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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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