ml-session/app.js

const ai = new Vue({
  el: "#ai",
  data: {
    error:    "",
    forecast: [],
    max:      undefined,
    min:      undefined,
    net:      new brain.recurrent.LSTMTimeStep(),
    trained:  false,
    training: false
  },
  methods: {
    predict: (values) => {
      final = [];
      for (value of ai.net.forecast(values, 30)) {
        final.push(value * (ai.max - ai.min) + ai.min);
      };
      ai.forecast = final;
    },
    train: (data) => {
      values = data.map((d) => { return d.value });
      trainData = [];
      ai.max = Math.max(...values);
      ai.min = Math.min(...values);

      for (value of values) {
        trainData.push( (value - ai.min) / (ai.max - ai.min) );
      }

      if (!ai.training && !ai.trained) {
        ai.error    = "";
        ai.training = true;

        setTimeout(
          () => {
            ai.net.train([ trainData ], { iterations: 5 });
            ai.training = false;
            ai.trained  = true;
            document.getElementById("network").innerHTML = brain.utilities.toSVG(
              ai.net, { height: 500 }
            );
            ai.predict(values);
          }, 250
        );
      } else {
        ai.predict(values);
      }
    }
  }
});

const graph = new Vue({
  el: "#graph",
  data: {
    candles: [],
    latest:  undefined
  },
  methods: {
    addCandle: (candle) => {
      graph.candles.push({
        date:  candle[0],
        value: candle[2]
      });
      graph.latest = new Date(candle[0]);
    }
  },
  watch: {
    candles: (values) => {
      sorted = values.slice();
      sorted.sort((a, b) => {
        if (a.date < b.date) return -1;
        if (a.date > b.date) return  1;
        return 0;
      });
      dates = sorted.map((d) => d.date);
      for (let i=0; i<30; i++) {
        last = dates[dates.length - 1];
        dates.push(last + 60000);
      }

      ai.train(sorted);

      d3.select("#data").select("g").remove();

      element = document.querySelector("#chart-space");
      margin  = {
        top:    10,
        right:  30,
        bottom: 100,
        left:   60
      }
      height  = element.clientHeight - margin.top  - margin.bottom;
      width   = element.clientWidth  - margin.left - margin.right;

      svg = d3.select("#data")
        .append("g")
        .attr("transform", `translate(${margin.left}, ${margin.top})`);
      x = d3.scaleTime()
        .domain(d3.extent(dates, (d) => { return d }))
        .range([ 0, width ]);
      svg.append("g")
        .attr("transform", `translate(0, ${height})`)
        .call(d3.axisBottom(x));
      y = d3.scaleLinear()
        .domain([0, d3.max(sorted, (d) => { return +d.value })])
        .range([ height, 0 ]);
      svg.append("g")
        .call(d3.axisLeft(y));

      regression = ss.linearRegression(sorted.map((d) => { return [+d.date, d.value] }));
      line       = ss.linearRegressionLine(regression);
      trendline  = x.domain().map((x) => {
        return {
          date:  x,
          value: line(+x)
        }
      });

      svg.append("path")
        .datum(trendline)
        .attr("fill", "none")
        .attr("stroke", "red")
        .attr("stroke-width", 1)
        .attr("d", d3.line().x((d) => {
          return x(d.date);
        }).y((d) => {
          return y(d.value);
        }));

      svg.append("path")
        .datum(sorted)
        .attr("fill", "none")
        .attr("stroke", "black")
        .attr("stroke-width", 2)
        .attr("d", d3.line().x((d) => {
          return x(d.date);
        }).y((d) => {
          return y(d.value);
        }));

      if (ai.forecast.length > 0) {
        forecast = [];
        position = 30;
        for (value of ai.forecast) {
          forecast.push({
            date:  dates[dates.length - position],
            value: value
          });
          position--;
        }
        svg.append("path")
          .datum(forecast)
          .attr("fill", "none")
          .attr("stroke", "green")
          .attr("stroke-width", 1.5)
          .attr("d", d3.line().x((d) => {
            return x(d.date);
          }).y((d) => {
            return y(d.value);
          }));
      }

    }
  }
});

const websocket = new Vue({
  el: "#websocket",
  created: () => {
    this.socket = new WebSocket("wss://api-pub.bitfinex.com/ws/2")
    this.socket.onopen = () => {
      websocket.connected = true
    };
    this.socket.onclose = () => {
      websocket.info      = "";
      websocket.connected = false;
    };
    this.socket.onmessage = (message) => {
      json = JSON.parse(message.data);
      if (json.event == "info") websocket.info = `Server ${json.serverId} v${json.version}`;
      if (Array.isArray(json) && json[1] !== "hb") websocket.processValues(json);
    };
  },
  data: {
    connected: false,
    info:      "",
    socket:    undefined
  },
  methods: {
    processCandle: (candle) => {
      graph.addCandle(candle);
    },
    processValues: (json) => {
      if (Array.isArray(json[1][0])) {
        json[1].forEach((data) => websocket.processCandle(data));
      } else {
        websocket.processCandle(json[1]);
      }
    }
  },
  watch: {
    connected: (connected) => {
      if (connected === true) this.socket.send('{"event":"subscribe","channel":"candles","key":"trade:1m:tBTCUSD"}');
    }
  }
});