but the indicator is composed of two parts: %K and %D, which is a moving average of %K. The crossover of

these lines indicates overbought and oversold areas. The formulas follow:

     Raw %K = 100 x (P − LowX )/(HighX − LowX)

     %K

t

 = [( %K

t−1

   x 2  ) + Raw      %K

t

 ] /3

     %D

t

 = [( %D

t−1

   x 2  ) + %K

t

 ] /3

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C++ Neural Networks and Fuzzy Logic:Preface

“Stochastics”

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