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mounirs-ga-version-2.js
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mounirs-ga-version-2.js
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// Created by https://github.com/mounirlabaied
var convnetjs = require('convnetjs')
var z = require('zero-fill')
var stats = require('stats-lite')
var n = require('numbro')
var math = require('mathjs')
const cluster = require('cluster');
const numCPUs = require('os').cpus().length;
var _ = require('lodash');
const deepqlearn = require('convnetjs/build/deepqlearn');
var log = require('../core/log.js');
// the below line starts you at 0 threads
global.forks = 0
// the below line is for calculating the last mean vs the now mean.
var oldmean = 0
getOption = function () {
}
// let's create our own method
var method = {};
options = "Options Set";
options.period = "1m";
options.period_length = "1m";
options.activation_1_type = "regression";
options.neurons_1 = 25;
options.depth = 1;
options.selector = "Gdax.BTC-USD";
options.min_periods = 1500;
options.min_predict = 1;
options.momentum =0.2;
options.decay = 0.1;
options.threads = 4;
options.learns = 2;
/*
[myStoch]
highThreshold = 80
lowThreshold = 20
optInFastKPeriod = 14
optInSlowKPeriod = 5
optInSlowDPeriod = 5
[myLongEma]
optInTimePeriod = 100
[myShortEma]
optInTimePeriod = 50
[stopLoss]
percent = 0.9*/
var hasbought = false;
var stochParams = {
optInFastKPeriod: 8,
optInSlowKPeriod: 3,
optInSlowDPeriod: 3
};
neural = undefined;
// prepare everything our method needs
method.init = function() {
this.requiredHistory = this.tradingAdvisor.historySize;
if (neural === undefined) {
// Create the net the first time it is needed and NOT on every run
neural = {
net : new convnetjs.Net(),
layer_defs : [
{type:'input', out_sx:4, out_sy:4, out_depth:options.depth},
{type:'fc', num_neurons:options.neurons_1, activation:options.activation_1_type},
{type:'regression', num_neurons:5}
],
neuralDepth: options.depth
}
neural.net.makeLayers(neural.layer_defs);
neural.trainer = new convnetjs.SGDTrainer(neural.net, {learning_rate:0.05, momentum:options.momentum, batch_size:10, l2_decay:options.decay});
}
}
var haspredicted = false;
var predictioncount = 0;
var maxaccuracy = 0;
var lowaccuracy = 0;
var highpeak =6;
var lowpeak =-100;
// what happens on every new candle?
method.update = function(candle) {
this.HCL = (this.candle.high + this.candle.close + this.candle.open) /3;
Price.push(candle.close);
if(Price.length > 2)
{
var tlp = []
var tll = []
var my_data = Price;
var learn = function () {
for (var i = 0; i < Price.length - 1; i++) {
var data = my_data.slice(i, i + 1);
var real_value = [my_data[i + 1]];
var x = new convnetjs.Vol(data);
neural.trainer.train(x, real_value);
var predicted_values =neural.net.forward(x);
var accuracy = predicted_values.w[0] -real_value
var accuracymatch = predicted_values.w[0] == real_value;
var rewardtheybitches = neural.net.backward(accuracymatch);
if(accuracy > 0)
{
if(accuracy > maxaccuracy) {maxaccuracy = accuracy}
}
if(accuracy <0)
{
if(accuracy < lowaccuracy) {lowaccuracy = accuracy}
}
predictioncount++;
haspredicted = true;
}
}
learn();
// var json = neural.net.toJSON();
// // the entire object is now simply string. You can save this somewhere
// var str = JSON.stringify(json);
// log.debug(str);
}
}
method.log = function() {
}
method.handleposition = function(){
}
var Price = [];
ManageSize = function(){
function per(num, amount){
return num*amount/100;
}
var calculatedpercent = per(Price.length,5);
Price.splice(0,calculatedpercent);
}
method.check = function() {
//Learn
var predict = function(data) {
var x = new convnetjs.Vol(data);
var predicted_value = neural.net.forward(x);
return predicted_value.w[0];
}
this.HCL = (this.candle.high + this.candle.close + this.candle.open) /3;
if(haspredicted && predictioncount > 1000)
{
var item = Price;
prediction = predict(item)
mean = Price[Price.length -1];
oldmean = prediction
meanp = math.mean(prediction, mean)
global.meanp = meanp
global.mean = mean
var percentvar = (meanp-mean)/mean * 100;
if(percentvar < 0) {
prediction += lowaccuracy;
percentvar += lowaccuracy;
if(lowpeak > percentvar) { lowpeak = percentvar;}
}
if(percentvar > 0) {
prediction -= maxaccuracy;
percentvar -= maxaccuracy;
if(highpeak < percentvar) { highpeak = percentvar;}
}
log.debug("IA - Buy - Predicted variation: ",percentvar);
global.sig0 = global.meanp < global.mean && meanp != 0
if (global.sig0 === false && percentvar> 1.70 )
{
log.debug("IA - Buy - Predicted variation: ",percentvar);
hasbought = true;
meanp = 0
mean = 0;
haspredicted = false;
ManageSize();
return this.advice('long');
}
else if
(global.sig0 === true && percentvar < -0.90)
{
log.debug("IA - Sell - Predicted variation: ",percentvar);
meanp = 0
mean = 0;
hasbought = false;
haspredicted = false;
return this.advice('short');
}
}
}
module.exports = method;