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Its a built-in function of Excel
Lionel Issen
lissen@xxxxxxxxxxxxxx
----- Original Message -----
From: "hengy" <hengy@xxxxxxxxxxxxxxxx>
To: <metastock@xxxxxxxxxxxxx>
Sent: Saturday, January 19, 2002 7:16 PM
Subject: RE: Forecast Oscillator
> Sorry for chiming in late but what is the formula for calculating the
linear
> regression in excel. thanks.
>
> -----Original Message-----
> From: owner-metastock@xxxxxxxxxxxxx
> [mailto:owner-metastock@xxxxxxxxxxxxx]On Behalf Of ERKAN BISEVAC
> Sent: Friday, January 11, 2002 11:52 AM
> To: metastock@xxxxxxxxxxxxx
> Subject: Re: Forecast Oscillator
>
>
> 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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