index.js

let input_dataset = [];
let result = [];
let data_raw = [];
let sma_vec = [];
let window_size = 50;
let trainingsize = 70;
let data_temporal_resolutions = 'Weekly';

$(document).ready(function(){
  $('select').formSelect();
});


function onClickChangeDataFreq(freq){
  console.log(freq.value);
  data_temporal_resolutions = freq.value;
  // $("#input_datafreq").text(freq);
}

function onClickFetchData(){

  let ticker = document.getElementById("input_ticker").value;
  let apikey = document.getElementById("input_apikey").value;

  $("#btn_fetch_data").hide();
  $("#load_fetch_data").show();

  let requestUrl = "";
  if(data_temporal_resolutions == 'Daily'){
    requestUrl = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol="+ticker+"&outputsize=full&apikey="+apikey;
  }else{
    requestUrl = "https://www.alphavantage.co/query?function=TIME_SERIES_WEEKLY_ADJUSTED&symbol="+ticker+"&apikey="+apikey;
  }

  $.getJSON(requestUrl
    ,function(data){
      // let data = gotten_data_raw;

      let message = "";
      $("#div_container_linegraph").show();

      let daily = [];
      if(data_temporal_resolutions == 'Daily'){
        daily = data['Time Series (Daily)'];
      }else{
        daily = data['Weekly Adjusted Time Series'];
      }

      if(daily){
        let symbol = data['Meta Data']['2. Symbol'];
        let last_refreshed = data['Meta Data']['3. Last Refreshed'];

        data_raw = [];
        sma_vec = [];

        let index = 0;
        for(let date in daily){
          data_raw.push({ timestamp: date, price: parseFloat(daily[date]['5. adjusted close']) });
          index++;
        }

        data_raw.reverse();

        message = "Symbol: " + symbol + " (last refreshed " + last_refreshed + ")";

        $("#btn_fetch_data").show();
        $("#load_fetch_data").hide();
        $("#div_linegraph_data_title").text(message);

        if(data_raw.length > 0){
          let timestamps = data_raw.map(function (val) { return val['timestamp']; });
          let prices = data_raw.map(function (val) { return val['price']; });

          let graph_plot = document.getElementById('div_linegraph_data');
          Plotly.newPlot( graph_plot, [{ x: timestamps, y: prices, name: "Stocks Prices" }], { margin: { t: 0 } } );
        }

        $("#div_container_getsma").show();
        $("#div_container_getsmafirst").hide();

      }else{
        $("#div_linegraph_data").text( data['Information'] );
      }

    }
  );

}

function onClickDisplaySMA(){
  $("#btn_draw_sma").hide();
  $("#load_draw_sma").show();
  $("#div_container_sma").show();

  window_size = parseInt(document.getElementById("input_windowsize").value);

  sma_vec = ComputeSMA(data_raw, window_size);

  let sma = sma_vec.map(function (val) { return val['avg']; });
  let prices = data_raw.map(function (val) { return val['price']; });

  let timestamps_a = data_raw.map(function (val) { return val['timestamp']; });
  let timestamps_b = data_raw.map(function (val) {
    return val['timestamp'];
  }).splice(window_size, data_raw.length);

  let graph_plot = document.getElementById('div_linegraph_sma');
  Plotly.newPlot( graph_plot, [{ x: timestamps_a, y: prices, name: "Stock Price" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot, [{ x: timestamps_b, y: sma, name: "SMA" }], { margin: { t: 0 } } );

  $("#div_linegraph_sma_title").text("Stock Price and Simple Moving Average (window: " + window_size + ")" );
  $("#btn_draw_sma").show();
  $("#load_draw_sma").hide();

  $("#div_container_train").show();
  $("#div_container_trainfirst").hide();

  displayTrainingData();
}


function displayTrainingData(){
  $("#div_container_trainingdata").show();

  let set = sma_vec.map(function (val) { return val['set']; });
  let data_output = "";
  for (let index = 0; index < 25; index++)
  {
     data_output += "<tr><td width=\"20px\">" + (index + 1) +
      "</td><td>[" + set[index].map(function (val) {
        return (Math.round(val['price'] * 10000) / 10000).toString();
      }).toString() +
      "]</td><td>" + sma_vec[index]['avg'] + "</td></tr>";
  }

  data_output = "<table class='striped'>" +
  "<thead><tr><th scope='col'>#</th>" +
  "<th scope='col'>Input (X)</th>" +
  "<th scope='col'>Label (Y)</th></thead>" +
  "<tbody>" + data_output + "</tbody>" +
  "</table>";

  $("#div_trainingdata").html(
    data_output
  );
}



async function onClickTrainModel(){

  let epoch_loss = [];

  $("#div_container_training").show();
  $("#btn_draw_trainmodel").hide();

  document.getElementById("div_traininglog").innerHTML = "";

  let inputs = sma_vec.map(function(inp_f){
    return inp_f['set'].map(function(val) { return val['price']; })
  });
  let outputs = sma_vec.map(function(outp_f) { return outp_f['avg']; });

  trainingsize = parseInt(document.getElementById("input_trainingsize").value);
  let n_epochs = parseInt(document.getElementById("input_epochs").value);
  let learningrate = parseFloat(document.getElementById("input_learningrate").value);
  let n_hiddenlayers = parseInt(document.getElementById("input_hiddenlayers").value);

  inputs = inputs.slice(0, Math.floor(trainingsize / 100 * inputs.length));
  outputs = outputs.slice(0, Math.floor(trainingsize / 100 * outputs.length));

  let callback = function(epoch, log) {
    let logHtml = document.getElementById("div_traininglog").innerHTML;
    logHtml = "<div>Epoch: " + (epoch + 1) + " (of "+ n_epochs +")" +
      ", loss: " + log.loss +
      // ", difference: " + (epoch_loss[epoch_loss.length-1] - log.loss) +
      "</div>" + logHtml;

    epoch_loss.push(log.loss);

    document.getElementById("div_traininglog").innerHTML = logHtml;
    document.getElementById("div_training_progressbar").style.width = Math.ceil(((epoch + 1) * (100 / n_epochs))).toString() + "%";
    document.getElementById("div_training_progressbar").innerHTML = Math.ceil(((epoch + 1) * (100 / n_epochs))).toString() + "%";

    let graph_plot = document.getElementById('div_linegraph_trainloss');
    Plotly.newPlot( graph_plot, [{x: Array.from({length: epoch_loss.length}, (v, k) => k+1), y: epoch_loss, name: "Loss" }], { margin: { t: 0 } } );
  };

  console.log('train X', inputs)
  console.log('train Y', outputs)
  result = await trainModel(inputs, outputs, window_size, n_epochs, learningrate, n_hiddenlayers, callback);

  let logHtml = document.getElementById("div_traininglog").innerHTML;
  logHtml = "<div>Model train completed</div>" + logHtml;
  document.getElementById("div_traininglog").innerHTML = logHtml;

  $("#div_container_validate").show();
  $("#div_container_validatefirst").hide();
  $("#div_container_predict").show();
  $("#div_container_predictfirst").hide();

}

function onClickValidate() {

  $("#div_container_validating").show();
  $("#load_validating").show();
  $("#btn_validation").hide();

  let inputs = sma_vec.map(function(inp_f) {
   return inp_f['set'].map(function (val) { return val['price']; });
  });

  // validate on training
  let val_train_x = inputs.slice(0, Math.floor(trainingsize / 100 * inputs.length));
  // let outputs = sma_vec.map(function(outp_f) { return outp_f['avg']; });
  // let outps = outputs.slice(0, Math.floor(trainingsize / 100 * inputs.length));
  console.log('val_train_x', val_train_x)
  let val_train_y = makePredictions(val_train_x, result['model']);
  console.log('val_train_y', val_train_y)

  // validate on unseen
  let val_unseen_x = inputs.slice(Math.floor(trainingsize / 100 * inputs.length), inputs.length);
  console.log('val_unseen_x', val_unseen_x)
  let val_unseen_y = makePredictions(val_unseen_x, result['model']);
  console.log('val_unseen_y', val_unseen_y)

  let timestamps_a = data_raw.map(function (val) { return val['timestamp']; });
  let timestamps_b = data_raw.map(function (val) {
    return val['timestamp'];
  }).splice(window_size, (data_raw.length - Math.floor((100-trainingsize) / 100 * data_raw.length))); //.splice(window_size, data_raw.length);
  // let timestamps_c = data_raw.map(function (val) {
  //   return val['timestamp'];
  // }).splice(window_size + Math.floor(trainingsize / 100 * val_unseen_x.length), data_raw.length);
  let timestamps_c = data_raw.map(function (val) {
    return val['timestamp'];
  }).splice(window_size + Math.floor(trainingsize / 100 * inputs.length), inputs.length);

  let sma = sma_vec.map(function (val) { return val['avg']; });
  let prices = data_raw.map(function (val) { return val['price']; });
  sma = sma.slice(0, Math.floor(trainingsize / 100 * sma.length));
  console.log('sma', sma)

  let graph_plot = document.getElementById('div_validation_graph');
  Plotly.newPlot( graph_plot, [{ x: timestamps_a, y: prices, name: "Actual Price" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot, [{ x: timestamps_b, y: sma, name: "Training Label (SMA)" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot, [{ x: timestamps_b, y: val_train_y, name: "Predicted (train)" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot, [{ x: timestamps_c, y: val_unseen_y, name: "Predicted (test)" }], { margin: { t: 0 } } );

  $("#load_validating").hide();
}

async function onClickPredict() {

  $("#div_container_predicting").show();
  $("#load_predicting").show();
  $("#btn_prediction").hide();

  let inputs = sma_vec.map(function(inp_f) {
   return inp_f['set'].map(function (val) { return val['price']; });
  });
  let pred_X = [inputs[inputs.length-1]];
  pred_X = pred_X.slice(Math.floor(trainingsize / 100 * pred_X.length), pred_X.length);
  let pred_y = makePredictions(pred_X, result['model']);

  window_size = parseInt(document.getElementById("input_windowsize").value);

  let timestamps_d = data_raw.map(function (val) {
    return val['timestamp'];
  }).splice((data_raw.length - window_size), data_raw.length);

  // date
  let last_date = new Date(timestamps_d[timestamps_d.length-1]);
  let add_days = 1;
  if(data_temporal_resolutions == 'Weekly'){
    add_days = 7;
  }
  last_date.setDate(last_date.getDate() + add_days);
  let next_date = await formatDate(last_date.toString());
  let timestamps_e = [next_date];

  let graph_plot = document.getElementById('div_prediction_graph');
  Plotly.newPlot( graph_plot, [{ x: timestamps_d, y: pred_X[0], name: "Latest Trends" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot, [{ x: timestamps_e, y: pred_y, name: "Predicted Price" }], { margin: { t: 0 } } );

  $("#load_predicting").hide();
}

function ComputeSMA(data, window_size)
{
  let r_avgs = [], avg_prev = 0;
  for (let i = 0; i <= data.length - window_size; i++){
    let curr_avg = 0.00, t = i + window_size;
    for (let k = i; k < t && k <= data.length; k++){
      curr_avg += data[k]['price'] / window_size;
    }
    r_avgs.push({ set: data.slice(i, i + window_size), avg: curr_avg });
    avg_prev = curr_avg;
  }
  return r_avgs;
}

function formatDate(date) {
  var d = new Date(date),
      month = '' + (d.getMonth() + 1),
      day = '' + d.getDate(),
      year = d.getFullYear();

  if (month.length < 2) month = '0' + month;
  if (day.length < 2) day = '0' + day;

  return [year, month, day].join('-');
}


// onClickFetchData();
// onClickDisplaySMA();
// onClickTrainModel();


// let gotten_data_raw = JSON.parse('{"Meta Data":{"1. Information":"Weekly Prices (open, high, low, close) and Volumes","2. Symbol":"MSFT","3. Last Refreshed":"2020-05-04","4. Time Zone":"US/Eastern"},"Weekly Time Series":{"2020-05-04":{"1. open":"174.4900","2. high":"179.0000","3. low":"173.8000","4. close":"178.8400","5. volume":"30035715"},"2020-05-01":{"1. open":"176.5900","2. high":"180.4000","3. low":"169.3900","4. close":"174.5700","5. volume":"212119968"},"2020-04-24":{"1. open":"176.6300","2. high":"178.7500","3. low":"166.1102","4. close":"174.5500","5. volume":"194621072"},"2020-04-17":{"1. open":"164.3500","2. high":"180.0000","3. low":"162.3000","4. close":"178.6000","5. volume":"238965670"},"2020-04-09":{"1. open":"160.3200","2. high":"170.0000","3. low":"157.5800","4. close":"165.1400","5. volume":"229630744"},"2020-04-03":{"1. open":"152.4400","2. high":"164.7800","3. low":"150.0100","4. close":"153.8300","5. volume":"290191457"},"2020-03-27":{"1. open":"137.0100","2. high":"156.6600","3. low":"132.5200","4. close":"149.7000","5. volume":"358866845"},"2020-03-20":{"1. open":"140.0000","2. high":"150.1500","3. low":"135.0000","4. close":"137.3500","5. volume":"421347734"},"2020-03-13":{"1. open":"151.0000","2. high":"161.9100","3. low":"138.5800","4. close":"158.8300","5. volume":"378231775"},"2020-03-06":{"1. open":"165.3100","2. high":"175.0000","3. low":"156.0000","4. close":"161.5700","5. volume":"313160520"},"2020-02-28":{"1. open":"167.7700","2. high":"174.8400","3. low":"152.0000","4. close":"162.0100","5. volume":"383019972"},"2020-02-21":{"1. open":"185.6050","2. high":"188.1800","3. low":"177.2500","4. close":"178.5900","5. volume":"143313345"},"2020-02-14":{"1. open":"183.5800","2. high":"190.7000","3. low":"181.8500","4. close":"185.3500","5. volume":"194512444"},"2020-02-07":{"1. open":"170.4300","2. high":"185.6300","3. low":"170.4000","4. close":"183.8900","5. volume":"167049170"},"2020-01-31":{"1. open":"161.1500","2. high":"174.0500","3. low":"160.2000","4. close":"170.2300","5. volume":"179845938"},"2020-01-24":{"1. open":"166.6800","2. high":"168.1900","3. low":"164.4500","4. close":"165.0400","5. volume":"97961146"},"2020-01-17":{"1. open":"161.7600","2. high":"167.4675","3. low":"161.2600","4. close":"167.1000","5. volume":"121822087"},"2020-01-10":{"1. open":"157.0800","2. high":"163.2200","3. low":"156.5100","4. close":"161.3400","5. volume":"112604365"},"2020-01-03":{"1. open":"158.9865","2. high":"160.7300","3. low":"156.4500","4. close":"158.6200","5. volume":"78506330"},"2019-12-27":{"1. open":"158.1200","2. high":"159.5500","3. low":"157.1150","4. close":"158.9600","5. volume":"59656712"},"2019-12-20":{"1. open":"155.1100","2. high":"158.4900","3. low":"153.7500","4. close":"157.4100","5. volume":"153142114"},"2019-12-13":{"1. open":"151.0700","2. high":"154.8900","3. low":"150.3300","4. close":"154.5300","5. volume":"100577839"},"2019-12-06":{"1. open":"151.8100","2. high":"151.8700","3. low":"146.6500","4. close":"151.7500","5. volume":"104537379"},"2019-11-29":{"1. open":"150.0000","2. high":"152.5000","3. low":"149.9200","4. close":"151.3800","5. volume":"74242278"},"2019-11-22":{"1. open":"150.0700","2. high":"151.3300","3. low":"148.4600","4. close":"149.5900","5. volume":"105652749"},"2019-11-15":{"1. open":"145.3400","2. high":"149.9900","3. low":"144.7300","4. close":"149.9700","5. volume":"93727027"},"2019-11-08":{"1. open":"144.8300","2. high":"145.9900","3. low":"143.2000","4. close":"145.9600","5. volume":"86277623"},"2019-11-01":{"1. open":"144.4000","2. high":"145.6700","3. low":"142.6500","4. close":"143.7200","5. volume":"131732463"},"2019-10-25":{"1. open":"138.4500","2. high":"141.1400","3. low":"135.6100","4. close":"140.7300","5. volume":"142631768"},"2019-10-18":{"1. open":"139.6900","2. high":"141.7900","3. low":"136.5600","4. close":"137.4100","5. volume":"107104735"},"2019-10-11":{"1. open":"137.1400","2. high":"141.0300","3. low":"135.6200","4. close":"139.6800","5. volume":"107510772"},"2019-10-04":{"1. open":"138.0500","2. high":"140.2500","3. low":"133.2200","4. close":"138.1200","5. volume":"123233428"},"2019-09-27":{"1. open":"139.2300","2. high":"140.6900","3. low":"136.0300","4. close":"137.7300","5. volume":"110281568"},"2019-09-20":{"1. open":"135.8300","2. high":"142.3700","3. low":"135.6600","4. close":"139.4400","5. volume":"135317290"},"2019-09-13":{"1. open":"139.5900","2. high":"139.7500","3. low":"134.5100","4. close":"137.3200","5. volume":"129776465"},"2019-09-06":{"1. open":"136.6100","2. high":"140.3837","3. low":"135.7000","4. close":"139.1000","5. volume":"83840107"},"2019-08-30":{"1. open":"134.9900","2. high":"139.1800","3. low":"133.5500","4. close":"137.8600","5. volume":"104909787"},"2019-08-23":{"1. open":"137.8550","2. high":"139.4935","3. low":"132.8000","4. close":"133.3900","5. volume":"117766903"},"2019-08-16":{"1. open":"137.0700","2. high":"138.8000","3. low":"132.2500","4. close":"136.1300","5. volume":"131659759"},"2019-08-09":{"1. open":"133.3000","2. high":"139.3800","3. low":"130.7800","4. close":"137.7100","5. volume":"159823950"},"2019-08-02":{"1. open":"141.5000","2. high":"141.5100","3. low":"135.0800","4. close":"136.9000","5. volume":"143400310"},"2019-07-26":{"1. open":"137.4100","2. high":"141.6750","3. low":"137.3300","4. close":"141.3400","5. volume":"101248083"},"2019-07-19":{"1. open":"139.4400","2. high":"140.6700","3. low":"134.6700","4. close":"136.6200","5. volume":"139389612"},"2019-07-12":{"1. open":"136.4000","2. high":"139.2200","3. low":"135.3701","4. close":"138.9000","5. volume":"102201981"},"2019-07-05":{"1. open":"136.6300","2. high":"137.7400","3. low":"134.9700","4. close":"137.0600","5. volume":"69662439"},"2019-06-28":{"1. open":"137.0000","2. high":"138.4000","3. low":"132.7300","4. close":"133.9600","5. volume":"124214457"},"2019-06-21":{"1. open":"132.6300","2. high":"137.7300","3. low":"132.5300","4. close":"136.9700","5. volume":"133967168"},"2019-06-14":{"1. open":"132.4000","2. high":"134.2400","3. low":"130.7100","4. close":"132.4500","5. volume":"102505844"},"2019-06-07":{"1. open":"123.8500","2. high":"132.2500","3. low":"119.0100","4. close":"131.4000","5. volume":"147636968"},"2019-05-31":{"1. open":"126.9800","2. high":"128.0000","3. low":"123.3200","4. close":"123.6800","5. volume":"89367881"},"2019-05-24":{"1. open":"126.5200","2. high":"128.2400","3. low":"124.7400","4. close":"126.2400","5. volume":"92123847"},"2019-05-17":{"1. open":"124.1100","2. high":"130.4600","3. low":"123.0400","4. close":"128.0700","5. volume":"139816701"},"2019-05-10":{"1. open":"126.3900","2. high":"128.5600","3. low":"123.5700","4. close":"127.1300","5. volume":"146827040"},"2019-05-03":{"1. open":"129.9000","2. high":"130.7000","3. low":"125.5200","4. close":"128.9000","5. volume":"119573665"},"2019-04-26":{"1. open":"122.6200","2. high":"131.3700","3. low":"122.5700","4. close":"129.8900","5. volume":"132620050"},"2019-04-18":{"1. open":"120.9400","2. high":"123.5200","3. low":"120.1000","4. 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  $("#div_linegraph_data_title").text("Symbol: MSFT (last refreshed 2019-03-08)");

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  let prices  = [127,135.25,138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06,76.87,77.56,80.69,79.56,78.12,85,84.94,92,93.25,99.44,90.25,85.81,85.12,84.69,83.37,93.25,95.87,95,96.44,90.94,89.98,94.94,88.06,92.69,92.56,91.56,89.19,86,91.12,96.12,93.87,115.25,117.44,116.75,111.44,112.25,103.75,98.25,106.56,99.94,95.06,91.31,96.12,101,99.37,111.69,106.25,89.06,74.12,78.94,69.75,71.12,68.81,65.06,61.44,66.31,68.81,72.56,77.69,80,82,78.94,72.31,69.69,69.12,72.44,71,70.62,70.19,69.31,64.19,63.25,60.31,55.56,53.75,65.19,67.69,68.25,67.37,69.06,69.94,56.63,54.44,49.19,46.44,43.38,49.13,53.5,61,64,60.81,59.13,57.31,56.75,56.69,56.69,54.56,56.56,54.69,56.19,62.18,69,67.12,70.75,69.4,68.09,70.91,70.34,73.19,68.02,68.83,73,66.06,71.34,69.18,65.47,66.89,65.52,61.88,62.05,57.05,55.4,57.58,49.71,51.17,57.72,56.38,57.9,62.2,61.4,65.21,65.75,64.71,64.21,67.83,67.44,67.54,67.87,68.9,68.61,66.1,63.8,62.66,60.65,60.23,57.99,61.37,63.95,62.49,60.45,60.31,55.87,55.93,57.2,51.5,49.56,50.05,56.03,53.26,50.91,51.98,55.25,52.28,54.7,54.85,51.86,49.56,45.35,44.41,48.12,50,52.22,49.08,47.82,47.91,47.46,45.25,43.77,48.87,53.15,52.68,53,55.1,56.69,58.22,57.68,55.47,52.5,53.04,52.97,53.79,55.92,51.46,49.85,47.46,46.58,48.3,24.63,23.7,23.56,24.86,26.57,24.67,25.09,24.2,25.5,25.22,26.1,26.36,25.57,24.22,24.61,23.67,24.65,26.33,25.63,26.5,27.31,26.89,26.89,26.17,25.58,25.54,26.22,26.52,28.38,28.34,29.96,28.19,29.08,28.91,28.93,26.61,26.14,26.1,25.5,25.11,25.71,25.98,26.65,27.36,27.21,27.45,27.66,27.81,28.48,27.65,27.08,26.59,26.57,26.53,26.35,25.38,24.63,25.03,25.85,25.48,25.16,27.54,26.13,25.78,25.86,25.89,26.23,25.95,26.77,28.35,28.57,28.57,27.86,27.48,28.03,28.49,27.14,27.02,27.2,27.46,27.11,27.49,27.51,27.29,28.25,27.99,27.99,27.74,27.97,29.31,29.97,26.86,26.6,27.23,27.08,26.96,27.01,26.72,26.67,26.12,25.65,26.18,26.32,25.97,25.48,25.25,25.17,25.09,24.31,24.28,24.12,24.94,24.46,24.98,25.3,25.22,25.3,25.74,26.07,25.43,25.43,25.04,25.04,24.71,25.09,25.79,25.68,25.61,27.76,27.05,26.72,26.97,27.02,26.58,26.07,25.27,25.73,24.59,24.67,24.78,25.53,26.66,27.28,28.07,27.76,28.01,27.71,26.9,26.64,26.15,26.91,27.19,26.41,27.79,27.54,26.69,26.7,26.63,26.93,27.17,27.5,27.01,27.21,27.25,27.07,27.15,24.15,23.8,23.17,22.56,23.72,22.76,22.03,22.1,22.5,23.3,23.3,22.29,23.87,24.25,24.29,24.43,25.79,25.85,25.84,25.6,26.85,26.66,27.35,27.87,28.37,28.43,28.34,28.73,29.24,29.4,29.76,29.12,29.4,30.19,29.64,29.86,29.64,31.21,31.11,30.6,30.19,28.98,28.74,28.9,27.76,27.28,27.33,28.02,27.87,28.55,28.61,29.02,30.12,30.56,30.89,30.83,30.48,30.59,30.05,30.49,29.49,29.47,29.97,29.82,31.16,29.3901,28.9601,28.71,28.25,28.81,28.73,28.44,29.04,28.6499,29.46,29.84,30.17,30.17,35.03,37.06,33.73,34.09,34.11,33.6,34.53,35.31,36.06,36.12,34.38,33.91,33.01,32.94,30.4498,28.56,28.42,27.68,27.1999,27.87,27.96,29.18,27.91,29.16,28.28,30,29.83,29.24,29.39,29.99,28.05,28.32,27.49,29.07,28.23,27.63,25.98,25.25,25.86,26.16,25.44,28.13,27.81,27.84,27.29,25.65,27.62,25.16,27.4,26.32,21.5,23.93,21.96,22.33,21.5,20.06,19.68,20.22,19.87,19.36,19.12,19.13,20.33,19.52,19.71,17.2,17.1,19.66,19.09,18,16.15,15.28,16.65,17.06,18.13,18.75,19.67,19.2,20.91,20.24,19.42,20.22,19.75,20.89,22.14,23.33,24.07,23.35,23.37,22.39,24.29,23.45,23.52,23.56,23.692,24.41,24.68,24.62,24.86,25.26,25.55,24.96,25.55,26.5,28.02,27.73,28.52,29.63,29.62,29.22,29.98,29.85,30.36,31,30.48,30.66,30.86,28.96,28.18,28.02,27.93,28.77,28.67,28.5875,29.27,29.59,29.66,29.16,30.34,30.67,30.96,30.535,28.21,28.93,26.84,25.8,25.79,25.66,26.44,24.5325,23.27,24.27,24.89,25.81,25.81,25.55,24.4,24.23,23.93,24.29,23.85,25.22,24.775,24.38,24.57,25.54,25.3775,26.665,26.85,26.27,25.69,25.25,27.02,27.34,27.9025,28.3,27.91,28.6,28.3,28.02,27.751,27.77,27.25,27.06,26.55,25.9525,25.68,24.8,25.62,25.48,26.07,25.37,25.52,25.92,25.87,25.03,24.49,24.76,23.905,23.705,24.26,24.3,26.02,26.92,26.78,27.53,27.4,25.68,25.1,24.05,25.25,25.8,25.74,27.12,25.06,24.89,26.25,27.27,27.16,26.98,26.25,26.91,25.3,24.3,25.22,25.7,26,26.03,25.96,28.105,28.25,29.71,29.23,30.24,30.495,31.25,31.48,32.075,31.99,32.6,32.01,32.255,31.52,30.81,32.42,31.98,30.98,31.16,29.27,29.06,28.45,29.65,30.02,30.7,30.59,30.185,29.39,30.115,29.755,29.75,30.42,30.9,30.56,30.82,30.95,31.21,31.19,29.76,29.85,29.2,28.64,28.21,29.4998,28.83,26.5209,27.7,26.615,26.455,26.81,27.45,26.55,26.74,26.83,27.25,27.88,27.93,27.55,28.01,27.76,27.95,28,28.035,28.25,28.605,28.7,28.79,29.765,31.79,33.49,32.69,34.87,34.269,34.9,35.67,34.4,33.265,34.545,34.21,35.67,31.4,31.62,31.89,32.7,31.8,34.75,33.4,31.152,33.03,32.791,33.27,33.88,34.13,34.96,35.73,35.525,37.78,37.841,37.57,38.13,38.36,36.69,36.8,37.29,36.91,36.04,36.38,36.805,37.84,36.56,37.62,37.98,38.31,37.9,37.7,40.16,40.3,39.87,39.209,40.01,39.91,39.69,39.54,39.83,40.12,40.94,41.48,41.23,41.68,42.25,41.8,42.09,44.69,44.5,42.86,43.2,44.79,45.15,45.43,45.91,46.695,47.52,46.41,46.09,44.03,43.63,46.13,46.95,48.68,49.58,47.98,47.81,48.42,46.95,47.66,47.88,46.76,47.19,46.24,47.18,40.4,42.41,43.87,43.855,43.85,42.36,41.38,42.88,40.97,40.29,41.72,41.615,47.87,48.655,47.75,48.295,46.9,46.86,46.14,45.97,46.1,45.26,44.4,44.61,46.62,45.94,46.7,46.74,47,43.07,43.93,42.61,43.48,43.48,43.94,45.57,47.11,47.51,52.87,52.64,54.92,52.84,54.19,53.93,55.91,54.06,54.13,55.67,55.48,52.33,50.99,52.29,55.09,50.16,50.5,51.82,51.3,52.03,53.07,53.49,54.21,55.57,54.42,55.65,51.78,49.87,50.39,51.08,50.62,52.32,51.79,51.48,50.13,49.83,51.16,52.3,53.7,56.57,56.68,57.96,57.94,57.62,58.03,57.67,56.21,57.25,57.43,57.6,57.8,57.42,59.66,59.87,58.71,59.02,60.35,60.53,59.25,61.97,62.3,63.24,62.14,62.84,62.7,62.74,65.78,63.68,64,64.62,64.62,64.25,64.93,64.87,64.98,65.86,65.68,64.95,66.4,68.46,69,68.38,67.69,69.96,71.76,70.32,70,71.21,68.93,69.46,72.78,73.79,73.04,72.68,72.5,72.49,72.82,73.94,73.98,75.31,74.41,74.49,76,77.49,78.81,83.81,84.14,83.87,82.4,83.26,84.26,84.16,86.85,85.51,85.54,88.19,89.6,90,94.06,91.78,88.18,92,94.06,93.05,96.54,94.6,87.18,91.27,90.23,93.08,95,95.82,95.16,97.7,96.36,98.36,100.79,101.63,100.13,100.41,98.61,101.16,105.43,106.27,107.68,108.04,109,107.58,108.4,112.33,108.21,113.37,114.26,114.37,112.13,109.57,108.66,106.96,106.16,109.57,108.29,103.07,110.89,104.82,106.03,98.23,100.39,101.93,102.8,107.71,107.17,102.78,105.67,108.22,110.97,112.53,110.51];

  let graph_plot1 = document.getElementById('div_linegraph_data');
  Plotly.newPlot( graph_plot1, [{ x: timestamps, y: prices, name: "Stocks Prices" }], { margin: { t: 0 } } );


  // SMA
  $("#div_container_getsmafirst").hide();
  $("#div_container_sma").show();
  $("#div_linegraph_sma_title").text("Stock Price and Simple Moving Average (window: 50)" );
  $("#div_container_trainingdata").show();

  $("#div_trainingdata").html(
    '<table class="striped"><thead><tr><th scope="col">#</th><th scope="col">Input (X)</th><th scope="col">Output (Y)</th></tr></thead><tbody><tr><td width="20px">1</td><td>[127,135.25,138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81]</td><td>107.96740000000003</td></tr><tr><td width="20px">2</td><td>[135.25,138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75]</td><td>108.26240000000003</td></tr><tr><td width="20px">3</td><td>[138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69]</td><td>108.33120000000002</td></tr><tr><td width="20px">4</td><td>[149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88]</td><td>108.56380000000001</td></tr><tr><td width="20px">5</td><td>[158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75]</td><td>108.57500000000002</td></tr><tr><td width="20px">6</td><td>[157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25]</td><td>108.53740000000003</td></tr><tr><td width="20px">7</td><td>[155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175]</td><td>108.88740000000003</td></tr><tr><td width="20px">8</td><td>[84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160]</td><td>108.98480000000002</td></tr><tr><td width="20px">9</td><td>[82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75]</td><td>110.44480000000003</td></tr><tr><td width="20px">10</td><td>[82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75]</td><td>111.74480000000003</td></tr><tr><td width="20px">11</td><td>[81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13]</td><td>113.10000000000002</td></tr><tr><td width="20px">12</td><td>[87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94]</td><td>114.56260000000002</td></tr><tr><td width="20px">13</td><td>[93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19]</td><td>116.01020000000001</td></tr><tr><td width="20px">14</td><td>[89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19]</td><td>117.57400000000001</td></tr><tr><td width="20px">15</td><td>[92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13]</td><td>119.35660000000001</td></tr><tr><td width="20px">16</td><td>[92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69]</td><td>119.36800000000002</td></tr><tr><td width="20px">17</td><td>[89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25]</td><td>119.41060000000003</td></tr><tr><td width="20px">18</td><td>[85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62]</td><td>119.35060000000003</td></tr><tr><td width="20px">19</td><td>[89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86]</td><td>119.35560000000002</td></tr><tr><td width="20px">20</td><td>[85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31]</td><td>119.19300000000001</td></tr><tr><td width="20px">21</td><td>[84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06]</td><td>119.063</td></tr><tr><td width="20px">22</td><td>[86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06,76.87]</td><td>118.90420000000002</td></tr><tr><td width="20px">23</td><td>[85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06,76.87,77.56]</td><td>118.7304</td></tr><tr><td width="20px">24</td><td>[94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06,76.87,77.56,80.69]</td><td>118.62920000000001</td></tr><tr><td width="20px">25</td><td>[104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75,138.69,149.88,149.75,156.25,175,160,157.75,147.75,150.13,154.94,160.19,171.19,178.13,92.69,94.25,86.62,86,81.31,79.06,76.87,77.56,80.69,79.56]</td><td>118.32660000000001</td></tr></tbody></table>'
  );

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  let sma = 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  let graph_plot2 = document.getElementById('div_linegraph_sma');
  Plotly.newPlot( graph_plot2, [{ x: timestamps_a, y: prices, name: "Stock Price" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot2, [{ x: timestamps_b, y: sma, name: "SMA" }], { margin: { t: 0 } } );


  // Training
  $("#div_container_trainfirst").hide();
  $("#div_container_training").show();

  document.getElementById("div_traininglog").innerHTML = "<div>Model trained</div><div>Epoch: 50 (of 50), loss: 0.03450336679816246</div><div>Epoch: 49 (of 50), loss: 0.08746889978647232</div><div>Epoch: 48 (of 50), loss: 0.12235825508832932</div><div>Epoch: 47 (of 50), loss: 0.11212839186191559</div><div>Epoch: 46 (of 50), loss: 0.05028475448489189</div><div>Epoch: 45 (of 50), loss: 0.015300695784389973</div><div>Epoch: 44 (of 50), loss: 0.0026924889534711838</div><div>Epoch: 43 (of 50), loss: 0.002561884233728051</div><div>Epoch: 42 (of 50), loss: 0.0014652956742793322</div><div>Epoch: 41 (of 50), loss: 0.0013217271771281958</div><div>Epoch: 40 (of 50), loss: 0.0030655807349830866</div><div>Epoch: 39 (of 50), loss: 0.005444769747555256</div><div>Epoch: 38 (of 50), loss: 0.005553357768803835</div><div>Epoch: 37 (of 50), loss: 0.006486714817583561</div><div>Epoch: 36 (of 50), loss: 0.004650598857551813</div><div>Epoch: 35 (of 50), loss: 0.007415720261633396</div><div>Epoch: 34 (of 50), loss: 0.008261540904641151</div><div>Epoch: 33 (of 50), loss: 0.006869967561215162</div><div>Epoch: 32 (of 50), loss: 0.007610109634697437</div><div>Epoch: 31 (of 50), loss: 0.0040054721757769585</div><div>Epoch: 30 (of 50), loss: 0.003657675813883543</div><div>Epoch: 29 (of 50), loss: 0.002818638924509287</div><div>Epoch: 28 (of 50), loss: 0.002496731234714389</div><div>Epoch: 27 (of 50), loss: 0.0032212012447416782</div><div>Epoch: 26 (of 50), loss: 0.007728812284767628</div><div>Epoch: 25 (of 50), loss: 0.012824371457099915</div><div>Epoch: 24 (of 50), loss: 0.020290391519665718</div><div>Epoch: 23 (of 50), loss: 0.011541156098246574</div><div>Epoch: 22 (of 50), loss: 0.009518957696855068</div><div>Epoch: 21 (of 50), loss: 0.013500338420271873</div><div>Epoch: 20 (of 50), loss: 0.04174267500638962</div><div>Epoch: 19 (of 50), loss: 0.08119461685419083</div><div>Epoch: 18 (of 50), loss: 0.044394541531801224</div><div>Epoch: 17 (of 50), loss: 0.07642599940299988</div><div>Epoch: 16 (of 50), loss: 0.15503166615962982</div><div>Epoch: 15 (of 50), loss: 0.11092785000801086</div><div>Epoch: 14 (of 50), loss: 0.08086691051721573</div><div>Epoch: 13 (of 50), loss: 0.1177196353673935</div><div>Epoch: 12 (of 50), loss: 0.1853371262550354</div><div>Epoch: 11 (of 50), loss: 0.3289039134979248</div><div>Epoch: 10 (of 50), loss: 0.3667738139629364</div><div>Epoch: 9 (of 50), loss: 0.5904757380485535</div><div>Epoch: 8 (of 50), loss: 1.4654392004013062</div><div>Epoch: 7 (of 50), loss: 4.224607944488525</div><div>Epoch: 6 (of 50), loss: 4.776238918304443</div><div>Epoch: 5 (of 50), loss: 6.680339336395264</div><div>Epoch: 4 (of 50), loss: 7.463838577270508</div><div>Epoch: 3 (of 50), loss: 7.559423446655273</div><div>Epoch: 2 (of 50), loss: 7.704190254211426</div><div>Epoch: 1 (of 50), loss: 14.499640464782715</div>";

  let loss = [14.499640464782715, 7.704190254211426, 7.559423446655273, 7.463838577270508, 6.680339336395264, 4.776238918304443, 4.224607944488525, 1.4654392004013062, 0.5904757380485535, 0.3667738139629364, 0.3289039134979248, 0.1853371262550354, 0.1177196353673935, 0.08086691051721573, 0.11092785000801086, 0.15503166615962982, 0.07642599940299988, 0.044394541531801224, 0.08119461685419083, 0.04174267500638962, 0.013500338420271873, 0.009518957696855068, 0.011541156098246574, 0.020290391519665718, 0.012824371457099915, 0.007728812284767628, 0.0032212012447416782, 0.002496731234714389, 0.002818638924509287, 0.003657675813883543, 0.0040054721757769585, 0.007610109634697437, 0.006869967561215162, 0.008261540904641151, 0.007415720261633396, 0.004650598857551813, 0.006486714817583561, 0.005553357768803835, 0.005444769747555256, 0.0030655807349830866, 0.0013217271771281958, 0.0014652956742793322, 0.002561884233728051, 0.0026924889534711838, 0.015300695784389973, 0.05028475448489189, 0.11212839186191559, 0.12235825508832932, 0.08746889978647232, 0.03450336679816246];
  let graph_plot3 = document.getElementById('div_linegraph_trainloss');
  Plotly.newPlot( graph_plot3, [{x: Array.from({length: loss.length}, (v, k) => k+1), y: loss, name: "Loss" }], { margin: { t: 0 } } );

  // Predict
  $("#div_container_validating").show();
  $("#div_container_validatefirst").hide();
  $("#div_container_predict").show();
  $("#div_container_predictfirst").hide();

  let pred_vals = [30.880271911621094,30.756319046020508,30.686813354492188,30.574687957763672,30.455322265625,30.414691925048828,30.37146759033203,30.261669158935547,30.20035743713379,30.206077575683594,30.166099548339844,30.20769500732422,30.27951431274414,30.41089630126953,30.460186004638672,30.54753875732422,30.661399841308594,30.743816375732422,30.833511352539062,30.9290771484375,30.962936401367188,31.101303100585938,31.168331146240234,31.2198543548584,31.229721069335938,31.20906639099121,31.213064193725586,31.356426239013672,31.342777252197266,31.393301010131836,31.59113121032715,31.525972366333008,31.621606826782227,31.75960922241211,31.942041397094727,32.01454544067383,32.187103271484375,32.330772399902344,32.529823303222656,32.76789855957031,33.018367767333984,33.3780632019043,33.553916931152344,33.76907730102539,34.15032196044922,34.301998138427734,34.50977325439453,34.76121520996094,34.92772674560547,35.17820739746094,35.31208419799805,35.566749572753906,35.8206787109375,35.98015213012695,36.20620346069336,36.523746490478516,36.84674072265625,37.05519104003906,37.301300048828125,37.620601654052734,37.84638977050781,38.01282501220703,38.09479522705078,38.280982971191406,38.35297393798828,38.43777084350586,38.594451904296875,38.667816162109375,38.75531005859375,38.89814376831055,38.97129821777344,39.05295181274414,39.204559326171875,39.47739791870117,39.669960021972656,39.878623962402344,40.08940124511719,40.33586502075195,40.57581329345703,40.74138641357422,41.0075569152832,41.375431060791016,41.680908203125,41.960845947265625,42.22312927246094,42.50804901123047,42.66658401489258,42.84552001953125,42.99861145019531,43.168792724609375,43.45603561401367,43.61700439453125,43.73627471923828,43.91419219970703,44.07825469970703,44.18304443359375,44.3232307434082,44.530033111572266,44.719879150390625,44.87042999267578,44.992645263671875,45.0815544128418,45.12534713745117,45.2662353515625,45.343135833740234,45.42549514770508,45.51020050048828,45.5604362487793,45.52619171142578,45.54057693481445,45.56728744506836,45.52531433105469,45.5361328125,45.63299560546875,45.79121017456055,45.892333984375,45.945945739746094,45.98936080932617,46.11119842529297,46.242759704589844,46.43060302734375,46.5166015625,46.54617691040039,46.666160583496094,46.76640319824219,46.761775970458984,46.80170440673828,46.80610275268555,46.9254150390625,47.066837310791016,47.09339141845703,46.92963409423828,46.78635025024414,46.73648452758789,46.729705810546875,46.661563873291016,46.7149772644043,46.705623626708984,46.72129821777344,46.774566650390625,46.75042724609375,46.68595886230469,46.75973892211914,46.85142517089844,47.08014678955078,47.18641662597656,47.31884765625,47.4283561706543,47.51057052612305,47.77496337890625,47.972713470458984,48.159950256347656,48.45866394042969,48.67660903930664,48.92796325683594,49.066158294677734,49.18317794799805,49.41706848144531,49.58464050292969,49.8792610168457,50.19385528564453,50.43202209472656,50.67279052734375,50.90032958984375,51.030662536621094,51.28118133544922,51.25638961791992,51.18509292602539,51.2841796875,51.58414840698242,51.68943405151367,51.82469177246094,52.018646240234375,52.30309295654297,52.437904357910156,52.5025520324707,52.50343704223633,52.67753982543945,52.89822006225586,53.123191833496094,53.378143310546875,53.666412353515625,53.97718048095703,54.40963363647461,54.72990036010742,55.11106872558594,55.38372802734375,55.635562896728516,55.90629959106445,56.144630432128906,56.22854232788086,56.215980529785156,56.40325927734375,56.66118240356445,56.82145690917969,56.85955047607422,56.942962646484375,56.99225997924805,57.04667663574219,57.15597152709961,57.28193664550781,57.53727340698242,57.82605743408203,58.00026321411133,58.20689392089844,58.41453170776367,58.68281555175781,58.94514083862305,59.298439025878906,59.544097900390625,59.760581970214844,60.02550506591797,60.27372360229492,60.41106414794922,60.568790435791016,60.84344482421875,61.19293975830078,61.475502014160156,61.77536392211914,62.11832046508789,62.48077392578125,62.79522705078125,62.98173904418945,63.213043212890625,63.37208557128906,63.52775573730469,63.693729400634766,63.85213851928711,63.9803466796875,64.12062072753906,64.2698745727539,64.37870025634766,64.56656646728516,64.71669006347656,64.7764892578125,64.91265106201172,65.09387969970703,65.24221801757812,65.49956512451172,65.70816802978516,65.9053955078125,66.29393768310547,66.71062469482422,67.01133728027344,67.4754638671875,67.97905731201172,68.535400390625,69.02253723144531,69.55972290039062,70.10680389404297,70.77141571044922,71.19349670410156,71.56824493408203,72.18843841552734,72.9797592163086,73.35125732421875,73.95635986328125,74.63835906982422,75.18717956542969,75.71568298339844,76.21609497070312,76.84970092773438,77.4169692993164,77.84510803222656,78.39065551757812,78.97591400146484,79.19047546386719,79.84211730957031,80.42247009277344,80.79270935058594,81.2449951171875,81.783935546875,82.43667602539062,82.80691528320312,83.5166015625,84.00643157958984,84.6805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  let timestamps_c = ["2013-02-22","2013-03-01","2013-03-08","2013-03-15","2013-03-22","2013-03-28","2013-04-05","2013-04-12","2013-04-19","2013-04-26","2013-05-03","2013-05-10","2013-05-17","2013-05-24","2013-05-31","2013-06-07","2013-06-14","2013-06-21","2013-06-28","2013-07-05","2013-07-12","2013-07-19","2013-07-26","2013-08-02","2013-08-09","2013-08-16","2013-08-23","2013-08-30","2013-09-06","2013-09-13","2013-09-20","2013-09-27","2013-10-04","2013-10-11","2013-10-18","2013-10-25","2013-11-01","2013-11-08","2013-11-15","2013-11-22","2013-11-29","2013-12-06","2013-12-13","2013-12-20","2013-12-27","2014-01-03","2014-01-10","2014-01-17","2014-01-24","2014-01-31","2014-02-07","2014-02-14","2014-02-21","2014-02-28","2014-03-07","2014-03-14","2014-03-21","2014-03-28","2014-04-04","2014-04-11","2014-04-17","2014-04-25","2014-05-02","2014-05-09","2014-05-16","2014-05-23","2014-05-30","2014-06-06","2014-06-13","2014-06-20","2014-06-27","2014-07-03","2014-07-11","2014-07-18","2014-07-25","2014-08-01","2014-08-08","2014-08-15","2014-08-22","2014-08-29","2014-09-05","2014-09-12","2014-09-19","2014-09-26","2014-10-03","2014-10-10","2014-10-17","2014-10-24","2014-10-31","2014-11-07","2014-11-14","2014-11-21","2014-11-28","2014-12-05","2014-12-12","2014-12-19","2014-12-26","2015-01-02","2015-01-09","2015-01-16","2015-01-23","2015-01-30","2015-02-06","2015-02-13","2015-02-20","2015-02-27","2015-03-06","2015-03-13","2015-03-20","2015-03-27","2015-04-02","2015-04-10","2015-04-17","2015-04-24","2015-05-01","2015-05-08","2015-05-15","2015-05-22","2015-05-29","2015-06-05","2015-06-12","2015-06-19","2015-06-26","2015-07-02","2015-07-10","2015-07-17","2015-07-24","2015-07-31","2015-08-07","2015-08-14","2015-08-21","2015-08-28","2015-09-04","2015-09-11","2015-09-18","2015-09-25","2015-10-02","2015-10-09","2015-10-16","2015-10-23","2015-10-30","2015-11-06","2015-11-13","2015-11-20","2015-11-27","2015-12-04","2015-12-11","2015-12-18","2015-12-24","2015-12-31","2016-01-08","2016-01-15","2016-01-22","2016-01-29","2016-02-05","2016-02-12","2016-02-19","2016-02-26","2016-03-04","2016-03-11","2016-03-18","2016-03-24","2016-04-01","2016-04-08","2016-04-15","2016-04-22","2016-04-29","2016-05-06","2016-05-13","2016-05-20","2016-05-27","2016-06-03","2016-06-10","2016-06-17","2016-06-24","2016-07-01","2016-07-08","2016-07-15","2016-07-22","2016-07-29","2016-08-05","2016-08-12","2016-08-19","2016-08-26","2016-09-02","2016-09-09","2016-09-16","2016-09-23","2016-09-30","2016-10-07","2016-10-14","2016-10-21","2016-10-28","2016-11-04","2016-11-11","2016-11-18","2016-11-25","2016-12-02","2016-12-09","2016-12-16","2016-12-23","2016-12-30","2017-01-06","2017-01-13","2017-01-20","2017-01-27","2017-02-03","2017-02-10","2017-02-17","2017-02-24","2017-03-03","2017-03-10","2017-03-17","2017-03-24","2017-03-31","2017-04-07","2017-04-13","2017-04-21","2017-04-28","2017-05-05","2017-05-12","2017-05-19","2017-05-26","2017-06-02","2017-06-09","2017-06-16","2017-06-23","2017-06-30","2017-07-07","2017-07-14","2017-07-21","2017-07-28","2017-08-04","2017-08-11","2017-08-18","2017-08-25","2017-09-01","2017-09-08","2017-09-15","2017-09-22","2017-09-29","2017-10-06","2017-10-13","2017-10-20","2017-10-27","2017-11-03","2017-11-10","2017-11-17","2017-11-24","2017-12-01","2017-12-08","2017-12-15","2017-12-22","2017-12-29","2018-01-05","2018-01-12","2018-01-19","2018-01-26","2018-02-02","2018-02-09","2018-02-16","2018-02-23","2018-03-02","2018-03-09","2018-03-16","2018-03-23","2018-03-29","2018-04-06","2018-04-13","2018-04-20","2018-04-27","2018-05-04","2018-05-11","2018-05-18","2018-05-25","2018-06-01","2018-06-08","2018-06-15","2018-06-22","2018-06-29","2018-07-06","2018-07-13","2018-07-20","2018-07-27","2018-08-03","2018-08-10","2018-08-17","2018-08-24","2018-08-31","2018-09-07","2018-09-14","2018-09-21","2018-09-28","2018-10-05","2018-10-12","2018-10-19","2018-10-26","2018-11-02","2018-11-09","2018-11-16","2018-11-23","2018-11-30","2018-12-07","2018-12-14","2018-12-21","2018-12-28","2019-01-04","2019-01-11","2019-01-18","2019-01-25","2019-02-01","2019-02-08","2019-02-15","2019-02-22","2019-03-01","2019-03-08"];

  let graph_plot4 = document.getElementById('div_validation_graph');
  Plotly.newPlot( graph_plot4, [{ x: timestamps_a, y: prices, name: "Actual Price" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot4, [{ x: timestamps_b, y: sma, name: "Training Label (SMA)" }], { margin: { t: 0 } } );
  Plotly.plot( graph_plot4, [{ x: timestamps_c, y: pred_vals, name: "Predicted" }], { margin: { t: 0 } } );
}