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>From Excel's help:
"FORECAST(x,known_y's,known_x's)
X is the data point for which you want to predict a value.
Known_y's is the dependent array or range of data.
Known_x's is the independent array or range of data."
In the case of a time series, Known_x's are just 1,2, ..., n. So, to
forecast the low for the 6th day ("L6"), based on the extension of a line
fit to days 1-5, you would have:
=FORECAST(6,{L1,L2,L3,L4,L5},{1,2,3,4,5})
David
----- Original Message -----
From: hengy <hengy@xxxxxxxxxxxxxxxx>
To: <metastock@xxxxxxxxxxxxx>
Sent: Sunday, January 20, 2002 12:31 PM
Subject: RE: Forecast Oscillator
> I did get that far. When using forecasting it asks you for 2 sets of
data.
> If I want the forecast for the low on the 6th day I would expect to use
days
> 1-5 for input data. It asks for an x and y series of data however. Maybe
> I'm missing something?
>
> -----Original Message-----
> From: owner-metastock@xxxxxxxxxxxxx
> [mailto:owner-metastock@xxxxxxxxxxxxx]On Behalf Of Lionel Issen
> Sent: Sunday, January 20, 2002 1:40 AM
> To: metastock@xxxxxxxxxxxxx
> Subject: Re: Forecast Oscillator
>
>
> 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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