# Neural Network (SDK Trading)

registerEA(
"sample_training_neuron_model",
"A test EA to train neuron model",
[{ // parameters
	name: "period",
	value: 20,
	required: true,
	type: PARAMETER_TYPE.INTEGER,
	range: [1, 100]
},{
	name: "inputNum",
	value: 20,
	required: true,
	type: PARAMETER_TYPE.INTEGER,
	range: [1, 100]
},{
	name: "hiddenNum",
	value: 50,
	required: true,
	type: PARAMETER_TYPE.INTEGER,
	range: [1, 100]
},{
	name: "diffPrice",
	value: 0.0001,
	required: true,
	type: PARAMETER_TYPE.NUMBER,
	range: [0, 10]
}],
function (context) { // Init()
	var account = getAccount(context, 0)
	var brokerName = getBrokerNameOfAccount(account)
	var accountId = getAccountIdOfAccount(account)
	var symbolName = "EUR/USD"

	window.chartHandle = getChartHandle(context, brokerName, accountId, symbolName, TIME_FRAME.M1)
	var period = getEAParameter(context, "period")
	window.indiHandle = getIndicatorHandle(context, brokerName, accountId, symbolName, TIME_FRAME.M1, "rsi", [{
		name: "period",
		value: period
	}])
},
function (context) { // Deinit()
	var period = getEAParameter(context, "period")
	var inputNum = getEAParameter(context, "inputNum")
	var hiddenNum = getEAParameter(context, "hiddenNum")
	var arrOpen = getData(context, window.chartHandle, DATA_NAME.OPEN)
	var arrClose = getData(context, window.chartHandle, DATA_NAME.CLOSE)
	var arrRsi = getData(context, window.indiHandle, "rsi")

	if (arrRsi.length <= period + 1) return
	if (inputNum + period - 1 > arrRsi.length) throw new Error("No enough data.")

	// extend the prototype chain
	Perceptron.prototype = new synaptic.Network()
	Perceptron.prototype.constructor = Perceptron

	var myPerceptron = new Perceptron(inputNum, hiddenNum, 1)
	var myTrainer = new synaptic.Trainer(myPerceptron)

	var diffPrice = getEAParameter(context, "diffPrice")
	var trainingSet = []
	var longCount = 0
	var shortCount = 0

	for (var i = period - 1; i < arrRsi.length - inputNum; i++) {
		if (arrClose[i * inputNum + inputNum] - arrOpen[i * inputNum + inputNum] > diffPrice) {
			var input = []

			for (var j = 0; j < inputNum; j++) {
				input.push(arrRsi[i * inputNum + j] / 100)
			}

			trainingSet.push({
				input: input,
				output: [0]
			})

			longCount++
		} else if (arrOpen[i * inputNum + inputNum] - arrClose[i * inputNum + inputNum] > diffPrice) {
			var input = []

			for (var j = 0; j < inputNum; j++) {
				input.push(arrRsi[i * inputNum + j] / 100)
			}

			trainingSet.push({
				input: input,
				output: [1]
			})

			shortCount++
		}
	}

	myTrainer.train(trainingSet)
	localStorage.sample_training_neuron_model = JSON.stringify(myPerceptron.toJSON())
	printMessage(longCount + ", " + shortCount)
	printMessage(JSON.stringify(trainingSet))
	printMessage(JSON.stringify(myPerceptron.toJSON()))
},
function (context) { // OnTick()
})

registerEA(
"sample_run_neuron_model",
"A test EA to run neuron model",
[{ // parameters
	name: "period",
	value: 20,
	required: true,
	type: PARAMETER_TYPE.INTEGER,
	range: [1, 100]
},{
	name: "inputNum",
	value: 20,
	required: true,
	type: PARAMETER_TYPE.INTEGER,
	range: [1, 100]
},{
	name: "threshold",
	value: 0.3,
	required: true,
	type: PARAMETER_TYPE.NUMBER,
	range: [0, 1]
},{
	name: "takeProfit",
	value: 0.0001,
	required: true,
	type: PARAMETER_TYPE.NUMBER,
	range: [0, 100]
}],
function (context) { // Init()
	if (typeof localStorage.sample_training_neuron_model == "undefined") return

	window.myPerceptron = synaptic.Network.fromJSON(JSON.parse(localStorage.sample_training_neuron_model))

	var account = getAccount(context, 0)
	var brokerName = getBrokerNameOfAccount(account)
	var accountId = getAccountIdOfAccount(account)
	var symbolName = "EUR/USD"

	getQuotes (context, brokerName, accountId, symbolName)
	window.chartHandle = getChartHandle(context, brokerName, accountId, symbolName, TIME_FRAME.M1)
	var period = getEAParameter(context, "period")
	window.indiHandle = getIndicatorHandle(context, brokerName, accountId, symbolName, TIME_FRAME.M1, "rsi", [{
		name: "period",
		value: period
	}])
},
function (context) { // Deinit()
	delete window.currTime
},
function (context) { // OnTick()
	var arrTime = getData(context, window.chartHandle, DATA_NAME.TIME)
	if (typeof window.currTime == "undefined") {
		window.currTime = arrTime[arrTime.length - 1]
	} else if (window.currTime != arrTime[arrTime.length - 1]) {
		window.currTime = arrTime[arrTime.length - 1]
	} else {
		return
	}

	var account = getAccount(context, 0)
	var brokerName = getBrokerNameOfAccount(account)
	var accountId = getAccountIdOfAccount(account)
	var symbolName = "EUR/USD"

	var period = getEAParameter(context, "period")
	var inputNum = getEAParameter(context, "inputNum")
	var threshold = getEAParameter(context, "threshold")
	var takeProfit = getEAParameter(context, "takeProfit")
	var arrRsi = getData(context, window.indiHandle, "rsi")

	if (inputNum + period - 1 > arrRsi.length) throw new Error("No enough data.")

	var input = []

	for (var i = arrRsi.length - inputNum - 1; i < arrRsi.length - 1; i++) {
		input.push(arrRsi[i] / 100)
	}

	var result = window.myPerceptron.activate(input)[0]
	printMessage(result)

	var ask = getAsk(context, brokerName, accountId, symbolName)
	var bid = getBid(context, brokerName, accountId, symbolName)
	var volume = 0.01

	if (result < 0.5 - threshold) {
		sendOrder(brokerName, accountId, symbolName, ORDER_TYPE.OP_BUY, 0, 0, volume, ask+takeProfit, bid-3*takeProfit, "")
	} else if (result > 0.5 + threshold) {
		sendOrder(brokerName, accountId, symbolName, ORDER_TYPE.OP_SELL, 0, 0, volume, bid-takeProfit, ask+3*takeProfit, "")
	}
})