2_8_1_Stock_Price_Prediction_using_Time_Series_GANs.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "jpydQU3AH7J6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Project Overview**\n",
        "- **Objective:**\n",
        "    - The goal of this project is to forecast stock prices using advanced deep learning techniques, specifically a **TimeGAN** (Time-series Generative Adversarial Network), to generate synthetic stock data.\n",
        "    - By training a **predictive model** on this synthetic data, we aim to predict future stock prices, leveraging the power of **LSTM** networks for sequence prediction.\n",
        "  \n",
        "- **Approach:**\n",
        "    1. **Data Collection:** Stock data is fetched using the **`yfinance`** library, and only the 'Close' price is used for modeling.\n",
        "    2. **Data Inspection:** The dataset is analyzed for quality by checking for missing values, duplicates, and its statistical properties.\n",
        "    3. **Data Normalization:** The stock data is normalized using **MinMaxScaler** to scale the stock prices between 0 and 1 for better model training.\n",
        "    4. **TimeGAN Architecture:** The TimeGAN model is implemented with separate components for **Generator**, **Discriminator**, **Embedder**, and **Recovery**, trained using synthetic data generated by the GAN.\n",
        "    5. **Predictive Model:** A predictive model is built and trained using synthetic data, and it is used to predict future stock prices.\n",
        "    6. **Evaluation & Visualization:** The model’s performance is evaluated using **Mean Squared Error (MSE)**, and the results are compared with real stock data.\n",
        "\n",
        "- **Outcome:**\n",
        "    - The final model provides synthetic stock data generation and price predictions for future stock movements.\n",
        "\n",
        "---\n",
        "\n",
        "### **Conclusion**\n",
        "- **Results:**\n",
        "    - The TimeGAN model successfully generated synthetic stock data, capturing the underlying patterns of stock price movements.\n",
        "    - The predictive model, trained on the synthetic data, provided reasonable predictions with a **Mean Squared Error (MSE)** score that can be further optimized.\n",
        "  \n",
        "- **Impact:**\n",
        "    - This approach demonstrates the power of **Generative Adversarial Networks** (GANs) in financial modeling, particularly in generating data where historical data might be scarce or limited.\n",
        "    - The use of synthetic data for training predictive models allows for experimentation and model testing without the need for extensive real-world data.\n",
        "\n",
        "- **Future Work:**\n",
        "    - The model can be expanded to include multiple stock features (e.g., volume, open, close) and trained with more sophisticated architectures to improve prediction accuracy.\n",
        "    - Further work could also include applying the model to other financial markets or integrating real-time data for live stock price prediction."
      ],
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      "cell_type": "markdown",
      "source": [
        "----"
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      "metadata": {
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      "cell_type": "code",
      "source": [
        "# Suppress warnings to keep the notebook clean"
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        "id": "whneSfOeIBM1"
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Import warnings module to suppress unnecessary warnings"
      ],
      "metadata": {
        "id": "t1mQq3yIIBQN"
      },
      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Suppress all warnings in the notebook"
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        "id": "Oz445kD0IBTf"
      },
      "execution_count": 3,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yLEnz0BlINy5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SWkiPSAqIN4R"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "NDnI4XNMIPJp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the working libraries"
      ],
      "metadata": {
        "id": "rDjkqRseISIZ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf  # For downloading stock data"
      ],
      "metadata": {
        "id": "0WjpIL1PISL5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation"
      ],
      "metadata": {
        "id": "r6lEtbWSISPL"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations"
      ],
      "metadata": {
        "id": "rh5_SAvuISS8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler  # For data normalization"
      ],
      "metadata": {
        "id": "amCKWzKTISWc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error  # For evaluating model performance\n"
      ],
      "metadata": {
        "id": "3HUGftP7IN7U"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf  # For deep learning models"
      ],
      "metadata": {
        "id": "j1BcidF4IN-d"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Model  # To create models"
      ],
      "metadata": {
        "id": "4jkFAZWtIOCA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input, LSTM, Dense  # Layers for building neural networks\n"
      ],
      "metadata": {
        "id": "MWIlh5WRIOFJ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For plotting data"
      ],
      "metadata": {
        "id": "Iol5sk3nIOT6"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # For creating statistical plots"
      ],
      "metadata": {
        "id": "U_AmXgVpIBcP"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lt0Vw0P3I_tL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vlY7UQwRJGF7"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EjuMfq1sJGUM"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "22IwGxy7JGXv"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "tghDEHJGJGyL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1: Download Stock Data\n",
        "- **Objective:** Download historical stock data for a given ticker symbol.\n",
        "- **Key Steps:**\n",
        "  - Use `yfinance` to fetch stock data within a specified date range.\n",
        "  - Clean the data by retaining only the 'Close' prices.\n",
        "  - Normalize the 'Close' prices using `MinMaxScaler` for better model performance."
      ],
      "metadata": {
        "id": "6NEo10PEOnZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def download_stock_data(ticker, start_date, end_date):\n",
        "    data = yf.download(ticker, start=start_date, end=end_date)  # Fetch stock data for given dates\n",
        "    return data[['Close']].dropna()  # Only keep 'Close' column and remove missing values\n"
      ],
      "metadata": {
        "id": "IstDUen9JTI_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5BNrQ8EwPvfJ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Fetch Apple stock data from January 2022 to January 2023"
      ],
      "metadata": {
        "id": "WdecEQs7JTMo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ticker = \"AAPL\"  # Ticker symbol for Apple stock"
      ],
      "metadata": {
        "id": "_ZBT8nCPJTQT"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = \"2022-01-01\"  # Start date for data download"
      ],
      "metadata": {
        "id": "Q4mR3vO1JTb8"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "end_date = \"2023-01-01\"  # End date for data download"
      ],
      "metadata": {
        "id": "5NmacoRVJTgD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data = download_stock_data(ticker, start_date, end_date)  # Fetch and clean data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqhf6_HrJTjy",
        "outputId": "a316fbc9-68ea-4cae-97f7-85fbc28a4ea4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()  # Initialize the MinMaxScaler to normalize the data\n"
      ],
      "metadata": {
        "id": "BwcOpiZ9JEmd"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data['Close'] = scaler.fit_transform(stock_data[['Close']])  # Normalize 'Close' column\n"
      ],
      "metadata": {
        "id": "SbOAu8O_JErO"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Stock data loaded and normalized.\")  # Confirm data loading and normalization\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQqKWhYAJBAK",
        "outputId": "7fea8ad2-c56e-4354-a3c2-000cbb96f5c1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock data loaded and normalized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "divEkI0oJuOp"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_BT4IyenJvWg"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "41VObkKSJvbL"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VRnqF2-xJvfh"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "rMlgHP0wJw9p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: Inspect the Dataset\n",
        "- **Objective:** Understand the structure and quality of the dataset.\n",
        "- **Key Steps:**\n",
        "  - **Head:** Display the first few rows of data.\n",
        "  - **Describe:** Show statistical summary of the dataset.\n",
        "  - **Info:** Check dataset information including data types and missing values.\n",
        "  - **Missing Values:** Identify and handle any missing values.\n",
        "  - **Duplicates:** Check and remove duplicate rows.\n",
        "  - **Visualizations:**\n",
        "    - **Histogram:** View the distribution of stock prices.\n",
        "    - **Correlation Matrix:** Understand relationships between features (if applicable).\n",
        "    - **Scatter Plot:** Visualize trends over time."
      ],
      "metadata": {
        "id": "sAeXf0AzPKvy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first few rows of the dataset to understand its structure"
      ],
      "metadata": {
        "id": "lNXtK2sZJyNS"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_data.head())  # Show the first 5 rows to get a snapshot of the dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saBUSpYcJvkY",
        "outputId": "15818fd5-4ccc-419f-bdf4-104e742191cd"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Price                         Close\n",
            "Ticker                         AAPL\n",
            "Date                               \n",
            "2022-01-03 00:00:00+00:00  1.000000\n",
            "2022-01-04 00:00:00+00:00  0.958728\n",
            "2022-01-05 00:00:00+00:00  0.873325\n",
            "2022-01-06 00:00:00+00:00  0.821154\n",
            "2022-01-07 00:00:00+00:00  0.824192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "djgy5UcSJvof"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i0i0HsuDJvwe"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display descriptive statistics (mean, std, min, 25%, 50%, 75%, max) to understand data distribution\n"
      ],
      "metadata": {
        "id": "4Xm0VUXCJ9ZU"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_data.describe())  # Show statistical summary for numerical columns\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kS_YCAUCJ9d9",
        "outputId": "5fff2b9d-47da-4af7-add9-9ea199e2fe8f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Price        Close\n",
            "Ticker        AAPL\n",
            "count   251.000000\n",
            "mean      0.514473\n",
            "std       0.233269\n",
            "min       0.000000\n",
            "25%       0.332410\n",
            "50%       0.501161\n",
            "75%       0.712435\n",
            "max       1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HF8CHO6bKCnI"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cfpIk_D_KCrG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display info about the dataset (columns, non-null counts, and data types)\n"
      ],
      "metadata": {
        "id": "au8BaIljKCvt"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_data.info())  # Show dataset info including datatypes and missing values\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABp4hAQ2KC0V",
        "outputId": "03424382-e31b-44e4-bd12-57391daca4b9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 251 entries, 2022-01-03 00:00:00+00:00 to 2022-12-30 00:00:00+00:00\n",
            "Data columns (total 1 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   (Close, AAPL)  251 non-null    float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 3.9 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-dMdofjbKKus"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yPP6f5iUKKz_"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in the dataset"
      ],
      "metadata": {
        "id": "NBOQMqQsKK4G"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_data.isnull().sum())  # Check for missing values in the data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-JA8IP8KK9F",
        "outputId": "5b4feb31-573b-4d76-d68b-48cf6d8a6e30"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Price  Ticker\n",
            "Close  AAPL      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6nvzHfI_KRQ7"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fUTzjgKvKRVA"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate rows in the dataset"
      ],
      "metadata": {
        "id": "oTU9hLLmKRZx"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(stock_data.duplicated().sum())  # Check if any duplicate rows exist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tScRrZy_KLBm",
        "outputId": "26722dbd-e8bd-47ad-927c-a138801fe959"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "09QmIlVhKXOZ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lFMzF-1tKXSs"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot histogram to understand the distribution of stock prices"
      ],
      "metadata": {
        "id": "kGTHOi9mKXZZ"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(stock_data['Close'], bins=30, edgecolor='k', color='skyblue')  # Histogram of stock prices\n",
        "plt.title(\"Histogram of Stock Prices (Normalized)\")  # Title for the histogram\n",
        "plt.xlabel(\"Normalized Close Price\")  # X-axis label\n",
        "plt.ylabel(\"Frequency\")  # Y-axis label\n",
        "plt.show()  # Show the histogram plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "D2mJRtRaKXdo",
        "outputId": "8bae8d6e-29e7-4cdf-b375-dfb6203205e8"
      },
      "execution_count": 35,
      "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": [],
      "metadata": {
        "id": "LPaMoYGZKXiz"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jDFB2ZX8KXnb"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot correlation matrix (useful when you have more features)"
      ],
      "metadata": {
        "id": "K1dJodkhKLF5"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(stock_data.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")  # Heatmap to show correlation\n",
        "plt.title(\"Correlation Matrix\")  # Title for the correlation matrix\n",
        "plt.show()  # Show the correlation plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "RTx8tPWvKLKq",
        "outputId": "8ab99e49-4d6b-4f5c-cda7-879a8eacddce"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YOkUmacMKLO8"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OGNDhD7fKLTc"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot to visualize trends over time"
      ],
      "metadata": {
        "id": "cyClNdm7KC5S"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(stock_data.index, stock_data['Close'], alpha=0.5, color='green')  # Scatter plot of stock prices\n",
        "plt.title(\"Scatter Plot of Stock Prices\")  # Title for the scatter plot\n",
        "plt.xlabel(\"Date\")  # X-axis label\n",
        "plt.ylabel(\"Normalized Close Price\")  # Y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.show()  # Show the scatter plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "jqRy879qKC91",
        "outputId": "70b31a66-2cab-42ea-a45c-b965bea6c3a7"
      },
      "execution_count": 39,
      "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": [],
      "metadata": {
        "id": "UFR8zvdlKtyS"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gl7Qg4IgKt30"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use a small subset of data for simplicity (uncomment to use entire dataset)\n"
      ],
      "metadata": {
        "id": "qoSRoDBVKt97"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# subset = stock_data[:50]  # Use first 50 rows for training"
      ],
      "metadata": {
        "id": "R5mmf8JYKuCu"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# real_data = subset['Close'].values.reshape(-1, 1)  # Reshape data into proper format for training\n"
      ],
      "metadata": {
        "id": "2mnZ4d7yKuIE"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NDoUpewXK5au"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using the entire dataset instead of a small subset"
      ],
      "metadata": {
        "id": "Pd4jzAd-K5jO"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_data = stock_data['Close'].values.reshape(-1, 1)  # Use all data (entire dataset) for training\n"
      ],
      "metadata": {
        "id": "7Ru__bD4K5oR"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ucviPd4wLCz1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lVP4sfAmLD8l"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HfZuASQPLEDJ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kr0ZBIfFLEKH"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "EEsonSQzLEeF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3: Define TimeGAN Architecture\n",
        "- **Objective:** Define the TimeGAN model for generating synthetic stock data.\n",
        "- **Key Components:**\n",
        "  - **Generator:** Generates synthetic data from latent noise.\n",
        "  - **Discriminator:** Distinguishes between real and generated data.\n",
        "  - **Embedder:** Encodes real data into latent space.\n",
        "  - **Recovery:** Recovers the original data from latent space.\n",
        "  - **Autoencoder:** Used for pretraining before GAN training."
      ],
      "metadata": {
        "id": "j8K2TwfZLN60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_timegan(input_dim, seq_length, latent_dim):\n",
        "    # Generator network\n",
        "    generator_input = Input(shape=(seq_length, latent_dim))  # Input layer for generator\n",
        "    generator_lstm = LSTM(50, activation=\"relu\", return_sequences=True)(generator_input)  # LSTM layer for generator\n",
        "    generator_output = Dense(input_dim)(generator_lstm)  # Dense layer to generate data\n",
        "    generator = Model(generator_input, generator_output, name=\"Generator\")  # Create the generator model\n",
        "\n",
        "    # Discriminator network\n",
        "    discriminator_input = Input(shape=(seq_length, input_dim))  # Input layer for discriminator\n",
        "    discriminator_lstm = LSTM(50, activation=\"relu\", return_sequences=False)(discriminator_input)  # LSTM layer for discriminator\n",
        "    discriminator_output = Dense(1, activation=\"sigmoid\")(discriminator_lstm)  # Sigmoid output for binary classification\n",
        "    discriminator = Model(discriminator_input, discriminator_output, name=\"Discriminator\")  # Create the discriminator model\n",
        "\n",
        "    # Embedding network\n",
        "    embedder_input = Input(shape=(seq_length, input_dim))  # Input layer for embedding\n",
        "    embedder_lstm = LSTM(50, activation=\"relu\", return_sequences=True)(embedder_input)  # LSTM layer for embedding\n",
        "    embedder_output = Dense(latent_dim)(embedder_lstm)  # Dense layer for embedding\n",
        "    embedder = Model(embedder_input, embedder_output, name=\"Embedder\")  # Create the embedder model\n",
        "\n",
        "    # Recovery network\n",
        "    recovery_input = Input(shape=(seq_length, latent_dim))  # Input layer for recovery\n",
        "    recovery_lstm = LSTM(50, activation=\"relu\", return_sequences=True)(recovery_input)  # LSTM layer for recovery\n",
        "    recovery_output = Dense(input_dim)(recovery_lstm)  # Dense layer to recover original data\n",
        "    recovery = Model(recovery_input, recovery_output, name=\"Recovery\")  # Create the recovery model\n",
        "\n",
        "    # Autoencoder for pretraining\n",
        "    autoencoder_input = Input(shape=(seq_length, input_dim))  # Input layer for autoencoder\n",
        "    latent_repr = embedder(autoencoder_input)  # Embedding step\n",
        "    recovered_data = recovery(latent_repr)  # Recovery step\n",
        "    autoencoder = Model(autoencoder_input, recovered_data, name=\"Autoencoder\")  # Define the autoencoder model\n",
        "\n",
        "    return generator, discriminator, embedder, recovery, autoencoder  # Return all components of TimeGAN\n"
      ],
      "metadata": {
        "id": "mZ0TkgUxLPJF"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iNCadNgTLWoU"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the TimeGAN components with sequence length, latent dimension, and input dimension\n"
      ],
      "metadata": {
        "id": "1HEPnTNaLWvZ"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seq_length = 5  # Sequence length for training data"
      ],
      "metadata": {
        "id": "mLcsEN6rLW2V"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "latent_dim = 10  # Latent dimension for data representation"
      ],
      "metadata": {
        "id": "tk8-R72nLW8G"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = 1  # Dimensionality of input data (1 feature: stock price)"
      ],
      "metadata": {
        "id": "w4qjXZh7LPQe"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator, discriminator, embedder, recovery, autoencoder = build_timegan(input_dim, seq_length, latent_dim)  # Initialize models\n"
      ],
      "metadata": {
        "id": "oB1kxPXsLPV4"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hc0g6XfvLouC"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the models with appropriate optimizers and loss functions"
      ],
      "metadata": {
        "id": "OzxOVpY8LozQ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator.compile(optimizer='adam', loss='binary_crossentropy')  # Compile discriminator\n"
      ],
      "metadata": {
        "id": "BQmTIyz2Lo5m"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "autoencoder.compile(optimizer='adam', loss='mse')  # Compile autoencoder"
      ],
      "metadata": {
        "id": "01s4iJH2LPdg"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "EiBaqFsyLx1c"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lZK19oNCLzKD"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wFB3fpSWLzR6"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vodobHXjLzcq"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Hus8JeZhL0XM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4: Prepare Data for TimeGAN\n",
        "- **Objective:** Create overlapping sequences from the stock price data for TimeGAN training.\n",
        "- **Key Steps:**\n",
        "  - Prepare sequences of data points with a specified sequence length (e.g., 5 days).\n",
        "  - These sequences will be used to teach the model temporal dependencies in the stock prices."
      ],
      "metadata": {
        "id": "nrMVrN0jL1hL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_sequences(data, seq_length):\n",
        "    # Create overlapping sequences of length seq_length from the data\n",
        "    return np.array([data[i:i+seq_length] for i in range(len(data) - seq_length)])\n"
      ],
      "metadata": {
        "id": "7r9_VfbgL3Dd"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rysqtYWIL3J-"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = prepare_sequences(real_data, seq_length)  # Prepare training data sequences\n"
      ],
      "metadata": {
        "id": "VD1b8cKIL3Qc"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "SVNZcvvhMAli"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OK7YO4z_MBjS"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8yCBk5UjMBqH"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YayCPaU2MBwi"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "LTVPh6IIMDa3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 5: Train TimeGAN with Fewer Epochs\n",
        "- **Objective:** Train the TimeGAN model with a focus on real and fake data generation.\n",
        "- **Key Phases:**\n",
        "  - **Pretraining Autoencoder:** Initially train the autoencoder to learn data representations.\n",
        "  - **GAN Training:** Train the discriminator and generator simultaneously to improve synthetic data quality.\n",
        "  - **Optimization:** Use adversarial loss to optimize the models."
      ],
      "metadata": {
        "id": "oSEnFKwYMEcz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Pretrain Autoencoder"
      ],
      "metadata": {
        "id": "AzFxF8GGMFqx"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "autoencoder.fit(train_data, train_data, epochs=50, batch_size=16, verbose=0)  # Pretrain autoencoder (50 epochs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPzgGATQMFyM",
        "outputId": "fbe5c7c3-1b09-4719-bc5e-b51c0a6e2c90"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a821732eb60>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Autoencoder pretraining complete.\")  # Confirm pretraining completion\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YL-T8I0IMF5v",
        "outputId": "c7a102fe-43cb-49d6-e8ca-1a2451b77fc5"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Autoencoder pretraining complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m65wyFJ2MRMV"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fdfy8VLhMRV1"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Train GAN with fewer epochs"
      ],
      "metadata": {
        "id": "Wvo35M2XMRcp"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 200  # Number of training epochs for GAN"
      ],
      "metadata": {
        "id": "7yIt849mMRuN"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16  # Batch size for training"
      ],
      "metadata": {
        "id": "LCuFIbL9MF_2"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(epochs):\n",
        "    # Sample random real sequences from training data\n",
        "    idx = np.random.randint(0, train_data.shape[0], batch_size)  # Random index selection\n",
        "    real_sequences = train_data[idx]  # Real data sequences\n",
        "\n",
        "    # Generate fake sequences from random noise\n",
        "    noise = np.random.normal(0, 1, (batch_size, seq_length, latent_dim))  # Noise for generator\n",
        "    fake_sequences = generator.predict(noise)  # Generate fake data sequences\n",
        "\n",
        "    # Train discriminator with real and fake data\n",
        "    d_loss_real = discriminator.train_on_batch(real_sequences, np.ones((batch_size, 1)))  # Train with real data\n",
        "    d_loss_fake = discriminator.train_on_batch(fake_sequences, np.zeros((batch_size, 1)))  # Train with fake data\n",
        "    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)  # Average discriminator loss\n",
        "\n",
        "    # Train generator using adversarial loss\n",
        "    g_loss = discriminator.train_on_batch(fake_sequences, np.ones((batch_size, 1)))  # Generator loss\n",
        "\n",
        "    if epoch % 50 == 0:  # Print progress every 50 epochs\n",
        "        print(f\"Epoch {epoch}/{epochs} | D Loss: {d_loss:.4f} | G Loss: {g_loss:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AH3ZL_dHMGGO",
        "outputId": "d6a30838-1fdb-4fc1-8c92-9167895e25c4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step\n",
            "Epoch 0/200 | D Loss: 0.6756 | G Loss: 0.6852\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 804 calls to <function TensorFlowTrainer.make_train_function.<locals>.one_step_on_iterator at 0x7a8200335d80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:6 out of the last 805 calls to <function TensorFlowTrainer.make_train_function.<locals>.one_step_on_iterator at 0x7a8200335d80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
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            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "Epoch 50/200 | D Loss: 0.5917 | G Loss: 0.5923\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "Epoch 100/200 | D Loss: 0.5408 | G Loss: 0.5414\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "Epoch 150/200 | D Loss: 0.5198 | G Loss: 0.5203\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wl8juQQMMhrt"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"TimeGAN training complete.\")  # Confirm GAN training completion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GoN7VZ5RMh2D",
        "outputId": "b3cf335e-8665-400e-c01e-4fd93beca43f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TimeGAN training complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "nmWTbHWvMmzE"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GA-Af8UiMn2N"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bq9JNNwSMn_h"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D2YrCxasMpqo"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "RX3sfyp_MqFW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 6: Generate Synthetic Data from Trained Generator\n",
        "- **Objective:** Generate synthetic stock data using the trained generator.\n",
        "- **Key Steps:**\n",
        "  - Input random noise to the trained generator to create synthetic stock price data.\n",
        "  - Reshape the generated data into the desired format for further use."
      ],
      "metadata": {
        "id": "bAZq18mWMrPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "noise = np.random.normal(0, 1, (100, seq_length, latent_dim))  # Random noise for data generation\n"
      ],
      "metadata": {
        "id": "rlWLWo9AMslV"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data = generator.predict(noise)  # Generate synthetic data from noise\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtZ2y8BCMstq",
        "outputId": "163ade48-cc0a-49f8-cdfb-3e00fda58ac9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 109ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QIwxuEW5QUUX"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data = synthetic_data.reshape(-1, input_dim)  # Reshape the generated data\n"
      ],
      "metadata": {
        "id": "Eh8axKg8Ms1A"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "WlkIlth2M3i-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UTcn2drDM4x_"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dRuskG02M45_"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "16qU2DRvM5AV"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "gGbO4a60M5QQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 7: Train Predictive Model Using Synthetic Data\n",
        "- **Objective:** Use the synthetic data to train a predictive model.\n",
        "- **Key Steps:**\n",
        "  - Create sequences from the synthetic data for training the predictive model.\n",
        "  - Train a model, such as LSTM, to predict future stock prices based on past data."
      ],
      "metadata": {
        "id": "Ha1QntYMM6_u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_sequences(data, time_steps):\n",
        "    X, y = [], []  # Lists to store sequences and corresponding labels\n",
        "    for i in range(len(data) - time_steps):\n",
        "        X.append(data[i:i+time_steps])  # Create input sequences\n",
        "        y.append(data[i+time_steps])  # Create output labels (next time step)\n",
        "    return np.array(X), np.array(y)  # Return as numpy arrays\n"
      ],
      "metadata": {
        "id": "pp7hF5gYMtIu"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ObedzWARMtRj"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = create_sequences(synthetic_data, seq_length)  # Create sequences for training\n"
      ],
      "metadata": {
        "id": "sfKJnZX0MoHO"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gGq9oJSOND9c"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g3dB8lE_NEIW"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define and train the predictive model"
      ],
      "metadata": {
        "id": "vermaxxtNEOq"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictive_model = tf.keras.Sequential([  # Sequential model with LSTM and Dense layers\n",
        "    LSTM(50, activation='relu', input_shape=(seq_length, input_dim)),  # LSTM layer for sequence input\n",
        "    Dense(1)  # Output layer to predict the next price\n",
        "])"
      ],
      "metadata": {
        "id": "hgERPUI-NEVo"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictive_model.compile(optimizer='adam', loss='mse')  # Compile the model with Adam optimizer and MSE loss\n"
      ],
      "metadata": {
        "id": "T8D3EymBNLK1"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictive_model.fit(X_train, y_train, epochs=50, verbose=0)  # Train the model with synthetic data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m35sFkcLNLRr",
        "outputId": "7a4cf660-1ad0-4785-d234-89c124b76d9d"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a81889c7b50>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-4waxHUuNLYJ"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Predictive model training complete.\")  # Confirm model training completion\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-G5OT8qzNLfW",
        "outputId": "a80508a4-b4dc-4432-fede-19a8a118f267"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictive model training complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "vmRDN_aTNXRd"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O0Nrbo-uNYiH"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I4LmXWuTNYqR"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-7GFuUvCNY5S"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "IIuDyHdgNZKs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 8: Evaluate Model on Real Data\n",
        "- **Objective:** Assess the model's performance using real stock data.\n",
        "- **Key Steps:**\n",
        "  - Prepare sequences from real stock data.\n",
        "  - Use the trained model to make predictions on real data.\n",
        "  - Calculate the Mean Squared Error (MSE) to evaluate the accuracy of predictions."
      ],
      "metadata": {
        "id": "CEXaTA-7NbWh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_real, y_real = create_sequences(real_data, seq_length)  # Prepare real data sequences for evaluation\n"
      ],
      "metadata": {
        "id": "ZTl4dBgkNcgq"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predictive_model.predict(X_real)  # Make predictions on real data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnvH1W5ZNcqs",
        "outputId": "ed673e26-375e-491e-db4a-352782b75071"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_real, predictions)  # Calculate Mean Squared Error\n"
      ],
      "metadata": {
        "id": "NP-sXCZfNc1n"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean Squared Error on real data: {mse}\")  # Print MSE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIUKymrNNaKT",
        "outputId": "d0061aad-652e-4a7d-c1dd-ce76321ad89b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error on real data: 0.20100431849719314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "fa8U7MfFNoE9"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D30cOeURNpLW"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uX_XGDp1NpUe"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XUCq728aNpir"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "mfb1zOzKNpyG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 9: Save Predictions to a File\n",
        "- **Objective:** Save the model's predictions for further analysis or reporting.\n",
        "- **Key Steps:**\n",
        "  - Convert predictions to a DataFrame.\n",
        "  - Save the predictions in a CSV file for easy access and review."
      ],
      "metadata": {
        "id": "Hk0ENsaANrWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df = pd.DataFrame(predictions, columns=[\"Predicted Close\"])  # Convert predictions to DataFrame\n"
      ],
      "metadata": {
        "id": "KqMxg_UsNT5K"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df.to_csv(\"predicted_stock_prices.csv\", index=False)  # Save predictions to CSV file\n"
      ],
      "metadata": {
        "id": "_Tpo9t9iNUAo"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Predictions saved to 'predicted_stock_prices.csv'.\")  # Confirm saving\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHn61gu-NUHi",
        "outputId": "669bc023-6c30-4754-e77d-d15314b9c595"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions saved to 'predicted_stock_prices.csv'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "g1WusIUuN7QZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tNjCVLPANLmE"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eDrvRPs7MogZ"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uCorAS_-Moq9"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "oyE_k8cIN9Bj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 10: Plot Results\n",
        "- **Objective:** Visualize the model’s predictions alongside real stock prices.\n",
        "- **Key Steps:**\n",
        "  - Plot actual vs predicted stock prices.\n",
        "  - Use a line plot to visually compare the trends in the data."
      ],
      "metadata": {
        "id": "2TLXk7A-N-pG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(y_real, label=\"Actual\")  # Plot actual stock prices\n",
        "plt.plot(predictions, label=\"Predicted\")  # Plot predicted stock prices\n",
        "plt.legend()  # Show legend\n",
        "plt.title(\"Real vs Predicted Stock Prices\")  # Title for the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "wMFLbdgjMh9f",
        "outputId": "4cb94aad-16c0-4db5-ec3b-bde3d6a1821f"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "9tDjxO9LQl84"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l3W09WHqMiD8"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kMZ_oSI_MB4h"
      },
      "execution_count": 81,
      "outputs": []
    }
  ]
}