Trading_Futures_Options.ipynb

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        "<a href=\"https://colab.research.google.com/github/baijuthomas528/B_Python/blob/main/Trading_Futures_Options.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "                                                                \n",
        "                                                                **Price Trends**"
      ],
      "metadata": {
        "id": "8X0hyaeExoc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "\n",
        "def plot_stock_returns(tickers, start_date, end_date):\n",
        "    # Create a dictionary to hold the results for each ticker\n",
        "    results_dict = {}\n",
        "\n",
        "    for ticker in tickers:\n",
        "        # Download stock data with auto_adjust=False to retain 'Adj Close'\n",
        "        stock_data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)\n",
        "\n",
        "        # Check if data is available\n",
        "        if stock_data.empty:\n",
        "            print(f\"No data found for ticker: {ticker}\")\n",
        "            continue  # Skip to the next ticker if no data\n",
        "\n",
        "        # Get the current price of the ticker\n",
        "        current_price = float(stock_data['Adj Close'].iloc[-1]) if 'Adj Close' in stock_data.columns else None\n",
        "\n",
        "        # Calculate daily returns\n",
        "        stock_data['Daily Return'] = stock_data['Adj Close'].pct_change()\n",
        "\n",
        "        # Resample to weekly data and calculate weekly returns\n",
        "        stock_data['Weekly Return'] = stock_data['Adj Close'].resample('W-FRI').ffill().pct_change()\n",
        "\n",
        "        # Resample to monthly data and calculate monthly returns\n",
        "        stock_data['Monthly Return'] = stock_data['Adj Close'].resample('M').ffill().pct_change()\n",
        "\n",
        "        # Calculate the required statistics for daily returns\n",
        "        max_daily_return = stock_data['Daily Return'].max()\n",
        "        min_daily_return = stock_data['Daily Return'].min()\n",
        "        median_daily_return = stock_data['Daily Return'].median()\n",
        "        avg_daily_return = stock_data['Daily Return'].mean()\n",
        "\n",
        "        # Find the dates for the max and min daily returns\n",
        "        max_daily_return_date = stock_data['Daily Return'].idxmax()\n",
        "        min_daily_return_date = stock_data['Daily Return'].idxmin()\n",
        "\n",
        "        # Calculate the required statistics for weekly returns\n",
        "        weekly_data = stock_data['Weekly Return'].dropna()  # Drop NaN values before calculating stats\n",
        "        max_weekly_return = weekly_data.max()\n",
        "        min_weekly_return = weekly_data.min()\n",
        "        median_weekly_return = weekly_data.median()\n",
        "        avg_weekly_return = weekly_data.mean()\n",
        "\n",
        "        # Find the dates for the max and min weekly returns\n",
        "        max_weekly_return_date = weekly_data.idxmax()\n",
        "        min_weekly_return_date = weekly_data.idxmin()\n",
        "\n",
        "        # Calculate the price after applying the min and max weekly returns\n",
        "        price_below_min_weekly = current_price * (1 + min_weekly_return)\n",
        "        price_above_max_weekly = current_price * (1 + max_weekly_return)\n",
        "\n",
        "        # Calculate the required statistics for monthly returns\n",
        "        monthly_data = stock_data['Monthly Return'].dropna()  # Drop NaN values before calculating stats\n",
        "        max_monthly_return = monthly_data.max()\n",
        "        min_monthly_return = monthly_data.min()\n",
        "        median_monthly_return = monthly_data.median()\n",
        "        avg_monthly_return = monthly_data.mean()\n",
        "\n",
        "        # Find the dates for the max and min monthly returns\n",
        "        max_monthly_return_date = monthly_data.idxmax()\n",
        "        min_monthly_return_date = monthly_data.idxmin()\n",
        "\n",
        "        # Calculate the price after applying the min and max monthly returns\n",
        "        price_below_min_monthly = current_price * (1 + min_monthly_return)\n",
        "        price_above_max_monthly = current_price * (1 + max_monthly_return)\n",
        "\n",
        "        # Count occurrences of specific thresholds\n",
        "        daily_1_percent_count = stock_data['Daily Return'].abs().ge(0.01).sum()\n",
        "        daily_2_percent_count = stock_data['Daily Return'].abs().ge(0.02).sum()\n",
        "        daily_3_percent_count = stock_data['Daily Return'].abs().ge(0.03).sum()\n",
        "        daily_4_percent_count = stock_data['Daily Return'].abs().ge(0.04).sum()\n",
        "        daily_5_percent_count = stock_data['Daily Return'].abs().ge(0.05).sum()\n",
        "        daily_10_percent_count = stock_data['Daily Return'].abs().ge(0.10).sum()\n",
        "\n",
        "        weekly_1_percent_count = weekly_data.abs().ge(0.01).sum()\n",
        "        weekly_2_percent_count = weekly_data.abs().ge(0.02).sum()\n",
        "        weekly_3_percent_count = weekly_data.abs().ge(0.03).sum()\n",
        "        weekly_4_percent_count = weekly_data.abs().ge(0.04).sum()\n",
        "        weekly_5_percent_count = weekly_data.abs().ge(0.05).sum()\n",
        "        weekly_10_percent_count = weekly_data.abs().ge(0.10).sum()\n",
        "\n",
        "        monthly_1_percent_count = monthly_data.abs().ge(0.01).sum()\n",
        "        monthly_2_percent_count = monthly_data.abs().ge(0.02).sum()\n",
        "        monthly_3_percent_count = monthly_data.abs().ge(0.03).sum()\n",
        "        monthly_4_percent_count = monthly_data.abs().ge(0.04).sum()\n",
        "        monthly_5_percent_count = monthly_data.abs().ge(0.05).sum()\n",
        "        monthly_10_percent_count = monthly_data.abs().ge(0.10).sum()\n",
        "\n",
        "        # Store the results in the dictionary\n",
        "        results_dict[ticker] = {\n",
        "            \"Current Price\": f\"{current_price:.2f}\",\n",
        "            \"Max Daily Return\": f\"{max_daily_return:.5%}\",\n",
        "            \"Max Daily Return Date\": max_daily_return_date,\n",
        "            \"Min Daily Return\": f\"{min_daily_return:.5%}\",\n",
        "            \"Min Daily Return Date\": min_daily_return_date,\n",
        "            \"Median Daily Return\": f\"{median_daily_return:.5%}\",\n",
        "            \"Average Daily Return\": f\"{avg_daily_return:.5%}\",\n",
        "            \"Max Weekly Return\": f\"{max_weekly_return:.5%}\",\n",
        "            \"Max Weekly Return Date\": max_weekly_return_date,\n",
        "            \"Min Weekly Return\": f\"{min_weekly_return:.5%}\",\n",
        "            \"Min Weekly Return Date\": min_weekly_return_date,\n",
        "            \"Price Above Max Weekly\": f\"{price_above_max_weekly:.2f}\",\n",
        "            \"Price Below Min Weekly\": f\"{price_below_min_weekly:.2f}\",\n",
        "            \"Median Weekly Return\": f\"{median_weekly_return:.5%}\",\n",
        "            \"Average Weekly Return\": f\"{avg_weekly_return:.5%}\",\n",
        "            \"Max Monthly Return\": f\"{max_monthly_return:.5%}\",\n",
        "            \"Max Monthly Return Date\": max_monthly_return_date,\n",
        "            \"Min Monthly Return\": f\"{min_monthly_return:.5%}\",\n",
        "            \"Min Monthly Return Date\": min_monthly_return_date,\n",
        "            \"Price Above Max Monthly\": f\"{price_above_max_monthly:.2f}\",\n",
        "            \"Price Below Min Monthly\": f\"{price_below_min_monthly:.2f}\",\n",
        "            \"Median Monthly Return\": f\"{median_monthly_return:.5%}\",\n",
        "            \"Average Monthly Return\": f\"{avg_monthly_return:.5%}\",\n",
        "            \"Times Daily Return Tested 1%\": daily_1_percent_count,\n",
        "            \"Times Daily Return Tested 2%\": daily_2_percent_count,\n",
        "            \"Times Daily Return Tested 3%\": daily_3_percent_count,\n",
        "            \"Times Daily Return Tested 4%\": daily_4_percent_count,\n",
        "            \"Times Daily Return Tested 5%\": daily_5_percent_count,\n",
        "            \"Times Daily Return Tested 10%\": daily_10_percent_count,\n",
        "            \"Times Weekly Return Tested 1%\": weekly_1_percent_count,\n",
        "            \"Times Weekly Return Tested 2%\": weekly_2_percent_count,\n",
        "            \"Times Weekly Return Tested 3%\": weekly_3_percent_count,\n",
        "            \"Times Weekly Return Tested 4%\": weekly_4_percent_count,\n",
        "            \"Times Weekly Return Tested 5%\": weekly_5_percent_count,\n",
        "            \"Times Monthly Return Tested 1%\": monthly_1_percent_count,\n",
        "            \"Times Monthly Return Tested 2%\": monthly_2_percent_count,\n",
        "            \"Times Monthly Return Tested 3%\": monthly_3_percent_count,\n",
        "            \"Times Monthly Return Tested 4%\": monthly_4_percent_count,\n",
        "            \"Times Monthly Return Tested 5%\": monthly_5_percent_count,\n",
        "        }\n",
        "\n",
        "    # Convert the results dictionary to a DataFrame for better readability\n",
        "    results_df = pd.DataFrame(results_dict)\n",
        "\n",
        "    # Display the results with tickers as headers\n",
        "    print(results_df)\n",
        "\n",
        "# Example usage:\n",
        "tickers = [\"^SPX\"]\n",
        "start_date = \"2024-01-01\"\n",
        "end_date = \"2024-12-28\"\n",
        "\n",
        "plot_stock_returns(tickers, start_date, end_date)\n"
      ],
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed"
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          "text": [
            "                                               ^SPX\n",
            "Current Price                               5970.84\n",
            "Max Daily Return                           2.52959%\n",
            "Max Daily Return Date           2024-11-06 00:00:00\n",
            "Min Daily Return                          -2.99688%\n",
            "Min Daily Return Date           2024-08-05 00:00:00\n",
            "Median Daily Return                        0.10911%\n",
            "Average Daily Return                       0.09568%\n",
            "Max Weekly Return                          4.65613%\n",
            "Max Weekly Return Date          2024-11-08 00:00:00\n",
            "Min Weekly Return                         -4.24864%\n",
            "Min Weekly Return Date          2024-09-06 00:00:00\n",
            "Price Above Max Weekly                      6248.85\n",
            "Price Below Min Weekly                      5717.16\n",
            "Median Weekly Return                       0.76396%\n",
            "Average Weekly Return                      0.48778%\n",
            "Max Monthly Return                         5.17206%\n",
            "Max Monthly Return Date         2024-02-29 00:00:00\n",
            "Min Monthly Return                        -4.16151%\n",
            "Min Monthly Return Date         2024-04-30 00:00:00\n",
            "Price Above Max Monthly                     6279.66\n",
            "Price Below Min Monthly                     5722.36\n",
            "Median Monthly Return                      1.57591%\n",
            "Average Monthly Return                     1.32914%\n",
            "Times Daily Return Tested 1%                     49\n",
            "Times Daily Return Tested 2%                      7\n",
            "Times Daily Return Tested 3%                      0\n",
            "Times Daily Return Tested 4%                      0\n",
            "Times Daily Return Tested 5%                      0\n",
            "Times Daily Return Tested 10%                     0\n",
            "Times Weekly Return Tested 1%                    29\n",
            "Times Weekly Return Tested 2%                     9\n",
            "Times Weekly Return Tested 3%                     5\n",
            "Times Weekly Return Tested 4%                     3\n",
            "Times Weekly Return Tested 5%                     0\n",
            "Times Monthly Return Tested 1%                    5\n",
            "Times Monthly Return Tested 2%                    4\n",
            "Times Monthly Return Tested 3%                    3\n",
            "Times Monthly Return Tested 4%                    3\n",
            "Times Monthly Return Tested 5%                    1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "<ipython-input-2-f489636018eb>:18: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
            "  current_price = float(stock_data['Adj Close'].iloc[-1]) if 'Adj Close' in stock_data.columns else None\n",
            "<ipython-input-2-f489636018eb>:27: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
            "  stock_data['Monthly Return'] = stock_data['Adj Close'].resample('M').ffill().pct_change()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Seasonality per Ticker**"
      ],
      "metadata": {
        "id": "jpx5zpSNkE4m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "\n",
        "def seasonality_grid(ticker, start_date, end_date):\n",
        "    # Download historical data for the given ticker\n",
        "    data = yf.download(ticker, start=start_date, end=end_date)\n",
        "\n",
        "    # Calculate monthly returns\n",
        "    data['Monthly Return'] = data['Close'].resample('M').ffill().pct_change() * 100\n",
        "\n",
        "    # Identify the last valid index before applying backward fill\n",
        "    last_valid_idx = data['Monthly Return'].last_valid_index()\n",
        "\n",
        "    # Apply backward fill only up to the last valid date\n",
        "    data.loc[:last_valid_idx, 'Monthly Return'] = data.loc[:last_valid_idx, 'Monthly Return'].fillna(method='bfill')\n",
        "\n",
        "    # Group data by year and month\n",
        "    data['Year'] = data.index.year\n",
        "    data['Month'] = data.index.strftime('%b')\n",
        "\n",
        "    # Pivot the data to create a grid with years as rows and months as columns\n",
        "    df = data.pivot_table(index='Year', columns='Month', values='Monthly Return', aggfunc='mean')\n",
        "\n",
        "    # Get the actual month abbreviations from the DataFrame columns\n",
        "    months_order = df.columns.tolist()\n",
        "\n",
        "    # Reorder the columns (if necessary) - This step might be redundant\n",
        "    # df = df[months_order]\n",
        "\n",
        "    # Add Average, Median, High, and Low\n",
        "    df.loc['Average'] = df.mean()\n",
        "    df.loc['Median'] = df.median()\n",
        "    df.loc['High'] = df.max()\n",
        "    df.loc['Low'] = df.min()\n",
        "\n",
        "    # Display the DataFrame\n",
        "    print(df)\n",
        "\n",
        "# Example usage:\n",
        "ticker = '^SPX'\n",
        "start_date = '2014-01-01'\n",
        "end_date = '2025-12-31'\n",
        "\n",
        "seasonality_grid(ticker, start_date, end_date)"
      ],
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            "\r[*********************100%***********************]  1 of 1 completed"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ticker                                                                 \\\n",
            "Month          Apr       Aug       Dec        Feb       Jan       Jul   \n",
            "Year                                                                    \n",
            "2014      0.620079 -1.551384 -0.418859   4.311703  4.311703 -1.507983   \n",
            "2015      0.852082 -6.258082 -1.753019  -1.739611 -1.739611  1.974203   \n",
            "2016      1.532460 -0.121924  1.788436  -0.412836 -0.412836 -0.121924   \n",
            "2017      1.157625  0.054643  5.617870   3.719816  1.788436  1.934883   \n",
            "2018      0.271877  3.026321 -9.177689  -3.894737  5.617870  3.602156   \n",
            "2019      3.931350  1.718118  2.858982   2.972889  7.868440  1.312815   \n",
            "2020     12.684404  7.006467  3.712146 -12.511928 -0.162809  5.510132   \n",
            "2021      5.242532  2.899042  4.361291   4.243863  4.243863  2.899042   \n",
            "2022      0.005318 -4.244013  6.175286  -3.136049 -5.258517 -4.244013   \n",
            "2023      0.248235 -1.771639  1.589571  -2.611249  6.175286  3.113893   \n",
            "2024     -4.161507  2.019688 -2.499014   5.172064  1.589571  1.132132   \n",
            "2025           NaN       NaN       NaN  -1.424209  2.701630       NaN   \n",
            "Average   2.034950  0.252476  1.114091  -0.442524  2.226919  1.418667   \n",
            "Median    1.004854  0.153560  1.689003  -0.442524  2.226919  1.676775   \n",
            "High     12.684404  7.006467  6.175286   5.172064  7.868440  5.510132   \n",
            "Low      -4.161507 -6.258082 -9.177689 -12.511928 -5.258517 -4.244013   \n",
            "\n",
            "Ticker                                                                  \n",
            "Month         Jun        Mar       May        Nov        Oct       Sep  \n",
            "Year                                                                    \n",
            "2014     1.905833   0.693217  1.905833  -0.418859   2.320146 -1.551384  \n",
            "2015    -2.101167  -1.739611 -2.101167   0.050487   0.050487 -2.644283  \n",
            "2016     0.091092   6.599111  1.532460   3.417452  -1.942568 -0.123445  \n",
            "2017     0.481378  -0.038920  1.157625   2.808263   2.218814  2.218814  \n",
            "2018     3.602156   0.271877  2.160834   1.785936  -6.940336 -6.940336  \n",
            "2019     1.312815   3.931350 -6.577773   2.858982   2.043177  1.718118  \n",
            "2020     1.838840 -12.511928  1.838840  10.754564  10.754564 -3.922797  \n",
            "2021     2.221401   4.243863  2.221401  -0.833371  -0.833371 -4.756917  \n",
            "2022    -8.392000   3.577329  0.005318   5.375289   7.986341 -9.339567  \n",
            "2023     6.472746   3.505161  0.248235   8.917927  -2.197969 -2.197969  \n",
            "2024     1.132132  -4.161507  4.802119  -2.499014  -0.989674  2.019688  \n",
            "2025          NaN  -5.754470       NaN        NaN        NaN       NaN  \n",
            "Average  0.778657  -0.115377  0.653975   2.928878   1.133601 -2.320007  \n",
            "Median   1.222473   0.271877  1.345043   2.833622   0.592044 -2.258988  \n",
            "High     6.472746   6.599111  4.802119  10.754564  10.754564  2.218814  \n",
            "Low     -8.392000 -12.511928 -6.577773  -2.499014  -6.940336 -9.339567  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "<ipython-input-5-9af00b8171f0>:10: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
            "  data['Monthly Return'] = data['Close'].resample('M').ffill().pct_change() * 100\n",
            "<ipython-input-5-9af00b8171f0>:16: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  data.loc[:last_valid_idx, 'Monthly Return'] = data.loc[:last_valid_idx, 'Monthly Return'].fillna(method='bfill')\n",
            "<ipython-input-5-9af00b8171f0>:23: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
            "  df = data.pivot_table(index='Year', columns='Month', values='Monthly Return', aggfunc='mean')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation values range from -1 to 1: +1: Perfect positive correlation (they move exactly together). 0: No correlation (independent movement). -1: Perfect negative correlation (they move exactly opposite).**"
      ],
      "metadata": {
        "id": "l_q4xIhZudha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define stock tickers to fetch\n",
        "tickers = ['AAPL', 'MSFT', 'GOOGL', 'NG=F', 'CL=F']\n",
        "\n",
        "# Fetch historical data for a specific date range with auto_adjust=True\n",
        "start_date = \"2021-11-01\"  # Specify start date\n",
        "end_date = \"2022-11-01\"    # Specify end date\n",
        "data = yf.download(tickers, start=start_date, end=end_date, auto_adjust=False)['Adj Close']\n",
        "\n",
        "# Calculate daily returns\n",
        "returns = data.pct_change().dropna()\n",
        "\n",
        "# Calculate correlation matrix\n",
        "correlation_matrix = returns.corr()\n",
        "\n",
        "# Plot the correlation matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='none', aspect='auto')\n",
        "plt.colorbar(label=\"Correlation Coefficient\")\n",
        "\n",
        "# Annotate the heatmap\n",
        "for (i, j), val in np.ndenumerate(correlation_matrix):\n",
        "    plt.text(j, i, f'{val:.2f}', ha='center', va='center', color='black')\n",
        "\n",
        "# Set labels and title\n",
        "plt.xticks(range(len(correlation_matrix)), correlation_matrix.columns, rotation=45)\n",
        "plt.yticks(range(len(correlation_matrix)), correlation_matrix.columns)\n",
        "plt.title(f\"Stock Correlation Matrix ({start_date} to {end_date})\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Display correlation matrix as a table\n",
        "print(correlation_matrix)\n"
      ],
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          "height": 946
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%***********************]  5 of 5 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ticker      AAPL      CL=F     GOOGL      MSFT      NG=F\n",
            "Ticker                                                  \n",
            "AAPL    1.000000  0.029698  0.765943  0.804884  0.014792\n",
            "CL=F    0.029698  1.000000  0.047325  0.026138  0.083936\n",
            "GOOGL   0.765943  0.047325  1.000000  0.836519  0.060074\n",
            "MSFT    0.804884  0.026138  0.836519  1.000000  0.086467\n",
            "NG=F    0.014792  0.083936  0.060074  0.086467  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Black Scholes calculator**"
      ],
      "metadata": {
        "id": "-p-q2YBXlfOo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Black Scholes calculator\n",
        "\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "# Parameters\n",
        "S = 5864  # Spot price\n",
        "K = 5650     # Strike price\n",
        "T = 10 / 365  # Time to expiration in years\n",
        "r = 0.012     # Risk-free interest rate\n",
        "sigma = 0.1661 # Volatility\n",
        "\n",
        "# Black-Scholes Put Price Calculation\n",
        "d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n",
        "d2 = d1 - sigma * np.sqrt(T)\n",
        "put_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)\n",
        "\n",
        "\n",
        "\n",
        "# Greeks Calculation\n",
        "\n",
        "# Delta\n",
        "delta = -norm.cdf(-d1)\n",
        "\n",
        "# Gamma\n",
        "gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))\n",
        "\n",
        "# Vega\n",
        "vega = S * norm.pdf(d1) * np.sqrt(T) / 100  # Vega is typically given in percentage terms\n",
        "\n",
        "# Theta\n",
        "theta = (-S * norm.pdf(d1) * sigma / (2 * np.sqrt(T)) - r * K * np.exp(-r * T) * norm.cdf(-d2)) / 365  # Theta per day\n",
        "\n",
        "# Rho\n",
        "rho = -K * T * np.exp(-r * T) * norm.cdf(-d2) / 100  # Rho is typically given in percentage terms\n",
        "\n",
        "# Output\n",
        "print(f\"Put Price: {put_price:.2f}\")\n",
        "print(f\"Delta: {delta:.4f}\")\n",
        "print(f\"Gamma: {gamma:.4f}\")\n",
        "print(f\"Vega: {vega:.4f}\")\n",
        "print(f\"Theta: {theta:.4f}\")\n",
        "print(f\"Rho: {rho:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hR2a1yoVpnhK",
        "outputId": "5057d641-76e9-4a93-8ae0-ca4ace6d1b37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Put Price: 6.27\n",
            "Delta: -0.0841\n",
            "Gamma: 0.0010\n",
            "Vega: 1.4985\n",
            "Theta: -1.2610\n",
            "Rho: -0.1369\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bachelier_pricing\n",
        "\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "def bachelier_call_price(S, K, T, r, sigma):\n",
        "    d1 = (S - K) / (sigma * np.sqrt(T))\n",
        "    call_price = (S - K) * norm.cdf(d1) + sigma * np.sqrt(T) * norm.pdf(d1)\n",
        "    return call_price\n",
        "\n",
        "def bachelier_put_price(S, K, T, r, sigma):\n",
        "    d1 = (S - K) / (sigma * np.sqrt(T))\n",
        "    put_price = (K - S) * norm.cdf(-d1) + sigma * np.sqrt(T) * norm.pdf(d1)\n",
        "    return put_price\n",
        "\n",
        "# Example parameters\n",
        "S = 90    # Spot price (can be negative in Bachelier model)\n",
        "K = 75     # Strike price\n",
        "T = 4 / 365  # Time to expiration in years\n",
        "r = 0.02     # Risk-free interest rate (not used directly in Bachelier model)\n",
        "sigma = 0.11  # Volatility (standard deviation)\n",
        "\n",
        "# Call and Put Prices\n",
        "call_price = bachelier_call_price(S, K, T, r, sigma)\n",
        "put_price = bachelier_put_price(S, K, T, r, sigma)\n",
        "\n",
        "call_price, put_price\n"
      ],
      "metadata": {
        "id": "Np5TcC5BelFd",
        "outputId": "bfcd90fc-c952-4498-dc11-499ab81e02e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15.0, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heston Model that uses stochasti volatility to price Vs  Black-Scholes, where volatility is assumed to be constant."
      ],
      "metadata": {
        "id": "czdnV4Y8Otig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Heston model parameters\n",
        "mu = 0.1             # drift of the asset price\n",
        "theta = 0.04         # long-term variance\n",
        "sigma = 0.5          # speed of mean reversion\n",
        "xi = 0.1             # volatility of volatility\n",
        "S0 = 100             # initial asset price\n",
        "V0 = 0.04            # initial variance\n",
        "T = 1                # time horizon\n",
        "dt = 1/252           # daily time step\n",
        "N = int(T / dt)     # number of time steps\n",
        "\n",
        "# Initialize arrays\n",
        "S = np.zeros(N)\n",
        "V = np.zeros(N)\n",
        "S[0] = S0\n",
        "V[0] = V0\n",
        "\n",
        "# Correlation between the two Brownian motions\n",
        "rho = -0.5  # correlation\n",
        "Z1 = np.random.normal(size=N)  # for asset price\n",
        "Z2 = np.random.normal(size=N)  # for volatility\n",
        "\n",
        "# Simulate the process\n",
        "for t in range(1, N):\n",
        "    # Generate correlated Brownian motions\n",
        "    Z2[t] = rho * Z1[t] + np.sqrt(1 - rho**2) * Z2[t]\n",
        "\n",
        "    # Update variance using the Heston model equation\n",
        "    V[t] = np.maximum(V[t-1] + theta * (V[t-1] - V0) * dt + xi * np.sqrt(V[t-1]) * np.sqrt(dt) * Z2[t], 0)\n",
        "\n",
        "    # Update asset price using the asset price dynamics\n",
        "    S[t] = S[t-1] * np.exp((mu - 0.5 * V[t-1]) * dt + np.sqrt(V[t-1]) * np.sqrt(dt) * Z1[t])\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(S)\n",
        "plt.title('Asset Price Simulation using Heston Model')\n",
        "plt.xlabel('Time (days)')\n",
        "plt.ylabel('Asset Price')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(V)\n",
        "plt.title('Stochastic Volatility Simulation using Heston Model')\n",
        "plt.xlabel('Time (days)')\n",
        "plt.ylabel('Variance')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "DC16v5onOmHu",
        "outputId": "fd7a0f93-31e6-49a5-bcae-9b748402b178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://quant-next.com/option-greeks-and-pl-decomposition-part-1/  - Pnl Explain\n"
      ],
      "metadata": {
        "id": "61bAMFujKIj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define function to calculate P&L components\n",
        "def calculate_pnl(delta, gamma, theta, vega, volga, vanna, cross_gamma, spot_price_day1, spot_price_day2, dsigma, dt, relative_difference=False):\n",
        "    # Calculate dS based on the relative or absolute difference\n",
        "    if relative_difference:\n",
        "        dS = (spot_price_day1 / spot_price_day2) - 1  # Relative difference (percentage change)\n",
        "        # For FX\n",
        "        ##dS = (spot_price_day2 / spot_price_day1) - 1  # Relative difference (percentage change)\n",
        "    else:\n",
        "        dS = spot_price_day1 - spot_price_day2  # Absolute difference\n",
        "\n",
        "    # Calculate each P&L component\n",
        "    delta_pnl = delta * dS\n",
        "    gamma_pnl = 0.5 * gamma * (dS ** 2)\n",
        "    theta_pnl = theta * dt\n",
        "    vega_pnl = vega * dsigma\n",
        "    volga_pnl = 0.5 * volga * (dsigma ** 2)\n",
        "    vanna_pnl = vanna * dS * dsigma\n",
        "\n",
        "    # Cross Gamma P&L: accounts for interaction between spot price and volatility changes\n",
        "    cross_gamma_pnl = cross_gamma * dS * dsigma\n",
        "\n",
        "    # Output each P&L component\n",
        "    print(f\"Delta P&L: {delta_pnl:.2f}\")\n",
        "    print(f\"Gamma P&L: {gamma_pnl:.2f}\")\n",
        "    print(f\"Theta P&L: {theta_pnl:.2f}\")\n",
        "    print(f\"Vega P&L: {vega_pnl:.2f}\")\n",
        "    print(f\"Volga P&L: {volga_pnl:.2f}\")\n",
        "    print(f\"Vanna P&L: {vanna_pnl:.2f}\")\n",
        "    print(f\"Cross Gamma P&L: {cross_gamma_pnl:.2f}\")\n",
        "\n",
        "    # Calculate and return total P&L\n",
        "    total_pnl = delta_pnl + gamma_pnl + theta_pnl + vega_pnl + volga_pnl + vanna_pnl + cross_gamma_pnl\n",
        "    print(f\"Total P&L: {total_pnl:.2f}\")\n",
        "    return total_pnl\n",
        "\n",
        "# Example input parameters\n",
        "delta_input = 7.26\n",
        "gamma_input = -0.29\n",
        "theta_input = 2.593\n",
        "vega_input = -11.88\n",
        "volga_input = 0  # Example input for volga\n",
        "vanna_input = 0  # Example input for vanna\n",
        "cross_gamma_input = 0  # Example input for cross-gamma\n",
        "spot_price_day1 = 475.34\n",
        "spot_price_day2 = 470.66\n",
        "dsigma_input = 0  # Solution needs - Vega Current MD - Vega Previous MD)\n",
        "dt_input = 1 / 365  # Input change in time (1 day)\n",
        "\n",
        "# Call the function with absolute difference\n",
        "print(\"Using Absolute Difference:\")\n",
        "calculate_pnl(delta_input, gamma_input, theta_input, vega_input, volga_input, vanna_input, cross_gamma_input, spot_price_day1, spot_price_day2, dsigma_input, dt_input, relative_difference=False)\n",
        "\n",
        "# Call the function with relative difference\n",
        "print(\"\\nUsing Relative Difference:\")\n",
        "calculate_pnl(delta_input, gamma_input, theta_input, vega_input, volga_input, vanna_input, cross_gamma_input, spot_price_day1, spot_price_day2, dsigma_input, dt_input, relative_difference=True)\n",
        "\n",
        "# Output statements\n",
        "print(\"\\nAsset Class Delta Explanation:\")\n",
        "print(\"1) Interest Rate (IR) Delta: Uses absolute basis point change.\")\n",
        "print(\"2) Credit/Repo Delta: Uses absolute basis point change.\")\n",
        "print(\"3) Foreign Exchange (FX) Delta: Uses relative shift in FX rate. (Also, change the formula above)\")\n",
        "print(\"4) Equity Delta: Uses relative price change.\")\n",
        "print(\"5) Commodity Delta: Uses relative price change.\")\n",
        "print(\"6) Vega and Volga is always Absolute change for all asset class.\")\n",
        "print(\"7) Theta change - TBD.\")\n",
        "print(\"8) Vanna in Solution the dsigma = (Vanna Previous MD - Vanna Current MD   # always Absolute Difference)\")\n",
        "print(\"8) Vanna's dS in solution is called (Vanna Previous MD2- Vanna Current MD2) it has the same values as the underlying's settlement price dS and the logic of the Diff values will follow the same as the above asset class rules of Relative vs Absolute  # in solution also drag and drop Risk Factor secondary)\")\n",
        "print(\"8) Vanna MDX saved in training file)\")\n",
        "print(\"9) Cross_gamma- TBD)\")\n",
        "print(\"10) MR solution asks for the shifts and not the prices so the above market data related to dS and dsigma is supplied as Shifts from the client and client can decide relative Vs Absolute)\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUufGUrvtmuf",
        "outputId": "9d7fd67d-6eb2-46a6-da1a-1013fc7577e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using Absolute Difference:\n",
            "Delta P&L: 33.98\n",
            "Gamma P&L: -3.18\n",
            "Theta P&L: 0.01\n",
            "Vega P&L: -0.00\n",
            "Volga P&L: 0.00\n",
            "Vanna P&L: 0.00\n",
            "Cross Gamma P&L: 0.00\n",
            "Total P&L: 30.81\n",
            "\n",
            "Using Relative Difference:\n",
            "Delta P&L: 0.07\n",
            "Gamma P&L: -0.00\n",
            "Theta P&L: 0.01\n",
            "Vega P&L: -0.00\n",
            "Volga P&L: 0.00\n",
            "Vanna P&L: 0.00\n",
            "Cross Gamma P&L: 0.00\n",
            "Total P&L: 0.08\n",
            "\n",
            "Asset Class Delta Explanation:\n",
            "1) Interest Rate (IR) Delta: Uses absolute basis point change.\n",
            "2) Credit/Repo Delta: Uses absolute basis point change.\n",
            "3) Foreign Exchange (FX) Delta: Uses relative shift in FX rate. (Also, change the formula above)\n",
            "4) Equity Delta: Uses relative price change.\n",
            "5) Commodity Delta: Uses relative price change.\n",
            "6) Vega and Volga is always Absolute change for all asset class.\n",
            "7) Theta change - TBD.\n",
            "8) Vanna in Solution the dsigma = (Vanna Previous MD - Vanna Current MD   # always Absolute Difference)\n",
            "8) Vanna's dS in solution is called (Vanna Previous MD2- Vanna Current MD2) it has the same values as the underlying's settlement price dS and the logic of the Diff values will follow the same as the above asset class rules of Relative vs Absolute  # in solution also drag and drop Risk Factor secondary)\n",
            "8) Vanna MDX saved in training file)\n",
            "9) Cross_gamma- TBD)\n",
            "10) MR solution asks for the shifts and not the prices so the above market data related to dS and dsigma is supplied as Shifts from the client and client can decide relative Vs Absolute)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import pandas as pd\n",
        "\n",
        "def black_scholes_option(S, K, T, r, sigma, multiplier, option_type='put'):\n",
        "    \"\"\"\n",
        "    Calculate the Black-Scholes price for put or call options.\n",
        "    \"\"\"\n",
        "    d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n",
        "    d2 = d1 - sigma * np.sqrt(T)\n",
        "\n",
        "    if option_type == 'put':\n",
        "        option_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)\n",
        "    elif option_type == 'call':\n",
        "        option_price = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)\n",
        "    else:\n",
        "        raise ValueError(\"Invalid option_type. Use 'put' or 'call'.\")\n",
        "\n",
        "    return option_price * multiplier\n",
        "\n",
        "def calculate_pnl(S, strike_long, strike_short, premium_long, premium_short, T, r, sigma, multiplier, lots, option_type):\n",
        "    # Define the percentage moves you want to analyze\n",
        "    percentages = [-0.05, -0.04, -0.03, -0.02, -0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05]  # Include -5% to +5%\n",
        "    S_values = [S * (1 + pct) for pct in percentages]\n",
        "\n",
        "    # Calculate the initial credit received from the spread\n",
        "    initial_credit = (premium_short - premium_long) * multiplier * lots\n",
        "\n",
        "    # Calculate PnL and store premiums for each scenario\n",
        "    pnls = []\n",
        "    premiums_long = []\n",
        "    premiums_short = []\n",
        "\n",
        "    for S_new in S_values:\n",
        "        # Calculate new premiums using Black-Scholes (keeping strikes constant)\n",
        "        new_premium_long = black_scholes_option(S_new, strike_long, T, r, sigma, multiplier, option_type)\n",
        "        new_premium_short = black_scholes_option(S_new, strike_short, T, r, sigma, multiplier, option_type)\n",
        "\n",
        "        # Calculate the spread value based on the option type\n",
        "        spread_value = (new_premium_short - new_premium_long) * lots\n",
        "\n",
        "        # Calculate PnL (starting with the initial credit received)\n",
        "        pnl = initial_credit - spread_value\n",
        "\n",
        "        # Append values for display\n",
        "        pnls.append(pnl)\n",
        "        premiums_long.append(new_premium_long)\n",
        "        premiums_short.append(new_premium_short)\n",
        "\n",
        "    return pnls, premiums_long, premiums_short, S_values, initial_credit, percentages\n",
        "\n",
        "# Template for Input Parameters\n",
        "S = 5864.67  # Spot price\n",
        "strike_long = 5600  # Long option strike (bought)\n",
        "strike_short = 5650  # Short option strike (sold)\n",
        "premium_long = 5.00  # Premium for long option\n",
        "premium_short = 6.50  # Premium for short option\n",
        "T = 10 / 365  # Time to expiration in years\n",
        "r = 0.012  # Risk-free rate\n",
        "sigma = 0.1663  # Volatility\n",
        "multiplier = 100  # Contract multiplier\n",
        "lots = 1  # Number of lots\n",
        "option_type = 'put'  # Change to 'call' for call options\n",
        "\n",
        "# Calculate PnL and premiums\n",
        "pnls, premiums_long, premiums_short, S_values, initial_credit, percentages = calculate_pnl(\n",
        "    S, strike_long, strike_short, premium_long, premium_short, T, r, sigma, multiplier, lots, option_type\n",
        ")\n",
        "\n",
        "# Create a DataFrame for display\n",
        "df_pnl = pd.DataFrame({\n",
        "    \"New Spot Price\": S_values,\n",
        "    \"Initial Credit\": [initial_credit] * len(S_values),\n",
        "    \"PnL\": pnls,\n",
        "    \"Premium Long\": premiums_long,\n",
        "    \"Premium Short\": premiums_short,\n",
        "    \"Percentage Change\": percentages  # Added column for percentage change\n",
        "})\n",
        "\n",
        "# Format the columns to show dollar amounts\n",
        "for col in df_pnl.columns[1:]:\n",
        "    if col == \"Percentage Change\":\n",
        "        df_pnl[col] = df_pnl[col].apply(lambda x: f\"{x * 100:.2f}%\")  # Format percentage change\n",
        "    else:\n",
        "        df_pnl[col] = df_pnl[col].apply(lambda x: f\"${x:.2f}\")  # Format monetary values\n",
        "\n",
        "# Move the Percentage Change column to the end\n",
        "df_pnl = df_pnl[['New Spot Price', 'Initial Credit', 'PnL', 'Premium Long', 'Premium Short', 'Percentage Change']]\n",
        "\n",
        "# Display the results without index\n",
        "print(f\"PnL Results for a {option_type.upper()} Spread for -5%, -4%, -3%, -2%, -1%, 0%, +1%, +2%, +3%, +4%, and +5% Move in the Spot Price:\")\n",
        "print(df_pnl.to_string(index=False))  # Show the DataFrame without the index\n"
      ],
      "metadata": {
        "id": "NZ7OIJWgdEtE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23b3d544-c224-42da-903b-ec15c45c4b11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PnL Results for a PUT Spread for -5%, -4%, -3%, -2%, -1%, 0%, +1%, +2%, +3%, +4%, and +5% Move in the Spot Price:\n",
            " New Spot Price Initial Credit       PnL Premium Long Premium Short Percentage Change\n",
            "      5571.4365        $150.00 $-3028.95     $7561.22     $10740.18            -5.00%\n",
            "      5630.0832        $150.00 $-2287.41     $4699.74      $7137.15            -4.00%\n",
            "      5688.7299        $150.00 $-1561.62     $2692.93      $4404.55            -3.00%\n",
            "      5747.3766        $150.00  $-942.80     $1414.38      $2507.18            -2.00%\n",
            "      5806.0233        $150.00  $-481.30      $678.03      $1309.32            -1.00%\n",
            "      5864.6700        $150.00  $-179.03      $295.80       $624.84             0.00%\n",
            "      5923.3167        $150.00    $-4.52      $117.23       $271.75             1.00%\n",
            "      5981.9634        $150.00    $84.64       $42.17       $107.53             2.00%\n",
            "      6040.6101        $150.00   $125.08       $13.76        $38.68             3.00%\n",
            "      6099.2568        $150.00   $141.43        $4.08        $12.65             4.00%\n",
            "      6157.9035        $150.00   $147.34        $1.10         $3.76             5.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/quant-science/vectorbt_backtesting/blob/master/01_ma_crossover.py?ck_subscriber_id=2765607720\n",
        "\n",
        "Back Testing link\n"
      ],
      "metadata": {
        "id": "mMAquKC7xTvU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import vectorbt as vbt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# Download data from Yahoo Finance\n",
        "price_aapl = yf.download('NVDA', start='2023-01-01', end='2024-08-01')['Close']\n",
        "\n",
        "# Optionally create the 'data' directory and save the data\n",
        "if not os.path.exists('data'):\n",
        "    os.makedirs('data')\n",
        "\n",
        "price_aapl.to_pickle('data/price_aapl.pkl')\n",
        "\n",
        "print(price_aapl.head())\n",
        "\n",
        "# Profit Level: Buy and Hold Strategy\n",
        "pf_buy_hold = vbt.Portfolio.from_holding(\n",
        "    close=price_aapl,\n",
        "    init_cash=10_000\n",
        ")\n",
        "print(\"Buy and Hold Strategy Total Profit:\", pf_buy_hold.total_profit())\n",
        "\n",
        "# 1.0 Simple Moving Average Crossover Strategy 5-20 Day (AAPL)\n",
        "\n",
        "# 1. Define the strategy\n",
        "fast_ma = vbt.MA.run(price_aapl, window=5)\n",
        "slow_ma = vbt.MA.run(price_aapl, window=20)\n",
        "\n",
        "entries = fast_ma.ma_crossed_above(slow_ma)\n",
        "exits = fast_ma.ma_crossed_below(slow_ma)\n",
        "\n",
        "# 2. Run the strategy\n",
        "pf_ma_strat = vbt.Portfolio.from_signals(\n",
        "    close=price_aapl,\n",
        "    entries=entries,\n",
        "    exits=exits,\n",
        "    init_cash=10_000,\n",
        "    fees=0.001,\n",
        ")\n",
        "\n",
        "print(\"Moving Average Crossover Strategy Total Profit:\", pf_ma_strat.total_profit())\n",
        "print(pf_ma_strat.stats())\n",
        "pf_ma_strat.plot().show()\n",
        "\n",
        "# 2.0 Backtesting 100 windows for the Moving Average Crossover Strategy (AAPL)\n",
        "\n",
        "windows = np.arange(2, 101)\n",
        "fast_ma, slow_ma = vbt.MA.run_combs(\n",
        "    close=price_aapl,\n",
        "    window=windows,\n",
        "    r=2,\n",
        "    short_names=['fast', 'slow']\n",
        ")\n",
        "entries = fast_ma.ma_crossed_above(slow_ma)\n",
        "exits = fast_ma.ma_crossed_below(slow_ma)\n",
        "\n",
        "pf_100_ma_strats = vbt.Portfolio.from_signals(\n",
        "    close=price_aapl,\n",
        "    entries=entries,\n",
        "    exits=exits,\n",
        "    size=np.inf,\n",
        "    fees=0.001,\n",
        "    freq='1D',\n",
        "    init_cash=10_000\n",
        ")\n",
        "\n",
        "max_profit = pf_100_ma_strats.total_profit().max()\n",
        "best_index = pf_100_ma_strats.total_profit().idxmax()\n",
        "\n",
        "print(\"Max Profit from 100 Strategies:\", max_profit)\n",
        "print(\"Best Index for Strategy with Max Profit:\", best_index)\n",
        "\n",
        "stats = pf_100_ma_strats[best_index].stats()\n",
        "print(stats)\n",
        "\n",
        "pf_100_ma_strats[best_index].plot().show()\n",
        "\n",
        "# Conclusions ----\n",
        "# You can do this!\n",
        "# There's a lot more to learn:\n",
        "# - More Trading Strategies\n",
        "# - Risk Management\n",
        "# - Portfolio Optimization\n",
        "# - Machine Learning\n",
        "# - Advanced Backtesting\n",
        "# - Live Trading & Execution\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_ECvCONwvUqm",
        "outputId": "f0a39882-996f-4426-f0f7-30d74022cc6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "/usr/local/lib/python3.10/dist-packages/vectorbt/generic/stats_builder.py:396: UserWarning:\n",
            "\n",
            "Metric 'sharpe_ratio' requires frequency to be set\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/vectorbt/generic/stats_builder.py:396: UserWarning:\n",
            "\n",
            "Metric 'calmar_ratio' requires frequency to be set\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/vectorbt/generic/stats_builder.py:396: UserWarning:\n",
            "\n",
            "Metric 'omega_ratio' requires frequency to be set\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/vectorbt/generic/stats_builder.py:396: UserWarning:\n",
            "\n",
            "Metric 'sortino_ratio' requires frequency to be set\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2023-01-03    14.315\n",
            "2023-01-04    14.749\n",
            "2023-01-05    14.265\n",
            "2023-01-06    14.859\n",
            "2023-01-09    15.628\n",
            "Name: Close, dtype: float64\n",
            "Buy and Hold Strategy Total Profit: 71746.41989052232\n",
            "Moving Average Crossover Strategy Total Profit: 20353.179356251338\n",
            "Start                         2023-01-03 00:00:00\n",
            "End                           2024-07-31 00:00:00\n",
            "Period                                        396\n",
            "Start Value                               10000.0\n",
            "End Value                            30353.179356\n",
            "Total Return [%]                       203.531794\n",
            "Benchmark Return [%]                   717.464199\n",
            "Max Gross Exposure [%]                      100.0\n",
            "Total Fees Paid                        380.402457\n",
            "Max Drawdown [%]                        19.643056\n",
            "Max Drawdown Duration                       127.0\n",
            "Total Trades                                   10\n",
            "Total Closed Trades                            10\n",
            "Total Open Trades                               0\n",
            "Open Trade PnL                                0.0\n",
            "Win Rate [%]                                 50.0\n",
            "Best Trade [%]                          69.985996\n",
            "Worst Trade [%]                        -10.381623\n",
            "Avg Winning Trade [%]                   35.519186\n",
            "Avg Losing Trade [%]                    -5.539802\n",
            "Avg Winning Trade Duration                   39.2\n",
            "Avg Losing Trade Duration                     7.0\n",
            "Profit Factor                             4.37775\n",
            "Expectancy                            2035.317936\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "Max Profit from 100 Strategies: 33498.4520538139\n",
            "Best Index for Strategy with Max Profit: (9, 11)\n",
            "Start                                2023-01-03 00:00:00\n",
            "End                                  2024-07-31 00:00:00\n",
            "Period                                 396 days 00:00:00\n",
            "Start Value                                      10000.0\n",
            "End Value                                   43498.452054\n",
            "Total Return [%]                              334.984521\n",
            "Benchmark Return [%]                          717.464199\n",
            "Max Gross Exposure [%]                             100.0\n",
            "Total Fees Paid                               828.056062\n",
            "Max Drawdown [%]                               16.431011\n",
            "Max Drawdown Duration                   89 days 00:00:00\n",
            "Total Trades                                          20\n",
            "Total Closed Trades                                   20\n",
            "Total Open Trades                                      0\n",
            "Open Trade PnL                                       0.0\n",
            "Win Rate [%]                                        55.0\n",
            "Best Trade [%]                                 83.784208\n",
            "Worst Trade [%]                                -8.202907\n",
            "Avg Winning Trade [%]                          19.002888\n",
            "Avg Losing Trade [%]                           -2.633076\n",
            "Avg Winning Trade Duration    16 days 10:54:32.727272727\n",
            "Avg Losing Trade Duration                7 days 08:00:00\n",
            "Profit Factor                                   5.247879\n",
            "Expectancy                                   1674.922603\n",
            "Sharpe Ratio                                    3.096087\n",
            "Calmar Ratio                                   17.509382\n",
            "Omega Ratio                                     1.851522\n",
            "Sortino Ratio                                    6.54699\n",
            "Name: (9, 11), dtype: object\n"
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      "source": [
        "# **VaR Calc Logic**\n",
        "\n",
        "Assume we have the following PnL vectors for Trader 1 with 4 trades over 10 days: Below is how its aggregated .\n",
        "\n",
        "Raw Data\n",
        "\n",
        "Example PnL vectors sent to atoti\n",
        "\n",
        "Trade 1 PnL Vector:\n",
        "[0.32, -0.75, 0.12, 0.56, -0.45, 0.68, -0.20, 0.50, -0.75, 0.12]\n",
        "\n",
        "Trade 2 PnL Vector:\n",
        "[-0.24, 0.15, -0.43, 0.21, 0.67, -0.12, -0.35, 0.44, 0.21, -0.33]\n",
        "\n",
        "Trade 3 PnL Vector:\n",
        "[0.67, -0.25, 0.92, 0.11, -0.65, 0.34, 0.13, 0.50, -0.44, 0.25]\n",
        "\n",
        "Trade 4 PnL Vector:\n",
        "[0.45, -0.23, 0.34, -0.55, 0.43, -0.12, 0.58, -0.34, 0.18, 0.30]\n",
        "\n",
        "-------------------------------------------------------------------------------\n",
        "Aggregate PnL:\n",
        "Day\tTrade 1\tTrade 2\tTrade 3\tTrade 4\tAggregated PnL\n",
        "\n",
        "Day 1\t0.32\t-0.24\t0.67\t0.45\t1.20\n",
        "\n",
        "Day 2\t-0.75\t0.15\t-0.25\t-0.23\t-1.08\n",
        "\n",
        "Day 3\t0.12\t-0.43\t0.92\t0.34\t0.95\n",
        "\n",
        "Day 4\t0.56\t0.21\t0.11\t-0.55\t0.33\n",
        "\n",
        "Day 5\t-0.45\t0.67\t-0.65\t0.43\t0.00\n",
        "\n",
        "---------------------------------------------------------------------------\n",
        "above aggregation example:\n",
        "Day 1 Aggregation:\n",
        "\n",
        "0.32 (Trade 1) + (-0.24) (Trade 2) + 0.67 (Trade 3) + 0.45 (Trade 4) = 1.20\n"
      ],
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Generate PnL vectors for 4 trades under each trader\n",
        "def generate_trader_pnl(trades=4, days=10):\n",
        "    return {f'Trade_{i}': np.random.normal(0, 1, size=days) for i in range(1, trades+1)}\n",
        "\n",
        "# Generate the hierarchical structure for Desk -> Equity -> 2 Traders -> 4 Trades per Trader\n",
        "hierarchy = {\n",
        "    'Desk': {\n",
        "        'Equity': {\n",
        "            'Trader_1': generate_trader_pnl(),\n",
        "            'Trader_2': generate_trader_pnl()\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "# Function to aggregate PnL vectors at each level\n",
        "def aggregate_pnl(trades_pnl):\n",
        "    return np.sum(np.array(list(trades_pnl.values())), axis=0)\n",
        "\n",
        "# Function to calculate VaR using historical simulation\n",
        "def calculate_var(pnl_vectors, confidence=0.99):\n",
        "    return np.percentile(pnl_vectors, (1-confidence) * 100)\n",
        "\n",
        "# VaR and PnL for Trades under Trader 1\n",
        "trader_1_pnl = aggregate_pnl(hierarchy['Desk']['Equity']['Trader_1'])\n",
        "trader_1_var = calculate_var(trader_1_pnl)\n",
        "\n",
        "# VaR and PnL for Trades under Trader 2\n",
        "trader_2_pnl = aggregate_pnl(hierarchy['Desk']['Equity']['Trader_2'])\n",
        "trader_2_var = calculate_var(trader_2_pnl)\n",
        "\n",
        "# VaR and PnL for Equity (aggregating both Trader 1 and Trader 2)\n",
        "equity_pnl = trader_1_pnl + trader_2_pnl\n",
        "equity_var = calculate_var(equity_pnl)\n",
        "\n",
        "# VaR and PnL for Desk (same as Equity in this case)\n",
        "desk_pnl = equity_pnl\n",
        "desk_var = calculate_var(desk_pnl)\n",
        "\n",
        "# Print only the VaR figures\n",
        "print(\"\\n--- VaR Figures ---\")\n",
        "print(f\"VaR for Trader 1: {trader_1_var:.2f}\")\n",
        "print(f\"VaR for Trader 2: {trader_2_var:.2f}\")\n",
        "print(f\"VaR for Equity: {equity_var:.2f}\")\n",
        "print(f\"VaR for Desk: {desk_var:.2f}\")\n"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- VaR Figures ---\n",
            "VaR for Trader 1: -3.54\n",
            "VaR for Trader 2: -2.17\n",
            "VaR for Equity: -1.72\n",
            "VaR for Desk: -1.72\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "**CFD BACKTEST**\n"
      ],
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        "id": "JJKl2xAgewvE"
      }
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "# Define the ticker and the period of interest\n",
        "ticker = '^SPX'  # SPX index\n",
        "start_date = '2020-01-01'\n",
        "end_date = '2024-10-10'\n",
        "\n",
        "# Fetch historical data from Yahoo Finance\n",
        "spx_data = yf.download(ticker, start=start_date, end=end_date)\n",
        "spx_data = spx_data[['Close']].rename(columns={'Close': 'Settlement Price'})\n",
        "\n",
        "# Reset index to get the date as a column\n",
        "spx_data.reset_index(inplace=True)\n",
        "spx_data['Date'] = pd.to_datetime(spx_data['Date'])\n",
        "spx_data.set_index('Date', inplace=True)\n",
        "\n",
        "# Calculate daily returns\n",
        "spx_data['Daily Return'] = spx_data['Settlement Price'].pct_change().fillna(0)\n",
        "\n",
        "# Leverage factor for leveraged investment\n",
        "leverage = 5\n",
        "\n",
        "# Initial investment and fees\n",
        "initial_investment = 10000  # Assuming starting with $10,000\n",
        "daily_fee = 15.30\n",
        "weekend_fee = 45.91\n",
        "\n",
        "# Calculate daily PnL before fees for leveraged investment\n",
        "spx_data['PnL Before Fees (Leveraged)'] = initial_investment * (1 + spx_data['Daily Return'] * leverage).cumprod()\n",
        "\n",
        "# Apply fees for leveraged investment\n",
        "spx_data['Daily Fee'] = daily_fee\n",
        "spx_data['Weekend Fee'] = spx_data['Daily Fee'].where(spx_data.index.dayofweek < 5, weekend_fee)  # Apply weekend fees on Saturdays and Sundays\n",
        "\n",
        "# Calculate total fees for leveraged investment\n",
        "spx_data['Total Fees'] = spx_data['Daily Fee'].cumsum() + spx_data['Weekend Fee'].cumsum()\n",
        "\n",
        "# Calculate PnL after fees for leveraged investment\n",
        "spx_data['PnL After Fees (Leveraged)'] = spx_data['PnL Before Fees (Leveraged)'] - spx_data['Total Fees']\n",
        "\n",
        "# Final PnL calculations for leveraged investment\n",
        "final_pnl_before_fees = spx_data['PnL Before Fees (Leveraged)'].iloc[-1]\n",
        "total_fees = spx_data['Total Fees'].iloc[-1]\n",
        "final_pnl_after_fees = final_pnl_before_fees - total_fees  # This ensures the relationship holds\n",
        "\n",
        "# Calculate PnL for unleveraged investment without any fees\n",
        "spx_data['PnL Before Fees (Unleveraged)'] = initial_investment * (1 + spx_data['Daily Return']).cumprod()\n",
        "\n",
        "# Calculate SPX growth for the same period\n",
        "spx_data['SPX Growth'] = (spx_data['Settlement Price'] / spx_data['Settlement Price'].iloc[0]) - 1\n",
        "\n",
        "# Plotting the results\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add PnL Before Fees (Leveraged)\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=spx_data.index,\n",
        "    y=spx_data['PnL Before Fees (Leveraged)'],\n",
        "    mode='lines',\n",
        "    name='PnL Before Fees (Leveraged)',\n",
        "    line=dict(color='blue')\n",
        "))\n",
        "\n",
        "# Add PnL After Fees (Leveraged)\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=spx_data.index,\n",
        "    y=spx_data['PnL After Fees (Leveraged)'],\n",
        "    mode='lines',\n",
        "    name='PnL After Fees (Leveraged)',\n",
        "    line=dict(color='red')\n",
        "))\n",
        "\n",
        "# Add PnL Before Fees (Unleveraged)\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=spx_data.index,\n",
        "    y=spx_data['PnL Before Fees (Unleveraged)'],\n",
        "    mode='lines',\n",
        "    name='PnL Before Fees (Unleveraged)',\n",
        "    line=dict(color='purple')\n",
        "))\n",
        "\n",
        "# Add SPX Growth\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=spx_data.index,\n",
        "    y=spx_data['SPX Growth'] * 100,  # Convert to percentage\n",
        "    mode='lines',\n",
        "    name='SPX Growth (%)',\n",
        "    line=dict(color='green', dash='dash')\n",
        "))\n",
        "\n",
        "# Customize layout\n",
        "fig.update_layout(\n",
        "    title='SPX PnL (Leveraged vs Unleveraged) & SPX Growth',\n",
        "    xaxis_title='Date',\n",
        "    yaxis_title='Value ($) / Growth (%)',\n",
        "    legend=dict(x=0, y=1),\n",
        "    hovermode='x unified'\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()\n",
        "\n",
        "# Displaying the results\n",
        "print(f\"Initial Investment: ${initial_investment:.2f}\")\n",
        "print(f\"Final PnL Before Fees (Leveraged): ${final_pnl_before_fees:.2f}\")\n",
        "print(f\"Total Fees: ${total_fees:.2f}\")\n",
        "print(f\"Final PnL After Fees (Leveraged): ${final_pnl_after_fees:.2f}\")\n"
      ],
      "metadata": {
        "id": "zWRP9WA2Of-x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "a4bc09ed-ebbb-4a84-ef9d-d715adc29bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"bf3c24e6-444a-4acb-bf7b-add479209844\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"bf3c24e6-444a-4acb-bf7b-add479209844\")) {                    Plotly.newPlot(                        \"bf3c24e6-444a-4acb-bf7b-add479209844\",                        [{\"line\":{\"color\":\"blue\"},\"mode\":\"lines\",\"name\":\"PnL Before Fees 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          "text": [
            "Initial Investment: $10000.00\n",
            "Final PnL Before Fees (Leveraged): $15769.27\n",
            "Total Fees: $36750.60\n",
            "Final PnL After Fees (Leveraged): $-20981.33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install graphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvjtFs7jeHGW",
        "outputId": "dd4b6eea-cff8-4261-9287-783ac89618cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (0.20.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from graphviz import Digraph\n",
        "\n",
        "# Initialize the Graphviz Digraph\n",
        "dot = Digraph(comment='AWS Architecture for AI and Data Analytics')\n",
        "\n",
        "# Define nodes by category\n",
        "categories = {\n",
        "    \"Data Exchange & Integration\": [\"AWS Data Exchange\", \"AWS Glue\", \"AWS Glue DataBrew\", \"AWS Lake Formation\"],\n",
        "    \"Analytics & BI\": [\"Amazon EMR\", \"Amazon OpenSearch Service\", \"Amazon QuickSight\", \"Amazon Redshift\"],\n",
        "    \"Cost Management\": [\"AWS Budgets\", \"AWS Cost Explorer\"],\n",
        "    \"Compute\": [\"Amazon EC2\", \"Amazon ECS\", \"Amazon EKS\"],\n",
        "    \"Database\": [\"Amazon DocumentDB\", \"Amazon DynamoDB\", \"Amazon ElastiCache\", \"Amazon MemoryDB\", \"Amazon Neptune\", \"Amazon RDS\"],\n",
        "    \"AI/ML\": [\"Amazon Augmented AI (A2I)\", \"Amazon Bedrock\", \"Amazon Comprehend\", \"Amazon Fraud Detector\",\n",
        "              \"Amazon Kendra\", \"Amazon Lex\", \"Amazon Personalize\", \"Amazon Polly\", \"Amazon Q\", \"Amazon Rekognition\",\n",
        "              \"Amazon SageMaker\", \"Amazon Textract\", \"Amazon Transcribe\", \"Amazon Translate\"],\n",
        "    \"Security & Compliance\": [\"AWS CloudTrail\", \"AWS IAM\", \"AWS Config\", \"AWS Trusted Advisor\", \"AWS Well-Architected Tool\",\n",
        "                              \"Amazon Inspector\", \"AWS KMS\", \"Amazon Macie\", \"AWS Artifact\", \"AWS Audit Manager\",\n",
        "                              \"AWS Secrets Manager\"],\n",
        "    \"Storage\": [\"Amazon S3\", \"Amazon S3 Glacier\"],\n",
        "    \"Monitoring\": [\"Amazon CloudWatch\"],\n",
        "    \"Networking\": [\"Amazon CloudFront\", \"Amazon VPC\"]\n",
        "}\n",
        "\n",
        "# Define each category as a cluster for organization\n",
        "for category, services in categories.items():\n",
        "    with dot.subgraph(name=f'cluster_{category.replace(\" \", \"_\")}') as c:\n",
        "        c.attr(label=category)\n",
        "        for service in services:\n",
        "            c.node(service, service)\n",
        "\n",
        "# Define relationships between major components (example connections)\n",
        "dot.edge(\"AWS Glue\", \"Amazon Redshift\", label=\"ETL to Redshift\")\n",
        "dot.edge(\"Amazon SageMaker\", \"Amazon DynamoDB\", label=\"Model Data\")\n",
        "dot.edge(\"Amazon SageMaker\", \"Amazon S3\", label=\"Model Artifacts\")\n",
        "dot.edge(\"Amazon S3\", \"Amazon QuickSight\", label=\"Data Visualization\")\n",
        "dot.edge(\"Amazon Kendra\", \"Amazon OpenSearch Service\", label=\"Search Integration\")\n",
        "dot.edge(\"AWS IAM\", \"Amazon EC2\", label=\"Access Control\")\n",
        "dot.edge(\"AWS IAM\", \"Amazon SageMaker\", label=\"Access Control\")\n",
        "dot.edge(\"Amazon CloudWatch\", \"Amazon EC2\", label=\"Monitoring\")\n",
        "dot.edge(\"AWS Config\", \"Amazon RDS\", label=\"Configuration Monitoring\")\n",
        "dot.edge(\"AWS Data Exchange\", \"Amazon S3\", label=\"Data Storage\")\n",
        "dot.edge(\"Amazon Lex\", \"Amazon Polly\", label=\"Text to Speech\")\n",
        "dot.edge(\"Amazon RDS\", \"Amazon EC2\", label=\"Database Connection\")\n",
        "dot.edge(\"AWS Glue\", \"Amazon S3\", label=\"Data Transformation\")\n",
        "\n",
        "# Render the graph to a file\n",
        "dot.render('aws_ai_architecture_diagram', format='png', view=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "3O12FF9DeN7A",
        "outputId": "03c4fefb-4316-42c8-fa46-b7d6ccd0049c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'aws_ai_architecture_diagram.png'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from graphviz import Digraph\n",
        "\n",
        "# Initialize the Graphviz Digraph\n",
        "dot = Digraph(comment='AWS Architecture for AI and Data Analytics')\n",
        "\n",
        "# Set the layout to stack items vertically within each cluster\n",
        "dot.attr(rankdir='LR', size='8.27,11.7!', ratio='compress')\n",
        "\n",
        "# Define nodes by category\n",
        "categories = {\n",
        "    \"Data Exchange & Integration\": [\"AWS Data Exchange\", \"AWS Glue\", \"AWS Glue DataBrew\", \"AWS Lake Formation\"],\n",
        "    \"Analytics & BI\": [\"Amazon EMR\", \"Amazon OpenSearch Service\", \"Amazon QuickSight\", \"Amazon Redshift\"],\n",
        "    \"Cost Management\": [\"AWS Budgets\", \"AWS Cost Explorer\"],\n",
        "    \"Compute\": [\"Amazon EC2\", \"Amazon ECS\", \"Amazon EKS\"],\n",
        "    \"Database\": [\"Amazon DocumentDB\", \"Amazon DynamoDB\", \"Amazon ElastiCache\", \"Amazon MemoryDB\", \"Amazon Neptune\", \"Amazon RDS\"],\n",
        "    \"AI/ML\": [\"Amazon Augmented AI (A2I)\", \"Amazon Bedrock\", \"Amazon Comprehend\", \"Amazon Fraud Detector\",\n",
        "              \"Amazon Kendra\", \"Amazon Lex\", \"Amazon Personalize\", \"Amazon Polly\", \"Amazon Q\", \"Amazon Rekognition\",\n",
        "              \"Amazon SageMaker\", \"Amazon Textract\", \"Amazon Transcribe\", \"Amazon Translate\"],\n",
        "    \"Security & Compliance\": [\"AWS CloudTrail\", \"AWS IAM\", \"AWS Config\", \"AWS Trusted Advisor\", \"AWS Well-Architected Tool\",\n",
        "                              \"Amazon Inspector\", \"AWS KMS\", \"Amazon Macie\", \"AWS Artifact\", \"AWS Audit Manager\",\n",
        "                              \"AWS Secrets Manager\"],\n",
        "    \"Storage\": [\"Amazon S3\", \"Amazon S3 Glacier\"],\n",
        "    \"Monitoring\": [\"Amazon CloudWatch\"],\n",
        "    \"Networking\": [\"Amazon CloudFront\", \"Amazon VPC\"]\n",
        "}\n",
        "\n",
        "# Define each category as a cluster for organization\n",
        "for category, services in categories.items():\n",
        "    with dot.subgraph(name=f'cluster_{category.replace(\" \", \"_\")}') as c:\n",
        "        c.attr(label=category)\n",
        "        for service in services:\n",
        "            c.node(service, service)\n",
        "\n",
        "# Define relationships between major components (example connections)\n",
        "dot.edge(\"AWS Glue\", \"Amazon Redshift\", label=\"ETL to Redshift\")\n",
        "dot.edge(\"Amazon SageMaker\", \"Amazon DynamoDB\", label=\"Model Data\")\n",
        "dot.edge(\"Amazon SageMaker\", \"Amazon S3\", label=\"Model Artifacts\")\n",
        "dot.edge(\"Amazon S3\", \"Amazon QuickSight\", label=\"Data Visualization\")\n",
        "dot.edge(\"Amazon Kendra\", \"Amazon OpenSearch Service\", label=\"Search Integration\")\n",
        "dot.edge(\"AWS IAM\", \"Amazon EC2\", label=\"Access Control\")\n",
        "dot.edge(\"AWS IAM\", \"Amazon SageMaker\", label=\"Access Control\")\n",
        "dot.edge(\"Amazon CloudWatch\", \"Amazon EC2\", label=\"Monitoring\")\n",
        "dot.edge(\"AWS Config\", \"Amazon RDS\", label=\"Configuration Monitoring\")\n",
        "dot.edge(\"AWS Data Exchange\", \"Amazon S3\", label=\"Data Storage\")\n",
        "dot.edge(\"Amazon Lex\", \"Amazon Polly\", label=\"Text to Speech\")\n",
        "dot.edge(\"Amazon RDS\", \"Amazon EC2\", label=\"Database Connection\")\n",
        "dot.edge(\"AWS Glue\", \"Amazon S3\", label=\"Data Transformation\")\n",
        "\n",
        "# Render the graph to a file\n",
        "dot.render('aws_ai_architecture_diagram_vertical', format='png', view=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "0AahKAj3f2Sv",
        "outputId": "36603d57-8294-45a4-e795-d455d7331852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'aws_ai_architecture_diagram_vertical.png'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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