## Thursday, 21 September 2017

It has been about 5 months since my last blog post and in this time I have been working away from home, been on summer holiday and spent some time mucking about on boats, so I have not been able to devote as much time to my blog as I would have liked. However, that has now changed, and this blog post is about obtaining historical data.

Many moons ago I used to download free, EOD data from tradingblox, but they stopped updating this in early 2013. I then started concentrating more on forex data because free sources of this data are more readily available. However, this still meant ( for me at least ) a laborious process of manually downloading .txt/.csv files, with the attendant problem of the data not being fully up to date and then resulting in me doing historical testing on data that I would not be able to trade in real time. With my present focus on machine learning derived trading algorithms this was becoming an untenable position.

My solution to this has been to personalize the ROandaAPI code that is freely available from this github, courtesy of IF.FranciscoME. I have stripped out some if statements, hard coded some variables particular to my own Oanda account, added some extra comments for my own enlightenment and broken the API code up into separate .R functions and .R scripts, which I use via RStudio.

The first such R script is called to initialise the variables and load the various functions into the R environment, shown below.
# Required Packages in order to use the R API functions
library("RCurl")
library("jsonlite")
library("httr")

# -- ---------------------------------------------------------------------------------- #
# -- Specific Values of Parameters in API for my primary account ---------------------- #
# -- ---------------------------------------------------------------------------------- #

# -- numeric ------ # My Account ID
AccountID = 123456
# -- character ---- # My Oanda Token
AccountToken = "blah-de-blah-de-blah"

# load the various function files
source("~/path/to/InstrumentsList.R")
source("~/path/to/R_Oanda_API_functions/ActualPrice.R")
source("~/path/to/HisPricesCount.R")
source("~/path/to/HisPricesDates.R")

# get list of all tradeable instruments
trade_instruments = InstrumentsList( AccountToken , AccountID )

# -- character ---- # Granularity of the prices
# granularity: The time range represented by each candlestick. The value specified will determine the alignment
# of the first candlestick.
# choose from S% S10 S15 S30 M1 M2 M3 M4 M5 M10 M15 M30 H1 H2 H3 H4 H6 H8 H12 D W M ( secs mins hours day week month )
Granularity = "D"

# -- numeric ------ # Hour of the "End of the Day"
# dailyAlignment: The hour of day used to align candles with hourly, daily, weekly, or monthly granularity.
# The value specified is interpretted as an hour in the timezone set through the alignmentTimezone parameter and must be
# an integer between 0 and 23.
DayAlign = 0 # original R code and Oanda default is 17 at "America%2FNew_York"

# -- character ---- # Time Zone in format "Continent/Zone
# alignmentTimezone: The timezone to be used for the dailyAlignment parameter. This parameter does NOT affect the
# returned timestamp, the start or end parameters, these will always be in UTC. The timezone format used is defined by
# the IANA Time Zone Database, a full list of the timezones supported by the REST API can be found at
# http://developer.oanda.com/docs/timezones.txt
# "America%2FMexico_City" was the originallly provided, but could use, for example,  "Europe%2FLondon" or "Europe%2FWarsaw"
TimeAlign = "Europe%2FLondon"

################################# IMPORTANT NOTE #####################################################################
# By setting DayAlign = 0 and TimeAlign = "Europe%2FLondon" the end of bar is midnight in London. Doing this ensures
# that the bar OHLC in data downloads matches the bars seen in the Oanda FXTrade software, which for my account is
# Europe Division, e.g. London time. The timestamps on downloads are, however, at GMT times, which means during summer
# daylight saving time the times shown on the Oanda software seem to be one hour ahead of GMT.
######################################################################################################################

Start = Sys.time() # Current system time
End = Sys.time() # Current system time
Count = 500 # Oanda default

# now cd to the working directory
setwd("~/path/to/oanda_data")
The code is liberally commented to describe reasons for my default choices. The InstrumentsList.R function called in the above script is shown next.
InstrumentsList = function( AccountToken , AccountID )
{
auth        = c(Authorization = paste("Bearer",AccountToken,sep=" "))
Queryhttp   = paste(httpaccount,"/v1/instruments?accountId=",sep="")
QueryInst   = paste(Queryhttp,AccountID,sep="")
InstJson    = fromJSON(QueryInst1, simplifyDataFrame = TRUE)
FinalData   = data.frame(InstJson)
FinalData$MaxTradeUnits = as.numeric(FinalData$MaxTradeUnits)
return(FinalData)
}
This downloads a list of all the available trading instruments for the associated Oanda account. The following R script actually downloads the historical data for all the trading instruments listed in the above mentioned list and writes the data to separate files; one file per instrument. It also keeps track of the all instruments and the date of the last complete OHLC bar in the historical record and writes this to file also.
# cd to the working directory
setwd("~/path/to/oanda_data")

# dataframe to keep track of updates
Instrument_update_file = data.frame( Instrument = character() , Date = as.Date( character() ) , stringsAsFactors = FALSE )

for( ii in 1 : nrow( trade_instruments ) ) {

instrument = trade_instruments[ ii , 1 ]

# write details of instrument to Instrument_update_file
Instrument_update_file[ ii , 1 ] = instrument

historical_prices = HisPricesCount( Granularity = "D", DayAlign , TimeAlign , AccountToken ,instrument , Count = 5000 )
last_row_ix = nrow( historical_prices )

if ( historical_prices[ last_row_ix , 7 ] == FALSE ){ # last obtained OHLC bar values are incomplete
# and do not want to save incomplete OHLC values, so add date of previous line of complete OHLC data
# to Instrument_update_file
Instrument_update_file[ ii , 2 ] = as.Date( historical_prices[ last_row_ix - 1 , 1 ] )

# and delete the row with these incomplete values
historical_prices = historical_prices[ 1 : last_row_ix - 1 , ]

} # end of if statement

# Write historical_prices to file
write.table( historical_prices , file = paste( instrument , "raw_OHLC_daily" , sep = "_" ) , row.names = FALSE , na = "" ,
col.names = FALSE , sep = "," )

} # end of for loop

# Write Instrument_update_file to file
write.table( Instrument_update_file , file = "Instrument_update_file" , row.names = FALSE , na = "" , col.names = TRUE , sep = "," )
This script repeatedly calls the actual download function, HisPricesCount.R, which does all the heavy lifting in a loop, and the code for this download function is
HisPricesCount = function( Granularity, DayAlign, TimeAlign, AccountToken, Instrument, Count ){

auth             = c(Authorization = paste("Bearer",AccountToken,sep=" "))
QueryHistPrec    = paste(httpaccount,"/v1/candles?instrument=",sep="")
QueryHistPrec1   = paste(QueryHistPrec,Instrument,sep="")
qcount           = paste("count=",Count,sep="")
qcandleFormat    = "candleFormat=midpoint"
qgranularity     = paste("granularity=",Granularity,sep="")
qdailyalignment  = paste("dailyAlignment=",DayAlign,sep="")
QueryHistPrec2   = paste(QueryHistPrec1,qcandleFormat,qgranularity,qdailyalignment,qcount,sep="&")
InstHistPjson    = fromJSON(InstHistP, simplifyDataFrame = TRUE)
Prices           = data.frame(InstHistPjson[[3]])
Prices$time = paste(substr(Prices$time,1,10),substr(Prices$time,12,19), sep=" ") colnames(Prices) = c("TimeStamp","Open","High","Low","Close","TickVolume","Complete") Prices$TimeStamp = as.POSIXct(strptime(Prices$TimeStamp, "%Y-%m-%d %H:%M:%OS"),origin="1970-01-01",tz = "UTC") attributes(Prices$TimeStamp)\$tzone = TimeAlign
return(Prices)

}
One of the input variables for this function is Count ( default = 5000 ), which means that the function downloads the last 5000 OHLC bar records up to and including the most recent, which may still be forming and hence is incomplete. The calling script ensures that any incomplete bar is stripped from the record so that only complete bars are printed to file.

All in all this is a vast improvement over my previous data collection regime, and kudos to IF.FranciscoME for making the base code available on his github.

## Thursday, 20 April 2017

### Using the BayesOpt Library to Optimise my Planned Neural Net

Following on from my last post, I have recently been using the BayesOpt library to optimise my planned neural net, and this post is a brief outline, with code, of what I have been doing.

My intent was to design a Nonlinear autoregressive exogenous model using my currency strength indicator as the main exogenous input, along with other features derived from the use of Savitzky-Golay filter convolution to model velocity, acceleration etc. I decided that rather than model prices directly, I would model the 20 period simple moving average because it would seem reasonable to assume that modelling a smooth function would be easier, and from this average it is a trivial matter to reverse engineer to get to the underlying price.

Given that my projected feature space/lookback length/number of nodes combination is/was a triple digit, discrete dimensional problem, I used the "bayesoptdisc" function from the BayesOpt library to perform a discrete Bayesian optimisation over these parameters, the main Octave script for this being shown below.
clear all ;

% all_rel_strengths_non_smooth = [ usd_rel_strength_non_smooth eur_rel_strength_non_smooth gbp_rel_strength_non_smooth chf_rel_strength_non_smooth ...
% jpy_rel_strength_non_smooth aud_rel_strength_non_smooth cad_rel_strength_non_smooth ] ;

% extract relevant data
% price = ( eurusd_daily_bars( : , 3 ) .+ eurusd_daily_bars( : , 4 ) ) ./ 2 ; % midprice
% price = ( gbpusd_daily_bars( : , 3 ) .+ gbpusd_daily_bars( : , 4 ) ) ./ 2 ; % midprice
% price = ( usdchf_daily_bars( : , 3 ) .+ usdchf_daily_bars( : , 4 ) ) ./ 2 ; % midprice
price = ( usdjpy_daily_bars( : , 3 ) .+ usdjpy_daily_bars( : , 4 ) ) ./ 2 ; % midprice
base_strength = all_rel_strengths_non_smooth( : , 1 ) .- 0.5 ;
term_strength = all_rel_strengths_non_smooth( : , 5 ) .- 0.5 ;

% clear unwanted data
% clear eurusd_daily_bars all_rel_strengths_non_smooth ;
% clear gbpusd_daily_bars all_rel_strengths_non_smooth ;
% clear usdchf_daily_bars all_rel_strengths_non_smooth ;
clear usdjpy_daily_bars all_rel_strengths_non_smooth ;

global start_opt_line_no = 200 ;
global stop_opt_line_no = 7545 ;

% get matrix coeffs
slope_coeffs = generalised_sgolay_filter_coeffs( 5 , 2 , 1 ) ;
accel_coeffs = generalised_sgolay_filter_coeffs( 5 , 2 , 2 ) ;
jerk_coeffs = generalised_sgolay_filter_coeffs( 5 , 3 , 3 ) ;

% create features
sma20 = sma( price , 20 ) ;
global targets = sma20 ;
[ sma_max , sma_min ] = adjustable_lookback_max_min( sma20 , 20 ) ;
global sma20r = zeros( size(sma20,1) , 5 ) ;
global sma20slope = zeros( size(sma20,1) , 5 ) ;
global sma20accel = zeros( size(sma20,1) , 5 ) ;
global sma20jerk = zeros( size(sma20,1) , 5 ) ;

global sma20diffs = zeros( size(sma20,1) , 5 ) ;
global sma20diffslope = zeros( size(sma20,1) , 5 ) ;
global sma20diffaccel = zeros( size(sma20,1) , 5 ) ;
global sma20diffjerk = zeros( size(sma20,1) , 5 ) ;

global base_strength_f = zeros( size(sma20,1) , 5 ) ;
global term_strength_f = zeros( size(sma20,1) , 5 ) ;

base_term_osc = base_strength .- term_strength ;
global base_term_osc_f = zeros( size(sma20,1) , 5 ) ;
slope_bt_osc = rolling_endpoint_gen_poly_output( base_term_osc , 5 , 2 , 1 ) ; % no_of_points(p),filter_order(n),derivative(s)
global slope_bt_osc_f = zeros( size(sma20,1) , 5 ) ;
accel_bt_osc = rolling_endpoint_gen_poly_output( base_term_osc , 5 , 2 , 2 ) ; % no_of_points(p),filter_order(n),derivative(s)
global accel_bt_osc_f = zeros( size(sma20,1) , 5 ) ;
jerk_bt_osc = rolling_endpoint_gen_poly_output( base_term_osc , 5 , 3 , 3 ) ; % no_of_points(p),filter_order(n),derivative(s)
global jerk_bt_osc_f = zeros( size(sma20,1) , 5 ) ;

slope_base_strength = rolling_endpoint_gen_poly_output( base_strength , 5 , 2 , 1 ) ; % no_of_points(p),filter_order(n),derivative(s)
global slope_base_strength_f = zeros( size(sma20,1) , 5 ) ;
accel_base_strength = rolling_endpoint_gen_poly_output( base_strength , 5 , 2 , 2 ) ; % no_of_points(p),filter_order(n),derivative(s)
global accel_base_strength_f = zeros( size(sma20,1) , 5 ) ;
jerk_base_strength = rolling_endpoint_gen_poly_output( base_strength , 5 , 3 , 3 ) ; % no_of_points(p),filter_order(n),derivative(s)
global jerk_base_strength_f = zeros( size(sma20,1) , 5 ) ;

slope_term_strength = rolling_endpoint_gen_poly_output( term_strength , 5 , 2 , 1 ) ; % no_of_points(p),filter_order(n),derivative(s)
global slope_term_strength_f = zeros( size(sma20,1) , 5 ) ;
accel_term_strength = rolling_endpoint_gen_poly_output( term_strength , 5 , 2 , 2 ) ; % no_of_points(p),filter_order(n),derivative(s)
global accel_term_strength_f = zeros( size(sma20,1) , 5 ) ;
jerk_term_strength = rolling_endpoint_gen_poly_output( term_strength , 5 , 3 , 3 ) ; % no_of_points(p),filter_order(n),derivative(s)
global jerk_term_strength_f = zeros( size(sma20,1) , 5 ) ;

min_max_range = sma_max .- sma_min ;

for ii = 51 : size( sma20 , 1 ) - 1 % one step ahead is target

targets(ii) = 2 * ( ( sma20(ii+1) - sma_min(ii) ) / min_max_range(ii) - 0.5 ) ;

% scaled sma20
sma20r(ii,:) = 2 .* ( ( flipud( sma20(ii-4:ii,1) )' .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) ;
sma20slope(ii,:) = fliplr( ( 2 .* ( ( sma20(ii-4:ii,1)' .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) ) * slope_coeffs ) ;
sma20accel(ii,:) = fliplr( ( 2 .* ( ( sma20(ii-4:ii,1)' .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) ) * accel_coeffs ) ;
sma20jerk(ii,:) = fliplr( ( 2 .* ( ( sma20(ii-4:ii,1)' .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) ) * jerk_coeffs ) ;

% scaled diffs of sma20
sma20diffs(ii,:) = fliplr( diff( 2.* ( ( sma20(ii-5:ii,1) .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) )' ) ;
sma20diffslope(ii,:) = fliplr( diff( 2.* ( ( sma20(ii-5:ii,1) .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) )' * slope_coeffs ) ;
sma20diffaccel(ii,:) = fliplr( diff( 2.* ( ( sma20(ii-5:ii,1) .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) )' * accel_coeffs ) ;
sma20diffjerk(ii,:) = fliplr( diff( 2.* ( ( sma20(ii-5:ii,1) .- sma_min(ii) ) ./ min_max_range(ii) .- 0.5 ) )' * jerk_coeffs ) ;

% base strength
base_strength_f(ii,:) = fliplr( base_strength(ii-4:ii)' ) ;
slope_base_strength_f(ii,:) = fliplr( slope_base_strength(ii-4:ii)' ) ;
accel_base_strength_f(ii,:) = fliplr( accel_base_strength(ii-4:ii)' ) ;
jerk_base_strength_f(ii,:) = fliplr( jerk_base_strength(ii-4:ii)' ) ;

% term strength
term_strength_f(ii,:) = fliplr( term_strength(ii-4:ii)' ) ;
slope_term_strength_f(ii,:) = fliplr( slope_term_strength(ii-4:ii)' ) ;
accel_term_strength_f(ii,:) = fliplr( accel_term_strength(ii-4:ii)' ) ;
jerk_term_strength_f(ii,:) = fliplr( jerk_term_strength(ii-4:ii)' ) ;

% base term oscillator
base_term_osc_f(ii,:) = fliplr( base_term_osc(ii-4:ii)' ) ;
slope_bt_osc_f(ii,:) = fliplr( slope_bt_osc(ii-4:ii)' ) ;
accel_bt_osc_f(ii,:) = fliplr( accel_bt_osc(ii-4:ii)' ) ;
jerk_bt_osc_f(ii,:) = fliplr( jerk_bt_osc(ii-4:ii)' ) ;

endfor

% create xset for bayes routine
% raw indicator
xset = zeros( 4 , 5 ) ; xset( 1 , : ) = 1 : 5 ;
to_add = zeros( 4 , 15 ) ;
to_add( 1 , : ) = [ 1 2 2 3 3 3 4 4 4 4 5 5 5 5 5 ] ;
to_add( 2 , : ) = [ 1 1 2 1 2 3 1 2 3 4 1 2 3 4 5 ] ;
xset = [ xset to_add ] ;
to_add = zeros( 4 , 21 ) ;
to_add( 1 , : ) = [ 1 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 ] ;
to_add( 2 , : ) = [ 1 1 2 2 1 2 2 3 3 3 1 2 3 4 2 3 3 4 4 4 4 ] ;
to_add( 3 , : ) = [ 1 1 1 2 1 1 2 1 2 3 1 1 1 1 2 2 3 1 2 3 4 ] ;
xset = [ xset to_add ] ;
to_add = zeros( 4 , 70 ) ;
to_add( 1 , : ) = [ 1 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 ] ;
to_add( 2 , : ) = [ 1 1 2 2 2 1 2 2 2 3 3 3 3 3 3 1 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 1 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 ] ;
to_add( 3 , : ) = [ 1 1 1 2 2 1 1 2 2 1 2 2 3 3 3 1 1 2 2 1 2 2 3 3 3 1 2 2 3 3 3 4 4 4 4 1 1 2 2 1 2 2 3 3 3 1 2 2 3 3 3 4 4 4 4 1 2 2 3 3 3 4 4 4 4 5 5 5 5 5 ] ;
to_add( 4 , : ) = [ 1 1 1 1 2 1 1 1 2 1 1 2 1 2 3 1 1 1 2 1 1 2 1 2 3 1 1 2 1 2 3 1 2 3 4 1 1 1 2 1 1 2 1 2 3 1 1 2 1 2 3 1 2 3 4 1 1 2 1 2 3 1 2 3 4 1 2 3 4 5 ] ;
xset = [ xset to_add ] ;
% construct all_xset for combinations of indicators and look back lengths
all_zeros = zeros( size( xset ) ) ;
all_xset = [ xset ; repmat( all_zeros , 3 , 1 ) ] ;
all_xset = [ all_xset [ xset ; xset ; all_zeros ; all_zeros ] ] ;
all_xset = [ all_xset [ xset ; all_zeros ; xset ; all_zeros ] ] ;
all_xset = [ all_xset [ xset ; all_zeros ; all_zeros ; xset ] ] ;
all_xset = [ all_xset [ xset ; xset ; xset ; all_zeros ] ] ;
all_xset = [ all_xset [ xset ; xset ; all_zeros ; xset ] ] ;
all_xset = [ all_xset [ xset ; all_zeros ; xset ; xset ] ] ;
all_xset = [ all_xset repmat( xset , 4 , 1 ) ] ;

ones_all_xset = ones( 1 , size( all_xset , 2 ) ) ;

% now add layer for number of neurons and extend as necessary
max_number_of_neurons_in_layer = 20 ;

parameter_matrix = [] ;

for ii = 2 : max_number_of_neurons_in_layer % min no. of neurons is 2, max = max_number_of_neurons_in_layer
parameter_matrix = [ parameter_matrix [ ii .* ones_all_xset ; all_xset ] ] ;
endfor

% now the actual bayes optimisation routine
% set the parameters
params.n_iterations = 190; % bayesopt library default is 190
params.n_init_samples = 10;
params.crit_name = 'cEIa'; % cEI is default. cEIa is an annealed version
params.surr_name = 'sStudentTProcessNIG';
params.noise = 1e-6;
params.kernel_name = 'kMaternARD5';
params.kernel_hp_mean = [1];
params.kernel_hp_std = [10];
params.verbose_level = 1; % 3 to path below
params.log_filename = '/home/dekalog/Documents/octave/cplusplus.oct_functions/nn_functions/optimise_narx_ind_lookback_nodes_log';
params.l_type = 'L_MCMC' ; % L_EMPIRICAL is default
params.epsilon = 0.5 ; % probability of performing a random (blind) evaluation of the target function.
% Higher values implies forced exploration while lower values relies more on the exploration/exploitation policy of the criterion. 0 is default

% the function to optimise
fun = 'optimise_narx_ind_lookback_nodes_rolling' ;

% the call to the Bayesopt library function
bayesoptdisc( fun , parameter_matrix , params ) ;
% result is the minimum as a vector (x_out) and the value of the function at the minimum (y_out)
What this script basically does is:
1. load all the relevant data ( in this case a forex pair )
2. creates a set of scaled features
3. creates a necessary parameter matrix for the discrete optimisation function
4. sets the parameters for the optimisation routine
5. and finally calls the "bayesoptdisc" function
Note that in step 2 all the features are declared as global variables, this being necessary because the "bayesoptdisc" function of the BayesOpt library does not appear to admit passing these variables as inputs to the function.

The actual function to be optimised is given in the following code box, and is basically a looped neural net training routine.
## Copyright (C) 2017 dekalog
##
## This program is free software; you can redistribute it and/or modify it
## 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} =} optimise_narx_ind_lookback_nodes_rolling (@var{input1})
##
## @seealso{}
## @end deftypefn

## Author: dekalog
## Created: 2017-03-21

function [ retval ] = optimise_narx_ind_lookback_nodes_rolling( input1 )

% declare all the global variables so the function can "see" them
global start_opt_line_no ;
global stop_opt_line_no ;
global targets ;
global sma20r ; % 2
global sma20slope ;
global sma20accel ;
global sma20jerk ;
global sma20diffs ;
global sma20diffslope ;
global sma20diffaccel ;
global sma20diffjerk ;
global base_strength_f ;
global slope_base_strength_f ;
global accel_base_strength_f ;
global jerk_base_strength_f ;
global term_strength_f ;
global slope_term_strength_f ;
global accel_term_strength_f ;
global jerk_term_strength_f ;
global base_term_osc_f ;
global slope_bt_osc_f ;
global accel_bt_osc_f ;
global jerk_bt_osc_f ;

% build feature matrix from the above global variable according to parameters in input1
hidden_layer_size = input1(1) ;

% training targets
Y = targets( start_opt_line_no:stop_opt_line_no , 1 ) ;

% create empty feature matrix
X = [] ;

% which will always have at least one element of the main price series for the NARX
X = [ X sma20r( start_opt_line_no:stop_opt_line_no , 1:input1(2) ) ] ;

% go through input1 values in turn and add to X if necessary
if input1(3) > 0
X = [ X sma20slope( start_opt_line_no:stop_opt_line_no , 1:input1(3) ) ] ;
endif

if input1(4) > 0
X = [ X sma20accel( start_opt_line_no:stop_opt_line_no , 1:input1(4) ) ] ;
endif

if input1(5) > 0
X = [ X sma20jerk( start_opt_line_no:stop_opt_line_no , 1:input1(5) ) ] ;
endif

if input1(6) > 0
X = [ X sma20diffs( start_opt_line_no:stop_opt_line_no , 1:input1(6) ) ] ;
endif

if input1(7) > 0
X = [ X sma20diffslope( start_opt_line_no:stop_opt_line_no , 1:input1(7) ) ] ;
endif

if input1(8) > 0
X = [ X sma20diffaccel( start_opt_line_no:stop_opt_line_no , 1:input1(8) ) ] ;
endif

if input1(9) > 0
X = [ X sma20diffjerk( start_opt_line_no:stop_opt_line_no , 1:input1(9) ) ] ;
endif

if input1(10) > 0 % input for base and term strengths together
X = [ X base_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(10) ) ] ;
X = [ X term_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(10) ) ] ;
endif

if input1(11) > 0
X = [ X slope_base_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(11) ) ] ;
X = [ X slope_term_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(11) ) ] ;
endif

if input1(12) > 0
X = [ X accel_base_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(12) ) ] ;
X = [ X accel_term_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(12) ) ] ;
endif

if input1(13) > 0
X = [ X jerk_base_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(13) ) ] ;
X = [ X jerk_term_strength_f( start_opt_line_no:stop_opt_line_no , 1:input1(13) ) ] ;
endif

if input1(14) > 0
X = [ X base_term_osc_f( start_opt_line_no:stop_opt_line_no , 1:input1(14) ) ] ;
endif

if input1(15) > 0
X = [ X slope_bt_osc_f( start_opt_line_no:stop_opt_line_no , 1:input1(15) ) ] ;
endif

if input1(16) > 0
X = [ X accel_bt_osc_f( start_opt_line_no:stop_opt_line_no , 1:input1(16) ) ] ;
endif

if input1(17) > 0
X = [ X jerk_bt_osc_f( start_opt_line_no:stop_opt_line_no , 1:input1(17) ) ] ;
endif

% now the X features matrix has been formed, get its size
X_rows = size( X , 1 ) ; X_cols = size( X , 2 ) ;

X = [ ones( X_rows , 1 ) X ] ; % add bias unit to X

fan_in = X_cols + 1 ; % no. of inputs to a node/unit, including bias
fan_out = 1 ; % no. of outputs from node/unit
r = sqrt( 6 / ( fan_in + fan_out ) ) ;

rolling_window_length = 100 ;
n_iters = 100 ;
n_iter_errors = zeros( n_iters , 1 ) ;
all_errors = zeros( X_rows - ( rolling_window_length - 1 ) - 1 , 1 ) ;
rolling_window_loop_iter = 0 ;

for rolling_window_loop = rolling_window_length : X_rows - 1

rolling_window_loop_iter = rolling_window_loop_iter + 1 ;

% train n_iters no. of nets and put the error stats in n_iter_errors
for ii = 1 : n_iters

% initialise weights
% see https://stats.stackexchange.com/questions/47590/what-are-good-initial-weights-in-a-neural-network

% One option is Orthogonal random matrix initialization for input_to_hidden weights
% w_i = rand( X_cols + 1 , hidden_layer_size ) ;
% [ u , s , v ] = svd( w_i ) ;
% input_to_hidden = [ ones( X_rows , 1 ) X ] * u ; % adding bias unit to X

% using fan_in and fan_out for tanh
w_i = ( rand( X_cols + 1 , hidden_layer_size ) .* ( 2 * r ) ) .- r ;
input_to_hidden = X( rolling_window_loop - ( rolling_window_length - 1 ) : rolling_window_loop , : ) * w_i ;

% push the input_to_hidden through the chosen sigmoid function
hidden_layer_output = sigmoid_lecun_m( input_to_hidden ) ;

% add bias unit for the output from hidden
hidden_layer_output = [ ones( rolling_window_length , 1 ) hidden_layer_output ] ;

% use hidden_layer_output as the input to a linear regression fit to targets Y
% a la Extreme Learning Machine
% w = ( inv( X' * X ) * X' ) * y ; the "classic" way for linear regression, where
% X = hidden_layer_output, but
w = ( ( hidden_layer_output' * hidden_layer_output ) \ hidden_layer_output' ) * Y( rolling_window_loop - ( rolling_window_length - 1 ) : rolling_window_loop , 1 ) ;
% is quicker and recommended

% use these current values of w_i and w for out of sample test
os_input_to_hidden = X( rolling_window_loop + 1 , : ) * w_i ;
os_hidden_layer_output = sigmoid_lecun_m( os_input_to_hidden ) ;
os_hidden_layer_output = [ 1 os_hidden_layer_output ] ; % add bias
os_output = os_hidden_layer_output * w ;
n_iter_errors( n_iters ) = abs( Y( rolling_window_loop + 1 , 1 ) - os_output ) ;

endfor

all_errors( rolling_window_loop_iter ) = mean( n_iter_errors ) ;

endfor % rolling_window_loop

retval = mean( all_errors ) ;

clear X w_i ;

endfunction

However, to speed things up for some rapid prototyping, rather than use backpropagation training this function uses the principles of an extreme learning machine and loops over 100 such trained ELMs per set of features contained in a rolling window of length 100 across the entire training data set. Walk forward cross validation is performed for each of the 100 ELMs, an average of the out of sample error obtained, and these averages across the whole data set are then averaged to provide the function return. The code was run on daily bars of the four major forex pairs; EURUSD, GBPUSD, USDCHF and USDYPY.

The results of running the above are quite interesting. The first surprise is that the currency strength indicator and features derived from it were not included in the optimal model for any of the four tested pairs. Secondly, for all pairs, a scaled version of a 20 bar price momentum function, and derived features, was included in the optimal model. Finally, again for all pairs, there was a symmetrically decreasing lookback period across the selected features, and when averaged across all pairs the following pattern results: 10 3 3 2 1 3 3 2 1, which is to be read as:
• 10 nodes (plus a bias node) in the hidden layer
• lookback length of 3 for the scaled values of the SMA20 and the 20 bar scaled momentum function
• lookback length of 3 for the slopes/rates of change of the above
• lookback length of 2 for the "accelerations" of the above
• lookback length of 1 for the "jerks" of the above
So it would seem that the 20 bar momentum function is a better exogenous input than the currency strength indicator. The symmetry across features is quite pleasing, and the selection of these "physical motion" features across all the tested pairs tends to confirm their validity. The fact that the currency strength indicator was not selected does not mean that this indicator is of no value, but perhaps it should not be used for regression purposes, but rather as a filter. More in due course.

## Tuesday, 7 February 2017

### Update on Currency Strength Smoothing, and a new direction?

Since my last two posts ( currency strength indicator and preliminary tests thereof ) I have been experimenting with different ways of smoothing the indicators without introducing lag, mostly along the lines of using an oscillator leading signal plus various schemes to smooth and compensate for introduced attenuation and making heavy use of my particle swarm optimisation code. Unfortunately I haven't found anything that really works to my satisfaction and so I have decided to forgo any further attempts at this and just use the indicator in its unsmoothed form as neural net input.

In the process of doing the above work I decided that my particle swarm routine wasn't fast enough and I started using the BayesOpt optimisation library, which is written in C++ and has an interface to Octave. Doing this has greatly decreased the time I've had to spend in my various optimisation routines and the framework provided by the BayesOpt library will enable more ambitious optimisations in the future.

Another discovery for me was this Predicting Stock Market Prices with Physical Laws paper, which has some really useful ideas for neural net input features. In particular I think the idea of combining position, velocity and acceleration with the ideas contained in an earlier post of mine on Savitzky Golay filter convolution and using the currency strength indicators as proxies for the arbitrary sine and cosine waves function posited in the paper hold some promise. More in due course.