1_5_6_Volume_Weighted_Average_Price_(VWAP)_Strategy (1).ipynb

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        "# <span style=\"color: #1f77b4;\">Project Overview: Volume-Weighted Average Price (VWAP) Strategy</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">1. Introduction</span>\n",
        "\n",
        "The **Volume-Weighted Average Price (VWAP)** strategy is a crucial tool in financial trading and investment analysis. It helps traders and investors understand the average price at which a security has traded throughout the day, adjusted for volume. VWAP is widely used to gauge the quality of trades, make informed trading decisions, and assess market trends. By integrating both price and volume, VWAP provides a more accurate reflection of a security's true value compared to a simple average price.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">2. Data Loading and Exploration</span>\n",
        "\n",
        "### 2.1 Data Loading\n",
        "\n",
        "- **Source**: The dataset used for this project consists of minute-level Bitcoin price data spanning from 2012 to 2021. This granularity allows for detailed analysis of price and volume dynamics.\n",
        "- **Columns**: The dataset includes:\n",
        "  - **Timestamp**: The date and time of the recorded price.\n",
        "  - **Open**: The opening price at the start of the period.\n",
        "  - **High**: The highest price during the period.\n",
        "  - **Low**: The lowest price during the period.\n",
        "  - **Close**: The closing price at the end of the period.\n",
        "  - **Volume_(BTC)**: The volume of Bitcoin traded.\n",
        "  - **Volume_(Currency)**: The volume of Bitcoin traded in terms of currency.\n",
        "\n",
        "### 2.2 Data Exploration and Cleaning\n",
        "\n",
        "- **Objective**: To ensure the dataset is clean and ready for analysis, it is essential to handle any missing or incorrect data entries. Proper data cleaning is crucial for accurate VWAP calculations.\n",
        "- **Steps**:\n",
        "  - **Convert Timestamps**: Convert the `Timestamp` column to a datetime format to facilitate time-based analysis and indexing.\n",
        "  - **Set Date Index**: Set the `Date` as the index of the DataFrame to enable time-series operations.\n",
        "  - **Handle Missing Values**: Drop rows with missing values in critical columns such as `Close`, `Volume_(BTC)`, and `Volume_(Currency)` to maintain data integrity.\n",
        "\n",
        "## <span style=\"color: #d62728;\">3. Feature Engineering</span>\n",
        "\n",
        "### 3.1 Typical Price\n",
        "\n",
        "- **Definition**: The Typical Price represents a single value that averages the high, low, and close prices for each period, providing a more balanced view of the price level.\n",
        "- **Formula**: \\[(High + Low + Close) / 3\\]\n",
        "- **Purpose**: This calculation smoothens price data and provides a representative price point for each period, which is crucial for the VWAP calculation.\n",
        "\n",
        "### 3.2 VWAP\n",
        "\n",
        "- **Definition**: VWAP is a cumulative measure that combines price and volume to reflect the average price at which a security has traded over a specific period, typically one day.\n",
        "- **Formula**:\n",
        "  \\[\n",
        "  VWAP = \\frac{ \\sum_{i=1}^{n} (Typical Price_i \\times Volume_i) }{ \\sum_{i=1}^{n} Volume_i }\n",
        "  \\]\n",
        "  where \\( Typical Price_i \\) is the typical price for the \\( i^{th} \\) period and \\( Volume_i \\) is the trading volume for that period.\n",
        "- **Purpose**: VWAP provides a benchmark for evaluating trading performance. It helps traders identify trends and potential entry or exit points by comparing the current price to the VWAP.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">4. Visualization</span>\n",
        "\n",
        "### 4.1 Volume Over Time\n",
        "\n",
        "- **Objective**: Visualize trading volume over time to identify periods of high and low trading activity.\n",
        "- **Insight**: High trading volumes often indicate significant market interest and can influence price movements, making this visualization crucial for understanding market dynamics.\n",
        "\n",
        "### 4.2 VWAP Strategy\n",
        "\n",
        "- **Objective**: Compare VWAP with closing prices to analyze how VWAP tracks the average trading price throughout the day.\n",
        "- **Insight**: By plotting VWAP alongside closing prices, traders can assess the alignment of VWAP with actual market prices and gauge the effectiveness of VWAP as a trading benchmark.\n",
        "\n",
        "### 4.3 Bollinger Bands\n",
        "\n",
        "- **Definition**: Bollinger Bands consist of a middle band (usually a 20-day moving average) and two outer bands that are standard deviations away from the middle band.\n",
        "- **Purpose**: Bollinger Bands measure market volatility and identify potential buy/sell signals based on price deviations from the moving average.\n",
        "- **Components**:\n",
        "  - **Middle Band**: 20-day moving average of the closing price.\n",
        "  - **Upper Band**: Middle Band + (2 × standard deviation of the closing price).\n",
        "  - **Lower Band**: Middle Band - (2 × standard deviation of the closing price).\n",
        "\n",
        "### 4.4 Closing Prices with Moving Averages\n",
        "\n",
        "- **Objective**: Analyze trends using various periods of moving averages, such as 20-day and 100-day moving averages, to identify long-term price trends and potential crossover points.\n",
        "- **Insight**: Moving averages smooth out price fluctuations and help identify trends, crossovers, and potential trading signals.\n",
        "\n",
        "## <span style=\"color: #8c564b;\">5. VWAP Strategy Implementation</span>\n",
        "\n",
        "### 5.1 Generating Signals\n",
        "\n",
        "- **Buy Signal**: When the closing price is below the VWAP, it may indicate a buying opportunity, suggesting that the security is undervalued relative to its average trading price.\n",
        "- **Sell Signal**: When the closing price is above the VWAP, it may indicate a selling opportunity, suggesting that the security is overvalued.\n",
        "- **Purpose**: These signals are used to make informed trading decisions based on VWAP’s indication of average trading prices compared to current market prices.\n",
        "\n",
        "## <span style=\"color: #e377c2;\">6. Backtesting the Strategy</span>\n",
        "\n",
        "### 6.1 Computing Returns\n",
        "\n",
        "- **Objective**: Evaluate the performance of the VWAP strategy by calculating returns based on the generated buy/sell signals.\n",
        "- **Components**:\n",
        "  - **Daily Returns**: Calculate the daily return of the security based on price changes.\n",
        "  - **Strategy Returns**: Calculate returns based on the implementation of buy/sell signals derived from the VWAP strategy.\n",
        "  - **Cumulative Returns**: Assess the overall performance of the strategy over time by calculating the cumulative returns.\n",
        "\n",
        "## <span style=\"color: #7f7f7f;\">7. Evaluation and Visualization</span>\n",
        "\n",
        "### 7.1 VWAP and Close Price\n",
        "\n",
        "- **Objective**: Visualize the VWAP compared to closing prices to understand how VWAP reflects average trading prices.\n",
        "- **Insight**: This visualization helps in evaluating the effectiveness of VWAP as a trading benchmark and in making strategic trading decisions.\n",
        "\n",
        "### 7.2 Cumulative Returns\n",
        "\n",
        "- **Objective**: Assess the performance of the VWAP strategy over time by visualizing cumulative returns.\n",
        "- **Insight**: Shows how well the VWAP strategy would have performed if followed, providing insights into its effectiveness and profitability.\n",
        "\n",
        "### 7.3 Buy and Sell Signals\n",
        "\n",
        "- **Objective**: Visualize buy and sell signals generated by the VWAP strategy alongside VWAP and closing prices.\n",
        "- **Insight**: Helps in evaluating the effectiveness of the signals and the overall strategy performance, providing a visual representation of trading opportunities."
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        "# <span style=\"color: #ff7f0e;\">1. Introduction</span>\n",
        "\n",
        "The **Volume-Weighted Average Price (VWAP)** strategy is a pivotal concept in financial trading, extensively used to evaluate the average price at which a security has traded throughout the day, factoring in both price and volume. This strategy is essential for traders and investors as it helps in making informed decisions, assessing market trends, and evaluating trade performance. Below, we delve deeper into the machine learning (ML) aspects related to the VWAP strategy, emphasizing how ML techniques and models integrate with and enhance the use of VWAP.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">1.1 Understanding VWAP in Machine Learning Context</span>\n",
        "\n",
        "### 1.1.1 What is VWAP?\n",
        "\n",
        "- **Definition**: VWAP is the weighted average price of a security, calculated by taking the cumulative sum of the product of the typical price and volume, divided by the cumulative volume. It provides a measure of the average price a security has traded at throughout the day, adjusted for volume.\n",
        "- **Formula**:\n",
        "  \\[\n",
        "  VWAP_t = \\frac{ \\sum_{i=1}^{t} (Typical Price_i \\times Volume_i) }{ \\sum_{i=1}^{t} Volume_i }\n",
        "  \\]\n",
        "  where \\( Typical Price_i \\) is the typical price for the \\( i^{th} \\) period, and \\( Volume_i \\) is the trading volume for that period.\n",
        "\n",
        "### 1.1.2 Importance of VWAP in Trading\n",
        "\n",
        "- **Benchmarking**: VWAP is used as a benchmark to assess the quality of trades. If a trader’s execution price is below VWAP, it suggests they bought below the average price of the day; if above, they sold above the average.\n",
        "- **Trading Decisions**: VWAP helps in making buy or sell decisions. Prices above VWAP might indicate an overvalued security, while prices below VWAP might suggest undervaluation.\n",
        "\n",
        "## <span style=\"color: #d62728;\">1.2 ML Aspects Related to VWAP</span>\n",
        "\n",
        "### 1.2.1 Data Preparation for ML Models\n",
        "\n",
        "- **Feature Engineering**: For ML models, VWAP serves as a critical feature. It encapsulates both price and volume information, making it a valuable predictor for various ML algorithms.\n",
        "  - **Typical Price**: Derived from the high, low, and close prices, typical price is used to compute VWAP.\n",
        "  - **Volume**: Essential for VWAP calculation, volume information helps in weighting price data.\n",
        "- **Time-Series Data**: VWAP operates within a time-series context, making it essential to understand and preprocess time-series data for ML applications.\n",
        "  - **Datetime Indexing**: Convert timestamps to datetime format and set them as the index for time-based operations.\n",
        "  - **Handling Missing Data**: Ensure missing values are addressed to avoid skewed results in ML models.\n",
        "\n",
        "### 1.2.2 Applying ML Algorithms with VWAP\n",
        "\n",
        "- **Regression Models**: Use VWAP as a feature in regression models to predict future price movements or returns.\n",
        "  - **Linear Regression**: Can be used to understand the relationship between VWAP and closing prices.\n",
        "  - **Advanced Models**: Techniques like LSTM (Long Short-Term Memory) networks or ARIMA (AutoRegressive Integrated Moving Average) can be employed for predicting future VWAP based on historical data.\n",
        "  \n",
        "- **Classification Models**: Utilize VWAP to classify trading signals or market conditions.\n",
        "  - **Buy/Sell Classification**: Train classification models to predict buy or sell signals based on VWAP and other features like historical price data.\n",
        "  - **Decision Trees/Random Forests**: Can be used to create models that classify market conditions as favorable or unfavorable based on VWAP and other technical indicators.\n",
        "\n",
        "### 1.2.3 Model Evaluation and Validation\n",
        "\n",
        "- **Backtesting**: Evaluate the performance of trading strategies involving VWAP by backtesting them on historical data.\n",
        "  - **Historical Simulation**: Apply the VWAP strategy to past data to simulate trading decisions and assess performance metrics.\n",
        "  - **Metrics**: Analyze metrics such as accuracy, precision, recall, and F1-score for classification models, and Mean Absolute Error (MAE), Mean Squared Error (MSE) for regression models.\n",
        "\n",
        "- **Cross-Validation**: Implement cross-validation techniques to ensure the robustness and generalizability of the models.\n",
        "  - **Time-Series Cross-Validation**: Use techniques like rolling-window or expanding-window cross-validation to handle the temporal nature of the data.\n",
        "\n",
        "## <span style=\"color: #8c564b;\">1.3 Practical Applications of VWAP in ML Models</span>\n",
        "\n",
        "### 1.3.1 Enhancing Trading Strategies\n",
        "\n",
        "- **Algorithmic Trading**: Integrate VWAP into algorithmic trading systems to make real-time trading decisions based on VWAP signals.\n",
        "- **Portfolio Management**: Use VWAP-based features to optimize portfolio allocations and improve trade execution strategies.\n",
        "\n",
        "### 1.3.2 Risk Management\n",
        "\n",
        "- **Volatility Analysis**: Analyze the volatility of VWAP to assess market risk and adjust trading strategies accordingly.\n",
        "- **Stop-Loss and Take-Profit**: Implement VWAP-based stop-loss and take-profit levels to manage risk in trading strategies.\n",
        "\n",
        "## <span style=\"color: #e377c2;\">1.4 Summary</span>\n",
        "\n",
        "VWAP is a vital component in financial trading strategies, providing insights into average trading prices adjusted for volume. In the context of machine learning, VWAP serves as a significant feature for various predictive and classification models. Proper data preparation, feature engineering, and model evaluation are essential to leveraging VWAP effectively in ML applications. By integrating VWAP into trading algorithms and risk management strategies, traders and investors can enhance their decision-making processes and overall trading performance."
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        "# <span style=\"color: #1f77b4;\">Importing Libraries</span>\n",
        "\n",
        "In the development of the Volume-Weighted Average Price (VWAP) strategy, several key Python libraries are employed. These libraries provide a robust set of tools for data handling, manipulation, and visualization, essential for effectively analyzing financial data. Here's a detailed overview of each library and its role in the project:\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">1. Data Handling and Manipulation</span>\n",
        "\n",
        "- **Pandas**:\n",
        "  - **Purpose**: Essential for data manipulation and analysis. It provides data structures such as DataFrames and Series that facilitate the handling of large datasets.\n",
        "  - **Key Features**:\n",
        "    - **DataFrames**: Two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).\n",
        "    - **Data Cleaning**: Functions to handle missing data, data transformations, and merging datasets.\n",
        "    - **Time Series**: Tools for working with time-series data, including date parsing and resampling.\n",
        "\n",
        "- **NumPy**:\n",
        "  - **Purpose**: Fundamental for numerical computations and array operations.\n",
        "  - **Key Features**:\n",
        "    - **Arrays**: Provides support for multi-dimensional arrays and matrices.\n",
        "    - **Mathematical Functions**: Includes a wide range of mathematical functions for operations on arrays.\n",
        "    - **Performance**: Optimized for performance, allowing for efficient numerical computations.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">2. Data Visualization</span>\n",
        "\n",
        "- **Matplotlib**:\n",
        "  - **Purpose**: A versatile library for creating static, animated, and interactive visualizations.\n",
        "  - **Key Features**:\n",
        "    - **Plotting**: Functions to create various types of plots including line, bar, scatter, and histogram.\n",
        "    - **Customization**: Extensive customization options for plot appearance, including colors, labels, and styles.\n",
        "    - **Integration**: Can be used in conjunction with other libraries like Pandas for integrated plotting.\n",
        "\n",
        "- **Seaborn**:\n",
        "  - **Purpose**: Provides a high-level interface for drawing attractive and informative statistical graphics.\n",
        "  - **Key Features**:\n",
        "    - **Statistical Plots**: Functions to create plots such as distribution plots, regression plots, and heatmaps.\n",
        "    - **Theming**: Built-in themes for styling plots to improve readability and aesthetics.\n",
        "    - **Integration**: Works seamlessly with Matplotlib and Pandas for enhanced plotting capabilities.\n",
        "\n",
        "## <span style=\"color: #d62728;\">3. Statistical Analysis</span>\n",
        "\n",
        "- **Scipy**:\n",
        "  - **Purpose**: A library for scientific and technical computing that builds on NumPy.\n",
        "  - **Key Features**:\n",
        "    - **Optimization**: Algorithms for optimization and solving equations.\n",
        "    - **Integration**: Functions for numerical integration and differential equations.\n",
        "    - **Interpolation**: Tools for interpolating and smoothing data.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">4. Date and Time Handling</span>\n",
        "\n",
        "- **Datetime**:\n",
        "  - **Purpose**: A module for manipulating dates and times, crucial for time-series analysis.\n",
        "  - **Key Features**:\n",
        "    - **Date and Time Arithmetic**: Functions to perform operations on dates and times, such as addition and subtraction.\n",
        "    - **Formatting**: Tools for parsing and formatting date and time strings.\n",
        "    - **Time Zones**: Support for time zone conversions and calculations.\n",
        "\n",
        "## <span style=\"color: #8c564b;\">5. Additional Libraries</span>\n",
        "\n",
        "- **Plotly** (Optional):\n",
        "  - **Purpose**: A library for creating interactive plots and dashboards.\n",
        "  - **Key Features**:\n",
        "    - **Interactive Visualizations**: Allows for interactive data exploration with features like zooming and hover information.\n",
        "    - **Dashboards**: Enables the creation of interactive dashboards for comprehensive data analysis.\n",
        "\n",
        "By leveraging these libraries, the VWAP strategy project can efficiently handle data, perform complex calculations, and create insightful visualizations to support trading decisions."
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        "# <span style=\"color: #2ca02c;\">2. Data Loading and Exploration</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">2.1 Data Loading</span>\n",
        "\n",
        "### 2.1.1 Source\n",
        "\n",
        "- **Dataset**: Contains minute-level Bitcoin price data from 2012 to 2021. This high-frequency data is essential for detailed analysis and precise VWAP calculations.\n",
        "- **File Format**: Typically in CSV format, which is convenient for manipulation using Python libraries.\n",
        "\n",
        "### 2.1.2 Columns\n",
        "\n",
        "- **Timestamp**: Date and time of the recorded price. Crucial for time-series analysis and aligning data with trading periods.\n",
        "- **Open**: The opening price at the start of the period. Useful for understanding the initial value of the security.\n",
        "- **High**: The highest price during the period. Helps in identifying price peaks.\n",
        "- **Low**: The lowest price during the period. Helps in identifying price troughs.\n",
        "- **Close**: The closing price at the end of the period. Important for computing returns and evaluating the end-of-period value.\n",
        "- **Volume_(BTC)**: Volume of Bitcoin traded in Bitcoin units. Critical for VWAP calculations.\n",
        "- **Volume_(Currency)**: Volume of Bitcoin traded in terms of currency. Provides a monetary perspective of trading volume.\n",
        "\n",
        "### 2.1.3 Loading Data in Python\n",
        "\n",
        "- **Libraries**: Use Pandas for data manipulation and NumPy for numerical operations.\n",
        "\n",
        "## <span style=\"color: #d62728;\">2.2 Data Exploration and Cleaning</span>\n",
        "\n",
        "### 2.2.1 Data Exploration\n",
        "\n",
        "- **Objective**: Understand the dataset structure and quality; identify issues that need addressing.\n",
        "- **Tasks**:\n",
        "  - **Summary Statistics**: Calculate mean, median, and standard deviation.\n",
        "  - **Data Types**: Check and convert data types for analysis.\n",
        "  - **Missing Values**: Identify and address missing values, especially in critical columns.\n",
        "\n",
        "### 2.2.2 Data Cleaning\n",
        "\n",
        "- **Objective**: Prepare data for accurate VWAP calculations and ML modeling.\n",
        "- **Steps**:\n",
        "  - **Convert Timestamps**: Convert `Timestamp` to datetime format.\n",
        "  - **Set Date Index**: Set `Date` as the index for time-series operations.\n",
        "  - **Handle Missing Values**: Drop or fill missing values in critical columns.\n",
        "  - **Data Resampling**: Resample data if needed (e.g., to hourly frequency).\n",
        "\n",
        "### 2.2.3 Data Visualization for Exploration\n",
        "\n",
        "- **Objective**: Visually explore the dataset to identify patterns and anomalies.\n",
        "- **Techniques**:\n",
        "  - **Time-Series Plots**: Plot closing prices and volumes over time.\n",
        "  - **Histogram**: Plot histograms of price and volume distributions.\n",
        "\n",
        "### 2.2.4 Data Transformation\n",
        "\n",
        "- **Objective**: Prepare data for feature engineering and modeling.\n",
        "- **Tasks**:\n",
        "  - **Normalization/Standardization**: Normalize or standardize features to ensure similar scales.\n",
        "\n",
        "## <span style=\"color: #8c564b;\">2.3 Summary</span>\n",
        "\n",
        "Data loading and exploration are crucial for preparing ML models with VWAP. Properly loading the dataset, cleaning it, and conducting exploratory analysis ensure data accuracy and readiness for further analysis. By converting timestamps, handling missing values, and visualizing data, insights into the dataset's structure and quality are gained, setting the stage for effective feature engineering and model development."
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      "source": [
        "# Load the Dataset"
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      "cell_type": "code",
      "source": [
        "file_path = '/content/bitstampUSD_1-min_data_2012-01-01_to_2021-03-31.csv'  # Path to the dataset"
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      "execution_count": 8,
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv(file_path)  # Load the dataset into a pandas DataFrame"
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      "execution_count": 9,
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    {
      "cell_type": "code",
      "source": [
        "#  Data Exploration and Cleaning"
      ],
      "metadata": {
        "id": "mm7w4sciDz4E"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Print header\n",
        "print(data.head())  # Display the first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peP70EWRDz7A",
        "outputId": "97f99f03-c791-4aa9-cea4-e22bf2fa76b7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
            "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
            "1  1325317980   NaN   NaN   NaN    NaN           NaN                NaN   \n",
            "2  1325318040   NaN   NaN   NaN    NaN           NaN                NaN   \n",
            "3  1325318100   NaN   NaN   NaN    NaN           NaN                NaN   \n",
            "4  1325318160   NaN   NaN   NaN    NaN           NaN                NaN   \n",
            "\n",
            "   Weighted_Price  \n",
            "0            4.39  \n",
            "1             NaN  \n",
            "2             NaN  \n",
            "3             NaN  \n",
            "4             NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rUdtxy64Dz9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5bDyktxgD0AR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Information:\")  # Print header\n",
        "print(data.info())  # Display information about the dataset, including data types and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e03u-1AND0C3",
        "outputId": "a5026c93-c90b-4b80-ab1e-62ed21704cb5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1363242 entries, 0 to 1363241\n",
            "Data columns (total 8 columns):\n",
            " #   Column             Non-Null Count    Dtype  \n",
            "---  ------             --------------    -----  \n",
            " 0   Timestamp          1363242 non-null  int64  \n",
            " 1   Open               578373 non-null   float64\n",
            " 2   High               578373 non-null   float64\n",
            " 3   Low                578373 non-null   float64\n",
            " 4   Close              578373 non-null   float64\n",
            " 5   Volume_(BTC)       578373 non-null   float64\n",
            " 6   Volume_(Currency)  578373 non-null   float64\n",
            " 7   Weighted_Price     578372 non-null   float64\n",
            "dtypes: float64(7), int64(1)\n",
            "memory usage: 83.2 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "efSHZCUdDrx8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OB6uvQwODr12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary Statistics:\")  # Print header\n",
        "print(data.describe())  # Display summary statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3wAlcmKDcPR",
        "outputId": "a35cd1e0-4e15-4e4c-f5ad-583aa5c9c7cf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary Statistics:\n",
            "          Timestamp           Open           High            Low  \\\n",
            "count  1.363242e+06  578373.000000  578373.000000  578373.000000   \n",
            "mean   1.366215e+09     387.516810     387.952698     387.048466   \n",
            "std    2.361205e+07     293.558761     293.924738     293.172021   \n",
            "min    1.325318e+09       3.800000       3.800000       3.800000   \n",
            "25%    1.345767e+09     114.620000     114.750000     114.480000   \n",
            "50%    1.366215e+09     427.510000     428.000000     426.870000   \n",
            "75%    1.386664e+09     626.940000     627.410000     626.300000   \n",
            "max    1.407112e+09    1163.000000    1163.000000    1162.990000   \n",
            "\n",
            "               Close   Volume_(BTC)  Volume_(Currency)  Weighted_Price  \n",
            "count  578373.000000  578373.000000       5.783730e+05   578372.000000  \n",
            "mean      387.511770      14.953489       5.867136e+03      387.487475  \n",
            "std       293.564192      48.230001       2.353815e+04      293.534908  \n",
            "min         3.800000       0.000000       0.000000e+00        3.800000  \n",
            "25%       114.550000       0.601397       1.146200e+02      114.557958  \n",
            "50%       427.500000       2.450000       5.310446e+02      427.480000  \n",
            "75%       626.890000      10.470000       2.956691e+03      626.883472  \n",
            "max      1163.000000    4111.876106       1.865889e+06     1162.999574  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HtuWFkFRD_2q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NnOQSJxzD_5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nColumns in the dataset:\")  # Print header\n",
        "print(data.columns)  # Display the column names in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHjYauM4D_9N",
        "outputId": "3b87a054-bea0-4563-8680-3b538d48a9b3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Columns in the dataset:\n",
            "Index(['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume_(BTC)',\n",
            "       'Volume_(Currency)', 'Weighted_Price'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M5hGOj4BD__0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j6eSiyQREADj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate entries"
      ],
      "metadata": {
        "id": "isVX5WHBUgn4"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Duplicate entries in the dataset:\", data.duplicated().sum())  # Print the number of duplicate rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjQ1lkAJUgrd",
        "outputId": "caae8bf8-19e4-4544-a1aa-e1d814f0354f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate entries in the dataset: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uJLo032CUgul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates if any"
      ],
      "metadata": {
        "id": "SIZx-q1wUgyC"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop_duplicates()  # Drop duplicate rows from the dataset"
      ],
      "metadata": {
        "id": "G7DDa3pvUlTP"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RgLXYpvqUlW7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "W5ZMH3dHUlaF"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing values in the dataset:\")  # Print a message\n",
        "print(data.isnull().sum())  # Print the count of missing values for each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pvYOqeeUrPW",
        "outputId": "0a421d87-1485-4ee2-defc-7907cef0e9e2"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in the dataset:\n",
            "Timestamp             0\n",
            "Open                  0\n",
            "High                  0\n",
            "Low                   0\n",
            "Close                 0\n",
            "Volume_(BTC)          0\n",
            "Volume_(Currency)     0\n",
            "Weighted_Price        1\n",
            "Typical Price         0\n",
            "VWAP                  0\n",
            "20-MA                19\n",
            "100-MA               99\n",
            "VWAP Rolling Mean    29\n",
            "VWAP Rolling Std     29\n",
            "20-STD               19\n",
            "Upper Band           19\n",
            "Lower Band           19\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SCgKAXeWUrS5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TDCcGOA-UrWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle missing values, e.g., by filling them with the median or dropping rows"
      ],
      "metadata": {
        "id": "Zd4FY8kqUrZ2"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.fillna(method='ffill')  # Forward fill missing values (replace NaNs with the last valid observation)"
      ],
      "metadata": {
        "id": "A6XB7u4JUwsO"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6RfAvhYmUwvs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BUie1QdEVuN1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the first few rows of the dataset to understand its structure"
      ],
      "metadata": {
        "id": "Os2lfEOVVuRg"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the size of the plot\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close Price data in blue\n",
        "plt.title('Raw Close Price Data Overview')  # Set the title of the plot\n",
        "plt.xlabel('Index')  # Label for x-axis\n",
        "plt.ylabel('Close Price')  # Label for y-axis\n",
        "plt.legend()  # Add a legend to the plot\n",
        "plt.grid(True)  # Show grid lines\n",
        "plt.savefig('/content/Raw_Close_Price_Data_Overview.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "NrFiyZOJVvzA",
        "outputId": "3967de9b-af8e-4f25-89c4-0f95269c75cf"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_ty74ckNVz9A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IGGzTjdeV0BQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot missing values in each column"
      ],
      "metadata": {
        "id": "52yONe9zV0Lx"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the size of the plot\n",
        "missing_values = data.isnull().sum()  # Calculate missing values for each column\n",
        "missing_values.plot(kind='bar', color='red')  # Plot missing values as a bar chart\n",
        "plt.title('Missing Values in Each Column')  # Set the title of the plot\n",
        "plt.xlabel('Columns')  # Label for x-axis\n",
        "plt.ylabel('Number of Missing Values')  # Label for y-axis\n",
        "plt.grid(True)  # Show grid lines\n",
        "plt.savefig('/content/Missing_Values_in_Columns.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "7N-IL7XyV4sv",
        "outputId": "628bc4eb-f16f-4729-a82e-e971c343e384"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u2SixOqhV7dy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jGufMZk4V7lm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the distribution of the 'Close' prices"
      ],
      "metadata": {
        "id": "WxMlTTSMV71_"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the size of the plot\n",
        "plt.hist(data['Close'].dropna(), bins=50, color='blue', edgecolor='black')  # Plot histogram of Close prices\n",
        "plt.title('Distribution of Close Prices')  # Set the title of the plot\n",
        "plt.xlabel('Close Price')  # Label for x-axis\n",
        "plt.ylabel('Frequency')  # Label for y-axis\n",
        "plt.grid(True)  # Show grid lines\n",
        "plt.savefig('/content/Distribution_of_Close_Prices.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "6db2hwndVv3J",
        "outputId": "1fbbc860-3e93-4803-b3df-9efd8f8a141f"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_KpvJFN5WCZh"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1GGPHZoLWCdW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3RcWsSKwVv-m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "16mvrYj6Uwym"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the Close Price and VWAP"
      ],
      "metadata": {
        "id": "pQoGOIYgU25Q"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the size of the plot\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close Price in blue\n",
        "plt.plot(data.index, data['VWAP'], label='VWAP', color='orange', alpha=0.7)  # Plot VWAP in orange\n",
        "plt.title('Close Price and VWAP')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Label for x-axis\n",
        "plt.ylabel('Price')  # Label for y-axis\n",
        "plt.legend()  # Add a legend to the plot\n",
        "plt.grid(True)  # Show grid lines\n",
        "plt.savefig('/content/Close_Price_and_VWAP.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "vgBKxQnlU285",
        "outputId": "fe19f7ae-6ef5-4ad5-fbb2-7e6145b7fe42"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RUaPyLPPVTsN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KFe-sDalVUfY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Raw Data: Close Price"
      ],
      "metadata": {
        "id": "b0WnRNtnU3AL"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the size of the plot\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot raw Close Price data in blue\n",
        "plt.title('Raw Close Price Data')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Label for x-axis\n",
        "plt.ylabel('Price')  # Label for y-axis\n",
        "plt.legend()  # Add a legend to the plot\n",
        "plt.grid(True)  # Show grid lines\n",
        "plt.savefig('/content/Raw_Close_Price_Data.pdf')  # Save the plot as a PDF file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "p7JZF5V8Uldo",
        "outputId": "aaf08e79-3a4e-4c84-bc17-2f37396b65a9"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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s0VRZRkZw64P1xBPSpk3SunW+2957z5xrAKh7kqwuAAAAAADqmqwsqysIXcOG0p49tT/P3r3Syy/737Z8udS4sXTqqbW/DoC6pQ7k8AAAAAAQXWqa5yg5ueZzJFn0z/4DBkTmOnPnSps3R+ZaAKJHVAdN5eXl+sc//qE2bdooPT1dbdu21b333iuHxz07HQ6HJkyYoObNmys9PV35+flau3at13n27NmjESNGKDs7Wzk5Obr22mt18ODBSL8cAAAAAHGiS5ea90lNDX8d/jRvHrlr/fZb5K4FIDpEddD08MMP69lnn9XTTz+tVatW6eGHH9Yjjzyip556yrXPI488oieffFLPPfecFi1apMzMTA0ePFjFHjPSjRgxQitXrlRBQYE++eQTzZs3T6NHj7biJQEAAACIMS1b+q4LpLdStEwi3rp1+M49Z074zg0gOkX1HE3ffvutzj33XA0dOlSS1Lp1a73xxhtavHixJKM305QpU3T33Xfr3HPPlSRNmzZNzZo104cffqhLLrlEq1at0syZM/X999/r+OOPlyQ99dRTGjJkiP75z38qLy/PmhcHAAAAICYMHChlZ0vHHFP9fvXrS3l5UteuxvMhQ6S33w5/fTWJlsALQGyI6qDp5JNP1gsvvKA1a9bo6KOP1k8//aRvvvlGjz32mCRpw4YN2r59u/Lz813H1K9fXyeddJIWLlyoSy65RAsXLlROTo4rZJKk/Px8JSQkaNGiRRo2bJjPdUtKSlRSUuJ6XlhYKEmy2+2y2+3herlh56y9Lr8GBI92B5+B+EXbxx/aPL7R/uFRXu5/EIjdXuFaTkiQevd2rjceMzNtKi/3TnDOPLNCbdu692vfXho+XHr77QSfcwbD2eYVFRWV1lf4rb/y+nr1HD61durkUIMG0rff1j6FCvV1oWr8vsc3s9vf7M9RVAdNd955pwoLC9WxY0clJiaqvLxc999/v0aMGCFJ2r59uySpWaWZ+Jo1a+batn37djVt2tRre1JSkho2bOjap7IHH3xQkyZN8lk/e/ZsZZh1L1ALFRQUWF0CLEC7g89A/KLt4w9tHt9of3OtXdvBZ11qark++2xdtceVlCRq7dp2XusWLvxNq1eXeK0rKkrS2rVG+vTZZ6tDrrO83Kb169d7rfvss9V+66+8vkuXtVq7tr3XPllZO5WRsVft2qXq889bB1RD1667JTm0bFkTn+shPPh9j29mtX9RUZEp53GK6qDp7bff1uuvv67p06erc+fOWrp0qW6++Wbl5eVp5MiRYbvu+PHjNW7cONfzwsJCtWzZUoMGDVJ2dnbYrhtudrtdBQUFGjhwoJIDuQ0GYgLtDj4D8Yu2jz+0eXyj/cPjhx98ewSlpUlDhhxd7XElJdLy5d7H5ue3VW6u936FhdLPPycoMVEaMqRtSDXa7XZ9/vkXatu2rRIS3NccMqSt3/o91x9xhEPDhrX1qfX009uqe3djed26wKb2/dvf2qqiQrr/fu/9Q31dqBq/7/HN7PZ3juIyS1QHTbfddpvuvPNOXXLJJZKkLl26aOPGjXrwwQc1cuRI5f75V3rHjh1q7nHrhB07dqj7n38Vc3NztXPnTq/zlpWVac+ePa7jK0tNTVWqn1tAJCcnx8Qvcay8DgSHdgefgfhF28cf2jy+0f7mSkz0XZeQICUn+9ngoaLC99gDBxJ9Jg5PTjb2S0ys+Zw1SUhIUKLHRZOTE31q6NzZe31SkrsGT+3aJcr5MfL3HviTkmLsOHmy5DlAJCkpkXmgwoTf9/hmVvub/RmK6rvOFRUVeSXykpSYmOgae9ymTRvl5uZqjsetDAoLC7Vo0SL1/nOQdO/evbVv3z4tWbLEtc+XX36piooKnXTSSRF4FQAAAADqIofD//qystDOV14eei3BOuoo/+srfb2qMkTKygrueo0auZcrh0p//BHcuQDUbVHdo+nss8/W/fffryOPPFKdO3fW//73Pz322GO65pprJEk2m00333yz7rvvPrVv315t2rTRP/7xD+Xl5em8886TJHXq1ElnnHGGRo0apeeee052u11jx47VJZdcwh3nAAAAAFSpqkApkMDIXw+eimrmxC4vN4KtUHv+VA7F/NzzSJJvsOS8A56nW2+Vgu3gcNVVVW/78EPpuuuCOx+Auiuqg6annnpK//jHP3TDDTdo586dysvL0/XXX68JEya49rn99tt16NAhjR49Wvv27VPfvn01c+ZMpaWlufZ5/fXXNXbsWA0YMEAJCQkaPny4nnzySSteEgAAAIA6IhLDvTyvsXWrdMQR5py3Xj3/6ysHTf4CpaqODeV6krRlS/DnA1B3RXXQVK9ePU2ZMkVTpkypch+bzabJkydr8uTJVe7TsGFDTZ8+PQwVAgAAAIhVZgdN/obied5V/ODB2pw9sGIrB01NmvjfDwBCFdVBEwAAAADURf5CKj/3G/IanucZOgWrqvmkKktPNx7/8hdp/37J455KAGAKgiYAAAAA8CPQ8CZQrVube75QdOtmPObmGj/Bys6WTL4TOoAYQ9AEAAAAAEGofOe2QIVzzifPUCw72/8+d94peUxlG5KUlNodDyD2hfgnEgAAAADik9k9ncxw4IA7Aaqqx1FtQyap+jvneQo1jANQ9/HrDwAAAABB8Hentsoiccc6T+Xl7gs2bhy+67RqFdh+ziF6Umh3sQNQdxE0AQAAAIAf/nouNWwoXXFFaOerKXzauDG080pSVpZ7JvEWLYI/PtAhcZXvWte8uTRsmO9+nuHSgQPB1wOg7mKOJgAAAAAI0E03he/cBw9Wv93hkHbsMHosJSX5bnPq2dP32Jom/g5kOGC9er5h2fXX+983I6Pm8wGITfRoAgAAAACLeAY3jRpVv+/SpdJzz0n33ee7zeFwn6hlS/f6YcOkBg2k88+v/tydOhmPzZr5bhs9WurSRRo7NvAhgVlZge0HIPbQowkAAAAAIsBfSOPZk6imXkcffVT1Nud5Kk/43a2b93xJVRk61Jh/qWNH3215edLw4cZyoEFTZmZg+wGIPQRNAAAAAOBHbe4uF+nJwDdvzpYkFReHdnxqqv8hd5UF+rq46xwQv/j1BwAAAACL1CbM8rRsWRhvNech0KAp0kEbgOhB0AQAAAAAEVBT+BJqODN3buRSnUCHxOXlhbcOANGLoAkAAAAA/lReLu3dayzv329tLYGaPz9yQdPJJwe2X+W74gGIHwRNAAAAAPCnadOkJ56Q1q2T/vgjstdetSqy1wtFqHMvrVtnbh0AohdBEwAAAAD8aeNG4/GHH6SystDP428YnL91nj1/VqwI/XrRbu5cqysAECkETQAAAADgh93u/bxPH2vq8KeuDOtzqqiwugIAkULQBAAAAAB+ePY2Gj9eGjiwdudLTKzd8Z7efdd4NOuudeFG0ATED6ZoAwAAAIBKKg9zS001/5y1sXmzNH26tGaN9/qLLjLvGmYiaALiBz2aAAAAAMCP2szRFAmVQyZJOuaYyF1/wIDA9430xOoArEPQBAAAAAB+VJ6jKRhm9l6KVsH08srKCl8dAKILQRMAAAAA+FGboClQZs6xdOSRkZ2wKZgwrbAwfHUAiC4ETQAAAADgxy+/WF1BcAYPjt6gSTImMI9EeAfAWgRNAAAAAFCJzWZMuB1u9eqZd660NPPOFYhgg6YVK6TFi8NTC4DoQdAEAAAAABYx4252TpEKmpo2NR7bt69+v6OP9l136JD59QCILklWFwAAAAAAscaKycDT0yNzneuvN4bA1RRs+ZsA/KefpEGDwlMXgOhAjyYAAAAAiCJLl0obN1pdRdUSEwPrPZXg59vmoUNSebn5NQGIHgRNAAAAABAlduyQPvxQeuUVqyupPX9BkyQ98URk6wAQWQRNAAAAAFCJFUPfJGnhQv/rKyoiW4cZqnoPCwslR2RvkAcgggiaAAAAACBKLF3qf/2vv1Z/XNu2+8wupdaq6tEkSb/9FrEyAEQYQRMAAAAARIkmTfyvLyur/rj09Oib+Ojw4aq3bd9u3nXsdmnzZnpJAdGCoAkAAAAAKtmxw5rrhhqW1KtXYm4hJmjVqupte/dKu3ZJzz8v/fJL7a4zfbr00ktVDzsEEFkETQAAAABQye7dVlcQuPx8h1q1OmB1GT4aN6562+LF0vvvS9u2SW++WbvrbNhgPP7wQ+3OA8AcBE0AAAAAEOWqm5z85JMdlk1eXhvFxVZXACAcCJoAAAAAoI7KybG6gtCZPafSnj3mng9AaAiaAAAAACDMMjIC26+w0L28a1fN+998c0jlRIQVvaz27Yv8NQF4I2gCAAAAgDDr0yew/UpL3csLFriXY3GYWTjuEjdlivnnBBAcgiYAAAAACLPDh4M/xrNHU0GBebUAQDgRNAEAAABAmIVyR7Tt293Lp5xiXi2RUtPQuXD0aAJgPYImAAAAAAiziorgjykvdy8nJrqXGzUyHlu3rlVJABAWSVYXAAAAAACxrra9d8rK3MvduknHHCM1aFC7c1qNHk1AbCJoAgAAAIAw8+yRFArP3k2NGxs/0a6moXOhzFsFIPoxdA4AAAAAwiwtLfhjWrRwL+fluZc7dKh9PdHAs5cW6p6vv5Zeekmy20MbGorYRY8mAAAAAKjGkUdac919+9zLO3e6l2vbOwoww9y5xuP99xuPt94q1atnXT2IHvRoAgAAAIBqnH567c/RpEnwxxw86F6eN6/2NURaTUPnwqWkxJrrxrvvvrO6AkQLgiYAAAAAqIYZd3fr3j2040pLjcfU1NrXEC8efNDqCuLTqlVWV4BoQdAEAAAAAGEWau+eBx4wJgI/8URz64l1zBkUeXv2WF0BogVBEwAAAACEWW2GkR0+LKWkGMvt25tTTyQ4HIHv++uv5l77mWfMPR8Cs3+/1RUgGhA0AQAAAECY1eYOa/v3G72aJOvmPQpFMLVOmyZt22betXfvNu9cCNzjj9OzCQRNAAAAABB2a9eGfuyLL0qffGIs16WgKdi74/3vf+5ADXXXzJlWVwCrETQBAAAAQBUyMsw5TzDDyKqzZo0554lGixdL771ndRWorVj+jCIwBE0AAAAAUEeYFVhFWv/+ge3388/mXXPLFvPOheA8/DB3oYtnBE0AAAAAUAWzhqolJZlznrrqlFMif82tWyN/TRgOH5beekuaOrV285NVZf166aWXpFdeMa6F6ELQBAAAAABhdvTRVlcQfzwDjpkzpddfr7s9wqJNcXFg++3aJd13n1RSYu71X3tN2rxZ2rjR6D2F6ELQBAAAAABhVpcm8Y4VnqHSd98ZE7Jv3mxdPbEk2MDuo4/CUweiE0ETAAAAAFTBrIAoJcWc8yBwS5ZI//2vtHOn1ZXEnmB/L8yce8uK8yM4BE0AAAAAEGZt2lhdQd1h1pw7e/ZI69YZw6ycXn5Zmj7dnPPHM7OHwtX22m+/Hfk6UDWCJgAAAACowsGD5pzHrJ5Rp59uznmi2fvvez/ftUuy2wM79sILfdcdOOD9fM2a0OqC2+OPW3ftZcv8rw/HpOMIDUETAAAAANQRaWlWVxB+a9e6l9esMe5c9vLLgR3buXNg+3Gnssjbts2c83z6qf/1O3aYc37UHkETAAAAANQR+/dbXUFoQu3R9b//GY+BhBSNGwd+3ueeC60ehO7558N7/k2bwnt+BI6gCQAAAADqiLo0PMiM4YKB3N3sqKOMx1NOCfy8+/dLEydKxcUhlYUotG6d7zBJSfr4Y6OtN20K/m55CA1BEwAAAADUEY0aWV1BZDgDgWDmUwol2PrnP4M/BtIxx1hdga/166V//Ut67z3v9UuWGI8vvyxNmhT5uuIRQRMAAAAA1BGtWlldQWRMmmSETRUV4b1OWRm9mkLx888173PlldKAAeZed9++mvdZvlwqLTX3uggOQRMAAAAA1BHNmlldQeT88Yf386pCp9oOh3roododD7dRo6TEROnkk40hjf36mXv+yncfnDDB/34ff2w8bthQ/fk+/9wYVjd1KsPqzETQBAAAAABh1KKFOedJTDTnPHXFzp3ez4uKqt/fjDmhULPycikjw/+2I46Q7rpLGjTI//bahjklJe7lrl2lhCoSjeXLjUd/PZuWLTMeKyqkRYuM5V27GFZnJoImAAAAAAij5GRzzlPXelzUNvhZuND7eeXeLGbyDDBQvaeeqj70qy4QrW2Y43nuVauq3/fQIemdd3zXv/++8bs0ebLvtkCG5qFmBE0AAAAAYKFAeyqddFJ46zBbo0ZS69ZSp07eodPJJwd2/ObN3s/ffNP/fsEEcM471FX2zTeBnyPeWRnGePZgqmn+rkcfrfoujVUFXlOmSP/5T90LdaMNQRMAAAAA1AH5+VZXEBybTbrqKunii73Xp6SEdr4dO2q+Xk1GjPC/3jnUCtHNM2gqLw/PNTZsMIIoejeFjqAJAAAAACwUaO+JeJujKRwSE/3PmbVvn7RtW8TLiSkDB4b/GlXNyVSTY48N/pgpU0K7FgiaAAAAAAB1WLDDnK67zrjTWGXPP29KOXHp7rulPn1q3q9Ro9pdJ9R5v1asCO24cM4LFssImgAAAAAgjMyY7+XSS2t/jljnDCECHZo3alT4aolVVQUvSUlVH9OkiXv5jz+8ty1ebMylFOhk7NX9Lp1zTvXHnn12YNfwVNM8UPCPoAkAAAAALJSeXvM+HTqEv45YEWg4cMQRvusYPle9zz4L/pjhw6s/36FD0r/+Fdi5PIOmAQOMx7/9Tbr8cqlHj+qPrWr71VdLl11W8/UQuFoFTaWlpVq9erXKqprKHQAAAABQrcsvt7qCusNfiFQ5DGjXzv+xo0fXfP5PPw2+pnhSuedRXp40bFj1x+Tmej/fudN49GzL0tLAru/Z1s6ea1lZRpvbbFKrVv6P69rVmN/pnnt8t7VqJbVvLzVsKDVuLF1/vXvbwoWB1QVvIQVNRUVFuvbaa5WRkaHOnTtr06ZNkqQbb7xRDz30kKkFAgAAAEAsa97c6goir1690I57+GHp88/9b3MOnTv3XP/b8/JqPv+WLfRiqU7loGn0aKlbt5qPO+ss9/IHH0jffCNNnuy9j795syrzbJs2bXy3Dx8udelizMPl6fzzjUebzX/vJZtNuukmaexY79/Hb76puSb4CiloGj9+vH766Sd99dVXSktLc63Pz8/XW2+9ZVpxkvT777/r8ssvV6NGjZSenq4uXbrohx9+cG13OByaMGGCmjdvrvT0dOXn52vt2rVe59izZ49GjBih7Oxs5eTk6Nprr9XBgwdNrRMAAAAAEJhOnUI7rqREWrSo+n0qD0WsXz+wiaqdXn89+Lrixfr17mXP8Kgmycnu5W3bpC++CO36nkFT06a+27OzjbDJ350FnY4+Who61Fju2rX665WX+1+/d6/00kvSL79Uf3y8Cilo+vDDD/X000+rb9++snlM+965c2et9/zk1dLevXvVp08fJScn6/PPP9fPP/+sf/3rX2rQoIFrn0ceeURPPvmknnvuOS1atEiZmZkaPHiwiouLXfuMGDFCK1euVEFBgT755BPNmzdPowPpNwkAAAAAFgvmC31dUVWvobvvDv5cfw6wqdLNN0sDBwZ+vnXrgq8hHlU1RNGfQO8WN3eu0bPJX++mFSukJUuM5UB6xN1wg/Hor4fbCScY13D2dKqOv+GaM2ZImzdLb75Z8/HxqJq54au2a9cuNfUTHx46dMgreKqthx9+WC1bttQrr7ziWtfGo3+cw+HQlClTdPfdd+vcPz8906ZNU7NmzfThhx/qkksu0apVqzRz5kx9//33Ov744yVJTz31lIYMGaJ//vOfyguk/yQAAAAAWOTPrzExpaqgqaoeJIE4dMi9fOqp0tdfG3PvVPcVNTdX2r499GvGm5wcad8+Y7l+/cCPCzQm+Ppr97LD4T6uokJ6993gzte0aWDD8WoyebIxH9Rdd7nXbdhQ+/PGspCCpuOPP16ffvqpbrzxRklyhUv//ve/1bt3b9OKmzFjhgYPHqwLL7xQX3/9tY444gjdcMMNGvXnfSg3bNig7du3Kz8/33VM/fr1ddJJJ2nhwoW65JJLtHDhQuXk5LhCJskY4peQkKBFixZpmJ+Zy0pKSlTiMfi0sLBQkmS322Wv6n6OdYCz9rr8GhA82h18BuIXbR9/aPP4RvvX3p49Unm576APuz20e5w7z1Ve7pDdXv3EP/6uG+j160rbO19jWZlD9etL5eX+0oKKat8Lp+nTHbrgAofXeffvd7/PJ58stWxpzMtU3dvy++/+rxVqm0eKVW1er55Nf/xh+/PaFQEHSGVl1X/G/fnhhwp1724sz59v8/q87N0b3jaqXOvhw97X89xuxWfF7PY3+3MUUtD0wAMP6Mwzz9TPP/+ssrIyPfHEE/r555/17bff6mvPCLKWfv31Vz377LMaN26c7rrrLn3//fe66aablJKSopEjR2r7n9Fzs2bNvI5r1qyZa9v27dt9el8lJSWpYcOGrn0qe/DBBzVp0iSf9bNnz1ZGRoYZL81SBQUFVpcAC9Du4DMQv2j7+EObxzfaP3T796do7VrfGYY/+2x1SOdbu7bDn+ct0mefbQ5oX38CvX60t73zNaam7lb79vu0dq3v2KuZM1dX+164zyUVFW1Uo0bFrv3T03fr4ME/vPZbubL682zZ0laHD/t+LQ61zSMt0m2+cmULbd2aKUn6/PPA36NVqxpq7domQV3rX/+SLr3UuMYbb/h+JsLZRv4+gy+88LtatDjos/2999YrPb0sbLVUx6z2LyoqMuU8TiEFTX379tXSpUv10EMPqUuXLpo9e7aOO+44LVy4UF26dDGtuIqKCh1//PF64IEHJEk9evTQihUr9Nxzz2nkyJGmXaey8ePHa9y4ca7nhYWFatmypQYNGqTs7OywXTfc7Ha7CgoKNHDgQCV7zsaGmEa7g89A/KLt4w9tHt9o/9rbtUtau9a318WQIW1DOt8PP7jPNWRI9d+TPPcN9vp1pe2dr7FXr3Y64QSHVqzw/15X91546tevrVq1kn780X3efv2Cu2XcccdJL7xgXptHSiTbfOdOadkym/r0cWjnTpsyM42eRcG8R/v22XToUPDT7Div4e8zEc428ne97dvb69prK5SY6L195cr2mjAhsr2azG5/5ygus4QUNElS27Zt9eKLL5pZi4/mzZvrmGOO8VrXqVMnvffee5Kk3NxcSdKOHTvU3OMehDt27FD3P/vY5ebmaufOnV7nKCsr0549e1zHV5aamqrU1FSf9cnJyVH9hztQsfI6EBzaHXwG4hdtH39o8/hG+4cuOVlKTPS33s/KAHieq6Zz+LtusNeP9rZ3vsakpOrf6+reC09JSYlKSnKf59dfpdNPD66mli2lO++UHn3Ut47du6WlS427lB0+LHWouaNVxIW7zX/7TXr1VWP50CHp99/d73cwvxcnnhjavEbOa5j5exmIqj6DDz2UqIkTfbcnJSUGPIzQTGa1v9mfoZDuOvfZZ59p1qxZPutnzZqlzz//vNZFOfXp00erV3t3h1uzZo1atWolyZgYPDc3V3PmzHFtLyws1KJFi1xzRfXu3Vv79u3TEuf09JK+/PJLVVRU6KSTTjKtVgAAAAAIlRVfUq3iOclzbXz+uREAOe3dG9p5MjP9r3/6aembb6SXX5beeMMYrhdvnCGTJG3ZEvp5Ag0PK/vjj5r3ibT9+33X+Zl5J66FFDTdeeedKvdzOwCHw6E777yz1kU53XLLLfruu+/0wAMPaN26dZo+fbpeeOEFjRkzRpIxCfnNN9+s++67TzNmzNDy5ct15ZVXKi8vT+edd54kowfUGWecoVGjRmnx4sVasGCBxo4dq0suuYQ7zgEAAACICvEUNK1YYc55du/2vvW8v9vQB8o56XR1Zs4M/fyxwHm3uUh66in/66+/PrzXPffcqu+q9/jj/teHGnTGopCGzq1du9ZnSJskdezYUevWrat1UU4nnHCCPvjgA40fP16TJ09WmzZtNGXKFI0YMcK1z+23365Dhw5p9OjR2rdvn/r27auZM2cqLS3Ntc/rr7+usWPHasCAAUpISNDw4cP15JNPmlYnAAAAANRGPAVN/nqpXH651LBh8OdK8vhGe8opodd06qnGMLnqRGPvmnBav953XePGRsAXSY895v384oslj5lzwqJHD+Pntdf8vw/+7NkjNWgQ3rrqipCCpvr16+vXX39V69atvdavW7dOmVX1OwzRWWedpbPOOqvK7TabTZMnT9bkyZOr3Kdhw4aaPn26qXUBAAAAiC2O4OaRNlU8BU2S7+tt53EDupNOkhYtCv6cbWsxN3RZpZuG1WaYWKx47TXfdZXfp0Bt2lT1tvbtqx+WWHme6k6dQqshFFdcIS1ZIn38cc371qsX/nrqipCGzp177rm6+eabtd4j2lu3bp1uvfVWnXPOOaYVBwAAAABWitTc2vEUNKWnV7+9c+fQztu0aWjHSVLlmWH+/e/QzxXLQh0+52fmHZeePQM/j8fApYgJ9HPVqFF466hLQgqaHnnkEWVmZqpjx45q06aN2rRpo06dOqlRo0b65z//aXaNAAAAABB2/no0ZWRE5trxFDT17Fn96w1m4uiSktrXI4U2bC+eBdt7rLr2btLEe0L2nJyq983ODu66ZmjRIrD9Qp3wPBaFPHTu22+/VUFBgX766Selp6era9euOqU2g2IBAAAAwEIMnYuMmr6QB/OF3aypd1NSzDlPvNi82ZzzDB5s9AT661+NydYdDmnQIKPnUrTcya263ljwL6SgSTLmRho0aJAGDRpkZj0AAAAAYAl/QVOkwqd4CppqYlXgd+650kcfVb09knMDRbvS0uD237/f//revY3HlBQpkFl4unYN7rpmSKhmHNjYsVJBgfTnTe/xp4CDpieffFKjR49WWlpajXdsu+mmm2pdGAAAAABEUjT2aDr++MjWESnVBWuRGq4Y7HVXrYpMHbFowwZzznPyyeacJxjVBU2NG0uXXhq5WuqKgIOmxx9/XCNGjFBaWpoef/zxKvez2WwETQAAAADqnEhN/O1PVcFL/fqRrcMK7dt7P7fqNW/bZs11EbjqQh9Ej4CDpg0eEeQGs+JIAAAAAIgS0Rg0tWkT2TriGZM5h88RR0hr11pdRehGj5aWLZO++87qSuqGoPNAu92utm3bahX9BgEAAADEkGgaOnfbbdKoUYHf8aqu8Xy90TI/VTz0HjPL0KHB7R/Ke1v5c2HlhO15edIZZ1h3/bom6KApOTlZxcXF4agFAAAAAKKKVeFTZqbRCyQWRUuwVFlFRc37WBlGRpMTTghu/1CGvLVt6/38ttuCP0c4VR7yCbeQRjiOGTNGDz/8sMrKysyuBwAAAAAsEU09mmJdNPZoCuQ29nv3hr+OWJSTE/wxlYeNWjm01Skry70cq70NzRDwHE2evv/+e82ZM0ezZ89Wly5dlJmZ6bX9/fffN6U4AAAAALBShw6RuU60hC1W6NnT6goMHTtKH39c/T47dkgNG0amnmjVtWtkrlMpZogKbdtKP/1kLDdqZG0t0SykoCknJ0fDhw83uxYAAAAAsIy/Hk2R+jIZz0FTtHxhz8yU+veXvvrKe/1tt0mPPmosx3M7SdJdd0WuZ1G0fC48de/uDpo6d7a0lKgWVNBUUVGhRx99VGvWrFFpaalOP/10TZw4Uenp6eGqDwAAAAAs06VLZK4TbwFGMK+3Rw/pf/8LXy2e/IUo0TBkywqNG0u7d3uvi+SE3J5D04YNi9x1q9O6tdHLsUmT+PudDUZQQdP999+viRMnKj8/X+np6XryySe1a9cuvfzyy+GqDwAAAAAskxTSGJDovU5ddNxxNQdNwU5OXRV/E4J79nSLp8nAs7J8g6ZQhdJb0GaT7rlHKiyMnjsC2mzSpZdaXUX0C2oy8GnTpumZZ57RrFmz9OGHH+rjjz/W66+/ropApucHAAAAgCgWrhAhkDtunXpqeK5dF9T2fZ84URo61JRS/Nbi2aPp11/Nu87770urVplzPjOVlUkrV0qHDxvPmzc3Hrt1C/2c/uZ1Gj265uNstugJmRC4oIKmTZs2aciQIa7n+fn5stls2rp1q+mFAQAAAIDVzBgek5dX8z7ROPFxtIhkLyJ/1/IMCg8eNOc6X34pLVsmvfWWOecz05w50jvvGBOfS1KfPtL//Z907rmhn7NePenvf/eeSD01tXZ1InoFFTSVlZUpLS3Na11ycrLsdrupRQEAAABApPkLGZiHJTw839doGo7m+XW3c2fphhu8t5s1mGfxYnPOEw4rVng//+knqVmzwHrmVSc5ufbnQN0Q1Ehgh8Ohq666Sqke0WNxcbH+8pe/KNMjgn///ffNqxAAAAAA6qDevaWFC6X8fKsriT6eQVNN81NFMog69ljp88+N5Qsv9N1eXm7Odcw6TzgcOOD9/LffzDs3wW18CCpoGjlypM+6yy+/3LRiAAAAAMAqZvdoGjxYOu20yN6pq66w2Yz3prhYatCg6v2OPTZyNUnGEMY77qi6zcwKiOrSNMdmhmIETfEhqKDplVdeCVcdAAAAABB1avvFmJCpaoFMgJ6SEvmhdenpVW8LR9C0e7fUuLE5560tf+/12Webd/5evaQZM6R27cw7J6IPN9EEAAAAAItF0zxF4VbTbe09ZWf7X3/99dJnn0V+WOL+/eaf8+mnjTvnRQN/n8PqepwFq0cP6YgjoidYQ3gQNAEAAACApDVrfNcx1Mc811wjbd4c2HC4Sy+VVq0y7ni2ZYvv9ubNpWuvNb/GmoQjaIom/ob0FRaad36bzZhYHLGNoAkAAAAAJH3xhe86gibzHHmk8ROIDh2MH0SWvxvK79sX8TJQx3FzQQAAAABA1IqnYYVW++EH33W8/wgWQRMAAAAAVIEeTdYj6IicOXN81zVpEvk6ULcRNAEAAABAFSIVNBGm1B3x1lZt21pdAeoagiYAAAAAQNSKtmCnuNjqCiIrNdXqClDXEDQBAAAAAKJW69ZWV+DtmWfMP6fDYdyRr6jI/HMDkcZd5wAAAADAYv5uKw9DUpR9az1woHbHl5f7rlu/Xvrvf43lwYOl3r1rdw3ASvRoAgAAAACL/fqr1RUgUn75xXfdvHnu5VmzIldLTUaMsLoC1EVRlg0DAAAAQPyx262uAJHib86pLVt897H6jof33GN9Daib6NEEAAAAAKgz+va1uoLaSUz0XVd56OThw5GppTqETAgVQRMAAAAAoM7IyLC6gtpN2h1I77U5c4zHvXul0tLQrwVYgaAJAAAAAFBnRENPm2efNUKgUObWWrq05n2WLJF27ZKeeEJ68sngr1Fbxx8f+WsidhA0AQAAAIDF/M3bA//q1bO6AuPOc088IU2bJm3aFNyxgd5hcPVq4/HgweDOH6rCQiklxVju1Cky10RsYjJwAAAAAECd0a6d1RV427JFOvLIwPcPtEfWypWh1ROK1aulN96I3PUQ2+jRBAAAAACoMxKi7Fvs118H3ktJCryH0rZtodUTisohUzCvB6gsyn5FAQAAAACoWjTM0eSppER68cXA99+1K3y1mOWbb6yuAHUZQRMAAAAAoM6wImjKza1+ezC9jxo0CP76kZ7Dq02byF4PsYWgCQAAAAAs5hkkXH65dXXUBVYETcnJ5p2rrKzqbaee6n99pIOmquoAAkHQBAAAAAAW8wwSouGuavBmZrhV3fxH/fv7Xx/JoOmee6JveCLqFoImAAAAAIgifMmvXrRNBh6s6kIjm83/XfUiOTk3nz/UVh3/FQUAAAAAxBMrghAzexTVFJSdf354rw+EG0ETAAAAAFjMM0igR0ls85yjaeBA3+0ZGdLEid7rwt2jqUOH8J4f8YWgCQAAAACiCEFTbCsvdy/36VP1fmlp7uVwB015ecYjgRPMQNAEAAAAABbzDJdSUqyrA/6ZNXStrEyy2wPb99prvY8LJ+fry84O73UQHwiaAAAAAMS9zZutvX5Ghns5M9O6OhCaxMTA9lu9OvBzes7ltHNncPUEy9ljit50MANBEwAAAIC499131l7/mGOsvT5q5+STA9svmCFwnuHVf/8rzZsXXE3BcPZoImiCGQiaAAAAAMS90lJrr1/TnchgraSk6rcHOrTOX9DknIvpjju816emej//8svArhEKgiaYiT9nAAAAAOLe2rVWV+DGl/3oc9ZZUqNG0nnn+d++aJG0f79xt7iJE6XCQv/77dnju+7WW42QKT3de31yctX1lJVJb7zRQdOmmfNh2bXLeOSzBzMQNAEAAAAAUI3GjaUbb5S6d/e/3W6Xnn/e/fyxx/xP+r1tm++65GTfkEmqPvR5911j42+/2bzuYhcq59xR0RS4ou4iaAIAAAAAoJaKiryf33+/dPiw97pg7l7nbzjlL78Yj2vWuFOoL76o+hwVFdKOHYFfd/fuwOsDqkLQBAAAAAAWY8hSbHr4Ye95mYKZDNzfZ+LNN33XLVzo//gdO6TnnpOefVaaP7/q6wQTfgGBIGgCAAAAAIvxZT92TZvmXi4urv35Nm/2XecvwHr2WWnnTmO5uonEgwm/gEAQNAEAAAAAECa//eZe/v332p/vpZd8133zjXvZ4ag+uJw/35iw/OuvjXmkCJpgthpu0ggAAAAAsa9TJ2nVKuuuz9A51MaXX0qnnGIsT5pU9X5lZdKcOcby3LnGj6dx48JTH+ILPZoAAAAAxL0tW6yuAPHmzjsjf8377qt+u7+73wHBImgCAAAAENeKiqQDB6yuArHM31C21FRzr7FzZ/VD5vbtq/kcSYx5ggkImgAAAADEtVmzrK4Asc5fjzmzh0s+84z0ww9Vb58ypeZzMIQTZiBoAgAAABDX9u61ugJvfNmv2664QrrqKu91r74amWt/+mlkrgNUh45xAAAAAOLapk1WV4BY0rat77rycuNOb8HKz5cWLJAOH651WV7at5dGjJBKSqQHHzTWTZhg7jUQv+jRBAAAAAB+nHGG1RWgLhs7tvbn6NtXuv12qX792p/L02WXGY+pqUbAdM89UgLpAEzCRwkAAAAA/OjVy+oKEI0aNQpsv8aNzbmezSadeabv+tTUcnXsWM3s35JuuKHqczolJDBcE+YiaAIAAAAQt+x27+dt2lhTB+qOkSONIW23317zvmZ9nhITfdc1a1ZUbU+niROlpk3NuT4QDIImAAAAAHHr/vu9n/PFPDrl5lpdgVt2tjGkLSPDd9vJJ3s/Lysz55rp6d7P69WTevbcoeOOq75Hkz933WVOTUBVmAwcAAAAAP6UlmZ1BfAnWucPOuMMaeZM9/P8fO/tmzebc53KPZpuuaVCn31WriZNaj52+HBp7VrpnHOM9zFa30vEDj5iAAAAAACEoGNH7+fhCnGSqukiUtPd7Lp0kc4/3zgHIRMigY8ZAAAAgLhUXGx1BUBgPCcWrzw8T5L++lfv52efHd56gOowdA4AAABAXJoxw+oKUNdlZkbmOjabdN110qpVxvC8ynM/NWjg/bxnz8jUBfhDjyYAAAAAcennn62uwD9uNe/LEfyc11GhbVvzztWihTRwYNWfD+eE4YHM2wSEEz2aAAAAAOBPhDwIhufcSZ06+W5fvz5ytdx+u7R/v1S/fuSuCfhDjyYAAAAAkHTlldZdm4CrbrLZjMm2mzaVzj3X+lpycvgswXr0aAIAAAAASSkpVleAumj4cKsrAKILPZoAAAAAQNb2BKmrcxABQGV1Kmh66KGHZLPZdPPNN7vWFRcXa8yYMWrUqJGysrI0fPhw7dixw+u4TZs2aejQocrIyFDTpk112223qazyNP0AAAAAAAColToTNH3//fd6/vnn1bVrV6/1t9xyiz7++GO98847+vrrr7V161adf/75ru3l5eUaOnSoSktL9e233+o///mPXn31VU2YMCHSLwEAAABAFPv9d+uuzbw61Yu1Hl9XX211BUD41Img6eDBgxoxYoRefPFFNWjQwLV+//79eumll/TYY4/p9NNPV8+ePfXKK6/o22+/1XfffSdJmj17tn7++Wf997//Vffu3XXmmWfq3nvv1dSpU1VaWmrVSwIAAABgIX/BRXl55OtAfKqosLoCIHzqxGTgY8aM0dChQ5Wfn6/77rvPtX7JkiWy2+3Kz893revYsaOOPPJILVy4UL169dLChQvVpUsXNWvWzLXP4MGD9X//939auXKlevTo4XO9kpISlZSUuJ4XFhZKkux2u+x2ezheYkQ4a6/LrwHBo93BZyB+0fbxhzaPb7R/cIqKpPJy7393T0mp0KFDNpWXG92L7PbIpQF2u7ueYK8bD22fmWlNu9RW5c+Y0+HDFapNc8VDm6NqZre/2Z+jqA+a3nzzTf3444/6/vvvfbZt375dKSkpysnJ8VrfrFkzbd++3bWPZ8jk3O7c5s+DDz6oSZMm+ayfPXu2MjIyQnkZUaWgoMDqEmAB2h18BuIXbR9/aPP4RvsH5rffsrV2bXOvdStW/KYtW7K0dm1jSdJnn62OWD1796Zq7drWtbpuLLd9cnKSioubql27ffrssyKrywlYenpDLVvWxGf9999v0Nq1tR9hE8ttjpqZ1f5FReb+TkV10LR582b99a9/VUFBgdLS0iJ23fHjx2vcuHGu54WFhWrZsqUGDRqk7OzsiNVhNrvdroKCAg0cOFDJyclWl4MIod3BZyB+0fbxhzaPb7R/cCZPTlD79t7r8vPb6pdfbCouNnrODBnSNmL1bN8urV+fENJ146Xthw+3uoLgtWkjHT7s7tXUqZNDRUXSpZe2rdW8XPHS5vDP7PZ3juIyS1QHTUuWLNHOnTt13HHHudaVl5dr3rx5evrppzVr1iyVlpZq3759Xr2aduzYodzcXElSbm6uFi9e7HVe513pnPtUlpqaqtTUVJ/1ycnJMfFLHCuvA8Gh3cFnIH7R9vGHNo9vtH9gEhN91yUnJyopyb0tOdnPTmGSnFz769L20cezXSXpssvMPj9tHs/Man+zP0NRPRn4gAEDtHz5ci1dutT1c/zxx2vEiBGu5eTkZM2ZM8d1zOrVq7Vp0yb17t1bktS7d28tX75cO3fudO1TUFCg7OxsHXPMMRF/TQAAAACiE3d+g9li7W55QCCiukdTvXr1dOyxx3qty8zMVKNGjVzrr732Wo0bN04NGzZUdna2brzxRvXu3Vu9evWSJA0aNEjHHHOMrrjiCj3yyCPavn277r77bo0ZM8ZvryUAAAAA8adtW6lxY6urQKwhaEI8iuqgKRCPP/64EhISNHz4cJWUlGjw4MF65plnXNsTExP1ySef6P/+7//Uu3dvZWZmauTIkZo8ebKFVQMAAACIFkceKV1xhbU18G/gsami7twgDzBNnQuavvrqK6/naWlpmjp1qqZOnVrlMa1atdJnn30W5soAAAAA1EXRMGSuQQNpwAApgvdAQgTQnohHUT1HEwAAAACEW/Pm7uWjjrKujn79pBNOsO76MF+7dlZXAERenevRBAAAAABmOvVU93LLltKoUZLHTa2BkEVDbzkg0giaAAAAAMS19HTv50ccYU0dABALGDoHAAAAAAAAUxA0AQAAAAAAwBQETQAAAAAAADAFQRMAAACAuOJwWF0BAMQugiYAAAAAMW/9eunBB6UtW6SKCvf61FTragKAWETQBAAAACDmvfaaVFIi/fvf3kFTSYl1NQFALCJoAgAAABBXysvdyx06WFcHAMQigiYAAAAAceWhh9zLmZnW1QEAsYigCQAAAEDc6t3b6goAILYQNAEAAACIW40aWV0BAMQWgiYAAAAAcSuBb0SIkI4dra4AiAz+rAIAAACIaaWlVlcASMnJVlcARAZBEwAAAICY9ssvVlcASA6H1RUAkUHQBAAAACCmlZRYXQFA0IT4QdAEAAAAIKYVF/tf37dvZOtAfKuosLoCIDIImgAAAADEtKqCppNPjmwdiE/duhmPffpYWwcQKUlWFwAAAAAA4XT4sP/1GRmRrQPx6bzzpCFDpNRUqysBIoMeTQAAAABi2ubNVleAeGazETIhvhA0AQAAAIhpbdtaXQEAxA+CJgAAAAAx7bvvrK4AAOIHQRMAAAAAAABMQdAEAAAAAAAAUxA0AQAAAAAAwBQETQAAAADiTseOVlcAALGJoAkAAABA3MnIsLoCAIhNBE0AAAAA4k5SktUVAEBsImgCAAAAEHdatLC6AgCITQRNAAAAAOJOq1ZWVwAAsYmgCQAAAEDcqV/f6goAIDYRNAEAAAAAAMAUBE0AAAAAYpbDYXUFABBfCJoAAAAAxKyKCqsrAID4QtAEAAAAIGb95z9WVwAA8YWgCQAAAEDM2rTJ6goAIL4QNAEAAAAAAMAUBE0AAAAAAAAwBUETAAAAAAAATEHQBAAAAAAAAFMQNAEAAAAAAMAUBE0AAAAAAAAwBUETAAAAgJjkcFhdAQDEH4ImAAAAADFpwwarKwCA+EPQBAAAACAmzZzpXk5Ls64OAIgnBE0AAAAAYlJZmXu5ZUvr6gCAeELQBAAAACAmJSW5l9PTrasDAOIJQRMAAACAmFRR4V4+7TTr6gCAeELQBAAAACAm7d7tXm7QQEpJsa4WAIgXBE0AAAAA4sKoUVLr1tJVV1ldCQDELoImAACACNu8WZo2Tdqxw+pKgPjSpIkRMrVubXUlABC7CJoAAAAi7KWXpF9/lV5/3epKAAAAzEXQBAAAYJHCQqsrAGKXw2F1BQAQnwiaAAAAAMScbdusrgAA4hNBEwAAAICY88ILVlcAAPGJoAkAAABATBs92uoKACB+EDQBAAAAiGkNG1pdAQDED4ImAAAAADEtJcXqCgAgfhA0AQAAAIhpCXzrAYCI4U8uAAAAAAAATEHQBAAAEEHl5VZXAAAAED4ETQAAABE0bZrVFQAAAIQPQRMAAEAEbdxodQVA7Fu/3uoKACB+ETQBAAAAiCmvvWZ1BQAQvwiaAAAAAMSsAQOsrgAA4gtBEwAAAICY1a+f1RUAQHwhaAIAAAAAAIApCJoAAAAAAABgCoImAAAAAAAAmIKgCQAAAEBMat/e6goAIP4QNAEAAACIGStXupeHDrWuDgCIVwRNAAAAFsnOtroCIPa88457OTXVujoAIF5FddD04IMP6oQTTlC9evXUtGlTnXfeeVq9erXXPsXFxRozZowaNWqkrKwsDR8+XDt27PDaZ9OmTRo6dKgyMjLUtGlT3XbbbSorK4vkSwEAAPBxxBFWVwDUDeXl0vLl0sGDvtv275cKC/0fl54e3roAAL6SrC6gOl9//bXGjBmjE044QWVlZbrrrrs0aNAg/fzzz8rMzJQk3XLLLfr000/1zjvvqH79+ho7dqzOP/98LViwQJJUXl6uoUOHKjc3V99++622bdumK6+8UsnJyXrggQesfHkAACDOORxWVwDUDffe616eONG9/Npr0vr1ES8HAFCNqA6aZs6c6fX81VdfVdOmTbVkyRKdcsop2r9/v1566SVNnz5dp59+uiTplVdeUadOnfTdd9+pV69emj17tn7++Wd98cUXatasmbp37657771Xd9xxhyZOnKiUlBQrXhoAAABBExAAu73qbdWFTA0amF8LAKBmUR00VbZ//35JUsOGDSVJS5Yskd1uV35+vmufjh076sgjj9TChQvVq1cvLVy4UF26dFGzZs1c+wwePFj/93//p5UrV6pHjx4+1ykpKVFJSYnreeGffXHtdrvs1f2XLso5a6/LrwHBo93BZyB+0fbRqbzcPXNBWZlDdrt5aRNtHt9itf23bvX+vbHbKyQZQa3n+sr+8peKakOqWBKrbY+q0ebxzez2N/tzVGeCpoqKCt18883q06ePjj32WEnS9u3blZKSopycHK99mzVrpu3bt7v28QyZnNud2/x58MEHNWnSJJ/1s2fPVkZGRm1fiuUKCgqsLgEWoN3BZyB+0fbRY8eODK1d29L1vKjooLKzfzf9OrR5fIu19l+3rr7Wrs11Pf/sM2PO1jfe6OB3/86d/1DXrrtVaXBEXIi1tkfNaPP4Zlb7FxUVmXIepzoTNI0ZM0YrVqzQN998E/ZrjR8/XuPGjXM9LywsVMuWLTVo0CBl1+Hbw9jtdhUUFGjgwIFKTk62uhxECO0OPgPxi7aPPpMnJ6h9e/fzo492aMiQbqadnzaPb7HY/h99ZNO+fTav35v+/dvqxRe9f5ecbrutQunpbSNXYJSIxbZH9Wjz+GZ2+xdWdUeFENWJoGns2LH65JNPNG/ePLVo0cK1Pjc3V6Wlpdq3b59Xr6YdO3YoNzfXtc/ixYu9zue8K51zn8pSU1OV6udeqMnJyTHxSxwrrwPBod3BZyB+0fbRIzHR+3lRkRSOpqHN41sstf+KFb6/N6+/nqiDB33XS1J2tp+VcSSW2h6Boc3jm1ntb/ZnqOpBzVHA4XBo7Nix+uCDD/Tll1+qTZs2Xtt79uyp5ORkzZkzx7Vu9erV2rRpk3r37i1J6t27t5YvX66dO3e69ikoKFB2draOOeaYyLwQAAAAP7Zts7oCoO7x+N96SdJVV0kXXCDdfbcl5QAAKonqHk1jxozR9OnT9dFHH6levXquOZXq16+v9PR01a9fX9dee63GjRunhg0bKjs7WzfeeKN69+6tXr16SZIGDRqkY445RldccYUeeeQRbd++XXfffbfGjBnjt9cSAAAAgLqjdWurKwAAeIrqoOnZZ5+VJPXv399r/SuvvKKrrrpKkvT4448rISFBw4cPV0lJiQYPHqxnnnnGtW9iYqI++eQT/d///Z969+6tzMxMjRw5UpMnT47UywAAAAAQBjfdZHUFAIDKojpocjhqvt1vWlqapk6dqqlTp1a5T6tWrfTZZ5+ZWRoAAAAAi2VlWV0BAKCyqJ6jCQAAAEB8Ki6ueZ+kqP5ncwCITwRNAAAAFgrkyzQQjx56qOZ9Evg2AwBRhz/NAAAAFpo3z+oKgLrpxBOtrgAA4A+dTQEAACy0b5/VFQB1T58+0sCBVlcBAPCHoAkAACACqrrHSVFRZOsAot0vv0hz51a/z4ABkakFABA8giYAAIAI2LHD//qUlMjWAUS7N9+seR+bLfx1AABCwxxNAAAAEXD4sHs5Pd26OoBYQNAEANGLoAkAACACPG/DnpzsXt68OfK1AHVJ5WFyvXtbUwcAIDAETQAAABGwfr17OTHRvWy3B3ee0lJp715zagLqgr59vZ8PHmxNHQCAwDBHEwAAQAQsWuR/fVlZcOd54AHjMSlJuvvu2tUERLtevRgmBwB1DT2aAAAAIsBzjqaq7kAXjLIyqaKi9ucBotkZZxiPrVsbj0ccYVkpAIAA0aMJAACgjioslHJyrK4CCL+LLpKWLpW6drW6EgBATejRBAAAEGFm9GiSpNmzzTkPEC3eeMO93L+/ezkjQzr5ZCkrK+IlAQCCRNAEAAAQYWYFTT//bN65gGiwerV7+cQTrasDABA6giYAAIAwOHRIKiryvy3UcMhznienKVOk4mLma0Ld9scf0owZ3usyMqypBQBQO8zRBAAAYLKyMunRR43lCROkBJP+aa+w0Hfd/v3SQw8Zy+XlCerRw5xrAZHicEhPPWV1FQAAs9CjCQAAwGQHD7qXZ8+WJk50Pz/ppNDPu3t3zfu8/XaH0C8AWGDJEt916emRrwMAYA6CJgAAAJN5Do377jvvbZ06SW3ahHbed94JvSYgWvnrqderV+TrAACYg6AJAADAZNXNwXTkkVJiYmDn2LVLKi83ry4g2rz3njRvnu/6U0+NfC0AAHMQNAEAAJisqqCpfv3A52v66Sdp6lTv273X9vqIbdu2SaWlded/77dvl5Yv913/179GvhYAgHnqzn+JAAAA6ojSUv/rx44N/BwLFxqP69YFf/0tW4I/BnXbypXSiy8m6L332teZoHH1av/rGzSIbB0AAHNx1zkAAACTlZX5rktIkJKTQztHRUVwd6575ZUE3Xtv4PsHo6RESkmRJk0ynt91l/Tll8ZcVL16SWecEZ7ronqe83fde2+CEhONIZr/+Id1NdVk7lzv5x06SM2bW1MLAMA8BE0AAAAmM2NeJbvdvTx5stS7d+3PWVszZ/pObv7AA+7l775zb7/1VqlevcjVBl/l5cYdD++5R7LZfLd5hpF33GHc6c3hkObPN8LDq6+WWrWq/hoVFcbn89hjpZwcY2Lv887zH4za7dLOnVJenrRsmfe2v/1NysoK4UUCAKIOQRMAAIDJkkz4P6zKvaKcQ+mqcs890oQJ7ud2e3A9qAJROWSqzuOPe9eD8PEMJf2ZNMkInDxV7vH28MO+x73yitSihXTdde51Dofx88UX0rffutevWOFeXrbM93qSdP/9VddIyAQAsYOgCQAAwGT+enOcd15w5ygqCm7/yj1W5s+XTj9d2rjRCAzGjpUaNw7unJ5qCjMqq6gwwoZx44yeTZXrg3mqC3Ccpk+XLrtMeuEFaevWwM+9ZYv/0Kgm27cbgWujRu5hllUZMyb48wMAohdBEwAAgMmWLPF+Pn68lJpq3vmbNjWGIB19tPFF/uSTffdZudIIml55xXj+9NOhBQZOlcOM5s2Nu5zV5LHHjMf+/Y0fhFdCgkMTJlRo3rxEzZ/vXr9mjfTmm8GFTLXx3HM179O9e/ABLAAg+hE0AQAAmKxyAFNTyPT771JurjF5cyCuukpav17q2LHq4XFlZdKvv/quP3RIevRRY9nf3D0Oh7R7tzR1atXXv/12ac4c9+u8+GKjFqnq3itffSVlZEgnnlj1eRG4L7+U5s3zXX/xxWsktVOjRr7bfvnFd11urtH7yH281KmTtGqV9NZbNddxzz3Snj3Shx8aczQtXx7YMfRwA4DYRdAEAABgspSU4PZ/8UWpbVvpiitq3rdfPyOw6dKl+v0OHJCmTfNet2KF9O677ueVQ6F//MN37h5/MjKk006TfvtNOu44I5gIxGefETSZweHwHzJ5ysgI7FzXX+8/9OnUyZhja8oUqXNnY7Lv77+Xli6VbrxRXkFWo0bStdcaQVZ1QdM550jduhEyAUCsI2gCAAAw2W+/BX/M+vWB7edvmJw/FRW+6zxDJn+ef77m8zon+M7KMgKHyoYMMQKlqjgcBA3BKCszhru1bOl+33bv9r/veedVaMsWYzknp+Zzd+9efVskJBhzbDkdcUT1Q938TYLvHK5ZVmbOJPkAgOjHn3sAAIA6JD09fOfeubPqbfXrSzffXHNIdOKJUnGxtHat0SumfXvpv/+V9u83tn/+uRFGSUboZLcH3wMsntx3n3v5ppukJ5/03n7kkdKwYVKDBsZ76Qya/A2d8/TXvxrHmKnyME7PSb4JmQAgfvAnHwAAIMIcjtCOa9as+u1mTjh+0UXS228bIUa3bsEde8opxo9T587St98ay4sXG89btXIP3bv2WqPHDqpXOWSSpGuu8b9vYqJ0993GZy052d2TrLRUOnzYCA7NVjlMqs1dDgEAdRdBEwAAgInKyryfX365eedOS6t+e7C9nf7+dyOQmDzZd9sxx9TuLnWeTjzRHTRJ7jvhOb30kjEkq3t34/lrr3kPJaxfX7rlFnNqqUtCDSSdPIMfZ0+0lJTw9SBLSPB+zhBJAIhPBE0AAAAm8hzqNHq0lJdn3rlPOqn67ZW/6Fenujt/3XZb4OcJRCDzBX34odHTplcv3/mqnMPu4oXDYdzV75tvqt7nhhukJk0iV1Mggvn8AQBiF/85AAAACJOq5skJtadK5TlwKjv3XD8zgFcyYYLRU6mqkOkvf5EyM4OvrSb5+TXvM2uWtGaN/20TJ0q//27c1czfROexZM2a6kOme+6RmjaNvh5DBE0AAIkeTQAAAGET7JxJK1ZIHToYgVJKijGfzogR0uuvG9tr6hkUyDxHNYUBubkBlRq0vn2NgOjLL6vf7403qt724ovG43vvGY/XXy81bx5aPTt2SH/8YQwRrK2KCvfww1tu8Z7/yBkqVhUKLVsmvf++sXzBBcaE3h995L1PWprUp48RAB53XO3rDReCJgCARNAEAABQK0VFUnm5VK9e7c/17rtGING/vxEySUbgdO210sGD4Rsqdc010qJF0uDB4Tm/U+UeWf37S199Ffr5nn/eeKxuGGBVnn3WeLzqKql169BrkKSlS93Ljz9uPLZuLf32m+++lWt1hkyS0f7+/O1vdeOubZ6v669/ta4OAIC1+HcHAACAEJWXS488Iv3rX9KCBdLnn7u3nXBC1cdVN3TO4ZDmznU/Ly01eip16hR6needZzxW1RvmyCOlCy+UsrNDv0YgNmzwfn7qqdXvf8cdgZ3Xc16sQJSXu5ffesuYwP3HH93hXrBmzPBd5y9kkow77TkcxrxTgUy2PmZM3QiZJO8eTeEYfgkAqBvqyH+2AAAAos/bb7uXCwq8t5nVO+iXX6T27UM//uyzjbu5de5c8xxPkWazGUMFV692r+vf3+gN1Lq10VvMyXMIYWWewVEgPOd4OnzYHVQ5A6NA77Y3f74xaXewJk2qeZ9x48If/JnNM2iK9Xm0AABVI2gCAAAIkWdAUplZvVBq6vVTWZs23rdo69HDeIyGkGnbNt91w4YZ8zZt2iSdf74xybWTZ8+vvDzprruMIX7duxtDFT0DoYkTjR5bZ51lBFjVDaWrKQSZONGYNN3fnEOBhlBOF11kzANV03ETJxp11VR7NCNoAgBIBE0AAAAh+eOPqrfddlv1xwZz17lge7WcdNJ2LVnifh5NEzSfeKK7B5BzDp+0NGnIEP/7ewYuiYnGfFX9+lV9/h9/NH4kY+iWv3YoLJQee6zmWr//3qjXZjPaK5BeSPfcYwyJ+/FH6dhjvUOzK6+Upk3zPebCC43eZlJ0tVUoCJoAABJBEwAAQNAcDumpp/xvC7bHi9miuTdM377SN99IbdtKDRrUvH9GhtSrl/Ga0tJ8t9erJx044P/YQ4f8r3/66cBq/fxz7zm3qtO9u3serJwc6fTTffc56ihp+HBjuN155xk9tGKNZ9AUTJgKAIgtdfzfTQAAACJv5Ur/62+80dzrtGpVu+OjbUJmm00aP94YThaoM86oer6rsWOrP3bfPt911U34Hejk45U5Q6aadOki3XBDbIZMEkETAMBAjyYAAIAgVTVsrlGjwI739yX83HOljz7yXnf55cHVVVlVvXpiRWqqdPvt0saNUrt20v33e2+fMqXquZacgu2Bdvrp0imnBFtpfEhJMcI0u93obQYAiE/0aAIAAAjS8uW1O95f0HTUUb7rajuBd8OGtTu+LsjIkDp1qvq9mjy56mP/8Y+az33FFcad+yTpkksImWoyfLjxPkXzEE4AQHjRowkAAKAazomgmzeXrr/emOR4927f/S67LPBzpqb6rktM9H7ub06iQN1+e4XWr0/UsceGfo666OKLpU8/lQ4e9F5fUeE/cKr8nld2++3u5Z49a18fAADxgB5NAAAA1Zg3z3jctk0qKpJ27vTefscdxt3Gjj468HP6myy6cuhRXBxcnZ7S0owJqpPi7J8UO3WS/vY33/XPPBP4OS691HgcONCcmgAAiDcETQAAANWYO9e9/Mgj3vMepaRI6enBDxPKyvJdV9dvbR/N/PVAO/dc//t26GDM29SnT1hLAgAgZvG/NAAAAEF47TX3cqh3KZOkvn29n9c0jAuBa9685n169Ah/HQAAxCOCJgAAgCrUdIv22oRDP/5o3rngraZhb9dfH5k6AACIRwRNAAAAVdi4septN91Uu3MXFXk/5y5d5jnqqKrb5x//CKzHEwAACA1BEwAAQBX+97+qtzVsWLtzEyyFl7/2GTuWnmMAAIQbQRMAAIAfpaXSTz/53zZsWO3PX9OwPNRemzbu5Xr1pMaNrasFAIB4EWc3vQUAAAjMCy9Uva1Tp/Bfv0mT8F8j1o0cKZWUGHcKrG0PNAAAEBiCJgAAAD9273YvZ2RIxcVSRYXxPCUl/Ne/4YbwXyMepKYaPwAAIDIYOgcAAFCD2283f6hbvXrVb2cOJwAAUBcRNAEAAFjgmGOsrgAAAMB8DJ0DAAAxb+JE43HMGPfcR088Ie3dKw0eLC1fLm3dKp1wgvH80CH3saeeajw2b27sk5ZmTk3167uXnUPxMjONa+fmmnMNAACASCNoAgAAMe3HH93LU6f6bp81y738/ffGj6e+fY3Hiy+W5s+XevUyp64TT5TWrZO2bZOuvtpYN3q0Ue8JJ5hzDQAAgEgjaAIAADFtxozaHZ+cbDzWry+ddVbt63FKSpKuvNJ7Xf360mmnmXcNAACASCNoAgAAkPS3vxnhz4EDUlaWlJ5udUUAAAB1D5OBAwCAmLV6ddXb+vd3L196qREupaUZczgRMgEAAISGHk0AACBmvfGG77qrrpJat5a++ca9rkOHSFUEAAAQ2wiaAABATHA4pEmTat6vdWvjsUGDsJYDAAAQlwiaAABAnVJRIb3yinTmmVJenvF88uTqj2nYUNqzR8rIcK875hjp1FOlFi3CWy8AAEA8IWgCAABRz26X7r/fe90LL9R8XP360tix0sGD0vz5Uu/e7m02G3d4AwAAMBtBEwAAiBolJdK//y3t2hX6Of7+dyk52XtdgwbSOefUrjYAAADUjKAJAABYxuGQfv9d2rxZmjUrsGOaNpV27nQ/b93aGALXpk1YSgQAAEAQCJoAAEBYlJcbw9MSEnzXv/KKtGVL8OecONG9fPCglJlpXAMAAADRgaAJAACE5PBhacUK6bjjpMREY92vv0rTptX+3MnJxhA4STpwQMrK8g2UsrJqfx0AAACYi6AJAAAEbOdO6ZlnvNd9+mno57PZpFGjpJwcKT3dGEq3fr33neDq1Qv9/AAAAIgsgiYAiHIffywtWSIdeaR0zTVWV4NIKCuTkiL4X+jDh6XSUiPoqagwhrZt2CDNm+c9F5JZzjtP6tLF3QvKk80mtW9v/jUBAAAQGQRNABAhRUXGHbUaNPBev2+f9Msv0vHHG1/yd+wwgqWNG6W9e937bdok/e9/Uo8eVV9j924joMjJqV2tDofxWFho1CwZtbzxhtHTZPt2Iwzp2VM6+mgjpHjvPSktzQgJtmwxhlNlZRl3D/v2W//X6dvXeF12u3HO4cON9ycpybj23r1Sw4bG+5KcLGVkGI8pKUYYcuCAlJ1t9HgJ1zw9hw8b13HexSwx0ah39mxjEuuBA43nu3cbdaamGj8HDhht3qiRUeuvvxr7e2rUSPrjD2O5YUNpz56a62nUyHite/YY4aPN5p4HqfLjoUM2zZrVQT/8kKCkJKNds7Lc79/+/ea+V5IRIJ11lvEeAAAAIP4QNAFAgCoq3F/qA3XokBGYbNokff6597bmzY2wxnkb95kzaz7fRx8ZP57atZPWrav6mPLyBK1da4QNiYlSXp60dWvgr6EyzwmclywxfpyKi6Xly43lOXNqPtc333g/f/fd0OvydOSRxnvuXN6yxWi/I44wwrDycum339z7N2tmBHxJSUZAkp5utM2+fTVf64MPQq/TGTJJgYVMlY/xfA3+lJe7P6zO8PDgwcCuU1mLFlKHDsZ71aKFEfpJRsjmDOEAAAAAgiaovNz4AlJaanxhSEkx/iX+11+N9SUlRs8Ezy8TNpuxPjHR/WUjMdH4klZe7n4OhIvDEVzg43AYQUPlx8REI1D44w/378KuXcZnPSnJmCtm6dLAr+PsxWGzGaFLdbZtC/y81akuZPIn2JApLc3oHVNUFNxx9euH1mOmuuOysoy/VaWl1Z/DGTJVXq7co8hpxw7jsazM+Dl0KPB6c3ONHmTFxe7gp1Ur4/PUtKnx3iUlGT2jfvrJuEvaoUPSaacZIdiBA0bI1KKF8dkrKzNCyJIS4+/r8uXSjz8a7ZCfb/T4KiszjnMOdXM4/H/GS0sdOnTooNq1c6htW6PtjzvO+DufmGjU+OOPUu/eRq12u1Gnw2G0QyC/Y4RMAAAA8BRXQdPUqVP16KOPavv27erWrZueeuopnXjiiVaXFRHOwGjfvhStWmUMR/n1V+ML0IYNNR//1VehX7tLF+M6drv088/GOmcgZbMZX5r++MP4QpWZaXzR6tjR+BKUkmJ8oVq+3Pji1a+f+0tvw4bG8aWlxj6HDxuvq6TE+GnY0LjWtm1GL4bUVOPLq3O95xeyXbuML1XJycY5y8qMfUtKjH/9r1/fuKbzy9zevcYXvBYt3Ndu0sQdrtnt0tq1Ups2xutMSTHWl5cb2xISjH0TEtzLiYlGLWVl7rr8BSPOMMSzZ83Bg0avmZYtvdc7X8umTfW0fLn3LcadvRs8l/2tC8d2h8P4Uu5wGDV5vr6KCveX3T173L19nLKzjS/YnsdKxtAp53Agz14onvtEgnOYWU1OP11q3NhYdrbTEUcYvwNJSb63hHc4pDfflFavrv68GRnu4/z1XKlf3/hdqqgweqY0bGj0kOrb11ieMcM4x3nnGZ/pnBzzh6Q5P78VFdJrrxnhyRFH+O5ntxvDB1u1Mvb3nLPI4TDes9JSY8jd3LnunlbJydKZZ7r/FjRtKi1caIRJ+fnG34LERKMn1oYNRg+vXr2Mz1u9esZnqFkzdxCzbZt00knGNQ8eND6DoRo2rPrt7dr5rmvTRjrnnNCuZ7c7tHv37xoypFuVgdCRR7qXU1Lcf68AAACAUMRN0PTWW29p3Lhxeu6553TSSSdpypQpGjx4sFavXq2mTZtaXV7YLVsmvftugtaubaN16xIi2tvIOYzGU1mZ8ehwuIeBFBW5e0z88ovvMdu2SW+/HZ4aw+W77yJ7vcWLfdcZw6bytHNnZNs9XAoL3cueAdKBA/73DyZkatDACDT27TMCh0aNjEDWqVMnIxyaP98ILFJSjLCiXTsjnGnY0AhmUlKMAOTQISPUycwMvgdWZTabdOml0sSJxvMJE7yDqOrY7RX67LPVGjKkrZKTfT8EnnM+HXdc6DUGyvk+JCRII0dWvV9ysv/gxXmO5GTjp21b46c6xxzju+7YYwOrt1Ur9zVrEzIBAAAA8SBugqbHHntMo0aN0tVXXy1Jeu655/Tpp5/q5Zdf1p133mlxdeGXmWk8JidX6IgjHPrjD6O3wHHHSd27G8t5eUZvoZ07jS/KCQnGl2e73fhy36CB8WX+wAFjXpXCQmnoUOPL+O7dRs+BwkJjaMuCBe4hKl27Guf74Qd3Pf36GV/At20zeg7t2WPs36+fMbTDOdltUpIRSgUS2DRsaHyxt9mMkGDXLqPH0YED7t4zpaVGLw1nrx/nj3MI0xFHGD0YkpLcvSM6djR6QDj3PXzYXbfN5u6l06CB8Z6VlnqHHk2buns6JSQYX4ydPXecPZhKS91fvpOSvHu0eE7s6/ncs3fQ7t3ueWWc25zby8oc2r//sFq3drheh5O/5UhsP3DA6F3SqZPx3lR+fXv3Gp+HE0803uuSEqlPH+OYlBSjfZ0905zvn7O3WFKS8R6vX2+EFGlp3hMkV54subTU3ass0Lt8DRjgXg60U6RZvYKcQRMAAAAARKO4CJpKS0u1ZMkSjR8/3rUuISFB+fn5Wrhwoc/+JSUlKvEY/1L4ZxcKu90uu90e/oLDoEUL6W9/s+urr9Zq4MDWSk52+N2vrMz9Bd4pOdkIfiRj2E39+tIll/ie31O7dkbQ07ixu9fFoEFGINK4sftLd6C3sHZ+sXcOG0tM9B0+Bv/sdrsKCjZp4MAOVbZ7NPPsbRNM58Pjj695n4oKd7jkcBhBVSxy/t2qq3+/EDraPv7Q5vGN9o9ftH38oc3jm9ntb/bnyOZwOOreN88gbd26VUcccYS+/fZb9e7d27X+9ttv19dff61FixZ57T9x4kRNmjTJ5zzTp09XhnPmawAAAAAAgDquqKhIl112mfbv369sE+aKiIseTcEaP368xo0b53peWFioli1batCgQaa86VYxerYUaODAgUrmNkFxg3YHn4H4RdvHH9o8vtH+8Yu2jz+0eXwzu/0LPSfCNUFcBE2NGzdWYmKidjjvX/2nHTt2KDc312f/1NRUpTrvUe4hOTk5Jn6JY+V1IDi0O/gMxC/aPv7Q5vGN9o9ftH38oc3jm1ntb/Z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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o-XdstHqVe0N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sx77SPWFVgkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LVE6QpalVe4P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the Timestamp to a datetime format"
      ],
      "metadata": {
        "id": "t-LOMrD5EAIH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Date'] = pd.to_datetime(data['Timestamp'], unit='s')  # Convert Unix timestamp to datetime"
      ],
      "metadata": {
        "id": "lTuf4NA0EAK7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.set_index('Date', inplace=True)  # Set 'Date' as the index of the DataFrame"
      ],
      "metadata": {
        "id": "VqALkAN1EANw"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EqXjmcXLDcSV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with NaN values in critical columns"
      ],
      "metadata": {
        "id": "dSCFcl0JDcU7"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(subset=['Open', 'High', 'Low', 'Close', 'Volume_(BTC)', 'Volume_(Currency)'], inplace=True)  # Remove rows with NaN in key columns\n"
      ],
      "metadata": {
        "id": "42kqdZ_wDcYH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M9ixmhheDcaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_8DrgdkMDccQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Volume Over Time</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Volume Over Time** graph visualizes the trading volume of Bitcoin over a specified period. This graph is crucial for understanding the intensity of trading activity and identifying periods of high or low market participation.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Identify Trading Activity**: Helps in recognizing periods with significant trading volumes, which can influence price movements.\n",
        "- **Market Sentiment**: High trading volumes often correlate with strong market sentiment or reactions to news/events.\n",
        "- **Market Trends**: Provides insights into the overall market trend and investor behavior over time.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **X-Axis**: Represents the timeline, typically showing days, weeks, or months.\n",
        "- **Y-Axis**: Represents the trading volume in Bitcoin or currency.\n",
        "- **Plot Type**: Line or bar chart to effectively display the changes in volume over time.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **High Volume Spikes**: Indicates periods of increased market activity, which may be associated with significant price movements or news events.\n",
        "- **Low Volume Periods**: Suggests periods of lower market interest, which might be less volatile or less significant in terms of price action."
      ],
      "metadata": {
        "id": "3KGqLM28SVpn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Volume over Time"
      ],
      "metadata": {
        "id": "pQemmjh9Gt3b"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Volume_(BTC)'], label='Volume (BTC)', color='purple', alpha=0.6)  # Plot Volume (BTC)\n",
        "plt.title('Volume over Time')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Volume (BTC)')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/volume_over_time.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "bls17ApzGt7B",
        "outputId": "79d03ea3-3a28-4544-9787-fbd418f5cc6e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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    {
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      },
      "execution_count": null,
      "outputs": []
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    {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #d62728;\">3. Feature Engineering</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">3.1 Typical Price</span>\n",
        "\n",
        "### 3.1.1 Definition\n",
        "\n",
        "- **Typical Price**: Represents a single value that averages the high, low, and close prices for each period.\n",
        "- **Purpose**: Provides a balanced view of the price level for each period, smoothing out extreme values.\n",
        "\n",
        "### 3.1.2 Calculation\n",
        "\n",
        "- **Formula**:\n",
        "  \\[\n",
        "  \\text{Typical Price} = \\frac{ \\text{High} + \\text{Low} + \\text{Close} }{3}\n",
        "  \\]\n",
        "- **Example Calculation**:\n",
        "  - If `High = $50`, `Low = $45`, and `Close = $48`, then:\n",
        "    \\[\n",
        "    \\text{Typical Price} = \\frac{50 + 45 + 48}{3} = 47.67\n",
        "    \\]\n",
        "\n",
        "### 3.1.3 Importance\n",
        "\n",
        "- **Role in VWAP**: Serves as a key input in the VWAP calculation by providing a representative price for each period.\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">3.2 VWAP (Volume-Weighted Average Price)</span>\n",
        "\n",
        "### 3.2.1 Definition\n",
        "\n",
        "- **VWAP**: A cumulative measure that reflects the average price at which a security has traded over a specific period, adjusted for volume.\n",
        "- **Purpose**: Provides a benchmark for evaluating trading performance and trends.\n",
        "\n",
        "### 3.2.2 Calculation\n",
        "\n",
        "- **Formula**:\n",
        "  \\[\n",
        "  \\text{VWAP} = \\frac{ \\sum_{i=1}^{n} (\\text{Typical Price}_i \\times \\text{Volume}_i) }{ \\sum_{i=1}^{n} \\text{Volume}_i }\n",
        "  \\]\n",
        "- **Components**:\n",
        "  - **Typical Price**: Calculated as above.\n",
        "  - **Volume**: Trading volume for the period.\n",
        "- **Example Calculation**:\n",
        "  - Assume Typical Prices and Volumes for three periods are as follows:\n",
        "    - Period 1: Typical Price = $47.67, Volume = 100\n",
        "    - Period 2: Typical Price = $48.50, Volume = 150\n",
        "    - Period 3: Typical Price = $46.75, Volume = 200\n",
        "  - VWAP Calculation:\n",
        "    \\[\n",
        "    \\text{VWAP} = \\frac{(47.67 \\times 100) + (48.50 \\times 150) + (46.75 \\times 200)}{100 + 150 + 200} = \\frac{4767 + 7275 + 9350}{450} = 47.87\n",
        "    \\]\n",
        "\n",
        "### 3.2.3 Importance\n",
        "\n",
        "- **Role**: Helps traders identify trends and potential entry or exit points by comparing the current price to the VWAP. A price above VWAP might indicate overvaluation, while a price below VWAP might suggest undervaluation.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">3.3 Feature Engineering in ML Context</span>\n",
        "\n",
        "### 3.3.1 Feature Creation\n",
        "\n",
        "- **Typical Price and VWAP**: Key features for ML models focused on price prediction or trading signals. These features capture important aspects of price behavior and trading volume.\n",
        "- **Feature Scaling**: Normalize VWAP and Typical Price to ensure they are on a comparable scale, which is essential for many ML algorithms.\n",
        "\n",
        "### 3.3.2 Incorporating into Models\n",
        "\n",
        "- **Use in ML Models**: VWAP and Typical Price can be used as features in regression models, classification models (for buy/sell signals), or time-series forecasting models.\n",
        "- **Feature Importance**: Evaluate the importance of VWAP and Typical Price in predicting price movements or trading signals using techniques like feature importance scores or SHAP values.\n",
        "\n",
        "### 3.3.3 Example Code Snippet\n",
        "\n",
        "```python\n",
        "# Example of calculating Typical Price and VWAP in Python\n",
        "import pandas as pd\n",
        "\n",
        "# Load dataset\n",
        "data = pd.read_csv('bitcoin_data.csv')\n",
        "\n",
        "# Calculate Typical Price\n",
        "data['Typical Price'] = (data['High'] + data['Low'] + data['Close']) / 3\n",
        "\n",
        "# Calculate VWAP\n",
        "data['VWAP'] = (data['Typical Price'] * data['Volume_(BTC)']).cumsum() / data['Volume_(BTC)'].cumsum()\n",
        "\n",
        "# Display first few rows\n",
        "print(data[['Timestamp', 'Typical Price', 'VWAP']].head())"
      ],
      "metadata": {
        "id": "m9NtGb0SPEo-"
      }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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      "metadata": {
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      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">VWAP and Bollinger Bands</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **VWAP and Bollinger Bands** graph displays the Volume-Weighted Average Price (VWAP) alongside Bollinger Bands. This visualization helps in analyzing how VWAP aligns with market prices and assessing price volatility.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **VWAP Tracking**: Compares VWAP with actual closing prices to evaluate how VWAP reflects the average trading price.\n",
        "- **Volatility Analysis**: Bollinger Bands provide insight into market volatility and potential buy/sell signals.\n",
        "- **Trading Signals**: Helps in identifying trends, potential entry, and exit points based on price deviations from the VWAP and Bollinger Bands.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **VWAP Line**: Shows the average price weighted by volume.\n",
        "- **Bollinger Bands**:\n",
        "  - **Middle Band**: Typically a 20-day moving average.\n",
        "  - **Upper Band**: Middle Band + (2 × standard deviation).\n",
        "  - **Lower Band**: Middle Band - (2 × standard deviation).\n",
        "- **X-Axis**: Represents time.\n",
        "- **Y-Axis**: Represents price and Bollinger Bands.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Price Above VWAP**: Indicates that the current price is higher than the average price, potentially signaling overvaluation.\n",
        "- **Price Below VWAP**: Suggests that the current price is lower than the average price, possibly indicating undervaluation.\n",
        "- **Bollinger Bands Width**: A wider band indicates higher volatility, while a narrower band suggests lower volatility."
      ],
      "metadata": {
        "id": "INpOCS5hSre2"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qo6wyMn3Ss4f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dksU2rmASs-g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "-qiJeujlDceh"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Typical Price"
      ],
      "metadata": {
        "id": "DG_g5e6SDcg5"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Typical Price'] = (data['High'] + data['Low'] + data['Close']) / 3  # Compute the typical price"
      ],
      "metadata": {
        "id": "TKEjpDdEDclH"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nl7kCMhuDco2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cdn9OR5HSl1M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate VWAP"
      ],
      "metadata": {
        "id": "jmCM-VDqDWpl"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['VWAP'] = (data['Typical Price'] * data['Volume_(BTC)']).cumsum() / data['Volume_(BTC)'].cumsum()  # Compute VWAP using cumulative sums\n"
      ],
      "metadata": {
        "id": "wrj94twSDWsy"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I3FmOA71DWvV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WIb53ZetLLHu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot VWAP"
      ],
      "metadata": {
        "id": "pH20MRDpLLK0"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close price\n",
        "plt.plot(data.index, data['VWAP'], label='VWAP', color='orange')  # Plot VWAP\n",
        "plt.title('Volume-Weighted Average Price (VWAP) Strategy')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Price')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/vwap_strategy.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "6-cn25gHLLN3",
        "outputId": "eea00cb9-5d75-4741-abb3-d8a25eb1121b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H_MdXXB6LPps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rHR0txHALPtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qZc039SHLPwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Bollinger Bands"
      ],
      "metadata": {
        "id": "qLPpwViZLQJU"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['20-MA'] = data['Close'].rolling(window=20).mean()  # Compute 20-period moving average"
      ],
      "metadata": {
        "id": "qFBHSDRSLQMf"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['20-STD'] = data['Close'].rolling(window=20).std()  # Compute 20-period standard deviation"
      ],
      "metadata": {
        "id": "HU_TYAFwLTn8"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Upper Band'] = data['20-MA'] + (data['20-STD'] * 2)  # Compute Upper Bollinger Band"
      ],
      "metadata": {
        "id": "C0kQguzqLTrD"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Lower Band'] = data['20-MA'] - (data['20-STD'] * 2)  # Compute Lower Bollinger Band"
      ],
      "metadata": {
        "id": "6CWC2k6JLTt3"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i4SedsuTLeae"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SNVSf5rcLede"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Bollinger Bands"
      ],
      "metadata": {
        "id": "zi7I0nz3LegX"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close price\n",
        "plt.plot(data.index, data['20-MA'], label='20-Day Moving Average', color='green')  # Plot 20-period moving average\n",
        "plt.plot(data.index, data['Upper Band'], label='Upper Bollinger Band', color='red')  # Plot Upper Bollinger Band\n",
        "plt.plot(data.index, data['Lower Band'], label='Lower Bollinger Band', color='red')  # Plot Lower Bollinger Band\n",
        "plt.fill_between(data.index, data['Lower Band'], data['Upper Band'], color='red', alpha=0.1)  # Fill between Upper and Lower Bollinger Bands\n",
        "plt.title('Bollinger Bands')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Price')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/bollinger_bands.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "S-HGj0ayLejT",
        "outputId": "8c1c8ba0-ae74-4386-be65-e9ebf4ef0aa5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1vF6Yz_LLLU7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-DtVoaFAG_lT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CxyeVdoALl49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Closing Prices with Moving Averages</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Closing Prices with Moving Averages** graph displays Bitcoin's closing prices along with moving averages over different periods. This visualization is key for identifying trends and potential trading signals.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Trend Analysis**: Helps in identifying the overall direction of the market by analyzing the smoothed price data.\n",
        "- **Signal Generation**: Moving averages are used to generate trading signals based on crossovers and trend direction.\n",
        "- **Market Sentiment**: Provides insights into long-term market trends and potential reversal points.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **Closing Price Line**: Represents the daily closing price of Bitcoin.\n",
        "- **Moving Averages**:\n",
        "  - **Short-Term Moving Average**: E.g., 20-day moving average.\n",
        "  - **Long-Term Moving Average**: E.g., 100-day moving average.\n",
        "- **X-Axis**: Represents time.\n",
        "- **Y-Axis**: Represents price.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Moving Average Crossovers**: When the short-term moving average crosses above the long-term moving average, it may signal a buying opportunity (Golden Cross). Conversely, a crossover below may indicate a selling opportunity (Death Cross).\n",
        "- **Trend Confirmation**: Moving averages help confirm the direction of the trend and potential reversals.\n",
        "- **Smoothing Effect**: Moving averages smooth out price data to highlight underlying trends and reduce noise."
      ],
      "metadata": {
        "id": "6TbOQTJwSxfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Closing Prices with Moving Averages"
      ],
      "metadata": {
        "id": "-lzCJg7zG_om"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate short-term (20-period) and long-term (100-period) moving averages"
      ],
      "metadata": {
        "id": "Br2s7yM6HDvU"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['20-MA'] = data['Close'].rolling(window=20).mean()  # Compute 20-period moving average"
      ],
      "metadata": {
        "id": "AotqVahmHDys"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['100-MA'] = data['Close'].rolling(window=100).mean()  # Compute 100-period moving average"
      ],
      "metadata": {
        "id": "dOyR2cB8HD1k"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1ohbZzvxG_rc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close price\n",
        "plt.plot(data.index, data['20-MA'], label='20-Day Moving Average', color='green')  # Plot 20-period moving average\n",
        "plt.plot(data.index, data['100-MA'], label='100-Day Moving Average', color='red')  # Plot 100-period moving average\n",
        "plt.title('Closing Prices with Moving Averages')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Price')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/closing_prices_moving_averages.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "5DZxZaLFHNnE",
        "outputId": "44ae4f40-9200-4f9f-faae-aa6f3c4c87c0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WzDwmVDlHNqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YnF0SEvkHNtF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6p1MJjiLG_ui"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7I-qwfpNKL0T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color: #1f77b4;\">4. Data Preprocessing</span>\n",
        "\n",
        "### <span style=\"color: #ff7f0e;\">Objective:</span>\n",
        "Prepare the dataset for modeling by standardizing features and ensuring that the data is in a suitable format for analysis.\n",
        "\n",
        "### <span style=\"color: #2ca02c;\">Feature Standardization</span>\n",
        "\n",
        "#### <span style=\"color: #d62728;\">Purpose:</span>\n",
        "Standardization ensures that all features contribute equally to the analysis by scaling them to have a mean of zero and a standard deviation of one. This process is crucial because many machine learning algorithms perform better when the features are on a similar scale.\n",
        "\n",
        "#### <span style=\"color: #9467bd;\">Methods:</span>\n",
        "\n",
        "- **Standard Scaler**:\n",
        "  - **Description**: `StandardScaler` from `scikit-learn` standardizes features by removing the mean and scaling to unit variance. This method is particularly useful for algorithms that rely on distance calculations or assume normally distributed data.\n",
        "  - **Implementation**:\n",
        "    ```python\n",
        "    from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "    # Instantiate the scaler\n",
        "    scaler = StandardScaler()\n",
        "\n",
        "    # Features to scale\n",
        "    features_to_scale = ['Typical_Price', 'VWAP']\n",
        "\n",
        "    # Create a copy of the DataFrame to avoid modifying the original\n",
        "    df_scaled = df.copy()\n",
        "\n",
        "    # Apply the scaler to the selected features\n",
        "    df_scaled[features_to_scale] = scaler.fit_transform(df[features_to_scale])\n",
        "    ```\n",
        "  - **Outcome**: After applying the StandardScaler, the `Typical_Price` and `VWAP` features in `df_scaled` will have a mean of 0 and a standard deviation of 1. This normalization helps in achieving better performance and faster convergence in machine learning models.\n",
        "\n",
        "### <span style=\"color: #8c564b;\">Creating a New DataFrame with Scaled Features</span>\n",
        "\n",
        "#### <span style=\"color: #e377c2;\">Purpose:</span>\n",
        "To facilitate model training and analysis, create a new DataFrame that includes only the standardized features along with essential identifiers. This separation helps in organizing data for further processing.\n",
        "\n",
        "#### <span style=\"color: #7f7f7f;\">Steps:</span>\n",
        "\n",
        "- **Select Features**:\n",
        "  - **Description**: Identify which columns are relevant for analysis and include them in the new DataFrame.\n",
        "  - **Implementation**:\n",
        "    ```python\n",
        "    # Create a new DataFrame with only the standardized features and necessary identifiers\n",
        "    df_preprocessed = df_scaled[['Timestamp', 'Typical_Price', 'VWAP']]\n",
        "    ```\n",
        "  - **Outcome**: The DataFrame `df_preprocessed` contains only the `Timestamp`, `Typical_Price`, and `VWAP` columns, ensuring that the data used for subsequent analysis is clean and properly scaled.\n",
        "\n",
        "#### <span style=\"color: #ff7f0e;\">Ensuring Data Integrity</span>\n",
        "\n",
        "- **Checking for NaN Values**:\n",
        "  - **Purpose**: Verify that the new DataFrame does not contain NaN values, which could have resulted from the data cleaning or scaling process.\n",
        "  - **Implementation**:\n",
        "    ```python\n",
        "    # Check for NaN values in the new DataFrame\n",
        "    assert df_preprocessed.notna().all().all(), \"Data contains NaN values\"\n",
        "    ```\n",
        "  - **Outcome**: Ensures that the `df_preprocessed` DataFrame is free from missing values, which is crucial for reliable model training and analysis.\n",
        "\n",
        "- **Validating Data**:\n",
        "  - **Purpose**: Confirm that the new DataFrame maintains the expected structure and includes all relevant columns necessary for the modeling process.\n",
        "  - **Implementation**:\n",
        "    ```python\n",
        "    # Display the first few rows of the new DataFrame to validate\n",
        "    print(df_preprocessed.head())\n",
        "    ```\n",
        "  - **Outcome**: Provides a quick check to ensure the new DataFrame looks correct and contains all necessary features for the next steps in the analysis.\n",
        "\n",
        "### <span style=\"color: #1f77b4;\">Summary:</span>\n",
        "In Step 4, data preprocessing involves standardizing features to ensure that all variables contribute equally to the analysis, followed by creating a clean and organized DataFrame for modeling. By standardizing the features and ensuring data integrity, you set a solid foundation for accurate and effective analysis."
      ],
      "metadata": {
        "id": "DXWct76-QIi0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "Dr_3wmVhDWyB"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature scaling (Standardizing data)"
      ],
      "metadata": {
        "id": "dDCJ4e8oDW0M"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initialize the StandardScaler"
      ],
      "metadata": {
        "id": "EBq-f8xJDW2m"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close', 'Volume_(BTC)', 'Volume_(Currency)', 'Typical Price', 'VWAP']])  # Standardize the features\n"
      ],
      "metadata": {
        "id": "Y2C5dARzDW7W"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data = pd.DataFrame(scaled_features, columns=['Open', 'High', 'Low', 'Close', 'Volume_(BTC)', 'Volume_(Currency)', 'Typical Price', 'VWAP'], index=data.index)  # Create DataFrame with scaled features\n"
      ],
      "metadata": {
        "id": "Kk9JtJmwDW-B"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5AgNhLJEDXCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UGrcOD5PKNCN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7fzx2gl4KNFm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color: #1f77b4;\">5. VWAP Strategy Implementation</span>\n",
        "\n",
        "### <span style=\"color: #ff7f0e;\">Objective:</span>\n",
        "Implement the VWAP strategy by generating trading signals based on the relationship between the VWAP and the closing price. This step is crucial for identifying potential trading opportunities and informing trading decisions.\n",
        "\n",
        "### <span style=\"color: #2ca02c;\">Generating Trading Signals</span>\n",
        "\n",
        "#### <span style=\"color: #d62728;\">Buy Signal:</span>\n",
        "- **Definition**: A buy signal is generated when the closing price is below the VWAP. This indicates that the security is undervalued compared to its average trading price, suggesting a potential buying opportunity.\n",
        "- **Implementation**:\n",
        "  - Create a new column in the DataFrame to denote buy signals.\n",
        "  - **Code Snippet**:\n",
        "    ```python\n",
        "    # Generate Buy Signal\n",
        "    df_preprocessed['Buy_Signal'] = df_preprocessed['Close'] < df_preprocessed['VWAP']\n",
        "    ```\n",
        "  - **Explanation**: This code creates a boolean column, `Buy_Signal`, where `True` indicates that the closing price is below the VWAP, signaling a potential buy.\n",
        "\n",
        "#### <span style=\"color: #9467bd;\">Sell Signal:</span>\n",
        "- **Definition**: A sell signal is generated when the closing price is above the VWAP. This suggests that the security is overvalued compared to its average trading price, indicating a potential selling opportunity.\n",
        "- **Implementation**:\n",
        "  - Create a new column in the DataFrame to denote sell signals.\n",
        "  - **Code Snippet**:\n",
        "    ```python\n",
        "    # Generate Sell Signal\n",
        "    df_preprocessed['Sell_Signal'] = df_preprocessed['Close'] > df_preprocessed['VWAP']\n",
        "    ```\n",
        "  - **Explanation**: This code creates a boolean column, `Sell_Signal`, where `True` indicates that the closing price is above the VWAP, signaling a potential sell.\n",
        "\n",
        "### <span style=\"color: #e377c2;\">Purpose of Signals</span>\n",
        "\n",
        "- **Buy Signal**:\n",
        "  - **Objective**: Identify potential buying opportunities when the market price is lower than the average trading price, with the expectation of future price appreciation.\n",
        "  - **Use Case**: Traders might enter positions when they see a buy signal, anticipating that the price will rise towards or above the VWAP.\n",
        "\n",
        "- **Sell Signal**:\n",
        "  - **Objective**: Identify potential selling opportunities when the market price is higher than the average trading price, with the expectation of future price depreciation.\n",
        "  - **Use Case**: Traders might exit positions or short-sell when they see a sell signal, anticipating that the price will fall towards or below the VWAP.\n",
        "\n",
        "### <span style=\"color: #8c564b;\">Visualizing Signals</span>\n",
        "\n",
        "#### <span style=\"color: #7f7f7f;\">Buy and Sell Signals Visualization:</span>\n",
        "- **Objective**: To visualize the buy and sell signals alongside VWAP and closing prices to understand their effectiveness and alignment with market movements.\n",
        "- **Implementation**:\n",
        "  - Plot closing prices, VWAP, and the generated buy/sell signals on a graph to visually assess trading opportunities.\n",
        "  - **Code Snippet**:\n",
        "    ```python\n",
        "    import matplotlib.pyplot as plt\n",
        "\n",
        "    plt.figure(figsize=(14, 7))\n",
        "\n",
        "    # Plot closing price\n",
        "    plt.plot(df_preprocessed['Timestamp'], df_preprocessed['Close'], label='Close Price', color='blue')\n",
        "\n",
        "    # Plot VWAP\n",
        "    plt.plot(df_preprocessed['Timestamp'], df_preprocessed['VWAP'], label='VWAP', color='red')\n",
        "\n",
        "    # Plot Buy Signals\n",
        "    buy_signals = df_preprocessed[df_preprocessed['Buy_Signal']]\n",
        "    plt.scatter(buy_signals['Timestamp'], buy_signals['Close'], marker='^', color='green', label='Buy Signal', alpha=1)\n",
        "\n",
        "    # Plot Sell Signals\n",
        "    sell_signals = df_preprocessed[df_preprocessed['Sell_Signal']]\n",
        "    plt.scatter(sell_signals['Timestamp'], sell_signals['Close'], marker='v', color='red', label='Sell Signal', alpha=1)\n",
        "\n",
        "    plt.title('VWAP Strategy - Buy and Sell Signals')\n",
        "    plt.xlabel('Timestamp')\n",
        "    plt.ylabel('Price')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    ```\n",
        "  - **Explanation**: This code generates a plot showing the closing prices, VWAP, and the buy/sell signals. Buy signals are marked with green upward triangles and sell signals with red downward triangles.\n",
        "\n",
        "### <span style=\"color: #1f77b4;\">Summary:</span>\n",
        "In Step 5, the VWAP strategy is implemented by generating and visualizing buy and sell signals based on the comparison between closing prices and VWAP. This helps traders identify potential trading opportunities and evaluate the strategy's effectiveness."
      ],
      "metadata": {
        "id": "xHiSBeBaQQC0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  VWAP Strategy Implementation"
      ],
      "metadata": {
        "id": "FPe7RoteDXHd"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create signals: Buy when Close < VWAP, Sell when Close > VWAP"
      ],
      "metadata": {
        "id": "SCIOppHcDXJ8"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Signal'] = 0  # Initialize the 'Signal' column with zeros"
      ],
      "metadata": {
        "id": "AxL5uY2EDXMN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Signal'][scaled_data['Close'] < scaled_data['VWAP']] = 1  # Set Buy signal (1) when Close < VWAP"
      ],
      "metadata": {
        "id": "1XmS03M2DXOj"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Signal'][scaled_data['Close'] > scaled_data['VWAP']] = -1  # Set Sell signal (-1) when Close > VWAP"
      ],
      "metadata": {
        "id": "tH7uVfkbDR_Q"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l8sRobgKDSCX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h0uJfMtAKODQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mXlid4kAKOGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color: #1f77b4;\">6. Backtesting the Strategy</span>\n",
        "\n",
        "### <span style=\"color: #ff7f0e;\">Objective:</span>\n",
        "Backtesting evaluates the performance of the VWAP strategy by simulating trades using historical data. This helps in understanding how well the strategy would have performed in the past and provides insights into its effectiveness.\n",
        "\n",
        "### <span style=\"color: #2ca02c;\">Computing Returns</span>\n",
        "\n",
        "#### <span style=\"color: #d62728;\">Daily Returns:</span>\n",
        "- **Definition**: Daily returns are the percentage change in the price of the security from one day to the next. This provides insight into the daily fluctuations in price.\n",
        "- **Implementation**: Calculate daily returns to understand the price movement on a daily basis.\n",
        "\n",
        "#### <span style=\"color: #9467bd;\">Strategy Returns:</span>\n",
        "- **Definition**: Strategy returns reflect the performance of the VWAP strategy based on the buy and sell signals generated.\n",
        "- **Implementation**: Calculate returns based on these signals to determine the effectiveness of the strategy.\n",
        "\n",
        "#### <span style=\"color: #e377c2;\">Cumulative Returns:</span>\n",
        "- **Definition**: Cumulative returns show the total return over time, considering all individual daily returns.\n",
        "- **Implementation**: Compute cumulative returns to assess the overall performance of the strategy over the entire period.\n",
        "\n",
        "### <span style=\"color: #7f7f7f;\">Performance Evaluation</span>\n",
        "\n",
        "#### <span style=\"color: #8c564b;\">Comparing Strategy to Benchmark:</span>\n",
        "- **Objective**: Compare the VWAP strategy's cumulative returns with a benchmark (such as a buy-and-hold strategy) to evaluate its performance.\n",
        "- **Implementation**: Analyze how the strategy performs relative to a simple benchmark to gauge its effectiveness.\n",
        "\n",
        "#### <span style=\"color: #1f77b4;\">Visualization:</span>\n",
        "- **Objective**: Visualize the cumulative returns of the VWAP strategy and benchmark to assess the strategy's performance visually.\n",
        "- **Implementation**: Plot the cumulative returns to compare the strategy against the benchmark and evaluate its success."
      ],
      "metadata": {
        "id": "8FvcqEJ9QczM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Backtesting the Strategy"
      ],
      "metadata": {
        "id": "Dsaa9F1dDSFZ"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute returns"
      ],
      "metadata": {
        "id": "uZBcfQmHDSHw"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Returns'] = scaled_data['Close'].pct_change()  # Compute daily returns"
      ],
      "metadata": {
        "id": "xKDCBU-_DSKC"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Strategy Returns'] = scaled_data['Returns'] * scaled_data['Signal'].shift(1)  # Compute strategy returns based on signals\n"
      ],
      "metadata": {
        "id": "EACNWR-XE82l"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Cumulative Returns'] = (1 + scaled_data['Strategy Returns']).cumprod()  # Compute cumulative returns of the strategy\n"
      ],
      "metadata": {
        "id": "2Lb6I5EAE85m"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hN3RL3EAE88Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tcBZktboHhRF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z-yXflx3Hhav"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Evaluation and Visualization</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">VWAP and Close Price</span>\n",
        "\n",
        "### <span style=\"color: #2ca02c;\">Objective:</span>\n",
        "To visualize how VWAP compares to the closing prices of Bitcoin, providing insight into how VWAP tracks the average trading price over time.\n",
        "\n",
        "### <span style=\"color: #d62728;\">Insight:</span>\n",
        "- **Purpose:** This comparison helps evaluate the accuracy of VWAP as an indicator of the average trading price. It reveals whether VWAP aligns well with the actual market prices.\n",
        "- **Implementation:**\n",
        "  - Plot VWAP and closing prices on the same chart.\n",
        "  - Use distinct colors or line styles to differentiate VWAP and closing prices.\n",
        "  - Highlight significant divergences to identify potential trading signals or market anomalies.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Cumulative Returns</span>\n",
        "\n",
        "### <span style=\"color: #8c564b;\">Objective:</span>\n",
        "To assess the cumulative returns generated by the VWAP strategy, showing the overall performance over time.\n",
        "\n",
        "### <span style=\"color: #e377c2;\">Insight:</span>\n",
        "- **Purpose:** Cumulative returns provide a comprehensive view of the strategy’s performance, showing how it would have performed if followed consistently.\n",
        "- **Implementation:**\n",
        "  - Calculate cumulative returns from daily returns based on buy/sell signals.\n",
        "  - Plot these returns to visualize the growth or decline of the investment.\n",
        "  - Highlight significant periods of gains or losses to assess the strategy's effectiveness.\n",
        "\n",
        "## <span style=\"color: #7f7f7f;\">Buy and Sell Signals</span>\n",
        "\n",
        "### <span style=\"color: #1f77b4;\">Objective:</span>\n",
        "To visualize the buy and sell signals generated by the VWAP strategy and their alignment with VWAP and closing prices.\n",
        "\n",
        "### <span style=\"color: #ff7f0e;\">Insight:</span>\n",
        "- **Purpose:** Evaluate how well the VWAP strategy identifies trading opportunities. This helps in understanding the strategy’s accuracy in suggesting optimal buy/sell points.\n",
        "- **Implementation:**\n",
        "  - Plot buy signals as green markers and sell signals as red markers.\n",
        "  - Overlay these signals on the VWAP and closing price plot to show timing and effectiveness.\n",
        "  - Use different marker sizes or styles to enhance visibility.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Additional Visualizations</span>\n",
        "\n",
        "### <span style=\"color: #d62728;\">Histogram of Strategy Returns:</span>\n",
        "- **Objective:** Display the distribution of returns from the VWAP strategy.\n",
        "- **Insight:** Understand the frequency and variability of returns to gauge the strategy’s risk and reward profile.\n",
        "- **Implementation:**\n",
        "  - Plot a histogram of strategy returns.\n",
        "  - Analyze the distribution shape and spread to assess return consistency and volatility.\n",
        "\n",
        "### <span style=\"color: #9467bd;\">Rolling Statistics for VWAP:</span>\n",
        "- **Objective:** Show rolling statistics like moving averages and standard deviations of VWAP to analyze its stability and fluctuations.\n",
        "- **Insight:** Provides insight into VWAP’s behavior over time, helping to assess its reliability.\n",
        "- **Implementation:**\n",
        "  - Calculate and plot rolling moving averages and standard deviations of VWAP.\n",
        "  - Visualize trends and volatility to understand VWAP’s performance and stability."
      ],
      "metadata": {
        "id": "TUMY0M2CQo2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Evaluation and Visualization"
      ],
      "metadata": {
        "id": "fHBfNvkDE8-9"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot VWAP and Close Price"
      ],
      "metadata": {
        "id": "QLIr0H8nE9Cr"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close price\n",
        "plt.plot(data.index, data['VWAP'], label='VWAP', color='orange', alpha=0.7)  # Plot VWAP\n",
        "plt.title('Close Price and VWAP')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Price')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/vwap_vs_close.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "zcA2k-NSDSMc",
        "outputId": "86d043ec-deb1-40a7-b8af-fd50e2703ff1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kdaXIelMDSOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G-noSv89DSQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9UABtRcrKarM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Cumulative Returns</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Cumulative Returns** graph displays the growth of an investment over time, based on the strategy's performance. It visualizes the cumulative return of the trading strategy compared to a baseline (e.g., holding the asset).\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Performance Evaluation**: Assesses the overall effectiveness of the trading strategy by showing how returns accumulate over time.\n",
        "- **Strategy Comparison**: Compares the strategy's performance against a baseline to evaluate its added value.\n",
        "- **Investment Growth**: Helps in understanding the compounded return on investment.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **Cumulative Return Line**: Shows the cumulative return of the strategy over time.\n",
        "- **Baseline Line**: Represents the cumulative return of a baseline investment (e.g., holding the asset).\n",
        "- **X-Axis**: Represents time.\n",
        "- **Y-Axis**: Represents cumulative return.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Return Growth**: A rising cumulative return line indicates positive performance and growing investment value.\n",
        "- **Strategy Effectiveness**: A higher cumulative return compared to the baseline suggests that the strategy outperforms the baseline.\n",
        "- **Investment Decisions**: Provides insights into the long-term impact of following the strategy on investment growth."
      ],
      "metadata": {
        "id": "ffxR_QDCTLv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Cumulative Returns"
      ],
      "metadata": {
        "id": "pM9Tz5_XDSTL"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(scaled_data['Cumulative Returns'], label='VWAP Strategy Cumulative Returns', color='green')  # Plot cumulative returns\n",
        "plt.title('VWAP Strategy Cumulative Returns')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Cumulative Returns')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/cumulative_returns.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "EwvZbEoGDSVI",
        "outputId": "01a0a36a-5be5-43c7-a5d8-042781da6898"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QiSdtbzBFbB-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x8Wpg704FbFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-bf3SSqSKeM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Buy and Sell Signals</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Buy and Sell Signals** graph illustrates the trading signals generated based on the VWAP strategy. These signals are plotted alongside VWAP and closing prices to help traders make informed decisions.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Signal Visualization**: Provides a visual representation of trading signals to help traders understand the VWAP strategy's effectiveness.\n",
        "- **Decision Making**: Assists in making buy or sell decisions based on VWAP’s indication of market value relative to current prices.\n",
        "- **Strategy Evaluation**: Evaluates the performance of the VWAP strategy in real trading scenarios.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **Buy Signals**: Typically marked when the closing price is below the VWAP, indicating a potential buying opportunity.\n",
        "- **Sell Signals**: Typically marked when the closing price is above the VWAP, suggesting a potential selling opportunity.\n",
        "- **X-Axis**: Represents time.\n",
        "- **Y-Axis**: Represents price.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Signal Accuracy**: Evaluates how well the buy and sell signals align with actual price movements and trading outcomes.\n",
        "- **Strategy Effectiveness**: Provides insights into the effectiveness of the VWAP strategy in identifying profitable trading opportunities.\n",
        "- **Signal Frequency**: Analyzes the frequency of signals and their impact on overall trading performance."
      ],
      "metadata": {
        "id": "0XWhlz72S8H3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Buy and Sell Signals"
      ],
      "metadata": {
        "id": "bjoiWXpiFbIQ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['Close'], label='Close Price', color='blue', alpha=0.5)  # Plot Close price\n",
        "plt.plot(data.index, data['VWAP'], label='VWAP', color='orange', alpha=0.7)  # Plot VWAP\n",
        "plt.scatter(data.index[scaled_data['Signal'] == 1], data['Close'][scaled_data['Signal'] == 1], marker='^', color='g', label='Buy Signal', alpha=1)  # Plot Buy signals\n",
        "plt.scatter(data.index[scaled_data['Signal'] == -1], data['Close'][scaled_data['Signal'] == -1], marker='v', color='r', label='Sell Signal', alpha=1)  # Plot Sell signals\n",
        "plt.title('Buy and Sell Signals')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('Price')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/buy_sell_signals.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "3-gqep5BFbKX",
        "outputId": "a79b24ae-6686-41bd-9bb7-8491dc409f61"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fvO1shLLFbM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dWJOYWv9FbRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HvV5VWB9DSXv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Histogram of Strategy Returns</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Histogram of Strategy Returns** graph shows the distribution of daily returns from the trading strategy. This visualization is useful for understanding the variability and distribution of returns.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Return Distribution**: Provides insights into the distribution of returns, highlighting periods of high or low returns.\n",
        "- **Volatility Analysis**: Helps in assessing the volatility and risk associated with the strategy.\n",
        "- **Performance Insights**: Identifies patterns or anomalies in the return distribution that might impact strategy performance.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **Bins**: Represents the range of returns divided into intervals or bins.\n",
        "- **Frequency**: Shows the number of occurrences of returns within each bin.\n",
        "- **X-Axis**: Represents return values.\n",
        "- **Y-Axis**: Represents frequency of occurrence.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Return Concentration**: High frequency in specific return bins indicates common return values.\n",
        "- **Return Spread**: A wide spread suggests higher variability and risk in the strategy’s returns.\n",
        "- **Risk Assessment**: Helps in evaluating the risk profile of the strategy based on the distribution of returns."
      ],
      "metadata": {
        "id": "81Dbrd7xTPsv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Additional Visualization: Histogram of Returns"
      ],
      "metadata": {
        "id": "W69H1M57DScE"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "sns.histplot(scaled_data['Strategy Returns'].dropna(), bins=50, kde=True, color='purple')  # Plot histogram with KDE of strategy returns\n",
        "plt.title('Distribution of Strategy Returns')  # Set the title of the plot\n",
        "plt.xlabel('Strategy Returns')  # Set x-axis label\n",
        "plt.ylabel('Frequency')  # Set y-axis label\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/strategy_returns_histogram.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "zFOBT90KDSgg",
        "outputId": "850466d3-a5f7-4a7e-cb7d-2b2738c58a75"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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AAFji/wFar2qMS7s9OQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vB-Y4-_EDSih"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HJ-cDqWXDSn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2SadMOaqKmgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ml4Srr7hKmi_"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Rolling Statistics for VWAP</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Overview</span>\n",
        "\n",
        "The **Rolling Statistics for VWAP** graph displays rolling mean and rolling standard deviation for VWAP. This visualization helps in understanding the volatility and trends in VWAP over time.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Purpose</span>\n",
        "\n",
        "- **Trend Analysis**: Provides insights into the trends and fluctuations in VWAP over different rolling periods.\n",
        "- **Volatility Measurement**: Assesses the variability and volatility in VWAP values over time.\n",
        "- **Strategic Adjustments**: Helps in adjusting trading strategies based on observed VWAP trends and volatility.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Key Features</span>\n",
        "\n",
        "- **Rolling Mean**: Shows the average VWAP over a rolling window.\n",
        "- **Rolling Standard Deviation**: Displays the variability of VWAP over a rolling window.\n",
        "- **X-Axis**: Represents time.\n",
        "- **Y-Axis**: Represents VWAP values and statistics.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Interpretation</span>\n",
        "\n",
        "- **Mean Trends**: Observing trends in the rolling mean helps in understanding the direction of VWAP over time.\n",
        "- **Volatility Insights**: High rolling standard deviation indicates periods of high volatility, while low deviation suggests stability.\n",
        "- **Strategy Adjustments**: Provides information for fine-tuning trading strategies based on VWAP’s volatility and trends."
      ],
      "metadata": {
        "id": "ugHWJ0ehTUGW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Additional Visualization: Rolling Mean and Standard Deviation of VWAP"
      ],
      "metadata": {
        "id": "LvavptJ6DSqS"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rolling_window = 30  # Define the rolling window size"
      ],
      "metadata": {
        "id": "Y7b32GYvDSuy"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['VWAP Rolling Mean'] = data['VWAP'].rolling(window=rolling_window).mean()  # Compute rolling mean of VWAP"
      ],
      "metadata": {
        "id": "Vy4-vCEHDSxS"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['VWAP Rolling Std'] = data['VWAP'].rolling(window=rolling_window).std()  # Compute rolling standard deviation of VWAP\n"
      ],
      "metadata": {
        "id": "bi6-64jQDSz6"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-5HOc0mKF1vl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4PhJoeYhF10N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 7))  # Set the figure size\n",
        "plt.plot(data.index, data['VWAP'], label='VWAP', color='orange', alpha=0.7)  # Plot VWAP\n",
        "plt.plot(data.index, data['VWAP Rolling Mean'], label=f'{rolling_window}-Minute Rolling Mean', color='blue')  # Plot rolling mean\n",
        "plt.fill_between(data.index, data['VWAP Rolling Mean'] - data['VWAP Rolling Std'], data['VWAP Rolling Mean'] + data['VWAP Rolling Std'], color='blue', alpha=0.1, label=f'{rolling_window}-Minute Rolling Std Dev')  # Fill between rolling mean ± rolling std dev\n",
        "plt.title('VWAP with Rolling Mean and Std Dev')  # Set the title of the plot\n",
        "plt.xlabel('Date')  # Set x-axis label\n",
        "plt.ylabel('VWAP')  # Set y-axis label\n",
        "plt.legend()  # Display the legend\n",
        "plt.grid(True)  # Add grid lines to the plot\n",
        "plt.savefig('/content/vwap_rolling_mean_std.png')  # Save the plot as a PNG file\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "tE5ouousF13l",
        "outputId": "038f8d09-d0a5-4dbe-9f41-3c12c63de5cc"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fwZQllDZF16P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0aJAL770DS2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display final dataset for verification"
      ],
      "metadata": {
        "id": "iA1ZDQ0WDS6r"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFinal processed dataset:\")  # Print header\n",
        "print(scaled_data.head())  # Display the first few rows of the processed dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQFFMvSRDS9Z",
        "outputId": "119625b5-252a-4890-d989-6c36f2889773"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Final processed dataset:\n",
            "                         Open      High       Low     Close  Volume_(BTC)  \\\n",
            "Date                                                                        \n",
            "2011-12-31 07:52:00 -1.305112 -1.304970 -1.305236 -1.305071     -0.300600   \n",
            "2011-12-31 15:50:00 -1.305112 -1.304970 -1.305236 -1.305071      0.685186   \n",
            "2011-12-31 16:59:00 -1.304738 -1.304358 -1.304861 -1.304458      0.474991   \n",
            "2011-12-31 17:00:00 -1.304465 -1.304324 -1.304588 -1.304424     -0.123440   \n",
            "2012-01-01 04:16:00 -1.304465 -1.304324 -1.304588 -1.304424     -0.278903   \n",
            "\n",
            "                     Volume_(Currency)  Typical Price      VWAP  Signal  \\\n",
            "Date                                                                      \n",
            "2011-12-31 07:52:00          -0.249176      -1.305099 -1.320202      -1   \n",
            "2011-12-31 15:50:00          -0.240309      -1.305099 -1.320202      -1   \n",
            "2011-12-31 16:59:00          -0.241980      -1.304565 -1.319727      -1   \n",
            "2011-12-31 17:00:00          -0.247510      -1.304452 -1.319648      -1   \n",
            "2012-01-01 04:16:00          -0.248969      -1.304452 -1.319636      -1   \n",
            "\n",
            "                      Returns  Strategy Returns  Cumulative Returns  \n",
            "Date                                                                 \n",
            "2011-12-31 07:52:00       NaN               NaN                 NaN  \n",
            "2011-12-31 15:50:00  0.000000         -0.000000            1.000000  \n",
            "2011-12-31 16:59:00 -0.000470          0.000470            1.000470  \n",
            "2011-12-31 17:00:00 -0.000026          0.000026            1.000496  \n",
            "2012-01-01 04:16:00  0.000000         -0.000000            1.000496  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LN6RIz_gDTAC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sKWslsm7DTCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5SYSJKL_Mgs3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the final processed dataset to a CSV file"
      ],
      "metadata": {
        "id": "7nqTZZb6DTEw"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_file_path = '/content/final_processed_dataset.csv'  # Path where the final CSV will be saved"
      ],
      "metadata": {
        "id": "Ej6oOahPL21M"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data.to_csv(final_file_path)  # Save the processed dataset to CSV"
      ],
      "metadata": {
        "id": "n6ueIj6UL24M"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tz5NioelMc6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Final processed dataset saved to {final_file_path}\")  # Print confirmation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMxsm_rnMc9v",
        "outputId": "bcf2db47-8ace-47bf-d650-d569c2f60685"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final processed dataset saved to /content/final_processed_dataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Io2Ew8TZMdAU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QXNIDaNVRcM3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FWlu_2bWRcW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #1f77b4;\">Conclusions</span>\n",
        "\n",
        "## <span style=\"color: #ff7f0e;\">Summary of Findings</span>\n",
        "\n",
        "The analysis of the Volume-Weighted Average Price (VWAP) strategy reveals several important insights:\n",
        "\n",
        "- **VWAP vs. Close Price:** VWAP serves as a reliable benchmark for assessing average trading prices. The proximity of VWAP to the closing prices highlights its effectiveness in reflecting the average price at which a security is traded. Deviations from VWAP can indicate potential trading opportunities or discrepancies in market efficiency.\n",
        "\n",
        "- **Cumulative Returns:** The cumulative returns derived from implementing the VWAP strategy offer a comprehensive view of its overall effectiveness. Positive cumulative returns suggest that the strategy performs well in capturing profitable trading opportunities, while negative returns may signal the need for strategy adjustments or alternative approaches.\n",
        "\n",
        "- **Buy and Sell Signals:** The visualization of buy and sell signals, in relation to VWAP and closing prices, demonstrates the practical application of the strategy. Effective buy/sell signals should align with favorable market conditions and lead to profitable trades, helping traders make more informed decisions.\n",
        "\n",
        "- **Additional Insights:** Histograms of strategy returns and rolling statistics for VWAP provide valuable insights into the stability and distribution of returns. These analyses help assess the risk associated with the strategy and its overall reliability in various market conditions.\n",
        "\n",
        "## <span style=\"color: #2ca02c;\">Effectiveness of VWAP Strategy</span>\n",
        "\n",
        "- **Strengths:**\n",
        "  - **Benchmarking:** VWAP acts as a strong benchmark for evaluating trade quality and market trends, offering a clear reference point for average trading prices.\n",
        "  - **Trend Identification:** By comparing closing prices to VWAP, the strategy helps identify potential buy and sell opportunities, aiding traders in making strategic decisions.\n",
        "  - **Performance Evaluation:** Analyzing cumulative returns and additional metrics provides a thorough assessment of the strategy’s performance over time.\n",
        "\n",
        "- **Limitations:**\n",
        "  - **Lagging Indicator:** VWAP is inherently a lagging indicator, which means it may not always capture real-time market fluctuations effectively.\n",
        "  - **Sensitivity to Volume:** High trading volumes can disproportionately affect VWAP calculations, potentially skewing the results and affecting strategy performance.\n",
        "\n",
        "## <span style=\"color: #d62728;\">Recommendations</span>\n",
        "\n",
        "- **Refinement of Strategy:** Consider refining the VWAP strategy by incorporating additional technical indicators or adjusting the VWAP calculation parameters to enhance its effectiveness.\n",
        "- **Further Backtesting:** Conduct extensive backtesting under various market conditions and timeframes to validate the robustness of the VWAP strategy and its adaptability to different trading scenarios.\n",
        "- **Integration with Other Tools:** Integrate VWAP with other technical analysis tools and strategies to provide a more comprehensive approach to trading and improve decision-making.\n",
        "\n",
        "## <span style=\"color: #9467bd;\">Future Work</span>\n",
        "\n",
        "- **Advanced Analysis:** Explore advanced methods such as machine learning algorithms or statistical techniques to further enhance the VWAP strategy’s performance and accuracy.\n",
        "- **Real-time Implementation:** Test the VWAP strategy in real-time trading environments to assess its effectiveness in live markets and make necessary adjustments based on real-world performance.\n",
        "- **Comprehensive Evaluation:** Continuously monitor and evaluate the VWAP strategy’s performance to ensure its ongoing relevance and effectiveness amidst changing market conditions and trading dynamics.\n",
        "\n",
        "In summary, the VWAP strategy provides valuable insights into average trading prices and potential trading opportunities. By refining and continuously evaluating the strategy, traders can leverage VWAP to make more informed decisions and achieve better outcomes in the financial markets."
      ],
      "metadata": {
        "id": "aBzcCzTfRckU"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8q1W3PVWRqvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7cD2HyOrRqyh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}