Ninjatrader to Matlab Error - futures io
futures io futures trading



Ninjatrader to Matlab Error


Discussion in NinjaTrader

Updated
    1. trending_up 1,239 views
    2. thumb_up 1 thanks given
    3. group 2 followers
    1. forum 4 posts
    2. attach_file 0 attachments




Welcome to futures io: the largest futures trading community on the planet, with well over 125,000 members
  • Genuine reviews from real traders, not fake reviews from stealth vendors
  • Quality education from leading professional traders
  • We are a friendly, helpful, and positive community
  • We do not tolerate rude behavior, trolling, or vendors advertising in posts
  • We are here to help, just let us know what you need
You'll need to register in order to view the content of the threads and start contributing to our community.  It's free and simple.

-- Big Mike, Site Administrator

(If you already have an account, login at the top of the page)

 
Search this Thread
 

Ninjatrader to Matlab Error

(login for full post details)
  #1 (permalink)
france
 
 
Posts: 2 since Aug 2017
Thanks: 1 given, 0 received

Hello,

My name is greg, I am trying to connect Ninjatrader with Matlab.
I have added "Interop.MLApp.dll" reference to Ninjatrader but I got an error that say "Error on calling 'OnBarUpdate' method on bar 19: The type innitializer for 'Globals' Threw an exception.
I have tryied many thing but nothing worked.

I use Matlab2017a and Ninjatrader 8.

This is the code I am using for ninjatrader strategy :

 
Code
#region Using declarations
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.ComponentModel.DataAnnotations;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows;
using System.Windows.Input;
using System.Windows.Media;
using System.Xml.Serialization;
using NinjaTrader.Cbi;
using NinjaTrader.Gui;
using NinjaTrader.Gui.Chart;
using NinjaTrader.Gui.SuperDom;
using NinjaTrader.Gui.Tools;
using NinjaTrader.Data;
using NinjaTrader.NinjaScript;
using NinjaTrader.Core.FloatingPoint;
using NinjaTrader.NinjaScript.Indicators;
using NinjaTrader.NinjaScript.DrawingTools;
using System.IO;
using MLApp;
#endregion

//This namespace holds Strategies in this folder and is required. Do not change it. 
namespace NinjaTrader.NinjaScript.Strategies
{
	public class MyCustomStrategy2 : Strategy
	{
		protected override void OnStateChange()
		{
			if (State == State.SetDefaults)
			{
				Description									= @"Enter the description for your new custom Strategy here.";
				Name										= "MyCustomStrategy2";
				Calculate									= Calculate.OnBarClose;
				EntriesPerDirection							= 1;
				EntryHandling								= EntryHandling.AllEntries;
				IsExitOnSessionCloseStrategy				= true;
				ExitOnSessionCloseSeconds					= 30;
				IsFillLimitOnTouch							= false;
				MaximumBarsLookBack							= MaximumBarsLookBack.TwoHundredFiftySix;
				OrderFillResolution							= OrderFillResolution.Standard;
				Slippage									= 0;
				StartBehavior								= StartBehavior.ImmediatelySubmitSynchronizeAccount;
				TimeInForce									= TimeInForce.Gtc;
				TraceOrders									= false;
				RealtimeErrorHandling						= RealtimeErrorHandling.StopCancelClose;
				StopTargetHandling							= StopTargetHandling.PerEntryExecution;
				BarsRequiredToTrade							= 20;
				// Disable this property for performance gains in Strategy Analyzer optimizations
				// See the Help Guide for additional information
				IsInstantiatedOnEachOptimizationIteration	= true;
			}
			else if (State == State.Configure)
			{
			}
		}
		
		public static class Globals
		{
			public static MLApp.MLApp matlab = new MLApp.MLApp();
		}

		protected override void OnBarUpdate()
		{
			//Add your custom strategy logic here.
			//PrintTo = PrintTo.OutputTab1; 
			//Print("Not real time");
			if (State == State.Realtime)
			{
				PrintTo = PrintTo.OutputTab1; 
				Print("updated2.8");
				string openfile = @"C:\Users\Manthan1412\Documents\MATLAB\open.csv";
				string closefile = @"C:\Users\Manthan1412\Documents\MATLAB\close.csv";
				//string logprefix = Time[0].Month.ToString("00") + "/" + Time[0].Day.ToString("00") + "/" + Time[0].Year + ", " + Time[0].Hour.ToString("00") + ", " + Time[0].Minute.ToString("00") + ", ";
			
				if(File.Exists(openfile))
				{
					File.Delete(openfile);
				}
				if (File.Exists(closefile))
				{
					File.Delete(closefile);
				}
				StreamWriter openLog;
				StreamWriter closeLog;
				openLog = File.AppendText(openfile);
				closeLog = File.AppendText(closefile);
				//log.WriteLine(logprefix + Open[0].ToString("0.00") + ", "  + Close[0].ToString("0.00"));
            	//log.Close();
				for(int barIndex = 0; barIndex <= ChartBars.Count; barIndex++)
				{
					// get the volume value at the selected bar index value
					long volumeValue = Bars.GetVolume(barIndex);
					double openValue = Bars.GetOpen(barIndex);
					double closeValue = Bars.GetClose(barIndex);
					PrintTo = PrintTo.OutputTab1; 
					Print("Bar #" + barIndex + " Open: " + openValue + " Close: " + closeValue + " Volume: " + volumeValue);
					openLog.WriteLine(openValue.ToString("0.00") + "," + volumeValue);
					closeLog.WriteLine(closeValue.ToString("0.00"));
				}
				openLog.Close();
				closeLog.Close();
				Globals.matlab.Execute(@"cd C:\Users\Manthan1412\Documents\MATLAB");

	            object result = null;
				
				PrintTo = PrintTo.OutputTab2;
				Print("Matlab function is called");

	            Globals.matlab.Feval("GoldzFunctionScript", 0, out result);

	            //object[] res = result as object[];
				PrintTo = PrintTo.OutputTab2; 
				Print("call completed");
			}
			
		}
	}
}
This is the .m file I am calling for matlab :
 
Code
% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by Neural Time Series app
% Created 11-Oct-2017 07:30:12
%
% This script assumes these variables are defined:
%
%   Input - input time series.
%   Output - feedback time series.

X = tonndata(Input,false,false);
T = tonndata(Output,false,false);

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainbr';  % Bayesian Regularization backpropagation.

% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 5;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);

% Choose Input and Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess
% Customize input parameters at: net.inputs{i}.processParam
% Customize output parameters at: net.outputs{i}.processParam
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
% shifting time by the minimum amount to fill input states and layer
% states. Using PREPARETS allows you to keep your original time series data
% unchanged, while easily customizing it for networks with differing
% numbers of delays, with open loop or closed loop feedback modes.
[x,xi,ai,t] = preparets(net,X,{},T);

% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'time';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse';  % Mean Squared Error

% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate', 'ploterrhist', ...
    'plotregression', 'plotresponse', 'ploterrcorr', 'plotinerrcorr'};

% Train the Network
[net,tr] = train(net,x,t,xi,ai);

% Test the Network
y = net(x,xi,ai);
e = gsubtract(t,y);
performance = perform(net,t,y)

% Recalculate Training, Validation and Test Performance
trainTargets = gmultiply(t,tr.trainMask);
valTargets = gmultiply(t,tr.valMask);
testTargets = gmultiply(t,tr.testMask);
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotresponse(t,y)
%figure, ploterrcorr(e)
%figure, plotinerrcorr(x,e)

% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the outout layer.
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
view(netc)
[xc,xic,aic,tc] = preparets(netc,X,{},T);
yc = netc(xc,xic,aic);
closedLoopPerformance = perform(net,tc,yc)

% Multi-step Prediction
% Sometimes it is useful to simulate a network in open-loop form for as
% long as there is known output data, and then switch to closed-loop form
% to perform multistep prediction while providing only the external input.
% Here all but 5 timesteps of the input series and target series are used
% to simulate the network in open-loop form, taking advantage of the higher
% accuracy that providing the target series produces:
numTimesteps = size(x,2);
knownOutputTimesteps = 1:(numTimesteps-5);
predictOutputTimesteps = (numTimesteps-4):numTimesteps;
X1 = X(:,knownOutputTimesteps);
T1 = T(:,knownOutputTimesteps);
[x1,xio,aio] = preparets(net,X1,{},T1);
[y1,xfo,afo] = net(x1,xio,aio);
% Next the the network and its final states will be converted to
% closed-loop form to make five predictions with only the five inputs
% provided.
x2 = X(1,predictOutputTimesteps);
[netc,xic,aic] = closeloop(net,xfo,afo);
[y2,xfc,afc] = netc(x2,xic,aic);
multiStepPerformance = perform(net,T(1,predictOutputTimesteps),y2)
% Alternate predictions can be made for different values of x2, or further
% predictions can be made by continuing simulation with additional external
% inputs and the last closed-loop states xfc and afc.

% Step-Ahead Prediction Network
% For some applications it helps to get the prediction a timestep early.
% The original network returns predicted y(t+1) at the same time it is
% given y(t+1). For some applications such as decision making, it would
% help to have predicted y(t+1) once y(t) is available, but before the
% actual y(t+1) occurs. The network can be made to return its output a
% timestep early by removing one delay so that its minimal tap delay is now
% 0 instead of 1. The new network returns the same outputs as the original
% network, but outputs are shifted left one timestep.
nets = removedelay(net);
nets.name = [net.name ' - Predict One Step Ahead'];
view(nets)
[xs,xis,ais,ts] = preparets(nets,X,{},T);
ys = nets(xs,xis,ais);
stepAheadPerformance = perform(nets,ts,ys)

% Deployment
% Change the (false) values to (true) to enable the following code blocks.
% See the help for each generation function for more information.
if (false)
    % Generate MATLAB function for neural network for application
    % deployment in MATLAB scripts or with MATLAB Compiler and Builder
    % tools, or simply to examine the calculations your trained neural
    % network performs.
    genFunction(net,'myNeuralNetworkFunction');
    y = myNeuralNetworkFunction(x,xi,ai);
end
if (false)
    % Generate a matrix-only MATLAB function for neural network code
    % generation with MATLAB Coder tools.
    genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');
    x1 = cell2mat(x(1,:));
    x2 = cell2mat(x(2,:));
    xi1 = cell2mat(xi(1,:));
    xi2 = cell2mat(xi(2,:));
    y = myNeuralNetworkFunction(x1,x2,xi1,xi2);
end
if (false)
    % Generate a Simulink diagram for simulation or deployment with.
    % Simulink Coder tools.
    gensim(net);
end
What a I doing wrong here?

Reply With Quote

Journal Challenge February 2021 results (so far):
Competing for $1500 in prizes from Topstep
looks_oneSBtrader82 's Trading Journalby SBtrader82
(170 thanks from 31 posts)
looks_twoJust BEING a Trader: Letting Go!!by iqgod
(116 thanks from 33 posts)
looks_3Wisdom is Emptinessby Mtype
(68 thanks from 25 posts)
looks_4Deetee’s DAX Trading Journal (time based)by Deetee
(31 thanks from 17 posts)
looks_5Journal for peanuts1956by peanuts1956
(23 thanks from 13 posts)
 
 
(login for full post details)
  #3 (permalink)
Gits (Hooglede) Belgium
 
Experience: Master
Platform: NinjaTrader, Proprietary,
Broker: Ninjabrokerage/IQfeed + Synthetic datafeed
Trading: 6A, 6B, 6C, 6E, 6J, 6S, ES, NQ, YM, AEX, CL, NG, ZB, ZN, ZC, ZS, GC
 
rleplae's Avatar
 
Posts: 2,991 since Sep 2013
Thanks: 2,437 given, 5,801 received


1) I would add some exception catching in the class Globals
Most likely the new fails

2) Did you correctly load mahtlab API DLL to NT8?

3) is the assembly of the correct .NET vesion ?

Follow me on Twitter Visit my futures io Trade Journal Reply With Quote
The following user says Thank You to rleplae for this post:
 
(login for full post details)
  #4 (permalink)
france
 
 
Posts: 2 since Aug 2017
Thanks: 1 given, 0 received


rleplae View Post
1) I would add some exception catching in the class Globals
Most likely the new fails

2) Did you correctly load mahtlab API DLL to NT8?

3) is the assembly of the correct .NET vesion ?

Yes, I have the dll correctly loaded, I first put it on the debug folder at this adress "c:/Users/greg64/Documents/NinjaTrader 8/bin/Custom/obj/Debug"

When I type "Using ML" on the top of my strategy, I have the popup that show me the MLApp, so it show that ninjatrader reconized the dll.

I use .NET 4.7.

What do you mean by "Exeption catching"?
I am new to Matlab and Ninjatrader, so I ask someone to help me with this code, he doesnt know how to fix this error, so I would like to find a solution for him.

Reply With Quote
 
(login for full post details)
  #5 (permalink)
Gits (Hooglede) Belgium
 
Experience: Master
Platform: NinjaTrader, Proprietary,
Broker: Ninjabrokerage/IQfeed + Synthetic datafeed
Trading: 6A, 6B, 6C, 6E, 6J, 6S, ES, NQ, YM, AEX, CL, NG, ZB, ZN, ZC, ZS, GC
 
rleplae's Avatar
 
Posts: 2,991 since Sep 2013
Thanks: 2,437 given, 5,801 received


greg2paris View Post
Yes, I have the dll correctly loaded, I first put it on the debug folder at this adress "c:/Users/greg64/Documents/NinjaTrader 8/bin/Custom/obj/Debug"

When I type "Using ML" on the top of my strategy, I have the popup that show me the MLApp, so it show that ninjatrader reconized the dll.

I use .NET 4.7.

What do you mean by "Exeption catching"?
I am new to Matlab and Ninjatrader, so I ask someone to help me with this code, he doesnt know how to fix this error, so I would like to find a solution for him.

I think the DLL must be compiled for version 4.5 if you want to load it in the address space of NT 8

in c# you can catch an error like this :


try
{
public static MLApp.MLApp matlab = new MLApp.MLApp();
}
catch (Exception ex)
{
Print("Something went wrong");
Print(ex.Message);
}

Follow me on Twitter Visit my futures io Trade Journal Reply With Quote


futures io Trading Community Platforms and Indicators NinjaTrader > Ninjatrader to Matlab Error


Last Updated on October 24, 2017


Upcoming Webinars and Events
 

NinjaTrader Indicator Challenge!

Ongoing
 

Journal Challenge w/$1500 prizes from Topstep!

February
 

Battlestations! Show us your trading desk - $1,500 in prizes!

March
 

Call Option Buying: The New Pain Trade? w/Carley Garner

Elite only
     



Copyright © 2021 by futures io, s.a., Av Ricardo J. Alfaro, Century Tower, Panama, +507 833-9432, info@futures.io
All information is for educational use only and is not investment advice.
There is a substantial risk of loss in trading commodity futures, stocks, options and foreign exchange products. Past performance is not indicative of future results.
no new posts