diff --git a/forex/forex.ipynb b/forex/forex.ipynb
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+++ b/forex/forex.ipynb
@@ -0,0 +1,489 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNP3uP/qqGwI61LNM2Z+Egl",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/anushka7220/Hedging-of-Financial-Derivatives/blob/main/forex/forex.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install tensorflow\n",
+        "!pip install keras\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zz0qhOpbN4o0",
+        "outputId": "989208a6-a16e-4042-81f1-f1a4a13a3b30"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
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+            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
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+            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
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+            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.25.2)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.0)\n",
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+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\n",
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+            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
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+            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.3)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.3)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.0)\n",
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+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
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+            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n",
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n",
+            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.15.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install yfinance\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mCDJ8EVFPcNw",
+        "outputId": "8a79b354-b0d2-4123-f60d-e2393e384c3b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.38)\n",
+            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.0.3)\n",
+            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.25.2)\n",
+            "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.31.0)\n",
+            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance) (0.0.11)\n",
+            "Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.9.4)\n",
+            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.4.4)\n",
+            "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2023.4)\n",
+            "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance) (2.4.4)\n",
+            "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.10/dist-packages (from yfinance) (3.17.5)\n",
+            "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (4.12.3)\n",
+            "Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.10/dist-packages (from yfinance) (1.1)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n",
+            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->yfinance) (2024.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance) (2024.2.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import yfinance as yf\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Step 1: Fetch historical exchange rate data\n",
+        "def get_exchange_rate_data(base_currency='USD', target_currency='EUR', start_date='2010-01-01', end_date='2024-01-01'):\n",
+        "    ticker = f'{base_currency}{target_currency}=X'\n",
+        "    data = yf.download(ticker, start=start_date, end=end_date)\n",
+        "    return data\n",
+        "\n",
+        "exchange_rates = get_exchange_rate_data(start_date='2010-01-01', end_date='2024-01-01')\n",
+        "\n",
+        "# Step 2: Prepare data for training\n",
+        "df = exchange_rates[['Close']].rename(columns={'Close': 'Rate'})\n",
+        "\n",
+        "# Step 3: Continue with the rest of your code to prepare the data, build and train the LSTM model, and make predictions.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xMdh_X7UPr0M",
+        "outputId": "20a3d30a-0244-4d9e-e208-acda23d6caf9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\r[*********************100%%**********************]  1 of 1 completed\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense, LSTM\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "AmrtbtVvP2W0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Step 2: Prepare data for training\n",
+        "data = df['Rate'].values.reshape(-1, 1)"
+      ],
+      "metadata": {
+        "id": "loi-lH3rP7Cf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Scale the data to a range between 0 and 1\n",
+        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+        "scaled_data = scaler.fit_transform(data)\n"
+      ],
+      "metadata": {
+        "id": "040JOl-mP8l5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create sequences of historical exchange rate values\n",
+        "sequence_length = 30  # Number of previous days to consider\n",
+        "X, y = [], []\n",
+        "for i in range(len(scaled_data) - sequence_length):\n",
+        "    X.append(scaled_data[i:i+sequence_length])\n",
+        "    y.append(scaled_data[i+sequence_length])\n",
+        "\n",
+        "X = np.array(X)\n",
+        "y = np.array(y)\n"
+      ],
+      "metadata": {
+        "id": "LxGnEtsXQGY4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+      ],
+      "metadata": {
+        "id": "ArKNJ3SaQMIE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Step 3: Build the LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(units=128, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
+        "model.add(LSTM(units=64, return_sequences=False))\n",
+        "model.add(Dense(units=1))\n"
+      ],
+      "metadata": {
+        "id": "GzvqrlKtQO5L"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='mean_squared_error')"
+      ],
+      "metadata": {
+        "id": "LL_cUiC4QSVf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Step 4: Train the model\n",
+        "history = model.fit(X_train, y_train, epochs=50, batch_size=64, validation_data=(X_test, y_test), verbose=1)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EIcXwQjAQWQZ",
+        "outputId": "3888df2c-dea8-483e-ec60-2067e20fcc76"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "46/46 [==============================] - 13s 184ms/step - loss: 0.0162 - val_loss: 0.0011\n",
+            "Epoch 2/50\n",
+            "46/46 [==============================] - 4s 97ms/step - loss: 7.7362e-04 - val_loss: 7.5109e-04\n",
+            "Epoch 3/50\n",
+            "46/46 [==============================] - 9s 209ms/step - loss: 6.6720e-04 - val_loss: 7.5947e-04\n",
+            "Epoch 4/50\n",
+            "46/46 [==============================] - 6s 130ms/step - loss: 6.5302e-04 - val_loss: 7.0224e-04\n",
+            "Epoch 5/50\n",
+            "46/46 [==============================] - 9s 198ms/step - loss: 6.2874e-04 - val_loss: 6.7900e-04\n",
+            "Epoch 6/50\n",
+            "46/46 [==============================] - 8s 168ms/step - loss: 6.0464e-04 - val_loss: 6.5665e-04\n",
+            "Epoch 7/50\n",
+            "46/46 [==============================] - 11s 240ms/step - loss: 5.9347e-04 - val_loss: 6.4056e-04\n",
+            "Epoch 8/50\n",
+            "46/46 [==============================] - 10s 214ms/step - loss: 5.8418e-04 - val_loss: 6.1300e-04\n",
+            "Epoch 9/50\n",
+            "46/46 [==============================] - 6s 123ms/step - loss: 6.3573e-04 - val_loss: 5.9115e-04\n",
+            "Epoch 10/50\n",
+            "46/46 [==============================] - 5s 108ms/step - loss: 5.4352e-04 - val_loss: 5.6128e-04\n",
+            "Epoch 11/50\n",
+            "46/46 [==============================] - 7s 155ms/step - loss: 5.0431e-04 - val_loss: 5.4673e-04\n",
+            "Epoch 12/50\n",
+            "46/46 [==============================] - 5s 101ms/step - loss: 4.9428e-04 - val_loss: 5.3500e-04\n",
+            "Epoch 13/50\n",
+            "46/46 [==============================] - 7s 151ms/step - loss: 4.8781e-04 - val_loss: 5.1928e-04\n",
+            "Epoch 14/50\n",
+            "46/46 [==============================] - 4s 98ms/step - loss: 4.5635e-04 - val_loss: 5.2700e-04\n",
+            "Epoch 15/50\n",
+            "46/46 [==============================] - 5s 101ms/step - loss: 4.7966e-04 - val_loss: 6.5309e-04\n",
+            "Epoch 16/50\n",
+            "46/46 [==============================] - 7s 147ms/step - loss: 4.5373e-04 - val_loss: 4.8669e-04\n",
+            "Epoch 17/50\n",
+            "46/46 [==============================] - 4s 97ms/step - loss: 4.4746e-04 - val_loss: 4.5985e-04\n",
+            "Epoch 18/50\n",
+            "46/46 [==============================] - 6s 134ms/step - loss: 4.2658e-04 - val_loss: 4.8124e-04\n",
+            "Epoch 19/50\n",
+            "46/46 [==============================] - 5s 111ms/step - loss: 4.4038e-04 - val_loss: 4.9066e-04\n",
+            "Epoch 20/50\n",
+            "46/46 [==============================] - 5s 98ms/step - loss: 3.9595e-04 - val_loss: 4.1977e-04\n",
+            "Epoch 21/50\n",
+            "46/46 [==============================] - 6s 139ms/step - loss: 4.0285e-04 - val_loss: 4.7019e-04\n",
+            "Epoch 22/50\n",
+            "46/46 [==============================] - 5s 106ms/step - loss: 4.0541e-04 - val_loss: 4.0423e-04\n",
+            "Epoch 23/50\n",
+            "46/46 [==============================] - 6s 129ms/step - loss: 3.9358e-04 - val_loss: 5.5367e-04\n",
+            "Epoch 24/50\n",
+            "46/46 [==============================] - 5s 116ms/step - loss: 3.9894e-04 - val_loss: 3.9787e-04\n",
+            "Epoch 25/50\n",
+            "46/46 [==============================] - 5s 99ms/step - loss: 3.7084e-04 - val_loss: 4.2828e-04\n",
+            "Epoch 26/50\n",
+            "46/46 [==============================] - 8s 185ms/step - loss: 3.6412e-04 - val_loss: 3.6421e-04\n",
+            "Epoch 27/50\n",
+            "46/46 [==============================] - 5s 107ms/step - loss: 3.5156e-04 - val_loss: 3.9828e-04\n",
+            "Epoch 28/50\n",
+            "46/46 [==============================] - 6s 135ms/step - loss: 3.2254e-04 - val_loss: 3.4698e-04\n",
+            "Epoch 29/50\n",
+            "46/46 [==============================] - 5s 102ms/step - loss: 3.2690e-04 - val_loss: 3.7925e-04\n",
+            "Epoch 30/50\n",
+            "46/46 [==============================] - 6s 125ms/step - loss: 3.1969e-04 - val_loss: 3.7955e-04\n",
+            "Epoch 31/50\n",
+            "46/46 [==============================] - 6s 137ms/step - loss: 3.3344e-04 - val_loss: 4.1169e-04\n",
+            "Epoch 32/50\n",
+            "46/46 [==============================] - 5s 108ms/step - loss: 2.9966e-04 - val_loss: 3.3044e-04\n",
+            "Epoch 33/50\n",
+            "46/46 [==============================] - 7s 146ms/step - loss: 2.9076e-04 - val_loss: 3.2060e-04\n",
+            "Epoch 34/50\n",
+            "46/46 [==============================] - 5s 100ms/step - loss: 2.9048e-04 - val_loss: 5.4903e-04\n",
+            "Epoch 35/50\n",
+            "46/46 [==============================] - 5s 107ms/step - loss: 2.9417e-04 - val_loss: 3.0579e-04\n",
+            "Epoch 36/50\n",
+            "46/46 [==============================] - 7s 150ms/step - loss: 2.6795e-04 - val_loss: 2.9819e-04\n",
+            "Epoch 37/50\n",
+            "46/46 [==============================] - 5s 107ms/step - loss: 2.5369e-04 - val_loss: 3.0082e-04\n",
+            "Epoch 38/50\n",
+            "46/46 [==============================] - 7s 151ms/step - loss: 2.5847e-04 - val_loss: 2.9346e-04\n",
+            "Epoch 39/50\n",
+            "46/46 [==============================] - 5s 109ms/step - loss: 2.3925e-04 - val_loss: 2.8057e-04\n",
+            "Epoch 40/50\n",
+            "46/46 [==============================] - 5s 107ms/step - loss: 2.3940e-04 - val_loss: 2.7471e-04\n",
+            "Epoch 41/50\n",
+            "46/46 [==============================] - 7s 144ms/step - loss: 2.4952e-04 - val_loss: 2.8277e-04\n",
+            "Epoch 42/50\n",
+            "46/46 [==============================] - 5s 106ms/step - loss: 2.3628e-04 - val_loss: 2.6541e-04\n",
+            "Epoch 43/50\n",
+            "46/46 [==============================] - 7s 147ms/step - loss: 2.5523e-04 - val_loss: 3.2260e-04\n",
+            "Epoch 44/50\n",
+            "46/46 [==============================] - 5s 98ms/step - loss: 2.3477e-04 - val_loss: 2.6329e-04\n",
+            "Epoch 45/50\n",
+            "46/46 [==============================] - 5s 106ms/step - loss: 2.2343e-04 - val_loss: 2.9458e-04\n",
+            "Epoch 46/50\n",
+            "46/46 [==============================] - 7s 152ms/step - loss: 2.1857e-04 - val_loss: 3.7469e-04\n",
+            "Epoch 47/50\n",
+            "46/46 [==============================] - 5s 102ms/step - loss: 2.9376e-04 - val_loss: 2.6570e-04\n",
+            "Epoch 48/50\n",
+            "46/46 [==============================] - 5s 119ms/step - loss: 2.0668e-04 - val_loss: 2.6872e-04\n",
+            "Epoch 49/50\n",
+            "46/46 [==============================] - 6s 123ms/step - loss: 2.1340e-04 - val_loss: 4.0894e-04\n",
+            "Epoch 50/50\n",
+            "46/46 [==============================] - 5s 109ms/step - loss: 2.1484e-04 - val_loss: 3.6445e-04\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Step 5: Evaluate the model\n",
+        "plt.plot(history.history['loss'], label='Training Loss')\n",
+        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "jF_j-mpVQaJQ",
+        "outputId": "61d11bda-5938-4e27-df71-a88810a81c43"
+      },
+      "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": [
+        "# Step 6: Make predictions\n",
+        "predictions = model.predict(X_test)\n",
+        "predictions = scaler.inverse_transform(predictions)\n",
+        "y_test = scaler.inverse_transform(y_test)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SYrKvB7YQeEL",
+        "outputId": "b891300a-00a8-4447-bb7d-7e765737e1af"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "23/23 [==============================] - 1s 22ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Step 7: Visualize the results\n",
+        "plt.plot(y_test, color='blue', label='Actual Exchange Rate')\n",
+        "plt.plot(predictions, color='red', label='Predicted Exchange Rate')\n",
+        "plt.xlabel('Time')\n",
+        "plt.ylabel('Exchange Rate')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "u3RyE0XpQhb4",
+        "outputId": "03f7c595-f289-48ba-f3a3-0c9cd467d734"
+      },
+      "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": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/forex/forex.py b/forex/forex.py
new file mode 100644
index 00000000..7b09c1bc
--- /dev/null
+++ b/forex/forex.py
@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+"""forex.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_JOkvQ9rMMC6ijgK6qo5g6mHB7OvOWvL
+"""
+
+!pip install tensorflow
+!pip install keras
+
+!pip install yfinance
+
+import yfinance as yf
+import pandas as pd
+
+# Step 1: Fetch historical exchange rate data
+def get_exchange_rate_data(base_currency='USD', target_currency='EUR', start_date='2010-01-01', end_date='2024-01-01'):
+    ticker = f'{base_currency}{target_currency}=X'
+    data = yf.download(ticker, start=start_date, end=end_date)
+    return data
+
+exchange_rates = get_exchange_rate_data(start_date='2010-01-01', end_date='2024-01-01')
+
+# Step 2: Prepare data for training
+df = exchange_rates[['Close']].rename(columns={'Close': 'Rate'})
+
+# Step 3: Continue with the rest of your code to prepare the data, build and train the LSTM model, and make predictions.
+
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from keras.models import Sequential
+from keras.layers import Dense, LSTM
+import matplotlib.pyplot as plt
+
+# Step 2: Prepare data for training
+data = df['Rate'].values.reshape(-1, 1)
+
+#Scale the data to a range between 0 and 1
+scaler = MinMaxScaler(feature_range=(0, 1))
+scaled_data = scaler.fit_transform(data)
+
+# Create sequences of historical exchange rate values
+sequence_length = 30  # Number of previous days to consider
+X, y = [], []
+for i in range(len(scaled_data) - sequence_length):
+    X.append(scaled_data[i:i+sequence_length])
+    y.append(scaled_data[i+sequence_length])
+
+X = np.array(X)
+y = np.array(y)
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Step 3: Build the LSTM model
+model = Sequential()
+model.add(LSTM(units=128, return_sequences=True, input_shape=(X_train.shape[1], 1)))
+model.add(LSTM(units=64, return_sequences=False))
+model.add(Dense(units=1))
+
+# Compile the model
+model.compile(optimizer='adam', loss='mean_squared_error')
+
+# Step 4: Train the model
+history = model.fit(X_train, y_train, epochs=50, batch_size=64, validation_data=(X_test, y_test), verbose=1)
+
+# Step 5: Evaluate the model
+plt.plot(history.history['loss'], label='Training Loss')
+plt.plot(history.history['val_loss'], label='Validation Loss')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+# Step 6: Make predictions
+predictions = model.predict(X_test)
+predictions = scaler.inverse_transform(predictions)
+y_test = scaler.inverse_transform(y_test)
+
+# Step 7: Visualize the results
+plt.plot(y_test, color='blue', label='Actual Exchange Rate')
+plt.plot(predictions, color='red', label='Predicted Exchange Rate')
+plt.xlabel('Time')
+plt.ylabel('Exchange Rate')
+plt.legend()
+plt.show()