# Dekalog Blog

"Trading is statistics and time series analysis." This blog details my progress in developing a systematic trading system for use on the futures and forex markets, with discussion of the various indicators and other inputs used in the creation of the system. Also discussed are some of the issues/problems encountered during this development process. Within the blog posts there are links to other web pages that are/have been useful to me.

## Tuesday, 12 June 2018

### candle.m Function Released

I have just noticed that my previously accepted candlestick plot function now appears to have been released, release date 14 December 2017, as part of the Octave financial package. The function reference is at https://octave.sourceforge.io/financial/function/candle.html

## Thursday, 7 June 2018

### Update on Improved Currency Strength Indicator

Following on from my previous post I have now slightly changed the logic and coding behind the idea, which can be seen in the code snippet below

which shows the optimisation errors for the "old" way of doing things, in black, and the revised way in blue. Note that this is a log scale, so the errors for the revised way are orders of magnitude smaller, implying a better model fit to the data.

This next chart shows the difference between the two methods of calculating a gold index ( black is old, blue is new ),

this one shows the calculated USD index

and this one the GBP index in blue, USD in green and the forex pair cross rate in black

The idea(s) I am going to look at next is using these various calculated indices as inputs to algorithms/trading decisions.

```
% aud_cad
mse_vector(1) = log( ( current_data(1,1) * ( aud_x / cad_x ) ) / current_data(2,1) )^2 ;
% xau_aud
mse_vector(46) = log( ( current_data(1,46) * ( gold_x / aud_x ) ) / current_data(2,46) )^2 ;
% xau_cad
mse_vector(47) = log( ( current_data(1,47) * ( gold_x / cad_x ) ) / current_data(2,47) )^2 ;
```

Essentially the change simultaneously optimises, using Octave's fminunc function, for both the gold_x and all currency_x geometric multipliers together rather than just optimising for gold and then analytically deriving the currency multipliers. The rationale for this change is shown in the chart below,which shows the optimisation errors for the "old" way of doing things, in black, and the revised way in blue. Note that this is a log scale, so the errors for the revised way are orders of magnitude smaller, implying a better model fit to the data.

This next chart shows the difference between the two methods of calculating a gold index ( black is old, blue is new ),

this one shows the calculated USD index

and this one the GBP index in blue, USD in green and the forex pair cross rate in black

The idea(s) I am going to look at next is using these various calculated indices as inputs to algorithms/trading decisions.

## Monday, 28 May 2018

### An Improved Currency Strength Indicator plus Gold and Silver Indices?

In the past I have blogged about creating a currency strength indicator ( e.g. here, here and here ) and this post talks about a new twist on this idea.

The motivation for this came about from looking at chart plots such as this,

which shows Gold prices in the first row, Silver in the second and a selection of forex cross rates in the third and final row. The charts are on a daily time scale and show prices since the beginning of 2018 up to and including 25th May, data from Oanda.

If one looks at the price of gold and asks oneself if the price is moving up or down, the answer will depend on which gold price currency denomination chart one looks at. In the latter part of the charts ( from about time ix 70 onwards ) the gold price goes up in pounds Sterling and Euro and down in US dollars. Obviously, by looking at the relevant exchange rates in the third row, a large part of this gold price movement is due to changes in the strength of the underlying currencies. Therefore, the problem to be addressed is that movements in the price of gold are confounded with movements in the price of the currencies, and it would be ideal if the gold price movement could be separated out from the currency movements, which would then allow for the currency strengths to also be determined.

One approach I have been toying with is to postulate a simple, geometric change model whereby the price of gold is multiplied by a constant, let's call it x_g, for example x_g = 1.01 represents a 1% increase in the "intrinsic" value of gold, and then adjust the obtained value of this multiplication to take in to account the change in the value of the currency. The code box below expresses this idea, in somewhat clunky Octave code.

The above would be repeated for all gold price currency denominations and the relevant forex pairs and be part of a function, for x_g, which is to be minimised by the Octave fminunc function. Observant readers might note that the error to be minimised is the square of the log of the accuracy ratio. Interested readers are referred to the paper A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation for an explanation of this and why it is a suitable error metric for a geometric model.

The chart below is a repeat of the one above, with the addition of a gold index, a silver index and currency strengths indices calculated from a preliminary subset of all gold price currency denominations using the above methodology.

In the first two rows, the blue lines are the calculated gold and silver indices, all normalised to start at the first price at the far left of each respective currency denomination. The silver index was calculated using the relationship between the gold x_g value and the xau xag ratio. Readers will see that these indices are similarly invariant to the currency in which they are expressed ( the geometric bar to bar changes in the indices are identical ) but each is highly correlated to its underlying currency. They could be calculated from an arbitrary index starting point, such as 100, and therefore can be considered to be an index of the changes in the intrinsic value of gold.

When it comes to currency strengths most indicators I have come across are variations of a single theme, namely: averages of all the changes for a given set of forex pairs, whether these changes be expressed as logs, percentages, values or whatever. Now that we have an absolute, intrinsic value gold index, it is a simple matter to parse out the change in the currency from the change in the gold price in this currency.

The third row of the second chart above shows these currency strengths for the two base currencies plotted - GBP and EUR - again normalised to the first charted price on the left. Although in this chart only observable for the Euro, it can be seen that the index again is invariant, similar to gold and silver above. Perhaps more interestingly, the red line is a cumulative product of the ratio of base currency index change to the term currency index change, normalised as described above. It can be seen that the red line almost exactly overwrites the underlying black line, which is the actual cross rate plot. This red line is plotted as a sanity check and it is gratifying to see such an accurate overwrite.

I think this idea shows great promise and for the nearest future I shall be working to extend it beyond the preliminary data set used above. More in due course.

The motivation for this came about from looking at chart plots such as this,

which shows Gold prices in the first row, Silver in the second and a selection of forex cross rates in the third and final row. The charts are on a daily time scale and show prices since the beginning of 2018 up to and including 25th May, data from Oanda.

If one looks at the price of gold and asks oneself if the price is moving up or down, the answer will depend on which gold price currency denomination chart one looks at. In the latter part of the charts ( from about time ix 70 onwards ) the gold price goes up in pounds Sterling and Euro and down in US dollars. Obviously, by looking at the relevant exchange rates in the third row, a large part of this gold price movement is due to changes in the strength of the underlying currencies. Therefore, the problem to be addressed is that movements in the price of gold are confounded with movements in the price of the currencies, and it would be ideal if the gold price movement could be separated out from the currency movements, which would then allow for the currency strengths to also be determined.

One approach I have been toying with is to postulate a simple, geometric change model whereby the price of gold is multiplied by a constant, let's call it x_g, for example x_g = 1.01 represents a 1% increase in the "intrinsic" value of gold, and then adjust the obtained value of this multiplication to take in to account the change in the value of the currency. The code box below expresses this idea, in somewhat clunky Octave code.

```
% xau_gbp using gbp_usd
new_val_gold_in_old_currency_value = current_data(1,1) * x_g ;
new_val_gold_in_new_currency_value = new_val_gold_in_old_currency_value * exp( -log( current_data(2,6) / current_data(1,6) ) ) ;
mse_vector(1) = log( current_data(2,1) / new_val_gold_in_new_currency_value )^2 ;
% xau_usd using gbp_usd
new_val_gold_in_old_currency_value = current_data(1,2) * x_g ;
new_val_gold_in_new_currency_value = new_val_gold_in_old_currency_value * exp( log( current_data(2,6) / current_data(1,6) ) ) ;
mse_vector(2) = log( current_data(2,2) / new_val_gold_in_new_currency_value )^2 ;
```

For this snippet, current_data is a 2-dimensional vector containing yesterday's and today's gold prices in GBP and USD, plus yesterday's and today's GBP_USD exchange rates.The above would be repeated for all gold price currency denominations and the relevant forex pairs and be part of a function, for x_g, which is to be minimised by the Octave fminunc function. Observant readers might note that the error to be minimised is the square of the log of the accuracy ratio. Interested readers are referred to the paper A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation for an explanation of this and why it is a suitable error metric for a geometric model.

The chart below is a repeat of the one above, with the addition of a gold index, a silver index and currency strengths indices calculated from a preliminary subset of all gold price currency denominations using the above methodology.

In the first two rows, the blue lines are the calculated gold and silver indices, all normalised to start at the first price at the far left of each respective currency denomination. The silver index was calculated using the relationship between the gold x_g value and the xau xag ratio. Readers will see that these indices are similarly invariant to the currency in which they are expressed ( the geometric bar to bar changes in the indices are identical ) but each is highly correlated to its underlying currency. They could be calculated from an arbitrary index starting point, such as 100, and therefore can be considered to be an index of the changes in the intrinsic value of gold.

When it comes to currency strengths most indicators I have come across are variations of a single theme, namely: averages of all the changes for a given set of forex pairs, whether these changes be expressed as logs, percentages, values or whatever. Now that we have an absolute, intrinsic value gold index, it is a simple matter to parse out the change in the currency from the change in the gold price in this currency.

The third row of the second chart above shows these currency strengths for the two base currencies plotted - GBP and EUR - again normalised to the first charted price on the left. Although in this chart only observable for the Euro, it can be seen that the index again is invariant, similar to gold and silver above. Perhaps more interestingly, the red line is a cumulative product of the ratio of base currency index change to the term currency index change, normalised as described above. It can be seen that the red line almost exactly overwrites the underlying black line, which is the actual cross rate plot. This red line is plotted as a sanity check and it is gratifying to see such an accurate overwrite.

I think this idea shows great promise and for the nearest future I shall be working to extend it beyond the preliminary data set used above. More in due course.

## Friday, 2 March 2018

### Hidden Markov Modelling of Synthetic Periodic Time Series Data

I am currently working on a method of predicting/projecting cyclic price action, based upon John Ehlers' sinewave indicator code, and to test it I am using Octave's implementation of a Hidden Markov model in the Octave statistics package hosted at Sourceforge.

Basically I measure the dominant cycle period ( using either the above linked sinewave indicator code or autocorrelation periodogram code ) and use the vector of measured dominant cycle periods as input to the hmmestimate function. Using the output from this, the hmmgenerate function is then used to generate a new period vector, these periods are converted to a slowly, periodically varying sine wave, and additive white Gaussian noise is added to the signal to produce a final signal upon which Monte Carlo testing of my proposed indicator can be conducted. A typical plot of the varying, dominant cycle periods looks like

whilst the noisy sine wave signal derived from this looks like this.

The relevant Octave code for all this is shown in the two code boxes below

Basically I measure the dominant cycle period ( using either the above linked sinewave indicator code or autocorrelation periodogram code ) and use the vector of measured dominant cycle periods as input to the hmmestimate function. Using the output from this, the hmmgenerate function is then used to generate a new period vector, these periods are converted to a slowly, periodically varying sine wave, and additive white Gaussian noise is added to the signal to produce a final signal upon which Monte Carlo testing of my proposed indicator can be conducted. A typical plot of the varying, dominant cycle periods looks like

whilst the noisy sine wave signal derived from this looks like this.

The relevant Octave code for all this is shown in the two code boxes below

```
clear all ;
pkg load statistics ;
% load all datafile names from Oanda
cd /home/dekalog/Documents/octave/oanda_data/daily ;
oanda_files_d = glob( "*_ohlc_daily" ) ; % cell with filenames matching *_ohlc_daily, e.g. eur_usd_ohlc_daily
all_transprobest = zeros( 75 , 75 ) ;
for ii = 1 : 124
filename = oanda_files_d{ ii } ;
current_data_d = load( "-binary" , filename ) ;
data = getfield( current_data_d , filename ) ;
midprice = ( data( : , 3 ) .+ data( : , 4 ) ) ./ 2 ;
period = autocorrelation_periodogram_2_5( midprice ) ;
period(1:49) = [] ;
min_val = min( period ) ; max_val = max( period ) ;
hmm_periods = period .- ( min_val - 1 ) ; hmm_states = hmm_periods ;
[ transprobest , outprobest ] = hmmestimate( hmm_periods , hmm_states ) ;
all_transprobest( min_val : max_val , min_val : max_val ) = all_transprobest( min_val : max_val , min_val : max_val ) .+ transprobest ;
endfor
all_transprobest = all_transprobest ./ 124 ;
[ i , j ] = find( all_transprobest ) ;
if ( min(i) == min(j) && max(i) == max(j) )
transprobest = all_transprobest( min(i):max(i) , min(j):max(j) ) ;
outprobest = eye( size(transprobest,1) ) ;
hmm_min_period_add = min(i) - 1 ;
endif
cd /home/dekalog/Documents/octave/period/hmm_period ;
save all_hmm_periods_daily transprobest outprobest hmm_min_period_add ;
```

and```
clear all ;
pkg load statistics ;
cd /home/dekalog/Documents/octave/snr ;
load all_snr ;
cd /home/dekalog/Documents/octave/period/hmm_period ;
load all_hmm_periods_daily ;
[ gen_period , gen_states ] = hmmgenerate( 2500 , transprobest , outprobest ) ;
gen_period = gen_period .+ hmm_min_period_add ;
gen_sine = sind( cumsum( 360 ./ gen_period ) ) ;
noise_val = mean( [ all_snr(:,1) ; all_snr(:,2) ] ) ;
noisy_sine = awgn( gen_sine , noise_val ) ;
[s,s1,s2,s3] = sinewave_indicator( noisy_sine ) ; s2(1:50) = s1(1:50) ;
figure(1) ; plot( gen_period , 'k' , 'linewidth' , 2 ) ;
figure(2) ; plot( gen_sine , 'k' , 'linewidth' , 2 , noisy_sine , 'b' , 'linewidth' , 2 , s , 'r' , 'linewidth' , 2 , ...
s1 , 'g' , 'linewidth' , 2 , s2 , 'm' , 'linewidth' , 2 ) ;
legend( "Gen Sine" , "Noisy Sine" , "Sine Ind" , "Sine Ind lead1" , "Sine Ind Lead2" ) ;
```

I hope readers find this useful if they need to generate synthetic, cyclic data for their own development/testing purposes too.
Labels:
Cycle Period,
Octave,
Synthetic Data

## Monday, 11 December 2017

### Time Warp Edit Distance

Part of my normal routine is to indulge in online research for use useful ideas, and I recently came across An Empirical Evaluation of Similarity Measures for Time Series Classification, and one standout from this paper is the Time Warp Edit Distance where, from the conclusion,

Below is my Octave

A = [1, 2, 3, 4, 5, 6, 7, 8, 9] ;

B1 = [1, 2, 3, 4, 5, 6, 7, 8, 12] ;

distance1 = twed( A , 1:9 , B1 , 1:9 , 1 , 0.001 )

distance1 = 3

B2 = [0, 3, 2, 5, 4, 7, 6, 9, 8] ;

distance2 = twed( A , 1:9 , B2 , 1:9 , 1 , 0.001 )

distance2 = 17

graphics_toolkit('fltk') ; plot(A,'k','linewidth',2,B1,'b','linewidth',2,B2,'r','linewidth',2);

legend( "A" , "B1" , "B2" ) ;

It can be seen that the twed algorithm correctly picks out B1 as being more like A than B2 (a lower twed distance, with default values for lambda and nu of 1 and 0.001 respectively, taken from the above survey paper) when compared with the simple squared error metric, which gives identical results for both B1 and B2.

More on this in due course.

*"...the TWED measure originally proposed by Marteau (2009) seems to consistently outperform all the considered distances..."*Below is my Octave

*.oct function version of the above linked MATLAB code.*```
#include octave oct.h
#include octave dmatrix.h
#include limits> // for infinity
#include math.h // for sqrt
DEFUN_DLD ( twed, args, nargout,
"-*- texinfo -*-\n\
@deftypefn {Function File} {} twed (@var{A , timeSA , B , timeSB , lambda, nu})\n\
Calculates the Time Warp Edit Distance between two univariate time series, A and B.\n\
timeSA and timeSB are the time stamps of the respective series, lambda is a penalty\n\
for a deletion operation and nu is an Elasticity parameter - nu >=0 needed for distance measure.\n\
@end deftypefn" )
{
octave_value_list retval_list ;
int nargin = args.length () ;
// check the input arguments
if ( nargin != 6 )
{
error ("Invalid number of arguments. See help twed.") ;
return retval_list ;
}
if ( args(0).length () < 2 )
{
error ("Invalid 1st argument length. Must be >= 2.") ;
return retval_list ;
}
if ( args(1).length () != args(0).length () )
{
error ("Arguments 1 and 2 must be vectors of the same length.") ;
return retval_list ;
}
if ( args(2).length () < 2 )
{
error ("Invalid 3rd argument length. Must be >= 2.") ;
return retval_list ;
}
if ( args(3).length () != args(2).length () )
{
error ("Arguments 3 and 4 must be vectors of the same length.") ;
return retval_list ;
}
if ( args(4).length () > 1 )
{
error ("Argument 5 must a single value for lambda.") ;
return retval_list ;
}
if ( args(5).length () > 1 )
{
error ("Argument 6 must a single value for nu >= 0.") ;
return retval_list ;
}
if ( error_state )
{
error ("Invalid arguments. See help twed.") ;
return retval_list ;
}
// end of input checking
Matrix A_input = args(0).matrix_value () ;
if( A_input.rows() == 1 && A_input.cols() >= 2 ) // is a row matrix, so transpose
{
A_input = A_input.transpose () ;
}
Matrix timeSA_input = args(1).matrix_value () ;
if( timeSA_input.rows() == 1 && timeSA_input.cols() >= 2 ) // is a row matrix, so transpose
{
timeSA_input = timeSA_input.transpose () ;
}
Matrix B_input = args(2).matrix_value () ;
if( B_input.rows() == 1 && B_input.cols() >= 2 ) // is a row matrix, so transpose
{
B_input = B_input.transpose () ;
}
Matrix timeSB_input = args(3).matrix_value () ;
if( timeSB_input.rows() == 1 && timeSB_input.cols() >= 2 ) // is a row matrix, so transpose
{
timeSB_input = timeSB_input.transpose () ;
}
double lambda = args(4).double_value () ;
double nu = args(5).double_value () ;
double inf = std::numeric_limits
```::infinity() ;
Matrix distance ( 1 , 1 ) ; distance.fill ( 0.0 ) ;
double cost ;
// Add padding of zero by using zero-filled distance matrix
Matrix A = distance.stack( A_input ) ;
Matrix timeSA = distance.stack( timeSA_input ) ;
Matrix B = distance.stack( B_input ) ;
Matrix timeSB = distance.stack( timeSB_input ) ;
Matrix DP ( A.rows() , B.rows() ) ; DP.fill ( inf ) ; DP( 0 , 0 ) = 0.0 ;
int n = timeSA.rows () ;
int m = timeSB.rows () ;
// Compute minimal cost
for ( octave_idx_type ii (1) ; ii < n ; ii++ )
{
for ( octave_idx_type jj (1) ; jj < m ; jj++ )
{
// Deletion in A
DP( ii , jj ) = DP(ii-1,jj) + sqrt( ( A(ii-1,0) - A(ii,0) ) * ( A(ii-1,0) - A(ii,0) ) ) + nu * ( timeSA(ii,0) - timeSA(ii-1,0) ) + lambda ;
// Deletion in B
cost = DP(ii,jj-1) + sqrt( ( B(jj-1,0) - B(jj,0) ) * ( B(jj-1,0) - B(jj,0) ) ) + nu * ( timeSB(jj,0) - timeSB(jj-1,0) ) + lambda ;
DP( ii , jj ) = cost < DP( ii , jj ) ? cost : DP( ii , jj ) ;
// Keep data points in both time series
cost = DP(ii-1,jj-1) + sqrt( ( A(ii,0) - B(jj,0) ) * ( A(ii,0) - B(jj,0) ) ) + sqrt( ( A(ii-1,0) - B(jj-1,0) ) * ( A(ii-1,0) - B(jj-1,0) ) ) + nu * ( abs( timeSA(ii,0) - timeSB(jj,0) ) + abs( timeSA(ii-1,0) - timeSB(jj-1,0) ) ) ;
DP( ii , jj ) = cost < DP( ii , jj ) ? cost : DP( ii , jj ) ;
} // end of jj loop
} // end of ii loop
distance( 0 , 0 ) = DP( n - 1 , m - 1 ) ;
retval_list(1) = DP ;
retval_list(0) = distance ;
return retval_list ;
} // end of function

As a quick test I took the example problem from this Cross Validated thread, the applicability I hope being quite obvious to readers:A = [1, 2, 3, 4, 5, 6, 7, 8, 9] ;

B1 = [1, 2, 3, 4, 5, 6, 7, 8, 12] ;

distance1 = twed( A , 1:9 , B1 , 1:9 , 1 , 0.001 )

distance1 = 3

B2 = [0, 3, 2, 5, 4, 7, 6, 9, 8] ;

distance2 = twed( A , 1:9 , B2 , 1:9 , 1 , 0.001 )

distance2 = 17

graphics_toolkit('fltk') ; plot(A,'k','linewidth',2,B1,'b','linewidth',2,B2,'r','linewidth',2);

legend( "A" , "B1" , "B2" ) ;

It can be seen that the twed algorithm correctly picks out B1 as being more like A than B2 (a lower twed distance, with default values for lambda and nu of 1 and 0.001 respectively, taken from the above survey paper) when compared with the simple squared error metric, which gives identical results for both B1 and B2.

More on this in due course.

## Friday, 8 December 2017

### candle.m Function Accepted

I have received an e-mail from the maintainer of the Octave Financial Package that my candlestick function will be rolled out with the next release of the financial package. For those readers who can't wait the final code, with revisions, is now available at the Octave-Forge here.

This represents a new milestone for me as this is my first, officially accepted contribution to any free and open source software (FOSS) project.

This represents a new milestone for me as this is my first, officially accepted contribution to any free and open source software (FOSS) project.

## Tuesday, 5 December 2017

### Candlestick Plotting Function Submitted for Inclusion in Octave Financial Package

I have today submitted an improved version of my basic candlestick plotting function, candle.m ( see previous post ) for inclusion in the Octave Financial package. As I am not sure when, or even if, it will be accepted, I provide a copy of it below.

```
## Copyright (C) 2017 dekalog
##
## This program 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 of the License, or
## (at your option) any later version.
##
## This program 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 this program. If not, see
```.
## -*- texinfo -*-
## @deftypefn {Function File} {@var{retval} =} candle (@var{highprices}, @var{lowprices}, @var{closeprices}, @var{openprices})
## @deftypefnx {Function File} {@var{retval} =} candle (@var{highprices}, @var{lowprices}, @var{closeprices}, @var{openprices}, @var{color})
## @deftypefnx {Function File} {@var{retval} =} candle (@var{highprices}, @var{lowprices}, @var{closeprices}, @var{openprices}, @var{color}, @var{dates})
## @deftypefnx {Function File} {@var{retval} =} candle (@var{highprices}, @var{lowprices}, @var{closeprices}, @var{openprices}, @var{color}, @var{dates}, @var{dateform})
##
## Plot the @var{highprices}, @var{lowprices}, @var{closeprices} and @var{openprices} of a security as a candlestick chart.
##
## HighPrices - High prices for a security. A column vector.
##
## LowPrices - Low prices for a security. A column vector.
##
## ClosePrices - Close prices for a security. A column vector.
##
## OpenPrices - Open prices for a security. A column vector.
##
## Color - (optional) Candlestick color is specified as a case insensitive four
## character row vector, e.g. "brwk". The characters that are accepted are
## k, b, c, r, m, w, g and y for black, blue, cyan, red, magenta, white, green
## and yellow respectively. Default colors are "brwk" applied in order to bars
## where the closing price is greater than the opening price, bars where the
## closing price is less than the opening price, the chart background color and
## the candlestick wicks. If fewer than four colors are specified, they are
## applied in turn in the above order with default colors for unspecified colors.
## For example, user supplied colors "gm" will plot green upbars and magenta
## downbars with a default white background and black wicks. If the user
## specified color for background is black, without specifying the wick color,
## e.g. "gmk", the default wick color is white. All other choices for background
## color will default to black for wicks. If all four colors are user specified,
## those colors will be used. Doji bars and single price bars, e.g. open = high
## = low = close, are plotted with the color for wicks, with single price bars
## being plotted as points/dots.
##
## Dates - (Optional) Dates for user specified x-axis tick labels. Dates can be
## a serial date number column (see datenum), a datevec matrix (See datevec)
## or a character vector of dates. If specified as either a datenum or a datevec,
## the Dateform argument is required. If the Dates argument is supplied, the
## Color argument must also be explicitly specified.
##
## Dateform - (Optional) Either a Date character string or a single integer code
## number used to format the x-axis tick labels (See datestr). Only required if
## Dates is specified as a serial date number column (See datenum) or a datevec
## matrix (See datevec).
##
## @seealso{datenum, datestr, datevec, highlow, bolling, dateaxis, movavg,
## pointfig}
## @end deftypefn
## Author: dekalog
## Created: 2017-12-05
function candle ( varargin )
if ( nargin < 4 || nargin > 7 )
print_usage ();
elseif ( nargin == 4 )
HighPrices = varargin{1}; LowPrices = varargin{2}; ClosePrices = varargin{3};
OpenPrices = varargin{4}; color = "brwk";
elseif ( nargin == 5 )
HighPrices = varargin{1}; LowPrices = varargin{2}; ClosePrices = varargin{3};
OpenPrices = varargin{4}; color = varargin{5};
elseif ( nargin == 6 )
HighPrices = varargin{1}; LowPrices = varargin{2}; ClosePrices = varargin{3};
OpenPrices = varargin{4}; color = varargin{5}; dates = varargin{6};
elseif ( nargin == 7 )
HighPrices = varargin{1}; LowPrices = varargin{2}; ClosePrices = varargin{3};
OpenPrices = varargin{4}; color = varargin{5}; dates = varargin{6};
dateform = varargin{7};
endif
if ( ! isnumeric ( HighPrices ) || ! isnumeric ( LowPrices ) || ...
! isnumeric ( ClosePrices ) || ! isnumeric ( OpenPrices ) ) # one of OHLC is not numeric
error ("candle: The inputs for HighPrices, LowPrices, ClosePrices and OpenPrices must be numeric vectors.");
endif
if ( size ( OpenPrices ) != size ( HighPrices ) )
error ("candle: OpenPrices and HighPrices vectors are different lengths.");
endif
if ( size ( HighPrices ) != size ( LowPrices ) )
error ("candle: HighPrices and LowPrices vectors are different lengths.");
endif
if ( size ( LowPrices ) != size ( ClosePrices ) )
error ("candle: LowPrices and ClosePrices vectors are different lengths.");
endif
if ( size ( ClosePrices, 1 ) == 1 && size ( ClosePrices, 2 ) > 1 ) # ohlc inputs are row vectors, so transpose them
OpenPrices = OpenPrices'; HighPrices = HighPrices'; LowPrices = LowPrices';
ClosePrices = ClosePrices';
warning ("candle: The HighPrices, LowPrices, ClosePrices and OpenPrices should be column vectors. They have been transposed.");
endif
## check the user input Color argument, if it's character row vector
if ( ( nargin >= 5 && ischar ( color ) ) && size ( color, 1 ) == 1 )
if ( size ( color, 2 ) == 1 ) # only one color has been user specified
color = [ tolower( color ) "rwk" ]; # so add default colors for down bars, background and wicks
elseif ( size ( color, 2 ) == 2 ) # two colors have been user specified
color = [ tolower( color ) "wk" ]; # so add default colors for background and wicks
elseif ( size ( color, 2 ) == 3 ) # three colors have been user specified
if ( color ( 3 ) == "k" || color ( 3 ) == "K" ) # if user selected background is black
color = [ tolower( color ) "w" ]; # set wicks to default white
else
color = [ tolower( color ) "k" ]; # else default black wicks
endif
elseif ( size ( color, 2 ) >= 4 ) # all four colors have been user specified, extra character inputs ignored
color = tolower( color ); # correct in case user input contains upper case e.g. "BRWK"
endif
elseif ( nargin >= 5 && ! ischar ( color ) ) # the user input for color is not a charcter vector
warning ("candle: The fifth input argument, Color, should be a character row vector for Color.\nThe chart has been plotted with default colors.");
color = "brwk";
elseif ( ischar ( color ) && size ( color, 1 ) != 1 ) # user input is more than one row of characters - a date character array by mistake?
warning ("candle: Color is not a single row character vector. Possibly a column Dates character vector?\nThe chart has been plotted with default colors.");
color = "brwk";
endif # end of nargin >= 5 && ischar ( color ) ) && size ( color, 1 ) == 1 if statement
wicks = HighPrices .- LowPrices;
body = ClosePrices .- OpenPrices;
up_down = sign ( body );
body_width = 20;
wick_width = 1;
doji_size = 10;
one_price_size = 15;
hold on;
## first, plot the chart background color
plot ( HighPrices, color( 3 ), LowPrices, color( 3 ) );
fill ( [ min( xlim ) max( xlim ) max( xlim ) min( xlim ) ], ...
[ min( ylim ) min( ylim ) max( ylim ) max( ylim ) ], color( 3 ) );
## plot the wicks
x = ( 1 : length ( ClosePrices ) ); # the x-axis
idx = x;
high_nan = nan ( size ( HighPrices ) ); high_nan( idx ) = HighPrices; # highs
low_nan = nan ( size ( LowPrices ) ); low_nan( idx ) = LowPrices; # lows
x = reshape ( [ x; x; nan( size ( x ) ) ], [], 1 );
y = reshape ( [ high_nan(:)'; low_nan(:)'; nan( 1 , length ( HighPrices ) ) ], ...
[] , 1 );
plot ( x, y, color( 4 ), "linewidth", wick_width ); # plot wicks
## plot the up bar bodies
x = ( 1 : length ( ClosePrices ) ); # the x-axis
idx = ( up_down == 1 ); idx = find ( idx ); # index by condition close > open
high_nan = nan ( size ( HighPrices ) ); high_nan( idx ) = ClosePrices( idx ); # body highs
low_nan = nan ( size ( LowPrices ) ); low_nan( idx ) = OpenPrices( idx ); # body lows
x = reshape ( [ x; x; nan( size ( x ) ) ], [], 1 );
y = reshape ( [ high_nan(:)'; low_nan(:)'; nan( 1, length ( HighPrices ) ) ], ...
[], 1 );
plot ( x, y, color( 1 ), "linewidth", body_width ); # plot bodies for up bars
## plot the down bar bodies
x = ( 1 : length ( ClosePrices ) ); # the x-axis
idx = ( up_down == -1 ); idx = find ( idx ); # index by condition close < open
high_nan = nan ( size ( HighPrices ) ); high_nan( idx ) = OpenPrices( idx ); # body highs
low_nan = nan ( size ( LowPrices ) ); low_nan( idx ) = ClosePrices( idx ); # body lows
x = reshape ( [ x; x; nan( size ( x ) ) ], [], 1 );
y = reshape ( [ high_nan(:)'; low_nan(:)'; nan( 1, length ( HighPrices ) ) ], ...
[], 1 );
plot ( x, y, color( 2 ), "linewidth", body_width ); # plot bodies for down bars
## plot special cases
## doji bars
doji_bar = ( HighPrices > LowPrices ) .* ( ClosePrices == OpenPrices ); ...
doji_ix = find ( doji_bar );
if ( length ( doji_ix ) >= 1 )
x = ( 1 : length ( ClosePrices ) ); # the x-axis
plot ( x( doji_ix ), ClosePrices( doji_ix ), [ "+" char ( color( 4 ) ) ], ...
"markersize", doji_size ); # plot the open/close as horizontal dash
endif
## OpenPrices == HighPrices == LowPrices == ClosePrices
one_price = ( HighPrices == LowPrices ) .* ( ClosePrices == OpenPrices ) .* ...
( OpenPrices == HighPrices ); one_price_ix = find ( one_price );
if ( length ( one_price_ix ) >= 1 )
x = ( 1 : length ( ClosePrices ) ); # the x-axis
plot ( x( one_price_ix ), ClosePrices( one_price_ix ), ...
[ "." char ( color( 4 ) ) ], "markersize", one_price_size ); # plot as a point/dot
endif
hold off;
## now add the x-axis tick labels, if the user has supplied the correct arguments
if ( nargin == 6 && isnumeric ( dates ) )
error ("candle: If the sixth input argument, Dates, is a serial date number column (See datenum) or a datevec matrix (See datevec), Dateform input is required.\nThe chart has been plotted without x-axis dates.");
endif
if ( nargin == 6 && ischar ( dates ) ) # user has given a character vector of dates for dates
if ( size ( dates, 1 ) != size ( ClosePrices, 1 ) )
error ("candle: The sixth input argument, Dates, and the OHLC prices vectors are different lengths.\nThe chart has been plotted without x-axis dates.");
else
ticks = cellstr ( dates ) ;
ax = "x" ;
xticks = 1 : length ( ClosePrices );
h = gca ();
set ( h, "xtick", xticks );
set ( h, [ ax "ticklabel" ], ticks );
endif
endif
if ( nargin == 7 && isnumeric ( dates ) ) # input arguments 6 and 7 assumed to be a dates vector and dateform respectively
if ( size ( dates, 1 ) != size ( ClosePrices, 1 ) )
error ("candle: The sixth input argument, Dates, and the OHLC prices vectors are different lengths.\nThe chart has been plotted without x-axis dates.");
endif
if ( size ( dates, 2 ) == 1 ) # user has given a possible serial date number column for dates
is_monotonically_increasing = sum ( dates == cummax ( dates ) ) / size ( dates, 1 );
if ( is_monotonically_increasing != 1 )
error ("candle: Dates does not appear to be a serial date number column as it is not monotonically increasing.\nThe chart has been plotted without x-axis dates.");
endif
if ( isnumeric ( dateform ) && ( dateform < 0 || dateform > 31 ) )
error ("candle: Dateform integer code number is out of bounds (See datestr).\nThe chart has been plotted without x-axis dates.");
endif
if ( isnumeric ( dateform ) && rem ( dateform, 1 ) > 0 )
error ("candle: Dateform code number should be an integer 0 - 31 (See datestr).\nThe chart has been plotted without x-axis dates.");
endif
ticks = datestr ( dates, dateform );
ticks = mat2cell ( ticks, ones ( size ( ticks, 1 ), 1 ), size ( ticks, 2 ) );
ax = "x";
xticks = 1 : length ( ClosePrices );
h = gca ();
set ( h, "xtick", xticks );
set ( h, [ ax "ticklabel" ], ticks );
elseif ( size ( dates, 2 ) == 6 ) # user has given a possible datevec matrix for dates
if ( isnumeric ( dateform ) && ( dateform < 0 || dateform > 31 ) )
error ("candle: Dateform integer code number is out of bounds (See datestr).\nThe chart has been plotted without x-axis dates.");
endif
if ( isnumeric ( dateform ) && rem ( dateform, 1 ) > 0 )
error ("candle: Dateform code number should be an integer 0 - 31 (See datestr).\nThe chart has been plotted without x-axis dates.");
endif
ticks = datestr ( dates, dateform );
ticks = mat2cell ( ticks, ones ( size ( ticks, 1 ), 1 ), size ( ticks, 2 ) );
ax = "x";
xticks = 1 : length ( ClosePrices );
h = gca ();
set ( h, "xtick", xticks );
set ( h, [ ax "ticklabel" ], ticks );
else
error ("candle: The numerical Dates input is neither a single column serial date number nor a six column datevec format.\nThe chart has been plotted without x-axis dates.");
endif
endif # end of ( nargin == 7 && isnumeric ( dates ) ) if statement
endfunction
%!demo 1
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open );
%! title("default plot.");
%!demo 2
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brk' );
%! title("default plot with user selected black background");
%!demo 3
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brkg' );
%! title("default color candlestick bodies and user selected background and wick colors");
%!demo 4
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'gmby' );
%! title("all four colors being user selected");
%!demo 5
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! datenum_vec = [ 7.3702e+05; 7.3702e+05 ;7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; ...
%! 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brwk', datenum_vec, "yyyy-mm-dd HH:MM" );
%! title("default plot with datenum dates and character dateform arguments");
%!demo 6
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! datenum_vec = [ 7.3702e+05; 7.3702e+05 ;7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; ...
%! 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05 ];
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brk', datenum_vec, 31 );
%! title("default plot with user selected black background with datenum dates and integer dateform arguments");
%!demo 7
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! datenum_vec = [ 7.3702e+05; 7.3702e+05 ;7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05; ...
%! 7.3702e+05; 7.3702e+05; 7.3702e+05; 7.3702e+05 ];
%! datevec_vec = datevec( datenum_vec );
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brwk', datevec_vec, 21 );
%! title("default plot with datevec dates and integer dateform arguments");
%!demo 8
%! Open = [ 1292.4; 1291.7; 1291.8; 1292.2; 1291.5; 1291.0; 1291.0; 1291.5; 1291.7; 1291.5; 1290.7 ];
%! High = [ 1292.6; 1292.1; 1292.5; 1292.3; 1292.2; 1292.2; 1292.7; 1292.4; 1292.3; 1292.1; 1292.9 ];
%! Low = [ 1291.3; 1291.3; 1291.7; 1291.1; 1290.7; 1290.2; 1290.3; 1291.1; 1291.2; 1290.5; 1290.4 ];
%! Close = [ 1291.8; 1291.7; 1292.2; 1291.5; 1291.0; 1291.1; 1291.5; 1291.7; 1291.6; 1290.8; 1292.8 ];
%! character_dates = char ( [] );
%! for i = 1 : 11
%! character_dates = [ character_dates ; "a date" ] ;
%! endfor
%! graphics_toolkit('fltk'); candle( High, Low, Close, Open, 'brk', character_dates );
%! title("default plot with user selected black background with character dates argument");

I hope readers who are Octave users find this useful.
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