chiuannica/predictethereumprices.ipynb

## predictethereumprices.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "predictEthereumPrices.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNMwTpj0nn6bW073GzW5kui",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/chiuannica/df4c95917450d64517750f4e5dd93b1b/predictethereumprices.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k81RHdrqjamn"
      },
      "source": [
        "import pandas as pd\r\n",
        "import datetime\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import math\r\n",
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "\r\n",
        "from keras.models import Sequential\r\n",
        "from keras.layers import Dense, LSTM"
      ],
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "av-klBEtNQRN"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\r\n",
        "#ETH for all of history\r\n",
        "#path = \"/ETH_USD_2015-08-09_2021-02-05-CoinDesk.csv\"\r\n",
        "#ETH past 3 months\r\n",
        "path = \"/ETH_USD_2020-11-06_2021-02-05-CoinDesk.csv\"\r\n",
        "#Doge past 3 months\r\n",
        "#path = \"/DOGE_USD_2020-11-06_2021-02-05-CoinDesk.csv\"\r\n",
        "eth_data = pd.read_csv(path)\r\n",
        "\r\n",
        "eth_data.shape\r\n",
        "eth_data.rename(columns={\"Date\": 'date', \"Closing Price (USD)\": 'close', \"24h Open (USD)\": 'open', '24h High (USD)': 'high', '24h Low (USD)': 'low'}, inplace=True)"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "rfRWUVZFlGBf",
        "outputId": "df7fb1c3-a066-4f4e-94a0-9b3bd26257c1"
      },
      "source": [
        "eth_data['date'] = pd.to_datetime(eth_data['date'])\r\n",
        "eth_data.index = eth_data['date']\r\n",
        "eth_data.tail()"
      ],
      "execution_count": 203,
      "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>Currency</th>\n",
              "      <th>date</th>\n",
              "      <th>close</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-01</th>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-02-01</td>\n",
              "      <td>1357.047990</td>\n",
              "      <td>1314.419019</td>\n",
              "      <td>1364.277491</td>\n",
              "      <td>1271.988289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-02</th>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-02-02</td>\n",
              "      <td>1540.651288</td>\n",
              "      <td>1374.482798</td>\n",
              "      <td>1549.033703</td>\n",
              "      <td>1361.935211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-02-03</td>\n",
              "      <td>1644.685967</td>\n",
              "      <td>1513.809609</td>\n",
              "      <td>1650.388472</td>\n",
              "      <td>1510.619675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>1628.468008</td>\n",
              "      <td>1665.892805</td>\n",
              "      <td>1697.107183</td>\n",
              "      <td>1558.583689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>ETH</td>\n",
              "      <td>2021-02-05</td>\n",
              "      <td>1704.832300</td>\n",
              "      <td>1596.991934</td>\n",
              "      <td>1761.354780</td>\n",
              "      <td>1592.854835</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Currency       date  ...         high          low\n",
              "date                            ...                          \n",
              "2021-02-01      ETH 2021-02-01  ...  1364.277491  1271.988289\n",
              "2021-02-02      ETH 2021-02-02  ...  1549.033703  1361.935211\n",
              "2021-02-03      ETH 2021-02-03  ...  1650.388472  1510.619675\n",
              "2021-02-04      ETH 2021-02-04  ...  1697.107183  1558.583689\n",
              "2021-02-05      ETH 2021-02-05  ...  1761.354780  1592.854835\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "mXypMwWQmDSP",
        "outputId": "6eaf6a76-7dcc-4a97-d1d1-86ee2aed061d"
      },
      "source": [
        "plt.figure(figsize=(16,8))\r\n",
        "plt.title(\"Close Price History\")\r\n",
        "plt.plot(eth_data[\"close\"])\r\n",
        "plt.xlabel(\"date\", fontsize=\"14\")\r\n",
        "plt.ylabel(\"close\", fontsize=\"14\")"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'close')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSxXnoOTr0mp"
      },
      "source": [
        "# get number of rows to train the model\r\n",
        "data = eth_data.filter(['close'])\r\n",
        "close_data = data.values\r\n",
        "\r\n",
        "training_data_len = math.ceil(len(close_data) * 0.8)"
      ],
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZCPEWmFHpGE"
      },
      "source": [
        "# Scale data\r\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\r\n",
        "# calculates the min and max and scales it between 0 and 1\r\n",
        "scaled_data = scaler.fit_transform(close_data)"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfOnTTDgJNKV",
        "outputId": "d0cc16f1-e0fc-498c-c925-d60e928642eb"
      },
      "source": [
        "# create training data set\r\n",
        "# trained data set\r\n",
        "train_data = scaled_data[0:training_data_len, : ]\r\n",
        "\r\n",
        "# split data into x_train and y_train\r\n",
        "x_train = []\r\n",
        "y_train = []\r\n",
        "\r\n",
        "for i in range(60, len(train_data)):\r\n",
        "  x_train.append(train_data[i-60:i, 0])\r\n",
        "  y_train.append(train_data[i, 0])\r\n",
        "  if i <= 60:\r\n",
        "    print(x_train)\r\n",
        "    print(y_train)\r\n",
        "    print()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.01353024, 0.        , 0.01472879, 0.00611447, 0.01375734,\n",
            "       0.02416674, 0.02112784, 0.03268596, 0.01938985, 0.00790625,\n",
            "       0.01985941, 0.03556397, 0.03352562, 0.02971903, 0.05655685,\n",
            "       0.08172075, 0.11237581, 0.13655346, 0.13055485, 0.10395402,\n",
            "       0.06696133, 0.06278543, 0.08680443, 0.10387958, 0.13388943,\n",
            "       0.12435109, 0.12797953, 0.14149066, 0.11011384, 0.12336599,\n",
            "       0.1235289 , 0.12120189, 0.10053498, 0.10957046, 0.10128878,\n",
            "       0.09024581, 0.10807734, 0.11856843, 0.11934253, 0.12012612,\n",
            "       0.15479468, 0.164869  , 0.17048041, 0.17942964, 0.1573893 ,\n",
            "       0.14294501, 0.14782203, 0.11714695, 0.13925682, 0.14638267,\n",
            "       0.15607803, 0.19339934, 0.22872332, 0.2275187 , 0.24445097,\n",
            "       0.24055481, 0.23182077, 0.26447762, 0.42635439, 0.45864017])]\n",
            "[0.5416211435515323]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DsjDVNdbLW7K",
        "outputId": "314f5714-cd2e-4841-a934-385834ce6ebc"
      },
      "source": [
        "# convert the x_train and y_train to numpy\r\n",
        "x_train = np.array(x_train)\r\n",
        "y_train = np.array(y_train)\r\n",
        "\r\n",
        "# reshape bc need 3 dimensions\r\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\r\n",
        "x_train.shape"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHVPJpcHMHYs"
      },
      "source": [
        "# LSTM Model\r\n",
        "model = Sequential()\r\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\r\n",
        "model.add(LSTM(50, return_sequences=False))\r\n",
        "model.add(Dense(25))\r\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVr-yVlMQlpz"
      },
      "source": [
        "# optimizer improves upon loss function\r\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\r\n"
      ],
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkhSHYETQ41K",
        "outputId": "8a1edc15-cc06-4104-ee2b-f78fe10431a0"
      },
      "source": [
        "model.fit(x_train, y_train, batch_size=1, epochs=1)"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14/14 [==============================] - 3s 23ms/step - loss: 0.2101\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff286f97780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NQFjB9oROdT"
      },
      "source": [
        "# create test data set\r\n",
        "# create a new array containing scaled values \r\n",
        "test_data = scaled_data[training_data_len - 60:, : ]\r\n",
        "# create the data sets x_test and y_test\r\n",
        "x_test = []\r\n",
        "y_test = close_data[training_data_len:, :]\r\n",
        "\r\n",
        "for i in range(60, len(test_data)):\r\n",
        "  x_test.append(test_data[i-60:i, 0])\r\n"
      ],
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkSJtLmiSIai"
      },
      "source": [
        "# convert the data to a numpy array\r\n",
        "x_test = np.array(x_test)\r\n",
        "# reshape the data\r\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zExqGysKSnEZ",
        "outputId": "2d55b70c-da45-4e43-dadb-1f4cb2eb6864"
      },
      "source": [
        "# Get the predicted price\r\n",
        "predictions = model.predict(x_test)\r\n",
        "predictions = scaler.inverse_transform(predictions)"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 17 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff2871419d8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nF6RTX0NTC6V",
        "outputId": "63cd8f24-7fe7-4725-b94e-f5828168dc90"
      },
      "source": [
        "# get the root mean squared error\r\n",
        "rmse = np.sqrt(np.mean(predictions - y_test)**2)\r\n",
        "rmse"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "286.90048122465555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "TYCY_oA9TqxV",
        "outputId": "17a15565-2d70-4f3b-cc6f-efc66baf961a"
      },
      "source": [
        "# train and validation\r\n",
        "train = data[0:training_data_len]\r\n",
        "valid = data[training_data_len:]\r\n",
        "\r\n",
        "valid[\"predictions\"] = predictions\r\n",
        "# plot\r\n",
        "plt.figure(figsize=(16,8))\r\n",
        "plt.title('Model')\r\n",
        "plt.xlabel('Date', fontsize=14)\r\n",
        "plt.ylabel('Close', fontsize=14)\r\n",
        "plt.plot(train['close'])\r\n",
        "plt.plot(valid[['close', 'predictions']])\r\n",
        "plt.legend(['train', 'validation', 'predictions'], loc='lower right')\r\n",
        "plt.show()"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "ukV3MOlbVJLu",
        "outputId": "27d51499-d28d-4245-c1ab-874eddcb6923"
      },
      "source": [
        "# show valid and predicted price\r\n",
        "valid.tail(20)"
      ],
      "execution_count": 217,
      "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",
              "      <th>predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-19</th>\n",
              "      <td>1411.833939</td>\n",
              "      <td>1526.171875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-20</th>\n",
              "      <td>1332.642513</td>\n",
              "      <td>1551.348145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-21</th>\n",
              "      <td>1117.483678</td>\n",
              "      <td>1575.788208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-22</th>\n",
              "      <td>1250.715166</td>\n",
              "      <td>1594.931396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-23</th>\n",
              "      <td>1235.091640</td>\n",
              "      <td>1612.669312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-24</th>\n",
              "      <td>1368.895605</td>\n",
              "      <td>1628.257080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-25</th>\n",
              "      <td>1324.858487</td>\n",
              "      <td>1644.711182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-26</th>\n",
              "      <td>1350.736956</td>\n",
              "      <td>1660.386108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-27</th>\n",
              "      <td>1247.656556</td>\n",
              "      <td>1675.862061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-28</th>\n",
              "      <td>1354.960394</td>\n",
              "      <td>1688.716431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>1407.681790</td>\n",
              "      <td>1701.690674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>1385.498675</td>\n",
              "      <td>1715.286255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-31</th>\n",
              "      <td>1315.243745</td>\n",
              "      <td>1728.608643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-01</th>\n",
              "      <td>1357.047990</td>\n",
              "      <td>1740.126587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-02</th>\n",
              "      <td>1540.651288</td>\n",
              "      <td>1750.996582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>1644.685967</td>\n",
              "      <td>1764.671021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>1628.468008</td>\n",
              "      <td>1781.898315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>1704.832300</td>\n",
              "      <td>1801.073486</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  close  predictions\n",
              "date                                \n",
              "2021-01-19  1411.833939  1526.171875\n",
              "2021-01-20  1332.642513  1551.348145\n",
              "2021-01-21  1117.483678  1575.788208\n",
              "2021-01-22  1250.715166  1594.931396\n",
              "2021-01-23  1235.091640  1612.669312\n",
              "2021-01-24  1368.895605  1628.257080\n",
              "2021-01-25  1324.858487  1644.711182\n",
              "2021-01-26  1350.736956  1660.386108\n",
              "2021-01-27  1247.656556  1675.862061\n",
              "2021-01-28  1354.960394  1688.716431\n",
              "2021-01-29  1407.681790  1701.690674\n",
              "2021-01-30  1385.498675  1715.286255\n",
              "2021-01-31  1315.243745  1728.608643\n",
              "2021-02-01  1357.047990  1740.126587\n",
              "2021-02-02  1540.651288  1750.996582\n",
              "2021-02-03  1644.685967  1764.671021\n",
              "2021-02-04  1628.468008  1781.898315\n",
              "2021-02-05  1704.832300  1801.073486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    }
  ]
}