#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");
			}
			
		}
	}
}

% 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

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