{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BTC-Stock-prediction.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLXH7XozYUus"
      },
      "source": [
        "![](https://blockchainstock.azureedge.net/features/C47CB7081D81D4B6A5F3C2D5F97D14376DF75D5E3DDC5733953EABB79263589D.jpg \"Creadit : https://blockchain.news/analysis/Why-bitcoin-price-correlation-stock-market-is-high-now\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ilRsupJoX37Q"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "This project will demonstrate Bitcoin (BTC-USD) stock closing price prediction using stacking bidirection LSTM (bi-LSTM)\n",
        "\n",
        "Data is collected using `yfinance` API  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tv3dly3HXsHM"
      },
      "source": [
        "# Libraries & Configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2obStAbg2d-q"
      },
      "source": [
        "%%capture\n",
        "!pip install yfinance --upgrade --no-cache-dir"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afgQ1-TXuiaE"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import yfinance as yf\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, CuDNNLSTM, Bidirectional\n",
        "from keras.layers import Activation, Dropout\n",
        "\n",
        "%matplotlib inline\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "plt.rcParams['figure.figsize'] = 12, 8"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaKRblgMYpLR"
      },
      "source": [
        "# Load Dataset\n",
        "\n",
        "[yfinance API](https://github.com/ranaroussi/yfinance)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qVyu9rl2WRz"
      },
      "source": [
        "btc_ticker = yf.Ticker('BTC-USD')\n",
        "\n",
        "df_btc_data = btc_ticker.history(period=\"max\")\n",
        "\n",
        "# to download for a specific period of time\n",
        "# yf.download(\"BTC-USD\", start=\"2014-01-01\", end=\"2019-04-30\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-8mq9iAY9m_"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "I2JZ0it9CuaL",
        "outputId": "9a727b6a-caac-4016-fddc-596a5bfe55ab"
      },
      "source": [
        "df_btc_data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-09-17</th>\n",
              "      <td>465.864014</td>\n",
              "      <td>468.174011</td>\n",
              "      <td>452.421997</td>\n",
              "      <td>457.334015</td>\n",
              "      <td>21056800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-18</th>\n",
              "      <td>456.859985</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>413.104004</td>\n",
              "      <td>424.440002</td>\n",
              "      <td>34483200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-19</th>\n",
              "      <td>424.102997</td>\n",
              "      <td>427.834991</td>\n",
              "      <td>384.532013</td>\n",
              "      <td>394.795990</td>\n",
              "      <td>37919700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-20</th>\n",
              "      <td>394.673004</td>\n",
              "      <td>423.295990</td>\n",
              "      <td>389.882996</td>\n",
              "      <td>408.903992</td>\n",
              "      <td>36863600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-21</th>\n",
              "      <td>408.084991</td>\n",
              "      <td>412.425995</td>\n",
              "      <td>393.181000</td>\n",
              "      <td>398.821014</td>\n",
              "      <td>26580100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-09</th>\n",
              "      <td>67549.734375</td>\n",
              "      <td>68530.335938</td>\n",
              "      <td>66382.062500</td>\n",
              "      <td>66971.828125</td>\n",
              "      <td>42357991721</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-10</th>\n",
              "      <td>66953.335938</td>\n",
              "      <td>68789.625000</td>\n",
              "      <td>63208.113281</td>\n",
              "      <td>64995.230469</td>\n",
              "      <td>48730828378</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-11</th>\n",
              "      <td>64978.890625</td>\n",
              "      <td>65579.015625</td>\n",
              "      <td>64180.488281</td>\n",
              "      <td>64949.960938</td>\n",
              "      <td>35880633236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-12</th>\n",
              "      <td>64863.980469</td>\n",
              "      <td>65460.816406</td>\n",
              "      <td>62333.914062</td>\n",
              "      <td>64155.941406</td>\n",
              "      <td>36084893887</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-13</th>\n",
              "      <td>64378.949219</td>\n",
              "      <td>64901.621094</td>\n",
              "      <td>63358.320312</td>\n",
              "      <td>64727.914062</td>\n",
              "      <td>30252488704</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2611 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Open          High  ...  Dividends  Stock Splits\n",
              "Date                                    ...                         \n",
              "2014-09-17    465.864014    468.174011  ...          0             0\n",
              "2014-09-18    456.859985    456.859985  ...          0             0\n",
              "2014-09-19    424.102997    427.834991  ...          0             0\n",
              "2014-09-20    394.673004    423.295990  ...          0             0\n",
              "2014-09-21    408.084991    412.425995  ...          0             0\n",
              "...                  ...           ...  ...        ...           ...\n",
              "2021-11-09  67549.734375  68530.335938  ...          0             0\n",
              "2021-11-10  66953.335938  68789.625000  ...          0             0\n",
              "2021-11-11  64978.890625  65579.015625  ...          0             0\n",
              "2021-11-12  64863.980469  65460.816406  ...          0             0\n",
              "2021-11-13  64378.949219  64901.621094  ...          0             0\n",
              "\n",
              "[2611 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYRHLpf_ZVvc"
      },
      "source": [
        "## Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmJR97HML7n7",
        "outputId": "8d5da9e2-fcbf-416b-964e-d017bce5dcdb"
      },
      "source": [
        "pd.date_range(df_btc_data.index.min(), df_btc_data.index.max()).difference(df_btc_data.index)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-04-17', '2020-10-09', '2020-10-12', '2020-10-13'], dtype='datetime64[ns]', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rj2m9bCZe3X"
      },
      "source": [
        "Since the ratio of missing dates is very low (~0.1%), no action is taken to fill missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEKzorYcax_i"
      },
      "source": [
        "## Target Column : `Close`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "82YliDLw-bn4",
        "outputId": "426b3094-2e61-48a1-b728-59b60887200b"
      },
      "source": [
        "df_btc_data.Close.plot();"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nSd4yAsa49W"
      },
      "source": [
        "### Zooming IN (2018-02 to 2021-11)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "k7fe9BJHaKMG",
        "outputId": "f07dccf4-1f48-4e5f-9d4a-f90d7780eabb"
      },
      "source": [
        "df_btc_data.loc['2018-01':'2021-11',\"Close\"].plot();"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW-DU7t2djwm"
      },
      "source": [
        "# Data Preparation\n",
        "### Univariate Time Series Dataset With One Feature - Stock closing price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "oOs0VXlZdVMO",
        "outputId": "2b9fa7af-b6b2-41e7-fd82-15265325158d"
      },
      "source": [
        "df_btc_close = df_btc_data[['Close']]\n",
        "df_btc_close"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-09-17</th>\n",
              "      <td>457.334015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-18</th>\n",
              "      <td>424.440002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-19</th>\n",
              "      <td>394.795990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-20</th>\n",
              "      <td>408.903992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-21</th>\n",
              "      <td>398.821014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-09</th>\n",
              "      <td>66971.828125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-10</th>\n",
              "      <td>64995.230469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-11</th>\n",
              "      <td>64949.960938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-12</th>\n",
              "      <td>64155.941406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-13</th>\n",
              "      <td>64727.914062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2611 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Close\n",
              "Date                    \n",
              "2014-09-17    457.334015\n",
              "2014-09-18    424.440002\n",
              "2014-09-19    394.795990\n",
              "2014-09-20    408.903992\n",
              "2014-09-21    398.821014\n",
              "...                  ...\n",
              "2021-11-09  66971.828125\n",
              "2021-11-10  64995.230469\n",
              "2021-11-11  64949.960938\n",
              "2021-11-12  64155.941406\n",
              "2021-11-13  64727.914062\n",
              "\n",
              "[2611 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLJcNvZ6cLIZ"
      },
      "source": [
        "## Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntdU-f_YdhEm"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "close_price = df_btc_close.Close.values.reshape(-1, 1)\n",
        "scaled_close = scaler.fit_transform(close_price)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWLVLGK4eTjK",
        "outputId": "3e401890-1014-4a12-9058-41c1eded31d1"
      },
      "source": [
        "scaled_close.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2611, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hcqk3FofdWL",
        "outputId": "f05e97b2-ffa0-4b5c-d12e-d574b4d662c8"
      },
      "source": [
        "scaled_close"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00414359],\n",
              "       [0.00365546],\n",
              "       [0.00321557],\n",
              "       ...,\n",
              "       [0.96116758],\n",
              "       [0.94938491],\n",
              "       [0.95787257]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpz1yd9-cQTg"
      },
      "source": [
        "**Check Missing value**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7tg2MrUeezc",
        "outputId": "882fd7cd-7969-431a-c191-31d96e84606b"
      },
      "source": [
        "np.isnan(scaled_close).sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_kHlVebeg_q",
        "outputId": "1bdc3b67-eda8-4103-a035-ae9792bcef2e"
      },
      "source": [
        "scaled_close.reshape(-1, 1).shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2611, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyJPvgQJcXwx"
      },
      "source": [
        "## Prepare Sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab1_THhJethc"
      },
      "source": [
        "Target Shape of The Data : `[batch_size, sequence_length, n_features]`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6XVMGQxesMb"
      },
      "source": [
        "sequence_length = 100 # history of 99 input time steps\n",
        "\n",
        "def to_sequences(data, seq_len):\n",
        "  d = []\n",
        "  for index in range(len(data) - seq_len):\n",
        "    d.append(data[index: index + seq_len])\n",
        "\n",
        "  return np.array(d)\n",
        "\n",
        "def preprocess_data(data_raw, seq_len, train_split):\n",
        "  data = to_sequences(data_raw, seq_len)\n",
        "  num_train = int(train_split * data.shape[0])\n",
        "\n",
        "  X_train = data[:num_train, :-1, :]\n",
        "  y_train = data[:num_train, -1, :]\n",
        "  \n",
        "  # input->X1: [1 2 3...99]-> y1: 100\n",
        "  # input->X2: [2 3 4...100]-> y2: 101\n",
        "  # input->X3: [3 4 5...101]-> y3: 102\n",
        "\n",
        "  X_test = data[num_train:, :-1, :]\n",
        "  y_test = data[num_train:, -1, :]\n",
        "\n",
        "  return X_train, y_train, X_test, y_test\n",
        "\n",
        "X_train, y_train, X_test, y_test = preprocess_data(scaled_close, sequence_length, 0.9)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "so8hhYKWfl1U",
        "outputId": "086b135a-fc82-4b48-ae79-c2d3a23472fe"
      },
      "source": [
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2259, 99, 1), (2259, 1), (252, 99, 1), (252, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTIQe-eIfvSr"
      },
      "source": [
        "# Modeling\n",
        "\n",
        "## Stacking LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0B8zIrVcfs3q",
        "outputId": "98f8b614-a514-4d5e-96cd-2bd2a34e754f"
      },
      "source": [
        "# https://machinelearningmastery.com/stacked-long-short-term-memory-networks/\n",
        "# https://machinelearningmastery.com/return-sequences-and-return-states-for-lstms-in-keras/\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "# CuDNNLSTM is roughly 15x faster than LSTM\n",
        "\n",
        "model.add(Bidirectional(\n",
        "CuDNNLSTM(X_train.shape[1], return_sequences=True), \n",
        "input_shape=(X_train.shape[1], X_train.shape[-1])\n",
        "))\n",
        "\n",
        "# set return_sequences=True\n",
        "# so that the second LSTM layer has a three-dimensional sequence input\n",
        "\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Bidirectional(CuDNNLSTM((X_train.shape[1] * 2), \n",
        "                                            return_sequences=True)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM(X_train.shape[1])))\n",
        "\n",
        "model.add(Dense(units=1))\n",
        "model.add(Activation('linear'))\n",
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional (Bidirectiona  (None, 99, 198)          80784     \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 99, 198)           0         \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 99, 396)          630432    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 99, 396)           0         \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 198)              393624    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 199       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 1)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,105,039\n",
            "Trainable params: 1,105,039\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qjjuc14ZI1Kf"
      },
      "source": [
        "# Setting up an early stop\n",
        "earlystop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=80,  verbose=1, mode='min')\n",
        "callbacks_list = [earlystop]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN9MMS5gqGkU",
        "outputId": "4c253e11-dea3-4e7c-a059-d4063ba3c9b0"
      },
      "source": [
        "BATCH_SIZE = 64\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=50, batch_size=BATCH_SIZE, \n",
        "                    shuffle=False,  validation_split=0.1 )"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "32/32 [==============================] - 16s 211ms/step - loss: 0.0027 - val_loss: 0.0525\n",
            "Epoch 2/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 0.0033 - val_loss: 0.0516\n",
            "Epoch 3/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 0.0020 - val_loss: 0.0338\n",
            "Epoch 4/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 0.0020 - val_loss: 0.0250\n",
            "Epoch 5/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 0.0015 - val_loss: 0.0131\n",
            "Epoch 6/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 0.0011 - val_loss: 0.0048\n",
            "Epoch 7/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 7.1743e-04 - val_loss: 0.0128\n",
            "Epoch 8/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 6.5780e-04 - val_loss: 0.0103\n",
            "Epoch 9/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 0.0011 - val_loss: 0.0381\n",
            "Epoch 10/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 9.3656e-04 - val_loss: 0.0217\n",
            "Epoch 11/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 0.0030 - val_loss: 0.0176\n",
            "Epoch 12/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 9.9426e-04 - val_loss: 0.0045\n",
            "Epoch 13/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 5.6209e-04 - val_loss: 0.0024\n",
            "Epoch 14/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 3.7187e-04 - val_loss: 0.0036\n",
            "Epoch 15/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.6282e-04 - val_loss: 0.0039\n",
            "Epoch 16/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.1326e-04 - val_loss: 0.0041\n",
            "Epoch 17/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.8399e-04 - val_loss: 0.0041\n",
            "Epoch 18/50\n",
            "32/32 [==============================] - 5s 155ms/step - loss: 1.7238e-04 - val_loss: 0.0040\n",
            "Epoch 19/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.7420e-04 - val_loss: 0.0041\n",
            "Epoch 20/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 1.6652e-04 - val_loss: 0.0036\n",
            "Epoch 21/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.9384e-04 - val_loss: 0.0033\n",
            "Epoch 22/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 1.8642e-04 - val_loss: 0.0034\n",
            "Epoch 23/50\n",
            "32/32 [==============================] - 5s 157ms/step - loss: 2.0777e-04 - val_loss: 0.0032\n",
            "Epoch 24/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.1582e-04 - val_loss: 0.0035\n",
            "Epoch 25/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 2.1004e-04 - val_loss: 0.0036\n",
            "Epoch 26/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 2.0698e-04 - val_loss: 0.0031\n",
            "Epoch 27/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.3536e-04 - val_loss: 0.0044\n",
            "Epoch 28/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.8200e-04 - val_loss: 0.0023\n",
            "Epoch 29/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 3.4417e-04 - val_loss: 0.0041\n",
            "Epoch 30/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.0592e-04 - val_loss: 0.0010\n",
            "Epoch 31/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 7.4489e-04 - val_loss: 0.0031\n",
            "Epoch 32/50\n",
            "32/32 [==============================] - 5s 155ms/step - loss: 2.5712e-04 - val_loss: 0.0028\n",
            "Epoch 33/50\n",
            "32/32 [==============================] - 5s 155ms/step - loss: 1.3549e-04 - val_loss: 0.0016\n",
            "Epoch 34/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.9134e-04 - val_loss: 0.0017\n",
            "Epoch 35/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 1.3780e-04 - val_loss: 0.0018\n",
            "Epoch 36/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.0649e-04 - val_loss: 0.0019\n",
            "Epoch 37/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.3328e-04 - val_loss: 0.0013\n",
            "Epoch 38/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 2.5050e-04 - val_loss: 0.0017\n",
            "Epoch 39/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.5038e-04 - val_loss: 8.8623e-04\n",
            "Epoch 40/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 3.8063e-04 - val_loss: 0.0013\n",
            "Epoch 41/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.7219e-04 - val_loss: 7.4542e-04\n",
            "Epoch 42/50\n",
            "32/32 [==============================] - 5s 152ms/step - loss: 4.9994e-04 - val_loss: 0.0017\n",
            "Epoch 43/50\n",
            "32/32 [==============================] - 5s 152ms/step - loss: 1.3355e-04 - val_loss: 0.0012\n",
            "Epoch 44/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.7310e-04 - val_loss: 0.0011\n",
            "Epoch 45/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 1.3199e-04 - val_loss: 9.3129e-04\n",
            "Epoch 46/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.7162e-04 - val_loss: 9.9057e-04\n",
            "Epoch 47/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.2942e-04 - val_loss: 7.9150e-04\n",
            "Epoch 48/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 1.8981e-04 - val_loss: 0.0010\n",
            "Epoch 49/50\n",
            "32/32 [==============================] - 5s 154ms/step - loss: 1.2551e-04 - val_loss: 6.1445e-04\n",
            "Epoch 50/50\n",
            "32/32 [==============================] - 5s 153ms/step - loss: 3.3240e-04 - val_loss: 8.9620e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZR4Rwg6Gk3Up"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKHuEzFhrS0y",
        "outputId": "9a2d26c5-fa97-4a2d-8bbc-8d3572c21b57"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 1s 66ms/step - loss: 0.0027\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0027122455649077892"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "R1PSIej4sKjK",
        "outputId": "f3125f26-cfe8-4fa2-cbbf-2533dd3453c2"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "MHGwXnx0sPdb",
        "outputId": "526666eb-86d9-4e90-9a45-328657b8cc87"
      },
      "source": [
        "y_hat = model.predict(X_test)\n",
        "\n",
        "y_test_inverse = scaler.inverse_transform(y_test)\n",
        "y_hat_inverse = scaler.inverse_transform(y_hat)\n",
        " \n",
        "plt.plot(y_test_inverse, label=\"Actual Price\", color='green')\n",
        "plt.plot(y_hat_inverse, label=\"Predicted Price\", color='red')\n",
        " \n",
        "plt.title('Bitcoin price prediction')\n",
        "plt.xlabel('Time [days]')\n",
        "plt.ylabel('Price')\n",
        "plt.legend(loc='best')\n",
        " \n",
        "plt.show();"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}