StockMarket2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from StockMarket2 import *\n",
    "from functools import partial\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "market_data = list(read_market_data())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cash Cushions and Ratcheting Withdrawals\n",
    "\n",
    "As I neared retirement, I did a lot of research on how much of my portfolio I could withdraw in a given year (or, conversely, how large a portfolio I'd need to sustain a given income).  Of course I came across the \"4% Safe Withdrawal Rate.\"  And that the S&P 500's long-term compound annual growth rate (CAGR) is about 7% after inflation.\n",
    "\n",
    "The natural question was why is the Safe Withdrawal Rate so much less than the S&P 500's inflation-adjusted CAGR?  That's because you might retire right before the start of a major downturn, and end up having to sell a larger percentage of your portfolio while the market is down (thus having a smaller portfolio left when the market recovers and keeps growing).\n",
    "\n",
    "Yet, most of the time (when retirement didn't start with a major downturn), the 4% SWR results in the portfolio's inflation-adjusted value growing over time -- often significantly.  So, most of the time, you should be able to either get away with a larger initial withdrawal, or else increase your withdrawals by more than inflation, and still not run out of money.\n",
    "\n",
    "I has also heard many recommendations to have 3 to 5 years of expenses out of the stock market (a \"cash cushion\"), so that you're not forced to sell stocks during the depths of a downturn.  Such a strategy should make it less likely that you run out of money for a given withdrawal rate, or let you increase the withdrawal rate with a similar chance of running out of money.\n",
    "\n",
    "I set out to find a way to safely increase retirement withdrawals.  And, since I retired early, I wanted to see what a \"safe\" withdrawal rate would be for a 40-year retirement (instead of the typical 30 years).  I wrote some code to simulate retirement periods, using Robert Shiller's historical stock market data from his book _Irrational Exuberance_.  I wanted to be able to evaluate various strategies for asset allocation and adjusting the withdrawal amount over time, as well as the duration of retirement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Phoenix 1's 10% raise rule does not significantly impact survivability or sustainability of the portfolio, nor does it significantly increase withdrawals.  Adjusting (\"ratcheting\") the withdrawal to match the initial withdrawal rate significantly increases withdrawals at the expense of survivability and sustainability of the portfolio.  Adding a cash cushion of 3 to 5 years of withdrawals significantly improves survivability of a portfolio with ratcheting withdrawals, while still allowing withdrawals to grow significantly over time (though somewhat less than without the cash cushion)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology\n",
    "\n",
    "My simulations are based on historical stock market data compiled and published by Robert Shiller.  This consists of monthly data from 1871 to present for S&P 500 price, dividends, and Consumer Price Index (to track inflation), plus other data.  I simulated the value of the portfolio over time, quarterly withdrawals and annual adjustments to the withdrawal amount.  Dividends were computed and reinvested quarterly.  The portfolio was 100% invested in stocks, unless a \"cash cushion\" was being simulated.  I used 3-month Treasury Bill yields (1934 to present) as an estimate for interest earned on cash (when a cash cushion was used)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terminology\n",
    "\n",
    "I decided to focus on a few key statistics for evaluating a particular strategy.\n",
    "\n",
    "**Survivability** is whether the portfolio is able to make all of the withdrawals for the given length of retirement.  If it runs out of money early, then it is not survivable.  I express it as the percentage of potential retirement periods that actually had enough money to make all of their withdrawals.\n",
    "\n",
    "**Sustainability** is whether the portfolio's ending inflation-adjusted value is close enough to its starting value, so it could likely provide withdrawals for much longer periods (possibly indefinitely).  I express it as the percentage of potential retirement periods where the ending value is at least 95% of the initial value, after adjusting for inflation.\n",
    "\n",
    "**Balance Growth**.  I calculated the median, mean, and standard deviation of the portfolio's inflation-adjusted compound annual growth rate for all periods simulated.  A positive growth rate means the portfolio grew by more than inflation over the retirement period.\n",
    "\n",
    "**Withdrawal Growth**.  I calculated the median, mean and standard deviation of the withdrawal's inflation-adjusted compound annual growth rate for all periods simulated.  A positive value means that the withdrawl amount grew by more than inflation over the retirement period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline\n",
    "\n",
    "As a baseline, I started with a 30-year retirement period and a 4% initial withdrawal rate as suggested by William Bengen.  As a stock-loving Fool, the baseline is a portfolio that is 100% invested in the S&P 500, with dividends reinvested quarterly.  Withdrawals are made quarterly.  The withdrawal amount is adjusted annually by the change in the Consumer Price Index (inflation).\n",
    "\n",
    "Here are the statistics for that baseline:\n",
    "\n",
    "    survivability:  98.36%\n",
    "    sustainability: 83.06%\n",
    "    balance     median:  3.64%  mean:  3.11%  stdev:  3.30%\n",
    "    withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.36%\n",
      "sustainability: 83.06%\n",
      "balance     median:  3.64%  mean:  3.11%  stdev:  3.30%\n",
      "withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio()\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.date(1929, 1, 1),\n",
       " datetime.date(1929, 2, 1),\n",
       " datetime.date(1929, 3, 1),\n",
       " datetime.date(1929, 4, 1),\n",
       " datetime.date(1929, 5, 1),\n",
       " datetime.date(1929, 6, 1),\n",
       " datetime.date(1929, 7, 1),\n",
       " datetime.date(1929, 8, 1),\n",
       " datetime.date(1929, 9, 1),\n",
       " datetime.date(1929, 10, 1),\n",
       " datetime.date(1930, 3, 1),\n",
       " datetime.date(1930, 4, 1),\n",
       " datetime.date(1930, 5, 1),\n",
       " datetime.date(1965, 10, 1),\n",
       " datetime.date(1965, 11, 1),\n",
       " datetime.date(1965, 12, 1),\n",
       " datetime.date(1966, 1, 1),\n",
       " datetime.date(1966, 2, 1),\n",
       " datetime.date(1968, 6, 1),\n",
       " datetime.date(1968, 9, 1),\n",
       " datetime.date(1968, 10, 1),\n",
       " datetime.date(1968, 11, 1),\n",
       " datetime.date(1968, 12, 1)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[period.date for period in periods if not period.survived]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What this means is that 98.36% of all 30-year periods \"surived\".  That is, the portfolio was able to make all 120 quarterly withdrawals.  In the other 1.64% of periods, the portfolio ran out of money in less than 30 years.\n",
    "\n",
    "In 83.06% of periods, the portfolio's ending value was at least 95% of the initial value, adjusted for inflation.  The portfolio could probably last at least another 30 years.\n",
    "\n",
    "In the other 6.94% of periods, the ending value was less.  This is typically due to a significant market decline at the start of the retirement period, or a decline right near the end of the retirement period.\n",
    "\n",
    "(**_TODO_**) Distribution of the ending value relative to the inflation-adjusted initial value?\n",
    "\n",
    "As you can see, the withdrawal amount grows with inflation.  The withdrawal growth rate is slightly negative because the withdrawal is only adjusted annualy, but the growth rate reflects inflation as of the last quarterly withdrawal.  So you're seeing the effects of the last year's inflation, right before the withdrawal amount would be adjusted again.\n",
    "\n",
    "The portfolio grew by 3.11% more than inflation, per year, on average.  Since withdrawals grew by inflation, this means the portfolio typically grew significantly more than the withdrawals.  This reflects the difference between the initial withdrawal rate and the long-term growth rate of the S&P 500.\n",
    "\n",
    "Note that the periods that didn't survive were:\n",
    "* January through October 1929\n",
    "* March through May 1930\n",
    "* October 1965 through February 1966\n",
    "* June, and September through December 1968\n",
    "\n",
    "The Great Depression was a brutal decline that lasted 29 years from initial peak to recovery on an inflation-adjusted basis.  The late 1960's saw two large declines with a brief growth spurt in between, and high inflation.  It's worth noting that William Bengen's study wouldn't have included retirement periods starting in the late 1960's because they wouldn't have had 30 years duration (his study was in the 1980's)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash Cushion\n",
    "\n",
    "Now let's take a look at using a cash cushion.  Foolish advice is that you should have the next 3 to 5 years of expenses out of the stock market so that you're not forced to sell in the depths of a market downturn.\n",
    "\n",
    "But when and how should you use this cash instead of selling stock?  How large should the cash cushion be?  When and how do you replenish this cash cushion after you have used some of it?  How do you increase it as your portfolio increases?\n",
    "\n",
    "It seems to me that you want to use cash from the cash cushion during a market decline, ideally at the lowest prices of the decline.  Using the cash cushion at the very beginning of the decline, or near the end of the recovery is less important since stock prices are higher than in the depths of the decline.  But we can't predict the depth or duration of any given decline in advance, not even during the decline itself.  So we have to make a guess.\n",
    "\n",
    "I chose to start using the cash cushion when the portfolio's value declined to 90% or less of its previous annual maximum value.  Once it drops below that value, withdrawals come first from the cash cushion.  If the remaining cash cushion isn't sufficient, then the balance comes from selling stock.  When the portfolio reaches its previous annual maximum (or sets a new maximum), the cash cushion begins to be rebuilt by selling more stock than is needed to pay that quarter's withdrawal.  I chose to sell (up to) 1.5 times the quarterly distribution.  That is, the cash cushion is increased by (up to) half of the quarterly distribution, each quarter, until it has reached the desired level.\n",
    "\n",
    "Note that when the annual withdrawal is increased, the previous cash cushion will be insufficient, so this triggers rebuilding the cash cushion even though it wasn't necessarily depleted during a decline.\n",
    "\n",
    "I chose a target amount of three times the annual withdrawal.  This does not try to adjust for future inflationary adjustments.  So it ends up being a little less than the next three years of withdrawals.\n",
    "\n",
    "Cash may earn interest.  I don't know of good historical data for interest rates.  I ended up using the 3-month Treasury Bill yield, at least as far back as it goes (to 1934).  Before that, I assume no interest on cash.  This rate seems in the right ballpark for high yield savings or money market accounts.\n",
    "\n",
    "Later, I'll discuss other values for the size of the cushion, triggering the use of the cash cushion, and the rate at which it is replenished.  It turns out that 3 years, 90% and 1.5x replenishment were pretty good, but not optimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.72%\n",
      "sustainability: 81.85%\n",
      "balance     median:  3.20%  mean:  2.71%  stdev:  3.10%\n",
      "withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.date(1929, 7, 1),\n",
       " datetime.date(1929, 8, 1),\n",
       " datetime.date(1929, 9, 1),\n",
       " datetime.date(1929, 10, 1)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[period.date for period in periods if not period.survived]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    survivability:  99.72%\n",
    "    sustainability: 81.85%\n",
    "    balance     median:  3.20%  mean:  2.71%  stdev:  3.10%\n",
    "    withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%\n",
    "\n",
    "Portfolio survivability goes up, relative to the baseline.  It's nearly 100% now.  The only failures were July to October 1929, right before the stock market's biggest crash.\n",
    "\n",
    "Sustainability is a little lower, and the balance's growth rate is somewhat lower.  These are because cash is typically growing more slowly than stocks.  We've traded some growth for safety."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raises\n",
    "\n",
    "Next, let's consider a rule that grows withdrawals when the market does sufficiently well.  I went with Phoenix 1's 10% rule.  If the portfolio's value is at least 10% higher than its previous annual maximum, then grow the withdrawal by the maximum of 10% and inflation, else just grow it by inflation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.15%\n",
      "sustainability: 81.57%\n",
      "balance     median:  3.51%  mean:  2.91%  stdev:  3.38%\n",
      "withdrawal  median:  0.21%  mean:  0.25%  stdev:  0.32%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(raise_enable=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    survivability:  98.15%\n",
    "    sustainability: 81.57%\n",
    "    balance     median:  3.51%  mean:  2.91%  stdev:  3.38%\n",
    "    withdrawal  median:  0.21%  mean:  0.25%  stdev:  0.32%\n",
    "\n",
    "Survivability and sustainability are slightly lower than the baseline safe withdrawal rate, but very close.  The withdrawal growth rate was slightly higher than inflation.  The portfolio growth rate was a little lower as more of the portfolio was withdrawn.\n",
    "\n",
    "It turns out that the 10% growth threshold doesn't happen all that often (at least for the S&P 500), so withdrawals mostly grew by inflation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash cushion and Raises\n",
    "\n",
    "Now let's look at a combination of both a cash cushion and Phoenix 1 style raises.\n",
    "\n",
    "    survivability:  99.43%\n",
    "    sustainability: 80.57%\n",
    "    balance     median:  3.07%  mean:  2.54%  stdev:  3.14%\n",
    "    withdrawal  median:  0.15%  mean:  0.19%  stdev:  0.32%\n",
    "\n",
    "Survivability and sustainability were slightly lower than with just a cash cushion.  Survivability was higher than the baseline.  Withdrawals grew, but less than with just raises.  Portfolio balance grew slightly less than with either cash cushion or raises alone, but still well more than inflation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.43%\n",
      "sustainability: 80.57%\n",
      "balance     median:  3.07%  mean:  2.54%  stdev:  3.14%\n",
      "withdrawal  median:  0.15%  mean:  0.19%  stdev:  0.32%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, raise_enable=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ratcheting Withdrawals\n",
    "\n",
    "Now let's try a different way of growing withdrawals.  I call this \"ratcheting.\"  Every time the withdrawal is adjusted (once a year), it grows to the maximum of the initial withdrawal rate or the previous year's withdrawal adjusted by inflation.  That is, if 4% of the current portfolio balance is more than the inflation-adjusted withdrawal, then the withdrawal is bumped up to 4% of the portfolio.  This is more aggressive than the 10% raise.  If the previous year was a high, and the portfolio grew by 20%, then the withdrawal also grows by 20%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  91.96%\n",
      "sustainability: 50.32%\n",
      "balance     median:  0.37%  mean: -0.04%  stdev:  3.40%\n",
      "withdrawal  median:  2.34%  mean:  2.71%  stdev:  1.72%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(ratchet=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    survivability:  91.96%\n",
    "    sustainability: 50.32%\n",
    "    balance     median:  0.37%  mean: -0.04%  stdev:  3.40%\n",
    "    withdrawal  median:  2.34%  mean:  2.71%  stdev:  1.72%\n",
    "\n",
    "Here, survivability and sustainability are both significantly lower.  But withdrawals grew significantly more (2.71% per year, on average; almost as much as stock prices grew).  The portfolio balance essentially kept up with inflation, on average.\n",
    "\n",
    "Sustainability being lower is because you significantly increase the chance that you increase your withdrawals right before experiencing a period with a poor sequence of returns, and the portfolio just barely keeps up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash Cushion and Ratcheting Withdrawals\n",
    "\n",
    "Let's try adding a cash cushion to a ratcheting withdrawal to see if the survivability improves.\n",
    "\n",
    "    survivability:  98.65%\n",
    "    sustainability: 53.81%\n",
    "    balance     median:  0.18%  mean:  0.01%  stdev:  2.51%\n",
    "    withdrawal  median:  2.05%  mean:  2.28%  stdev:  1.50%\n",
    "\n",
    "Survivability is  slightly better than the baseline.  Sustainability is still very low, but a little better than ratcheting withdrawals alone.\n",
    "\n",
    "The portfolio balance still just keeps up with inflation.  Withdrawals grow significantly more than just inflation, but a little less than with ratcheting alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.65%\n",
      "sustainability: 53.81%\n",
      "balance     median:  0.18%  mean:  0.01%  stdev:  2.51%\n",
      "withdrawal  median:  2.05%  mean:  2.28%  stdev:  1.50%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 40-year Retirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.13%\n",
      "sustainability: 88.17%\n",
      "balance     median:  3.79%  mean:  3.52%  stdev:  2.79%\n",
      "withdrawal  median: -0.05%  mean: -0.06%  stdev:  0.10%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.038)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.61%\n",
      "sustainability: 94.94%\n",
      "balance     median:  4.08%  mean:  3.94%  stdev:  2.37%\n",
      "withdrawal  median: -0.05%  mean: -0.06%  stdev:  0.10%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.035)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.22%\n",
      "sustainability: 73.00%\n",
      "balance     median:  0.83%  mean:  0.83%  stdev:  1.54%\n",
      "withdrawal  median:  2.07%  mean:  2.15%  stdev:  1.05%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.035)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  95.25%\n",
      "sustainability: 83.35%\n",
      "balance     median:  3.66%  mean:  3.35%  stdev:  2.91%\n",
      "withdrawal  median: -0.05%  mean: -0.06%  stdev:  0.10%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.04)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  96.42%\n",
      "sustainability: 82.88%\n",
      "balance     median:  3.13%  mean:  2.93%  stdev:  2.86%\n",
      "withdrawal  median: -0.05%  mean: -0.06%  stdev:  0.10%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, annual_withdrawal_rate=0.04)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  89.42%\n",
      "sustainability: 40.93%\n",
      "balance     median: -0.28%  mean: -0.77%  stdev:  2.32%\n",
      "withdrawal  median:  1.55%  mean:  1.69%  stdev:  0.99%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.04)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  95.64%\n",
      "sustainability: 52.53%\n",
      "balance     median:  0.11%  mean: -0.18%  stdev:  2.10%\n",
      "withdrawal  median:  1.73%  mean:  1.82%  stdev:  1.03%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.038)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.44%\n",
      "sustainability: 65.76%\n",
      "balance     median:  0.60%  mean:  0.52%  stdev:  1.69%\n",
      "withdrawal  median:  1.94%  mean:  2.03%  stdev:  1.05%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.036)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.22%\n",
      "sustainability: 73.00%\n",
      "balance     median:  0.83%  mean:  0.83%  stdev:  1.54%\n",
      "withdrawal  median:  2.07%  mean:  2.15%  stdev:  1.05%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.035)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.84%\n",
      "sustainability: 98.99%\n",
      "balance     median:  4.38%  mean:  4.32%  stdev:  2.10%\n",
      "withdrawal  median: -0.05%  mean: -0.06%  stdev:  0.10%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  97.74%\n",
      "sustainability: 84.82%\n",
      "balance     median:  1.62%  mean:  1.49%  stdev:  1.62%\n",
      "withdrawal  median:  2.72%  mean:  2.81%  stdev:  1.05%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(ratchet=True, annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.92%\n",
      "sustainability: 91.83%\n",
      "balance     median:  1.55%  mean:  1.58%  stdev:  1.31%\n",
      "withdrawal  median:  2.47%  mean:  2.55%  stdev:  0.99%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=480)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 45-Year Retirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.43%\n",
      "sustainability: 94.45%\n",
      "balance     median:  4.08%  mean:  4.07%  stdev:  2.33%\n",
      "withdrawal  median: -0.04%  mean: -0.05%  stdev:  0.09%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.035)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.37%\n",
      "sustainability: 73.71%\n",
      "balance     median:  0.79%  mean:  0.69%  stdev:  1.41%\n",
      "withdrawal  median:  1.92%  mean:  1.94%  stdev:  0.94%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.035)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  93.22%\n",
      "sustainability: 85.47%\n",
      "balance     median:  3.72%  mean:  3.64%  stdev:  2.65%\n",
      "withdrawal  median: -0.05%  mean: -0.05%  stdev:  0.09%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.04)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  94.20%\n",
      "sustainability: 84.65%\n",
      "balance     median:  3.29%  mean:  3.25%  stdev:  2.60%\n",
      "withdrawal  median: -0.05%  mean: -0.05%  stdev:  0.09%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, annual_withdrawal_rate=0.04)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.76%\n",
      "sustainability: 98.86%\n",
      "balance     median:  4.34%  mean:  4.45%  stdev:  2.01%\n",
      "withdrawal  median: -0.05%  mean: -0.05%  stdev:  0.09%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  96.49%\n",
      "sustainability: 80.65%\n",
      "balance     median:  1.49%  mean:  1.36%  stdev:  1.55%\n",
      "withdrawal  median:  2.62%  mean:  2.62%  stdev:  0.89%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(ratchet=True, annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.92%\n",
      "sustainability: 90.04%\n",
      "balance     median:  1.41%  mean:  1.46%  stdev:  1.25%\n",
      "withdrawal  median:  2.35%  mean:  2.38%  stdev:  0.86%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True, annual_withdrawal_rate=0.032)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data, period_length=540)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cash Cushion Exhaustion\n",
    "\n",
    "Cash cushions are not perfect.  That's why a safe withdrawal rate is still significantly less than the stock market's long term growth rate.  Over the last 100 years, there have been many periods where a 3-year cash cushion would have been exhausted during a major market decline.  The deepest declines took a very long time to recover (and thus the cash cushion ran out).  Plus, we've had several periods where we had a major decline, which recovers, and very soon another major decline (without enough time to rebuild a cash cushion).\n",
    "\n",
    "### 1929\n",
    "\n",
    "From peak to trough took just under 3 years.  It was another 22 years before the nominal stock price recovered to its 1929 level.  But there was inflation that whole time.  Inflation-adjusted prices took a total of 29 years (a full retirement!) from initial peak to recovery.  A saving grace was that dividend yields were relatively high, so you could mostly live off of dividends and sell very little stock while it was down.\n",
    "\n",
    "### Late 1960's and 1970's\n",
    "\n",
    "There was a 36% decline that started in late 1968.  It took about 18 months to hit bottom, and about 21 months to recover (nominal prices).  The cash cushion would have lasted for most of that decline, but you'd be out of cash at that point.  10 months later, in early 1973, a 48% decline started.  It took about 21 months to hit bottom, and almost 6 years to (nominally) recover.  You would have had little to no cushion to weather that decline, and you'd still be selling for long periods at depressed prices, during a time of high inflation.\n",
    "\n",
    "### Late 1980\n",
    "\n",
    "Late 1980 saw the start of a 27% decline that hit bottom in about 21 months, but recovered in 3 months(!) in late 1982.  It lasted a total of about 2 years, so you'd have had some cash cushion left.  You had about 1 year to rebuild the cushion before a 14% drop in late 1983.  The cash cushion would have worked OK here.\n",
    "\n",
    "### Late 1980's to early 1990's\n",
    "\n",
    "Late 1987 saw a brutal 33% drop in just over 3 months.  Recovery was about 20 months, so the whole decline lasted about 2 years.  Just over 2 months later (no time for recovery!), a 10% drop took 4 months to fall and 4 months to recover.  We're now 33 months from the start of the 1987 decline, and the portfolio is nearly unchanged.  You could view that as a single 3-year decline.  Six weeks later, a 19% decline begins, takes 3 months to bottom, and about 15 months to recover.  You would not have had a cash cushion for that decline; it was exhausted in the 1987 crash.\n",
    "\n",
    "### Late 1990's\n",
    "\n",
    "In mid-to-late 1998, the market fell 19% in six weeks (faster than in 1987), but was recovered 4 months later.  A cash cushion would have worked well here, especially since stocks grew strongly right afterward.\n",
    "\n",
    "### Dot.com Crash of 2000, Financial Crisis of Late 2000's\n",
    "\n",
    "Late march 2000, stocks declined 49% in about 5.5 months.  The recovery was 4 years and 8 months.  Six weeks later (so no time to rebuild), a 9% drop in one month, followed by two months of recovery.  Four days later was the start of the 57% decline (normally associated with 2008-2009, but the peak was October 2007).  The decline lasted about 17 months, but took 4 years to recover.  The dot.com crash wiped out your cash cushion, leaving you unprotected in the financial crisis of the late 2000's.\n",
    "\n",
    "You had about 2 years to rebuild before a 15% decline in mid 2015, which took 9 months to fall, and 5 months to recover.  You just might have had your cash cushion rebuilt enough by then to last through that one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = Portfolio(cash_cushion=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  99.72%\n",
      "sustainability: 81.85%\n",
      "balance     median:  3.20%  mean:  2.71%  stdev:  3.10%\n",
      "withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%\n"
     ]
    }
   ],
   "source": [
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash Cushion Variables\n",
    "\n",
    "If you're using a cash cushion, what is the best combination of size of the cushion (number of years worth of withdrawals), threshold for the decline before using the cash, and rate at which you rebuild the cushion?  For the purposes of this section, I'm using a 4% initial withdrawal rate and 30 year retirement period.  I varied the size of the cushion from 1 to 5 years of withdrawals.  I varied the portfolio balance threshold for using the cash between 85%, 90% and 95% of the previous annual maximum.  And I varied the rebuild rate between 1.25, 1.5, 1.75 and 2.0 times the withdrawal rate (note that this is how much stock to sell compared to the quarterly withdrawal; 1.0 of that is the quarterly withdrawal itself, leaving the rest to rebuild the cash cushion).\n",
    "\n",
    "When using a ratcheting withdrawal, the highest survivability (99.5%) were for a 5-year cushion, and either a threshold of 95% and rebuild rate of 1.75, or a threshold of 90% and rebuild rate of 2.0.  In general, 5 years was better than 4 years, which was better than shorter durations, the rebuild rate was 1.75 or 2.0, and the threshold didn't matter as much. Note that 3 years, 90% threshold, and 1.5 rebuild rate had a survivability of 98.65%, which is still pretty close.  The worst survivability was 92.67% for 1 year, 95% threshold, and a rebuild rate of 1.5 to 2.0.\n",
    "\n",
    "When not using a ratcheting withdrawal, the highest survivability (99.86%) were for a 4-year cushion, followed by a 5-year cushion.  The threshold and rebuild rate didn't matter as far as survivability was concerned, though it changed sustainability a little.  Note that 3 years, 90% threshold and 1.5x rebuild rate had a survivability of 99.715%.  The worst survivability was 98.65% for 1-year, 95% threshold, and 1.25x rebuild rate.\n",
    "\n",
    "With or without a ratcheting withdrawal, 1-year cushions were worst, then 2-year cushions.  Size of the cushion was most important (4 or 5 years being best).  If you're using ratcheting withdrawals, you want a larger cushion and to rebuild it faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = []\n",
    "for ratchet in [False, True]:\n",
    "    for withdrawal_rate in [0.04]:\n",
    "        for period_length in [360]:\n",
    "            for cash_use_threshold in [0.85, 0.90, 0.95]:\n",
    "                for rebuild_rate in [1.25, 1.5, 1.75, 2.0]:\n",
    "                    for cash_cushion_target in [1, 2, 3, 4, 5]:\n",
    "                        portfolio = Portfolio(cash_cushion=True,\n",
    "                                              ratchet=ratchet,\n",
    "                                              annual_withdrawal_rate=withdrawal_rate,\n",
    "                                              cash_cushion_target=cash_cushion_target,\n",
    "                                              cash_use_threshold=cash_use_threshold,\n",
    "                                              cash_rebuild_rate=rebuild_rate)\n",
    "\n",
    "                        (survivability, sustainability,\n",
    "                        balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "                        withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "                        periods) = portfolio.sim_periods(market_data, period_length=period_length)\n",
    "\n",
    "                        table.append((ratchet, withdrawal_rate, period_length,\n",
    "                                      cash_cushion_target, cash_use_threshold, rebuild_rate,\n",
    "                                      survivability, sustainability,\n",
    "                                      balance_growth_mean, balance_growth_mean, balance_growth_stdev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame.from_records(table, columns=\"ratchet withdrawal_rate period_length cash_cushion_target cash_use_threshold rebuild_rate survivability sustainability balance_growth_mean balance_growth_mean balance_growth_stdev\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <th>balance_growth_stdev</th>\n",
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       "      <td>0.85</td>\n",
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       "      <th>74</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>0.487544</td>\n",
       "      <td>-0.006334</td>\n",
       "      <td>-0.006334</td>\n",
       "      <td>0.025603</td>\n",
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       "      <th>93</th>\n",
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       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
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       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
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       "      <th>113</th>\n",
       "      <td>True</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
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       "      <td>0.542349</td>\n",
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       "      <td>0.023736</td>\n",
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       "      <th>98</th>\n",
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       "      <td>0.000109</td>\n",
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       "      <td>0.024442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.986477</td>\n",
       "      <td>0.525267</td>\n",
       "      <td>-0.001572</td>\n",
       "      <td>-0.001572</td>\n",
       "      <td>0.024266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.985765</td>\n",
       "      <td>0.544484</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.025408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.985053</td>\n",
       "      <td>0.479715</td>\n",
       "      <td>-0.004048</td>\n",
       "      <td>-0.004048</td>\n",
       "      <td>0.024120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.984342</td>\n",
       "      <td>0.530961</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.024760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.984342</td>\n",
       "      <td>0.526690</td>\n",
       "      <td>-0.000297</td>\n",
       "      <td>-0.000297</td>\n",
       "      <td>0.024547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.984342</td>\n",
       "      <td>0.504626</td>\n",
       "      <td>-0.003112</td>\n",
       "      <td>-0.003112</td>\n",
       "      <td>0.024377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.984342</td>\n",
       "      <td>0.469039</td>\n",
       "      <td>-0.005323</td>\n",
       "      <td>-0.005323</td>\n",
       "      <td>0.024744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.982918</td>\n",
       "      <td>0.533808</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.025391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.981495</td>\n",
       "      <td>0.523843</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>0.025216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.981495</td>\n",
       "      <td>0.520285</td>\n",
       "      <td>-0.000707</td>\n",
       "      <td>-0.000707</td>\n",
       "      <td>0.025181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.981495</td>\n",
       "      <td>0.473310</td>\n",
       "      <td>-0.004800</td>\n",
       "      <td>-0.004800</td>\n",
       "      <td>0.024970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.980071</td>\n",
       "      <td>0.453381</td>\n",
       "      <td>-0.007224</td>\n",
       "      <td>-0.007224</td>\n",
       "      <td>0.025908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.979359</td>\n",
       "      <td>0.491815</td>\n",
       "      <td>-0.002878</td>\n",
       "      <td>-0.002878</td>\n",
       "      <td>0.025214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.979359</td>\n",
       "      <td>0.482562</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>-0.004039</td>\n",
       "      <td>0.024838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.978648</td>\n",
       "      <td>0.522420</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>0.025574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.977936</td>\n",
       "      <td>0.498932</td>\n",
       "      <td>-0.002441</td>\n",
       "      <td>-0.002441</td>\n",
       "      <td>0.025121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.977224</td>\n",
       "      <td>0.472598</td>\n",
       "      <td>-0.003959</td>\n",
       "      <td>-0.003959</td>\n",
       "      <td>0.024987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.977224</td>\n",
       "      <td>0.444840</td>\n",
       "      <td>-0.007007</td>\n",
       "      <td>-0.007007</td>\n",
       "      <td>0.025574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.974377</td>\n",
       "      <td>0.518149</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.026932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.973665</td>\n",
       "      <td>0.498932</td>\n",
       "      <td>-0.001756</td>\n",
       "      <td>-0.001756</td>\n",
       "      <td>0.025853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.972954</td>\n",
       "      <td>0.513879</td>\n",
       "      <td>-0.000900</td>\n",
       "      <td>-0.000900</td>\n",
       "      <td>0.026608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.972954</td>\n",
       "      <td>0.512456</td>\n",
       "      <td>-0.000721</td>\n",
       "      <td>-0.000721</td>\n",
       "      <td>0.025782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.970819</td>\n",
       "      <td>0.520996</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.027276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.970107</td>\n",
       "      <td>0.524555</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>0.027362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.967972</td>\n",
       "      <td>0.524555</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.027828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.967260</td>\n",
       "      <td>0.520996</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.027419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.964413</td>\n",
       "      <td>0.520996</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.027763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.963701</td>\n",
       "      <td>0.498932</td>\n",
       "      <td>-0.000662</td>\n",
       "      <td>-0.000662</td>\n",
       "      <td>0.028176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.960854</td>\n",
       "      <td>0.500356</td>\n",
       "      <td>-0.000719</td>\n",
       "      <td>-0.000719</td>\n",
       "      <td>0.028511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.959431</td>\n",
       "      <td>0.503915</td>\n",
       "      <td>-0.000491</td>\n",
       "      <td>-0.000491</td>\n",
       "      <td>0.028396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.956584</td>\n",
       "      <td>0.503203</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>0.028591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.953737</td>\n",
       "      <td>0.500356</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>0.029954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.949466</td>\n",
       "      <td>0.500356</td>\n",
       "      <td>-0.000586</td>\n",
       "      <td>-0.000586</td>\n",
       "      <td>0.029283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.938078</td>\n",
       "      <td>0.506050</td>\n",
       "      <td>-0.000347</td>\n",
       "      <td>-0.000347</td>\n",
       "      <td>0.031146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.936655</td>\n",
       "      <td>0.503203</td>\n",
       "      <td>-0.000275</td>\n",
       "      <td>-0.000275</td>\n",
       "      <td>0.031234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.933808</td>\n",
       "      <td>0.500356</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>-0.000647</td>\n",
       "      <td>0.031662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.933096</td>\n",
       "      <td>0.504626</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.030653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.931673</td>\n",
       "      <td>0.505338</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.030940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.931673</td>\n",
       "      <td>0.505338</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.030477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.931673</td>\n",
       "      <td>0.503203</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>0.031073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.930961</td>\n",
       "      <td>0.503915</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.030343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.930249</td>\n",
       "      <td>0.503915</td>\n",
       "      <td>-0.000117</td>\n",
       "      <td>-0.000117</td>\n",
       "      <td>0.030911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.503203</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.030445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.502491</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.030552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>True</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.501068</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>0.031191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.812811</td>\n",
       "      <td>0.026060</td>\n",
       "      <td>0.026060</td>\n",
       "      <td>0.030480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.807829</td>\n",
       "      <td>0.025370</td>\n",
       "      <td>0.025370</td>\n",
       "      <td>0.030381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.025521</td>\n",
       "      <td>0.025521</td>\n",
       "      <td>0.030854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.804270</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>0.030943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.802847</td>\n",
       "      <td>0.025006</td>\n",
       "      <td>0.025006</td>\n",
       "      <td>0.031023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.801423</td>\n",
       "      <td>0.024878</td>\n",
       "      <td>0.024878</td>\n",
       "      <td>0.030819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.800712</td>\n",
       "      <td>0.024565</td>\n",
       "      <td>0.024565</td>\n",
       "      <td>0.030939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.796441</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.030636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.796441</td>\n",
       "      <td>0.024210</td>\n",
       "      <td>0.024210</td>\n",
       "      <td>0.031179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.796441</td>\n",
       "      <td>0.024431</td>\n",
       "      <td>0.024431</td>\n",
       "      <td>0.029675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.791459</td>\n",
       "      <td>0.023575</td>\n",
       "      <td>0.023575</td>\n",
       "      <td>0.031187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.788612</td>\n",
       "      <td>0.023450</td>\n",
       "      <td>0.023450</td>\n",
       "      <td>0.030527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.788612</td>\n",
       "      <td>0.022759</td>\n",
       "      <td>0.022759</td>\n",
       "      <td>0.030992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.787189</td>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.031374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.787189</td>\n",
       "      <td>0.023213</td>\n",
       "      <td>0.023213</td>\n",
       "      <td>0.029860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.787189</td>\n",
       "      <td>0.023166</td>\n",
       "      <td>0.023166</td>\n",
       "      <td>0.030759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>4</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.780783</td>\n",
       "      <td>0.022846</td>\n",
       "      <td>0.022846</td>\n",
       "      <td>0.031732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.780783</td>\n",
       "      <td>0.022382</td>\n",
       "      <td>0.022382</td>\n",
       "      <td>0.030755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.776512</td>\n",
       "      <td>0.021876</td>\n",
       "      <td>0.021876</td>\n",
       "      <td>0.031239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.774377</td>\n",
       "      <td>0.021508</td>\n",
       "      <td>0.021508</td>\n",
       "      <td>0.031404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.738078</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>0.033389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.998577</td>\n",
       "      <td>0.736655</td>\n",
       "      <td>0.020819</td>\n",
       "      <td>0.020819</td>\n",
       "      <td>0.031589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.997865</td>\n",
       "      <td>0.735943</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.032347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>5</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.997865</td>\n",
       "      <td>0.734520</td>\n",
       "      <td>0.019609</td>\n",
       "      <td>0.019609</td>\n",
       "      <td>0.032959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.821352</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>0.031012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.818505</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>0.027085</td>\n",
       "      <td>0.031014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.816370</td>\n",
       "      <td>0.026462</td>\n",
       "      <td>0.026462</td>\n",
       "      <td>0.031042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.813523</td>\n",
       "      <td>0.026239</td>\n",
       "      <td>0.026239</td>\n",
       "      <td>0.031073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.812100</td>\n",
       "      <td>0.026653</td>\n",
       "      <td>0.026653</td>\n",
       "      <td>0.031254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.812100</td>\n",
       "      <td>0.026485</td>\n",
       "      <td>0.026485</td>\n",
       "      <td>0.031334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.807117</td>\n",
       "      <td>0.025767</td>\n",
       "      <td>0.025767</td>\n",
       "      <td>0.031431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.997153</td>\n",
       "      <td>0.806406</td>\n",
       "      <td>0.025555</td>\n",
       "      <td>0.025555</td>\n",
       "      <td>0.031696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.996441</td>\n",
       "      <td>0.825623</td>\n",
       "      <td>0.027615</td>\n",
       "      <td>0.027615</td>\n",
       "      <td>0.031278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.996441</td>\n",
       "      <td>0.815658</td>\n",
       "      <td>0.027114</td>\n",
       "      <td>0.027114</td>\n",
       "      <td>0.031386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.996441</td>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.026951</td>\n",
       "      <td>0.026951</td>\n",
       "      <td>0.031413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.996441</td>\n",
       "      <td>0.814235</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.027366</td>\n",
       "      <td>0.031335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.993594</td>\n",
       "      <td>0.827758</td>\n",
       "      <td>0.028230</td>\n",
       "      <td>0.028230</td>\n",
       "      <td>0.032222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.993594</td>\n",
       "      <td>0.820641</td>\n",
       "      <td>0.027804</td>\n",
       "      <td>0.027804</td>\n",
       "      <td>0.032334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.993594</td>\n",
       "      <td>0.817794</td>\n",
       "      <td>0.027654</td>\n",
       "      <td>0.027654</td>\n",
       "      <td>0.032491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.993594</td>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.027617</td>\n",
       "      <td>0.027617</td>\n",
       "      <td>0.032500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>0.828470</td>\n",
       "      <td>0.028776</td>\n",
       "      <td>0.028776</td>\n",
       "      <td>0.032126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>0.823488</td>\n",
       "      <td>0.028400</td>\n",
       "      <td>0.028400</td>\n",
       "      <td>0.032185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>0.820641</td>\n",
       "      <td>0.028323</td>\n",
       "      <td>0.028323</td>\n",
       "      <td>0.032159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>0.820641</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>0.032160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.829893</td>\n",
       "      <td>0.029005</td>\n",
       "      <td>0.029005</td>\n",
       "      <td>0.032017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.822064</td>\n",
       "      <td>0.028513</td>\n",
       "      <td>0.028513</td>\n",
       "      <td>0.032390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.822064</td>\n",
       "      <td>0.028575</td>\n",
       "      <td>0.028575</td>\n",
       "      <td>0.032050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.821352</td>\n",
       "      <td>0.028670</td>\n",
       "      <td>0.028670</td>\n",
       "      <td>0.032038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.988612</td>\n",
       "      <td>0.824911</td>\n",
       "      <td>0.029790</td>\n",
       "      <td>0.029790</td>\n",
       "      <td>0.032443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.988612</td>\n",
       "      <td>0.824911</td>\n",
       "      <td>0.029946</td>\n",
       "      <td>0.029946</td>\n",
       "      <td>0.032443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.988612</td>\n",
       "      <td>0.824199</td>\n",
       "      <td>0.029911</td>\n",
       "      <td>0.029911</td>\n",
       "      <td>0.032058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.988612</td>\n",
       "      <td>0.824199</td>\n",
       "      <td>0.030019</td>\n",
       "      <td>0.030019</td>\n",
       "      <td>0.032220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.827758</td>\n",
       "      <td>0.029981</td>\n",
       "      <td>0.029981</td>\n",
       "      <td>0.032483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.826335</td>\n",
       "      <td>0.029735</td>\n",
       "      <td>0.029735</td>\n",
       "      <td>0.032734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.824911</td>\n",
       "      <td>0.029755</td>\n",
       "      <td>0.029755</td>\n",
       "      <td>0.032515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.824199</td>\n",
       "      <td>0.029926</td>\n",
       "      <td>0.029926</td>\n",
       "      <td>0.032568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.823488</td>\n",
       "      <td>0.030018</td>\n",
       "      <td>0.030018</td>\n",
       "      <td>0.032553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.987900</td>\n",
       "      <td>0.823488</td>\n",
       "      <td>0.030040</td>\n",
       "      <td>0.030040</td>\n",
       "      <td>0.032484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.987189</td>\n",
       "      <td>0.824911</td>\n",
       "      <td>0.030093</td>\n",
       "      <td>0.030093</td>\n",
       "      <td>0.032447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>False</td>\n",
       "      <td>0.04</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.986477</td>\n",
       "      <td>0.825623</td>\n",
       "      <td>0.030156</td>\n",
       "      <td>0.030156</td>\n",
       "      <td>0.032383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ratchet  withdrawal_rate  period_length  cash_cushion_target  \\\n",
       "114     True             0.04            360                    5   \n",
       "99      True             0.04            360                    5   \n",
       "119     True             0.04            360                    5   \n",
       "94      True             0.04            360                    5   \n",
       "79      True             0.04            360                    5   \n",
       "74      True             0.04            360                    5   \n",
       "93      True             0.04            360                    4   \n",
       "73      True             0.04            360                    4   \n",
       "113     True             0.04            360                    4   \n",
       "98      True             0.04            360                    4   \n",
       "78      True             0.04            360                    4   \n",
       "118     True             0.04            360                    4   \n",
       "88      True             0.04            360                    4   \n",
       "87      True             0.04            360                    3   \n",
       "67      True             0.04            360                    3   \n",
       "108     True             0.04            360                    4   \n",
       "107     True             0.04            360                    3   \n",
       "109     True             0.04            360                    5   \n",
       "92      True             0.04            360                    3   \n",
       "72      True             0.04            360                    3   \n",
       "68      True             0.04            360                    4   \n",
       "89      True             0.04            360                    5   \n",
       "112     True             0.04            360                    3   \n",
       "97      True             0.04            360                    3   \n",
       "77      True             0.04            360                    3   \n",
       "84      True             0.04            360                    5   \n",
       "69      True             0.04            360                    5   \n",
       "83      True             0.04            360                    4   \n",
       "104     True             0.04            360                    5   \n",
       "117     True             0.04            360                    3   \n",
       "103     True             0.04            360                    4   \n",
       "63      True             0.04            360                    4   \n",
       "64      True             0.04            360                    5   \n",
       "66      True             0.04            360                    2   \n",
       "62      True             0.04            360                    3   \n",
       "102     True             0.04            360                    3   \n",
       "82      True             0.04            360                    3   \n",
       "86      True             0.04            360                    2   \n",
       "61      True             0.04            360                    2   \n",
       "81      True             0.04            360                    2   \n",
       "106     True             0.04            360                    2   \n",
       "101     True             0.04            360                    2   \n",
       "71      True             0.04            360                    2   \n",
       "76      True             0.04            360                    2   \n",
       "91      True             0.04            360                    2   \n",
       "96      True             0.04            360                    2   \n",
       "111     True             0.04            360                    2   \n",
       "116     True             0.04            360                    2   \n",
       "60      True             0.04            360                    1   \n",
       "80      True             0.04            360                    1   \n",
       "65      True             0.04            360                    1   \n",
       "100     True             0.04            360                    1   \n",
       "70      True             0.04            360                    1   \n",
       "75      True             0.04            360                    1   \n",
       "90      True             0.04            360                    1   \n",
       "95      True             0.04            360                    1   \n",
       "85      True             0.04            360                    1   \n",
       "105     True             0.04            360                    1   \n",
       "110     True             0.04            360                    1   \n",
       "115     True             0.04            360                    1   \n",
       "43     False             0.04            360                    4   \n",
       "23     False             0.04            360                    4   \n",
       "48     False             0.04            360                    4   \n",
       "53     False             0.04            360                    4   \n",
       "58     False             0.04            360                    4   \n",
       "28     False             0.04            360                    4   \n",
       "33     False             0.04            360                    4   \n",
       "3      False             0.04            360                    4   \n",
       "38     False             0.04            360                    4   \n",
       "44     False             0.04            360                    5   \n",
       "8      False             0.04            360                    4   \n",
       "49     False             0.04            360                    5   \n",
       "59     False             0.04            360                    5   \n",
       "13     False             0.04            360                    4   \n",
       "24     False             0.04            360                    5   \n",
       "54     False             0.04            360                    5   \n",
       "18     False             0.04            360                    4   \n",
       "29     False             0.04            360                    5   \n",
       "34     False             0.04            360                    5   \n",
       "39     False             0.04            360                    5   \n",
       "19     False             0.04            360                    5   \n",
       "4      False             0.04            360                    5   \n",
       "9      False             0.04            360                    5   \n",
       "14     False             0.04            360                    5   \n",
       "22     False             0.04            360                    3   \n",
       "27     False             0.04            360                    3   \n",
       "2      False             0.04            360                    3   \n",
       "7      False             0.04            360                    3   \n",
       "32     False             0.04            360                    3   \n",
       "37     False             0.04            360                    3   \n",
       "12     False             0.04            360                    3   \n",
       "17     False             0.04            360                    3   \n",
       "42     False             0.04            360                    3   \n",
       "52     False             0.04            360                    3   \n",
       "57     False             0.04            360                    3   \n",
       "47     False             0.04            360                    3   \n",
       "1      False             0.04            360                    2   \n",
       "6      False             0.04            360                    2   \n",
       "11     False             0.04            360                    2   \n",
       "16     False             0.04            360                    2   \n",
       "21     False             0.04            360                    2   \n",
       "26     False             0.04            360                    2   \n",
       "31     False             0.04            360                    2   \n",
       "36     False             0.04            360                    2   \n",
       "41     False             0.04            360                    2   \n",
       "51     False             0.04            360                    2   \n",
       "56     False             0.04            360                    2   \n",
       "46     False             0.04            360                    2   \n",
       "10     False             0.04            360                    1   \n",
       "30     False             0.04            360                    1   \n",
       "15     False             0.04            360                    1   \n",
       "35     False             0.04            360                    1   \n",
       "20     False             0.04            360                    1   \n",
       "0      False             0.04            360                    1   \n",
       "5      False             0.04            360                    1   \n",
       "25     False             0.04            360                    1   \n",
       "45     False             0.04            360                    1   \n",
       "50     False             0.04            360                    1   \n",
       "55     False             0.04            360                    1   \n",
       "40     False             0.04            360                    1   \n",
       "\n",
       "     cash_use_threshold  rebuild_rate  survivability  sustainability  \\\n",
       "114                0.95          1.75       0.995018        0.517438   \n",
       "99                 0.90          2.00       0.995018        0.510320   \n",
       "119                0.95          2.00       0.994306        0.525267   \n",
       "94                 0.90          1.75       0.994306        0.501779   \n",
       "79                 0.85          2.00       0.994306        0.491815   \n",
       "74                 0.85          1.75       0.992883        0.487544   \n",
       "93                 0.90          1.75       0.992171        0.532384   \n",
       "73                 0.85          1.75       0.992171        0.518149   \n",
       "113                0.95          1.75       0.991459        0.542349   \n",
       "98                 0.90          2.00       0.990747        0.538790   \n",
       "78                 0.85          2.00       0.990747        0.523132   \n",
       "118                0.95          2.00       0.990036        0.540925   \n",
       "88                 0.90          1.50       0.987189        0.514591   \n",
       "87                 0.90          1.50       0.986477        0.538078   \n",
       "67                 0.85          1.50       0.986477        0.528114   \n",
       "108                0.95          1.50       0.986477        0.525267   \n",
       "107                0.95          1.50       0.985765        0.544484   \n",
       "109                0.95          1.50       0.985053        0.479715   \n",
       "92                 0.90          1.75       0.984342        0.530961   \n",
       "72                 0.85          1.75       0.984342        0.526690   \n",
       "68                 0.85          1.50       0.984342        0.504626   \n",
       "89                 0.90          1.50       0.984342        0.469039   \n",
       "112                0.95          1.75       0.982918        0.533808   \n",
       "97                 0.90          2.00       0.981495        0.523843   \n",
       "77                 0.85          2.00       0.981495        0.520285   \n",
       "84                 0.90          1.25       0.981495        0.473310   \n",
       "69                 0.85          1.50       0.980071        0.453381   \n",
       "83                 0.90          1.25       0.979359        0.491815   \n",
       "104                0.95          1.25       0.979359        0.482562   \n",
       "117                0.95          2.00       0.978648        0.522420   \n",
       "103                0.95          1.25       0.977936        0.498932   \n",
       "63                 0.85          1.25       0.977224        0.472598   \n",
       "64                 0.85          1.25       0.977224        0.444840   \n",
       "66                 0.85          1.50       0.974377        0.518149   \n",
       "62                 0.85          1.25       0.973665        0.498932   \n",
       "102                0.95          1.25       0.972954        0.513879   \n",
       "82                 0.90          1.25       0.972954        0.512456   \n",
       "86                 0.90          1.50       0.970819        0.520996   \n",
       "61                 0.85          1.25       0.970107        0.524555   \n",
       "81                 0.90          1.25       0.967972        0.524555   \n",
       "106                0.95          1.50       0.967260        0.520996   \n",
       "101                0.95          1.25       0.964413        0.520996   \n",
       "71                 0.85          1.75       0.963701        0.498932   \n",
       "76                 0.85          2.00       0.960854        0.500356   \n",
       "91                 0.90          1.75       0.959431        0.503915   \n",
       "96                 0.90          2.00       0.956584        0.503203   \n",
       "111                0.95          1.75       0.953737        0.500356   \n",
       "116                0.95          2.00       0.949466        0.500356   \n",
       "60                 0.85          1.25       0.938078        0.506050   \n",
       "80                 0.90          1.25       0.936655        0.503203   \n",
       "65                 0.85          1.50       0.933808        0.500356   \n",
       "100                0.95          1.25       0.933096        0.504626   \n",
       "70                 0.85          1.75       0.931673        0.505338   \n",
       "75                 0.85          2.00       0.931673        0.505338   \n",
       "90                 0.90          1.75       0.931673        0.503203   \n",
       "95                 0.90          2.00       0.930961        0.503915   \n",
       "85                 0.90          1.50       0.930249        0.503915   \n",
       "105                0.95          1.50       0.926690        0.503203   \n",
       "110                0.95          1.75       0.926690        0.502491   \n",
       "115                0.95          2.00       0.926690        0.501068   \n",
       "43                 0.95          1.25       0.998577        0.812811   \n",
       "23                 0.90          1.25       0.998577        0.807829   \n",
       "48                 0.95          1.50       0.998577        0.804270   \n",
       "53                 0.95          1.75       0.998577        0.804270   \n",
       "58                 0.95          2.00       0.998577        0.802847   \n",
       "28                 0.90          1.50       0.998577        0.801423   \n",
       "33                 0.90          1.75       0.998577        0.800712   \n",
       "3                  0.85          1.25       0.998577        0.796441   \n",
       "38                 0.90          2.00       0.998577        0.796441   \n",
       "44                 0.95          1.25       0.998577        0.796441   \n",
       "8                  0.85          1.50       0.998577        0.791459   \n",
       "49                 0.95          1.50       0.998577        0.788612   \n",
       "59                 0.95          2.00       0.998577        0.788612   \n",
       "13                 0.85          1.75       0.998577        0.787189   \n",
       "24                 0.90          1.25       0.998577        0.787189   \n",
       "54                 0.95          1.75       0.998577        0.787189   \n",
       "18                 0.85          2.00       0.998577        0.780783   \n",
       "29                 0.90          1.50       0.998577        0.780783   \n",
       "34                 0.90          1.75       0.998577        0.776512   \n",
       "39                 0.90          2.00       0.998577        0.774377   \n",
       "19                 0.85          2.00       0.998577        0.738078   \n",
       "4                  0.85          1.25       0.998577        0.736655   \n",
       "9                  0.85          1.50       0.997865        0.735943   \n",
       "14                 0.85          1.75       0.997865        0.734520   \n",
       "22                 0.90          1.25       0.997153        0.821352   \n",
       "27                 0.90          1.50       0.997153        0.818505   \n",
       "2                  0.85          1.25       0.997153        0.816370   \n",
       "7                  0.85          1.50       0.997153        0.813523   \n",
       "32                 0.90          1.75       0.997153        0.812100   \n",
       "37                 0.90          2.00       0.997153        0.812100   \n",
       "12                 0.85          1.75       0.997153        0.807117   \n",
       "17                 0.85          2.00       0.997153        0.806406   \n",
       "42                 0.95          1.25       0.996441        0.825623   \n",
       "52                 0.95          1.75       0.996441        0.815658   \n",
       "57                 0.95          2.00       0.996441        0.814947   \n",
       "47                 0.95          1.50       0.996441        0.814235   \n",
       "1                  0.85          1.25       0.993594        0.827758   \n",
       "6                  0.85          1.50       0.993594        0.820641   \n",
       "11                 0.85          1.75       0.993594        0.817794   \n",
       "16                 0.85          2.00       0.993594        0.814947   \n",
       "21                 0.90          1.25       0.992883        0.828470   \n",
       "26                 0.90          1.50       0.992883        0.823488   \n",
       "31                 0.90          1.75       0.992883        0.820641   \n",
       "36                 0.90          2.00       0.992883        0.820641   \n",
       "41                 0.95          1.25       0.991459        0.829893   \n",
       "51                 0.95          1.75       0.991459        0.822064   \n",
       "56                 0.95          2.00       0.991459        0.822064   \n",
       "46                 0.95          1.50       0.991459        0.821352   \n",
       "10                 0.85          1.75       0.988612        0.824911   \n",
       "30                 0.90          1.75       0.988612        0.824911   \n",
       "15                 0.85          2.00       0.988612        0.824199   \n",
       "35                 0.90          2.00       0.988612        0.824199   \n",
       "20                 0.90          1.25       0.987900        0.827758   \n",
       "0                  0.85          1.25       0.987900        0.826335   \n",
       "5                  0.85          1.50       0.987900        0.824911   \n",
       "25                 0.90          1.50       0.987900        0.824199   \n",
       "45                 0.95          1.50       0.987900        0.823488   \n",
       "50                 0.95          1.75       0.987900        0.823488   \n",
       "55                 0.95          2.00       0.987189        0.824911   \n",
       "40                 0.95          1.25       0.986477        0.825623   \n",
       "\n",
       "     balance_growth_mean  balance_growth_mean  balance_growth_stdev  \n",
       "114            -0.003102            -0.003102              0.024118  \n",
       "99             -0.003514            -0.003514              0.023787  \n",
       "119            -0.002333            -0.002333              0.023334  \n",
       "94             -0.004287            -0.004287              0.024119  \n",
       "79             -0.005808            -0.005808              0.025571  \n",
       "74             -0.006334            -0.006334              0.025603  \n",
       "93             -0.001137            -0.001137              0.023701  \n",
       "73             -0.002544            -0.002544              0.024629  \n",
       "113            -0.000556            -0.000556              0.023736  \n",
       "98             -0.001084            -0.001084              0.024147  \n",
       "78             -0.002293            -0.002293              0.024312  \n",
       "118            -0.000545            -0.000545              0.024262  \n",
       "88             -0.002301            -0.002301              0.024677  \n",
       "87              0.000109             0.000109              0.025057  \n",
       "67             -0.000415            -0.000415              0.024442  \n",
       "108            -0.001572            -0.001572              0.024266  \n",
       "107             0.000174             0.000174              0.025408  \n",
       "109            -0.004048            -0.004048              0.024120  \n",
       "92              0.000259             0.000259              0.024760  \n",
       "72             -0.000297            -0.000297              0.024547  \n",
       "68             -0.003112            -0.003112              0.024377  \n",
       "89             -0.005323            -0.005323              0.024744  \n",
       "112             0.000252             0.000252              0.025391  \n",
       "97             -0.000240            -0.000240              0.025216  \n",
       "77             -0.000707            -0.000707              0.025181  \n",
       "84             -0.004800            -0.004800              0.024970  \n",
       "69             -0.007224            -0.007224              0.025908  \n",
       "83             -0.002878            -0.002878              0.025214  \n",
       "104            -0.004039            -0.004039              0.024838  \n",
       "117            -0.000060            -0.000060              0.025574  \n",
       "103            -0.002441            -0.002441              0.025121  \n",
       "63             -0.003959            -0.003959              0.024987  \n",
       "64             -0.007007            -0.007007              0.025574  \n",
       "66              0.000135             0.000135              0.026932  \n",
       "62             -0.001756            -0.001756              0.025853  \n",
       "102            -0.000900            -0.000900              0.026608  \n",
       "82             -0.000721            -0.000721              0.025782  \n",
       "86              0.000284             0.000284              0.027276  \n",
       "61             -0.000014            -0.000014              0.027362  \n",
       "81              0.000185             0.000185              0.027828  \n",
       "106             0.000416             0.000416              0.027419  \n",
       "101             0.000222             0.000222              0.027763  \n",
       "71             -0.000662            -0.000662              0.028176  \n",
       "76             -0.000719            -0.000719              0.028511  \n",
       "91             -0.000491            -0.000491              0.028396  \n",
       "96             -0.000398            -0.000398              0.028591  \n",
       "111            -0.000993            -0.000993              0.029954  \n",
       "116            -0.000586            -0.000586              0.029283  \n",
       "60             -0.000347            -0.000347              0.031146  \n",
       "80             -0.000275            -0.000275              0.031234  \n",
       "65             -0.000647            -0.000647              0.031662  \n",
       "100             0.000024             0.000024              0.030653  \n",
       "70             -0.000043            -0.000043              0.030940  \n",
       "75              0.000132             0.000132              0.030477  \n",
       "90             -0.000148            -0.000148              0.031073  \n",
       "95              0.000222             0.000222              0.030343  \n",
       "85             -0.000117            -0.000117              0.030911  \n",
       "105             0.000301             0.000301              0.030445  \n",
       "110             0.000238             0.000238              0.030552  \n",
       "115            -0.000007            -0.000007              0.031191  \n",
       "43              0.026060             0.026060              0.030480  \n",
       "23              0.025370             0.025370              0.030381  \n",
       "48              0.025521             0.025521              0.030854  \n",
       "53              0.025305             0.025305              0.030943  \n",
       "58              0.025006             0.025006              0.031023  \n",
       "28              0.024878             0.024878              0.030819  \n",
       "33              0.024565             0.024565              0.030939  \n",
       "3               0.024122             0.024122              0.030636  \n",
       "38              0.024210             0.024210              0.031179  \n",
       "44              0.024431             0.024431              0.029675  \n",
       "8               0.023575             0.023575              0.031187  \n",
       "49              0.023450             0.023450              0.030527  \n",
       "59              0.022759             0.022759              0.030992  \n",
       "13              0.023244             0.023244              0.031374  \n",
       "24              0.023213             0.023213              0.029860  \n",
       "54              0.023166             0.023166              0.030759  \n",
       "18              0.022846             0.022846              0.031732  \n",
       "29              0.022382             0.022382              0.030755  \n",
       "34              0.021876             0.021876              0.031239  \n",
       "39              0.021508             0.021508              0.031404  \n",
       "19              0.019181             0.019181              0.033389  \n",
       "4               0.020819             0.020819              0.031589  \n",
       "9               0.020200             0.020200              0.032347  \n",
       "14              0.019609             0.019609              0.032959  \n",
       "22              0.027259             0.027259              0.031012  \n",
       "27              0.027085             0.027085              0.031014  \n",
       "2               0.026462             0.026462              0.031042  \n",
       "7               0.026239             0.026239              0.031073  \n",
       "32              0.026653             0.026653              0.031254  \n",
       "37              0.026485             0.026485              0.031334  \n",
       "12              0.025767             0.025767              0.031431  \n",
       "17              0.025555             0.025555              0.031696  \n",
       "42              0.027615             0.027615              0.031278  \n",
       "52              0.027114             0.027114              0.031386  \n",
       "57              0.026951             0.026951              0.031413  \n",
       "47              0.027366             0.027366              0.031335  \n",
       "1               0.028230             0.028230              0.032222  \n",
       "6               0.027804             0.027804              0.032334  \n",
       "11              0.027654             0.027654              0.032491  \n",
       "16              0.027617             0.027617              0.032500  \n",
       "21              0.028776             0.028776              0.032126  \n",
       "26              0.028400             0.028400              0.032185  \n",
       "31              0.028323             0.028323              0.032159  \n",
       "36              0.028290             0.028290              0.032160  \n",
       "41              0.029005             0.029005              0.032017  \n",
       "51              0.028513             0.028513              0.032390  \n",
       "56              0.028575             0.028575              0.032050  \n",
       "46              0.028670             0.028670              0.032038  \n",
       "10              0.029790             0.029790              0.032443  \n",
       "30              0.029946             0.029946              0.032443  \n",
       "15              0.029911             0.029911              0.032058  \n",
       "35              0.030019             0.030019              0.032220  \n",
       "20              0.029981             0.029981              0.032483  \n",
       "0               0.029735             0.029735              0.032734  \n",
       "5               0.029755             0.029755              0.032515  \n",
       "25              0.029926             0.029926              0.032568  \n",
       "45              0.030018             0.030018              0.032553  \n",
       "50              0.030040             0.030040              0.032484  \n",
       "55              0.030093             0.030093              0.032447  \n",
       "40              0.030156             0.030156              0.032383  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values(by=['ratchet', 'survivability', 'sustainability'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sustainability and Ending Values\n",
    "\n",
    "How are the ending values distributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.36%\n",
      "sustainability: 83.06%\n",
      "balance     median:  3.64%  mean:  3.11%  stdev:  3.30%\n",
      "withdrawal  median: -0.07%  mean: -0.07%  stdev:  0.13%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio()\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11eab2860>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAY2pa5A/D1Y+8d+3PxZdfNHSNXXrh17ggrxfMCAMDo7LEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNA2VWyr6pGLDgIAAABfi83usf2dqnpXVf0fVXXKQhMBAADAMdhUse3u707yo0nOSvKeqnpNVf3AQpMBAADAJmz6O7bd/ZEkv5TkF5J8X5LfrKoPVdU/XlQ4AAAA2Mhmv2P7D6rqZUluTfL4JP+ou79jmn7ZAvMBAADAUW3Z5Hr/b5JXJHlBd3/x4MLuvrOqfmkhyQAAAGATNltsn5Tki939lSSpqm9I8oDu/kJ3v2ph6QAAAGADm/2O7VuSnLxu/oHTMgAAAJjVZovtA7r7wMGZafqBi4kEAAAAm7fZYvv5qjrv4ExVnZ/ki0dZHwAAAJZis9+x/bkkv19Vd07zpyf5XxcTCQAAADZvU8W2u99dVf9jkocnqSQf6u6/WWgyAAAA2ITN7rFNku9KcvZ0m8dUVbr7dxeSCgAAADZpU8W2ql6V5NuSvC/JV6bFnUSxBQAAYFab3WO7I8kjursXGQYAAACO1WbPinxzkr+7yCAAAADwtdjsHtvTknywqt6V5EsHF3b3kxeSCgAAADZps8X2hYsMAQAAAF+rzf65n7dV1d9Lcm53v6WqHpjkpMVGAwAAgI1t6ju2VfVTSf4gyb+dFp2R5A8XFQoAAAA2a7Mnj3pOkscl+WySdPdHkjx0UaEAAABgszZbbL/U3V8+OFNVW7L2d2wBAABgVpsttm+rqhckObmqfiDJ7yf5o8XFAgAAgM3ZbLG9LMlfJdmb5KeTXJ/klxYVCgAAADZrs2dF/tskr5guAAAAsDI2VWyr6mM5zHdqu/tbj3siAAAAOAabKrZJdqybfkCSpyV5yPGPAwAAAMdmU9+x7e6/Xne5o7v/dZLHLzgbAAAAbGizhyKft272G7K2B/dBC0kEAAAAx2CzhyK/ZN30vUluT/Ijxz0NAAAAHKPNnhV516KDAAAAwNdis4ci/7OjXd/dLz0+cQAAAODYHMtZkb8rybXT/D9K8vYkn1hEKAAAANiszRbb05Kc192fS5KqemGS3+/un1xUMAAAANiMTf25nyTfkuTL6+a/nOTs454GAAAAjtFm99i+Ksm7quqNSTrJU5P87sJSAQAAwCZt9qzIL6qq/5Dke6ZFP9Hdf7a4WAAAALA5mz0UOUkemOSz3f3/JNlXVecsKBMAAABs2qaKbVX9SpJfSPL8adH9krx6UaEAAABgsza7x/apSZ6c5PNJ0t13JnnQokIBAADAZm325FFf7u6uqk6Sqtq60Q2q6pVJfjjJPd39yGnZC5P8VJK/mlZ7QXdfP133/CTPTvKVJD/b3X98LP8Q2Ky9d+zPxZddN3eMXHnhhv+NAACATdjsHtvXVdW/TXJKVf1UkrckecUGt7kyyYWHWf6y7n70dDlYah+R5OlJ/v50m39TVSdtMhsAAABfxzZ7VuTfqKofSPLZJA9P8svdfcMGt3l7VZ29yRwXJdnd3V9K8rGqui3JBUn+dJO3BwAA4OvUhsV22nP6x939/UmOWmY36blV9eNJ3pPk0u7+dJIzkrxj3Tr7pmUAAABwVNXdG69UdW2SH+vu/cd052t7bN+07ju225N8Mkkn+bUkp3f3s6rqt5L8aXe/elrviiTXd/frD3OflyS5JEm2b99+/u7du48l0tLd86n9ufuLc6dItp+c2XOsQoZVynHOg0/Ktm3b5o6xMq/RVXk8Dhw4MHuOVcgghxyrnkEOOUbIsQoZ5JBj1TNsZNeuXTd1946N1tvsyaP+S5K9VXVDpjMjJ0l3/+yxhOruuw9OV9Urkrxpmt2X5Kx1q56Z5M4j3MflSS5Pkh07dvTOnTuPJcLSvfzqa/KSvZt9mBfn0kfdO3uOVciwSjmuvHBrVuH1uyqv0VV5PPbs2TN7jlXIIIccq55BDjlGyLEKGeSQY9UzHC+bfTd73XS5T6rq9O6+a5p9apKbp+lrk7ymql6a5GFJzk3yrvu6PQAAAE58Ry22VfUt3f3x7r7qWO+4ql6bZGeS06pqX5JfSbKzqh6dtUORb0/y00nS3bdU1euSfDDJvUme091fOdZtAgAA8PVnoz22f5jkvCSpqtd39z/Z7B139zMOs/iKo6z/oiQv2uz9AwAAQLLx37GtddPfusggAAAA8LXYqNj2EaYBAABgJWx0KPJ3VtVns7bn9uRpOtN8d/c3LjQdAAAAbOCoxba7T1pWEAAAAPhabHQoMgAAAKw0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaFvmDgCQJHvv2J+LL7tu7hi58sKtc0cAAOAY2WMLAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhrZl7gDw9WrvHftz8WXXzR0jlz5q7gQAAHDf2GMLAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBoii0AAABDU2wBAAAYmmILAADA0BRbAAAAhrawYltVr6yqe6rq5nXLHlJVN1TVR6afp07Lq6p+s6puq6oPVNV5i8oFAADAiWWRe2yvTHLhIcsuS3Jjd5+b5MZpPkmemOTc6XJJkt9eYC4AAABOIAsrtt399iSfOmTxRUmumqavSvKUdct/t9e8I8kpVXX6orIBAABw4qjuXtydV52d5E3d/chp/jPdfcq66z/d3adW1ZuSvLi7/2RafmOSX+ju9xzmPi/J2l7dbN++/fzdu3cvLP/xcM+n9ufuL86dItl+cmbPsQoZ5JBjI+c8+KRs27Zt1gwHDhyYPYMccqx6BjnkGCHHKmSQQ45Vz7CRXbt23dTdOzZab8sywmxCHWbZYRt3d1+e5PIk2bFjR+/cuXOBse67l199TV6yd/6H+dJH3Tt7jlXIIIccG7nywq2Ze1zZs2fP7BnkkGPVM8ghxwg5ViGDHHKseobjZdlnRb774CHG0897puX7kpy1br0zk9y55GwAAAAMaNnF9tokz5ymn5nkmnXLf3w6O/Jjk+zv7ruWnA0AAIABLey4v6p6bZKdSU6rqn1JfiXJi5O8rqqeneTjSZ42rX59kicluS3JF5L8xKJyAQAAcGJZWLHt7mcc4aonHGbdTvKcRWUBAADgxLXsQ5EBAADguFJsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGJpiCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ1NsAQAAGNqWuQMArJK9d+zPxZddN2uGKy/cOuv2AQBGY48tAAAAQ1NsAQAAGNoshyJX1e1JPpfkK0nu7e4dVfWQJL+X5Owktyf5ke7+9Bz5AAAAGMece2x3dfeju3vHNH9Zkhu7+9wkN07zAAAAcFSrdCjyRUmumqavSvKUGbMAAAAwiLmKbSd5c1XdVFWXTMu2d/ddSTL9fOhM2QAAABhIdffyN1r1sO6+s6oemuSGJD+T5NruPmXdOp/u7lMPc9tLklySJNu3bz9/9+7dy4r9NbnnU/tz9xfnTpFsPzmz51iFDHLIMUKOcx58UrZt2zZviCQHDhyQQ46VzSCHHCPkWIUMcsix6hk2smvXrpvWfX31iGY5eVR33zn9vKeq3pjkgiR3V9Xp3X1XVZ2e5J4j3PbyJJcnyY4dO3rnzp1LSv21efnV1+Qle+f/c8GXPure2XOsQgY55Bghx5UXbs0qjG179uyRQ46VzSCHHCPkWIUMcsix6hmOl6UfilxVW6vqQQenk/xgkpuTXJvkmdNqz0xyzbKzAQAAMJ45dktsT/LGqjq4/dd093+sqncneV1VPTvJx5M8bYZsAAAADGbpxba7/yLJdx5m+V8necKy8wAAADC2VfpzPwAAAHDMFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGhb5g4AwFfbe8f+XHzZdXPHyJUXbp07AgDApthjCwAAwNAUWwAAAIam2AIAADA0xRYAAIChKbYAAAAMTbEFAABgaIotAAAAQ/N3bAE4LH9PFwAYhT22AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNH/HFoCV5u/pAgAbsccWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwNMUWAACAoSm2AAAADE2xBQAAYGiKLQAAAENTbAEAABiaYgsAAMDQFFsAAACGptgCAAAwtC1zBwCAEey9Y38uvuy6uWPkygu3zh0BAFaOPbYAAAAMTbEFAABgaIotAAAAQ/MdWwAYyCp819f3fAHI56clAAAKgUlEQVRYNfbYAgAAMLSVK7ZVdWFVfbiqbquqy+bOAwAAwGpbqWJbVScl+a0kT0zyiCTPqKpHzJsKAACAVbZq37G9IMlt3f0XSVJVu5NclOSDs6YCAP6rVfieb7I63/X1eKymVXheLn3UvbNnSLw2DrUKr43E83K8rdQe2yRnJPnEuvl90zIAAAA4rOruuTP8V1X1tCQ/1N0/Oc3/WJILuvtn1q1zSZJLptmHJ/nw0oMem9OSfHLuEFmNHKuQIZHjUHJ8tVXIsQoZEjkOJcdqZUjkOJQcX20VcqxChkSOQ8mxWhk28ve6+5s3WmnVDkXel+SsdfNnJrlz/QrdfXmSy5cZ6r6oqvd09w45ViODHHKMkGMVMsghx6pnkEOOEXKsQgY55Fj1DMfLqh2K/O4k51bVOVV1/yRPT3LtzJkAAABYYSu1x7a7762q5yb54yQnJXlld98ycywAAABW2EoV2yTp7uuTXD93juNoVQ6bXoUcq5AhkeNQcny1VcixChkSOQ4lx3+zChkSOQ4lx1dbhRyrkCGR41By/DerkOG4WKmTRwEAAMCxWrXv2AIAAMAxUWwXpKourKoPV9VtVXXZjDleWVX3VNXNM2Y4q6reWlW3VtUtVfW8mXI8oKreVVXvn3L86hw5piwnVdWfVdWbZsxwe1Xtrar3VdV7ZsxxSlX9QVV9aHqN/E8zZHj49DgcvHy2qn5u2TmmLD8/vT5vrqrXVtUDZsrxvCnDLct8LA43ZlXVQ6rqhqr6yPTz1JlyPG16PP62qhZ+BskjZPhX0/+VD1TVG6vqlJly/NqU4X1V9eaqetgcOdZd98+rqqvqtDlyVNULq+qOdWPIk+bIMS3/men9xy1V9S+XnaGqfm/d43B7Vb1vkRmOkuPRVfWOg7/jquqCmXJ8Z1X96fT79o+q6huXkOOw77uWOZYeJcOyx9Ej5VjqWHqUHEsdS4+UY931SxtLF6K7XY7zJWsnvvpokm9Ncv8k70/yiJmyfG+S85LcPOPjcXqS86bpByX58zkejySVZNs0fb8k70zy2Jkek3+W5DVJ3jTj83J7ktPm2v66HFcl+clp+v5JTpk5z0lJ/jJrfzNt2ds+I8nHkpw8zb8uycUz5HhkkpuTPDBr52J4S5Jzl7Tt/27MSvIvk1w2TV+W5NdnyvEdWfv76XuS7Jgpww8m2TJN//qMj8U3rpv+2SS/M0eOaflZWTvp5H9exph2hMfjhUn++aK3vYkcu6b/r39nmn/oHM/JuutfkuSXZ3os3pzkidP0k5LsmSnHu5N83zT9rCS/toQch33ftcyx9CgZlj2OHinHUsfSo+RY6lh6pBzT/FLH0kVc7LFdjAuS3Nbdf9HdX06yO8lFcwTp7rcn+dQc216X4a7ufu80/bkkt2btDfyyc3R3H5hm7zddlv4l86o6M8k/TPLvlr3tVTN9cv29Sa5Iku7+cnd/Zt5UeUKSj3b3f55p+1uSnFxVW7JWLO/cYP1F+I4k7+juL3T3vUneluSpy9jwEcasi7L2AUimn0+ZI0d339rdH170tjfI8ObpOUmSd2Tt773PkeOz62a3Zglj6VF+n70syb9YRoYNcizVEXL80yQv7u4vTevcM0OGJElVVZIfSfLaRWY4So5OcnDv6IOzhLH0CDkenuTt0/QNSf7JEnIc6X3X0sbSI2WYYRw9Uo6ljqVHybHUsXSD9+RLHUsXQbFdjDOSfGLd/L7MUORWUVWdneQxWdtbOsf2T5oOi7onyQ3dPUeOf521geNvZ9j2ep3kzVV1U1VdMlOGb03yV0n+fa0dmv3vqmrrTFkOenqW8EbscLr7jiS/keTjSe5Ksr+73zxDlJuTfG9VfVNVPTBrezvOmiHHQdu7+65k7ZdykofOmGWVPCvJf5hr41X1oqr6RJIfTfLLM2V4cpI7uvv9c2z/EM+dDil85SIP8dzAtyf5nqp6Z1W9raq+a6YcSfI9Se7u7o/MtP2fS/KvptfobyR5/kw5bk7y5Gn6aVnyWHrI+65ZxtK53/ttIsdSx9JDc8w1lq7PsWJj6ddMsV2MOsyyYT/9OF6qaluS1yf5uUM+oVqa7v5Kdz86a5/MXVBVj1zm9qvqh5Pc0903LXO7R/C47j4vyROTPKeqvneGDFuydtjWb3f3Y5J8PmuHR82iqu6ftTcgvz/T9k/N2ifq5yR5WJKtVfW/LTtHd9+atUOzbkjyH7P2dYp7j3ojlqqqfjFrz8nVc2Xo7l/s7rOmDM9d9vanD11+MTOV6kP8dpJvS/LorH0o9ZKZcmxJcmqSxyb5P5O8btpzOodnZKYPCSf/NMnPT6/Rn890ZNAMnpW137E3Ze3Qzy8va8Or8L5rFTIcLceyx9LD5ZhjLF2fI2v//lUZS+8TxXYx9uWrP5E7M/McTrgyqup+WfsPdHV3v2HuPNPhrnuSXLjkTT8uyZOr6vasHaL++Kp69ZIzJEm6+87p5z1J3pi1Q+iXbV+Sfev2nP9B1oruXJ6Y5L3dffdM2//+JB/r7r/q7r9J8oYk//McQbr7iu4+r7u/N2uH1s211yVJ7q6q05Nk+rnQwytXXVU9M8kPJ/nR7l6FD01fkyUcXnkY35a1D4HeP42pZyZ5b1X93WUH6e67pw9O/zbJKzLPeJqsjalvmL56866sHRm09JPATF+l+MdJfm/Z217nmVkbQ5O1DytneU66+0Pd/YPdfX7Wiv5Hl7HdI7zvWupYuirv/Y6UY9lj6SYej6WMpYfJsTJj6X2l2C7Gu5OcW1XnTHuAnp7k2pkzzWb6tPiKJLd290tnzPHNB896V1UnZ61EfGiZGbr7+d19ZnefnbXXxX/q7qXvkauqrVX1oIPTWTuJwtLPnN3df5nkE1X18GnRE5J8cNk51pl7D8PHkzy2qh44/b95Qta+/7J0VfXQ6ee3ZO0N6pyPy7VZe5Oa6ec1M2aZVVVdmOQXkjy5u78wY45z180+OUseS5Oku/d290O7++xpTN2XtZOi/OWysxwsC5OnZobxdPKHSR6fJFX17Vk7Id8nZ8jx/Uk+1N37Ztj2QXcm+b5p+vGZ6cO5dWPpNyT5pSS/s4RtHul919LG0hV673fYHMseS4+SY6lj6eFyrNJYep/1CpzB6kS8ZO07aX+etU/mfnHGHK/N2mFRf5O1F+qzZ8jw3Vk7FPsDSd43XZ40Q45/kOTPphw3Zwlnatwgz87MdFbkrH239f3T5ZaZX6OPTvKe6Xn5wySnzpTjgUn+OsmDZ35d/GrWfrHdnORVmc5uOkOO/y9rHzK8P8kTlrjd/27MSvJNSW7M2hvTG5M8ZKYcT52mv5Tk7iR/PEOG27J2DoeDY+kyzkZ8uByvn16jH0jyR1k7CcrScxxy/e1ZzlmRD/d4vCrJ3unxuDbJ6TPluH+SV0/PzXuTPH6O5yTJlUn+90U/Bhs8Ft+d5KZpDHtnkvNnyvG8rL0f/PMkL05SS8hx2PddyxxLj5Jh2ePokXIsdSw9So6ljqVHynHIOksZSxdxqekfAAAAAENyKDIAAABDU2wBAAAYmmILAADA0BRbAAAAhqbYAgAAMDTFFgAAgKEptgAAAAxNsQUAAGBo/z/+wz/GhiwYtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ending = pd.Series(data=[period.last_real_fraction for period in periods])\n",
    "ending.plot.hist(bins=range(25), figsize=(16,12), xticks=range(25), grid=True)  # Bins should really be 0.0 to 5.0 by 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survivability:  98.65%\n",
      "sustainability: 53.81%\n",
      "balance     median:  0.18%  mean:  0.01%  stdev:  2.51%\n",
      "withdrawal  median:  2.05%  mean:  2.28%  stdev:  1.50%\n"
     ]
    }
   ],
   "source": [
    "portfolio = Portfolio(cash_cushion=True, ratchet=True)\n",
    "\n",
    "(survivability, sustainability,\n",
    "balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "periods) = portfolio.sim_periods(market_data)\n",
    "\n",
    "print(\"survivability:  {:.2%}\".format(survivability))\n",
    "print(\"sustainability: {:.2%}\".format(sustainability))\n",
    "print(\"balance     median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(balance_growth_median, balance_growth_mean, balance_growth_stdev))\n",
    "print(\"withdrawal  median: {:6.2%}  mean: {:6.2%}  stdev: {:6.2%}\".format(withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11da355c0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ending = pd.Series(data=[period.last_real_fraction for period in periods])\n",
    "ending.plot.hist(bins=list(i/10 for i in range(51)), figsize=(20,12), xticks=list(i/10 for i in range(51)), grid=True)  # Bins should really be 0.0 to 5.0 by 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dc25160>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ending = pd.Series(data=[period.last_real_fraction for period in periods])\n",
    "ending.hist(bins=50, cumulative=True)  # Bins should really be 0.0 to 5.0 by 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is causing the ending values significantly larger than 1.0?  The ratchet mechanism should have caused withdrawals to increase to drain the portfolio back down to a value equal to the starting value (adjusted for inflation).\n",
    "\n",
    "Oh, I think I see.  It's because the withdrawal rate (and ratchet_to_rate) stays constant as the time remaining goes to zero.  If the market keeps growing consistently faster than the nominal withdrawal rate, could the portfolio grow like this?\n",
    "\n",
    "Or is this just keeping in line with the compounding growth of withdrawals?  After all, a larger withdrawal is supported by a larger portfolio balance.  2.28% compounded over 30 years is 96.66% growth (or an ending factor of 1.9666)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TODO** It would be interesting to look at the non-sustained periods and see how much withdrawals grew.\n",
    "\n",
    "It would be interesting to plot last_real_fraction vs. withdrawal_rate_real.  It should be sufficient to graph growth_rate_real vs. withdrawal_rate_real.  Probably a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('date',\n",
       " 'survived',\n",
       " 'sustained',\n",
       " 'min_real',\n",
       " 'max_real',\n",
       " 'last_real',\n",
       " 'last_real_fraction',\n",
       " 'growth_rate_real',\n",
       " 'withdrawal_rate_real',\n",
       " 'last_withdrawal_rate',\n",
       " 'history')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Period._fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_records(periods, columns=Period._fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['color'] = 'b'\n",
    "df.loc[~df.survived, 'color'] = 'r'\n",
    "df.loc[df.growth_rate_real >= 0.0, 'color'] = 'g'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do I get it to show a legend for the colors?\n",
    "\n",
    "I think I would have to do multiple plots using the same axes object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[df.color == 'r'].plot.scatter(x='growth_rate_real', y='withdrawal_rate_real', figsize=(12,9), grid=True,\n",
    "                c='r', label='Failed')\n",
    "ax = df[df.color == 'b'].plot.scatter(x='growth_rate_real', y='withdrawal_rate_real', figsize=(12,9), grid=True,\n",
    "                ax=ax, c='b', label='Survived')\n",
    "ax = df[df.color == 'g'].plot.scatter(x='growth_rate_real', y='withdrawal_rate_real', figsize=(12,9), grid=True,\n",
    "                ax=ax, c='g', label='Thrived')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f59c780>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.growth_rate_real.plot.hist(bins=100, grid=True, figsize=(12,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = df.groupby('color')\n",
    "h = pd.DataFrame({\"Failed\": g.get_group('r').growth_rate_real,\n",
    "                  \"Survived\": g.get_group('b').growth_rate_real,\n",
    "                  \"Thrived\": g.get_group('g').growth_rate_real})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.215, -0.21 , -0.205, -0.2  , -0.195, -0.19 , -0.185, -0.18 ,\n",
       "       -0.175, -0.17 , -0.165, -0.16 , -0.155, -0.15 , -0.145, -0.14 ,\n",
       "       -0.135, -0.13 , -0.125, -0.12 , -0.115, -0.11 , -0.105, -0.1  ,\n",
       "       -0.095, -0.09 , -0.085, -0.08 , -0.075, -0.07 , -0.065, -0.06 ,\n",
       "       -0.055, -0.05 , -0.045, -0.04 , -0.035, -0.03 , -0.025, -0.02 ,\n",
       "       -0.015, -0.01 , -0.005,  0.   ,  0.005,  0.01 ,  0.015,  0.02 ,\n",
       "        0.025,  0.03 ,  0.035,  0.04 ,  0.045,  0.05 ,  0.055])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(-0.215, 0.055, num=55, endpoint=True)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1204426a0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qznaSvDTJvM5jWZL/p4d1AQBAT/QsXNdav5rkZ5vtfnmSizrPL0ryinH7P1nHfDPJjFLKrF7VBgAAvdDvNdcza62rkqTz61M6+w9O8sNx41Z29gEAwNAotdbeHbyUOUm+UGs9orO9utY6Y9zrP6+1PrGU8s9J/rLW+rXO/quS/Nda6+gWjrksY0tHMnPmzIXLly/vWf2T3bp16zJ9+vRBl8FO0MPhp4fDTw/7Y/Rxf6Nv3cKF3Y/tZf9GV02g6AlYOGsCH3A3MCzn4OLFi0drrYu2N25qP4oZ50ellFm11lWdZR8/7uxfmeSQceNmJ7lvSweotV6Q5IIkWbRoUR0ZGelhuZPbihUrsjt//l2BHg4/PRx+etgfixd3P3Yi83697N/icyZQ9ATU03o3sTmMdrVzsN/LQq5Icnrn+elJLh+3/1Wdq4Y8L8majctHAABgWPRs5rqU8vdJRpIcVEpZmeSsJO9Lcmkp5XVJ7k1ycmf4F5O8LMmdSX6Z5DW9qgsAAHqlZ+G61nraVl564RbG1iRv7lUtAADQD+7QCAAAjQjXAADQiHANAACNCNcAANCIcA0AAI0I1wAA0IhwDQAAjQjXAADQiHANAACNCNcAANCIcA0AAI1MHXQBAMBwKmXQFcDkY+YaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAamTroAgAAdtToqtEsPmdx1+PrWbWH1XSnnFMmNH4y1Ez3zFwDAEAjwjUAADQiXAMAQCPCNQAANCJcAwBAI8I1AAA0IlwDAEAjwjUAADQiXAMAQCPCNQAANCJcAwBAI8I1AAA0IlwDAEAjwjUAADQiXAMAQCPCNQAANCJcAwBAI8I1AAA0IlwDAEAjwjUAADQiXAMAQCPCNQAANCJcAwBAI8I1AAA0IlwDAEAjwjUAADQiXAMAQCNTB10AAEC/lHPKoEtgF2fmGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAWAXVkr3j8liGGuGjYRrAABoRLgGAIBGhGsAAGhEuAYAgEYGEq5LKf9XKeWWUsrNpZS/L6VMK6XMLaVcV0q5o5RySSllz0HUBgAAO6rv4bqUcnCStyVZVGs9IsmUJKcm+ask59Va5yX5eZLX9bs2AADYGYNaFjI1yd6llKlJ9kmyKslxSS7rvH5RklcMqDYAANghpdba/x9ayhlJzk3yYJIvJzkjyTdrrc/qvH5Ikn/pzGxv/t5lSZYlycyZMxcuX768b3VPNuvWrcv06dMHXQY7QQ+Hnx4Ov129h6Ojg66gt2bP/VFWPrxy0GX01MJZCwddQk8Nyzm4ePHi0Vrrou2Nm9qPYsYrpTwxycuTzE2yOslnk7x0C0O3mPprrRckuSBJFi1aVEdGRnpT6BBYsWJFdufPvyvQw+Gnh8NvV+/h4sWDrqC3PvCZv86Zt5856DJ6qp7W/4nQftrVzsFBLAs5Psndtdaf1FrXJ/mHJL+VZEZnmUiSzE5y3wBqAwCAHTaIcH1vkueVUvYppZQkL0zyvSRXJzmpM+b0JJcPoDYAANhhfQ/XtdbrMvbFxW8nualTwwVJ3pHk7aWUO5McmORj/a4NAAB2Rt/XXCdJrfWsJGdttvuuJMcMoBwAAGjCHRoBAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKCRqYMuAACArSvnlK7H1rNqDyuhG2auAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKCRrsJ1KeWIXhcCAADDrtuZ64+UUr5VSnlTKWVGTysCAIAh1VW4rrX+dpI/SHJIkutLKZ8ppbyop5UBAMCQ6XrNda31jiR/luQdSV6Q5EOllO+XUn6/V8UBAMAw6XbN9fxSynlJbk1yXJLfq7U+u/P8vB7WBwAAQ2Nql+P+R5K/S/KntdYHN+6std5XSvmznlQGAABDpttw/bIkD9ZaH02SUsoTkkyrtf6y1vqpnlUHAABDpNs11/+WZO9x2/t09gEAAB3dzlxPq7Wu27hRa11XStmnRzUBALADyjml67H1rNrDSnZf3c5cP1BKOWrjRillYZIHtzEeAAB2O93OXP9Rks+WUu7rbM9KsrQ3JQEAwHDqKlzXWv+9lPKfkhyapCT5fq11fU8rAwCAIdPtzHWSHJ1kTuc9R5ZSUmv9ZE+qAgCAIdRVuC6lfCrJM5PckOTRzu6aRLgGAICObmeuFyU5rNbqa6UAALAV3V4t5OYkT+1lIQAAMOy6nbk+KMn3SinfSvLwxp211iU9qQoAgJ6ayDWxE9fF7la34frsXhYBAAC7gm4vxXdNKeXpSebVWv+tc3fGKb0tDQAAhktXa65LKa9PclmS8zu7Dk7yj70qCgAAhlG3X2h8c5LnJ/lFktRa70jylF4VBQAAw6jbcP1wrfWRjRullKkZu841AADQ0W24vqaU8qdJ9i6lvCjJZ5P8U+/KAgCA4dNtuH5nkp8kuSnJHyb5YpI/61VRAAAwjLq9Wsivkvxd57HTSikzknw0yREZW17y2iS3JbkkyZwk9yQ5pdb68xY/DwAA+qHbq4XcXUq5a/PHTvzcDyb5Uq31PyV5bpJbMzY7flWtdV6SqzrbAAAwNLq9icyicc+nJTk5yZN25AeWUvZP8jtJXp0knS9KPlJKeXmSkc6wi5KsSPKOHfkZAAAwCF3NXNda7x/3+I9a698kOW4Hf+YzMrZ++xOllO+UUj5aStk3ycxa66rOz1sVl/oDAGDIlFq3f0W9UspR4zafkLGZ7DfWWp874R9YyqIk30zy/FrrdaWUD2bs+tlvrbXOGDfu57XWJ27h/cuSLEuSmTNnLly+fPlES9hlrFu3LtOnTx90GewEPRx+ejj8dvUejo4OuoLemj33R1n58MpBl7FbWDhrYU+OOyzn4OLFi0drrYu2N67bcH31uM0NGfvC4QdqrbdNtLBSylOTfLPWOqezfWzG1lc/K8lIrXVVKWVWkhW11kO3daxFixbV66+/fqIl7DJWrFiRkZGRQZfBTtDD4aeHw29X72Epg66gtz7wmb/OmbefOegydgv1rN7c4mRYzsFSSlfhuturhSze+ZI2Het/lVJ+WEo5tBPOX5jke53H6Une1/n18lY/EwAA+qGrcF1Kefu2Xq+1/vcJ/ty3Jrm4lLJnkruSvCZjy00uLaW8Lsm9GfvSJAAADI2JXC3k6CRXdLZ/L8lXk/xwR35orfWGPPYKJBu9cEeOBwAAk0G34fqgJEfVWtcmSSnl7CSfrbX+l14VBgAAw6bb25//WpJHxm0/krE7KQIAAB3dzlx/Ksm3Simfz9jtyv9zkk/2rCoAABhC3V4t5NxSyr8kObaz6zW11u/0riwAABg+3S4LSZJ9kvyi1vrBJCtLKXN7VBMAAAylrsJ1KeWsJO9I8q7Orj2SfLpXRQEAwDDqdub6PydZkuSBJKm13pdkv14VBQAAw6jbcP1IHbtPek2SUsq+vSsJAACGU7fh+tJSyvlJZpRSXp/k35L8Xe/KAgCA4dPt1UI+UEp5UZJfJDk0yf9da72yp5UBAMCQ2W64LqVMSfKvtdbjkwjUAACwFdtdFlJrfTTJL0spB/ShHgAAGFrd3qHxoSQ3lVKuTOeKIUlSa31bT6oCAIAh1G24/ufOAwAA2IpthutSyq/VWu+ttV7Ur4IAAGBYbW/N9T9ufFJK+VyPawEAgKG2vXBdxj1/Ri8LAQCAYbe9cF238hwAANjM9r7Q+NxSyi8yNoO9d+d5Otu11rp/T6sDAIAhss1wXWud0q9CAABg2G33JjIAAEB3hGsAAGhEuAYAgEa6vUMjADBJlLL9McBgmLkGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARgYWrkspU0op3ymlfKGzPbeUcl0p5Y5SyiWllD0HVRsAAOyIQc5cn5Hk1nHbf5XkvFrrvCQ/T/K6gVQFAAA7aCDhupQyO8nvJvloZ7skOS7JZZ0hFyV5xSBqAwCAHTWomeu/SfJfk/yqs31gktW11g2d7ZVJDh5EYQAAsKNKrbW/P7CUE5O8rNb6plLKSJIzk7wmyTdqrc/qjDkkyRdrrc/ZwvuXJVmWJDNnzly4fPnyvtU+2axbty7Tp08fdBnsBD0cfno4/Iaxh6Ojg65g8pg990dZ+fDKQZexW1g4a2FPjjss5+DixYtHa62Ltjduaj+K2czzkywppbwsybQk+2dsJntGKWVqZ/Z6dpL7tvTmWusFSS5IkkWLFtWRkZG+FD0ZrVixIrvz598V6OHw08PhN4w9XLx40BVMHh/4zF/nzNvPHHQZu4V6Wm8mZIfxHNyWvi8LqbW+q9Y6u9Y6J8mpSb5Sa/2DJFcnOakz7PQkl/e7NgAA2BmT6TrX70jy9lLKnRlbg/2xAdcDAAATMohlIZvUWlckWdF5fleSYwZZDwAMQimDrgBoZTLNXAMAwFATrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAamTroAgBgWJTS/dhae1cHDEI5p/sToJ61+54AZq4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoJGpgy4AAHZFpQy6AmAQzFwDAEAjwjUAADQiXAMAQCPCNQAANCJcAwBAI8I1AAA0IlwDAEAjwjUAADTS93BdSjmklHJ1KeXWUsotpZQzOvufVEq5spRyR+fXJ/a7NgAA2BmDmLnekOSPa63PTvK8JG8upRyW5J1Jrqq1zktyVWcbAACGRt/Dda11Va31253na5PcmuTgJC9PclFn2EVJXtHv2gAAYGeUWuvgfngpc5J8NckRSe6ttc4Y99rPa62PWxpSSlmWZFmSzJw5c+Hy5cv7U+wktG7dukyfPn3QZbAT9HD46eHwm0gPR0d7XAwTNnvuj7Ly4ZWDLoPNLJy1sOuxw/Ln6OLFi0drrYu2N25g4bqUMj3JNUnOrbX+QylldTfherxFixbV66+/vtelTlorVqzIyMjIoMtgJ+jh8NPD4TeRHpbS21qYuA985q9z5u1nDroMNlPP6j5fDsufo6WUrsL1QK4WUkrZI8nnklxca/2Hzu4flVJmdV6fleTHg6gNAAB21CCuFlKSfCzJrbW3nw9DAAAIdElEQVTW/z7upSuSnN55fnqSy/tdGwAA7IypA/iZz0/yyiQ3lVJu6Oz70yTvS3JpKeV1Se5NcvIAagMAgB3W93Bda/1akq2tWnthP2sBAICW3KERAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAdillDKxx+ho92MBtke4BgCARoRrAABoRLgGAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARqYOugAA2B7XmAaGhZlrAABoRLgGAIBGhGsAAGjEmmsABsI6amBXZOYaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAaEa4BAKAR4RoAABoRrgEAoBHhGgAAGhGuAQCgEeEaAAAamTroAgDYdZQy6AoABsvMNQAANCJcAwBAI8I1AAA0Ys01AFtlDTWwI8o53f/hcfULru5hJf1n5hoAABoRrgEAoBHhGgAAGrHmml3GRNaG1jqZDt4jk6XmyVIHm1hHDdA7Zq4BAKAR4RoAABoRrgEAoBFrrtll1ExkIam1veNNaFn0hH6fAWD3YuYaAAAaEa4BAKAR4RoAABqZdGuuSykvSfLBJFOSfLTW+r4Bl9RfE1n8evXVvauDHderHvbw4sSTZh11rz6j62cDTFqjq0az+JzFXY2tZ03+P88n1cx1KWVKkg8neWmSw5KcVko5bLBVAQBAdyZVuE5yTJI7a6131VofSbI8ycsHXBMAAHRlsoXrg5P8cNz2ys4+AACY9EqdRGsRSyknJzmh1vpfOtuvTHJMrfWt48YsS7Kss3loktv6XujkcVCSnw66CHaKHg4/PRx+ejjc9G/4DUsPn15rffL2Bk22LzSuTHLIuO3ZSe4bP6DWekGSC/pZ1GRVSrm+1rpo0HWw4/Rw+Onh8NPD4aZ/w29X6+FkWxby70nmlVLmllL2THJqkisGXBMAAHRlUs1c11o3lFLekuRfM3Ypvo/XWm8ZcFkAANCVSRWuk6TW+sUkXxx0HUPC8pjhp4fDTw+Hnx4ON/0bfrtUDyfVFxoBAGCYTbY11wAAMLSE60mulPKkUsqVpZQ7Or8+cQtjFpRSvlFKuaWUcmMpZem41+aWUq7rvP+SzhdF6aNuetgZ96VSyupSyhc2239hKeXuUsoNnceC/lTORg166DwcoAn07/TOmDtKKaeP27+ilHLbuHPwKf2rfvdWSnlJ5/f+zlLKO7fw+l6dc+rOzjk2Z9xr7+rsv62UckI/6+Z/29EellLmlFIeHHfefaTfte8o4Xrye2eSq2qt85Jc1dne3C+TvKrWeniSlyT5m1LKjM5rf5XkvM77f57kdX2omcfqpodJ8t+SvHIrr/1JrXVB53FDL4pkm3a2h87Dwdpu/0opT0pyVpLfyNjdgs/aLIT/wbhz8Mf9KHp3V0qZkuTDSV6a5LAkp5VSDtts2OuS/LzW+qwk52XsXEtn3KlJNv69+D87x6OPdqaHHT8Yd969oS9FNyBcT34vT3JR5/lFSV6x+YBa6+211js6z+9L8uMkTy6llCTHJblsW++n57bbwySptV6VZG2/imJCdriHzsNJoZv+nZDkylrrz2qtP09yZcZCGYNzTJI7a6131VofSbI8Y70cb3xvL0vyws459/Iky2utD9da705yZ+d49NfO9HBoCdeT38xa66ok6fy6zX+OLKUck2TPJD9IcmCS1bXWDZ2X3U5+MCbUw604t7Pk57xSyl5ty6MLO9ND5+HgddO/g5P8cNz25n36ROefpt8z7H/xD5Ht9eQxYzrn2JqMnXPdvJfe25keJsncUsp3SinXlFKO7XWxrUy6S/Htjkop/5bkqVt46d0TPM6sJJ9Kcnqt9Vdb+QvA5WF6oFUPt+JdSf5Xxv6n6YIk70jy3gbHZZwe9tB52AcN+retPv1BrfU/Sin7Jflcxpb+fHLiVTJB3Zw7WxvjvJscdqaHq5L8Wq31/lLKwiT/WEo5vNb6i9ZFtiZcTwK11uO39lop5UellFm11lWd8LzFtX6llP2T/HOSP6u1frOz+6dJZpRSpnb+b/Bxt5OnjRY93MaxV3WePlxK+USSM3eiVLaihz10HvZBg/6tTDIybnt2khWdY/9H59e1pZTPZOyfuoXr3luZ5JBx21s6dzaOWVlKmZrkgCQ/6/K99N4O97COXSv64SSptY6WUn6Q5NeTXN/zqneSZSGT3xVJNn5r/fQkl28+oHPlgc8n+WSt9bMb93f+w7w6yUnbej89t90ebksnDGxcu/uKJDc3rY5u7HAPnYeTQjf9+9ckLy6lPLHzRcYXJ/nXUsrUUspBSVJK2SPJiXEO9su/J5nXudrOnhn7guIVm40Z39uTknylc85dkeTUzpUo5iaZl+Rbfaqb/22He1hKefLGL6GWUp6RsR7e1ae6d06t1WMSPzK27uiqJHd0fn1SZ/+iJB/tPP8/k6xPcsO4x4LOa8/I2B8odyb5bJK9Bv2ZdrdHNz3sbP+/SX6S5MGM/Z/8CZ39X0lyU8b+Qv90kumD/ky726NBD52Hw9G/13Z6dGeS13T27ZtkNMmNSW5J8sEkUwb9mXaXR5KXJbk9Y98jendn33uTLOk8n9Y5p+7snGPPGPfed3fed1uSlw76s+yujx3tYZL/o3POfTfJt5P83qA/S7cPd2gEAIBGLAsBAIBGhGsAAGhEuAYAgEaEawAAaES4BgCARoRrAABoRLgGAIBGhGsAAGjk/wfk+p/6jFsI6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h.plot.hist(bins=bins, figsize=(12,9), grid=True, stacked=True, colormap=matplotlib.colors.ListedColormap(['r', 'b', 'g']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How should we define *Sustainable*?\n",
    "\n",
    "While comparing the portfolio's ending value to its starting value is interesting, is it the right way to think about a *sustainable* portfolio?  When you're only increasing the withdrawal by inflation, then it's perfectly reasonable.  If the ending value is at least as the initial value (adjusting for inflation), then you could start a new retirement at the end of another, keep the same withdrawal amount, and expect it to last another full retirement length.  But it's maybe not as relevant when the withdrawal has been increased above inflation (by Phoenix 1's 10% Raise rule, or a ratcheting withdrawal).  This definitition of *sustainable* would have you reset your withdrawal to the same amount as at the beginning of retirement -- throwing away any increases since then.\n",
    "\n",
    "I think what I'm really after with *sustainable* is to know whether the portfolio could survive longer than planned.  How much longer?  I don't know.\n",
    "\n",
    "Maybe it would be better to compare the ending withdrawal rate to the ending balance, and compare that ratio (a percentage) to the initial withdrawal rate.  If they're close enough, then you could begin a new retirement period with the same or similar withdrawal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(0,0.50,num=51,endpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1386.000000\n",
       "mean        0.121324\n",
       "std         0.968587\n",
       "min         0.030222\n",
       "25%         0.048842\n",
       "50%         0.077121\n",
       "75%         0.109270\n",
       "max        35.860670\n",
       "Name: last_withdrawal_rate, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.survived].last_withdrawal_rate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_real</th>\n",
       "      <th>max_real</th>\n",
       "      <th>last_real</th>\n",
       "      <th>last_real_fraction</th>\n",
       "      <th>growth_rate_real</th>\n",
       "      <th>withdrawal_rate_real</th>\n",
       "      <th>last_withdrawal_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>155488.661460</td>\n",
       "      <td>2.462230e+06</td>\n",
       "      <td>155488.661460</td>\n",
       "      <td>0.155147</td>\n",
       "      <td>-0.071305</td>\n",
       "      <td>0.023748</td>\n",
       "      <td>2.843394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>113836.841287</td>\n",
       "      <td>1.363551e+06</td>\n",
       "      <td>113836.841287</td>\n",
       "      <td>0.113213</td>\n",
       "      <td>0.033928</td>\n",
       "      <td>0.021395</td>\n",
       "      <td>8.545743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4166.512905</td>\n",
       "      <td>9.977631e+05</td>\n",
       "      <td>4166.512905</td>\n",
       "      <td>0.004136</td>\n",
       "      <td>-0.166973</td>\n",
       "      <td>-0.000745</td>\n",
       "      <td>0.353520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96276.266159</td>\n",
       "      <td>1.127465e+06</td>\n",
       "      <td>96276.266159</td>\n",
       "      <td>0.096511</td>\n",
       "      <td>-0.075052</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.391565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>105641.010783</td>\n",
       "      <td>1.969105e+06</td>\n",
       "      <td>105641.010783</td>\n",
       "      <td>0.105905</td>\n",
       "      <td>-0.072186</td>\n",
       "      <td>0.022184</td>\n",
       "      <td>0.467246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218532.751381</td>\n",
       "      <td>3.530906e+06</td>\n",
       "      <td>218532.751381</td>\n",
       "      <td>0.218358</td>\n",
       "      <td>-0.049430</td>\n",
       "      <td>0.044059</td>\n",
       "      <td>0.696222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>409604.119459</td>\n",
       "      <td>4.365621e+06</td>\n",
       "      <td>409604.119459</td>\n",
       "      <td>0.407086</td>\n",
       "      <td>-0.029314</td>\n",
       "      <td>0.050560</td>\n",
       "      <td>35.860670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            min_real      max_real      last_real  last_real_fraction  \\\n",
       "count      17.000000  1.700000e+01      17.000000           17.000000   \n",
       "mean   155488.661460  2.462230e+06  155488.661460            0.155147   \n",
       "std    113836.841287  1.363551e+06  113836.841287            0.113213   \n",
       "min      4166.512905  9.977631e+05    4166.512905            0.004136   \n",
       "25%     96276.266159  1.127465e+06   96276.266159            0.096511   \n",
       "50%    105641.010783  1.969105e+06  105641.010783            0.105905   \n",
       "75%    218532.751381  3.530906e+06  218532.751381            0.218358   \n",
       "max    409604.119459  4.365621e+06  409604.119459            0.407086   \n",
       "\n",
       "       growth_rate_real  withdrawal_rate_real  last_withdrawal_rate  \n",
       "count         17.000000             17.000000             17.000000  \n",
       "mean          -0.071305              0.023748              2.843394  \n",
       "std            0.033928              0.021395              8.545743  \n",
       "min           -0.166973             -0.000745              0.353520  \n",
       "25%           -0.075052              0.000674              0.391565  \n",
       "50%           -0.072186              0.022184              0.467246  \n",
       "75%           -0.049430              0.044059              0.696222  \n",
       "max           -0.029314              0.050560             35.860670  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.survived & (df.last_withdrawal_rate > 0.35)].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d8879b0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0,0.50,num=51,endpoint=True)\n",
    "df.last_withdrawal_rate.plot.hist(bins=bins, figsize=(12,9), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ecf2be0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 0.20, num=21, endpoint=True)\n",
    "df.last_withdrawal_rate.plot.hist(bins=bins, figsize=(12,9), grid=True, xticks=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fec4a20>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.last_withdrawal_rate.plot.hist(bins=bins, cumulative=True, figsize=(12,9), grid=True, xticks=bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yuck!  I was hoping to see more samples near 0.04 (4%).  About half of the survived periods are above 8%.  I'm disturbed that half are above that, and about 1/3 above 10%.  Those portfolios would have a hard time sustaining for much longer.  This further reinforces that the withdrawal rate is key to the length of time your portfolio can last."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps a better way to look at this is to see what happens at longer retirement periods, for a variety of withdrawal rates.\n",
    "\n",
    "I'm thinking the X axis is duration, from 30 to 45 years.  The Y axis is probability of surviving.  Have multiple lines (i.e. DataFrame columns) for each withdrawal rate (3.5 to 4.0, by 0.1?).  Duration could also be the index for the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "durations = range(30,46) # in years\n",
    "rates = [3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0]\n",
    "d = {}\n",
    "for rate in rates:\n",
    "    values = []\n",
    "    for duration in durations:\n",
    "        portfolio = Portfolio(annual_withdrawal_rate=rate/100, cash_cushion=True, ratchet=True)\n",
    "        \n",
    "        (survivability, sustainability,\n",
    "         balance_growth_median, balance_growth_mean, balance_growth_stdev,\n",
    "         withdrawal_growth_median, withdrawal_growth_mean, withdrawal_growth_stdev,\n",
    "         periods) = portfolio.sim_periods(market_data, period_length=duration*12)\n",
    "        values.append(survivability * 100.0)\n",
    "    d[rate] = values\n",
    "df2 = pd.DataFrame(d, index=durations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3.3</th>\n",
       "      <th>3.4</th>\n",
       "      <th>3.5</th>\n",
       "      <th>3.6</th>\n",
       "      <th>3.7</th>\n",
       "      <th>3.8</th>\n",
       "      <th>3.9</th>\n",
       "      <th>4.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.928826</td>\n",
       "      <td>99.786477</td>\n",
       "      <td>99.715302</td>\n",
       "      <td>99.644128</td>\n",
       "      <td>99.288256</td>\n",
       "      <td>99.074733</td>\n",
       "      <td>98.647687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>99.928212</td>\n",
       "      <td>99.928212</td>\n",
       "      <td>99.784637</td>\n",
       "      <td>99.641062</td>\n",
       "      <td>99.497487</td>\n",
       "      <td>99.138550</td>\n",
       "      <td>98.779612</td>\n",
       "      <td>98.348887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>99.927589</td>\n",
       "      <td>99.927589</td>\n",
       "      <td>99.782766</td>\n",
       "      <td>99.637944</td>\n",
       "      <td>99.420710</td>\n",
       "      <td>98.986242</td>\n",
       "      <td>98.624185</td>\n",
       "      <td>98.117306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>99.926954</td>\n",
       "      <td>99.926954</td>\n",
       "      <td>99.780862</td>\n",
       "      <td>99.634770</td>\n",
       "      <td>99.269540</td>\n",
       "      <td>98.758218</td>\n",
       "      <td>98.246896</td>\n",
       "      <td>97.516435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>99.926308</td>\n",
       "      <td>99.926308</td>\n",
       "      <td>99.778924</td>\n",
       "      <td>99.557848</td>\n",
       "      <td>99.042004</td>\n",
       "      <td>98.452469</td>\n",
       "      <td>98.010317</td>\n",
       "      <td>96.831245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>99.925651</td>\n",
       "      <td>99.776952</td>\n",
       "      <td>99.628253</td>\n",
       "      <td>99.256506</td>\n",
       "      <td>98.810409</td>\n",
       "      <td>98.289963</td>\n",
       "      <td>97.472119</td>\n",
       "      <td>95.985130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>99.924981</td>\n",
       "      <td>99.774944</td>\n",
       "      <td>99.474869</td>\n",
       "      <td>99.099775</td>\n",
       "      <td>98.574644</td>\n",
       "      <td>98.049512</td>\n",
       "      <td>96.924231</td>\n",
       "      <td>94.973743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>99.924300</td>\n",
       "      <td>99.697199</td>\n",
       "      <td>99.470098</td>\n",
       "      <td>98.864497</td>\n",
       "      <td>98.334595</td>\n",
       "      <td>97.728993</td>\n",
       "      <td>96.139288</td>\n",
       "      <td>93.489780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>99.923606</td>\n",
       "      <td>99.618029</td>\n",
       "      <td>99.388846</td>\n",
       "      <td>98.701299</td>\n",
       "      <td>98.090145</td>\n",
       "      <td>97.326203</td>\n",
       "      <td>95.416348</td>\n",
       "      <td>92.131398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>99.922899</td>\n",
       "      <td>99.614495</td>\n",
       "      <td>99.306091</td>\n",
       "      <td>98.535081</td>\n",
       "      <td>97.841172</td>\n",
       "      <td>96.453354</td>\n",
       "      <td>94.294526</td>\n",
       "      <td>90.902082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>99.922179</td>\n",
       "      <td>99.610895</td>\n",
       "      <td>99.221790</td>\n",
       "      <td>98.443580</td>\n",
       "      <td>97.431907</td>\n",
       "      <td>95.642023</td>\n",
       "      <td>93.073930</td>\n",
       "      <td>89.416342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>99.764336</td>\n",
       "      <td>99.607227</td>\n",
       "      <td>98.821681</td>\n",
       "      <td>98.036135</td>\n",
       "      <td>97.093480</td>\n",
       "      <td>94.815397</td>\n",
       "      <td>91.751767</td>\n",
       "      <td>87.745483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>99.762094</td>\n",
       "      <td>99.603489</td>\n",
       "      <td>98.731166</td>\n",
       "      <td>97.858842</td>\n",
       "      <td>96.510706</td>\n",
       "      <td>93.338620</td>\n",
       "      <td>90.166534</td>\n",
       "      <td>85.646312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>99.599680</td>\n",
       "      <td>99.439552</td>\n",
       "      <td>98.638911</td>\n",
       "      <td>97.518014</td>\n",
       "      <td>95.516413</td>\n",
       "      <td>92.073659</td>\n",
       "      <td>88.710969</td>\n",
       "      <td>83.666934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>99.595796</td>\n",
       "      <td>99.434115</td>\n",
       "      <td>98.464026</td>\n",
       "      <td>97.170574</td>\n",
       "      <td>94.502829</td>\n",
       "      <td>90.703314</td>\n",
       "      <td>86.984640</td>\n",
       "      <td>81.487470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>99.591837</td>\n",
       "      <td>99.428571</td>\n",
       "      <td>98.367347</td>\n",
       "      <td>96.816327</td>\n",
       "      <td>93.551020</td>\n",
       "      <td>89.469388</td>\n",
       "      <td>85.061224</td>\n",
       "      <td>78.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           3.3        3.4        3.5        3.6        3.7        3.8  \\\n",
       "30  100.000000  99.928826  99.786477  99.715302  99.644128  99.288256   \n",
       "31   99.928212  99.928212  99.784637  99.641062  99.497487  99.138550   \n",
       "32   99.927589  99.927589  99.782766  99.637944  99.420710  98.986242   \n",
       "33   99.926954  99.926954  99.780862  99.634770  99.269540  98.758218   \n",
       "34   99.926308  99.926308  99.778924  99.557848  99.042004  98.452469   \n",
       "35   99.925651  99.776952  99.628253  99.256506  98.810409  98.289963   \n",
       "36   99.924981  99.774944  99.474869  99.099775  98.574644  98.049512   \n",
       "37   99.924300  99.697199  99.470098  98.864497  98.334595  97.728993   \n",
       "38   99.923606  99.618029  99.388846  98.701299  98.090145  97.326203   \n",
       "39   99.922899  99.614495  99.306091  98.535081  97.841172  96.453354   \n",
       "40   99.922179  99.610895  99.221790  98.443580  97.431907  95.642023   \n",
       "41   99.764336  99.607227  98.821681  98.036135  97.093480  94.815397   \n",
       "42   99.762094  99.603489  98.731166  97.858842  96.510706  93.338620   \n",
       "43   99.599680  99.439552  98.638911  97.518014  95.516413  92.073659   \n",
       "44   99.595796  99.434115  98.464026  97.170574  94.502829  90.703314   \n",
       "45   99.591837  99.428571  98.367347  96.816327  93.551020  89.469388   \n",
       "\n",
       "          3.9        4.0  \n",
       "30  99.074733  98.647687  \n",
       "31  98.779612  98.348887  \n",
       "32  98.624185  98.117306  \n",
       "33  98.246896  97.516435  \n",
       "34  98.010317  96.831245  \n",
       "35  97.472119  95.985130  \n",
       "36  96.924231  94.973743  \n",
       "37  96.139288  93.489780  \n",
       "38  95.416348  92.131398  \n",
       "39  94.294526  90.902082  \n",
       "40  93.073930  89.416342  \n",
       "41  91.751767  87.745483  \n",
       "42  90.166534  85.646312  \n",
       "43  88.710969  83.666934  \n",
       "44  86.984640  81.487470  \n",
       "45  85.061224  78.857143  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d966a20>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.line(figsize=(12,9), grid=True,\n",
    "              xticks=np.linspace(30, 45, num=16, endpoint=True),\n",
    "              yticks=np.linspace(75, 100, num=26, endpoint=True),\n",
    "              colormap='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       3.3    3.4    3.5    3.6    3.7    3.8    3.9    4.0\n",
      "30  100.00  99.93  99.79  99.72  99.64  99.29  99.07  98.65\n",
      "31   99.93  99.93  99.78  99.64  99.50  99.14  98.78  98.35\n",
      "32   99.93  99.93  99.78  99.64  99.42  98.99  98.62  98.12\n",
      "33   99.93  99.93  99.78  99.63  99.27  98.76  98.25  97.52\n",
      "34   99.93  99.93  99.78  99.56  99.04  98.45  98.01  96.83\n",
      "35   99.93  99.78  99.63  99.26  98.81  98.29  97.47  95.99\n",
      "36   99.92  99.77  99.47  99.10  98.57  98.05  96.92  94.97\n",
      "37   99.92  99.70  99.47  98.86  98.33  97.73  96.14  93.49\n",
      "38   99.92  99.62  99.39  98.70  98.09  97.33  95.42  92.13\n",
      "39   99.92  99.61  99.31  98.54  97.84  96.45  94.29  90.90\n",
      "40   99.92  99.61  99.22  98.44  97.43  95.64  93.07  89.42\n",
      "41   99.76  99.61  98.82  98.04  97.09  94.82  91.75  87.75\n",
      "42   99.76  99.60  98.73  97.86  96.51  93.34  90.17  85.65\n",
      "43   99.60  99.44  98.64  97.52  95.52  92.07  88.71  83.67\n",
      "44   99.60  99.43  98.46  97.17  94.50  90.70  86.98  81.49\n",
      "45   99.59  99.43  98.37  96.82  93.55  89.47  85.06  78.86\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.precision\", 2):\n",
    "    print(repr(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}