VaR_calculated_by_Monte_Carlo_simulations.ipynb

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      "source": [
        "# Application of Monte Carlo Simulations to Calculate VaR"
      ]
    },
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      },
      "source": [
        "#### Importing Libraries"
      ]
    },
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          "text": [
            "C:\\Users\\princ\\AppData\\Local\\Temp\\ipykernel_3052\\1271272703.py:2: DeprecationWarning: \n",
            "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
            "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
            "but was not found to be installed on your system.\n",
            "If this would cause problems for you,\n",
            "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
            "        \n",
            "  import pandas as pd\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import norm"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fd20b24-61b0-4257-aa45-4d7d66c46b45",
      "metadata": {
        "id": "4fd20b24-61b0-4257-aa45-4d7d66c46b45"
      },
      "source": [
        "#### Setting Time Range and Ticker List"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "years = 15\n",
        "\n",
        "endDate = dt.datetime.now()\n",
        "startDate = endDate - dt.timedelta(days = 365*years)\n",
        "\n",
        "\n",
        "tickers = ['SPY','BND','GLD','QQQ','VTI']"
      ]
    },
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      "metadata": {
        "id": "22c1d9d5-110e-44fa-a88f-db3449fef4dc"
      },
      "source": [
        "#### Downloading Adjusted Close Prices"
      ]
    },
    {
      "cell_type": "code",
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          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed"
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          "text": [
            "                   SPY        BND         GLD         QQQ         VTI\n",
            "Date                                                                 \n",
            "2009-03-06   51.787926  49.502071   92.290001   22.981503   25.791931\n",
            "2009-03-09   51.179276  49.320179   90.570000   22.492159   25.481903\n",
            "2009-03-10   54.230045  49.372143   88.139999   23.881533   27.107609\n",
            "2009-03-11   54.583195  49.476112   89.220001   24.248545   27.205910\n",
            "2009-03-12   56.732292  49.586559   91.099998   25.052464   28.325001\n",
            "...                ...        ...         ...         ...         ...\n",
            "2024-02-26  505.989990  71.848480  188.199997  436.549988  251.240005\n",
            "2024-02-27  506.929993  71.738785  188.000000  437.600006  251.940002\n",
            "2024-02-28  506.260010  71.898331  188.339996  435.269989  251.520004\n",
            "2024-02-29  508.079987  72.017998  189.309998  439.000000  252.580002\n",
            "2024-03-01  512.849976  72.320000  192.889999  445.609985  254.899994\n",
            "\n",
            "[3773 rows x 5 columns]\n"
          ]
        },
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          "name": "stderr",
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          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "adjusted_cp_df = pd.DataFrame()\n",
        "\n",
        "for ticker in tickers:\n",
        "    data = yf.download(ticker, start = startDate, end = endDate)\n",
        "    adjusted_cp_df[ticker] = data['Adj Close']\n",
        "\n",
        "print(adjusted_cp_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1e57022-ca53-4ce3-9c75-6bc2aa43673f",
      "metadata": {
        "id": "e1e57022-ca53-4ce3-9c75-6bc2aa43673f"
      },
      "source": [
        "#### Calculating Daily Log retuns"
      ]
    },
    {
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      "execution_count": null,
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          "text": [
            "                 SPY       BND       GLD       QQQ       VTI\n",
            "Date                                                        \n",
            "2009-03-09 -0.011822 -0.003681 -0.018813 -0.021523 -0.012093\n",
            "2009-03-10  0.057900  0.001053 -0.027197  0.059939  0.061846\n",
            "2009-03-11  0.006491  0.002104  0.012179  0.015251  0.003620\n",
            "2009-03-12  0.038618  0.002230  0.020853  0.032616  0.040311\n",
            "2009-03-13  0.007784 -0.000787  0.002193  0.002438  0.010621\n",
            "...              ...       ...       ...       ...       ...\n",
            "2024-02-26 -0.003669 -0.001803 -0.002229 -0.000527 -0.003020\n",
            "2024-02-27  0.001856 -0.001528 -0.001063  0.002402  0.002782\n",
            "2024-02-28 -0.001323  0.002222  0.001807 -0.005339 -0.001668\n",
            "2024-02-29  0.003588  0.001663  0.005137  0.008533  0.004206\n",
            "2024-03-01  0.009344  0.004185  0.018734  0.014945  0.009143\n",
            "\n",
            "[3772 rows x 5 columns]\n"
          ]
        }
      ],
      "source": [
        "daily_log = np.log(adjusted_cp_df/adjusted_cp_df.shift(1))\n",
        "daily_log  = daily_log.dropna()\n",
        "\n",
        "print(daily_log)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e14c1d2e-838f-4289-b679-6dc02703b958",
      "metadata": {
        "id": "e14c1d2e-838f-4289-b679-6dc02703b958"
      },
      "source": [
        "#### Functions For Calculating Portfolio Returns and Expected Deviation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f8158aaf-73f6-4ae3-903f-34f0673d590e",
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      "source": [
        "def return_expected(weights, daily_log):\n",
        "    return np.sum(daily_log.mean()*weights)\n",
        "\n",
        "\n",
        "def calculate_sd (weights, cv_matrix):\n",
        "    variance = weights.T @ cv_matrix @ weights\n",
        "    return np.sqrt(variance)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "66ce7e7f-1753-4a1b-bc8f-4f80d3bd3453",
      "metadata": {
        "id": "66ce7e7f-1753-4a1b-bc8f-4f80d3bd3453"
      },
      "source": [
        "#### Creating a Covariance Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "acc4a53c-88e2-4571-b430-ae1a03a1c5fa",
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          "text": [
            "              SPY           BND       GLD           QQQ           VTI\n",
            "SPY  1.244105e-04 -3.948832e-07  0.000006  1.347973e-04  1.270218e-04\n",
            "BND -3.948832e-07  9.245981e-06  0.000009  4.885527e-07 -2.160260e-07\n",
            "GLD  6.384992e-06  8.917798e-06  0.000098  6.844613e-06  6.887696e-06\n",
            "QQQ  1.347973e-04  4.885527e-07  0.000007  1.702644e-04  1.379197e-04\n",
            "VTI  1.270218e-04 -2.160260e-07  0.000007  1.379197e-04  1.307833e-04\n"
          ]
        }
      ],
      "source": [
        "cv_matrix = daily_log.cov()\n",
        "print(cv_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c3528535-f327-4db9-b07c-590cb62549d7",
      "metadata": {
        "id": "c3528535-f327-4db9-b07c-590cb62549d7"
      },
      "source": [
        "#### Calculating Portfolio Expected Return and Standard Deviation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2b269f2-421f-4ff5-99b3-abac1e3c38f6",
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        "id": "d2b269f2-421f-4ff5-99b3-abac1e3c38f6"
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      "outputs": [],
      "source": [
        "portfolio_value = 1000000\n",
        "weights = np.array([1/len(tickers)]*len(tickers))\n",
        "portfolio_return_expected = return_expected(weights, daily_log)\n",
        "portfolio_sd = calculate_sd (weights, cv_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d6b408fd-299e-40ae-869b-2450c9702399",
      "metadata": {
        "id": "d6b408fd-299e-40ae-869b-2450c9702399"
      },
      "source": [
        "#### Defining Functions for Monte Carlo Simulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "120d750b-f2aa-4a86-b785-ab356c47fb19",
      "metadata": {
        "id": "120d750b-f2aa-4a86-b785-ab356c47fb19"
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      "outputs": [],
      "source": [
        "def random_z_score():\n",
        "    return np.random.normal(0, 1) # mu=0 , sigma =1\n",
        "\n",
        "### Function to calculate scenarioGainLoss\n",
        "days = 20\n",
        "\n",
        "def scenario_gain_loss(portfolio_value, portfolio_sd, z_score, days):\n",
        "    return portfolio_value * portfolio_return_expected * days + portfolio_value * portfolio_sd * z_score * np.sqrt(days)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ded3a244-df82-4bcc-b2b2-e49231b0af81",
      "metadata": {
        "id": "ded3a244-df82-4bcc-b2b2-e49231b0af81"
      },
      "source": [
        "#### Running Monte Carlo Simulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b73462b9-0ad5-4c98-b4bb-aebd27cf57a0",
      "metadata": {
        "id": "b73462b9-0ad5-4c98-b4bb-aebd27cf57a0"
      },
      "outputs": [],
      "source": [
        "simulations = 10000\n",
        "scenarioReturn = []\n",
        "\n",
        "for i in range(simulations):\n",
        "    z_score = random_z_score()\n",
        "    scenarioReturn.append(scenario_gain_loss(portfolio_value, portfolio_sd, z_score, days))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4be4797-fdb5-4a1f-9e24-cd7e4edfb53b",
      "metadata": {
        "id": "a4be4797-fdb5-4a1f-9e24-cd7e4edfb53b"
      },
      "source": [
        "#### Calculating Value at Risk (VaR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e849d3e-be92-48c2-888d-22d45b71eb4d",
      "metadata": {
        "id": "2e849d3e-be92-48c2-888d-22d45b71eb4d",
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "68667.18569730023\n"
          ]
        }
      ],
      "source": [
        "confidence_interval = 0.99\n",
        "VaR = -np.percentile(scenarioReturn, 100 * (1 - confidence_interval))\n",
        "print(VaR)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6a7b95f-3eba-4fa1-aa85-8a6f61570c04",
      "metadata": {
        "id": "c6a7b95f-3eba-4fa1-aa85-8a6f61570c04"
      },
      "source": [
        "#### Plotting the Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e820754-b379-4b2b-b8bc-56a7c273d622",
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        {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(scenarioReturn, bins=50, density=True)\n",
        "plt.xlabel('Scenario Gain/Loss ($)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title(f'Distribution of Portfolio Gain/Loss Over {days} Days')\n",
        "plt.axvline(-VaR, color='r', linestyle='dashed', linewidth=2, label=f'VaR at {confidence_interval:.0%} confidence level')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}