stock_dashboard.ipynb

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        "<a href=\"https://colab.research.google.com/github/snehaaprabhu/GenSec/blob/main/stock_dashboard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "%%capture\n",
        "!pip install torch==\"2.3.0\"\n",
        "!pip install pandas==\"2.2.2\"\n",
        "!pip install matplotlib==\"3.8.4\"\n",
        "!pip install scikit-learn==\"1.4.2\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhmFo8WGd4j2"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/g5XM0-gTrOquyZcBxcJAfw/StockData.csv\")\n",
        "df.drop(['Unnamed: 0'], axis=1, inplace = True)"
      ],
      "metadata": {
        "id": "ZeoTJBqYfNG9"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "         Date    Open    High     Low    Close\n",
              "0  2018-03-27  173.68  175.15  166.92  168.340\n",
              "1  2018-03-26  168.07  173.10  166.44  172.770\n",
              "2  2018-03-23  168.39  169.92  164.94  164.940\n",
              "3  2018-03-22  170.00  172.68  168.60  168.845\n",
              "4  2018-03-21  175.04  175.09  171.26  171.270"
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
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              "      <th>Close</th>\n",
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              "      <th>0</th>\n",
              "      <td>2018-03-27</td>\n",
              "      <td>173.68</td>\n",
              "      <td>175.15</td>\n",
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              "      <td>168.340</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-03-26</td>\n",
              "      <td>168.07</td>\n",
              "      <td>173.10</td>\n",
              "      <td>166.44</td>\n",
              "      <td>172.770</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-03-23</td>\n",
              "      <td>168.39</td>\n",
              "      <td>169.92</td>\n",
              "      <td>164.94</td>\n",
              "      <td>164.940</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-03-22</td>\n",
              "      <td>170.00</td>\n",
              "      <td>172.68</td>\n",
              "      <td>168.60</td>\n",
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              "      <th>4</th>\n",
              "      <td>2018-03-21</td>\n",
              "      <td>175.04</td>\n",
              "      <td>175.09</td>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9400,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 9400,\n        \"samples\": [\n          \"2003-05-13\",\n          \"1999-01-04\",\n          \"2000-03-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135.16930707269765,\n        \"min\": 11.12,\n        \"max\": 702.41,\n        \"num_unique_values\": 4551,\n        \"samples\": [\n          107.88,\n          136.57,\n          171.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 136.29400536238688,\n        \"min\": 11.12,\n        \"max\": 705.07,\n        \"num_unique_values\": 4471,\n        \"samples\": [\n          38.87,\n          87.0,\n          137.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133.82952818585267,\n        \"min\": 11.0,\n        \"max\": 699.57,\n        \"num_unique_values\": 4474,\n        \"samples\": [\n          123.71,\n          81.6,\n          136.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135.08734966638877,\n        \"min\": 11.0,\n        \"max\": 702.1,\n        \"num_unique_values\": 4643,\n        \"samples\": [\n          468.83,\n          292.32,\n          59.723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 4
        }
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      "cell_type": "code",
      "source": [
        "stock_data = df\n",
        "# Convert the date column into a Datetime object:\n",
        "stock_data['Date'] = pd.to_datetime(df.Date)\n",
        "print(\"Information about the dataset\", end = \"\\n\")\n",
        "print(stock_data.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfmgzR8VfczS",
        "outputId": "baa4638d-cd9a-4a6e-8a30-16eae6fb2fb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Information about the dataset\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9400 entries, 0 to 9399\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   Date    9400 non-null   datetime64[ns]\n",
            " 1   Open    9400 non-null   float64       \n",
            " 2   High    9400 non-null   float64       \n",
            " 3   Low     9400 non-null   float64       \n",
            " 4   Close   9400 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(4)\n",
            "memory usage: 367.3 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First five elements in the dataset\", end = \"\\n\")\n",
        "print(stock_data.head(5))\n",
        "print(\"Last five elements in the dataset\", end = \"\\n\")\n",
        "print(stock_data.tail(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFAhSEuufhAa",
        "outputId": "2898253d-02e6-4140-ec0f-9a3eacf0989c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First five elements in the dataset\n",
            "        Date    Open    High     Low    Close\n",
            "0 2018-03-27  173.68  175.15  166.92  168.340\n",
            "1 2018-03-26  168.07  173.10  166.44  172.770\n",
            "2 2018-03-23  168.39  169.92  164.94  164.940\n",
            "3 2018-03-22  170.00  172.68  168.60  168.845\n",
            "4 2018-03-21  175.04  175.09  171.26  171.270\n",
            "Last five elements in the dataset\n",
            "           Date   Open   High    Low  Close\n",
            "9395 1980-12-18  26.63  26.75  26.63  26.63\n",
            "9396 1980-12-17  25.87  26.00  25.87  25.87\n",
            "9397 1980-12-16  25.37  25.37  25.25  25.25\n",
            "9398 1980-12-15  27.38  27.38  27.25  27.25\n",
            "9399 1980-12-12  28.75  28.87  28.75  28.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data = stock_data.sort_values(by=\"Date\")\n",
        "print(stock_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ii7KYIsgPNR",
        "outputId": "637dfd48-0dac-4dad-cf72-d996ca07256d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Date   Open   High    Low  Close\n",
            "9399 1980-12-12  28.75  28.87  28.75  28.75\n",
            "9398 1980-12-15  27.38  27.38  27.25  27.25\n",
            "9397 1980-12-16  25.37  25.37  25.25  25.25\n",
            "9396 1980-12-17  25.87  26.00  25.87  25.87\n",
            "9395 1980-12-18  26.63  26.75  26.63  26.63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "price = stock_data[['High','Low','Open','Close']]\n",
        "print(price[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpmPxYtQgVfK",
        "outputId": "b13655ce-cf2f-466e-fe00-1d235950a354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       High    Low   Open  Close\n",
            "9399  28.87  28.75  28.75  28.75\n",
            "9398  27.38  27.25  27.38  27.25\n",
            "9397  25.37  25.25  25.37  25.25\n",
            "9396  26.00  25.87  25.87  25.87\n",
            "9395  26.75  26.63  26.63  26.63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "price = scaler.fit_transform(price.values)\n",
        "print(price[:5])"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Cc96vC45gY94",
        "outputId": "ed802566-a163-48d3-f7f9-991ca40a8277"
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.94884358 -0.94844388 -0.94899391 -0.94863261]\n",
            " [-0.95313783 -0.95280073 -0.95295751 -0.95297352]\n",
            " [-0.95893076 -0.95860987 -0.95877273 -0.95876139]\n",
            " [-0.95711507 -0.95680904 -0.95732616 -0.95696715]\n",
            " [-0.95495353 -0.95460157 -0.95512737 -0.95476776]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_window = 7\n",
        "import numpy as np\n",
        "def create_in_out_sequences(price, tw):\n",
        "    inout_seq = []\n",
        "    L = len(price)\n",
        "    print('Length = ', L)\n",
        "\n",
        "    for i in range(L-tw):\n",
        "        data_seq = price[i:i+tw]\n",
        "        data_label = price[i+tw:i+tw+1][0][3]\n",
        "        inout_seq.append((data_seq ,data_label))\n",
        "\n",
        "    data = inout_seq;\n",
        "    print('size of data : ', len(data))\n",
        "\n",
        "    test_set_size = 20\n",
        "    train_set_size = len(data) - (test_set_size);\n",
        "    print('size of test : ', test_set_size)\n",
        "    print('size of train : ', train_set_size)\n",
        "\n",
        "    train = data[:train_set_size]\n",
        "    test = data[train_set_size:]\n",
        "    train = train[:-(tw-1)]\n",
        "    return train, test\n",
        "\n",
        "train,test = create_in_out_sequences(price, train_window)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQBDltYsiAJj",
        "outputId": "9583d5d2-37e0-4476-d563-2058f032f37b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length =  9400\n",
            "size of data :  9393\n",
            "size of test :  20\n",
            "size of train :  9373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test[0])\n",
        "print(train[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8rDgILziD-b",
        "outputId": "46013bc1-eb91-49ae-a477-1d8995392c48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([[-0.52820808, -0.53303223, -0.53350981, -0.53283172],\n",
            "       [-0.52982203, -0.53404883, -0.53440669, -0.5345102 ],\n",
            "       [-0.53022552, -0.5352397 , -0.53215004, -0.53676747],\n",
            "       [-0.53071547, -0.5332065 , -0.53512997, -0.53233975],\n",
            "       [-0.52581598, -0.52789114, -0.5297198 , -0.52378816],\n",
            "       [-0.51503711, -0.52013593, -0.52196618, -0.51390537],\n",
            "       [-0.51189567, -0.51447202, -0.51401004, -0.51558385]]), -0.5163652148748373)\n",
            "(array([[-0.56112112, -0.56992463, -0.56034371, -0.57013457],\n",
            "       [-0.56803804, -0.58165473, -0.56843004, -0.58234698],\n",
            "       [-0.57700122, -0.59556763, -0.57774595, -0.58046592],\n",
            "       [-0.55970891, -0.5744514 , -0.57360876, -0.56096079],\n",
            "       [-0.55723035, -0.56242648, -0.56362742, -0.55624367],\n",
            "       [-0.54918942, -0.55885386, -0.56045943, -0.54747504],\n",
            "       [-0.53319403, -0.54107789, -0.54094519, -0.53121111]]), -0.5328317175517292)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "    def __init__(self, input_size=4, hidden_layer_size=100, output_size=1):\n",
        "        super().__init__()\n",
        "        self.hidden_layer_size = hidden_layer_size\n",
        "\n",
        "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
        "\n",
        "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
        "\n",
        "    def forward(self, input_seq):\n",
        "        hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),\n",
        "                       torch.zeros(1,1,self.hidden_layer_size),\n",
        "                      )\n",
        "        lstm_out, hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), hidden_cell)\n",
        "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
        "        return predictions[-1]"
      ],
      "metadata": {
        "id": "DycdhvNGiHf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTM()\n",
        "loss_function = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "tQP58tvuiMpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.notebook import tqdm\n",
        "\n",
        "epochs = 5\n",
        "for i in tqdm(range(epochs)):\n",
        "    epoch_loss = 0\n",
        "    for seq, labels in tqdm(train):\n",
        "        seq = torch.from_numpy(np.array(seq)).type(torch.FloatTensor)\n",
        "        labels = torch.from_numpy(np.array(labels)).type(torch.FloatTensor)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        y_pred = model(seq)\n",
        "\n",
        "        labels = labels.view(1)\n",
        "\n",
        "        single_loss = loss_function(y_pred, labels)\n",
        "        single_loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        epoch_loss += single_loss.item()\n",
        "\n",
        "    average_loss = epoch_loss / len(train)\n",
        "    print(f'epoch: {i:3} loss: {average_loss:.10f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
          "referenced_widgets": [
            "03bbb1f0541a444a9ac67f6ea1a473eb",
            "55e491711d504777aa72d98c1ed43a15",
            "50466efc9e4c4bae896c6f76ed7e98cb",
            "45d650deba834cf38cfaf258813065bb",
            "6a58381905bc4ec59ddffaafb8b97554",
            "93bc5aa894e646e1a17a8cba16873bc0",
            "588cd0ebe55b42fbbe1b695420d3a87d",
            "7ac67811d2a34f7992cd3b02e5f20326",
            "10294fd4453e4a28a22eb90c430ae6c6",
            "6367961a71134ccaa2c9a1f043042df8",
            "f6dd939ba5864ba2ba3b4e309f55f750",
            "2cf1c5566b2c41d0918cebe156310918",
            "c3453b801c9140c4ae8031f47cba9f13",
            "4333603957b046cea0a8dc9f137db0a0",
            "06ef69d8ebb345f4867413d9743416de",
            "d4ab54ba987146a185bb0fa19976dad3",
            "604a80d6cbfe43f294c3a802ee349773",
            "b02522659e3e4f76a070eb4f64a4b55a",
            "14061cd19cfa4916926f3a89c5612476",
            "4d03a7318b2b45dcb1a4565dd177ef9e",
            "237ad3f8852549348c153ebc9c85fabf",
            "bc2a39a71c624a16a06a16b2c45136bb",
            "e930eb56b4ef41bea8b5e3638fd3bc03",
            "4230d5c69f0644d496dafbfc79145564",
            "b472dd3c64064f5fb083ffb597959a39",
            "0a0725d4575d46c890513fc9750594fe",
            "233ee1292b62446f956178fb7e7fc656",
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            "1c85082268924a83967f6acd2786629c",
            "dccc611a9719434695634021d1d43567",
            "cb8dd527ef404eb8bb1571501d8f9371",
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            "9ec37ec8d9fc4e85974d0afd6a5cd91a",
            "03f23d8ecb76448c9b2ae544afdd5f42",
            "8ea73b5ee4324827b4f922db8ed1ee74",
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            "918af226f4564dcaac1303040f34b4ee",
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            "0f3ad98c1afa4188aef4c58344076820",
            "053f9e43d45445f0b666ca6f030c42c0",
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            "767e2c0ed79a4741ba7aed69024f6a87",
            "56d55fdfcc544b218c334295f06d4422",
            "f9719c9fc6c84ce3ad1ef4d7484eae8a",
            "d902f54f502a46d492e36e7027f01403",
            "bb271584b22f4e46b77a1a8d99a4c4ee",
            "2ebb4e12a4ac4845adfec19b6b3dba59",
            "42d72f57a25047fc8b2540dccaa70728",
            "a731fb598cc4462d9f6a94cfbc2db783",
            "e540de2509db4d108dd303f21915ad85",
            "c0dc42d6ecaf4b2385c7ddfd131c17ac",
            "b7801c5c443a426ca6558e8fa0780831",
            "bce344cbf4194eee84cd3912785059ae",
            "5d55d55fac1945fb982a7f24956d8814",
            "7431c366667b416298c53ad7c468bf96",
            "43f1b324b5be4f77a51f13f31044bffa",
            "0d618030764648118d7c78abd524e9f4",
            "e7d2b13573874249a4269d4266380164",
            "e78cdfa83e1e409a8a5dd1b30da7bcc1",
            "075b2c54e39f47bd8fe343eb5c7ca4e7",
            "08be8e0d9fd24282a24c1805d06d8375",
            "30a82ea44ff74a88829328dd5279c5be",
            "777d44b65f8645d2bccd40ece6b2609c",
            "48ab9334995d4a9ab54b76280353e487"
          ]
        },
        "id": "0K67L7M_iQPv",
        "outputId": "3a601d8f-0e32-49f0-feb9-0e5196188232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03bbb1f0541a444a9ac67f6ea1a473eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9367 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cf1c5566b2c41d0918cebe156310918"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch:   0 loss: 0.0018638977\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9367 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e930eb56b4ef41bea8b5e3638fd3bc03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch:   1 loss: 0.0010040112\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9367 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03f23d8ecb76448c9b2ae544afdd5f42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch:   2 loss: 0.0009904117\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9367 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56d55fdfcc544b218c334295f06d4422"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch:   3 loss: 0.0008824414\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9367 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d55d55fac1945fb982a7f24956d8814"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch:   4 loss: 0.0007968503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "actual = []\n",
        "pred = []\n",
        "\n",
        "for seq, labels in test:\n",
        "    seq = torch.from_numpy(np.array(seq)).type(torch.FloatTensor)\n",
        "    labels = torch.from_numpy(np.array(labels)).type(torch.FloatTensor)\n",
        "    actual.append(labels.item())\n",
        "    with torch.no_grad():\n",
        "        pred.append(model(seq).item())\n",
        "\n",
        "actual = np.array(actual)\n",
        "pred = np.array(pred)"
      ],
      "metadata": {
        "id": "Ed4qQ-5AiVXc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = torch.from_numpy(np.array(pred)).type(torch.Tensor)\n",
        "actual = torch.from_numpy(np.array(actual)).type(torch.Tensor)"
      ],
      "metadata": {
        "id": "uc6kWuiKi-Up"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(pred)\n",
        "print(actual)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCVXAFTpjBXl",
        "outputId": "3e3449c3-4166-479d-cb16-7317b04bc5c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([-0.5417, -0.5416, -0.5473, -0.5477, -0.5448, -0.5444, -0.5486, -0.5436,\n",
            "        -0.5410, -0.5376, -0.5395, -0.5423, -0.5415, -0.5436, -0.5475, -0.5458,\n",
            "        -0.5521, -0.5544, -0.5597, -0.5499])\n",
            "tensor([-0.5164, -0.5254, -0.5219, -0.5201, -0.5206, -0.5253, -0.5198, -0.5110,\n",
            "        -0.5059, -0.5110, -0.5154, -0.5148, -0.5167, -0.5245, -0.5247, -0.5362,\n",
            "        -0.5432, -0.5545, -0.5318, -0.5447])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "pred_new = scaler.inverse_transform(np.c_[np.zeros(20),np.zeros(20),np.zeros(20),np.array(pred)])\n",
        "print(pred_new[:,3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLAz22L1jK9G",
        "outputId": "e14e51dc-1bd4-44c2-e089-55580efa0212"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[169.36375061 169.39441862 167.44618597 167.29177487 168.2898345\n",
            " 168.44447215 166.98229359 168.70135027 169.59593365 170.78669306\n",
            " 170.10993704 169.15119591 169.44749551 168.72099922 167.34427506\n",
            " 167.93349644 165.76164883 164.96852324 163.15789503 166.51714483]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "actual_new = scaler.inverse_transform(np.c_[np.zeros(20),np.zeros(20),np.zeros(20),np.array(actual)])\n",
        "print(actual_new[:,3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mz_gxHuojOBU",
        "outputId": "8a1ebe94-9098-4436-f833-1a24dd405cb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[178.11999474 174.99999256 176.21000958 176.81999212 176.67000924\n",
            " 175.03000149 176.94000725 179.97999249 181.71999569 179.97000324\n",
            " 178.44000078 178.65000152 178.0199993  175.2999995  175.24000223\n",
            " 171.27000783 168.84501005 164.93999725 172.77000135 168.34000729]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "\n",
        "mae = mean_absolute_error(actual_new, pred_new)\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(actual_new, pred_new))\n",
        "print(\"Root Mean Squared Error (RMSE):\", rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8H6usNDEjQ3m",
        "outputId": "08f0ce80-961f-488e-ad77-f437f97b0888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE): 1.8615344881638891\n",
            "Root Mean Squared Error (RMSE): 4.034319519247493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(actual_new[:,3], 'r-', label='Actual')\n",
        "ax.plot(pred_new[:,3], 'c-', label='Predicted')\n",
        "\n",
        "ax.set_ylabel('Stock Value (dollars)')\n",
        "\n",
        "ax.set_ylim(min(min(actual_new[:,3]), min(pred_new[:,3])) - 5, max(max(actual_new[:,3]), max(pred_new[:,3])) + 5)\n",
        "plt.xticks([])\n",
        "\n",
        "ax.legend(loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "X0LhUZljjUa0",
        "outputId": "7f944a23-7ef5-4d87-d673-4c6a3ab83fa4"
      },
      "execution_count": null,
      "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",
      "source": [
        "difference = actual_new - pred_new\n",
        "fig = plt.figure()\n",
        "diffGraph = fig.add_axes([0,0,1,1])\n",
        "diffGraph.plot(difference[:, 3], 'b')\n",
        "diffGraph.set_ylabel('Difference between Actual and Predicted Stock Value (dollars)')\n",
        "plt.xticks([])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "dyyu-Me6jYoP",
        "outputId": "1c6f4273-cd9a-4902-e774-da99b150908d"
      },
      "execution_count": null,
      "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",
      "source": [
        "pip install dash\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYfLO5TDjcMs",
        "outputId": "50290993-3894-4a62-ead4-1e0f1530fc75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting dash\n",
            "  Downloading dash-2.18.1-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from dash) (2.2.5)\n",
            "Requirement already satisfied: Werkzeug<3.1 in /usr/local/lib/python3.10/dist-packages (from dash) (3.0.4)\n",
            "Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from dash) (5.24.1)\n",
            "Collecting dash-html-components==2.0.0 (from dash)\n",
            "  Downloading dash_html_components-2.0.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting dash-core-components==2.0.0 (from dash)\n",
            "  Downloading dash_core_components-2.0.0-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting dash-table==5.0.0 (from dash)\n",
            "  Downloading dash_table-5.0.0-py3-none-any.whl.metadata (2.4 kB)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from dash) (8.5.0)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from dash) (4.12.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from dash) (2.32.3)\n",
            "Collecting retrying (from dash)\n",
            "  Downloading retrying-1.3.4-py3-none-any.whl.metadata (6.9 kB)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (from dash) (1.6.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from dash) (71.0.4)\n",
            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash) (3.1.4)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash) (2.2.0)\n",
            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from Flask<3.1,>=1.0.4->dash) (8.1.7)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=5.0.0->dash) (9.0.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly>=5.0.0->dash) (24.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from Werkzeug<3.1->dash) (3.0.1)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->dash) (3.20.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->dash) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->dash) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->dash) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->dash) (2024.8.30)\n",
            "Requirement already satisfied: six>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from retrying->dash) (1.16.0)\n",
            "Downloading dash-2.18.1-py3-none-any.whl (7.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m52.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)\n",
            "Downloading dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)\n",
            "Downloading dash_table-5.0.0-py3-none-any.whl (3.9 kB)\n",
            "Downloading retrying-1.3.4-py3-none-any.whl (11 kB)\n",
            "Installing collected packages: dash-table, dash-html-components, dash-core-components, retrying, dash\n",
            "Successfully installed dash-2.18.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 retrying-1.3.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import dash\n",
        "from dash import dcc, html\n",
        "import plotly.graph_objs as go\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "\n",
        "# (Your trained model import here)\n",
        "# from your_project import model, scaler\n"
      ],
      "metadata": {
        "id": "dGJDcFL5jvU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have already defined your model, loss function, and optimizer\n",
        "\n",
        "epochs = 5\n",
        "for i in range(epochs):\n",
        "    epoch_loss = 0\n",
        "    for seq, labels in train:\n",
        "        seq = torch.from_numpy(np.array(seq)).type(torch.FloatTensor)\n",
        "        labels = torch.from_numpy(np.array(labels)).type(torch.FloatTensor)\n",
        "\n",
        "        optimizer.zero_grad()  # Cleartorch.save(model, 'model.pth')  # Save the trained model to a file\n",
        " previous gradients\n",
        "\n",
        "        y_pred = model(seq)  # Forward pass\n",
        "\n",
        "        labels = labels.view(1)  # Reshape labels to match predictions\n",
        "\n",
        "        single_loss = loss_function(y_pred, labels)  # Calculate loss\n",
        "        single_loss.backward()  # Backpropagation\n",
        "        optimizer.step()  # Update weights\n",
        "\n",
        "        epoch_loss += single_loss.item()\n",
        "\n",
        "    average_loss = epoch_loss / len(train)  # Average loss for the epoch\n",
        "    print(f'epoch: {i+1:3} loss: {average_loss:.10f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "stqkQjpIj1j8",
        "outputId": "ea027a08-3e98-4b89-d409-8ad0e435bf25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndentationError",
          "evalue": "unindent does not match any outer indentation level (<tokenize>, line 11)",
          "traceback": [
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      "source": [
        "torch.save(model, 'model.pth')  # Save the trained model to a file\n"
      ],
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        "id": "K_D9Yn9qj7yg"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "model = torch.load('model.pth')  # Load the trained model\n"
      ],
      "metadata": {
        "id": "wIA9IMOrmSmA"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Load the dataset (or replace it with real-time data source)\n",
        "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/g5XM0-gTrOquyZcBxcJAfw/StockData.csv\")\n",
        "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
        "\n",
        "# Load your trained model (ensure the model is saved in a .pt or .pkl file)\n",
        "model = torch.load('model.pth')  # Use your saved model here\n",
        "\n",
        "# Normalize data for the LSTM model\n",
        "price = df[['High', 'Low', 'Open', 'Close']]\n",
        "price_scaled = scaler.fit_transform(price.values)\n"
      ],
      "metadata": {
        "id": "j66PVHjUmZJh"
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      "outputs": []
    },
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      "source": [
        "app = dash.Dash(__name__)\n",
        "\n",
        "app.layout = html.Div(children=[\n",
        "    html.H1(children='Stock Price Prediction Dashboard'),\n",
        "\n",
        "    dcc.Graph(\n",
        "        id='prediction-graph',\n",
        "        figure={}\n",
        "    ),\n",
        "\n",
        "    html.Button('Predict Next Value', id='predict-button', n_clicks=0)\n",
        "])\n"
      ],
      "metadata": {
        "id": "lVkz_57cmj-g"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "from dash.dependencies import Input, Output\n",
        "\n",
        "@app.callback(\n",
        "    Output('prediction-graph', 'figure'),\n",
        "    [Input('predict-button', 'n_clicks')]\n",
        ")\n",
        "def update_graph(n_clicks):\n",
        "    # Make prediction for the next value using the model\n",
        "    model.eval()\n",
        "\n",
        "    actual = df['Close'].values[-20:]  # Last 20 days actual data for comparison\n",
        "    pred = []  # Store predictions\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for seq in price_scaled[-20:]:\n",
        "            seq = torch.tensor(seq, dtype=torch.float32).unsqueeze(0)\n",
        "            prediction = model(seq)\n",
        "            pred.append(prediction.item())\n",
        "\n",
        "    pred_unscaled = scaler.inverse_transform(np.array(pred).reshape(-1, 1))  # Inverse scaling\n",
        "\n",
        "    # Create the figure for the graph\n",
        "    fig = go.Figure()\n",
        "\n",
        "    fig.add_trace(go.Scatter(x=np.arange(len(actual)), y=actual, mode='lines', name='Actual'))\n",
        "    fig.add_trace(go.Scatter(x=np.arange(len(pred_unscaled)), y=pred_unscaled.flatten(), mode='lines', name='Predicted'))\n",
        "\n",
        "    fig.update_layout(title='Stock Price Prediction', xaxis_title='Days', yaxis_title='Price')\n",
        "    return fig\n"
      ],
      "metadata": {
        "id": "TtvC2sf3m3YB"
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        "    app.run_server(debug=True)\n"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 671
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              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
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              "    iframe.style.border = 0;\n",
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