# Copyright 2007, 2009, 2010, 2011 Kevin Ryde
# This file is part of Chart.
#
# Chart is free software; you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation; either version 3, or (at your option) any later version.
#
# Chart is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along
# with Chart. If not, see <http://www.gnu.org/licenses/>.
package App::Chart::Series::Derived::AdaptiveLaguerre;
use 5.010;
use strict;
use warnings;
use Carp;
use List::Util qw(min max);
use Locale::TextDomain ('App-Chart');
use base 'App::Chart::Series::Indicator';
use App::Chart::Series::Derived::LaguerreFilter;
use App::Chart::Series::Derived::Median;
use App::Chart::Series::Derived::WilliamsR;
# http://www.mesasoftware.com/technicalpapers.htm
# http://www.mesasoftware.com/Papers/TIME%20WARP.pdf
# Paper by John Elhers.
#
# http://www.mesasoftware.com/seminars.htm
# http://www.mesasoftware.com/Seminars/TradeStation%20World%2005.pdf
# http://www.mesasoftware.com/Seminars/Seminars/TSWorld05.ppt
# (View the powerpoint with google.)
# Powerpoint summary by John Ehlers of several of his and other averages.
# View in google,
# * A Laguerre filter warps time in the filter coefficients
# - Enables extreme smoothing with just a few filter terms
# * A NonLinear Laguerre filter measures the difference between the
# current price and the last computed filter output.
# - Objective is to drive this "error" to zero
# - The "error", normalized to the error range over a selected period
# is the alpha of the Laguerre filter
#
sub longname { __('Adaptive Laguerre Filter') }
sub shortname { __('Adaptive Laguerre') }
sub manual { __p('manual-node','Adaptive Laguerre Filter') }
use constant
{ type => 'average',
parameter_info => [ { name => __('Days'),
key => 'adaptive_laguerre_filter_days',
type => 'integer',
minimum => 1,
default => 20 } ],
};
sub new {
my ($class, $parent, $N) = @_;
$N //= parameter_info()->[0]->{'default'};
($N >= 1) || croak "Adaptive Laguerre Filter bad N: $N";
return $class->SUPER::new
(parent => $parent,
parameters => [ $N ],
arrays => { values => [] },
array_aliases => { });
}
sub proc {
my ($class, $N) = @_;
my $proc_laguerre_and_alpha = $class->proc_laguerre_and_alpha($N);
return sub {
return ($proc_laguerre_and_alpha->(@_))[0];
};
}
sub proc_laguerre_and_alpha {
my ($class, $N) = @_;
my $laguerre_proc
= App::Chart::Series::Derived::LaguerreFilter->proc_for_alpha();
my $williams_proc = App::Chart::Series::Derived::WilliamsR->proc($N);
my $median_proc = App::Chart::Series::Derived::Median->proc(5);
my $alpha = 0.2;
my $prev;
return sub {
my ($value) = @_;
if (defined $prev) {
my $w = $williams_proc->(undef, undef, abs ($value - $prev));
$alpha = $median_proc->(0.01 * ($w + 100)); # 0 to 1
}
return (($prev = $laguerre_proc->($value, $alpha)),
$alpha);
};
}
# warmup_count() gives a fixed amount, based on the worst-case EMA alphas
# all the slowest possible. It ends up being 1656 which is hugely more than
# needed in practice.
#
# warmup_count_for_position() calculates a value on actual data, working
# backwards. In practice it's as little as about 100.
#
sub warmup_count {
my ($self_or_class, $N) = @_;
# FIXME: this is a big over-estimate
return $N + App::Chart::Series::Derived::LaguerreFilter->warmup_count(0.01);
}
### AdaptiveLaguerre warmup_count(): __PACKAGE__->warmup_count(parameter_info()->[0]->{'default'})
1;
__END__
# =head1 NAME
#
# App::Chart::Series::Derived::AdaptiveLaguerre -- Laguerre Filter moving average
#
# =head1 SYNOPSIS
#
# my $series = $parent->AdaptiveLaguerre($alpha);
#
# =head1 DESCRIPTION
#
# ...
#
# =head1 SEE ALSO
#
# L<App::Chart::Series>, L<App::Chart::Series::Derived::LaguerreFilter>
#
# =cut