full_potential.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nasdaqdatalink\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from data_loader import get_data, get_data_multiple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = \"4582031f930830a9e531b95f4f79f677\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = get_data(api_key, \"USREC\")\n",
    "target['USREC'] = target['USREC'].astype(float)\n",
    "target.reset_index(inplace = True)\n",
    "target = target.rename(columns = {'date': 'DATE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>DATE</th>\n",
       "      <th>USREC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1854-12-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1855-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1855-02-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1855-03-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1855-04-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2017 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE  USREC\n",
       "0    1854-12-01    1.0\n",
       "1    1855-01-01    0.0\n",
       "2    1855-02-01    0.0\n",
       "3    1855-03-01    0.0\n",
       "4    1855-04-01    0.0\n",
       "...         ...    ...\n",
       "2012 2022-08-01    0.0\n",
       "2013 2022-09-01    0.0\n",
       "2014 2022-10-01    0.0\n",
       "2015 2022-11-01    0.0\n",
       "2016 2022-12-01    0.0\n",
       "\n",
       "[2017 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ten_year_treas = get_data(api_key, \"DGS10\")\n",
    "ten_year_treas = ten_year_treas.rename(columns = {'DGS10': 'Value'})\n",
    "three_month_treas = get_data(api_key, \"DTB3\")\n",
    "three_month_treas = three_month_treas.rename(columns = {'DTB3': 'Value'})\n",
    "combined = three_month_treas.join(ten_year_treas, how='left', lsuffix='_3mon', rsuffix='_10yr')\n",
    "combined = combined.dropna()\n",
    "# drop all rows where the value is just \".\"\n",
    "combined = combined[combined.Value_3mon != \".\"]\n",
    "combined = combined[combined.Value_10yr != \".\"]\n",
    "# export combined to csv\n",
    "combined.to_csv('combined.csv')\n",
    "# convert columns to float64\n",
    "combined['Value_3mon'] = combined['Value_3mon'].astype('float64')\n",
    "combined['Value_10yr'] = combined['Value_10yr'].astype('float64')\n",
    "# create new column for spread\n",
    "combined['spread'] = combined['Value_10yr'] - combined['Value_3mon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Value_3mon</th>\n",
       "      <th>Value_10yr</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-02</th>\n",
       "      <td>2.70</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-03</th>\n",
       "      <td>2.73</td>\n",
       "      <td>4.03</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-04</th>\n",
       "      <td>2.72</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-05</th>\n",
       "      <td>2.74</td>\n",
       "      <td>4.02</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-08</th>\n",
       "      <td>2.78</td>\n",
       "      <td>4.03</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-18</th>\n",
       "      <td>4.54</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-19</th>\n",
       "      <td>4.55</td>\n",
       "      <td>3.39</td>\n",
       "      <td>-1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-20</th>\n",
       "      <td>4.57</td>\n",
       "      <td>3.48</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-23</th>\n",
       "      <td>4.58</td>\n",
       "      <td>3.52</td>\n",
       "      <td>-1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-24</th>\n",
       "      <td>4.57</td>\n",
       "      <td>3.46</td>\n",
       "      <td>-1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15250 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value_3mon  Value_10yr  spread\n",
       "date                                      \n",
       "1962-01-02        2.70        4.06    1.36\n",
       "1962-01-03        2.73        4.03    1.30\n",
       "1962-01-04        2.72        3.99    1.27\n",
       "1962-01-05        2.74        4.02    1.28\n",
       "1962-01-08        2.78        4.03    1.25\n",
       "...                ...         ...     ...\n",
       "2023-01-18        4.54        3.37   -1.17\n",
       "2023-01-19        4.55        3.39   -1.16\n",
       "2023-01-20        4.57        3.48   -1.09\n",
       "2023-01-23        4.58        3.52   -1.06\n",
       "2023-01-24        4.57        3.46   -1.11\n",
       "\n",
       "[15250 rows x 3 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take average by month in yield\n",
    "yield_by_month_mean = combined.resample('M').mean()\n",
    "# format yield into monthly datetime\n",
    "yield_by_month_mean.index = yield_by_month_mean.index.strftime('%Y-%m')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take average by month in yield\n",
    "yield_by_month_mean = combined.groupby(pd.Grouper(freq='M')).mean()\n",
    "\n",
    "#Format yield into monthly datetime\n",
    "yield_by_month_mean = yield_by_month_mean.set_index(yield_by_month_mean.index.strftime('%Y-%m'))\n",
    "\n",
    "#Format target into YYYY-MM\n",
    "target['DATE'] = target['DATE'].apply(lambda x: x.strftime('%Y-%m'))\n",
    "target = target.set_index(target['DATE']).drop(columns = ['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recession_vs_yield = yield_by_month_mean.join(target, how = 'left').dropna()\n",
    "recession_vs_yield = recession_vs_yield.set_index(pd.DatetimeIndex(recession_vs_yield.index))\n",
    "recession_vs_yield.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Value_3mon    float64\n",
       "Value_10yr    float64\n",
       "spread        float64\n",
       "USREC         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recession_vs_yield.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "nasdaqdatalink.read_key(\"/data/.apikey\")\n",
    "SOC35 = nasdaqdatalink.get('UMICH/SOC35')\n",
    "#Include michigan state university data on current levels of fear WRT the economy\n",
    "SOC35 = SOC35.set_index(SOC35.index.strftime('%Y-%m'))\n",
    "date_index = recession_vs_yield.index.strftime('%Y-%m')\n",
    "recession_vs_yield = recession_vs_yield.set_index(date_index)\n",
    "recession_vs_yield = SOC35.join(recession_vs_yield, how = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Good time to buy</th>\n",
       "      <th>Uncertain - depends</th>\n",
       "      <th>Bad time to buy</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Value_3mon</th>\n",
       "      <th>Value_10yr</th>\n",
       "      <th>spread</th>\n",
       "      <th>USREC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.719545</td>\n",
       "      <td>4.083182</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.732778</td>\n",
       "      <td>4.039444</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.723182</td>\n",
       "      <td>3.930455</td>\n",
       "      <td>1.207273</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.729500</td>\n",
       "      <td>3.843000</td>\n",
       "      <td>1.113500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.685000</td>\n",
       "      <td>3.873636</td>\n",
       "      <td>1.188636</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.630870</td>\n",
       "      <td>2.897826</td>\n",
       "      <td>0.266957</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.126667</td>\n",
       "      <td>3.519048</td>\n",
       "      <td>0.392381</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.717000</td>\n",
       "      <td>3.983500</td>\n",
       "      <td>0.266500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.151500</td>\n",
       "      <td>3.891000</td>\n",
       "      <td>-0.260500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12</th>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.252381</td>\n",
       "      <td>3.616190</td>\n",
       "      <td>-0.636190</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Good time to buy  Uncertain - depends  Bad time to buy  Relative  \\\n",
       "date                                                                        \n",
       "1962-01               NaN                  NaN              NaN       NaN   \n",
       "1962-02               NaN                  NaN              NaN       NaN   \n",
       "1962-03               NaN                  NaN              NaN       NaN   \n",
       "1962-04               NaN                  NaN              NaN       NaN   \n",
       "1962-05               NaN                  NaN              NaN       NaN   \n",
       "...                   ...                  ...              ...       ...   \n",
       "2022-08              32.0                  5.0             63.0      69.0   \n",
       "2022-09              34.0                  5.0             61.0      73.0   \n",
       "2022-10              42.0                  6.0             52.0      90.0   \n",
       "2022-11              34.0                  5.0             61.0      73.0   \n",
       "2022-12              36.0                  5.0             59.0      77.0   \n",
       "\n",
       "         Value_3mon  Value_10yr    spread  USREC  \n",
       "date                                              \n",
       "1962-01    2.719545    4.083182  1.363636    0.0  \n",
       "1962-02    2.732778    4.039444  1.306667    0.0  \n",
       "1962-03    2.723182    3.930455  1.207273    0.0  \n",
       "1962-04    2.729500    3.843000  1.113500    0.0  \n",
       "1962-05    2.685000    3.873636  1.188636    0.0  \n",
       "...             ...         ...       ...    ...  \n",
       "2022-08    2.630870    2.897826  0.266957    0.0  \n",
       "2022-09    3.126667    3.519048  0.392381    0.0  \n",
       "2022-10    3.717000    3.983500  0.266500    0.0  \n",
       "2022-11    4.151500    3.891000 -0.260500    0.0  \n",
       "2022-12    4.252381    3.616190 -0.636190    0.0  \n",
       "\n",
       "[732 rows x 8 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recession_vs_yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment = get_data(api_key, \"UNRATE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "if unemployment.index.dtype == 'datetime64[ns]':\n",
    "    unemployment.index = unemployment.index.strftime('%Y-%m')\n",
    "combined2 = recession_vs_yield.join(unemployment, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Good time to buy</th>\n",
       "      <th>Uncertain - depends</th>\n",
       "      <th>Bad time to buy</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Value_3mon</th>\n",
       "      <th>Value_10yr</th>\n",
       "      <th>spread</th>\n",
       "      <th>USREC</th>\n",
       "      <th>UNRATE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.719545</td>\n",
       "      <td>4.083182</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.732778</td>\n",
       "      <td>4.039444</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.723182</td>\n",
       "      <td>3.930455</td>\n",
       "      <td>1.207273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.729500</td>\n",
       "      <td>3.843000</td>\n",
       "      <td>1.113500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.685000</td>\n",
       "      <td>3.873636</td>\n",
       "      <td>1.188636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.630870</td>\n",
       "      <td>2.897826</td>\n",
       "      <td>0.266957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.126667</td>\n",
       "      <td>3.519048</td>\n",
       "      <td>0.392381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.717000</td>\n",
       "      <td>3.983500</td>\n",
       "      <td>0.266500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.151500</td>\n",
       "      <td>3.891000</td>\n",
       "      <td>-0.260500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12</th>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.252381</td>\n",
       "      <td>3.616190</td>\n",
       "      <td>-0.636190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Good time to buy  Uncertain - depends  Bad time to buy  Relative  \\\n",
       "date                                                                        \n",
       "1962-01               NaN                  NaN              NaN       NaN   \n",
       "1962-02               NaN                  NaN              NaN       NaN   \n",
       "1962-03               NaN                  NaN              NaN       NaN   \n",
       "1962-04               NaN                  NaN              NaN       NaN   \n",
       "1962-05               NaN                  NaN              NaN       NaN   \n",
       "...                   ...                  ...              ...       ...   \n",
       "2022-08              32.0                  5.0             63.0      69.0   \n",
       "2022-09              34.0                  5.0             61.0      73.0   \n",
       "2022-10              42.0                  6.0             52.0      90.0   \n",
       "2022-11              34.0                  5.0             61.0      73.0   \n",
       "2022-12              36.0                  5.0             59.0      77.0   \n",
       "\n",
       "         Value_3mon  Value_10yr    spread  USREC UNRATE  \n",
       "date                                                     \n",
       "1962-01    2.719545    4.083182  1.363636    0.0    5.8  \n",
       "1962-02    2.732778    4.039444  1.306667    0.0    5.5  \n",
       "1962-03    2.723182    3.930455  1.207273    0.0    5.6  \n",
       "1962-04    2.729500    3.843000  1.113500    0.0    5.6  \n",
       "1962-05    2.685000    3.873636  1.188636    0.0    5.5  \n",
       "...             ...         ...       ...    ...    ...  \n",
       "2022-08    2.630870    2.897826  0.266957    0.0    3.7  \n",
       "2022-09    3.126667    3.519048  0.392381    0.0    3.5  \n",
       "2022-10    3.717000    3.983500  0.266500    0.0    3.7  \n",
       "2022-11    4.151500    3.891000 -0.260500    0.0    3.6  \n",
       "2022-12    4.252381    3.616190 -0.636190    0.0    3.5  \n",
       "\n",
       "[732 rows x 9 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_market_data = nasdaqdatalink.get('YALE/RHPI')\n",
    "housing_market_data = housing_market_data.rename(columns = {'Index': 'Yale_Housing'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "fred_housing = get_data(api_key, \"CSUSHPINSA\")\n",
    "fred_housing = fred_housing[fred_housing.CSUSHPINSA != \".\"]\n",
    "fred_housing['CSUSHPINSA'] = fred_housing['CSUSHPINSA'].astype(float)\n",
    "fred_housing = fred_housing.rename(columns = {'CSUSHPINSA': 'FRED_Housing'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust index to match other data\n",
    "fred_housing.index = fred_housing.index.strftime('%Y-%m')\n",
    "housing_market_data.index = housing_market_data.index.strftime('%Y-%m')\n",
    "combined_housing = fred_housing.join(housing_market_data, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>FRED_Housing</th>\n",
       "      <th>Yale_Housing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-01</th>\n",
       "      <td>63.734</td>\n",
       "      <td>122.638054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02</th>\n",
       "      <td>64.134</td>\n",
       "      <td>122.963501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-03</th>\n",
       "      <td>64.469</td>\n",
       "      <td>123.056393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04</th>\n",
       "      <td>64.973</td>\n",
       "      <td>123.360053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-05</th>\n",
       "      <td>65.547</td>\n",
       "      <td>124.009712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>308.371</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>307.177</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>303.706</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>300.588</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>298.990</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FRED_Housing  Yale_Housing\n",
       "date                               \n",
       "1987-01        63.734    122.638054\n",
       "1987-02        64.134    122.963501\n",
       "1987-03        64.469    123.056393\n",
       "1987-04        64.973    123.360053\n",
       "1987-05        65.547    124.009712\n",
       "...               ...           ...\n",
       "2022-06       308.371           NaN\n",
       "2022-07       307.177           NaN\n",
       "2022-08       303.706           NaN\n",
       "2022-09       300.588           NaN\n",
       "2022-10       298.990           NaN\n",
       "\n",
       "[430 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#adjust fred housing with cpi\n",
    "cpi = get_data(api_key, \"CPIAUCSL\")\n",
    "cpi = cpi.rename(columns = {'CPIAUCSL': 'Value'})\n",
    "cpi = cpi.set_index(cpi.index.strftime('%Y-%m'))\n",
    "cpi = cpi.join(combined_housing, how = 'left')\n",
    "cpi = cpi.dropna()\n",
    "cpi['Value'] = cpi['Value'].astype(float)\n",
    "cpi['FRED_Housing'] = cpi['FRED_Housing'].astype(float)\n",
    "cpi['adjusted_housing'] = (cpi['FRED_Housing']/ cpi['Value'])\n",
    "cpi = cpi.drop(columns = ['Value'])\n",
    "cpi.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213.0298949761555"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# do linear regression to get scaling factor between fred and yale\n",
    "X = cpi['adjusted_housing'].values.reshape(-1, 1)\n",
    "y = cpi['Yale_Housing'].values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "reg.score(X, y)\n",
    "reg.coef_[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpi_series = get_data(api_key, \"CPIAUCSL\")\n",
    "cpi_series = cpi_series.set_index(cpi_series.index.strftime('%Y-%m'))\n",
    "#adjusted_housing = fred_housing * reg.coef_[0][0] / cpi_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>FRED_Housing</th>\n",
       "      <th>CPIAUCSL</th>\n",
       "      <th>adjusted_housing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-01</th>\n",
       "      <td>63.734</td>\n",
       "      <td>111.400</td>\n",
       "      <td>121.878342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02</th>\n",
       "      <td>64.134</td>\n",
       "      <td>111.800</td>\n",
       "      <td>122.204466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-03</th>\n",
       "      <td>64.469</td>\n",
       "      <td>112.200</td>\n",
       "      <td>122.404851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04</th>\n",
       "      <td>64.973</td>\n",
       "      <td>112.700</td>\n",
       "      <td>122.814475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-05</th>\n",
       "      <td>65.547</td>\n",
       "      <td>113.000</td>\n",
       "      <td>123.570536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>308.371</td>\n",
       "      <td>295.328</td>\n",
       "      <td>222.438244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>307.177</td>\n",
       "      <td>295.271</td>\n",
       "      <td>221.619746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>303.706</td>\n",
       "      <td>295.620</td>\n",
       "      <td>218.856834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>300.588</td>\n",
       "      <td>296.761</td>\n",
       "      <td>215.777107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>298.990</td>\n",
       "      <td>298.062</td>\n",
       "      <td>213.693152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FRED_Housing  CPIAUCSL  adjusted_housing\n",
       "date                                             \n",
       "1987-01        63.734   111.400        121.878342\n",
       "1987-02        64.134   111.800        122.204466\n",
       "1987-03        64.469   112.200        122.404851\n",
       "1987-04        64.973   112.700        122.814475\n",
       "1987-05        65.547   113.000        123.570536\n",
       "...               ...       ...               ...\n",
       "2022-06       308.371   295.328        222.438244\n",
       "2022-07       307.177   295.271        221.619746\n",
       "2022-08       303.706   295.620        218.856834\n",
       "2022-09       300.588   296.761        215.777107\n",
       "2022-10       298.990   298.062        213.693152\n",
       "\n",
       "[430 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_housing = fred_housing.join(cpi_series, how='left')\n",
    "adjusted_housing['CPIAUCSL'] = adjusted_housing['CPIAUCSL'].astype(float)\n",
    "adjusted_housing['FRED_Housing'] = adjusted_housing['FRED_Housing'].astype(float)\n",
    "# remove all rows where CPIAUCSL is NaN or FRED_Housing is '.'ArithmeticError\n",
    "#adjusted_housing = adjusted_housing[adjusted_housing.CPIAUCSL.notna()]\n",
    "adjusted_housing = adjusted_housing[adjusted_housing.FRED_Housing != \".\"]\n",
    "adjusted_housing.dtypes\n",
    "adjusted_housing['adjusted_housing'] = (adjusted_housing['FRED_Housing'] * reg.coef_[0][0] / adjusted_housing['CPIAUCSL']).astype(float)\n",
    "adjusted_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FRED_Housing</th>\n",
       "      <th>Yale_Housing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-01</th>\n",
       "      <td>63.734</td>\n",
       "      <td>122.638054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02</th>\n",
       "      <td>64.134</td>\n",
       "      <td>122.963501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-03</th>\n",
       "      <td>64.469</td>\n",
       "      <td>123.056393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04</th>\n",
       "      <td>64.973</td>\n",
       "      <td>123.360053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-05</th>\n",
       "      <td>65.547</td>\n",
       "      <td>124.009712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>308.371</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>307.177</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>303.706</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>300.588</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>298.990</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FRED_Housing  Yale_Housing\n",
       "date                               \n",
       "1987-01        63.734    122.638054\n",
       "1987-02        64.134    122.963501\n",
       "1987-03        64.469    123.056393\n",
       "1987-04        64.973    123.360053\n",
       "1987-05        65.547    124.009712\n",
       "...               ...           ...\n",
       "2022-06       308.371           NaN\n",
       "2022-07       307.177           NaN\n",
       "2022-08       303.706           NaN\n",
       "2022-09       300.588           NaN\n",
       "2022-10       298.990           NaN\n",
       "\n",
       "[430 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>adjusted_housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1890-12</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891-12</th>\n",
       "      <td>88.011791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892-12</th>\n",
       "      <td>95.421736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893-12</th>\n",
       "      <td>92.297385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-12</th>\n",
       "      <td>123.980483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06</th>\n",
       "      <td>222.438244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07</th>\n",
       "      <td>221.619746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>218.856834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>215.777107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>213.693152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>901 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         adjusted_housing\n",
       "1890-12        100.000000\n",
       "1891-12         88.011791\n",
       "1892-12         95.421736\n",
       "1893-12         92.297385\n",
       "1894-12        123.980483\n",
       "...                   ...\n",
       "2022-06        222.438244\n",
       "2022-07        221.619746\n",
       "2022-08        218.856834\n",
       "2022-09        215.777107\n",
       "2022-10        213.693152\n",
       "\n",
       "[901 rows x 1 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop everything after 1987-01\n",
    "housing_market_data = housing_market_data[housing_market_data.index < '1987-01']\n",
    "# concat with adjusted housing\n",
    "adjusted_housing = pd.concat([housing_market_data['Yale_Housing'], adjusted_housing['adjusted_housing']], axis = 0)\n",
    "adjusted_housing = adjusted_housing.to_frame(name='adjusted_housing')\n",
    "adjusted_housing = adjusted_housing.set_index(pd.to_datetime(adjusted_housing.index).strftime('%Y-%m'))\n",
    "adjusted_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined3 = combined2.join(adjusted_housing, how = 'left')\n",
    "combined3 = combined3.rename(columns = {'adjusted_housing': 'housing market', 'UNRATE': 'Value'})\n",
    "# rename the date index column to Date\n",
    "combined3 = combined3.rename_axis('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Good time to buy       192\n",
       "Uncertain - depends    192\n",
       "Bad time to buy        192\n",
       "Relative               192\n",
       "Value_3mon               0\n",
       "Value_10yr               0\n",
       "spread                   0\n",
       "USREC                    0\n",
       "Value                    0\n",
       "housing market           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined3.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Good time to buy</th>\n",
       "      <th>Uncertain - depends</th>\n",
       "      <th>Bad time to buy</th>\n",
       "      <th>Relative</th>\n",
       "      <th>Value_3mon</th>\n",
       "      <th>Value_10yr</th>\n",
       "      <th>spread</th>\n",
       "      <th>USREC</th>\n",
       "      <th>Value</th>\n",
       "      <th>housing market</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.719545</td>\n",
       "      <td>4.083182</td>\n",
       "      <td>1.363636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>109.981774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.732778</td>\n",
       "      <td>4.039444</td>\n",
       "      <td>1.306667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>109.380905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.723182</td>\n",
       "      <td>3.930455</td>\n",
       "      <td>1.207273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>109.380905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.729500</td>\n",
       "      <td>3.843000</td>\n",
       "      <td>1.113500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>109.018717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.685000</td>\n",
       "      <td>3.873636</td>\n",
       "      <td>1.188636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>109.605470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08</th>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.630870</td>\n",
       "      <td>2.897826</td>\n",
       "      <td>0.266957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>218.856834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.126667</td>\n",
       "      <td>3.519048</td>\n",
       "      <td>0.392381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>215.777107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.717000</td>\n",
       "      <td>3.983500</td>\n",
       "      <td>0.266500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>213.693152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.151500</td>\n",
       "      <td>3.891000</td>\n",
       "      <td>-0.260500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12</th>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.252381</td>\n",
       "      <td>3.616190</td>\n",
       "      <td>-0.636190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Good time to buy  Uncertain - depends  Bad time to buy  Relative  \\\n",
       "Date                                                                        \n",
       "1962-01               NaN                  NaN              NaN       NaN   \n",
       "1962-02               NaN                  NaN              NaN       NaN   \n",
       "1962-03               NaN                  NaN              NaN       NaN   \n",
       "1962-04               NaN                  NaN              NaN       NaN   \n",
       "1962-05               NaN                  NaN              NaN       NaN   \n",
       "...                   ...                  ...              ...       ...   \n",
       "2022-08              32.0                  5.0             63.0      69.0   \n",
       "2022-09              34.0                  5.0             61.0      73.0   \n",
       "2022-10              42.0                  6.0             52.0      90.0   \n",
       "2022-11              34.0                  5.0             61.0      73.0   \n",
       "2022-12              36.0                  5.0             59.0      77.0   \n",
       "\n",
       "         Value_3mon  Value_10yr    spread  USREC Value  housing market  \n",
       "Date                                                                    \n",
       "1962-01    2.719545    4.083182  1.363636    0.0   5.8      109.981774  \n",
       "1962-02    2.732778    4.039444  1.306667    0.0   5.5      109.380905  \n",
       "1962-03    2.723182    3.930455  1.207273    0.0   5.6      109.380905  \n",
       "1962-04    2.729500    3.843000  1.113500    0.0   5.6      109.018717  \n",
       "1962-05    2.685000    3.873636  1.188636    0.0   5.5      109.605470  \n",
       "...             ...         ...       ...    ...   ...             ...  \n",
       "2022-08    2.630870    2.897826  0.266957    0.0   3.7      218.856834  \n",
       "2022-09    3.126667    3.519048  0.392381    0.0   3.5      215.777107  \n",
       "2022-10    3.717000    3.983500  0.266500    0.0   3.7      213.693152  \n",
       "2022-11    4.151500    3.891000 -0.260500    0.0   3.6             NaN  \n",
       "2022-12    4.252381    3.616190 -0.636190    0.0   3.5             NaN  \n",
       "\n",
       "[732 rows x 10 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined3.to_csv('preprocessed_data.csv')"
   ]
  }
 ],
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