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Re: Forecast Oscillator



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HOW YOU CALCULATE R2?
THANKS
ERKAN
> ----- Original Message ----- 
>   From: Peter Gialames 
>   To: metastock@xxxxxxxxxxxxx 
>   Cc: kernish@xxxxxxxxxxxx 
>   Sent: Thursday, January 10, 2002 9:45 AM
>   Subject: RE: Forecast Oscillator
> 
> 
>   Not sure if this is what you are looking for but
> ...
>    
>   Peter Gialames
>    
>   Here is the text from S&C V. 10:5 (220-224):
> Forecasting Tomorrow's Trading Day by Tushar S.
> Chande, Ph.D.
>    
>   Using linear regression as a crystal ball for
> forecasting the market? After all, if you were to be
> able to
> 
>   determine tomorrow's high, low and close for trend
> changes and placement of stop points, it would
> 
>   simplify your life immeasurably. Can it work?
> Tushar Chande explains how it can be done.
> 
>   Wouldn't you trade better It you could "see" the
> future? A simple linear regression can provide an
> 
>   objective forecast for the next day's high, low
> and close. These ingredients are essential for a
> trading
> 
>   game plan, which can help you trade more
> mechanically and less emotionally. Best of all, a
> regression
> 
>   forecast oscillator, %F, gives early warning of
> impending trend changes. The linear regression
> method is
> 
>   well known for finding a "best-fit" straight line
> for a given set of data. The output of the
> regression are
> 
>   the slope (m) and constant (c) of the equation
> 
>   (1)Y = mX + c
> 
>   Here, m and c are derived from a known set of
> values of the independent variable X and dependent
> 
>   variable Y. The relative strength of the linear
> relationship between X and Y is measured by the
> 
>   coefficient of determination r 2 , which is the
> ratio of the variation explained by the regression
> line to the
> 
>   total variation in Y. Here is a table to help
> interpret the values of r 2 , which range from 0 to
> 1:
> 
>   The coining of the term "regression" can be
> attributed to Sir Francis Galton, who observed in
> the late
> 
>   1800s that tall fathers appeared to have as a rule
> short sons, while short fathers appeared to have as
> a rule
> 
>   tall sons. Galton suggested that the heights of
> the sons "regressed" or reverted to the average.
> Technician
> 
>   Arthur Merrill also had a good explanation in a
> recent issue of STOCKS & COMMODITIES, and Patrick
> 
>   Lafferty recently wrote on an application of
> multiple regression to gold trading. Virtually all
> introductory
> 
>   books on statistics have a detailed discussion of
> the linear regression method.
> 
>   Successful professional traders emphasize the
> importance of having a trading plan. A trading game
> plan,
> 
>   much like that of a football team, clearly defines
> specific actions under different conditions. The
> linear
> 
>   regression method is very useful in developing a
> forecast for the next trading day's high, low and
> close
> 
>   based on the last five trading sessions. The
> method is general and broad-based enough so that it
> can be
> 
>   used with stocks, indices or commodities. The
> forecast is the basis of my trading plan: I can
> define what I
> 
>   should do if the market rises above the forecast
> high, falls below the forecast low or stays within
> the
> 
>   forecast range. This way, I can avoid being
> emotional and trade as mechanically as possible by
> having a
> 
>   plan to rely on.
> 
>   FORECASTING WITH LINEAR REGRESSION
> 
>   I like to use at least 10 days of data and develop
> a forecast for the high, low and close. The five-day
> 
>   regression is a good choice for short-term
> trading. You can use any length of regression you
> like. Here
> 
>   are the calculations with the daily close in a
> spreadsheet format:
> 
>   1 Perform a linear regression with the first five
> days of data to obtain the slope m and constant c
> such
> 
>   that
> 
> 
> 
>   X Value    Daily Close
> 
>      1                Day 1
> 
>      2                Day 2
> 
>     ....
> 
>      5                Day 5
> 
>    
> 
>   2 Forecast the next day's close with the slope m
> and constant c from step 1:
> 
>   (2) Forecast close (Day 6) = 6m + c
> 
>   3 Record m, c and r 2 on the same line as Day 5.
> Record the forecast from step 2 one day ahead, with
> 
>   Day 6. Note when we are using five days' data, the
> first forecast is for Day 6.
> 
>   4 Step the calculation ahead one day such that
> 
>   5 Record m, c and r 2 as in step 3.
> 
>   6 Calculate the regression forecast oscillator,
> %F, as
> 
>   (3)
> 
> 
=== message truncated ===

> ATTACHMENT part 2 image/gif name=3.gif



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