App-Chart
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# Old code:
#
# # version 0.06 for bug fix of a struct size for perl 5.10 (there's some
# # fragile duplication)
# require Encode; # Encode::PERLQQ
# require PerlIO::locale; PerlIO::locale->VERSION(0.06);
# { no warnings 'once';
# local $PerlIO::encoding::fallback = Encode::PERLQQ; # \x{1234} style
# (binmode (STDOUT, ':locale') && binmode (STDERR, ':locale'))
# or die "Cannot set :encoding on stdout/stderr: $!\n";
# }
# Makefile: 'PerlIO::locale' => '0.06',
# , libperlio-locale-perl (>= 0.06)
}
my $option_output;
my $option_mode;
my @args;
{
doc/chart.texi view on Meta::CPAN
@cindex Deviation
@cindex Standard error
@cindex Kirshenbaum, Paul
Kirshenbaum bands are channel lines drawn around an exponential moving average
(@pxref{Exponential Moving Average}). The channel width is a multiple of the
``standard error'' from a linear regression of a past N days (@pxref{Linear
Regression}, and the EMA is smoothed using the same N days.
Kirshenbaum bands are similar to Bollinger bands (@pxref{Bollinger Bands}),
but with a linear regression standard error (stderr) instead of a standard
deviation (stddev, @pxref{Standard Deviation}). The difference is that stddev
takes no account of a trend, so the Bollinger channel widens when a trend is
in progress. But stderr is based on deviation from a fitted sloping line, so
if prices are making steady progress up or down the channel width remains
small.
The standard error values (ie.@: the channel width) can be viewed directly as
an indicator too as ``Linear Regression Stderr'' (@pxref{Linear Regression}).
@c ---------------------------------------------------------------------------
@node Parabolic SAR, , Kirshenbaum Bands, Channels and Boxes
@section Parabolic SAR
doc/chart.texi view on Meta::CPAN
called the @dfn{regression coefficient}. This is available as an indicator
(Linear Regression Slope), to show how steep the fitted trend line is. The
units are price change per day, which is negative for a downward sloping line.
This may or may not be particularly useful so it's under ``Low Priority'' in
the indicator lists.
@cindex Standard error
@anchor{Linear Regression Standard Error}
@subsection Standard Error
Standard error (stderr) is a statistical measure of how much values differ
from an assumed underlying curve. It's calculated as the quadratic mean of
the vertical distances from each point to the curve.
Standard error from a linear regression line @math{y=a+bx} is
@tex
$$ Stderr = \sqrt { (y_1 - (a + bx_1))^2 + \cdots + (y_N - (a + bx_N))^2
\over N } $$
@end tex
@ifnottex
@example
/ (y1 - (a+b*x1))^2 + ... + (yN - (a+b*xN))^2 \
Stderr = sqrt | ------------------------------------------- |
\ N /
@end example
@end ifnottex
Notice the numerator is the same SumSquares which was minimized above.
Standard error is similar to standard deviation (@pxref{Standard Deviation});
but where stddev takes differences from a horizontal line (the @math{Y} mean),
stderr here goes from the sloping linear regression line.
For reference, there's no need to actually calculate the linear regression
@math{a} and @math{b}, the stderr can be formed directly as
@tex
$$ Stderr = \sqrt { Variance(Y) - { Covariance(X,Y)^2 \over Variance(X) }} $$
@end tex
@ifnottex
@example
/ Covariance(X,Y)^2 \
Stderr = sqrt | Variance(Y) - ----------------- |
\ Variance(X) /
@end example
doc/chart.texi view on Meta::CPAN
Standard error from a linear regression like this is used as a channel width
in Kirshenbaum Bands (@pxref{Kirshenbaum Bands}). It can also be viewed
directly as an indicator, but this is probably of limited use and for that
reason is under ``Low Priority'' in the indicator lists.
@subsection Additional Resources
@itemize
@item
@uref{http://mathworld.wolfram.com/LeastSquaresFitting.html} -- on calculating
stderr without the a,b parameters
@end itemize
@c ---------------------------------------------------------------------------
@node Standard Deviation, True Range, Linear Regression, Common Calculations
@section Standard Deviation
@cindex Standard deviation
@cindex Stddev
Standard deviation (stddev) is a statistical measure of how much the values in
lib/App/Chart/EmacsMain.pm view on Meta::CPAN
binmode (STDIN, ':utf8') or die;
# dup-ed to a new descriptor to talk to emacs
open $emacs_fh, '>&STDOUT' or die;
$emacs_fh->autoflush(1); # emacs_write() does single-string prints
# ENHANCE-ME: use one of the IO::Capture or via layer or whatnot to get
# perl prints to STDOUT/STDERR and send them up to an emacs buffer, or
# message area
#
# stdout/stderr fds 1 and 2 put to /dev/null to discard other prints
my $devnull = File::Spec->devnull;
fdreopen (1, $devnull, POSIX::O_WRONLY())
// die "Cannot send STDOUT to $devnull: ", Glib::strerror($!);
POSIX::dup2 (1, 2) // die;
if (DEBUG) {
# fds 1 and 2 changed (again) to DEBUG_TTY_FILENAME, if it's possible to
# open that
if (fdreopen (1, DEBUG_TTY_FILENAME, POSIX::O_WRONLY())) {
POSIX::dup2 (1, 2) // die "Cannot dup fd 1 to fd 2: $!";
lib/App/Chart/Series/Derived/Kirshenbaum.pm view on Meta::CPAN
sub manual { __p('manual-node','Kirshenbaum Bands') }
use constant
{ type => 'average',
parameter_info => [ { name => __('Days'),
key => 'kirshenbaum_days',
type => 'integer',
minimum => 1,
default => 20 },
{ name => __('Stderrs'),
key => 'kirshenbaum_stderrs',
type => 'float',
default => 1.75,
decimals => 2,
step => 0.25,
minimum => 0 }],
line_colours => { upper => App::Chart::BAND_COLOUR(),
lower => App::Chart::BAND_COLOUR() },
};
sub new {
my ($class, $parent, $N, $stderr_factor) = @_;
$N //= parameter_info()->[0]->{'default'};
($N > 0) || croak "Kirshenbaum bad N: $N";
$stderr_factor //= parameter_info()->[1]->{'default'};
return $class->SUPER::new
(parent => $parent,
parameters => [ $N, $stderr_factor ],
arrays => { middle => [],
upper => [],
lower => [] },
array_aliases => { values => 'middle' });
}
sub proc {
my ($class_or_self, $N, $stderr_factor) = @_;
my $ema_proc = App::Chart::Series::Derived::EMA->proc($N);
my $stderr_proc = App::Chart::Series::Derived::LinRegStderr->proc($N);
return sub {
my ($value) = @_;
my $ema = $ema_proc->($value);
my $stderr = $stderr_proc->($value) * $stderr_factor;
return ($ema, $ema + $stderr, $ema - $stderr);
};
}
sub warmup_count {
my ($self_or_class, $N, $stderr_factor) = @_;
return max (App::Chart::Series::Derived::EMA->warmup_count($N),
App::Chart::Series::Derived::LinRegStderr->warmup_count($N));
}
*fill_part = \&App::Chart::Series::Derived::Bollinger::fill_part;
1;
__END__
# =head1 NAME
#
# App::Chart::Series::Derived::Kirshenbaum -- kirshenbaum bands
#
# =head1 SYNOPSIS
#
# my $series = $parent->Kirshenbaum($N);
# my $series = $parent->Kirshenbaum($N, $stderr_factor);
#
# =head1 DESCRIPTION
#
# ...
#
# =head1 SEE ALSO
#
# L<App::Chart::Series>, L<App::Chart::Series::Derived::SMA>
#
# =cut
lib/App/Chart/Series/Derived/LinRegStderr.pm view on Meta::CPAN
use App::Chart::Series::Derived::SMA;
# http://mathworld.wolfram.com/LeastSquaresFitting.html
# Showing how to calculate the variance in the error amounts (e[i])
# without calculating a,b parameters.
#
#
#
# Sum (y - (a+b*x))^2
# stderr = sqrt -------------------
# N^2
#
# Sum ((y - My) - b*x)^2
# = sqrt ---------------------- where My = Mean(Y)
# N^2
#
# Sum (y - My)^2 - 2*My*b*x + b^2*x^2
# = sqrt -----------------------------------
# N^2
#
lib/App/Chart/Series/Derived/LinRegStderr.pm view on Meta::CPAN
sub longname { __('Linear Regression Stderr') }
sub shortname { __('Linreg Stderr') }
sub manual { __p('manual-node','Linear Regression Standard Error') }
use constant
{ type => 'indicator',
units => 'price',
priority => -10,
minimum => 0,
parameter_info => [ { name => __('Days'),
key => 'linregstderr_days',
type => 'integer',
minimum => 1,
default => 20 } ],
};
sub new {
my ($class, $parent, $N) = @_;
$N //= parameter_info()->[0]->{'default'};
($N > 0) || croak "LinRegStderr bad N: $N";
return $class->SUPER::new
(parent => $parent,
N => $N,
parameters => [ $N ],
arrays => { values => [] },
array_aliases => { });
}
*warmup_count = \&App::Chart::Series::Derived::SMA::warmup_count; # $N-1
# Return the factor for 1/Variance(X) arising in the stderr formula. With
# X values -1.5,-0.5,0.5, 1.5 this means
#
# 1
# --------------------------------------------
# n * ((-1.5)^2 + (-0.5)^2 + (0.5)^2 + (1.5)^2)
#
# and the denominator here is (n^4 - n^2)/12. See devel/ema-omitted.pl for
# checking this. Same func in RSquared.pm too.
#
sub xfactor {
lib/App/Chart/doc/chart.html view on Meta::CPAN
<a class="index-entry-id" id="index-Variance-1"></a>
<a class="index-entry-id" id="index-Deviation-1"></a>
<a class="index-entry-id" id="index-Standard-error"></a>
<a class="index-entry-id" id="index-Kirshenbaum_002c-Paul"></a>
<p>Kirshenbaum bands are channel lines drawn around an exponential moving average
(see <a class="pxref" href="#Exponential-Moving-Average">Exponential Moving Average</a>). The channel width is a multiple of the
“standard error” from a linear regression of a past N days (see <a class="pxref" href="#Linear-Regression">Linear Regression</a>, and the EMA is smoothed using the same N days.
</p>
<p>Kirshenbaum bands are similar to Bollinger bands (see <a class="pxref" href="#Bollinger-Bands">Bollinger Bands</a>),
but with a linear regression standard error (stderr) instead of a standard
deviation (stddev, see <a class="pxref" href="#Standard-Deviation">Standard Deviation</a>). The difference is that stddev
takes no account of a trend, so the Bollinger channel widens when a trend is
in progress. But stderr is based on deviation from a fitted sloping line, so
if prices are making steady progress up or down the channel width remains
small.
</p>
<p>The standard error values (ie. the channel width) can be viewed directly as
an indicator too as “Linear Regression Stderr” (see <a class="pxref" href="#Linear-Regression">Linear Regression</a>).
</p>
<hr>
</div>
<div class="section-level-extent" id="Parabolic-SAR">
lib/App/Chart/doc/chart.html view on Meta::CPAN
(Linear Regression Slope), to show how steep the fitted trend line is. The
units are price change per day, which is negative for a downward sloping line.
This may or may not be particularly useful so it’s under “Low Priority” in
the indicator lists.
</p>
<a class="index-entry-id" id="index-Standard-error-1"></a>
<a class="anchor" id="Linear-Regression-Standard-Error"></a></div>
<div class="subsection-level-extent" id="Standard-Error">
<h4 class="subsection"><span>11.1.2 Standard Error<a class="copiable-link" href="#Standard-Error"> ¶</a></span></h4>
<p>Standard error (stderr) is a statistical measure of how much values differ
from an assumed underlying curve. It’s calculated as the quadratic mean of
the vertical distances from each point to the curve.
</p>
<p>Standard error from a linear regression line <em class="math">y=a+bx</em> is
</p>
<div class="example">
<pre class="example-preformatted"> / (y1 - (a+b*x1))^2 + ... + (yN - (a+b*xN))^2 \
Stderr = sqrt | ------------------------------------------- |
\ N /
</pre></div>
<p>Notice the numerator is the same SumSquares which was minimized above.
Standard error is similar to standard deviation (see <a class="pxref" href="#Standard-Deviation">Standard Deviation</a>);
but where stddev takes differences from a horizontal line (the <em class="math">Y</em> mean),
stderr here goes from the sloping linear regression line.
</p>
<p>For reference, there’s no need to actually calculate the linear regression
<em class="math">a</em> and <em class="math">b</em>, the stderr can be formed directly as
</p>
<div class="example">
<pre class="example-preformatted"> / Covariance(X,Y)^2 \
Stderr = sqrt | Variance(Y) - ----------------- |
\ Variance(X) /
</pre></div>
<p>where variance and covariance are as follows (and notice they simplify if
<em class="math">X</em> values are chosen to make <em class="math">Mean(X)</em> zero),
</p>
lib/App/Chart/doc/chart.html view on Meta::CPAN
<p>Standard error from a linear regression like this is used as a channel width
in Kirshenbaum Bands (see <a class="pxref" href="#Kirshenbaum-Bands">Kirshenbaum Bands</a>). It can also be viewed
directly as an indicator, but this is probably of limited use and for that
reason is under “Low Priority” in the indicator lists.
</p>
</div>
<div class="subsection-level-extent" id="Additional-Resources-11">
<h4 class="subsection"><span>11.1.3 Additional Resources<a class="copiable-link" href="#Additional-Resources-11"> ¶</a></span></h4>
<ul class="itemize mark-bullet">
<li><a class="uref" href="http://mathworld.wolfram.com/LeastSquaresFitting.html">http://mathworld.wolfram.com/LeastSquaresFitting.html</a> – on calculating
stderr without the a,b parameters
</li></ul>
<hr>
</div>
</div>
<div class="section-level-extent" id="Standard-Deviation">
<div class="nav-panel">
<p>
Next: <a href="#True-Range" accesskey="n" rel="next">True Range</a>, Previous: <a href="#Linear-Regression" accesskey="p" rel="prev">Linear Regression</a>, Up: <a href="#Common-Calculations" accesskey="u" rel="up">Common Calculations</a> [<a h...
my $wstat = system
"scrollkeeper-install -v -p $tempdir $omffile >omf.out 2>omf.err";
is ($wstat, 0, 'scrollkeeper-install exit status');
# show output only on error, the normal output is only confusing
if ($wstat) {
require File::Slurp;
diag "omf.out stdout:";
diag File::Slurp::read_file('omf.out');
diag "omf.err stderr:";
diag File::Slurp::read_file('omf.err');
}
# chdir out of directory so File::Temp can remove it
chdir(File::Spec->rootdir);
exit 0;
( run in 1.303 second using v1.01-cache-2.11-cpan-49f99fa48dc )