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      "name": "python"
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        "<a href=\"https://colab.research.google.com/github/haribharadwaj/notebooks/blob/main/FINANCE/RetirementBootstrapMonteCarlo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Monte Carlo Retirement Simulator\n",
        "\n",
        "A **Monte Carlo simulation** is used to model the potential outcomes of a retirement portfolio, starting with an initial withdrawal rate and running calculations on a **monthly basis**. The simulation uses historical data from **Robert Shiller**, including the **S&P 500 index**, dividends, **Consumer Price Index (CPI)**, and **10-year Treasury bond interest rates (GS10)** between the years of 1871 and 2023. The portfolio consists of two asset classes: the S&P 500 to represent equities and an **intermediate-term bond fund based on 7-year-duration** approximations using GS10 data for fixed income. A target allocation can be specified (e.g., **70% stocks, 30% bonds**), and the portfolio is periodically **rebalanced** to maintain this allocation.\n",
        "\n",
        "To maintain realism, all calculations are performed in an **inflation-adjusted** manner using the CPI to account for changes in purchasing power over time. The simulation applies a **block bootstrap** approach, where blocks of consecutive months are resampled from historical data to preserve patterns of autocorrelation, providing a realistic range of outcomes that consider the historical relationship between stocks and bonds. **Transaction costs** are included during the rebalancing process, but it is assumed that the funds are held in **tax-advantaged accounts**, so no taxes on capital gains or dividends are considered.\n",
        "\n",
        "The simulation now includes two withdrawal strategies:\n",
        "\n",
        "- **Constant Withdrawal Strategy:** Withdraws a fixed inflation-adjusted amount annually based on the initial withdrawal rate (e.g., 4%). This amount remains constant throughout the simulation, providing predictable income but not adjusting for changes in portfolio performance.\n",
        "\n",
        "- **Dynamic Withdrawal Strategy (akin to Vanguard's Dynamic Spending Rule):** Implements an optional dynamic withdrawal strategy inspired by Vanguard's dynamic spending rule. In this approach, the annual withdrawal amount is adjusted each year based on the portfolio's performance, within specified **ceiling** and **floor** limits to prevent drastic changes in spending. Specifically, the withdrawal amount is recalculated annually as a fixed percentage (e.g., 4%) of the current portfolio value. The year-over-year change in the withdrawal amount is then constrained by the ceiling (maximum increase) and floor (maximum decrease). This strategy aims to balance the need for stable income with the sustainability of the portfolio, potentially reducing the likelihood of depleting funds during retirement.\n",
        "\n",
        "If we set the block size equal to the full length of the simulation (e.g., 30 years), the approach essentially turns into *backtesting* rather than a Monte Carlo simulation, which one may prefer to do. For example, with a block size equal to the full simulation, each \"block\" would cover an entire historical sequence of returns and inflation from the dataset instead of resampling shorter blocks and stitching them together to create new sequences.\n",
        "\n",
        "## Some Caveats of the Simulation Approach\n",
        "\n",
        "- **Assumption of Stationarity:** The simulation assumes that the historical returns, correlations, volatilities, and inflation are representative of future conditions (stationarity).\n",
        "\n",
        "- **Only S&P 500 Equity:** No small-cap stocks, international stocks, or real estate diversification included.\n",
        "\n",
        "- **Independence of Simulated Blocks:** Even though block bootstrapping is used to preserve autocorrelation, it still treats different resampled blocks as independent. This approach may not fully account for long-term trends or extended periods of poor market performance.\n",
        "\n",
        "- **Ignoring Tax Implications:** The simulation does not consider taxes on capital gains, dividends, or interest income. This is reasonable assuming that the funds are in tax-advantaged accounts like 401(k)s and IRAs (i.e., rebalancing doesn't generate taxable events).\n",
        "\n",
        "- **Transaction Cost Simplifications:** The model applies fixed and percentage-based transaction costs during rebalancing but does not account for other potential costs, such as bid-ask spreads or market-impact costs, which can be significant for large portfolios.\n",
        "\n",
        "- **Withdrawal Strategy Considerations:** While the simulation now includes both constant and dynamic withdrawal strategies, it does not account for more complex spending needs or changes in personal circumstances that may affect withdrawal amounts.\n",
        "\n",
        "- **No Modeling of Sequence of Returns Risk Beyond Block Size:** Although block bootstrapping helps preserve some patterns of returns, it may not fully capture the impact of sequence of returns risk, where the timing of poor market performance (especially early in retirement) can have a disproportionate effect on the outcome. The dynamic withdrawal strategy may help mitigate this risk by adjusting spending in response to portfolio performance.\n",
        "\n",
        "- **Assumption of Constant Asset Allocation:** The model assumes a fixed target allocation between stocks and bonds, which may not be realistic over a long time horizon. Investors may choose to adjust their asset allocation as they age or in response to market conditions.\n",
        "\n",
        "- **No Incorporation of Cash Flows Outside Withdrawals:** The simulation does not account for additional contributions or withdrawals, such as Social Security benefits, pensions, or large expenses, which can impact the portfolio's longevity.\n",
        "\n",
        "- **Simplified Rebalancing:** Rebalancing is done at fixed intervals (e.g., every three months). In reality, investors may rebalance based on market conditions or when the asset allocation drifts beyond certain thresholds.\n",
        "\n",
        "\n",
        "# License Information\n",
        "\n",
        "## Code License: BSD 3-Clause License\n",
        "\n",
        "Copyright &copy; 2024, Hari Bharadwaj.\n",
        "All rights reserved.\n",
        "\n",
        "Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n",
        "\n",
        "1. Redistributions of source code must retain the above copyright notice, this list of conditions, and the following disclaimer.\n",
        "\n",
        "2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions, and the following disclaimer in the documentation and/or other materials provided with the distribution.\n",
        "\n",
        "3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n",
        "\n",
        "**Disclaimer**: THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n",
        "\n",
        "\n",
        "## Text/Images License: CC-BY-NC-SA 4.0\n",
        "\n",
        "Any accompanying text and images, including plots, are licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).\n",
        "\n",
        "![Creative Commons License](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/by-nc-sa.svg)\n",
        "\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Download and prepare  historical data\n",
        "First, download CSV file from Dropbox and load it into a pandas DataFrame. The CSV file was generated by downloading Shiller's data from [his official link](http://www.econ.yale.edu/~shiller/data.htm) and selecting a subset of columns. The selected columns include the S&P 500 price, dividends, Consumer Price Index (CPI), and the 10-year Treasury bond interest rate (GS10). We'll calculate the historical monthly returns for stocks (S&P 500) and an intermediate-term bond fund (i.e., assuming GS10 rates and a 7 year duration). We'll also calculate monthly changes in the CPI for inflation adjustments. All simulations will be done with inflation adjusted historical data (i.e., \"real\" returns). As a sanity check, histograms of (inflation-adjusted) stock and bond returns, and a histogram of inflation is plotted and annotated with annualized returns and volatility."
      ],
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    {
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        "outputId": "f03c865f-3f20-4709-920f-099d92ab48d8"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Crazy outliers\n",
            " ==============\n",
            "\n",
            "         Date  Real Stock Return\n",
            "704   1929.10          -0.261909\n",
            "729   1931.11          -0.169881\n",
            "733   1932.03          -0.226760\n",
            "737   1932.07           0.513473\n",
            "746   1933.04           0.293179\n",
            "747   1933.05           0.175874\n",
            "808   1938.06           0.204865\n",
            "1651  2008.09          -0.200805\n",
            "1788  2020.02          -0.191403\n"
          ]
        }
      ],
      "source": [
        "#@title # Code block for downloading and preparing the data + sanity-checks\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the data (assuming it has been downloaded from Shiller's data)\n",
        "url = \"https://www.dropbox.com/scl/fi/98a5ppoa3d0qhp3rui31m/ShillerSP500.csv?rlkey=zsgql64k7h6mcpmr6b65njmzs&st=11ivsbec&dl=1\"\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "\n",
        "# Calculate monthly inflation rate\n",
        "df['Inflation Rate'] = df['CPI'].pct_change()\n",
        "\n",
        "# Convert trailing 12-month dividend to a monthly dividend\n",
        "df['Monthly Dividend'] = df['D'] / 12\n",
        "\n",
        "# Calculate real stock return using inflation-adjusted prices and dividends\n",
        "# df['Stock Return'] = ((df['P'].shift(-1) / df['P']) * (1 + df['Monthly Dividend'] / df['P'])) - 1\n",
        "df['Stock Return'] = (df['P'].shift(-1) + df['Monthly Dividend']) / df['P'] - 1\n",
        "df['Real Stock Return'] = ((1 + df['Stock Return']) /\n",
        "                           (1 + df['Inflation Rate'])) - 1\n",
        "\n",
        "# Calculate real bond returns\n",
        "duration = 7  # Assume a duration of 7 years for bond fund\n",
        "df['Delta Yield'] = df['GS10'].diff()\n",
        "df['Bond Return'] = (df['GS10'] / 12 / 100) - duration * (df['Delta Yield'] / 100)\n",
        "df['Real Bond Return'] = ((1 + df['Bond Return']) / (1 + df['Inflation Rate'])) - 1\n",
        "\n",
        "# Drop rows with missing values after calculating real returns\n",
        "df = df.dropna().reset_index(drop=True)\n",
        "\n",
        "\n",
        "# Calculate the annualized inflation-adjusted return and volatility for stocks\n",
        "N_stock = len(df['Real Stock Return'])\n",
        "# Cumulative real return over the entire period\n",
        "cumulative_stock_return = (1 + df['Real Stock Return']).prod()\n",
        "# Annualize and convert to percentage\n",
        "annualized_real_stock_return = (cumulative_stock_return ** (12 / N_stock) - 1) * 100\n",
        "annualized_real_stock_volatility = df['Real Stock Return'].std() * np.sqrt(12) * 100\n",
        "\n",
        "# Similarly for bonds\n",
        "N_bond = len(df['Real Bond Return'])\n",
        "cumulative_bond_return = (1 + df['Real Bond Return']).prod()\n",
        "annualized_real_bond_return = (cumulative_bond_return ** (12 / N_bond) - 1) * 100\n",
        "annualized_real_bond_volatility = df['Real Bond Return'].std() * np.sqrt(12) * 100\n",
        "\n",
        "# Similarly for inflation rate\n",
        "N_inflation = len(df['Inflation Rate'].dropna())\n",
        "cumulative_inflation = (1 + df['Inflation Rate'].dropna()).prod()\n",
        "annualized_inflation_rate = (cumulative_inflation ** (12 / N_inflation) - 1) * 100\n",
        "annualized_inflation_volatility = df['Inflation Rate'].dropna().std() * np.sqrt(12) * 100\n",
        "\n",
        "# Plot histograms of the monthly real stock, bond returns, and inflation\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "# Histogram for real stock returns (converted to percentages)\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.hist(df['Real Stock Return'] * 100, bins=50, color='blue',\n",
        "         alpha=0.7, edgecolor='black', density=True)\n",
        "plt.title('Histogram of Monthly Real Stock Returns')\n",
        "plt.xlabel('Monthly Real Return (%)')\n",
        "plt.ylabel('Probability Density')\n",
        "plt.text(0.05, 0.95,\n",
        "         f'Annualized Return: {annualized_real_stock_return:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "plt.text(0.05, 0.90,\n",
        "         f'Annualized Volatility: {annualized_real_stock_volatility:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "\n",
        "# Histogram for real bond returns (converted to percentages)\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.hist(df['Real Bond Return'] * 100, bins=50, color='green',\n",
        "         alpha=0.7, edgecolor='black', density=True)\n",
        "plt.title('Histogram of Monthly Real Bond Returns')\n",
        "plt.xlabel('Monthly Real Return (%)')\n",
        "plt.ylabel('Probability Density')\n",
        "plt.text(0.05, 0.95,\n",
        "         f'Annualized Return: {annualized_real_bond_return:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "plt.text(0.05, 0.90,\n",
        "         f'Annualized Volatility: {annualized_real_bond_volatility:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "\n",
        "# Histogram for monthly inflation rates (converted to percentages)\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.hist(df['Inflation Rate'].dropna() * 100, bins=50, color='orange',\n",
        "         alpha=0.7, edgecolor='black', density=True)\n",
        "plt.title('Histogram of Monthly Inflation Rates')\n",
        "plt.xlabel('Monthly Inflation Rate (%)')\n",
        "plt.ylabel('Probability Density')\n",
        "plt.text(0.05, 0.95,\n",
        "         f'Annualized Inflation Rate: {annualized_inflation_rate:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "plt.text(0.05, 0.90,\n",
        "         f'Annualized Volatility: {annualized_inflation_volatility:.2f}%',\n",
        "         transform=plt.gca().transAxes, fontsize=10, verticalalignment='top')\n",
        "\n",
        "# Adjust layout and show the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Identify months where the stock return is greater than 15% or less than -15%\n",
        "outliers = df[(df['Real Stock Return'] > 0.15) | (df['Stock Return'] < -0.15)]\n",
        "\n",
        "# Display the outlier months and their returns\n",
        "print(f'\\n Crazy outliers\\n ==============\\n')\n",
        "print(outliers[['Date', 'Real Stock Return']])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The core of the Monte Carlo simulation using block bootstrap draws from historical data"
      ],
      "metadata": {
        "id": "B1mjeBKBnT5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Function Definitions\n",
        "\n",
        "def block_bootstrap(df, block_size, num_blocks):\n",
        "    \"\"\"\n",
        "    Perform block bootstrap resampling on a DataFrame with historical returns.\n",
        "\n",
        "    Parameters:\n",
        "        df (DataFrame): DataFrame with 'Real Stock Return', 'Real Bond Return'.\n",
        "        block_size (int): The number of consecutive months in each block.\n",
        "        num_blocks (int): The number of blocks to sample.\n",
        "\n",
        "    Returns:\n",
        "        np.ndarray: Array of resampled stock and bond returns.\n",
        "    \"\"\"\n",
        "    combined_returns = []\n",
        "    n = len(df)\n",
        "\n",
        "    for _ in range(num_blocks):\n",
        "        # Randomly choose a starting index for the block\n",
        "        start_idx = np.random.randint(0, n - block_size + 1)\n",
        "        block = df.iloc[\n",
        "            start_idx:start_idx + block_size][\n",
        "                ['Real Stock Return', 'Real Bond Return']].values\n",
        "        combined_returns.append(block)\n",
        "\n",
        "    # Concatenate the sampled blocks\n",
        "    combined_returns = np.concatenate(combined_returns, axis=0)\n",
        "    return combined_returns\n",
        "\n",
        "\n",
        "\n",
        "def simulate_portfolio(df, initial_value=1e6, years=30, stock_weight=0.70,\n",
        "                       rebalance_frequency=3, transaction_cost_rate=0.002,\n",
        "                       fixed_transaction_cost=50, withdrawal_strategy='constant',\n",
        "                       initial_withdrawal_rate=0.045, ceiling=0.05, floor=-0.025,\n",
        "                       block_size=12, num_simulations=1000):\n",
        "    # Convert years to months\n",
        "    months = years * 12\n",
        "\n",
        "    results = []\n",
        "    spending_results = []\n",
        "    depletion_count = 0\n",
        "\n",
        "    # Run multiple simulations\n",
        "    for _ in range(num_simulations):\n",
        "        # Resample blocks of returns using block bootstrapping\n",
        "        num_blocks = int(np.ceil(months / block_size))\n",
        "        resampled_returns = block_bootstrap(df, block_size, num_blocks)\n",
        "        # Trim to exact number of months just in case\n",
        "        resampled_returns = resampled_returns[:months]\n",
        "\n",
        "        # Extract resampled stock and bond returns\n",
        "        resampled_stock_returns = resampled_returns[:, 0]\n",
        "        resampled_bond_returns = resampled_returns[:, 1]\n",
        "\n",
        "        # Initialize portfolio values\n",
        "        stock_value = initial_value * stock_weight\n",
        "        bond_value = initial_value * (1 - stock_weight)\n",
        "\n",
        "        # Track the portfolio value and spending over time\n",
        "        portfolio_values = np.zeros(months)\n",
        "        spending_values = np.zeros(months)\n",
        "\n",
        "        # Initialize withdrawal amounts\n",
        "        if withdrawal_strategy == 'constant':\n",
        "            # Monthly withdrawal in real terms\n",
        "            monthly_withdrawal = initial_value * initial_withdrawal_rate / 12\n",
        "        elif withdrawal_strategy == 'dynamic':\n",
        "            # Initial annual withdrawal\n",
        "            annual_withdrawal = initial_value * initial_withdrawal_rate\n",
        "            monthly_withdrawal = annual_withdrawal / 12\n",
        "            previous_annual_withdrawal = annual_withdrawal\n",
        "        else:\n",
        "            raise ValueError(\"withdrawal_strategy must be 'constant' or 'dynamic'\")\n",
        "\n",
        "        # Flag to check if the portfolio runs out of money\n",
        "        portfolio_depleted = False\n",
        "\n",
        "        # Simulate month by month using the resampled returns\n",
        "        for month in range(months):\n",
        "            # Apply returns first\n",
        "            stock_return = resampled_stock_returns[month]\n",
        "            bond_return = resampled_bond_returns[month]\n",
        "\n",
        "            stock_value *= (1 + stock_return)\n",
        "            bond_value *= (1 + bond_return)\n",
        "\n",
        "            # Calculate total portfolio value\n",
        "            total_value = stock_value + bond_value\n",
        "\n",
        "            # Check for portfolio depletion after returns\n",
        "            if total_value <= 0:\n",
        "                portfolio_depleted = True\n",
        "                portfolio_values[month:] = 0\n",
        "                spending_values[month:] = 0\n",
        "                break\n",
        "\n",
        "            # Adjust withdrawal amount annually if using dynamic strategy\n",
        "            if withdrawal_strategy == 'dynamic' and (month % 12 == 0 and month != 0):\n",
        "                # Calculate baseline annual withdrawal\n",
        "                baseline_withdrawal = total_value * initial_withdrawal_rate\n",
        "                # Calculate the percentage change from previous withdrawal\n",
        "                change = (baseline_withdrawal - previous_annual_withdrawal) / previous_annual_withdrawal\n",
        "                # Apply ceiling and floor constraints\n",
        "                change = max(min(change, ceiling), floor)\n",
        "                # Calculate new annual withdrawal\n",
        "                annual_withdrawal = previous_annual_withdrawal * (1 + change)\n",
        "                monthly_withdrawal = annual_withdrawal / 12\n",
        "                previous_annual_withdrawal = annual_withdrawal\n",
        "\n",
        "            # Record the monthly spending amount\n",
        "            spending_values[month] = monthly_withdrawal\n",
        "\n",
        "            # Apply the monthly withdrawal proportionally\n",
        "            stock_proportion = stock_value / total_value\n",
        "            bond_proportion = bond_value / total_value\n",
        "\n",
        "            stock_value -= monthly_withdrawal * stock_proportion\n",
        "            bond_value -= monthly_withdrawal * bond_proportion\n",
        "\n",
        "            # Ensure asset values don't go negative\n",
        "            stock_value = max(stock_value, 0)\n",
        "            bond_value = max(bond_value, 0)\n",
        "\n",
        "            # Update total portfolio value after withdrawal\n",
        "            total_value = stock_value + bond_value\n",
        "\n",
        "            # Check for portfolio depletion after withdrawal\n",
        "            if total_value <= 0:\n",
        "                portfolio_depleted = True\n",
        "                portfolio_values[month:] = 0\n",
        "                spending_values[month:] = 0\n",
        "                break\n",
        "\n",
        "            # Record the portfolio value\n",
        "            portfolio_values[month] = total_value\n",
        "\n",
        "            # Rebalance the portfolio if required\n",
        "            if total_value > 0 and (month + 1) % rebalance_frequency == 0:\n",
        "                # Rebalance to target weights\n",
        "                target_stock_value = total_value * stock_weight\n",
        "                target_bond_value = total_value * (1 - stock_weight)\n",
        "\n",
        "                # Calculate the total amount that needs to be rebalanced\n",
        "                stock_rebalance_amount = abs(stock_value - target_stock_value)\n",
        "                bond_rebalance_amount = abs(bond_value - target_bond_value)\n",
        "                rebalance_amount = stock_rebalance_amount + bond_rebalance_amount\n",
        "\n",
        "                # Apply transaction costs\n",
        "                transaction_cost = (transaction_cost_rate * rebalance_amount +\n",
        "                                    fixed_transaction_cost)\n",
        "\n",
        "                # Deduct transaction cost from the total portfolio value\n",
        "                total_value -= transaction_cost\n",
        "\n",
        "                # Check if transaction costs depleted the portfolio\n",
        "                if total_value <= 0:\n",
        "                    portfolio_depleted = True\n",
        "                    portfolio_values[month:] = 0\n",
        "                    spending_values[month:] = 0\n",
        "                    break\n",
        "\n",
        "                # Update stock and bond values after transaction cost\n",
        "                stock_value = total_value * stock_weight\n",
        "                bond_value = total_value * (1 - stock_weight)\n",
        "\n",
        "        # Track if the portfolio ran out of money\n",
        "        if portfolio_depleted:\n",
        "            depletion_count += 1\n",
        "        else:\n",
        "            # Ensure the last month's portfolio and spending values are recorded\n",
        "            portfolio_values[month] = total_value\n",
        "            spending_values[month] = monthly_withdrawal\n",
        "\n",
        "        # Append the simulation results\n",
        "        results.append(portfolio_values)\n",
        "        spending_results.append(spending_values)\n",
        "\n",
        "    # Convert results to numpy arrays for easier analysis\n",
        "    results_array = np.array(results)\n",
        "    spending_array = np.array(spending_results)\n",
        "\n",
        "    return results_array, spending_array, depletion_count"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Lp3ibKG6Plx4"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Description of Function Parameters\n",
        "\n",
        "We'll simulate the portfolio growth over time using the specified withdrawal rates (inflation-adjusted), rebalancing, and transaction costs. To perform the Monte Carlo simulations, we sample from historical returns in blocks (i.e., capturing some of the temporal autocorrelation and the correlation between asset classes).\n",
        "\n",
        "## Inputs\n",
        "\n",
        "- **`df` (DataFrame)**: This parameter represents the historical data, containing monthly real stock returns, real bond returns, and inflation rates. It is used for resampling and calculating portfolio returns in the simulation.\n",
        "\n",
        "- **`initial_value` (float)**: The starting value of the portfolio, specified in dollars. This represents the initial investment amount, with a default of $1,000,000. It serves as the baseline for calculating withdrawals and tracking portfolio growth.\n",
        "\n",
        "- **`years` (int)**: The duration of the simulation, specified in years. It determines the total length of time over which the portfolio is simulated, with the default set to 30 years. The duration is converted to months internally (`years * 12`) for monthly calculations.\n",
        "\n",
        "- **`stock_weight` (float)**: The target weight for stocks in the portfolio, expressed as a proportion of the total portfolio value. For example, a value of 0.70 means 70% of the portfolio is allocated to stocks, while the remaining 30% is allocated to bonds. The default value is 0.70.\n",
        "\n",
        "- **`rebalance_frequency` (int)**: Specifies how often the portfolio should be rebalanced to maintain the target stock and bond allocation, measured in months. For instance, a value of 3 means the portfolio is rebalanced every three months. This helps keep the asset allocation close to the intended target over time.\n",
        "\n",
        "- **`transaction_cost_rate` (float)**: Represents the percentage of the rebalanced amount that is incurred as a transaction cost, simulating the cost of trading. For example, a value of 0.002 means a 0.2% fee is applied to the amount being rebalanced. The default value is 0.002 (0.2%).\n",
        "\n",
        "- **`fixed_transaction_cost` (float)**: A fixed dollar amount charged per rebalancing event, in addition to the percentage-based transaction cost. This simulates flat fees that brokers may charge for transactions. The default value is $50.\n",
        "\n",
        "- **`withdrawal_strategy` (str)**: Specifies the withdrawal strategy to be used in the simulation. It can be either `'constant'` or `'dynamic'`. The `'constant'` strategy withdraws a fixed amount annually (adjusted for inflation), based on the initial withdrawal rate. The `'dynamic'` strategy adjusts the withdrawal amount annually based on a fixed percentage of the current portfolio value, constrained by a ceiling and floor to limit year-over-year changes. The default value is `'constant'`.\n",
        "\n",
        "- **`initial_withdrawal_rate` (float)**: The initial annual withdrawal rate, expressed as a percentage of the portfolio's value. This rate determines the amount withdrawn each year in real terms. For example, a value of 0.045 indicates a 4.5% annual withdrawal rate. In the constant strategy, this withdrawal amount remains the same throughout the simulation. In the dynamic strategy, this rate is used both for the initial withdrawal and as the target percentage for annual adjustments. The default value is 0.045 (4.5%).\n",
        "\n",
        "- **`ceiling` (float)**: Applicable only when using the `'dynamic'` withdrawal strategy. Represents the maximum allowed percentage **increase** in the annual withdrawal amount compared to the previous year. For example, a value of 0.05 means the withdrawal amount cannot increase by more than 5% from one year to the next. This parameter helps prevent large jumps in spending when the portfolio performs exceptionally well. The default value is 0.05 (5%).\n",
        "\n",
        "- **`floor` (float)**: Applicable only when using the `'dynamic'` withdrawal strategy. Represents the maximum allowed percentage **decrease** in the annual withdrawal amount compared to the previous year. For example, a value of -0.025 means the withdrawal amount cannot decrease by more than 2.5% from one year to the next. This parameter helps prevent drastic cuts in spending when the portfolio underperforms. The default value is -0.025 (-2.5%).\n",
        "\n",
        "- **`block_size` (int)**: The number of consecutive months in each block used for block bootstrapping. This parameter helps preserve patterns of autocorrelation in the returns by resampling chunks of consecutive data instead of individual months. The default value is 12 months.\n",
        "\n",
        "- **`num_simulations` (int)**: The number of Monte Carlo simulations to run. Each simulation represents a different possible path for the portfolio's growth, based on resampling the historical data. More simulations help improve the robustness of the results by exploring a wider range of potential outcomes. The default is set to 1,000 simulations.\n"
      ],
      "metadata": {
        "id": "QkLQnv5PPkvA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Set parameters, run simulations, and show results\n",
        "\n",
        "This is the part where we can set/alter inputs to the simulation."
      ],
      "metadata": {
        "id": "tTERa3lGQLVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the parameters for the simulation\n",
        "simulation_params = {\n",
        "    'initial_value': 1e6,            # $1 million starting portfolio\n",
        "    'years': 30,                     # 30-year simulation\n",
        "    'stock_weight': 0.7,             # e.g., 0.7 for 70% stocks, 30% bonds\n",
        "    'rebalance_frequency': 3,        # Rebalance every N months\n",
        "    'transaction_cost_rate': 0.002,  # Proportion of the rebalanced amount\n",
        "    'fixed_transaction_cost': 0,     # Fixed cost ($) per rebalancing\n",
        "    'withdrawal_strategy': 'dynamic',    # 'constant' or 'dynamic' withdrawal strategy\n",
        "    'initial_withdrawal_rate': 0.045,     # Initial annual withdrawal rate (real)\n",
        "    'ceiling': 0.05,                     # Ceiling for dynamic strategy (e.g., 0.05 for 5%)\n",
        "    'floor': -0.05,                     # Floor for dynamic strategy (e.g., -0.025 for -2.5%)\n",
        "    'block_size': 12,                # Block size in months\n",
        "    'num_simulations': 500           # Number of Monte Carlo simulations\n",
        "}\n",
        "\n",
        "\n",
        "# Run the inflation-adjusted Monte Carlo simulations with block bootstrapping\n",
        "results_array, spending_array, depletion_count = simulate_portfolio(df, **simulation_params)\n",
        "\n",
        "# Calculate percentiles for the results\n",
        "results_in_millions = results_array / 1e6\n",
        "spending_in_thousands = spending_array / 1e3  # Convert spending to thousands for plotting\n",
        "\n",
        "# Number of years and months for the simulation\n",
        "years = simulation_params['years']\n",
        "months = years * 12\n",
        "\n",
        "# Generate the x-axis values in years\n",
        "x_values_years = np.linspace(0, years, months)\n",
        "\n",
        "# Calculate percentiles for portfolio values\n",
        "median_portfolio = np.percentile(results_in_millions, 50, axis=0)\n",
        "percentile_10 = np.percentile(results_in_millions, 10, axis=0)\n",
        "percentile_90 = np.percentile(results_in_millions, 90, axis=0)\n",
        "\n",
        "# Calculate percentiles for spending amounts\n",
        "median_spending = np.percentile(spending_in_thousands, 50, axis=0)\n",
        "spending_percentile_10 = np.percentile(spending_in_thousands, 10, axis=0)\n",
        "spending_percentile_90 = np.percentile(spending_in_thousands, 90, axis=0)\n",
        "\n",
        "depletion_percentage = (depletion_count /\n",
        "                        simulation_params['num_simulations']) * 100\n",
        "\n",
        "# Plot the portfolio value results in log scale\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(x_values_years, median_portfolio, label='Median Portfolio',\n",
        "         color='black')\n",
        "plt.fill_between(x_values_years, percentile_10, percentile_90, alpha=0.2,\n",
        "                 label='10th-90th Percentile', color='grey')\n",
        "plt.title(f'Inflation-Adjusted Portfolio Value Over Time '\n",
        "          f'({simulation_params[\"num_simulations\"]} Simulations)\\n'\n",
        "          f'{depletion_percentage:.2f}% of Portfolios Ran Out of Money')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Portfolio Value (Million $)')\n",
        "plt.yscale('log')  # Set y-axis to log scale\n",
        "\n",
        "# Customize the ticks using LogLocator for better control over tick appearance\n",
        "ax = plt.gca()  # Get the current axis\n",
        "ax.yaxis.set_major_locator(ticker.LogLocator(base=2.0, numticks=10))\n",
        "ax.yaxis.set_minor_locator(ticker.LogLocator(base=2.0, subs='auto', numticks=10))\n",
        "ax.yaxis.set_major_formatter(ticker.ScalarFormatter())  # Ensure tick labels are integers\n",
        "\n",
        "plt.grid(True, which='both', axis='both')  # Grid for both major and minor ticks on y-axis\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot the monthly spending amounts\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(x_values_years, median_spending, label='Median Spending', color='black')\n",
        "plt.fill_between(x_values_years, spending_percentile_10, spending_percentile_90, alpha=0.2,\n",
        "                 label='10th-90th Percentile', color='grey')\n",
        "plt.title(f'Monthly Spending Over Time ({simulation_params[\"num_simulations\"]} Simulations)')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Monthly Spending (Thousand $)')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4oh6J4M3Ps6a",
        "outputId": "f2d6c953-328d-405a-b367-ceb4e3941c15"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}