A Second Orderbook Visualisation Chart

Below is code for a second way of charting ohlc price with Oanda's order book levels. First, the function loads the relevant data and plots the order book levels with Octave's pcolor function and then plots the price ohlc as candlesticks over the pcolor plot. The candlestick part of the function reuses some of the code I wrote for the financial package's candle plotting function.

## Copyright (C) 2020 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 {} candle_with_order_levels (@var{cross}, @var{n_bars})
##
## Plots a 20 minute candlestick chart of the most recent N_BARS of currency CROSS with
## the background highlighted with Oanda's historical orderbook levels for
## this CROSS.
##
## @seealso{candle,snake_and_canyon_plot}
## @end deftypefn

## Author: dekalog 
## Created: 2020-05-06

function candle_with_order_levels ( curr_cross , n_bars )

price_name = tolower( curr_cross ) ;

## get price data of *_ohlc_20m
unix_command = [ "wc" , " " , "-l" , " " , [ price_name , '_ohlc_20m' ] ] ;
## the 'wc' with '-l' flag command counts the number of lines in [ price_name , '_ohlc_20m' ] } 
[ ~ , system_out ] = system( unix_command ) ;
cstr = strsplit( system_out , " " ) ; 
lines_in_file = str2double( cstr( 1 , 1 ) ) ;
## read *_ohlc_20m file
price_data = dlmread( [ price_name , '_ohlc_20m' ] , ',' , [ lines_in_file - n_bars , 0 , lines_in_file , 21 ] ) ;
open = price_data(:,18) ; high = price_data(:,19) ; low = price_data(:,20) ; close = price_data(:,21) ; vol = price_data(:,22) ;

## get orderbook data of *_historical_orderbook_snapshots
unix_command = [ "wc" , " " , "-l" , " " , [ price_name , '_historical_orderbook_snapshots' ] ] ;
## the 'wc' with '-l' flag command counts the number of lines in [ price_name , '_historical_orderbook_snapshots' ] } 
[ ~ , system_out ] = system( unix_command ) ;
cstr = strsplit( system_out , " " ) ; 
lines_in_file = str2double( cstr( 1 , 1 ) ) ;
## read *_ohlc_20m file
orderbook_data = dlmread( [ price_name , '_historical_orderbook_snapshots' ] , ',' , [ lines_in_file - n_bars , 0 , lines_in_file , 87 ] ) ;
## combine buys and sell % and delete unnecessary data
orderbook_data( : , 7 : 47 ) = orderbook_data( : , 7 : 47 ) .+ orderbook_data( : , 48 : 88 ) ;
orderbook_data( : , 48 : 88 ) = [] ; ## col( : , 27 ) is the orderbook price level column

## create the backgound heatmap of levels
if ( strcmp( price_name , 'aud_jpy' ) || strcmp( price_name , 'eur_jpy' ) || strcmp( price_name , 'gbp_jpy' ) || ...
     strcmp( price_name , 'usd_jpy' ) )
 bucket_size = 0.05 ;
elseif ( strcmp( price_name , 'xau_usd' ) )
 bucket_size = 0.5 ;
else
 bucket_size = 0.0005 ;
endif

rounded_order_price = round( orderbook_data( : , 6 ) ./ bucket_size ) .* bucket_size ;

## create and fill image mesh for backgound plot
y_max = max( rounded_order_price .+ ( 20 * bucket_size ) ) ;
y_min = min( rounded_order_price .- ( 20 * bucket_size ) ) ;
y_ax = ( y_min - 5 * bucket_size : bucket_size : y_max + 5 * bucket_size )' ;
x_ax = ( 1 : 1 : numel( open ) + 3 )' ;

z = zeros( numel( x_ax ) , numel( y_ax ) ) ;

##for ii = 1 : size( z , 2 )
##[ ~ , ix ] = min( abs( y_ax .- rounded_order_price( ii ) ) ) ;
##z( ix - 20 : ix + 20 , ii ) = orderbook_data( ii , 7 : 47 )' ;
##endfor

[ ~ , ix ] = min( abs( y_ax .- rounded_order_price( 1 ) ) ) ;
z( 1 , ix - 20 : ix + 20 ) = orderbook_data( 1 , 7 : 47 ) ;
for ii = 2 : numel( x_ax ) - 3
z( ii , : ) = z( ii - 1 , : ) ;
[ ~ , ix ] = min( abs( y_ax .- rounded_order_price( ii ) ) ) ;
z( ii , ix - 20 : ix + 20 ) = orderbook_data( ii , 7 : 47 ) ;
endfor

for ii = numel( x_ax ) - 2 : numel( x_ax )
z( ii , : ) = z( ii - 1 , : ) ;
endfor

## For pcolor(), if x and y are vectors, then a typical vertex is (x(j), y(i), c(i,j)). 
## Thus, columns of c correspond to different x values and rows of c correspond to different y values.
## create the background ( best choices - viridis and ocean? ) 
colormap( 'ocean' ) ; figure( 20 ) ; pcolor( x_ax , y_ax , z' ) ; shading interp ; axis tight ;

hold on ; 

## plot candlesticks
wicks = high .- low ;
body = close .- open ;
up_down = sign ( body );
body_width = 20 ;
wick_width = 2 ;
doji_size = 10 ;
one_price_size = 15 ;

## plot the wicks
x = ( 1 : numel( close ) ) ;  # the x-axis
idx = x ;
high_nan = nan( size ( high ) ) ; high_nan( idx ) = high ; # highs
low_nan = nan( size ( low ) ) ; low_nan( idx ) = low ;     # lows
x = reshape( [ x ; x ; nan( size ( x ) ) ] , [] , 1 ) ;
y = reshape( [ high_nan( : )' ; low_nan( : )' ; nan( 1 , length ( high ) ) ] , [] , 1 ) ;
figure( 20 ) ; plot( x , y , 'w' , 'linewidth' , wick_width ) ; # plot wicks

## plot the up bar bodies
x = ( 1 : numel( close ) ) ; # the x-axis
idx = ( up_down == 1 ) ; idx = find ( idx ) ;                      # index by condition close > open
high_nan = nan( size ( high ) ) ; high_nan( idx ) = close( idx ) ; # body highs
low_nan = nan( size ( low ) ) ; low_nan( idx ) = open( idx ) ;     # body lows
x = reshape( [ x ; x ; nan( size ( x ) ) ] , [] , 1 ) ;
y = reshape( [ high_nan( : )' ; low_nan( : )' ; nan( 1 , length ( high ) ) ] , [] , 1 ) ;
figure( 20 ) ; plot( x , y , 'c' , 'linewidth' , body_width ) ; # plot bodies for up bars

## plot the down bar bodies
x = ( 1 : numel( close ) ) ; # the x-axis
idx = ( up_down == -1 ) ; idx = find ( idx ) ;                    # index by condition close < open
high_nan = nan( size ( high ) ) ; high_nan( idx ) = open( idx ) ; # body highs
low_nan = nan( size ( low ) ) ; low_nan( idx ) = close( idx ) ;   # body lows
x = reshape( [ x ; x ; nan( size ( x ) ) ] , [] , 1 ) ;
y = reshape( [ high_nan( : )' ; low_nan( : )' ; nan( 1 , length ( high ) ) ] , [] , 1 ) ;
figure( 20 ) ; plot( x , y , 'r' , 'linewidth', body_width ) ; # plot bodies for down bars

## plot special cases
## doji bars
doji_bar = ( high > low ) .* ( close == open ) ; doji_ix = find ( doji_bar ) ;

if ( length ( doji_ix ) >= 1 )
  x = ( 1 : length ( close ) ) ; # the x-axis
  figure( 20 ) ; plot( x( doji_ix ) , close( doji_ix ) , '+k' , 'markersize' , doji_size ) ; # plot the open/close as horizontal dash
endif

## open == high == low == close
one_price = ( high == low ) .* ( close == open ) .* ( open == high ) ; one_price_ix = find ( one_price ) ;

if ( length ( one_price_ix ) >= 1 )
  x = ( 1 : numel( close ) ) ; # the x-axis
  figure( 20 ) ; plot ( x( one_price_ix ) , close( one_price_ix ) , '.k' , 'markersize' , one_price_size ) ; # plot as a point/dot
endif

hold off ;

endfunction

The 20 orderbook levels above and below the open of each candlestick are plotted and a typical looking chart output, with the "ocean" colourmap, is:

where the lighter colours show a higher proportion of accumulated orders.

The next little project I have set myself is to do the same as above, but to plot a different version of a market profile chart.