Risk_Adjusted_Returns AND Drawdown.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = pd.read_csv('data/Portfolios_Formed_on_ME_monthly_EW.csv',\n",
    "                     header=0, index_col=0, parse_dates=True, na_values=-99.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>&lt;= 0</th>\n",
       "      <th>Lo 30</th>\n",
       "      <th>Med 40</th>\n",
       "      <th>Hi 30</th>\n",
       "      <th>Lo 20</th>\n",
       "      <th>Qnt 2</th>\n",
       "      <th>Qnt 3</th>\n",
       "      <th>Qnt 4</th>\n",
       "      <th>Hi 20</th>\n",
       "      <th>Lo 10</th>\n",
       "      <th>Dec 2</th>\n",
       "      <th>Dec 3</th>\n",
       "      <th>Dec 4</th>\n",
       "      <th>Dec 5</th>\n",
       "      <th>Dec 6</th>\n",
       "      <th>Dec 7</th>\n",
       "      <th>Dec 8</th>\n",
       "      <th>Dec 9</th>\n",
       "      <th>Hi 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192607</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.68</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.47</td>\n",
       "      <td>3.33</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192608</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.33</td>\n",
       "      <td>5.12</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.72</td>\n",
       "      <td>4.72</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.98</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192609</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192610</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>-3.52</td>\n",
       "      <td>-3.06</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-4.83</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-2.43</td>\n",
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       "    <tr>\n",
       "      <th>192611</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>3.82</td>\n",
       "      <td>3.09</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>2.18</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.16</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.70</td>\n",
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      "text/plain": [
       "        <= 0  Lo 30  Med 40  Hi 30  Lo 20  Qnt 2  Qnt 3  Qnt 4  Hi 20  Lo 10  \\\n",
       "192607   NaN  -0.43    1.52   2.68  -0.57   0.59   1.60   1.47   3.33  -1.45   \n",
       "192608   NaN   3.90    3.04   2.09   3.84   3.59   3.71   1.61   2.33   5.12   \n",
       "192609   NaN  -1.08   -0.54   0.16  -0.48  -1.40   0.00  -0.50  -0.09   0.93   \n",
       "192610   NaN  -3.32   -3.52  -3.06  -3.29  -4.10  -2.89  -3.36  -2.95  -4.84   \n",
       "192611   NaN  -0.46    3.82   3.09  -0.55   2.18   3.41   3.39   3.16  -0.78   \n",
       "\n",
       "        Dec 2  Dec 3  Dec 4  Dec 5  Dec 6  Dec 7  Dec 8  Dec 9  Hi 10  \n",
       "192607   0.29  -0.15   1.33   1.24   1.98   1.55   1.38   3.38   3.29  \n",
       "192608   2.59   4.03   3.15   2.72   4.72   1.60   1.63   0.98   3.70  \n",
       "192609  -1.87  -2.27  -0.53   0.07  -0.07  -1.64   0.64  -0.86   0.67  \n",
       "192610  -1.77  -3.36  -4.83  -2.98  -2.80  -3.45  -3.27  -3.47  -2.43  \n",
       "192611  -0.32  -0.29   4.65   3.24   3.57   3.82   2.95   3.61   2.70  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Lo 10</th>\n",
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       "      <td>5.12</td>\n",
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       "      <th>192609</th>\n",
       "      <td>0.93</td>\n",
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       "      <th>192610</th>\n",
       "      <td>-4.84</td>\n",
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      "text/plain": [
       "        Lo 10  Hi 10\n",
       "192607  -1.45   3.29\n",
       "192608   5.12   3.70\n",
       "192609   0.93   0.67\n",
       "192610  -4.84  -2.43\n",
       "192611  -0.78   2.70"
      ]
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     "execution_count": 20,
     "metadata": {},
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   "source": [
    "columns = ['Lo 10', 'Hi 10']\n",
    "returns = returns[columns]\n",
    "returns.head()"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0512</td>\n",
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      "text/plain": [
       "         Lo 10   Hi 10\n",
       "192607 -0.0145  0.0329\n",
       "192608  0.0512  0.0370\n",
       "192609  0.0093  0.0067\n",
       "192610 -0.0484 -0.0243\n",
       "192611 -0.0078  0.0270"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "returns=returns/100\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SmallCap</th>\n",
       "      <th>LargeCap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>192607</th>\n",
       "      <td>-0.0145</td>\n",
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       "      <th>192609</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0067</td>\n",
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       "      <th>192610</th>\n",
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      "text/plain": [
       "        SmallCap  LargeCap\n",
       "192607   -0.0145    0.0329\n",
       "192608    0.0512    0.0370\n",
       "192609    0.0093    0.0067\n",
       "192610   -0.0484   -0.0243\n",
       "192611   -0.0078    0.0270"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.columns = ['SmallCap','LargeCap']\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ac11b43a0>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.106288\n",
       "LargeCap    0.053900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.368193\n",
       "LargeCap    0.186716\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_vol = returns.std()*np.sqrt(12)\n",
    "annualized_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.012986\n",
       "LargeCap    0.007423\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_months = returns.shape[0]\n",
    "return_per_month = (returns+1).prod()**(1/n_months)-1\n",
    "return_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.167463\n",
       "LargeCap    0.092810\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annua_return = (return_per_month+1)**12-1\n",
    "annua_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.454825\n",
       "LargeCap    0.497063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annua_return/annualized_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.373346\n",
       "LargeCap    0.336392\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riskfree_rate = 0.03\n",
    "excessReturn = annua_return-riskfree_rate\n",
    "sharp_ratio = excessReturn/annualized_vol\n",
    "sharp_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([192607, 192608, 192609, 192610, 192611, 192612, 192701, 192702,\n",
       "            192703, 192704,\n",
       "            ...\n",
       "            201803, 201804, 201805, 201806, 201807, 201808, 201809, 201810,\n",
       "            201811, 201812],\n",
       "           dtype='int64', length=1110)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns.index = pd.to_datetime(returns.index, format = \"%Y%m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>SmallCap</th>\n",
       "      <th>LargeCap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926-07-01</th>\n",
       "      <td>-0.0145</td>\n",
       "      <td>0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-08-01</th>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-09-01</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-10-01</th>\n",
       "      <td>-0.0484</td>\n",
       "      <td>-0.0243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-11-01</th>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.0270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SmallCap  LargeCap\n",
       "1926-07-01   -0.0145    0.0329\n",
       "1926-08-01    0.0512    0.0370\n",
       "1926-09-01    0.0093    0.0067\n",
       "1926-10-01   -0.0484   -0.0243\n",
       "1926-11-01   -0.0078    0.0270"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'PeriodIndex' object has no attribute 'to_period'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-46d5841692fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mreturns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"M\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mreturns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'PeriodIndex' object has no attribute 'to_period'"
     ]
    }
   ],
   "source": [
    "returns.index = returns.index.to_period(\"M\")\n",
    "returns.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1926-07', '1926-08', '1926-09', '1926-10', '1926-11', '1926-12',\n",
       "             '1927-01', '1927-02', '1927-03', '1927-04',\n",
       "             ...\n",
       "             '2018-03', '2018-04', '2018-05', '2018-06', '2018-07', '2018-08',\n",
       "             '2018-09', '2018-10', '2018-11', '2018-12'],\n",
       "            dtype='period[M]', length=1110, freq='M')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>SmallCap</th>\n",
       "      <th>LargeCap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998-01</th>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.0089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-02</th>\n",
       "      <td>0.0473</td>\n",
       "      <td>0.0767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-03</th>\n",
       "      <td>0.0509</td>\n",
       "      <td>0.0549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04</th>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-05</th>\n",
       "      <td>-0.0397</td>\n",
       "      <td>-0.0222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-06</th>\n",
       "      <td>-0.0490</td>\n",
       "      <td>0.0304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-07</th>\n",
       "      <td>-0.0498</td>\n",
       "      <td>-0.0126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-08</th>\n",
       "      <td>-0.2050</td>\n",
       "      <td>-0.1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-09</th>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-10</th>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-11</th>\n",
       "      <td>0.0967</td>\n",
       "      <td>0.0578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-12</th>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SmallCap  LargeCap\n",
       "1998-01    0.0324    0.0089\n",
       "1998-02    0.0473    0.0767\n",
       "1998-03    0.0509    0.0549\n",
       "1998-04    0.0422    0.0123\n",
       "1998-05   -0.0397   -0.0222\n",
       "1998-06   -0.0490    0.0304\n",
       "1998-07   -0.0498   -0.0126\n",
       "1998-08   -0.2050   -0.1508\n",
       "1998-09    0.0062    0.0680\n",
       "1998-10    0.0134    0.0919\n",
       "1998-11    0.0967    0.0578\n",
       "1998-12    0.0019    0.0553"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns['1998']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Drawdowns\n",
    "\n",
    "1. Compute a wealth index\n",
    "2. Compute previous peaks\n",
    "3. Compute drawdown-which is the wealth value as a percentage of the previous peak\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1926-07    1032.900000\n",
       "1926-08    1071.117300\n",
       "1926-09    1078.293786\n",
       "1926-10    1052.091247\n",
       "1926-11    1080.497711\n",
       "Freq: M, Name: LargeCap, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_index = 1000*(1+returns[\"LargeCap\"]).cumprod()\n",
    "wealth_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127fca8ef70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wealth_index.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127fce28d30>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/Rw8AMxt1S9MgDXoJuXPuk2EUIiIjX6FFPV1BHSh1fYhIYA4oqCtCQS0igTnY0UvjuCrqqnW/tyApqEUkMAc6epmh1nTgFNQiEoi2rjSvvHtEQV0BCmoRCcSD33mLrnSOG86dFnUpo46CWkSG7aUdR3hu6yH+w1Ut3HPlvKjLGXUU1CIybBvebwfg/mvmR1zJ6KSgFpFh23ush8n11cyZpCeNV4KCWkSGxTnHqzuPslhPGa8YBbWIlC2Ty/OnP97KriNd3HTRjKjLGbUU1CJStlVrdvLXL+yiqa6KFUumRl3OqKXLh0SkLK/uPMqjP93GzRfN4PFPLsPMoi5p1FKLWkTK8o2XdtNQk+LR31iqkK4wBbWIDNmuI138ZPNB7rp8LuOqk1GXM+opqEVkyL7y7L8BcOvSmRFXMjYoqEVkSN472sVPNx/iqoWTuWBWY9TljAkKahEZkidf3IUD/tfHLoy6lDFDQS0iJcvk8nx3/T5uWzqTeZProy5nzFBQi0jJvv36Hjp7s9y+bFbUpYwpGkctIv3q6Mmwas27PP9vreTyjmw+z/72Xi5raeKDi6ZEXd6YoqAWkdNkcnm+9+Y+Hv7e22TzjqkNNSybO5FUIsEFsxr59AcXaNx0yBTUInLShvfbefi7b7PlwHGSCeOrn1jKrUtnkUwomKOkoBYZ43ozOdq60jz1ym7+6hc7mVxfzWOfuJjrFjfTVF8ddXmCglpkTNt9pIuPPLaGdDYPwAemjufxu5exZPqEiCuTYgpqkTGovTvNH39/Mz/YuB+Ah359CctbJnHpvKaIK5P+KKhFxpjjvRk+8tU1HO7s49J5Tdx9+VzuuHR21GXJABTUIqNYe3eaV3e2se69NlZva2V/ew9d6RwAf/rRC7n7irkRVyilUFCLjFL5vOPWx19iT1s3ANMn1HLThTNoHFfFvCn1CukRREEtMsL1ZXO0d2d+af5f/WIne9q6+a83LOI/fXA+E2qrIqhOgqCgFhnBOnoy3Pb4i+w+2t3v8qsWTuazH1pITUr3jB7JFNQiI9SP3z7AH39/M8e60/zhTedSX3P6r3NddZIbL5iukB4FFNQiI4hzjjd2H+OlHUf4s59vZ86kcTx444V8fPmcqEuTClJQi4wQ6Wyelf93Lau3tQJQnUrw5L+/jEXTGiKuTCpNQS0yAuTzjge/s5HV21r5j1e38JlrF9JUX6VujTFCQS0SU845Xnn3KH/xrzvYdqiTtq4091w5l/9xy/lRlyYhU1CLROz9tm6eWb+Pjp4MDnAODnf2sn5PO/vae6hOJbjloplcPKeRe66cF3W5EgEFtUgFOefYdqiTnnSOvANw5J0Xxr2ZHM+s38e/bDpITyZHXXUSA8yMVNK4cFYjv3HpbO67Zj6N4zQGeixTUIsMwjmHc/itXXey1evw5qdzeXrTOfqyeVpP9HGgvZfdR7s42NHLC9tbzzrGGaC2KsFtS2ex8roFLGweH9q/SUaWkoLazG4E/gxIAn/jnPtSRauSUa0vm+NEb5YTfd4rk3Nkc3myeUcu78jk8mRyjnQ2TzqXoy+TpzfjBWFvJk9f1ptOF165PLm8I+8Hat5507m8F6x55+jL5mnrSnPkRB9dfbmTIes3ck/7vjiMh6O2KsHS2RP52CWzuXB2IwYkzDDzvwILmsczvbF2uKdURrlBg9rMksATwIeBvcAbZvYD59yWShc3ljk/JQph4Yrm55wjn4dsPu8/y86RzXkB5017QZfN508LwUwuT7Z4vv811+82p2/flc7Snc6dPE7hWOnsqa9dfVky+Tz5POTyhTpPfc07TgbrcKQSRk0qQXXRK2lGImEkzEgUgtCfTiaMVMKYM6mOZXObqK9OYuZ1MRiAgeEFqNf1cPr3+OsVQvbkOv7jqGpSCcZVJ6lOJqirTrGguZ4ZjbVMrNNN9yUYpbSoLwd2OOd2ApjZt4HbgLMG9TuHOlnxv1d73/QTNKemC8tOhdKZrZiTgXWW9U/f98mtzrLc9bv+mfP7q7nf7c44Tv/7GFotcWIGVYkEqaRRV52iviZJKmFUJRP+y5uuq07SOK6Klin1VCW8wCwEZzIBST80kwmjOpVgfE2KhtoU9dUp6mtS1KQSJ8M0lUyQTEB1MnkyhGv8V21VkppUglQyEfWpEQlVKUE9C3i/6Pu9wBVnrmRmK4GVABNmLuDcGROwU8u8rye/56zLCq2bwnq/vN0Zy4o3PMs23vdnW376s+AGX//sdZ+aLK8W+jlP/a2fTNjJYEv4Hzyl/ECt8qerTpuXOBmCxfMLoZsq3sZftyqZ0HPyRGKilKDu77f1l9p/zrlVwCqA5cuXuyfuvmSYpYmICEApf0PuBYpvJDAb2F+ZckRE5EylBPUbwCIzm29m1cBdwA8qW5aIiBQM2vXhnMua2W8Bz+INz3vSObe54pWJiAhQ4jhq59yPgR9XuBYREemHxjmJiMScglpEJOYU1CIiMaegFhGJOXMVuHbZzFqB9wZZbQpwJIDDNQIdAexH9QwsbvVA/GpSPQMbrfVAMDXNc84197vEu4Vj+C9gbUD7WaV6xl49caxJ9YzNeoKs6Wyv0dD18cOoCziD6hlY3OqB+NWkegYWt3qgwjWN+KB2zsXqh6Z6Bha3eiB+NamegcWtHqh8TVEG9aoIj90f1TOwuNUD8atJ9QxM9ZSpIh8miohIcEZ814eIyGinoBYRibnAgtrMnjSzw2a2qWjeUjN7xczeNrMfmtkEf/6HzWydP3+dma0o2uYTZvaWmW02s0dCqudyM9vgvzaa2UejrKdo+VwzO2Fmnwu6nqHWZGYtZtZTdJ6+FnRNQz1HZnaRv2yzv7w2qnrM7FNF52aDmeXN7OII66kys6f8+VvN7KGibaJ6D1Wb2df9+RvN7PogazKzOWb2vP/v3WxmD/jzJ5nZz8xsu/+1qWibh8xsh5ltM7NfC7KeQAU4jvBa4BJgU9G8N4Dr/On7gC/608uAmf70BcA+f3oysAdo9r9/CrghhHrqgJQ/PQM4jHdnwUjqKVr+z8A/AZ8L+vyUcY5aitcrWj+qn1kKeAtYWlRHMuqfmT//QmBnxOfnbuDbRe/v3f7PMMr30GeBr/vTU4F1eI3FQGrC+929xJ9uAN4BzgMeAf7An/8HwJf96fOAjUANMB94N+j3UFCvwFrUzrk1QNsZs88B1vjTPwPu8Ndd75wrPCVmM1BrZjXAAuAd51yrv+y5wjYVrqfbOZf159dy6lFjkdQDYGa3Azvxzk9BYPWUU9NZRHWOPgK85Zzb6G971DmXi7CeYp8Envano6rHAfVmlgLGAWngeJD1lFHTecDP/e0OA+3A8qBqcs4dcM696U93Alvxnvl6G17Y4n+93Z++De8/sz7n3C5gB97DvAM9R0GodB/1JuBWf/pOTn+kV8EdwHrnXB/eiVri/5mdwjuh/W0TeD1mdoWZbQbeBv6zH9yR1GNm9cDvA184Y/1K13PWmnzzzWy9mf3CzD4YUk1nq2cx4MzsWTN708wejLieYp/gVFBHVc93gC7gAF7r8FHnXFsI9QxU00bgNjNLmdl84FJ/WeA1mVkL3l/urwHTnHMHwAtzvNY89P/g7lmVqGe4Kh3U9wGfNbN1eH+KpIsXmtn5wJeBzwA4544Bvwn8A/AC3p9rWYJz1nqcc685584HLgMeMrPaCOv5AvBV59yJ4pVDqGegmg4Ac51zy4DfBb5lZhMiPEcp4BrgU/7Xj5rZDVG+h8D7Dx/ods5tgkjf05cDOWAm3p/1v2dmCyJ+Dz2JF4ZrgceAl4Fs0DWZ2Xi8bsPfcc4dH2jVfua5kM7R0ATZj8JZ+jH9ZYuB14u+n43Xh3T1APtbCTwSRj1nLHseWB5VPZx6c+zG+/OwDfitoOsZ5jlaHfE5ugv4RtGy/w58Pur3EPBV4OEB9hfW+XkCuLdo2ZPAx2P2HnoZOC/ImoAqvMcG/m7RvG3ADH96BrDNn34IeKhovWeBX6nEORruK9idnfEDA6b6XxPAN4H7/O8n4v0ZdEc/+yhs0wRsABaHUM98Tn2YOA/vKetToqrnjG3+BP/DxKDrGeI5agaS/vQCYB8wKcKfWRPwJv4HwXj9iDdH+TPz5+0FFsTgPf37wNfxWo31wBbgoojfQ3VAvT/9YWBNkOfI/7d+E3jsjPlf4fQPEx/xp8/n9A8Tdxa9xwM9R8N9Bbcjr0/uAJDx36z3Aw/gtZrfAb7EqSsh/wiv/2xD0Wtq0X62+K+7QqrnXrwP7Tbg/fLffsZ+Qq3njO3+hNODOpB6yjhHd/jnaKN/jm6J+hwB9/g1baKoxRNhPdcDr55lP2G/p8fjjRja7B/380HXU0ZNLXit2614/7HOC7ImvC4whzcaqJArN+GN4vg5sN3/Oqlomz/EG+2xDfj1SpyjIF66hFxEJOZ0ZaKISMwpqEVEYk5BLSIScwpqEZGYU1CLiMScglpEJOYU1CIiMff/ARSXnzTJzxmRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "previous_peaks = wealth_index.cummax()\n",
    "previous_peaks.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127fcecdfd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawdown = (wealth_index - previous_peaks)/previous_peaks\n",
    "drawdown.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1926-07    0.0000\n",
       "1926-08    0.0000\n",
       "1926-09    0.0000\n",
       "1926-10   -0.0243\n",
       "1926-11    0.0000\n",
       "Freq: M, Name: LargeCap, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8400375277943123"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009-02 -0.5280945042309304\n"
     ]
    }
   ],
   "source": [
    "print(drawdown['1975':].idxmin(),drawdown['1975':].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('1932-05', 'M')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawdown(returns_series: pd.Series):\n",
    "    \"\"\"\n",
    "    takes a times series od asset returns\n",
    "    Computes and returns a DF that contains:\n",
    "    the wealth index\n",
    "    the previous peaks\n",
    "    percent drawdowns\n",
    "    \"\"\"\n",
    "    wealth_index = 1000*(1+returns_series).cumprod()\n",
    "    previous_peaks = wealth_index.cummax()\n",
    "    drawdowns = (wealth_index - previous_peaks)/previous_peaks\n",
    "    return pd.DataFrame({\n",
    "        \"Wealth\": wealth_index,\n",
    "        \"Peaks\": previous_peaks,\n",
    "        \"Drawdown\": drawdowns\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "DD=drawdown(returns['LargeCap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Wealth</th>\n",
       "      <th>Peaks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926-07</th>\n",
       "      <td>1.032900e+03</td>\n",
       "      <td>1.032900e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-08</th>\n",
       "      <td>1.071117e+03</td>\n",
       "      <td>1.071117e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-09</th>\n",
       "      <td>1.078294e+03</td>\n",
       "      <td>1.078294e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-10</th>\n",
       "      <td>1.052091e+03</td>\n",
       "      <td>1.078294e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-11</th>\n",
       "      <td>1.080498e+03</td>\n",
       "      <td>1.080498e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>4.175915e+06</td>\n",
       "      <td>4.175915e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>4.212246e+06</td>\n",
       "      <td>4.212246e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>3.935501e+06</td>\n",
       "      <td>4.212246e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>4.035069e+06</td>\n",
       "      <td>4.212246e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>3.675948e+06</td>\n",
       "      <td>4.212246e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1110 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Wealth         Peaks\n",
       "1926-07  1.032900e+03  1.032900e+03\n",
       "1926-08  1.071117e+03  1.071117e+03\n",
       "1926-09  1.078294e+03  1.078294e+03\n",
       "1926-10  1.052091e+03  1.078294e+03\n",
       "1926-11  1.080498e+03  1.080498e+03\n",
       "...               ...           ...\n",
       "2018-08  4.175915e+06  4.175915e+06\n",
       "2018-09  4.212246e+06  4.212246e+06\n",
       "2018-10  3.935501e+06  4.212246e+06\n",
       "2018-11  4.035069e+06  4.212246e+06\n",
       "2018-12  3.675948e+06  4.212246e+06\n",
       "\n",
       "[1110 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DD[[\"Wealth\",\"Peaks\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127fe09c730>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DD[[\"Wealth\",\"Peaks\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127fe132940>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawdown(returns[:\"1950\"]['LargeCap'])[[\"Wealth\",\"Peaks\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8400375277943123"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns['LargeCap'])[\"Drawdown\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8330007793945303"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns['SmallCap'])[\"Drawdown\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('1932-05', 'M')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns['SmallCap'])[\"Drawdown\"].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('1932-05', 'M')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns['LargeCap'])[\"Drawdown\"].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7000596091967917"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1940\":]['SmallCap'])[\"Drawdown\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('1974-12', 'M')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1940\":]['SmallCap'])[\"Drawdown\"].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6312068077252386"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1975\":]['SmallCap'])[\"Drawdown\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009-02', 'M')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1975\":]['SmallCap'])[\"Drawdown\"].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5280945042309303"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1975\":]['LargeCap'])[\"Drawdown\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009-02', 'M')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown(returns[\"1975\":]['LargeCap'])[\"Drawdown\"].idxmin()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}