Visualising Oanda's Orderbook

My earlier post of 26th March shows code to visualise the most recent instantaneous snapshot of Oanda's order book, realised as a horizontal bar chart superimposed over a price chart. Below is a screen shot of a different type of chart

designed to show the historical order book, which is similar to the proprietary Bookmap software. The background of the chart is a heatmap of the 20 order book levels above and below the order book price, with the lighter colours representing a higher percentage of the total order book order volume, the spheres are sized proportionally to the tick volume of the relevant OHLC bar and the blue and red lines represent the high and low of the bar respectively. The lighter colours nicely highlight the areas where orders are accumulated for potential support and resistance.

However, the above screenshot is actually a two dimensional view, from above, of a three dimensional surface plot, as can be seen in the short video below

The first 25 seconds or so shows panning along the two dimensional view, whilst the remainder of the video shows panning in 3 dimensions. I have whimsically named this a "Snake and Canyon" plot as it resembles watching a river flowing/snaking along a canyon/valley floor and bouncing off the cliffs/hills that are the support and resistance zones. The higher the peaks, the higher the percentage of resting orders, and thus a greater number of incoming market orders is needed to breakout out of the canyon/valley.

I created this so that I can visually look for patterns in the historical data, the reason being that my statistical search for useful features, using the Boruta package, has not shown any useful results yet. The Octave code to produce a "Snake and Canyon" plot is given below. Of course, use of this code presupposes that you have the data available for loading from csv files. Enjoy!

clear all ;
cd /home/dekalog/Documents/octave/oanda_data/20m ;

orderbook_snapshots_files = glob( '*_historical_orderbook_snapshots' ) ;
## {
##   [1,1] = aud_jpy_historical_orderbook_snapshots
##   [2,1] = aud_usd_historical_orderbook_snapshots
##   [3,1] = eur_aud_historical_orderbook_snapshots
##   [4,1] = eur_chf_historical_orderbook_snapshots
##   [5,1] = eur_gbp_historical_orderbook_snapshots
##   [6,1] = eur_jpy_historical_orderbook_snapshots
##   [7,1] = eur_usd_historical_orderbook_snapshots
##   [8,1] = gbp_chf_historical_orderbook_snapshots
##   [9,1] = gbp_jpy_historical_orderbook_snapshots
##   [10,1] = gbp_usd_historical_orderbook_snapshots
##   [11,1] = nzd_usd_historical_orderbook_snapshots
##   [12,1] = usd_cad_historical_orderbook_snapshots
##   [13,1] = usd_chf_historical_orderbook_snapshots
##   [14,1] = usd_jpy_historical_orderbook_snapshots
##   [15,1] = xag_usd_historical_orderbook_snapshots
##   [16,1] = xau_usd_historical_orderbook_snapshots
## }

file_no = input( 'Enter file no. from list, a number 1 to 16 inclusive. ' ) ;
filename = orderbook_snapshots_files{ file_no } ;
str_split = strsplit( filename , "_" ) ;
price_name = strjoin( str_split( 1 : 2 ) , "_" ) ;
ohlc_20m = dlmread( [ price_name , '_ohlc_20m' ] ) ; ## price data
orders = dlmread( [ price_name , '_historical_orderbook_snapshots' ] ) ; ## get latest orderbook levels

if ( file_no == 1 || file_no == 6 || file_no == 9 || file_no == 14 )
 bucket_size = 0.05 ;
elseif ( file_no == 16 )
 bucket_size = 0.5 ;
else
 bucket_size = 0.0005 ;
endif

order_price = orders( : , 6 ) ;
price_length = 250 ;
plot_price = order_price( end - price_length : end ) ;
rounded_price = round( plot_price ./ bucket_size ) .* bucket_size ;
x_length = ( 1 : 1 : ( price_length + 1 ) )' ;

y_max = max( rounded_price .+ ( 20 * bucket_size ) ) ;
y_min = min( rounded_price .- ( 20 * bucket_size ) ) ;
y = ( y_min : bucket_size : y_max )' ;

z = zeros( size( y , 1 ) , size( x_length , 1 ) ) ;
zz = orders( end - price_length : end , 7 : 47 ) .+ orders( end - price_length : end , 48 : 88 ) ;

for ii = 1 : size( z , 2 )
[ ~ , ix ] = min( abs( y .- plot_price( ii ) ) ) ;
z( ix - 20 : ix + 20 , ii ) = zz( ii , : ) ;
endfor

z_high = max( max( z ) ) ;
mid_y = ( ohlc_20m( end - price_length : end , 19 ) .+ ohlc_20m( end - price_length : end , 20 ) ) ./ 2 ;
vol = ohlc_20m( end - price_length : end , 22 ) ; vol = vol ./ max( vol ) ;

up_scatter_ix = find( ohlc_20m( end - price_length : end , 21 ) >= ohlc_20m( end - price_length : end , 18 ) ) ;
down_scatter_ix = find( ohlc_20m( end - price_length : end , 21 ) < ohlc_20m( end - price_length : end , 18 ) ) ;

figure(1) ;
colormap( 'cubehelix' ) ; surf( x_length , y , z , 'edgecolor' , 'none' , 'facecolor' , 'interp' , 'facealpha' , 0.75 ) ; view( 2 ) ; 
axis( [ 1 price_length y_min y_max 0 z_high ] ) ; grid off ;
hold on ;
scatter3( up_scatter_ix , mid_y( up_scatter_ix ) , ones( size( up_scatter_ix ) ) .* z_high ./ 2 , 1000.*vol( up_scatter_ix ) , 'c' , 'o' , 'filled' ) ;
scatter3( down_scatter_ix , mid_y( down_scatter_ix ) , ones( size( down_scatter_ix ) ) .* z_high ./ 2 , 1000.*vol( down_scatter_ix ) , 'm' , 'o' , 'filled' ) ;
line( 'xdata' , x_length , 'ydata' , ohlc_20m( end - price_length : end , 19 ) , 'zdata' , ones(size(x_length)).*z_high ./ 2 , 'color','b' , 'linewidth' , 5 ) ;
line( 'xdata',x_length ,'ydata', ohlc_20m( end - price_length : end , 20 ) ,'zdata', ones(size(x_length)).*z_high ./ 2 , 'color','r' , 'linewidth' , 5 ) ;
xlabel( 'Time' ) ; ylabel( 'Levels - Price' ) ; zlabel( 'OrderBook Percent' ) ; title( strjoin( str_split( 1 : 2 ) , "-" ) ) ;
hold off ;

Generic Octave_Oanda_API Function

My last two posts have shown Octave functions that use the Oanda API to access and download data. In the first of these posts I said that I would post more code for further functions as and when I write them. However, on further reflection this would be unnecessary as the generic form of any such function is:

1) create the required headers

## set up the headers
query = [ 'curl -s --compressed -H "Content-Type: application/json"' ] ; ## -s is silent mode for Curl for no paging to terminal
query = [ query , ' -H "Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"' ] ;

2) create the required API call

## construct the actual API call for instrument, year, month, day, hour and min
query = [ query , ' "https://api-fxtrade.oanda.com/v3/instruments/' , toupper( cross ) , "/orderBook?time=" , num2str( year ) , '-' , ...
num2str( month , "%02d" ) , '-' , num2str( day , "%02d" ) , 'T' , num2str( hour , "%02d" ) , '%3A' , num2str( min , "%02d" ) , '%3A00.000Z"' ] ;

3) extract the data from the returned JSON object

## call to use external Unix systems/Curl and return result
[ ~ , ret_JSON ] = system( query , RETURN_OUTPUT = 'TRUE' ) ;

## convert the returned JSON object to Octave structure
ret_JSON = load_json( ret_JSON ) ;

Providing the required libraries are installed on your system (see first post) the above is all that is needed for any API call - just change the details in the second code box - in the example above the code is to download the historical orderbook for a given forex currency cross/tradable at a specific date/time, given as function input variables.

I have found that the main difficulty lies in parsing the returned data structure, which can consist of nested Structures/Cell-Arrays. Below is code, liberally commented, that shows how to parse the above orderbook API call and add the 20 orderbook levels above/below the orderbook price to historical orderbook files already on disc

clear all ;
cd /home/dekalog/Documents/octave/oanda_data/20m ;

orderbooks = glob( '*_historical_orderbook_snapshots' ) ;
## {
##   [1,1] = aud_jpy_historical_orderbook_snapshots
##   [2,1] = aud_usd_historical_orderbook_snapshots
##   [3,1] = eur_aud_historical_orderbook_snapshots
##   [4,1] = eur_chf_historical_orderbook_snapshots
##   [5,1] = eur_gbp_historical_orderbook_snapshots
##   [6,1] = eur_jpy_historical_orderbook_snapshots
##   [7,1] = eur_usd_historical_orderbook_snapshots
##   [8,1] = gbp_chf_historical_orderbook_snapshots
##   [9,1] = gbp_jpy_historical_orderbook_snapshots
##   [10,1] = gbp_usd_historical_orderbook_snapshots
##   [11,1] = nzd_usd_historical_orderbook_snapshots
##   [12,1] = usd_cad_historical_orderbook_snapshots
##   [13,1] = usd_chf_historical_orderbook_snapshots
##   [14,1] = usd_jpy_historical_orderbook_snapshots
##   [15,1] = xag_usd_historical_orderbook_snapshots
##   [16,1] = xau_usd_historical_orderbook_snapshots
## }
data_20m = glob( '*_ohlc_20m' ) ;

for ii = 1 : 1 ## begin the instrument loop

str_split = strsplit( data_20m{ ii } , "_" ) ;
cross = strjoin( str_split( 1 : 2 ) , "_" ) ; ## get the tradable, e.g. 'eur_usd'

## create a unix system command to read the last row of 20 min ohlc of filename
unix_command = [ "tail -1" , " " , data_20m{ ii } ] ; 
[ ~ , data ] = system( unix_command ) ;
data = strsplit( data , { "," , "\n" } ) ; ## gives a cell arrayfun
## covert last date/time to numeric format
data_20m_last = [ str2num(data{1}) , str2num(data{2}) , str2num(data{3}) , str2num(data{4}) ,str2num(data{5}) ] ;

## create a unix system command to read the last row of historical_orderbook_snapshots of filename
unix_command = [ "tail -1" , " " , orderbooks{ ii } ] ; 
[ ~ , data ] = system( unix_command ) ;
data = strsplit( data , { "," , "\n" } ) ; ## gives a cell arrayfun
## covert last date/time to numeric format
data_ordbk_last = [ str2num(data{1}) , str2num(data{2}) , str2num(data{3}) , str2num(data{4}) ,str2num(data{5}) ] ;

time_diff = datenum( data_20m_last ) - datenum( data_ordbk_last ) ;
## only run following code if there is more orderbook data to download to match 20min data already on file
 if ( time_diff > 0 )

 no_rows_diff = ceil( time_diff * 72 ) ; ## there are 72 x 20 minute bars per day
 ## create a unix system command to read the last no_rows_diff of 20 min ohlc of filename
 unix_command = [ "tail -" , num2str( no_rows_diff ) , " " , data_20m{ ii } ] ; 
 [ ~ , data ] = system( unix_command ) ;
 data = strsplit( data , { "," , "\n" } ) ; ## gives a cell arrayfun
 data = data( 1 : size( data , 2 ) - 1 ) ; ## get rid of last empty cell
 data = reshape( data , 22 , no_rows_diff )' ;
 
  for jj = 1 : no_rows_diff
   if ( str2double(data{jj,1})==data_ordbk_last(1) && str2double(data{jj,2})==data_ordbk_last(2) && ...
        str2double(data{jj,3})==data_ordbk_last(3) && str2double(data{jj,4})==data_ordbk_last(4) && ...
        str2double(data{jj,5})==data_ordbk_last(5) )
    begin_ix = jj + 1 ;
    break ;
   endif
  endfor ## end of jj = 1 : no_rows_diff loop

 new_orderbook_data = zeros( no_rows_diff - ( begin_ix - 1 ) , 88 ) ;

 kk = 0 ; ## initialise kk counter to loop over new_orderbook_data rows
  for jj = begin_ix : no_rows_diff ## loop over structure S from begin_ix to no_rows_diff
   kk = kk + 1 ; ## increment counter
   
   ## write dates and times to new_orderbook_data
   new_orderbook_data( kk , 1 ) = str2double( data{ jj , 1 } ) ;
   new_orderbook_data( kk , 2 ) = str2double( data{ jj , 2 } ) ;
   new_orderbook_data( kk , 3 ) = str2double( data{ jj , 3 } ) ;
   new_orderbook_data( kk , 4 ) = str2double( data{ jj , 4 } ) ;
   new_orderbook_data( kk , 5 ) = str2double( data{ jj , 5 } ) ;
   
   ## download the orderbook structure, S
   S = get_historical_orderbook( cross , new_orderbook_data( kk , 1 ) , new_orderbook_data( kk , 2 ) , new_orderbook_data( kk , 3 ) , ...
                                  new_orderbook_data( kk , 4 ) , new_orderbook_data( kk , 5 ) ) ;

   ## write the orderBook Price to new_orderbook_data
   new_orderbook_data( kk , 6 ) = str2double( S.orderBook.price ) ;

    ######## find where str2double( S.orderBook.price ) is within S ########
    for ix = 1 : size( S.orderBook.buckets , 2 )
     if ( str2double( S.orderBook.buckets{ ix }.price ) >= str2double( S.orderBook.price ) )
      mid_ix = ix ;  
      break ;
     endif
    endfor ## end ix loop

    if ( ( str2double( S.orderBook.price ) - str2double( S.orderBook.buckets{ mid_ix - 1 }.price ) ) < ...
         ( str2double( S.orderBook.buckets{ mid_ix }.price ) ) - str2double( S.orderBook.price ) ) ## refine accuracy of mid_ix
      mid_ix = mid_ix - 1 ;
    endif
    ########## index for str2double( S.orderBook.price ) found #############

  ## actual writing to file: +/- 20 lines around mid_ix, the orderbook_price
  orderbook_begin_ix = mid_ix - 20 ; orderbook_end_ix = mid_ix + 20 ;
  
  ## format of file to write is:
  ## year month day hour min orderbook_price long% short%
   xx = 7 ; ## initialise column counter
   for zz = orderbook_begin_ix : orderbook_end_ix
    new_orderbook_data( kk , xx ) = str2double( S.orderBook.buckets{ zz }.longCountPercent ) ;
    new_orderbook_data( kk , xx + 41 ) = str2double( S.orderBook.buckets{ zz }.shortCountPercent ) ;
    xx = xx + 1 ; ## increment column counter
   endfor ## end of zz filling loop
   
  endfor ## end of jj for loop to fill one line of new_orderbook_data

 endif ## end of ( time_diff > 0 ) if statement

dlmwrite( orderbooks{ ii } , new_orderbook_data , '-append' ) ;

endfor ## end of ii instrument for loop

## The downloaded Structure S looks like
## parse the character structure S and write to new_orderbook_data
##  isstruct(S)
##  ans = 1
##  fieldnames(S)
##  ans =
##  {
##    [1,1] = orderBook
##  }
##
##  >> fieldnames(S.orderBook)
##  ans =
##  {
##    [1,1] = instrument
##    [2,1] = time
##    [3,1] = unixTime
##    [4,1] = price
##    [5,1] = bucketWidth
##    [6,1] = buckets
##  }
##
##  S.orderBook.instrument
##  ans = AUD_JPY
##
##  S.orderBook.time
##  ans = 2020-04-05T21:00:00Z
##
##  S.orderBook.unixTime
##  ans = 1586120400
##
##  S.orderBook.price
##  ans = 65.080
##
##  S.orderBook.bucketWidth
##  ans = 0.050
##
##  iscell( S.orderBook.buckets )
##  ans = 1

The code uses loops, which is usually frowned upon in favour of vectorised code, but I am not aware of how to vectorise the parsing of the structure. This code also shows the use of the unix_command to use -tail to read just the last 'n' lines of the files on disc and thus avoid loading complete, and perhaps very large, files.

Get Latest Pricing Octave_Oanda_API Function

Following on from my my earlier, simple account summary API function, here is a function which downloads the latest pricing information for a given currency cross/tradable

## 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 {} {@var{best_bid}, @var{best_ask} =} get_current_pricing (@var{currency_cross})
##
## Returns the current pricing (best bid and ask prices) for CURRENCY_CROSS given by the Oanda API call,
## for example,
##
## "https://api-fxtrade.oanda.com/v3/accounts//pricing?instruments=EUR_USD"
##
## for CURRENCY_CROSS == 'eur_usd' (input is a character vector)
##
## Internally the function runs system() which calls the Curl
## library for the actual API download. The function is hard coded
## with the account token and account ID.
##
## @seealso{}
## @end deftypefn

## Author: dekalog 
## Created: 2020-04-05

function [ best_bid , best_ask ] = get_current_pricing ( cross )
 
if ( ischar( cross ) == 0 )
   error( 'Input must be a character vector for currency crosss/tradable, e.g. "eur_usd"' ) ;
endif
 
## set up the headers
query = [ 'curl -s -H "Content-Type: application/json"' ] ; ## -s is silent mode for Curl for no paging to terminal
query = [ query , ' -H "Authorization: Bearer 63926d856d2f6d7ed16ff014c8227042-3ceb540b828a7380b02dfdb273e7ab68"' ] ;

## construct the API call
query = [ query , ' "https://api-fxtrade.oanda.com/v3/accounts/001-004-225017-001/pricing?instruments=' ] ;
query = [ query , toupper( cross ) , '"' ] ;

## call to use external Unix systems/Curl and return result
[ ~ , ret_JSON ] = system( query , RETURN_OUTPUT = 'TRUE' ) ;

## convert the returned JSON object to Octave structure
s = load_json( ret_JSON ) ;

best_bid = s.prices{1}.bids{1}.price ;
best_ask = s.prices{1}.asks{1}.price ;

endfunction

## Typically, the retval output structure s will look like this:-
##
## s =
##
##   scalar structure containing the fields:
##
##     time = 2020-04-10T17:42:13.317424312Z
##     prices =
##     {
##       [1,1] =
##
##         scalar structure containing the fields:
##
##           type = PRICE
##           time = 2020-04-10T17:42:09.734257300Z
##           bids =
##           {
##             [1,1] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,2] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,3] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,4] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##           }
##
##           asks =
##           {
##             [1,1] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,2] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,3] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##             [1,4] =
##
##               scalar structure containing the fields:
##
##                 price: 1x7 sq_string
##                 liquidity: 1x1 uint32 scalar
##
##           }
##
##           closeoutBid = 1.09358
##           closeoutAsk = 1.09388
##           status = tradeable
##           tradeable = 1
##           unitsAvailable =
##
##             scalar structure containing the fields:
##
##               default: 1x1 scalar struct
##               openOnly: 1x1 scalar struct
##               reduceFirst: 1x1 scalar struct
##               reduceOnly: 1x1 scalar struct
##
##           quoteHomeConversionFactors =
##
##             scalar structure containing the fields:
##
##               positiveUnits: 1x10 sq_string
##               negativeUnits: 1x10 sq_string
##
##           instrument = EUR_USD
##
##     }
##
## where bid and ask fields etc. just give information rather than values.
## Can access values by, e.g. values = s.prices
## to get
##
## b =
## {
##   [1,1] =
##
##     scalar structure containing the fields:
##
##       type = PRICE
##       time = 2020-04-10T17:48:17.265291655Z
##       bids =
##       {
##         [1,1] =
##
##           scalar structure containing the fields:
##
##             price = 1.09352
##             liquidity = 1000000
##
##         [1,2] =
##
##           scalar structure containing the fields:
##
##             price = 1.09351
##             liquidity = 2000000
##
##         [1,3] =
##
##           scalar structure containing the fields:
##
##             price = 1.09350
##             liquidity = 2000000
##
##         [1,4] =
##
##           scalar structure containing the fields:
##
##             price = 1.09348
##             liquidity = 5000000
##
##       }
##
##       asks =
##       {
##         [1,1] =
##
##           scalar structure containing the fields:
##
##             price = 1.09393
##             liquidity = 1000000
##
##         [1,2] =
##
##           scalar structure containing the fields:
##
##             price = 1.09395
##             liquidity = 2000000
##
##         [1,3] =
##
##           scalar structure containing the fields:
##
##             price = 1.09396
##             liquidity = 2000000
##
##         [1,4] =
##
##           scalar structure containing the fields:
##
##             price = 1.09397
##             liquidity = 5000000
##
##       }
##
##       closeoutBid = 1.09348
##       closeoutAsk = 1.09397
##       status = tradeable
##       tradeable = 1
##       unitsAvailable =
##
##         scalar structure containing the fields:
##
##           default =
##
##             scalar structure containing the fields:
##
##               long: 1x6 sq_string
##               short: 1x6 sq_string
##
##           openOnly =
##
##             scalar structure containing the fields:
##
##               long: 1x6 sq_string
##               short: 1x6 sq_string
##
##           reduceFirst =
##
##             scalar structure containing the fields:
##
##               long: 1x6 sq_string
##               short: 1x6 sq_string
##
##           reduceOnly =
##
##             scalar structure containing the fields:
##
##               long: 1x1 sq_string
##               short: 1x1 sq_string
##
##
##       quoteHomeConversionFactors =
##
##         scalar structure containing the fields:
##
##           positiveUnits = 0.80113441
##           negativeUnits = 0.80178317
##
##       instrument = EUR_USD
##
## }
##
## where some of the values are now "viewable" in terminal.
##
## to do this directly do
##
## s.prices{1}.asks{1}.price, which  will give 1.09393
##
## and 
##
## s.prices{1}.asks{1}.liquidity, which will give 1000000

The function returns are the best bid and ask prices; however, it would be a simple enough task for readers to edit the function to get further levels, a la Market depth, liquidity at these levels and some other price metrics. There is a comment section at the end of the function which shows how to do this.

I wrote this function with a view to it perhaps becoming the basis of a client-side, trailing stop functionality. Enjoy!

First Octave Function using Oanda API

As part of my on-going code revision I have written my first Octave function to use the Oanda API. This is just a simple "proof of concept" function which downloads an account summary.

## 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 {} {@var{retval} =} account_summary ()
##
## Returns the Oanda account summary given by the Oanda API call
##
## "https://api-fxtrade.oanda.com/v3/accounts//summary"
##
## Internally the function runs system() which calls the Curl
## library for the actual API download. The function is hard coded
## with the account token and account ID.
## 
## @seealso{}
## @end deftypefn

## Author: dekalog 
## Created: 2020-04-04

function retval = account_summary ()

## set up the headers
query = [ 'curl -s -H "Content-Type: application/json"' ] ; ## -s is silent mode for Curl for no paging to terminal
query = [ query , ' -H "Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"' ] ;

## construct the API call
query = [ query , ' "https://api-fxtrade.oanda.com/v3/accounts/XXX-XXX-XXXXXX-XXX/summary"' ] ;

## call to use external Unix systems/Curl and return result
[ ~ , retval ] = system( query , RETURN_OUTPUT = 'TRUE' ) ;

## convert the returned json object to Octave structure
retval = load_json( retval ) ;

endfunction

The function uses the Curl library, so obviously this must be installed on your system, and to convert the returned JSON object to a native Octave structure the Octave wrapper function available from this Github, https://github.com/Andy1978/octave-rapidjson, is used. For this wrapper function to work rapidjson must also be installed.

I shall probably write more such Octave-OandaAPI functions for my particular use cases and will blog about them as and when I do so.