notebooks/Amortization-Corrected-Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updated Notebook from Practical Business Python.\n",
    "\n",
    "Original article had a model that did not work correctly. This notebook is for the [updated article](http://pbpython.com/amortization-model-revised.html).\n",
    "\n",
    "Many thanks to the individuals that helped me fix the errors. The solution below is based heavily on this [gist](https://gist.github.com/sjmallon/e1ca2aee4574d5517b8d31c93832222a) and comments on [reddit](https://www.reddit.com/r/Python/comments/5e3xab/building_a_financial_model_with_pandas/?st=iwjk8alv&sh=d721fcd7)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import date\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "from dateutil.relativedelta import *\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.pylabtools import figsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a payment schedule using a generator that can be easily read into a pandas dataframe for additional analysis and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def amortize(principal, interest_rate, years, pmt, addl_principal, start_date, annual_payments):\n",
    "    \"\"\"\n",
    "    Calculate the amortization schedule given the loan details.\n",
    "\n",
    "    :param principal: Amount borrowed\n",
    "    :param interest_rate: The annual interest rate for this loan\n",
    "    :param years: Number of years for the loan\n",
    "    :param pmt: Payment amount per period\n",
    "    :param addl_principal: Additional payments to be made each period.\n",
    "    :param start_date: Start date for the loan.\n",
    "    :param annual_payments: Number of payments in a year.\n",
    "\n",
    "    :return: \n",
    "        schedule: Amortization schedule as an Ortdered Dictionary\n",
    "    \"\"\"\n",
    "\n",
    "    # initialize the variables to keep track of the periods and running balances\n",
    "    p = 1\n",
    "    beg_balance = principal\n",
    "    end_balance = principal\n",
    "    \n",
    "    while end_balance > 0:\n",
    "        \n",
    "        # Recalculate the interest based on the current balance\n",
    "        interest = round(((interest_rate/annual_payments) * beg_balance), 2)\n",
    "        \n",
    "        # Determine payment based on whether or not this period will pay off the loan\n",
    "        pmt = min(pmt, beg_balance + interest)\n",
    "        principal = pmt - interest\n",
    "        \n",
    "        # Ensure additional payment gets adjusted if the loan is being paid off\n",
    "        addl_principal = min(addl_principal, beg_balance - principal)\n",
    "        end_balance = beg_balance - (principal + addl_principal)\n",
    "\n",
    "        yield OrderedDict([('Month',start_date),\n",
    "                           ('Period', p),\n",
    "                           ('Begin Balance', beg_balance),\n",
    "                           ('Payment', pmt),\n",
    "                           ('Principal', principal),\n",
    "                           ('Interest', interest),\n",
    "                           ('Additional_Payment', addl_principal),\n",
    "                           ('End Balance', end_balance)])\n",
    "        \n",
    "        # Increment the counter, balance and date\n",
    "        p += 1\n",
    "        start_date += relativedelta(months=1)\n",
    "        beg_balance = end_balance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrapper function to call `amortize`.\n",
    "\n",
    "This function primarily cleans up the table and provides summary stats so it is easy to compare various scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def amortization_table(principal, interest_rate, years,\n",
    "                       addl_principal=0, annual_payments=12, start_date=date.today()):\n",
    "    \"\"\"\n",
    "    Calculate the amortization schedule given the loan details as well as summary stats for the loan\n",
    "\n",
    "    :param principal: Amount borrowed\n",
    "    :param interest_rate: The annual interest rate for this loan\n",
    "    :param years: Number of years for the loan\n",
    "    \n",
    "    :param annual_payments (optional): Number of payments in a year. DEfault 12.\n",
    "    :param addl_principal (optional): Additional payments to be made each period. Default 0.\n",
    "    :param start_date (optional): Start date. Default first of next month if none provided\n",
    "\n",
    "    :return: \n",
    "        schedule: Amortization schedule as a pandas dataframe\n",
    "        summary: Pandas dataframe that summarizes the payoff information\n",
    "    \"\"\"\n",
    "    \n",
    "    # Payment stays constant based on the original terms of the loan\n",
    "    payment = -round(np.pmt(interest_rate/annual_payments, years*annual_payments, principal), 2)\n",
    "    \n",
    "    # Generate the schedule and order the resulting columns for convenience\n",
    "    schedule = pd.DataFrame(amortize(principal, interest_rate, years, payment,\n",
    "                                     addl_principal, start_date, annual_payments))\n",
    "    schedule = schedule[[\"Period\", \"Month\", \"Begin Balance\", \"Payment\", \"Interest\", \n",
    "                         \"Principal\", \"Additional_Payment\", \"End Balance\"]]\n",
    "    \n",
    "    # Convert to a datetime object to make subsequent calcs easier\n",
    "    schedule[\"Month\"] = pd.to_datetime(schedule[\"Month\"])\n",
    "    \n",
    "    #Create a summary statistics table\n",
    "    payoff_date = schedule[\"Month\"].iloc[-1]\n",
    "    stats = pd.Series([payoff_date, schedule[\"Period\"].count(), interest_rate,\n",
    "                       years, principal, payment, addl_principal,\n",
    "                       schedule[\"Interest\"].sum()],\n",
    "                       index=[\"Payoff Date\", \"Num Payments\", \"Interest Rate\", \"Years\", \"Principal\",\n",
    "                             \"Payment\", \"Additional Payment\", \"Total Interest\"])\n",
    "    \n",
    "    return schedule, stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example showing how to call the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df, stats = amortization_table(700000, .04, 30, addl_principal=200, start_date=date(2016, 1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Payoff Date           2042-12-01 00:00:00\n",
       "Num Payments                          324\n",
       "Interest Rate                        0.04\n",
       "Years                                  30\n",
       "Principal                          700000\n",
       "Payment                           3341.91\n",
       "Additional Payment                    200\n",
       "Total Interest                     444406\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Month</th>\n",
       "      <th>Begin Balance</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Additional_Payment</th>\n",
       "      <th>End Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>700000.00</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>2333.33</td>\n",
       "      <td>1008.58</td>\n",
       "      <td>200.0</td>\n",
       "      <td>698791.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>698791.42</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>2329.30</td>\n",
       "      <td>1012.61</td>\n",
       "      <td>200.0</td>\n",
       "      <td>697578.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>697578.81</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>2325.26</td>\n",
       "      <td>1016.65</td>\n",
       "      <td>200.0</td>\n",
       "      <td>696362.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>696362.16</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>2321.21</td>\n",
       "      <td>1020.70</td>\n",
       "      <td>200.0</td>\n",
       "      <td>695141.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>695141.46</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>2317.14</td>\n",
       "      <td>1024.77</td>\n",
       "      <td>200.0</td>\n",
       "      <td>693916.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Period      Month  Begin Balance  Payment  Interest  Principal  \\\n",
       "0       1 2016-01-01      700000.00  3341.91   2333.33    1008.58   \n",
       "1       2 2016-02-01      698791.42  3341.91   2329.30    1012.61   \n",
       "2       3 2016-03-01      697578.81  3341.91   2325.26    1016.65   \n",
       "3       4 2016-04-01      696362.16  3341.91   2321.21    1020.70   \n",
       "4       5 2016-05-01      695141.46  3341.91   2317.14    1024.77   \n",
       "\n",
       "   Additional_Payment  End Balance  \n",
       "0               200.0    698791.42  \n",
       "1               200.0    697578.81  \n",
       "2               200.0    696362.16  \n",
       "3               200.0    695141.46  \n",
       "4               200.0    693916.69  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Month</th>\n",
       "      <th>Begin Balance</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Additional_Payment</th>\n",
       "      <th>End Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>320</td>\n",
       "      <td>2042-08-01</td>\n",
       "      <td>14413.65</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>48.05</td>\n",
       "      <td>3293.86</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10919.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>321</td>\n",
       "      <td>2042-09-01</td>\n",
       "      <td>10919.79</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>36.40</td>\n",
       "      <td>3305.51</td>\n",
       "      <td>200.0</td>\n",
       "      <td>7414.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>322</td>\n",
       "      <td>2042-10-01</td>\n",
       "      <td>7414.28</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>24.71</td>\n",
       "      <td>3317.20</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3897.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>323</td>\n",
       "      <td>2042-11-01</td>\n",
       "      <td>3897.08</td>\n",
       "      <td>3341.91</td>\n",
       "      <td>12.99</td>\n",
       "      <td>3328.92</td>\n",
       "      <td>200.0</td>\n",
       "      <td>368.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>324</td>\n",
       "      <td>2042-12-01</td>\n",
       "      <td>368.16</td>\n",
       "      <td>369.39</td>\n",
       "      <td>1.23</td>\n",
       "      <td>368.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Period      Month  Begin Balance  Payment  Interest  Principal  \\\n",
       "319     320 2042-08-01       14413.65  3341.91     48.05    3293.86   \n",
       "320     321 2042-09-01       10919.79  3341.91     36.40    3305.51   \n",
       "321     322 2042-10-01        7414.28  3341.91     24.71    3317.20   \n",
       "322     323 2042-11-01        3897.08  3341.91     12.99    3328.92   \n",
       "323     324 2042-12-01         368.16   369.39      1.23     368.16   \n",
       "\n",
       "     Additional_Payment  End Balance  \n",
       "319               200.0     10919.79  \n",
       "320               200.0      7414.28  \n",
       "321               200.0      3897.08  \n",
       "322               200.0       368.16  \n",
       "323                 0.0         0.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make multiple calls to compare scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule1, stats1 = amortization_table(100000, .04, 30, addl_principal=50, start_date=date(2016,1,1))\n",
    "schedule2, stats2 = amortization_table(100000, .05, 30, addl_principal=200, start_date=date(2016,1,1))\n",
    "schedule3, stats3 = amortization_table(100000, .04, 15, addl_principal=0, start_date=date(2016,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payoff Date</th>\n",
       "      <th>Num Payments</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Years</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Additional Payment</th>\n",
       "      <th>Total Interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2041-01-01</td>\n",
       "      <td>301</td>\n",
       "      <td>0.04</td>\n",
       "      <td>30</td>\n",
       "      <td>100000</td>\n",
       "      <td>477.42</td>\n",
       "      <td>50</td>\n",
       "      <td>58441.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2032-09-01</td>\n",
       "      <td>201</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>100000</td>\n",
       "      <td>536.82</td>\n",
       "      <td>200</td>\n",
       "      <td>47708.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2030-12-01</td>\n",
       "      <td>180</td>\n",
       "      <td>0.04</td>\n",
       "      <td>15</td>\n",
       "      <td>100000</td>\n",
       "      <td>739.69</td>\n",
       "      <td>0</td>\n",
       "      <td>33143.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Payoff Date  Num Payments  Interest Rate  Years  Principal  Payment  \\\n",
       "0  2041-01-01           301           0.04     30     100000   477.42   \n",
       "1  2032-09-01           201           0.05     30     100000   536.82   \n",
       "2  2030-12-01           180           0.04     15     100000   739.69   \n",
       "\n",
       "   Additional Payment  Total Interest  \n",
       "0                  50        58441.08  \n",
       "1                 200        47708.38  \n",
       "2                   0        33143.79  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([stats1, stats2, stats3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make some plots to show scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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osz5EjP0b/HIY59xncT7/OM5Na5FlpXXSDkWpa+dKo8+aNYvAwEAmTJjAl19+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\nWEqdmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff388774a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "schedule1.plot(x='Month', y='End Balance', label=\"Scenario 1\", ax=ax)\n",
    "schedule2.plot(x='Month', y='End Balance', label=\"Scenario 2\", ax=ax)\n",
    "schedule3.plot(x='Month', y='End Balance', label=\"Scenario 3\", ax=ax)\n",
    "plt.title(\"Pay Off Timelines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot_data(schedule, stats):\n",
    "    \"\"\"Create a dataframe with annual interest totals, and a descriptive label\"\"\"\n",
    "    y = schedule.set_index('Month')['Interest'].resample(\"A\").sum().reset_index()\n",
    "    y[\"Year\"] = y[\"Month\"].dt.year\n",
    "    y.set_index('Year', inplace=True)\n",
    "    y.drop('Month', 1, inplace=True)\n",
    "    label=\"{} years at {}% with additional payment of ${}\".format(stats['Years'], stats['Interest Rate']*100, stats['Additional Payment'])\n",
    "    return y, label\n",
    "    \n",
    "y1, label1 = make_plot_data(schedule1, stats1)\n",
    "y2, label2 = make_plot_data(schedule2, stats2)\n",
    "y3, label3 = make_plot_data(schedule3, stats3)\n",
    "\n",
    "y = pd.concat([y1, y2, y3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGSJOHPCuvN52VVJQhaCkFixYAE9PTyxYsKDI99+c1SczMxNDhgyBu7s7PD09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\nspjkiIhIspjkiIhIspjkiIhIsv4//4y58SPBwgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff38847a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(7,5)\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "y.plot(kind=\"bar\", ax=ax)\n",
    "\n",
    "plt.legend([label1, label2, label3], loc=1, prop={'size':10})\n",
    "plt.title(\"Interest Payments\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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49sYl4eSMMvA+lDc+RRn/Enj5oi7/EtM/H8X05buoWRm1PldJuhPExcUxcODA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\nSZITycIqSZIkSU70/wPAX6hvnyGMsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff38823b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "additional_payments = [0, 50, 200, 500]\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "for pmt in additional_payments:\n",
    "    result, _ = amortization_table(100000, .04, 30, addl_principal=pmt, start_date=date(2016,1,1))\n",
    "    ax.plot(result['Month'], result['End Balance'], label='Addl Payment = ${}'.format(str(pmt)))\n",
    "plt.title(\"Pay Off Timelines\")\n",
    "plt.ylabel(\"Balance\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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