trx_Coin_Predict.ipynb

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  "metadata": {
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/user960507/DeepMachineLearning/blob/master/trx_Coin_Predict.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vULPNVrwumom"
      },
      "source": [
        "#INSTALL THESE PACKAGES IF NEEDED"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Dliz1_buaOT",
        "outputId": "50f3da48-e847-411a-f70f-5af87b4fdbc4"
      },
      "source": [
        "!pip install yfinance\n",
        "!pip install yahoofinancials"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.18)\n",
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            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.22.4)\n",
            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.27.1)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.9.2)\n",
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            "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2022.7.1)\n",
            "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.3.7)\n",
            "Requirement already satisfied: cryptography>=3.3.2 in /usr/local/lib/python3.10/dist-packages (from yfinance) (40.0.2)\n",
            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.11.2)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yfinance) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yfinance) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yfinance) (3.4)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.21)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yahoofinancials\n",
            "  Downloading yahoofinancials-1.14.tar.gz (41 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.5/41.5 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.10/dist-packages (from yahoofinancials) (2.27.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yahoofinancials) (1.26.15)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yahoofinancials) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.26->yahoofinancials) (3.4)\n",
            "Building wheels for collected packages: yahoofinancials\n",
            "  Building wheel for yahoofinancials (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yahoofinancials: filename=yahoofinancials-1.14-py3-none-any.whl size=28627 sha256=0496c2f1188bab707b72a7c9d799d0823fc2d30e0bcd936dafd75c20d5069a3b\n",
            "  Stored in directory: /root/.cache/pip/wheels/32/a3/b6/b5b33187f2d42f095fecc236b957b46173fa09e78a106e309f\n",
            "Successfully built yahoofinancials\n",
            "Installing collected packages: yahoofinancials\n",
            "Successfully installed yahoofinancials-1.14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BsPA3hGurwL"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import yfinance as yf\n",
        "from yahoofinancials import YahooFinancials"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrxZ48fDvOhp"
      },
      "source": [
        "#CREATING THE DATAFRAME TO STORE DATA\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "iHEL_aFmvLV7",
        "outputId": "97626281-9314-48ec-95c0-c09d28095cbb"
      },
      "source": [
        "df = yf.download('TRX-USD', \n",
        "                      start='2021-01-01', \n",
        "                      end='2023-05-20', \n",
        "                      progress=False)\n",
        "df.head(2)\n",
        "df.tail(2)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume\n",
              "Date                                                                    \n",
              "2023-05-18  0.071058  0.071341  0.069950  0.070276   0.070276  188382376\n",
              "2023-05-19  0.070273  0.070778  0.070142  0.070675   0.070675  138133276"
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              "      <th>2023-05-18</th>\n",
              "      <td>0.071058</td>\n",
              "      <td>0.071341</td>\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "3-bAAVw7vfT5"
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      "source": [
        "series = df['Close'].values.reshape(-1, 1)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(series[0:len(series) // 2])\n",
        "series = scaler.transform(series).flatten()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Plot it"
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
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        "outputId": "7ee7e42a-a201-4317-f34d-127fe19c6c14"
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      "source": [
        "plt.plot(series)\n",
        "plt.show()"
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      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yR4omfevlgA",
        "outputId": "1dd608b4-b690-408f-bca3-7539faccbea4"
      },
      "source": [
        "T = 10\n",
        "D = 1\n",
        "X = []\n",
        "Y = []\n",
        "for t in range(len(series) - T):\n",
        "  x = series[t:t+T]\n",
        "  X.append(x)\n",
        "  y = series[t+T]\n",
        "  Y.append(y)\n",
        "\n",
        "X = np.array(X).reshape(-1, T)\n",
        "Y = np.array(Y)\n",
        "N = len(X)\n",
        "print(\"X.shape\", X.shape, \"Y.shape\", Y.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X.shape (859, 10) Y.shape (859,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0Xn-uYivn2y"
      },
      "source": [
        "class BaselineModel:\n",
        "  def predict(self, X):\n",
        "    return X[:,-1]"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4puG0kkYvsId"
      },
      "source": [
        "Xtrain, Ytrain = X[:-N//2], Y[:-N//2]\n",
        "Xtest, Ytest = X[-N//2:], Y[-N//2:]"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwtUlJiOvtc9"
      },
      "source": [
        "model = BaselineModel()\n",
        "Ptrain = model.predict(Xtrain)\n",
        "Ptest = model.predict(Xtest)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3WmAKnavu6n"
      },
      "source": [
        "Ytrain2 = scaler.inverse_transform(Ytrain.reshape(-1, 1)).flatten()\n",
        "Ytest2 = scaler.inverse_transform(Ytest.reshape(-1, 1)).flatten()\n",
        "Ptrain2 = scaler.inverse_transform(Ptrain.reshape(-1, 1)).flatten()\n",
        "Ptest2 = scaler.inverse_transform(Ptest.reshape(-1, 1)).flatten()"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eerVVANPvwd1",
        "outputId": "e6291557-3df1-43f7-c98b-4e970cbe4427"
      },
      "source": [
        "print(\"baseline train mae:\", mae(Ytrain2, Ptrain2))\n",
        "print(\"baseline test mae:\", mae(Ytest2, Ptest2))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "baseline train mae: 0.003366797146472064\n",
            "baseline test mae: 0.0013765768949375596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "7e_eG0A7v184",
        "outputId": "34a8e658-ca98-42eb-d75c-ee6553947570"
      },
      "source": [
        "forecast = []\n",
        "count = 0\n",
        "\n",
        "\n",
        "while len(forecast) < len(Ytest):\n",
        "  input_ = Xtest[count]\n",
        "  f = model.predict(input_.reshape(1, T))[0]\n",
        "  forecast.append(f)\n",
        "  count = count + 1\n",
        "\n",
        "  # make a new input with the latest forecast\n",
        "  input_ = np.roll(input_, -1)\n",
        "  input_[-1] = f\n",
        "plt.plot(Ytest, label='target')\n",
        "plt.plot(forecast, label='prediction')\n",
        "plt.legend()\n",
        "plt.title(\"forecast\")\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByvmjRsYv4Ju"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}