LSTM_Trading_Bot.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yRW1zYi4FQtR",
        "outputId": "eb95ed00-c453-46fe-fb90-85e1946b0b3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "last_trainingdate=2023-01-20:\n",
            "Above is data_training\n",
            "Above  is data_test\n",
            "Above is training_data.head())\n",
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_32 (LSTM)              (None, 60, 50)            11200     \n",
            "                                                                 \n",
            " dropout_32 (Dropout)        (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_33 (LSTM)              (None, 60, 60)            26640     \n",
            "                                                                 \n",
            " dropout_33 (Dropout)        (None, 60, 60)            0         \n",
            "                                                                 \n",
            " lstm_34 (LSTM)              (None, 60, 80)            45120     \n",
            "                                                                 \n",
            " dropout_34 (Dropout)        (None, 60, 80)            0         \n",
            "                                                                 \n",
            " lstm_35 (LSTM)              (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_35 (Dropout)        (None, 120)               0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 179,561\n",
            "Trainable params: 179,561\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "4/4 [==============================] - 8s 463ms/step - loss: 0.0643 - val_loss: 0.0195\n",
            "Epoch 2/50\n",
            "4/4 [==============================] - 1s 227ms/step - loss: 0.0267 - val_loss: 0.0163\n",
            "Epoch 3/50\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 0.0241 - val_loss: 0.0133\n",
            "Epoch 4/50\n",
            "4/4 [==============================] - 1s 233ms/step - loss: 0.0205 - val_loss: 0.0138\n",
            "Epoch 5/50\n",
            "4/4 [==============================] - 1s 239ms/step - loss: 0.0175 - val_loss: 0.0122\n",
            "Epoch 6/50\n",
            "4/4 [==============================] - 1s 340ms/step - loss: 0.0132 - val_loss: 0.0124\n",
            "Epoch 7/50\n",
            "4/4 [==============================] - 2s 409ms/step - loss: 0.0156 - val_loss: 0.0122\n",
            "Epoch 8/50\n",
            "4/4 [==============================] - 1s 251ms/step - loss: 0.0116 - val_loss: 0.0135\n",
            "Epoch 9/50\n",
            "4/4 [==============================] - 1s 229ms/step - loss: 0.0126 - val_loss: 0.0131\n",
            "Epoch 10/50\n",
            "4/4 [==============================] - 1s 220ms/step - loss: 0.0106 - val_loss: 0.0134\n",
            "Epoch 11/50\n",
            "4/4 [==============================] - 1s 224ms/step - loss: 0.0107 - val_loss: 0.0155\n",
            "Epoch 12/50\n",
            "4/4 [==============================] - 1s 226ms/step - loss: 0.0105 - val_loss: 0.0148\n",
            "Epoch 13/50\n",
            "4/4 [==============================] - 1s 233ms/step - loss: 0.0091 - val_loss: 0.0150\n",
            "Epoch 14/50\n",
            "4/4 [==============================] - 1s 251ms/step - loss: 0.0080 - val_loss: 0.0157\n",
            "Epoch 15/50\n",
            "4/4 [==============================] - 1s 231ms/step - loss: 0.0083 - val_loss: 0.0150\n",
            "Epoch 16/50\n",
            "4/4 [==============================] - 1s 247ms/step - loss: 0.0095 - val_loss: 0.0165\n",
            "Epoch 17/50\n",
            "4/4 [==============================] - 1s 235ms/step - loss: 0.0095 - val_loss: 0.0168\n",
            "Epoch 18/50\n",
            "4/4 [==============================] - 1s 242ms/step - loss: 0.0079 - val_loss: 0.0146\n",
            "Epoch 19/50\n",
            "4/4 [==============================] - 2s 412ms/step - loss: 0.0076 - val_loss: 0.0145\n",
            "Epoch 20/50\n",
            "4/4 [==============================] - 2s 447ms/step - loss: 0.0092 - val_loss: 0.0173\n",
            "Epoch 21/50\n",
            "4/4 [==============================] - 1s 281ms/step - loss: 0.0087 - val_loss: 0.0160\n",
            "Epoch 22/50\n",
            "4/4 [==============================] - 2s 428ms/step - loss: 0.0074 - val_loss: 0.0139\n",
            "Epoch 23/50\n",
            "4/4 [==============================] - 1s 282ms/step - loss: 0.0079 - val_loss: 0.0175\n",
            "Epoch 24/50\n",
            "4/4 [==============================] - 1s 321ms/step - loss: 0.0075 - val_loss: 0.0166\n",
            "Epoch 25/50\n",
            "4/4 [==============================] - 1s 319ms/step - loss: 0.0069 - val_loss: 0.0128\n",
            "Epoch 26/50\n",
            "4/4 [==============================] - 1s 274ms/step - loss: 0.0071 - val_loss: 0.0144\n",
            "Epoch 27/50\n",
            "4/4 [==============================] - 1s 230ms/step - loss: 0.0072 - val_loss: 0.0189\n",
            "Epoch 28/50\n",
            "4/4 [==============================] - 1s 247ms/step - loss: 0.0063 - val_loss: 0.0165\n",
            "Epoch 29/50\n",
            "4/4 [==============================] - 2s 443ms/step - loss: 0.0062 - val_loss: 0.0146\n",
            "Epoch 30/50\n",
            "4/4 [==============================] - 2s 381ms/step - loss: 0.0048 - val_loss: 0.0145\n",
            "Epoch 31/50\n",
            "4/4 [==============================] - 1s 225ms/step - loss: 0.0073 - val_loss: 0.0108\n",
            "Epoch 32/50\n",
            "4/4 [==============================] - 1s 229ms/step - loss: 0.0063 - val_loss: 0.0129\n",
            "Epoch 33/50\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 0.0063 - val_loss: 0.0159\n",
            "Epoch 34/50\n",
            "4/4 [==============================] - 1s 224ms/step - loss: 0.0062 - val_loss: 0.0093\n",
            "Epoch 35/50\n",
            "4/4 [==============================] - 1s 220ms/step - loss: 0.0070 - val_loss: 0.0147\n",
            "Epoch 36/50\n",
            "4/4 [==============================] - 1s 230ms/step - loss: 0.0060 - val_loss: 0.0158\n",
            "Epoch 37/50\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 0.0047 - val_loss: 0.0161\n",
            "Epoch 38/50\n",
            "4/4 [==============================] - 1s 223ms/step - loss: 0.0053 - val_loss: 0.0175\n",
            "Epoch 39/50\n",
            "4/4 [==============================] - 1s 229ms/step - loss: 0.0049 - val_loss: 0.0140\n",
            "Epoch 40/50\n",
            "4/4 [==============================] - 1s 383ms/step - loss: 0.0061 - val_loss: 0.0175\n",
            "Epoch 41/50\n",
            "4/4 [==============================] - 2s 456ms/step - loss: 0.0071 - val_loss: 0.0122\n",
            "Epoch 42/50\n",
            "4/4 [==============================] - 2s 371ms/step - loss: 0.0064 - val_loss: 0.0094\n",
            "Epoch 43/50\n",
            "4/4 [==============================] - 1s 220ms/step - loss: 0.0055 - val_loss: 0.0160\n",
            "Epoch 44/50\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 0.0057 - val_loss: 0.0158\n",
            "Epoch 45/50\n",
            "4/4 [==============================] - 1s 249ms/step - loss: 0.0052 - val_loss: 0.0164\n",
            "Epoch 46/50\n",
            "4/4 [==============================] - 1s 239ms/step - loss: 0.0041 - val_loss: 0.0114\n",
            "Epoch 47/50\n",
            "4/4 [==============================] - 1s 232ms/step - loss: 0.0039 - val_loss: 0.0097\n",
            "Epoch 48/50\n",
            "4/4 [==============================] - 1s 243ms/step - loss: 0.0041 - val_loss: 0.0128\n",
            "Epoch 49/50\n",
            "4/4 [==============================] - 1s 228ms/step - loss: 0.0043 - val_loss: 0.0193\n",
            "Epoch 50/50\n",
            "4/4 [==============================] - 1s 222ms/step - loss: 0.0048 - val_loss: 0.0093\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <canvas id=\"myChart\"></canvas>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "        <script src=\"https://cdn.jsdelivr.net/npm/chart.js@2.8.0\"></script>\n",
              "        <script>\n",
              "          var ctx = document.getElementById('myChart').getContext('2d');\n",
              "          var chart = new Chart(ctx, {\n",
              "              // The type of chart we want to create\n",
              "              type: 'line',\n",
              "\n",
              "              // The data for our dataset\n",
              "              data: {\n",
              "                  labels: [getDateTime(-10), getDateTime(-20), getDateTime(-30),\n",
              "                                  getDateTime(-40), getDateTime(-50), getDateTime(-60) ],\n",
              "                  datasets: [{\n",
              "                      label: 'Actual',\n",
              "                      borderColor: 'rgb(255, 99, 132)',\n",
              "                      data: [0,1,2,3,4,5]\n",
              "                  }, \n",
              "                  {\n",
              "                      label: 'Predicted',\n",
              "                      borderColor: 'rgb(155, 199, 32)',\n",
              "                      data: [0,1,2,3,4,5]\n",
              "                  }]\n",
              "              },\n",
              "\n",
              "              // Configuration options go here\n",
              "              options: { animation: {duration: 0} ,\n",
              "                scales: {x: {\n",
              "                           type: 'time',\n",
              "                           time: { unit: 'minute',displayFormats: {minute: 'HH:mm'},tooltipFormat: 'HH:mm'},\n",
              "                           title: {display: true, text: 'Date'}},\n",
              "                         y: {\n",
              "                           title: { display: true, text: 'value'}},\n",
              "                         xAxes: [{ scaleLabel: { display: true, labelString: 'Timestamp [YYYY-MM-DD hh:mm:ss]'}}],\n",
              "                        yAxes: [{scaleLabel: {display: true, labelString: 'BitCoin Price [USD $]'} }], },\n",
              "                title: { display: true, text: 'Bitcoin Price - Realtime Prediction'}}});\n",
              "\n",
              "          function getEpoch(offset_sec=0) {\n",
              "             var now     = new Date(); \n",
              "             return Math.floor((now.getTime() - offset_sec*1000)/1000);}\n",
              "\n",
              "          function getDateTime(offset_sec=0) {\n",
              "             var now     = new Date(); \n",
              "             var numberOfMlSeconds = now.getTime() - offset_sec*1000;\n",
              "             var update_now = new Date (numberOfMlSeconds);\n",
              "             var year    = update_now.getFullYear();\n",
              "             var month   = update_now.getMonth()+1; \n",
              "             var day     = update_now.getDate();\n",
              "             var hour    = update_now.getHours();\n",
              "             var minute  = update_now.getMinutes();\n",
              "             var second  = update_now.getSeconds(); \n",
              "             if(month.toString().length == 1) {\n",
              "                 month = '0'+month;}\n",
              "             if(day.toString().length == 1) {\n",
              "                 day = '0'+day;}   \n",
              "             if(hour.toString().length == 1) {\n",
              "                 hour = '0'+hour;}\n",
              "             if(minute.toString().length == 1) {\n",
              "                 minute = '0'+minute; }\n",
              "             if(second.toString().length == 1) {\n",
              "                 second = '0'+second;}   \n",
              "             var dateTime = year+'-'+month+'-'+day+' '+hour+':'+minute+':'+second;   \n",
              "             return dateTime;\n",
              "          }\n",
              "\n",
              "          function addData(value, value2){\n",
              "            chart.data.labels.push(getDateTime())\n",
              "            chart.data.datasets[0].data.push(value)\n",
              "            chart.data.datasets[1].data.push(value2)\n",
              "            // optional windowing\n",
              "            if(chart.data.labels.length > 100) {\n",
              "              chart.data.labels.shift()\n",
              "              chart.data.datasets[0].data.shift()\n",
              "              chart.data.datasets[1].data.shift() }\n",
              "\n",
              "            chart.update();\n",
              "          }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-3a78175e2945>:368: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df= part_60_days.append(live_data, ignore_index = True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "days= 0      2022-11-21\n",
            "1      2022-11-22\n",
            "2      2022-11-23\n",
            "3      2022-11-24\n",
            "4      2022-11-25\n",
            "          ...    \n",
            "176    2023-05-16\n",
            "177    2023-05-17\n",
            "178    2023-05-18\n",
            "179    2023-05-19\n",
            "180    2023-05-20\n",
            "Name: Date, Length: 181, dtype: object\n",
            "inputs.shape[0]= 181\n",
            "x1= [0.30633151 0.30693383 0.32423658 0.33111528 0.12076167]\n",
            "y1= 0.33122974168956754\n",
            "1/1 [==============================] - 1s 668ms/step\n",
            "days= 2023-01-20\n"
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            "x1= [0.33122974 0.40253697 0.3391371  0.43044406 0.18947541]\n",
            "y1= 0.43066943179366546\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "days= 2023-01-21\n"
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            "y1= 0.436950318691782\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "days= 2023-01-22\n"
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            "x1= [0.43695032 0.42531482 0.43276643 0.43318466 0.1530551 ]\n",
            "y1= 0.4333964439481015\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "days= 2023-01-23\n"
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            "x1= [0.43339644 0.42967543 0.4497488  0.44655643 0.16898205]\n",
            "y1= 0.44642178778158215\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "days= 2023-01-24\n"
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            "x1= [0.44642179 0.43014586 0.44308342 0.4279396  0.1679609 ]\n",
            "y1= 0.4282859495439981\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "days= 2023-01-25\n"
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            "y1= 0.4576226173275517\n",
            "1/1 [==============================] - 0s 56ms/step\n",
            "days= 2023-01-26\n"
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            "y1= 0.45273585653946746\n",
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            "days= 2023-01-27\n"
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            "y1= 0.4558118881430928\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "days= 2023-01-28\n"
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            "y1= 0.4527816035965879\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "days= 2023-01-29\n"
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            "y1= 0.4992018055070062\n",
            "1/1 [==============================] - 0s 54ms/step\n",
            "days= 2023-01-30\n"
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            "y1= 0.4408734636565561\n",
            "1/1 [==============================] - 0s 88ms/step\n",
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current price of BTC: $ 25728\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 45ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(25728,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Predicted future price does not meet the minimum growth requirement (2.0%) for buy trigger\n",
            "Remaining balance (USD): $ 1000000 \n",
            "\n",
            "Current price of BTC: $ 20586\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 41ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(20586,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Predicted future price does not meet the minimum growth requirement (2.0%) for buy trigger\n",
            "Remaining balance (USD): $ 1000000 \n",
            "\n",
            "Current price of BTC: $ 15458\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 45ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(15458,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "New buy order placed: Transaction ID: 812ddcb0-2771-4253-8aef-0fc2afdccc1d, Transaction Type: TransactionTypes.BUY, Price: 15458, Amount: 1000000 BTC, Volume: 64.69142191745375, Profit/Loss: Predicted future growth, Transaction Trigger: None\n",
            "Remaining balance (USD): $ 0 \n",
            "\n",
            "Current price of BTC: $ 15896\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 61ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(15896,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0 \n",
            "\n",
            "Current price of BTC: $ 14902\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 46ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(14902,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0 \n",
            "\n",
            "Current price of BTC: $ 13879\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 47ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(13879,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "New sell order placed: Transaction ID: dee28644-466e-4975-a046-cb5284110f68, Transaction Type: TransactionTypes.SELL, Price: 13879, Amount: 897852.2447923407 BTC, Volume: 64.69142191745375, Profit/Loss: -102147.75520765947, Transaction Trigger: Current price triggered stoploss\n",
            "Sell order fulfilled. Profit: $ -102147.75520765947\n",
            "Remaining balance (USD): $ 897852.2447923407 \n",
            "\n",
            "Current price of BTC: $ 9615\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 53ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(9615,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "New buy order placed: Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 9763\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 41ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(9763,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 10144\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 42ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(10144,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 10971\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 45ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(10971,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 10405\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 38ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(10405,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 10258\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 40ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(10258,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 11177\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 70ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(11177,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 10552\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 44ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(10552,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 11353\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 46ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(11353,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 11742\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 47ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(11742,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 12400\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 47ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(12400,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "Waiting for price to reach sell threshold\n",
            "Remaining balance (USD): $ 0.0 \n",
            "\n",
            "Current price of BTC: $ 13072\n",
            "Min 15787.28418 Max 31792.310547\n",
            "1/1 [==============================] - 0s 42ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "addData(13072,[[19158.738]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted future price of BTC: $ [[19158.738]]\n",
            "New sell order placed: Transaction ID: 25b25efc-bb24-4e0d-a840-57fea410b0b0, Transaction Type: TransactionTypes.SELL, Price: 13072, Amount: 1220668.1792954216 BTC, Volume: 93.38036867315036, Profit/Loss: 322815.9345030808, Transaction Trigger: Investment goal reached\n",
            "Sell order fulfilled. Profit: $ 322815.9345030808\n",
            "Investment goal reached! Stop trading.\n",
            "Remaining balance (USD): $ 1220668.1792954216 \n",
            "\n",
            "Transaction record: [(Transaction ID: 812ddcb0-2771-4253-8aef-0fc2afdccc1d, Transaction Type: TransactionTypes.BUY, Price: 15458, Amount: 1000000 BTC, Volume: 64.69142191745375, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 812ddcb0-2771-4253-8aef-0fc2afdccc1d, Transaction Type: TransactionTypes.BUY, Price: 15458, Amount: 1000000 BTC, Volume: 64.69142191745375, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 812ddcb0-2771-4253-8aef-0fc2afdccc1d, Transaction Type: TransactionTypes.BUY, Price: 15458, Amount: 1000000 BTC, Volume: 64.69142191745375, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: dee28644-466e-4975-a046-cb5284110f68, Transaction Type: TransactionTypes.SELL, Price: 13879, Amount: 897852.2447923407 BTC, Volume: 64.69142191745375, Profit/Loss: -102147.75520765947, Transaction Trigger: Current price triggered stoploss), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 4dc0fcd0-ee1b-4f48-9849-4b1bf20f5437, Transaction Type: TransactionTypes.BUY, Price: 9615, Amount: 897852.2447923407 BTC, Volume: 93.38036867315036, Profit/Loss: Predicted future growth, Transaction Trigger: None), (Transaction ID: 25b25efc-bb24-4e0d-a840-57fea410b0b0, Transaction Type: TransactionTypes.SELL, Price: 13072, Amount: 1220668.1792954216 BTC, Volume: 93.38036867315036, Profit/Loss: 322815.9345030808, Transaction Trigger: Investment goal reached)]\n",
            "Profit and Loss: [{UUID('dee28644-466e-4975-a046-cb5284110f68'): -102147.75520765947}, {UUID('25b25efc-bb24-4e0d-a840-57fea410b0b0'): 322815.9345030808}]\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import uuid\n",
        "import numpy as np\n",
        "import random\n",
        "import requests\n",
        "import time\n",
        "import pandas as pd\n",
        "from enum import Enum\n",
        "from prophet import Prophet\n",
        "from datetime import date, datetime, timedelta\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import IPython\n",
        "from IPython.display import display, HTML, Javascript\n",
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
        "\n",
        "# Constants\n",
        "INITIAL_BALANCE = 1000000  # $1 million\n",
        "INTERVALS = 5  # seconds\n",
        "MINIMUM_GROWTH = 0.02  # minimum 2% increase in predicted price to trigger buy\n",
        "STOP_LOSS_PERCENTAGE = 0.05  # limit loss to 5%\n",
        "GOAL = 1200000  # $1.2 million\n",
        "\n",
        "\"\"\" LSTM Prediction Functions \"\"\"\n",
        "def preprocess_data(data_url):\n",
        "    df = pd.read_csv(data_url)\n",
        "    df = df.drop(['Adj Close'], axis=1)\n",
        "    #df.rename(columns={'Date': 'ds', 'Close': 'y'}, inplace=True)\n",
        "    return df\n",
        "\n",
        "\n",
        "def train_lstm_model(x_train):\n",
        "    #Initialize the RNN\n",
        "    model = Sequential() \n",
        "    model.add(LSTM(units = 50, \n",
        "                   activation = 'relu', \n",
        "                   return_sequences = True, \n",
        "                   input_shape = (x_train.shape[1], 5)))\n",
        "\n",
        "    model.add(Dropout(0.2)) \n",
        "    model.add(LSTM(units = 60, activation = 'relu', return_sequences = True))\n",
        "    model.add(Dropout(0.3)) \n",
        "    model.add(LSTM(units = 80, activation = 'relu', return_sequences = True))\n",
        "\n",
        "    model.add(Dropout(0.4)) \n",
        "    model.add(LSTM(units = 120, activation = 'relu'))\n",
        "    model.add(Dropout(0.5)) \n",
        "    model.add(Dense(units =1))\n",
        "    model.summary()\n",
        "\n",
        "    model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "    return model\n",
        "\n",
        "def display_training_validation_loss(history):                      \n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    epochs = range(len(loss))\n",
        "    plt.figure()\n",
        "    plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "    plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "    plt.title(\"Training and Validation Loss\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "def predict_lstm_price(model, data_test, data, days, scaler):\n",
        "    # Prepare input data for prediction\n",
        "    last_60_days = data_test.tail(60)  # Take the most recent 60 data points\n",
        "    last_60_days = last_60_days.drop(['Date'], axis = 1)\n",
        "    inputs = scaler.transform(last_60_days)  # Scale the input data\n",
        "    reshaped_inputs = np.reshape(inputs, (1, 60, 5))  # Reshape the input data\n",
        "\n",
        "    # Obtain the scaling parameters\n",
        "    price_min = data['Close'].min()\n",
        "    price_max = data['Close'].max()\n",
        "    print(\"Min\", price_min, \"Max\", price_max)\n",
        "\n",
        "    # Reverse the scaling transformation\n",
        "    predicted_price = model.predict(reshaped_inputs)\n",
        "    predicted_price_rescaled = predicted_price * (price_max - price_min) + price_min\n",
        "    next_day_date = days.iloc[-1]\n",
        "    return predicted_price_rescaled\n",
        "    # # Print the real predicted price for the next day\n",
        "    # print(\"Predicted price for next day:\", predicted_price)\n",
        "    # print(\"Real predicted price for the next day:\", next_day_date, \":\", predicted_price_rescaled)\n",
        "\n",
        "\n",
        "class BitcoinTransaction:\n",
        "    def __init__(\n",
        "        self,\n",
        "        transaction_type,\n",
        "        price,\n",
        "        amount,\n",
        "        volume,\n",
        "        profit_or_loss=None,\n",
        "        transaction_trigger=None,\n",
        "    ):\n",
        "        self.transaction_type = transaction_type\n",
        "        self.price = price\n",
        "        self.amount = amount\n",
        "        self.volume = volume\n",
        "        self.profit_or_loss = profit_or_loss\n",
        "        self.transaction_trigger = transaction_trigger\n",
        "        self.transaction_id = uuid.uuid4()\n",
        "\n",
        "    def __str__(self):\n",
        "        return f\"Transaction ID: {self.transaction_id}, Transaction Type: {self.transaction_type}, Price: {self.price}, Amount: {self.amount} BTC, Volume: {self.volume}, Profit/Loss: {self.profit_or_loss}, Transaction Trigger: {self.transaction_trigger}\"\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f\"({str(self)})\"\n",
        "\n",
        "\n",
        "class TransactionTypes(Enum):\n",
        "    BUY = 1\n",
        "    SELL = 2\n",
        "\n",
        "\n",
        "\"\"\" Trading Bot Functions \"\"\"\n",
        "\n",
        "\n",
        "def get_price():\n",
        "    url = \"https://pro-api.coinmarketcap.com/v1/cryptocurrency/quotes/latest\"\n",
        "    headers = {\"X-CMC_PRO_API_KEY\": \"f16d5846-68ea-4cbc-88c2-b2b0ae91ae25\"}\n",
        "    params = {\"symbol\": \"BTC\"}\n",
        "    response = requests.get(url, headers=headers, params=params)\n",
        "    if response.status_code == 200:\n",
        "        data = response.json()\n",
        "        return data[\"data\"][\"BTC\"][\"quote\"][\"USD\"][\"price\"]\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "\n",
        "def take_decision(current_price, predicted_price, balance, buy_order, sell_order):\n",
        "    # no buy orders placed yet\n",
        "    if buy_order is None:\n",
        "        # Check if price is predicted to grow beyond minimum required growth\n",
        "        if predicted_price >= current_price * (1 + MINIMUM_GROWTH):\n",
        "            # Check if we have sufficient balance\n",
        "            if balance > 0:\n",
        "                # Calculate the volume of BTC we can buy with our balance and the dollar amount\n",
        "                volume = balance / current_price\n",
        "                amount = balance\n",
        "                # Place the buy order\n",
        "                trigger = \"Predicted future growth\"\n",
        "                buy_order = BitcoinTransaction(\n",
        "                    TransactionTypes.BUY, current_price, amount, volume, trigger\n",
        "                )\n",
        "                print(\"New buy order placed:\", buy_order)\n",
        "                balance -= amount\n",
        "            else:\n",
        "                print(\"Insufficient balance to place a new buy order\")\n",
        "        else:\n",
        "            print(\n",
        "                f\"Predicted future price does not meet the minimum growth requirement ({MINIMUM_GROWTH * 100}%) for buy trigger\"\n",
        "            )\n",
        "    elif sell_order is None:\n",
        "        # Check if current price has fallen to trigger stoploss sell, minimize loss\n",
        "        if current_price <= buy_order.price * (1 - STOP_LOSS_PERCENTAGE):\n",
        "            volume = buy_order.volume\n",
        "            amount = current_price * volume\n",
        "            # calculate the loss (negative profit) incurred in this sell order\n",
        "            profit_or_loss = volume * (current_price - buy_order.price)\n",
        "            trigger = \"Current price triggered stoploss\"\n",
        "            sell_order = BitcoinTransaction(\n",
        "                TransactionTypes.SELL,\n",
        "                current_price,\n",
        "                amount,\n",
        "                volume,\n",
        "                profit_or_loss,\n",
        "                trigger,\n",
        "            )\n",
        "            print(\"New sell order placed:\", sell_order)\n",
        "            balance += amount\n",
        "        # Check if investment goal has been reached\n",
        "        elif balance + (current_price * buy_order.volume) >= GOAL:\n",
        "            volume = buy_order.volume\n",
        "            amount = current_price * volume\n",
        "            # calculate the profit/loss incurred in this sell order\n",
        "            profit_or_loss = volume * (current_price - buy_order.price)\n",
        "            trigger = \"Investment goal reached\"\n",
        "            sell_order = BitcoinTransaction(\n",
        "                TransactionTypes.SELL,\n",
        "                current_price,\n",
        "                amount,\n",
        "                volume,\n",
        "                profit_or_loss,\n",
        "                trigger,\n",
        "            )\n",
        "            print(\"New sell order placed:\", sell_order)\n",
        "            balance += amount\n",
        "        # Check if predicted future price will fall below stoploss of current price, prevent possible loss\n",
        "        elif predicted_price <= current_price * (1 - STOP_LOSS_PERCENTAGE):\n",
        "            volume = buy_order.volume\n",
        "            amount = current_price * volume\n",
        "            # calculate the profit/loss incurred in this sell order\n",
        "            profit_or_loss = volume * (current_price - buy_order.price)\n",
        "            trigger = \"Predicted future price triggered stoploss\"\n",
        "            sell_order = BitcoinTransaction(\n",
        "                TransactionTypes.SELL,\n",
        "                current_price,\n",
        "                amount,\n",
        "                volume,\n",
        "                profit_or_loss,\n",
        "                trigger,\n",
        "            )\n",
        "            print(\"New sell order placed:\", sell_order)\n",
        "            balance += amount\n",
        "        else:\n",
        "            print(\"Waiting for price to reach sell threshold\")\n",
        "\n",
        "    return buy_order, sell_order, balance\n",
        "\n",
        "\n",
        "def configure_browser_state():\n",
        "    display(\n",
        "        IPython.core.display.HTML(\n",
        "            \"\"\"\n",
        "    <canvas id=\"myChart\"></canvas>\n",
        "  \"\"\"\n",
        "        )\n",
        "    )\n",
        "    display(\n",
        "        IPython.core.display.HTML(\n",
        "            \"\"\"\n",
        "        <script src=\"https://cdn.jsdelivr.net/npm/chart.js@2.8.0\"></script>\n",
        "        <script>\n",
        "          var ctx = document.getElementById('myChart').getContext('2d');\n",
        "          var chart = new Chart(ctx, {\n",
        "              // The type of chart we want to create\n",
        "              type: 'line',\n",
        "\n",
        "              // The data for our dataset\n",
        "              data: {\n",
        "                  labels: [getDateTime(-10), getDateTime(-20), getDateTime(-30),\n",
        "                                  getDateTime(-40), getDateTime(-50), getDateTime(-60) ],\n",
        "                  datasets: [{\n",
        "                      label: 'Actual',\n",
        "                      borderColor: 'rgb(255, 99, 132)',\n",
        "                      data: [0,1,2,3,4,5]\n",
        "                  }, \n",
        "                  {\n",
        "                      label: 'Predicted',\n",
        "                      borderColor: 'rgb(155, 199, 32)',\n",
        "                      data: [0,1,2,3,4,5]\n",
        "                  }]\n",
        "              },\n",
        "\n",
        "              // Configuration options go here\n",
        "              options: { animation: {duration: 0} ,\n",
        "                scales: {x: {\n",
        "                           type: 'time',\n",
        "                           time: { unit: 'minute',displayFormats: {minute: 'HH:mm'},tooltipFormat: 'HH:mm'},\n",
        "                           title: {display: true, text: 'Date'}},\n",
        "                         y: {\n",
        "                           title: { display: true, text: 'value'}},\n",
        "                         xAxes: [{ scaleLabel: { display: true, labelString: 'Timestamp [YYYY-MM-DD hh:mm:ss]'}}],\n",
        "                        yAxes: [{scaleLabel: {display: true, labelString: 'BitCoin Price [USD $]'} }], },\n",
        "                title: { display: true, text: 'Bitcoin Price - Realtime Prediction'}}});\n",
        "\n",
        "          function getEpoch(offset_sec=0) {\n",
        "             var now     = new Date(); \n",
        "             return Math.floor((now.getTime() - offset_sec*1000)/1000);}\n",
        "\n",
        "          function getDateTime(offset_sec=0) {\n",
        "             var now     = new Date(); \n",
        "             var numberOfMlSeconds = now.getTime() - offset_sec*1000;\n",
        "             var update_now = new Date (numberOfMlSeconds);\n",
        "             var year    = update_now.getFullYear();\n",
        "             var month   = update_now.getMonth()+1; \n",
        "             var day     = update_now.getDate();\n",
        "             var hour    = update_now.getHours();\n",
        "             var minute  = update_now.getMinutes();\n",
        "             var second  = update_now.getSeconds(); \n",
        "             if(month.toString().length == 1) {\n",
        "                 month = '0'+month;}\n",
        "             if(day.toString().length == 1) {\n",
        "                 day = '0'+day;}   \n",
        "             if(hour.toString().length == 1) {\n",
        "                 hour = '0'+hour;}\n",
        "             if(minute.toString().length == 1) {\n",
        "                 minute = '0'+minute; }\n",
        "             if(second.toString().length == 1) {\n",
        "                 second = '0'+second;}   \n",
        "             var dateTime = year+'-'+month+'-'+day+' '+hour+':'+minute+':'+second;   \n",
        "             return dateTime;\n",
        "          }\n",
        "\n",
        "          function addData(value, value2){\n",
        "            chart.data.labels.push(getDateTime())\n",
        "            chart.data.datasets[0].data.push(value)\n",
        "            chart.data.datasets[1].data.push(value2)\n",
        "            // optional windowing\n",
        "            if(chart.data.labels.length > 100) {\n",
        "              chart.data.labels.shift()\n",
        "              chart.data.datasets[0].data.shift()\n",
        "              chart.data.datasets[1].data.shift() }\n",
        "\n",
        "            chart.update();\n",
        "          }\n",
        "        </script>\n",
        "        \"\"\"\n",
        "        )\n",
        "    )\n",
        "\n",
        "\n",
        "# Main program\n",
        "def main():\n",
        "    balance = INITIAL_BALANCE\n",
        "    buy_order = None\n",
        "    sell_order = None\n",
        "    goal_reached = False\n",
        "\n",
        "    # url = 'https://raw.githubusercontent.com/yetanotherpassword/COMS4507/main/BTC-USD.csv'\n",
        "    # update this url to new dataset for future retraining.\n",
        "    url = \"https://raw.githubusercontent.com/AnsonCNS/COMS4507/main/BTC-USD_2023-05-21.csv\"\n",
        "    data = preprocess_data(url)\n",
        "    last_date = datetime.strptime(data['Date'][data.shape[0]-1],'%Y-%m-%d')\n",
        "\n",
        "    #separate the last 120 days to simulate as live daily data\n",
        "    last_trainingdate = str(last_date+timedelta(days=-120))\n",
        "\n",
        "    print(\"last_trainingdate=\"+last_trainingdate[0:10]+\":\")\n",
        "\n",
        "    data_training = data[data['Date']< last_trainingdate[0:10]].copy()\n",
        "    data_training\n",
        "    print(\"Above is data_training\")\n",
        "\n",
        "    data_test = data[data['Date']< last_trainingdate[0:10]].copy()\n",
        "    data_test\n",
        "    print(\"Above  is data_test\")\n",
        "\n",
        "    live_data = data[data['Date'] >= last_trainingdate[0:10]].copy()\n",
        "\n",
        "    training_data = data_training.drop(['Date'], axis = 1)\n",
        "    training_data.head()\n",
        "    print(\"Above is training_data.head())\")\n",
        "\n",
        "    scaler = MinMaxScaler()\n",
        "    training_data = scaler.fit_transform(training_data)\n",
        "    training_data\n",
        "\n",
        "    X_train = [] \n",
        "    Y_train = []\n",
        "    training_data.shape[0]\n",
        "    for i in range(60, training_data.shape[0]):\n",
        "      X_train.append(training_data[i-60:i])\n",
        "      Y_train.append(training_data[i,0])\n",
        "    X_train, Y_train = np.array(X_train), np.array(Y_train)\n",
        "    X_train.shape\n",
        "\n",
        "    model = train_lstm_model(X_train)\n",
        "    history= model.fit(X_train, \n",
        "                       Y_train, \n",
        "                       epochs = 50, \n",
        "                       batch_size =50, \n",
        "                       validation_split=0.1)\n",
        "    \n",
        "    # Display training and Validation loss graph\n",
        "    display_training_validation_loss(history)\n",
        "\n",
        "    # Ready graph display\n",
        "    configure_browser_state() \n",
        "    \n",
        "    part_60_days = data_training.tail(60)\n",
        "    df= part_60_days.append(live_data, ignore_index = True)\n",
        "    days = df['Date']\n",
        "    df = df.drop(['Date'], axis = 1)\n",
        "    df.head()\n",
        "\n",
        "    print(\"days=\",days)\n",
        "    inputs = scaler.transform(df) \n",
        "    inputs\n",
        "\n",
        "    X_test = []\n",
        "    Y_test = []\n",
        "    Y_pred = -1\n",
        "    print(\"inputs.shape[0]=\",inputs.shape[0])\n",
        "    for i in range (60, inputs.shape[0]):\n",
        "        X_test.append(inputs[i-60:i]) \n",
        "        Y_test.append(inputs[i, 0])\n",
        "        x1 = X_test[-1][-1]\n",
        "        y1 = Y_test[-1]\n",
        "        print(\"x1=\",x1)\n",
        "        print(\"y1=\",y1)\n",
        "        #if Y_pred != -1:\n",
        "        #    model.train_on_batch(x1, y1)\n",
        "        Y_pred = model.predict(np.array(X_test))\n",
        "        print(\"days=\",days[i])\n",
        "        display(Javascript('addData('+str(Y_test[-1])+','+str(Y_pred[-1])+',\"'+str(days[i])+'\")'))\n",
        "\n",
        "    plt.figure(figsize=(14,5))\n",
        "    plt.plot(Y_test, color = 'red', label = 'Real Bitcoin Price')\n",
        "    plt.plot(Y_pred, color = 'green', label = 'Predicted Bitcoin Price')\n",
        "    plt.title('Bitcoin Price Prediction using RNN-LSTM')\n",
        "    plt.xlabel('Time')\n",
        "    plt.ylabel('Price')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    ##FIXME\n",
        "    # predict_lstm_price(model, data_test, data, days, scaler)\n",
        "\n",
        "    # Trading Bot Section\n",
        "    transaction_record = []\n",
        "    profit_and_loss_record = []\n",
        "\n",
        "    # FIXME the line below is for experimental demonstration, remove line to get real prices\n",
        "    current_price = get_price()\n",
        "\n",
        "    # keep running the bot if there is positive balance or an existing buy order has been placed.\n",
        "    while (balance > 0 or buy_order) and not goal_reached:\n",
        "        # FIXME\n",
        "        # uncomment the following line to get real prices\n",
        "        # current_price = get_price()\n",
        "\n",
        "        # FIXME\n",
        "        # the following lines are for experimental demonstration, remove lines to get real prices\n",
        "        if not buy_order:\n",
        "          current_price = random.randint(int(current_price * (1 - 0.4)), int(current_price))\n",
        "        else:\n",
        "          current_price = random.randint(int(current_price * (1 - 0.1)), int(current_price * (1 + 0.1)))\n",
        "\n",
        "        if current_price is not None:\n",
        "            print(\"Current price of BTC: $\", current_price)\n",
        "            predicted_price = predict_lstm_price(model, data_test, data, days, scaler)\n",
        "            display(\n",
        "                Javascript(\n",
        "                    \"addData(\" + str(current_price) + \",\" + str(predicted_price) + \")\"\n",
        "                )\n",
        "            )\n",
        "            print(\"Predicted future price of BTC: $\", predicted_price)\n",
        "            buy_order, sell_order, balance = take_decision(\n",
        "                current_price, predicted_price, balance, buy_order, sell_order\n",
        "            )\n",
        "\n",
        "            if sell_order:\n",
        "                # record transaction\n",
        "                transaction_record.append(sell_order)\n",
        "                print(\"Sell order fulfilled. Profit: $\", sell_order.profit_or_loss)\n",
        "                profit_and_loss_record.append(\n",
        "                    {sell_order.transaction_id: sell_order.profit_or_loss}\n",
        "                )\n",
        "\n",
        "                # reset orders\n",
        "                buy_order = None\n",
        "                sell_order = None\n",
        "\n",
        "            elif buy_order:\n",
        "                # record transaction\n",
        "                transaction_record.append(buy_order)\n",
        "\n",
        "            if balance >= GOAL:\n",
        "                goal_reached = True\n",
        "                print(\"Investment goal reached! Stop trading.\")\n",
        "\n",
        "            print(\"Remaining balance (USD): $\", balance, \"\\n\")\n",
        "\n",
        "        else:\n",
        "            print(\"Error getting price from CoinMarketCap API\")\n",
        "\n",
        "        time.sleep(INTERVALS)\n",
        "\n",
        "    print(\"Transaction record:\", transaction_record)\n",
        "    print(\"Profit and Loss:\", profit_and_loss_record)\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ]
    }
  ]
}