Merge pull request #588 from Luis-Fernando-Molina/develop
Contribution: Contributions Plotter implemented
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@@ -3039,9 +3039,188 @@
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"actionFunction": "payload.executeAction"
|
||||
},
|
||||
{
|
||||
"action": "Copy Node Type",
|
||||
"label": "Copy Node Type",
|
||||
"iconPathOn": "copy-type",
|
||||
"iconPathOff": "copy-type",
|
||||
"actionFunction": "payload.executeAction"
|
||||
},
|
||||
{
|
||||
"action": "Delete UI Object",
|
||||
"askConfirmation": true,
|
||||
"confirmationLabel": "Confirm to Delete",
|
||||
"label": "Delete",
|
||||
"iconPathOn": "delete-entity",
|
||||
"iconPathOff": "delete-entity",
|
||||
"actionFunction": "payload.executeAction"
|
||||
}
|
||||
],
|
||||
"editors": {
|
||||
"config": true
|
||||
},
|
||||
"initialValues": {
|
||||
"config": "{\n\"initialValue\": 0\n}"
|
||||
},
|
||||
"addLeftIcons": true,
|
||||
"level": 3,
|
||||
"attachingRules": {
|
||||
"compatibleTypes": "->Predictions->"
|
||||
},
|
||||
"propertyNameAtParent": "predictions",
|
||||
"childrenNodesProperties": [
|
||||
{
|
||||
"name": "begin",
|
||||
"type": "node",
|
||||
"childType": "Begin",
|
||||
"autoAdd": true
|
||||
},
|
||||
{
|
||||
"name": "end",
|
||||
"type": "node",
|
||||
"childType": "End",
|
||||
"autoAdd": true
|
||||
},
|
||||
{
|
||||
"name": "predictionValue",
|
||||
"type": "node",
|
||||
"childType": "Prediction Value",
|
||||
"autoAdd": true
|
||||
}
|
||||
],
|
||||
"icon": "bitcoin"
|
||||
},
|
||||
{
|
||||
"type": "Prediction Value",
|
||||
"menuItems": [
|
||||
{
|
||||
"action": "Configure",
|
||||
"label": "Configure",
|
||||
"iconPathOn": "configuration",
|
||||
"iconPathOff": "configuration",
|
||||
"actionFunction": "uiObject.configEditor.activate"
|
||||
},
|
||||
{
|
||||
"action": "Copy Node Path",
|
||||
"label": "Copy Node Path",
|
||||
"iconPathOn": "copy-path",
|
||||
"iconPathOff": "copy-path",
|
||||
"actionFunction": "payload.executeAction"
|
||||
},
|
||||
{
|
||||
"action": "Copy Node Value",
|
||||
"label": "Copy Node Value",
|
||||
"iconPathOn": "copy-value",
|
||||
"iconPathOff": "copy-value",
|
||||
"actionFunction": "payload.executeAction"
|
||||
},
|
||||
{
|
||||
"action": "Copy Node Type",
|
||||
"label": "Copy Node Type",
|
||||
"iconPathOn": "copy-type",
|
||||
"iconPathOff": "copy-type",
|
||||
"actionFunction": "payload.executeAction"
|
||||
},
|
||||
{
|
||||
"action": "Delete UI Object",
|
||||
"askConfirmation": true,
|
||||
"confirmationLabel": "Confirm to Delete",
|
||||
"label": "Delete",
|
||||
"iconPathOn": "delete-entity",
|
||||
"iconPathOff": "delete-entity",
|
||||
"actionFunction": "payload.executeAction"
|
||||
}
|
||||
],
|
||||
"editors": {
|
||||
"config": true
|
||||
},
|
||||
"initialValues": {
|
||||
"config": "{\n\"initialValue\": 0\n}"
|
||||
},
|
||||
"addLeftIcons": true,
|
||||
"level": 3,
|
||||
"attachingRules": {
|
||||
"compatibleTypes": "->Prediction->"
|
||||
},
|
||||
"propertyNameAtParent": "predictionValue",
|
||||
"childrenNodesProperties": [
|
||||
],
|
||||
"icon": "bitcoin"
|
||||
},
|
||||
{
|
||||
"type": "Dynamic Indicators",
|
||||
"menuItems": [
|
||||
@@ -7794,7 +7973,7 @@
|
||||
"addLeftIcons": true,
|
||||
"level": 3,
|
||||
"attachingRules": {
|
||||
"compatibleTypes": "->Trading Episode->Learning Episode->Strategy->Position->Candle->Cycle->Market Order->Limit Order->Strategy Trigger Stage->Strategy Open Stage->Strategy Manage Stage->Strategy Close Stage->Stop Loss->Take Profit->"
|
||||
"compatibleTypes": "->Trading Episode->Learning Episode->Strategy->Position->Candle->Cycle->Market Order->Limit Order->Strategy Trigger Stage->Strategy Open Stage->Strategy Manage Stage->Strategy Close Stage->Stop Loss->Take Profit->Prediction->"
|
||||
},
|
||||
"propertyNameAtParent": "begin"
|
||||
},
|
||||
@@ -7848,7 +8027,7 @@
|
||||
"addLeftIcons": true,
|
||||
"level": 3,
|
||||
"attachingRules": {
|
||||
"compatibleTypes": "->Trading Episode->Learning Episode->Strategy->Position->Candle->Cycle->Market Order->Limit Order->Strategy Trigger Stage->Strategy Open Stage->Strategy Manage Stage->Strategy Close Stage->Stop Loss->Take Profit->"
|
||||
"compatibleTypes": "->Trading Episode->Learning Episode->Strategy->Position->Candle->Cycle->Market Order->Limit Order->Strategy Trigger Stage->Strategy Open Stage->Strategy Manage Stage->Strategy Close Stage->Stop Loss->Take Profit->Prediction->"
|
||||
},
|
||||
"propertyNameAtParent": "end"
|
||||
},
|
||||
@@ -10430,7 +10609,7 @@
|
||||
}
|
||||
],
|
||||
"initialValues": {
|
||||
"config": "{ \n\"codeName\": \"Time Series Forcasting Tensor Flow JS\"\n}"
|
||||
"config": "{ \n\"backLearningPredictingRatio\": 0.8\n}"
|
||||
},
|
||||
"addLeftIcons": true,
|
||||
"editors": {
|
||||
|
||||
-6
@@ -143,9 +143,6 @@ exports.newSuperalgosBotModulesLearningRecords = function (processIndex) {
|
||||
the Root Node specifically for this property only.
|
||||
*/
|
||||
if (recordProperty.config.nodePath !== undefined) {
|
||||
if (recordProperty.config.nodePath === "learningEngine.exchangeOrders.marketBuyOrders.marketOrders[index].orderBaseAsset") {
|
||||
let a = learningEngine
|
||||
}
|
||||
try {
|
||||
propertyRootNode = eval(recordProperty.config.nodePath)
|
||||
} catch (err) {
|
||||
@@ -291,9 +288,6 @@ exports.newSuperalgosBotModulesLearningRecords = function (processIndex) {
|
||||
When we are not dealing with objects, we add every record to the existing file except
|
||||
for the ones that are filtered out at the Product Definition.
|
||||
*/
|
||||
if (product.config.codeName === "Learning-System-Errors") {
|
||||
let a = 1
|
||||
}
|
||||
if (product.config.propertyNameThatDefinesStatus !== undefined && product.config.propertyValueThatPreventsSavingObject !== undefined) {
|
||||
for (let j = 0; j < product.record.properties.length; j++) {
|
||||
let recordProperty = product.record.properties[j]
|
||||
|
||||
@@ -143,9 +143,6 @@ exports.newSuperalgosBotModulesTradingRecords = function (processIndex) {
|
||||
the Root Node specifically for this property only.
|
||||
*/
|
||||
if (recordProperty.config.nodePath !== undefined) {
|
||||
if (recordProperty.config.nodePath === "tradingEngine.exchangeOrders.marketBuyOrders.marketOrders[index].orderBaseAsset") {
|
||||
let a = tradingEngine
|
||||
}
|
||||
try {
|
||||
propertyRootNode = eval(recordProperty.config.nodePath)
|
||||
} catch (err) {
|
||||
@@ -291,9 +288,6 @@ exports.newSuperalgosBotModulesTradingRecords = function (processIndex) {
|
||||
When we are not dealing with objects, we add every record to the existing file except
|
||||
for the ones that are filtered out at the Product Definition.
|
||||
*/
|
||||
if (product.config.codeName === "Trading-System-Errors") {
|
||||
let a = 1
|
||||
}
|
||||
if (product.config.propertyNameThatDefinesStatus !== undefined && product.config.propertyValueThatPreventsSavingObject !== undefined) {
|
||||
for (let j = 0; j < product.record.properties.length; j++) {
|
||||
let recordProperty = product.record.properties[j]
|
||||
|
||||
@@ -118,7 +118,8 @@ function newSuperalgosFunctionLibraryLearningSessionFunctions() {
|
||||
'Begin->End->Exit Type->Status->Begin Rate->End Rate->Stage Base Asset->Stage Quoted Asset->Size Placed->Target Size->Size Filled->Fees Paid->Stage Defined In->' +
|
||||
'Serial Number->Identifier->Begin->End->Begin Rate->End Rate->Strategy Name->Status->Exit Type->' +
|
||||
'Balance->Begin Balance->End Balance->' +
|
||||
'Index->Situation Name->Formula->Periods->'
|
||||
'Index->Situation Name->Formula->Periods->' +
|
||||
'Predictions->Prediction->Begin->End->Prediction Value->'
|
||||
|
||||
let learningEngine = UI.projects.superalgos.functionLibraries.protocolNode.getProtocolNode(node.learningEngineReference.payload.referenceParent, false, true, true, false, false, lightingPath)
|
||||
|
||||
|
||||
@@ -358,9 +358,6 @@ function newUiObjectTitle() {
|
||||
let phrases = splitTextIntoPhrases(label, 2)
|
||||
let lineSeparator = thisObject.payload.floatingObject.currentFontSize * 1.2
|
||||
|
||||
if (phrases.length > 3) {
|
||||
let a = 1
|
||||
}
|
||||
for (let i = 0; i < phrases.length; i++) {
|
||||
let phrase = phrases[i]
|
||||
let point = {
|
||||
|
||||
+98
-50
@@ -375,65 +375,113 @@ exports.newTensorFlowBotModulesTensorFlowLibrary = function (processIndex) {
|
||||
}
|
||||
|
||||
/*
|
||||
Building the Labels Tensor
|
||||
At this point we need to decide if we are going to learn or going to predict.
|
||||
This is regulated by the backLearningPredictingRatio property at the config
|
||||
of the Learning Algorithm of the Learning Session.
|
||||
*/
|
||||
if (layersModel.outputLayer === undefined) {
|
||||
// TODO
|
||||
}
|
||||
if (layersModel.outputLayer.outputLabels === undefined) {
|
||||
// TODO
|
||||
}
|
||||
learningSystem.evalFormulas(layersModel.outputLayer.outputLabels, layersModel.outputLayer.outputLabels.type)
|
||||
let backLearningPredictingRatio = 0.8 // this is the default value.
|
||||
|
||||
for (let i = 0; i < layersModel.outputLayer.outputLabels.dataLabels.length; i++) {
|
||||
let dataLabel = layersModel.outputLayer.outputLabels.dataLabels[i]
|
||||
if (dataLabel.labelFormula === undefined) { continue }
|
||||
let labelValue = learningSystem.formulas.get(dataLabel.labelFormula.id)
|
||||
labels.push(labelValue)
|
||||
}
|
||||
|
||||
tensorFlowData = require("@tensorflow/tfjs-data")
|
||||
|
||||
function* featuresGenerator() {
|
||||
//console.log('Features: ' + features)
|
||||
yield features
|
||||
}
|
||||
|
||||
function* labelsGenerator() {
|
||||
//console.log('Labels: ' + labels)
|
||||
yield labels
|
||||
}
|
||||
|
||||
const xs = tensorFlowData.generator(featuresGenerator)
|
||||
const ys = tensorFlowData.generator(labelsGenerator)
|
||||
const ds = tensorFlowData.zip({ xs, ys }).shuffle(1).batch(1)
|
||||
|
||||
let verbose = 0
|
||||
let ecpochs = 1
|
||||
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose.config.value !== undefined) {
|
||||
verbose = learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose.config.value
|
||||
}
|
||||
if (sessionParameters.learningAlgorithm !== undefined) {
|
||||
if (sessionParameters.learningAlgorithm.config.backLearningPredictingRatio !== undefined) {
|
||||
backLearningPredictingRatio = sessionParameters.learningAlgorithm.config.backLearningPredictingRatio
|
||||
}
|
||||
}
|
||||
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs.config.value !== undefined) {
|
||||
ecpochs = learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs.config.value
|
||||
}
|
||||
}
|
||||
let initialCandle = Math.trunc(sessionParameters.timeRange.config.initialDatetime / sessionParameters.timeFrame.config.value)
|
||||
let finalCandle = Math.trunc(sessionParameters.timeRange.config.finalDatetime / sessionParameters.timeFrame.config.value)
|
||||
let currentCandle = Math.trunc(learningEngine.learningCurrent.learningEpisode.candle.begin.value / sessionParameters.timeFrame.config.value)
|
||||
let thresholdCandle = (finalCandle - initialCandle) * backLearningPredictingRatio + initialCandle
|
||||
|
||||
if (currentCandle < thresholdCandle) {
|
||||
await learn()
|
||||
} else {
|
||||
predict()
|
||||
}
|
||||
|
||||
await tensorFlowModel.fitDataset(ds, {
|
||||
verbose: verbose,
|
||||
epochs: ecpochs
|
||||
})
|
||||
async function learn() {
|
||||
/*
|
||||
Building the Labels Tensor
|
||||
*/
|
||||
if (layersModel.outputLayer === undefined) {
|
||||
// TODO
|
||||
}
|
||||
if (layersModel.outputLayer.outputLabels === undefined) {
|
||||
// TODO
|
||||
}
|
||||
learningSystem.evalFormulas(layersModel.outputLayer.outputLabels, layersModel.outputLayer.outputLabels.type)
|
||||
|
||||
//tensorFlowModel.summary()
|
||||
for (let i = 0; i < layersModel.outputLayer.outputLabels.dataLabels.length; i++) {
|
||||
let dataLabel = layersModel.outputLayer.outputLabels.dataLabels[i]
|
||||
if (dataLabel.labelFormula === undefined) { continue }
|
||||
let labelValue = learningSystem.formulas.get(dataLabel.labelFormula.id)
|
||||
labels.push(labelValue)
|
||||
}
|
||||
|
||||
tensorFlowData = require("@tensorflow/tfjs-data")
|
||||
|
||||
function* featuresGenerator() {
|
||||
//console.log('Features: ' + features)
|
||||
yield features
|
||||
}
|
||||
|
||||
function* labelsGenerator() {
|
||||
//console.log('Labels: ' + labels)
|
||||
yield labels
|
||||
}
|
||||
|
||||
const xs = tensorFlowData.generator(featuresGenerator)
|
||||
const ys = tensorFlowData.generator(labelsGenerator)
|
||||
const ds = tensorFlowData.zip({ xs, ys }).shuffle(1).batch(1)
|
||||
|
||||
let verbose = 0
|
||||
let ecpochs = 1
|
||||
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose.config.value !== undefined) {
|
||||
verbose = learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.verbose.config.value
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs !== undefined) {
|
||||
if (learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs.config.value !== undefined) {
|
||||
ecpochs = learningSystem.machineLearningLibrary.typeOfLearning.typeOfModel.model.fitDataset.epochs.config.value
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
await tensorFlowModel.fitDataset(ds, {
|
||||
verbose: verbose,
|
||||
epochs: ecpochs
|
||||
})
|
||||
|
||||
}
|
||||
|
||||
async function predict() {
|
||||
let featuresTensor = tensorFlowBackend.tensor([features])
|
||||
let predictions = tensorFlowModel.predict(featuresTensor, { verbose: 1 })
|
||||
|
||||
/*
|
||||
We will download the predictions to an array, and we will store each one at the
|
||||
learning engine data structure.
|
||||
*/
|
||||
let predictionsArray = predictions.dataSync()
|
||||
predictions.dispose()
|
||||
featuresTensor.dispose()
|
||||
|
||||
for (let i = 0; i < predictionsArray.length; i++) {
|
||||
let prediction = predictionsArray[i]
|
||||
if (isNaN(prediction)) {
|
||||
prediction = 0
|
||||
}
|
||||
if (learningEngine.predictions.predictions[i] === undefined) { continue }
|
||||
learningEngine.predictions.predictions[i].predictionValue.value = prediction
|
||||
learningEngine.predictions.predictions[i].begin.value = learningEngine.learningCurrent.learningEpisode.candle.begin.value
|
||||
learningEngine.predictions.predictions[i].end.value = learningEngine.learningCurrent.learningEpisode.candle.end.value
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function badDefinitionUnhandledException(err, message, node) {
|
||||
|
||||
Reference in new issue
Block a user