LSTM_TRF_Improved_version.ipynb

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        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from datetime import date\n",
        "\n",
        "color_pal = sns.color_palette()\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Bidirectional\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout, Layer\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import ReduceLROnPlateau, EarlyStopping"
      ]
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        "Preprocessing Part"
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      "execution_count": 2,
      "metadata": {
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      "source": [
        "start_date ='2000-02-01'\n",
        "end_date = '2024-12-13'\n"
      ]
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        "def creating_features(data):\n",
        "  data = data.copy()\n",
        "  data.columns = ['_'.join(col).strip() if isinstance(col, tuple) else col for col in data.columns]\n",
        "  data = data.filter(regex='^Close')\n",
        "  #data['Date'] = data.index.date\n",
        "  #data['Date'] = pd.to_datetime(data['Date'])\n",
        "  return data"
      ]
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            "\r[*********************100%***********************]  1 of 1 completed\n"
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      "source": [
        "#Features_agglomorated\n",
        "tickers = ['GC=F']\n",
        "\n",
        "# Download historical data\n",
        "features = yf.download(tickers, start= start_date, end=end_date, interval='1d')\n",
        "#features = yf.download(tickers, end=end_date, interval='1d')"
      ]
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        "final_data= creating_features(features).dropna()"
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        "final_data.shape"
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              "            Close_GC=F\n",
              "Date                  \n",
              "2000-08-30  273.899994\n",
              "2000-08-31  278.299988\n",
              "2000-09-01  277.000000\n",
              "2000-09-05  275.799988\n",
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              "             Close_GC=F\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f32e4fd5-ca36-49ae-ab3f-7c2954bb5295 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f32e4fd5-ca36-49ae-ab3f-7c2954bb5295');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e9ecc6e3-856d-4ae7-82d1-c30a1dc5fa35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9ecc6e3-856d-4ae7-82d1-c30a1dc5fa35')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9ecc6e3-856d-4ae7-82d1-c30a1dc5fa35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"final_data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-12-06 00:00:00\",\n        \"max\": \"2024-12-12 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-12-09 00:00:00\",\n          \"2024-12-12 00:00:00\",\n          \"2024-12-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close_GC=F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.71438870474407,\n        \"min\": 2638.60009765625,\n        \"max\": 2733.800048828125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2664.89990234375,\n          2687.5,\n          2697.60009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seq_length = 63"
      ],
      "metadata": {
        "id": "WPfzrHu3YtPo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_sequences(data, seq_len):\n",
        "    X, y = [], []\n",
        "    for i in range(len(data) - seq_len):\n",
        "        X.append(data[i:(i + seq_len)])\n",
        "        y.append(data[i + seq_len])\n",
        "    return np.array(X), np.array(y)"
      ],
      "metadata": {
        "id": "QK4uBps6KG7x"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_data(df, feature_column, seq_len=seq_length):\n",
        "    data = df[feature_column].values.reshape(-1, 1)\n",
        "\n",
        "    X, y = create_sequences(data, seq_len)\n",
        "\n",
        "    train_size = int(len(X) * 0.7)\n",
        "    val_size = int(len(X) * 0.2)\n",
        "\n",
        "    X_train = X[:train_size]\n",
        "    y_train = y[:train_size]\n",
        "\n",
        "    X_val = X[train_size:train_size+val_size]\n",
        "    y_val = y[train_size:train_size+val_size]\n",
        "\n",
        "    X_test = X[train_size+val_size:]\n",
        "    y_test = y[train_size+val_size:]\n",
        "\n",
        "    scaler = MinMaxScaler()\n",
        "\n",
        "    X_train_reshaped = X_train.reshape(-1, 1)\n",
        "    scaler = scaler.fit(X_train_reshaped)\n",
        "\n",
        "    X_train_scaled = scaler.transform(X_train_reshaped).reshape(X_train.shape)\n",
        "    X_val_scaled = scaler.transform(X_val.reshape(-1, 1)).reshape(X_val.shape)\n",
        "    X_test_scaled = scaler.transform(X_test.reshape(-1, 1)).reshape(X_test.shape)\n",
        "\n",
        "    y_train_scaled = scaler.transform(y_train)\n",
        "    y_val_scaled = scaler.transform(y_val)\n",
        "    y_test_scaled = scaler.transform(y_test)\n",
        "\n",
        "    return X_train_scaled, X_val_scaled, X_test_scaled, y_train_scaled, y_val_scaled, y_test_scaled, scaler"
      ],
      "metadata": {
        "id": "fgCqZNGwHx57"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def forecast_future(model, last_sequence, scaler, horizon):\n",
        "\n",
        "    future_predictions = []\n",
        "    current_sequence = last_sequence.copy()\n",
        "\n",
        "    for _ in range(horizon):\n",
        "\n",
        "        next_pred = model.predict(current_sequence.reshape(1, -1, 1), verbose=0)\n",
        "        future_predictions.append(next_pred[0, 0])\n",
        "\n",
        "        current_sequence = np.roll(current_sequence, -1)\n",
        "        current_sequence[-1] = next_pred\n",
        "\n",
        "    future_predictions = np.array(future_predictions).reshape(-1, 1)\n",
        "    future_predictions = scaler.inverse_transform(future_predictions)\n",
        "\n",
        "    return future_predictions"
      ],
      "metadata": {
        "id": "UapyvPkVJSix"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AttentionLayer(Layer):\n",
        "   def __init__(self, **kwargs):\n",
        "       super(AttentionLayer, self).__init__(**kwargs)\n",
        "\n",
        "   def build(self, input_shape):\n",
        "       self.W = self.add_weight(name='attention_weight',\n",
        "                               shape=(input_shape[-1], 1),\n",
        "                               initializer='random_normal',\n",
        "                               trainable=True)\n",
        "       self.b = self.add_weight(name='attention_bias',\n",
        "                               shape=(input_shape[1], 1),\n",
        "                               initializer='zeros',\n",
        "                               trainable=True)\n",
        "\n",
        "   def call(self, inputs):\n",
        "       e = tf.tanh(tf.matmul(inputs, self.W) + self.b)\n",
        "       a = tf.nn.softmax(e, axis=1)\n",
        "       output = inputs * a\n",
        "       return tf.reduce_sum(output, axis=1)"
      ],
      "metadata": {
        "id": "Y9a-5XVMdH-U"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResidualWrapper(tf.keras.Model):\n",
        "    def __init__(self, model):\n",
        "        super().__init__()\n",
        "        self.model = model\n",
        "\n",
        "    def call(self, inputs, *args, **kwargs):\n",
        "        delta = self.model(inputs, *args, **kwargs)\n",
        "        return inputs + delta"
      ],
      "metadata": {
        "id": "IgLJGUbTEApk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def lstm_trf(seq_len, features=1):\n",
        "\n",
        "    model2 = Sequential([\n",
        "\n",
        "        # First LSTM stack with residual connection\n",
        "        ResidualWrapper(\n",
        "            Sequential([\n",
        "                Bidirectional(LSTM(128, activation='tanh', return_sequences=True,\n",
        "                                 input_shape=(seq_len, features))),\n",
        "                BatchNormalization(),\n",
        "                Dropout(0.3)\n",
        "            ])\n",
        "        ),\n",
        "\n",
        "        # Second LSTM stack\n",
        "        Bidirectional(LSTM(64, activation='tanh', return_sequences=True)),\n",
        "        BatchNormalization(),\n",
        "        AttentionLayer(),\n",
        "        Dropout(0.3),\n",
        "\n",
        "        # Deeper dense layers with gradual dimension reduction\n",
        "        Dense(128, activation='relu'),\n",
        "        BatchNormalization(),\n",
        "        Dropout(0.3),\n",
        "\n",
        "        Dense(64, activation='relu'),\n",
        "        BatchNormalization(),\n",
        "        Dropout(0.2),\n",
        "\n",
        "        Dense(32, activation='relu'),\n",
        "        BatchNormalization(),\n",
        "        Dropout(0.2),\n",
        "\n",
        "        Dense(1)\n",
        "    ])\n",
        "\n",
        "    optimizer = Adam(learning_rate=0.001)\n",
        "    model2.compile(optimizer=optimizer,\n",
        "                  loss='huber',\n",
        "                  metrics=['mae', 'mse'])\n",
        "\n",
        "    return model2,get_callbacks()"
      ],
      "metadata": {
        "id": "4KrGTuiMF_Jk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_callbacks():\n",
        "    return [\n",
        "        ReduceLROnPlateau(\n",
        "            monitor='val_loss',\n",
        "            factor=0.5,\n",
        "            patience=5,\n",
        "            min_lr=1e-6,\n",
        "            verbose=1\n",
        "        ),\n",
        "        EarlyStopping(\n",
        "            monitor='val_loss',\n",
        "            patience=15,\n",
        "            restore_best_weights=True,\n",
        "            verbose=1\n",
        "        )\n",
        "    ]"
      ],
      "metadata": {
        "id": "KX2S9HZ3N6qN"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "_5ozX8VcGnHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, X_test, y_train, y_val, y_test, scaler = prepare_data(final_data, feature_column='Close_GC=F', seq_len=seq_length)"
      ],
      "metadata": {
        "id": "LI6D9kR3J6R4"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_val.shape, X_test.shape, y_train.shape, y_val.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otdAJVOpSPaV",
        "outputId": "2f23b38a-6e18-4a49-abe3-ef8d77efba6e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4221, 63, 1), (1206, 63, 1), (604, 63, 1), (4221, 1), (1206, 1), (604, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(patience=10, restore_best_weights=True)\n",
        "lr_reducer = ReduceLROnPlateau(factor=0.5, patience=5)\n"
      ],
      "metadata": {
        "id": "60sJtkmkSW3A"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lstm_trf, callbacks = lstm_trf(seq_length)"
      ],
      "metadata": {
        "id": "cm8YhXm5Kq5_"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_lstm_trf = model_lstm_trf.fit(X_train, y_train,\n",
        "                  epochs= 100,\n",
        "                  batch_size=32,\n",
        "                  validation_data=(X_val, y_val),\n",
        "                  callbacks=[early_stopping, lr_reducer],\n",
        "                  verbose=1)"
      ],
      "metadata": {
        "id": "cZK0fasaGOFA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79ec8ce-cb3b-4c3c-ced2-649f6c9ef293"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 328ms/step - loss: 0.5003 - mae: 0.8712 - mse: 1.3147 - val_loss: 0.0305 - val_mae: 0.2059 - val_mse: 0.0609 - learning_rate: 0.0010\n",
            "Epoch 2/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 323ms/step - loss: 0.1421 - mae: 0.4154 - mse: 0.2968 - val_loss: 0.0651 - val_mae: 0.3279 - val_mse: 0.1302 - learning_rate: 0.0010\n",
            "Epoch 3/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 343ms/step - loss: 0.0734 - mae: 0.2880 - mse: 0.1506 - val_loss: 0.0441 - val_mae: 0.2589 - val_mse: 0.0882 - learning_rate: 0.0010\n",
            "Epoch 4/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 321ms/step - loss: 0.0513 - mae: 0.2385 - mse: 0.1037 - val_loss: 0.0124 - val_mae: 0.1446 - val_mse: 0.0248 - learning_rate: 0.0010\n",
            "Epoch 5/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 325ms/step - loss: 0.0308 - mae: 0.1843 - mse: 0.0618 - val_loss: 0.0041 - val_mae: 0.0850 - val_mse: 0.0083 - learning_rate: 0.0010\n",
            "Epoch 6/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 325ms/step - loss: 0.0203 - mae: 0.1522 - mse: 0.0406 - val_loss: 0.0028 - val_mae: 0.0645 - val_mse: 0.0056 - learning_rate: 0.0010\n",
            "Epoch 7/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 324ms/step - loss: 0.0140 - mae: 0.1264 - mse: 0.0281 - val_loss: 7.5718e-04 - val_mae: 0.0307 - val_mse: 0.0015 - learning_rate: 0.0010\n",
            "Epoch 8/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 324ms/step - loss: 0.0113 - mae: 0.1125 - mse: 0.0226 - val_loss: 0.0017 - val_mae: 0.0503 - val_mse: 0.0034 - learning_rate: 0.0010\n",
            "Epoch 9/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 335ms/step - loss: 0.0078 - mae: 0.0945 - mse: 0.0155 - val_loss: 0.0013 - val_mae: 0.0423 - val_mse: 0.0025 - learning_rate: 0.0010\n",
            "Epoch 10/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 327ms/step - loss: 0.0061 - mae: 0.0850 - mse: 0.0121 - val_loss: 6.9176e-04 - val_mae: 0.0284 - val_mse: 0.0014 - learning_rate: 0.0010\n",
            "Epoch 11/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 324ms/step - loss: 0.0055 - mae: 0.0813 - mse: 0.0111 - val_loss: 0.0019 - val_mae: 0.0512 - val_mse: 0.0037 - learning_rate: 0.0010\n",
            "Epoch 12/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 325ms/step - loss: 0.0041 - mae: 0.0707 - mse: 0.0082 - val_loss: 6.1295e-04 - val_mae: 0.0285 - val_mse: 0.0012 - learning_rate: 0.0010\n",
            "Epoch 13/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 330ms/step - loss: 0.0041 - mae: 0.0720 - mse: 0.0081 - val_loss: 4.2354e-04 - val_mae: 0.0234 - val_mse: 8.4707e-04 - learning_rate: 0.0010\n",
            "Epoch 14/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 327ms/step - loss: 0.0040 - mae: 0.0712 - mse: 0.0080 - val_loss: 3.0580e-04 - val_mae: 0.0184 - val_mse: 6.1160e-04 - learning_rate: 0.0010\n",
            "Epoch 15/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 327ms/step - loss: 0.0036 - mae: 0.0676 - mse: 0.0073 - val_loss: 9.8892e-04 - val_mae: 0.0385 - val_mse: 0.0020 - learning_rate: 0.0010\n",
            "Epoch 16/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 330ms/step - loss: 0.0035 - mae: 0.0664 - mse: 0.0070 - val_loss: 5.5147e-04 - val_mae: 0.0238 - val_mse: 0.0011 - learning_rate: 0.0010\n",
            "Epoch 17/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 347ms/step - loss: 0.0037 - mae: 0.0680 - mse: 0.0075 - val_loss: 8.5936e-04 - val_mae: 0.0345 - val_mse: 0.0017 - learning_rate: 0.0010\n",
            "Epoch 18/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 328ms/step - loss: 0.0034 - mae: 0.0650 - mse: 0.0068 - val_loss: 3.1794e-04 - val_mae: 0.0177 - val_mse: 6.3588e-04 - learning_rate: 0.0010\n",
            "Epoch 19/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 332ms/step - loss: 0.0038 - mae: 0.0704 - mse: 0.0076 - val_loss: 6.5060e-04 - val_mae: 0.0263 - val_mse: 0.0013 - learning_rate: 0.0010\n",
            "Epoch 20/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 332ms/step - loss: 0.0034 - mae: 0.0656 - mse: 0.0067 - val_loss: 5.9683e-04 - val_mae: 0.0298 - val_mse: 0.0012 - learning_rate: 5.0000e-04\n",
            "Epoch 21/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 349ms/step - loss: 0.0030 - mae: 0.0617 - mse: 0.0061 - val_loss: 8.5019e-04 - val_mae: 0.0368 - val_mse: 0.0017 - learning_rate: 5.0000e-04\n",
            "Epoch 22/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 327ms/step - loss: 0.0033 - mae: 0.0642 - mse: 0.0065 - val_loss: 5.0941e-04 - val_mae: 0.0269 - val_mse: 0.0010 - learning_rate: 5.0000e-04\n",
            "Epoch 23/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 329ms/step - loss: 0.0030 - mae: 0.0617 - mse: 0.0060 - val_loss: 3.7788e-04 - val_mae: 0.0214 - val_mse: 7.5576e-04 - learning_rate: 5.0000e-04\n",
            "Epoch 24/100\n",
            "\u001b[1m132/132\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 328ms/step - loss: 0.0034 - mae: 0.0662 - mse: 0.0068 - val_loss: 0.0016 - val_mae: 0.0520 - val_mse: 0.0031 - learning_rate: 5.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(history_lstm_trf.history['loss'], label='Training Loss')\n",
        "plt.plot(history_lstm_trf.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss LSTM  and TRF')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "M5gUvxbbVwoK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "outputId": "f986dd22-7120-4336-956d-392bbcd078da",
        "collapsed": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the price for a month in the horizon\n",
        "\n",
        "horizon = 30\n",
        "\n",
        "last_date = final_data.index[-1]\n",
        "future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=horizon, freq='B')\n",
        "last_sequence = X_test[-1]"
      ],
      "metadata": {
        "id": "_uja8la7Gz8G"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction using lstm and transformer\n",
        "future_pred_lstm_trf = forecast_future(model_lstm_trf, last_sequence, scaler, horizon)"
      ],
      "metadata": {
        "id": "gnndE5utKC0i"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25, 6))\n",
        "\n",
        "#plt.plot(final_data.index[-15:], final_data['Close_GC=F'].values[-15:], label='Historical Prices', color='green')\n",
        "\n",
        "plt.plot(future_dates, future_pred_lstm_trf, label='Forecasted Prices', linestyle='--', color= 'red')\n",
        "plt.title('Gold Price Forecast LSTM  and TRF')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "pOCaKL0zJN5I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "6b5342be-3e2d-48be-b261-656a37cc0bf1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 1 Axes>"
            ],
            "image/png": 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hoaFasWKFhgwZkqruVmitfv36WrduXZrvV6tWrfTMM8/o+vXr9mMuLi6qUaOGrl27Zj/m7++vGjVqpOkjr/Ts2VPvvvuuLl26pDNnzqhBgwZq3bq1WrVqpbp166p27dry9vbOt/4ee+wxzZgxQydOnNDhw4dTfY5urbZWo0YNh6sA5iQ/Pz8dPXo03bBsq1at9NRTT+m5557T0qVLNWvWLD333HMqXLhwhvP9/PPPmjVrVqrnSu3atdW+fXv16tVLO3fu1OrVqzV58mQVLVo01di33nrLHqidOXNmqjnq1q2rnj17qnPnzjp06NDdvu08s337dvvz65FHHnG4hbOUsg3vP+/N7UqXLp2leQAAAAAAcFZGxyUAAAAAAOB+d3tgJbN/ZJdStqM7fvx4uv9duXIlVe3Bgwe1f/9+SdKAAQPSBIEkyd3dXbNnz7YHbNJbvSczy5YtU2xsrCTpnXfeSXdVqJYtW2ro0KHZmje74uLitG/fPj322GP27R99fHxShaRu5+Pjo48++ihVECs7duzYoQMHDkiSnnzyyTShtdt5eXmlCkfYbDZNnz7dPja974skeXh42EN458+fV1BQ0B31esv8+fPVtGnTDP979913U9XPmzdPUsq9+vjjj9O9V08//bQ97Ld+/XqFh4dneP3mzZtn+P3IqXsydepUSZKbm5sWLlyYJrR2u7Jly6Y5ll5o7XatW7dW586dJSnNNqOS9Ndff0lKWW0ts5BhZn0VBJ6enlq6dKk9hJWcnKytW7dqwoQJ6tatm/z9/dWyZUu9++67+v333/O8v+rVq9tXhVu+fLn9uMVi0bfffitJGjhwYJ725OnpmeEKj1LKNtCTJk2SyWRSTEyMduzYkel8Xbt2Tfe5YjQaNXr0aElSUlKSfv7551TnL1++rLVr10pK+bymN4e3t7dmzJjh6C3lu4SEBJ06dUr/+9//7O/Dy8tL//73v7M0/vnnn8/0mZfezzAAAAAAAPcSgmsAAAAAAMCh6Oho+2tPT89Ma59++ukM/xH+888/T1X7448/2l//c2Wo21WoUEGtW7eWJAUHByshISHLvd8KX3h5ealnz54Z1j3++ONZnjMrpkyZIl9fX/t/pUqVUocOHbRx40ZJKWGrRYsWqVixYumO79Spk3x8fO74+reuI0n/+te/sjX25MmTOnPmjCRles8kqVq1avaQ0759+7LZ5Z37888/7dvy9ejRI9NViYYNGyYpZavEn376KcO6/v37Z3guJ+7JjRs37CGe7t2758hWglevXtXp06dTBURvfaZu35L2llvBpY0bN6YKpDqjunXrau/evXrttdfS3Eur1aqjR4/qo48+UsOGDfX222+n2vI1LwwYMECStHLlSvu1t23bpr/++ksmk0l9+/bN037+KSkpSX/88YdOnTpl/+xcunTJ/tm9fbvY9GT283L7Fp9nz55NdS4oKMh+PzIL1NavX1/Vq1d39DbyVHh4eKrnup+fnxo3bqz33ntPcXFxqlevnlavXu10W5wCAAAAAJBf2CoUAAAAAAA45OXlZX8dExOTY/OeOHFCUsoKPfXq1cu0tkGDBtq6dasSEhIUFhamhx56KEvXuLV120MPPSQ3N7cM62rWrClXV1clJiZmsfs7U7ZsWXXp0kWjR49WuXLlMu3nbhw5ckSSVLx4cVWuXDlbY2/fnq979+5ZHndrNa87NX78eL3++utZqr19S9GGDRtmWtugQYN0x/1TZvc8J+7J0aNHZbPZJEmBgYFZnuOf9u7dqzlz5mjHjh26ceNGhnXpBdMGDRqk3bt368yZM6pbt666deum1q1bq0mTJjkSpMtrvr6+eu211/Taa68pNDRUP//8s44cOaJffvlFR44ckc1mU3JysmbMmKGrV69q1qxZedZbv3799O9//1t//fWXtm3bpg4dOmjZsmWSUrbmLFWqVJ71cktSUpIWLFig5cuX6+jRo5k+727fLjY9Dz74YIbnHnjgAfvr24PPUuqfQUfP/Xr16tn/nijo3N3d9cQTTzh8Ht3uhx9+yHC7aAAAAAAA7gcE1wAAAAAAgEO3bxvoaJWmrVu3pvo6KCgow6DPrdCNt7e33N3dM53Xz88vzbisuFWb0cpmt5jNZj3wwAO6fPlylufOzPDhwzV8+HD71+7u7ipSpEimK4PdrnDhwnd1/Vvfp9vvW1ZdvXr1jq55a0vWvHD7Z8DR9zarn53Mvjc5cU9u/9m5k++LJE2ePFlTpkzJUm1cXFyaY4MHD9a5c+c0bdo0RUZGaunSpVq6dKkkqVy5curUqZOefPJJ1ahR4476y09VqlRRlSpV7KsnXrhwQVOmTNHixYslSUuWLNETTzyhJk2a5Ek/xYsXV9u2bbVp0yYtX75cjRs31oYNGyRJjz32WJ70cLsbN26oV69eOnz4cJbq0/v83M7DwyPDc7dv2/vPle6y87NbokSJTM/ntVKlSmnlypX2r69fv66jR49q9uzZCg8P17hx4xQVFaUxY8bkY5cAAAAAADgPgmsAAAAAAMCh6tWry2g0ymq12lfyykkGgyHH58yPa9yuWLFidxX+MZlMOdhN9tweNFm0aFGWV2zLaigvp+XU9/b2sM0/FYR7snPnTntorUKFCho9erSaNGmismXLytPTU2Zzyv+pb9KkSfrggw8ynOeNN97QkCFDtHLlSu3cuVP79u1TdHS0wsPDNW/ePH3++ed69dVXs7z6XUFVtmxZzZw5U7Gxsfaw0XfffZdnwTUpJaC2adMmrV+/XnXq1FF8fLy8vb3VrVu3POvhlvHjx9tDa127dtXjjz+uhx56SMWLF5e7u7v95+jhhx/WhQsX7KsD5qa8fi7fLbPZnOa53rx5cw0aNEgdOnRQSEiI/vvf/6p58+YOV5MDAAAAAAAE1wAAAAAAQBYULlxYtWrV0uHDhxUSEqKzZ8+qQoUKdz3vre3kIiMjFR8fn+mqa7evhHb7NnSO+Pr66vLly7py5UqmdcnJydlaya2gK1q0qCTd0Qpyt8ZKko+PT4Fcfev2z4Cj7+2dfnZulxP35PY57uT7snDhQkkpn+mtW7dmuFpVRESEw7nKli2rsWPHauzYsbJYLDp8+LB++OEHffHFF4qMjNSUKVNUu3ZtdenSJdt9FjS3QnqSdObMmTy9dufOnVW4cGHdvHlTkyZNkpSy1WyhQoXytI/IyEitXr1aktS/f3/NnTs3w9qsfH7uxu1hzitXrsjHxyfD2rvdfjiv+Pr6avbs2WrXrp2Sk5P15ptv2lfXAwAAAAAAGcv4/40UAAAAAADgNgMHDpQk2Ww2ffbZZzkyZ/Xq1SVJVqtVhw4dyrT2wIEDkiQ3N7csr3YlyR4w+u2335SYmJhh3bFjxzI972xq164tKSUYEhoamq2xtWrVsr8ODg7O0b5yyq3PjiTt378/09pbnx1JdxzCy4l7UqtWLfsKU3v27Mn2+JMnT0qSWrRokekWi45+lv7JZDKpfv36+s9//qNvv/3Wfvy7775LVedsq2PdUqpUKfvrvH4P7u7uevTRRyVJ8fHxkvJnm9AzZ84oKSlJktSrV68M60JCQhQdHZ2rvdz+M3jw4MFMa7P7Wc5P9evXV48ePSSlPCP+uW02AAAAAABIi+AaAAAAAADIkiFDhsjPz0+SNG/ePAUFBd31nI888oj99VdffZVh3blz5/Tjjz9KkgIDA+Xm5pbla7Ru3VqSFB0dre+//z7Dusyu74w6d+5sf/3pp59ma2ytWrVUtmxZSdLixYtzPchyJ0qVKqVq1apJkn744QfdvHkzw9pbK5UZjUa1bNnyjq6XE/fkgQcesG9TuXbtWp0/fz5b45OTkyVJsbGxGdYcOXLEYZAvM40aNbKvBnbt2rVU525fETG/Q57Z2cby9vCTv79/brSTqYEDB8rNzU1ubm6qWLGiWrRokec93PrsSJl/fr744otc76VFixb2rZCXLVuWYd3Bgwd1/PjxXO8nJ73yyiv2cGRm2/UCAAAAAIAUBNcAAAAAAECWeHh4aO7cuTKZTLJYLBowYICWL1/uMECS2fab9erVU/369SWlBBg2b96cpiYhIUHPPfecPXgxcuTIbPU9cOBAeXh4SJLefvttXbp0KU3Nrl27tGDBgmzNW9C1atVKdevWlSQtWLBAS5cuzbA2JiYm1faARqNRL730kiTp4sWLGj58eKZhl/j4eM2dO9e+olReGTFihKSUrQ1feumldD+LX375pXbs2CFJ6tKli8qVK3dH18qpe/LCCy9ISvlcDx06NNOfjwsXLqT6ulKlSpKkvXv3prvl5dWrV/XMM89k+j6+/vpr+8pb6QkODra/r3+GvG4FVyXp999/z/Q6ue3999/XhAkT9Mcff2Rad+7cOb377rv2r7t165bbraXRpEkTXb58WZcvX9ahQ4fyZeW6SpUq2a+7bNmydH9WNmzYoHnz5uV6LyVLlrRvQbt9+3Z9/fXXaWqio6PtPyvO5OGHH7aHhn/++Wft3LkznzsCAAAAAKBgM+d3AwAAAAAAwHm0atVKs2bN0tixYxUbG6tRo0ZpxowZ6t69u+rXr69ixYrJZDIpIiJCoaGh2rp1q7Zs2WIff2slp9vNmDFDbdu2VUJCggYNGqThw4era9eu8vHx0YkTJzRz5kz7qjuPPvqoPfCQVSVKlNAbb7xhD7k88sgjGjdunBo2bKiEhARt2bJFn376qUqVKqW4uDhdvXr17m5SATJ37ly1adNGUVFR+te//qXvv/9effv2VUBAgGw2m86fP69du3Zp5cqVWrRoUaqVoIYNG6YdO3ZozZo12rRpkxo1aqQnn3xSDRs2lK+vr2JiYnT69GkFBwdr7dq1unnzpgYNGpSn72/YsGH69ttvFRwcrG+//VZ//PGHRo4cqYoVK+ratWv69ttv7aEYX19fTZky5a6vd7f3pGPHjho2bJgWLFigQ4cOqVGjRnr66acVGBgoX19fRURE6Ndff9XatWtlMpm0du1a+9iBAwd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          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "crysto_env",
      "language": "python",
      "name": "crysto_env"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.0"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}