Transformer_Close_prediction.ipynb

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      "source": [
        "\n",
        "# Read Csv\n",
        "import pandas as pd\n",
        "data = pd.read_csv('drive/MyDrive/Nifty50(15-24).csv')"
      ]
    },
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              "         Date     Open     High      Low    Close  Shares Traded  \\\n",
              "0  2015-11-09  7788.25  7937.75  7771.70  7915.20    218422388.0   \n",
              "1  2015-11-10  7877.60  7885.10  7772.85  7783.35    170267413.0   \n",
              "2  2015-11-11  7838.80  7847.95  7819.10  7825.00     22380435.0   \n",
              "3  2015-11-13  7762.45  7775.10  7730.90  7762.25    165876819.0   \n",
              "4  2015-11-16  7732.95  7838.85  7714.15  7806.60    154134885.0   \n",
              "\n",
              "   Turnover (₹ Cr)  \n",
              "0          9376.17  \n",
              "1          7153.47  \n",
              "2          1123.44  \n",
              "3          7731.55  \n",
              "4          6871.15  "
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              "      <td>2015-11-10</td>\n",
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              "      <td>2015-11-11</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eDi0qwbg4Wj",
        "outputId": "11fccbfc-7c11-4701-be16-6f51aa49207d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All Columns are Filled with Some values\n"
          ]
        }
      ],
      "source": [
        "## Suggestion :- Can make a function for each step to use it on the go for multiple csv's\n",
        "\n",
        "# Remove WhiteSpaces in Columns\n",
        "data.columns = data.columns.str.strip()\n",
        "\n",
        "# Check Null Values in Every Column\n",
        "if data.isna().sum().all()==0:\n",
        "    print('All Columns are Filled with Some values')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "Oo08Z6mqhAmQ",
        "outputId": "0181cca3-4d06-4b8b-d927-e2ed27f9eab4"
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        {
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          "name": "stdout",
          "text": [
            "         Date     Open     High      Low    Close  Shares Traded  \\\n",
            "0  2015-11-09  7788.25  7937.75  7771.70  7915.20    218422388.0   \n",
            "1  2015-11-10  7877.60  7885.10  7772.85  7783.35    170267413.0   \n",
            "2  2015-11-11  7838.80  7847.95  7819.10  7825.00     22380435.0   \n",
            "3  2015-11-13  7762.45  7775.10  7730.90  7762.25    165876819.0   \n",
            "4  2015-11-16  7732.95  7838.85  7714.15  7806.60    154134885.0   \n",
            "5  2015-11-17  7848.75  7860.45  7793.00  7837.55    149451211.0   \n",
            "6  2015-11-18  7823.15  7843.40  7725.05  7731.80    148037721.0   \n",
            "7  2015-11-19  7788.50  7854.90  7765.45  7842.75    136702518.0   \n",
            "8  2015-11-20  7841.90  7906.95  7817.80  7856.55    156610433.0   \n",
            "9  2015-11-23  7869.50  7877.50  7825.20  7849.25    130871603.0   \n",
            "\n",
            "   Turnover (₹ Cr)    SMA_5     SMA_20        EMA_5  ...  Avg_Volume_5  \\\n",
            "0          9376.17  7818.48  7840.1525  7915.200000  ...   146216388.0   \n",
            "1          7153.47  7818.48  7840.1525  7871.250000  ...   146216388.0   \n",
            "2          1123.44  7818.48  7840.1525  7855.833333  ...   146216388.0   \n",
            "3          7731.55  7818.48  7840.1525  7824.638889  ...   146216388.0   \n",
            "4          6871.15  7818.48  7840.1525  7818.625926  ...   146216388.0   \n",
            "5          6367.14  7802.95  7840.1525  7824.933951  ...   132422152.6   \n",
            "6          6112.32  7792.64  7840.1525  7793.889300  ...   127976214.2   \n",
            "7          7410.15  7796.19  7840.1525  7810.176200  ...   150840630.8   \n",
            "8          7298.01  7815.05  7840.1525  7825.634134  ...   148987353.6   \n",
            "9          6099.09  7823.58  7840.1525  7833.506089  ...   144334697.2   \n",
            "\n",
            "   Volume_Ratio  Rolling_Std_5  Bollinger_High  Bollinger_Low  Price_Change  \\\n",
            "0      1.054156      59.021888     8019.483632    7660.821368     -0.016658   \n",
            "1      1.054156      59.021888     8019.483632    7660.821368     -0.016658   \n",
            "2      1.054156      59.021888     8019.483632    7660.821368      0.005351   \n",
            "3      1.054156      59.021888     8019.483632    7660.821368     -0.008019   \n",
            "4      1.054156      59.021888     8019.483632    7660.821368      0.005714   \n",
            "5      1.128597      30.566873     8019.483632    7660.821368      0.003965   \n",
            "6      1.156760      44.395994     8019.483632    7660.821368     -0.013493   \n",
            "7      0.906271      48.178955     8019.483632    7660.821368      0.014350   \n",
            "8      1.051166      49.994387     8019.483632    7660.821368      0.001760   \n",
            "9      0.906723      51.798137     8019.483632    7660.821368     -0.000929   \n",
            "\n",
            "        RSI       EMA_12       EMA_26       MACD  \n",
            "0  4.433069  7915.200000  7915.200000   0.000000  \n",
            "1  4.433069  7894.915385  7905.433333 -10.517949  \n",
            "2  4.433069  7884.159172  7899.475309 -15.316137  \n",
            "3  4.433069  7865.403914  7889.310471 -23.906557  \n",
            "4  4.433069  7856.357158  7883.183769 -26.826611  \n",
            "5  4.433069  7853.463749  7879.803490 -26.339741  \n",
            "6  4.433069  7834.746249  7868.840269 -34.094019  \n",
            "7  4.433069  7835.977596  7866.907656 -30.930060  \n",
            "8  4.433069  7839.142581  7866.140422 -26.997841  \n",
            "9  4.433069  7840.697569  7864.889280 -24.191711  \n",
            "\n",
            "[10 rows x 21 columns]\n"
          ]
        }
      ],
      "source": [
        "# Add Simple Moving Averages\n",
        "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n",
        "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n",
        "\n",
        "# Add Exponential Moving Averages\n",
        "data['EMA_5'] = data['Close'].ewm(span=5, adjust=False).mean()\n",
        "data['EMA_20'] = data['Close'].ewm(span=20, adjust=False).mean()\n",
        "\n",
        "# Add Volume-related Features\n",
        "data['Avg_Volume_5'] = data['Shares Traded'].rolling(window=5).mean()\n",
        "data['Volume_Ratio'] = data['Shares Traded'] / data['Avg_Volume_5']\n",
        "\n",
        "# Add Volatility Features\n",
        "data['Rolling_Std_5'] = data['Close'].rolling(window=5).std()\n",
        "data['Bollinger_High'] = data['SMA_20'] + 2 * data['Rolling_Std_5']\n",
        "data['Bollinger_Low'] = data['SMA_20'] - 2 * data['Rolling_Std_5']\n",
        "\n",
        "# Add Momentum Indicators\n",
        "data['Price_Change'] = data['Close'].pct_change()\n",
        "data['RSI'] = 100 - (100 / (1 + data['Price_Change'].rolling(window=14).mean() / data['Price_Change'].rolling(window=14).std()))\n",
        "\n",
        "# Define EMA_12 and EMA_26 for MACD calculation\n",
        "data['EMA_12'] = data['Close'].ewm(span=12, adjust=False).mean()\n",
        "data['EMA_26'] = data['Close'].ewm(span=26, adjust=False).mean()\n",
        "data['MACD'] = data['EMA_12'] - data['EMA_26']\n",
        "\n",
        "# Fill NaN values using Interpolation can be more accurate for time series data.\n",
        "data.interpolate(method='linear', inplace=True)\n",
        "\n",
        "# Fill any remaining NaNs using forward fill and backward fill\n",
        "data.fillna(method='ffill', inplace=True)\n",
        "data.fillna(method='bfill', inplace=True)\n",
        "\n",
        "print(data.head(10))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XU9ICM49hj8E"
      },
      "outputs": [],
      "source": [
        "# Drop Date Column\n",
        "X = data.drop(['Date'],axis=1)\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_data = scaler.fit_transform(data[X.columns.values])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "1Vv3pdYzhnm6",
        "outputId": "387466f4-8e9b-4a33-f8bc-21a8618c641b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Fit a separate scaler for the Close prices\n",
        "close_scaler = MinMaxScaler()\n",
        "close_scaler.fit(data[['Close']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[:,4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBQp_Ho30cMl",
        "outputId": "066f049e-38ef-4d55-a5d7-bdd5d165b75e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        7915.20\n",
              "1        7783.35\n",
              "2        7825.00\n",
              "3        7762.25\n",
              "4        7806.60\n",
              "          ...   \n",
              "2143    24320.55\n",
              "2144    24433.20\n",
              "2145    24324.45\n",
              "2146    24315.95\n",
              "2147    24502.15\n",
              "Name: Close, Length: 2148, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQxSk5qJhti4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def create_sequences(data, time_steps=5):\n",
        "    X, y = [], []\n",
        "    for i in range(len(data) - time_steps):\n",
        "        X.append(data[i:i + time_steps])\n",
        "        y.append(data[i + time_steps, 3])  # Close price\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "X, y = create_sequences(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMVs57Xfh1Kc"
      },
      "outputs": [],
      "source": [
        "# Split into training and test sets\n",
        "train_size = int(len(X) * 0.8)\n",
        "X_train, X_test = X[:train_size], X[train_size:]\n",
        "y_train, y_test = y[:train_size], y[train_size:]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape[1:])"
      ],
      "metadata": {
        "id": "SFUzfK3wo-Xl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_dSieGS3h3k0"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# Define Transformer Model\n",
        "def create_transformer_model(input_shape):\n",
        "    inputs = layers.Input(shape=input_shape)\n",
        "    x = layers.LayerNormalization(epsilon=1e-6)(inputs)\n",
        "\n",
        "    # Transformer Encoder\n",
        "    for _ in range(8):  # Number of layers\n",
        "        x = layers.MultiHeadAttention(num_heads=8, key_dim=128)(x, x)\n",
        "        x = layers.LayerNormalization(epsilon=1e-6)(x + inputs)\n",
        "\n",
        "        x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(x)\n",
        "        x = layers.Conv1D(filters=input_shape[-1], kernel_size=1)(x)\n",
        "        x = layers.LayerNormalization(epsilon=1e-6)(x + inputs)\n",
        "\n",
        "    x = layers.GlobalAveragePooling1D()(x)\n",
        "    x = layers.Dense(64, activation='relu')(x)\n",
        "    outputs = layers.Dense(1)(x)\n",
        "\n",
        "    model = keras.Model(inputs, outputs)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Ozv0rP5h9JQ"
      },
      "outputs": [],
      "source": [
        "# Create and compile the model\n",
        "model = create_transformer_model(X_train.shape[1:])\n",
        "model.compile(optimizer='adam', loss='mse')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLVRgKpVh_r0",
        "outputId": "db81edab-db5a-4da9-fabc-f0b79457d222"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/80\n",
            "86/86 [==============================] - 29s 134ms/step - loss: 0.0115 - val_loss: 0.0091\n",
            "Epoch 2/80\n",
            "86/86 [==============================] - 11s 134ms/step - loss: 0.0011 - val_loss: 0.0054\n",
            "Epoch 3/80\n",
            "86/86 [==============================] - 13s 146ms/step - loss: 9.5800e-04 - val_loss: 0.0103\n",
            "Epoch 4/80\n",
            "86/86 [==============================] - 12s 143ms/step - loss: 8.7194e-04 - val_loss: 0.0038\n",
            "Epoch 5/80\n",
            "86/86 [==============================] - 9s 107ms/step - loss: 8.1571e-04 - val_loss: 0.0018\n",
            "Epoch 6/80\n",
            "86/86 [==============================] - 11s 123ms/step - loss: 8.5171e-04 - val_loss: 0.0021\n",
            "Epoch 7/80\n",
            "86/86 [==============================] - 14s 158ms/step - loss: 7.9724e-04 - val_loss: 0.0076\n",
            "Epoch 8/80\n",
            "86/86 [==============================] - 12s 138ms/step - loss: 0.0013 - val_loss: 0.0051\n",
            "Epoch 9/80\n",
            "86/86 [==============================] - 21s 243ms/step - loss: 7.8690e-04 - val_loss: 0.0038\n",
            "Epoch 10/80\n",
            "86/86 [==============================] - 13s 149ms/step - loss: 9.2830e-04 - val_loss: 0.0066\n",
            "Epoch 11/80\n",
            "86/86 [==============================] - 11s 130ms/step - loss: 7.5404e-04 - val_loss: 0.0117\n",
            "Epoch 12/80\n",
            "86/86 [==============================] - 11s 133ms/step - loss: 6.8982e-04 - val_loss: 0.0201\n",
            "Epoch 13/80\n",
            "86/86 [==============================] - 11s 124ms/step - loss: 7.9452e-04 - val_loss: 0.0054\n",
            "Epoch 14/80\n",
            "86/86 [==============================] - 13s 150ms/step - loss: 0.0011 - val_loss: 0.0077\n",
            "Epoch 15/80\n",
            "86/86 [==============================] - 10s 117ms/step - loss: 0.0012 - val_loss: 0.0153\n",
            "Epoch 16/80\n",
            "86/86 [==============================] - 10s 119ms/step - loss: 6.2280e-04 - val_loss: 0.0078\n",
            "Epoch 17/80\n",
            "86/86 [==============================] - 12s 145ms/step - loss: 6.5763e-04 - val_loss: 0.0092\n",
            "Epoch 18/80\n",
            "86/86 [==============================] - 11s 135ms/step - loss: 7.0395e-04 - val_loss: 0.0099\n",
            "Epoch 19/80\n",
            "86/86 [==============================] - 10s 112ms/step - loss: 6.4897e-04 - val_loss: 0.0100\n",
            "Epoch 20/80\n",
            "86/86 [==============================] - 8s 99ms/step - loss: 8.4671e-04 - val_loss: 0.0228\n",
            "Epoch 21/80\n",
            "86/86 [==============================] - 10s 118ms/step - loss: 9.8341e-04 - val_loss: 0.0106\n",
            "Epoch 22/80\n",
            "86/86 [==============================] - 11s 129ms/step - loss: 5.8040e-04 - val_loss: 0.0037\n",
            "Epoch 23/80\n",
            "86/86 [==============================] - 10s 113ms/step - loss: 6.6944e-04 - val_loss: 0.0109\n",
            "Epoch 24/80\n",
            "86/86 [==============================] - 9s 104ms/step - loss: 6.3325e-04 - val_loss: 0.0075\n",
            "Epoch 25/80\n",
            "86/86 [==============================] - 10s 119ms/step - loss: 5.7074e-04 - val_loss: 0.0077\n",
            "Epoch 26/80\n",
            "86/86 [==============================] - 11s 131ms/step - loss: 5.7854e-04 - val_loss: 0.0065\n",
            "Epoch 27/80\n",
            "86/86 [==============================] - 11s 127ms/step - loss: 5.1726e-04 - val_loss: 0.0055\n",
            "Epoch 28/80\n",
            "86/86 [==============================] - 11s 125ms/step - loss: 5.4128e-04 - val_loss: 0.0120\n",
            "Epoch 29/80\n",
            "86/86 [==============================] - 12s 137ms/step - loss: 5.1758e-04 - val_loss: 0.0048\n",
            "Epoch 30/80\n",
            "86/86 [==============================] - 8s 93ms/step - loss: 8.8775e-04 - val_loss: 0.0166\n",
            "Epoch 31/80\n",
            "86/86 [==============================] - 10s 113ms/step - loss: 6.0881e-04 - val_loss: 0.0058\n",
            "Epoch 32/80\n",
            "86/86 [==============================] - 9s 102ms/step - loss: 7.0429e-04 - val_loss: 0.0136\n",
            "Epoch 33/80\n",
            "86/86 [==============================] - 10s 113ms/step - loss: 5.8802e-04 - val_loss: 0.0035\n",
            "Epoch 34/80\n",
            "86/86 [==============================] - 11s 128ms/step - loss: 4.3757e-04 - val_loss: 0.0146\n",
            "Epoch 35/80\n",
            "86/86 [==============================] - 12s 142ms/step - loss: 4.5579e-04 - val_loss: 0.0059\n",
            "Epoch 36/80\n",
            "86/86 [==============================] - 9s 108ms/step - loss: 6.1450e-04 - val_loss: 0.0111\n",
            "Epoch 37/80\n",
            "86/86 [==============================] - 12s 142ms/step - loss: 4.8601e-04 - val_loss: 0.0060\n",
            "Epoch 38/80\n",
            "86/86 [==============================] - 10s 122ms/step - loss: 5.3873e-04 - val_loss: 0.0083\n",
            "Epoch 39/80\n",
            "86/86 [==============================] - 11s 126ms/step - loss: 5.1433e-04 - val_loss: 0.0060\n",
            "Epoch 40/80\n",
            "86/86 [==============================] - 13s 149ms/step - loss: 6.3733e-04 - val_loss: 0.0083\n",
            "Epoch 41/80\n",
            "86/86 [==============================] - 12s 143ms/step - loss: 6.7452e-04 - val_loss: 0.0030\n",
            "Epoch 42/80\n",
            "86/86 [==============================] - 11s 127ms/step - loss: 5.1237e-04 - val_loss: 0.0021\n",
            "Epoch 43/80\n",
            "86/86 [==============================] - 10s 116ms/step - loss: 4.9292e-04 - val_loss: 0.0041\n",
            "Epoch 44/80\n",
            "86/86 [==============================] - 10s 122ms/step - loss: 4.1563e-04 - val_loss: 0.0060\n",
            "Epoch 45/80\n",
            "86/86 [==============================] - 12s 143ms/step - loss: 5.3555e-04 - val_loss: 0.0026\n",
            "Epoch 46/80\n",
            "86/86 [==============================] - 10s 118ms/step - loss: 4.7148e-04 - val_loss: 0.0067\n",
            "Epoch 47/80\n",
            "86/86 [==============================] - 10s 113ms/step - loss: 4.1923e-04 - val_loss: 0.0021\n",
            "Epoch 48/80\n",
            "86/86 [==============================] - 14s 160ms/step - loss: 4.0939e-04 - val_loss: 0.0021\n",
            "Epoch 49/80\n",
            "86/86 [==============================] - 11s 134ms/step - loss: 4.8088e-04 - val_loss: 0.0025\n",
            "Epoch 50/80\n",
            "86/86 [==============================] - 9s 103ms/step - loss: 4.8009e-04 - val_loss: 0.0030\n",
            "Epoch 51/80\n",
            "86/86 [==============================] - 9s 102ms/step - loss: 5.3261e-04 - val_loss: 0.0074\n",
            "Epoch 52/80\n",
            "86/86 [==============================] - 11s 126ms/step - loss: 4.6043e-04 - val_loss: 0.0180\n",
            "Epoch 53/80\n",
            "86/86 [==============================] - 10s 121ms/step - loss: 4.0802e-04 - val_loss: 0.0053\n",
            "Epoch 54/80\n",
            "86/86 [==============================] - 9s 104ms/step - loss: 4.2763e-04 - val_loss: 0.0062\n",
            "Epoch 55/80\n",
            "86/86 [==============================] - 13s 154ms/step - loss: 4.5127e-04 - val_loss: 0.0297\n",
            "Epoch 56/80\n",
            "86/86 [==============================] - 10s 117ms/step - loss: 4.3199e-04 - val_loss: 0.0059\n",
            "Epoch 57/80\n",
            "86/86 [==============================] - 11s 133ms/step - loss: 4.3290e-04 - val_loss: 0.0088\n",
            "Epoch 58/80\n",
            "86/86 [==============================] - 11s 134ms/step - loss: 5.9460e-04 - val_loss: 0.0074\n",
            "Epoch 59/80\n",
            "86/86 [==============================] - 8s 94ms/step - loss: 4.5621e-04 - val_loss: 0.0078\n",
            "Epoch 60/80\n",
            "86/86 [==============================] - 12s 135ms/step - loss: 3.3394e-04 - val_loss: 0.0052\n",
            "Epoch 61/80\n",
            "86/86 [==============================] - 11s 130ms/step - loss: 3.8877e-04 - val_loss: 0.0073\n",
            "Epoch 62/80\n",
            "86/86 [==============================] - 12s 138ms/step - loss: 2.9521e-04 - val_loss: 0.0087\n",
            "Epoch 63/80\n",
            "86/86 [==============================] - 10s 119ms/step - loss: 3.6129e-04 - val_loss: 0.0065\n",
            "Epoch 64/80\n",
            "86/86 [==============================] - 9s 107ms/step - loss: 2.7916e-04 - val_loss: 0.0079\n",
            "Epoch 65/80\n",
            "86/86 [==============================] - 12s 139ms/step - loss: 3.1342e-04 - val_loss: 0.0065\n",
            "Epoch 66/80\n",
            "86/86 [==============================] - 13s 151ms/step - loss: 2.7725e-04 - val_loss: 0.0058\n",
            "Epoch 67/80\n",
            "86/86 [==============================] - 9s 107ms/step - loss: 3.5589e-04 - val_loss: 0.0131\n",
            "Epoch 68/80\n",
            "86/86 [==============================] - 13s 147ms/step - loss: 6.1316e-04 - val_loss: 0.0109\n",
            "Epoch 69/80\n",
            "86/86 [==============================] - 9s 107ms/step - loss: 4.2759e-04 - val_loss: 0.0198\n",
            "Epoch 70/80\n",
            "86/86 [==============================] - 11s 125ms/step - loss: 3.9987e-04 - val_loss: 0.0118\n",
            "Epoch 71/80\n",
            "86/86 [==============================] - 12s 136ms/step - loss: 3.6953e-04 - val_loss: 0.0229\n",
            "Epoch 72/80\n",
            "86/86 [==============================] - 10s 112ms/step - loss: 3.3511e-04 - val_loss: 0.0028\n",
            "Epoch 73/80\n",
            "86/86 [==============================] - 10s 122ms/step - loss: 3.6312e-04 - val_loss: 0.0189\n",
            "Epoch 74/80\n",
            "86/86 [==============================] - 11s 129ms/step - loss: 3.1724e-04 - val_loss: 0.0151\n",
            "Epoch 75/80\n",
            "86/86 [==============================] - 12s 140ms/step - loss: 2.9053e-04 - val_loss: 0.0215\n",
            "Epoch 76/80\n",
            "86/86 [==============================] - 11s 134ms/step - loss: 3.0496e-04 - val_loss: 0.0195\n",
            "Epoch 77/80\n",
            "86/86 [==============================] - 10s 118ms/step - loss: 2.2028e-04 - val_loss: 0.0202\n",
            "Epoch 78/80\n",
            "86/86 [==============================] - 13s 153ms/step - loss: 2.8503e-04 - val_loss: 0.0243\n",
            "Epoch 79/80\n",
            "86/86 [==============================] - 12s 144ms/step - loss: 2.4761e-04 - val_loss: 0.0250\n",
            "Epoch 80/80\n",
            "86/86 [==============================] - 14s 168ms/step - loss: 2.5663e-04 - val_loss: 0.0097\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7893640ebfa0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=40, batch_size=16, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HmMMpi7hiL46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d18e7328-8069-4e81-a3f7-061a85774554"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 1s 25ms/step\n"
          ]
        }
      ],
      "source": [
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "# Inverse transform to get original scale using close_scaler\n",
        "y_pred_inverse = close_scaler.inverse_transform(y_pred)\n",
        "y_test_inverse = close_scaler.inverse_transform(y_test.reshape(-1, 1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WpZWnVijiOwV"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "predictions_df = pd.DataFrame({\n",
        "    'Date': data['Date'][train_size + 5:].reset_index(drop=True),\n",
        "    'Actual Close': y_test_inverse.flatten(),\n",
        "    'Predicted Close': y_pred_inverse.flatten()\n",
        "})\n",
        "\n",
        "# Save to CSV\n",
        "#predictions_df.to_csv('predictions.csv', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g42BK6AniRGb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "outputId": "62be03fa-8d32-4995-c843-1531488ac53a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(predictions_df['Date'], predictions_df['Actual Close'], label='Actual Close', color='blue')\n",
        "plt.plot(predictions_df['Date'], predictions_df['Predicted Close'], label='Predicted Close', color='orange')\n",
        "plt.title('Stock Price Prediction')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the next year's data\n",
        "last_sequence = X_test[-1]\n",
        "future_predictions = []\n",
        "\n",
        "for _ in range(10):  # Assuming 365 trading days\n",
        "    next_pred = model.predict(last_sequence[np.newaxis, :, :])\n",
        "    future_predictions.append(next_pred[0, 0])\n",
        "\n",
        "    next_pred_scaled = close_scaler.transform(next_pred)\n",
        "    new_sequence = np.append(last_sequence[1:], [scaled_data[-1][:20] + [next_pred_scaled[0, 0]]], axis=0)\n",
        "    last_sequence = new_sequence"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjmNumbbd-iX",
        "outputId": "d21bca6c-9900-4572-cfef-afc0fa329d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 1s 750ms/step\n",
            "1/1 [==============================] - ETA: 0s"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 833ms/step\n",
            "1/1 [==============================] - ETA: 0s"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 297ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inverse transform future predictions\n",
        "future_predictions_inverse = close_scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1))\n",
        "# Create a date range for future predictions\n",
        "last_date = pd.to_datetime(data['Date'].iloc[-1])\n",
        "future_dates = pd.date_range(last_date, periods=10, freq='B')"
      ],
      "metadata": {
        "id": "eCpD0HtcaK2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the future predictions\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(future_dates, future_predictions_inverse, color='green', label='Future Predicted Close')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.title('Future Stock Price Prediction')\n",
        "plt.legend()\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Z6mzESrYaP43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "outputId": "8ac1edc2-8762-4cbe-dc70-4a3bcf5e4bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure(data=[go.Candlestick(x=data['Date'],\n",
        "                open=data['Open'],\n",
        "                high=data['High'],\n",
        "                low=data['Low'],\n",
        "                close=data['Close'])] )\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "MFCOXD2TUTPR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4atQszR1Us5Y"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}