Week 1 - Lab session - Risk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = pd.read_csv(\"data/sample_prices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = prices.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.023621</td>\n",
       "      <td>0.039662</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.021807</td>\n",
       "      <td>-0.033638</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.031763</td>\n",
       "      <td>0.082232</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.034477</td>\n",
       "      <td>0.044544</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.037786</td>\n",
       "      <td>-0.026381</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.011452</td>\n",
       "      <td>-0.049187</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.032676</td>\n",
       "      <td>0.117008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.012581</td>\n",
       "      <td>0.067353</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.029581</td>\n",
       "      <td>0.078249</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.006151</td>\n",
       "      <td>-0.168261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.012162</td>\n",
       "      <td>0.024041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.021149</td>\n",
       "      <td>-0.055623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "        BLUE    ORANGE\n",
       "0        NaN       NaN\n",
       "1   0.023621  0.039662\n",
       "2  -0.021807 -0.033638\n",
       "3  -0.031763  0.082232\n",
       "4   0.034477  0.044544\n",
       "5   0.037786 -0.026381\n",
       "6  -0.011452 -0.049187\n",
       "7   0.032676  0.117008\n",
       "8  -0.012581  0.067353\n",
       "9   0.029581  0.078249\n",
       "10  0.006151 -0.168261\n",
       "11  0.012162  0.024041\n",
       "12  0.021149 -0.055623"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = returns.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.023621</td>\n",
       "      <td>0.039662</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.021807</td>\n",
       "      <td>-0.033638</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.031763</td>\n",
       "      <td>0.082232</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.034477</td>\n",
       "      <td>0.044544</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.037786</td>\n",
       "      <td>-0.026381</td>\n",
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       "      <th>6</th>\n",
       "      <td>-0.011452</td>\n",
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       "      <th>7</th>\n",
       "      <td>0.032676</td>\n",
       "      <td>0.117008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.012581</td>\n",
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       "      <th>9</th>\n",
       "      <td>0.029581</td>\n",
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       "      <th>10</th>\n",
       "      <td>0.006151</td>\n",
       "      <td>-0.168261</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.012162</td>\n",
       "      <td>0.024041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.021149</td>\n",
       "      <td>-0.055623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        BLUE    ORANGE\n",
       "1   0.023621  0.039662\n",
       "2  -0.021807 -0.033638\n",
       "3  -0.031763  0.082232\n",
       "4   0.034477  0.044544\n",
       "5   0.037786 -0.026381\n",
       "6  -0.011452 -0.049187\n",
       "7   0.032676  0.117008\n",
       "8  -0.012581  0.067353\n",
       "9   0.029581  0.078249\n",
       "10  0.006151 -0.168261\n",
       "11  0.012162  0.024041\n",
       "12  0.021149 -0.055623"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "deviations = returns - returns.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sq_deviations = deviations**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "variance = sq_deviations.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "volatility = np.sqrt(variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.022957\n",
       "ORANGE    0.076212\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_observations = returns.shape[0] # or .count()\n",
    "variance = sq_deviations.sum() / (number_of_observations - 1 )\n",
    "volatility = variance ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.083060\n",
       "ORANGE    0.275747\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annualizing volatilty \n",
    "returns.std() * np.sqrt(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = pd.read_csv(\"data/Portfolios_Formed_on_ME_monthly_EW.csv\",\n",
    "                        header=0,\n",
    "                        index_col=0,\n",
    "                        parse_dates=True,\n",
    "                        na_values=-99.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
       "    </tr>\n",
       "    <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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       "      <th>...</th>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>201808</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.09</td>\n",
       "      <td>5.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.41</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.30</td>\n",
       "      <td>4.70</td>\n",
       "      <td>3.02</td>\n",
       "      <td>5.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201809</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201810</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.76</td>\n",
       "      <td>-10.88</td>\n",
       "      <td>-7.63</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>-11.74</td>\n",
       "      <td>-10.55</td>\n",
       "      <td>-9.45</td>\n",
       "      <td>-7.41</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-11.98</td>\n",
       "      <td>-11.89</td>\n",
       "      <td>-11.52</td>\n",
       "      <td>-11.58</td>\n",
       "      <td>-9.23</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-8.28</td>\n",
       "      <td>-6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201811</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.49</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201812</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.28</td>\n",
       "      <td>-12.41</td>\n",
       "      <td>-9.76</td>\n",
       "      <td>-14.77</td>\n",
       "      <td>-12.44</td>\n",
       "      <td>-12.22</td>\n",
       "      <td>-11.34</td>\n",
       "      <td>-9.21</td>\n",
       "      <td>-15.31</td>\n",
       "      <td>-13.19</td>\n",
       "      <td>-11.94</td>\n",
       "      <td>-13.13</td>\n",
       "      <td>-12.73</td>\n",
       "      <td>-11.57</td>\n",
       "      <td>-11.87</td>\n",
       "      <td>-10.75</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>-8.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1110 rows × 19 columns</p>\n",
       "</div>"
      ],
      "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",
       "201808   NaN   3.47    4.04   2.87   3.09   5.05   3.90   3.54   2.49   2.41   \n",
       "201809   NaN  -2.24   -1.85   0.08  -2.04  -2.38  -2.48  -0.74   0.19  -1.68   \n",
       "201810   NaN -10.76  -10.88  -7.63 -10.52 -11.74 -10.55  -9.45  -7.41 -10.02   \n",
       "201811   NaN  -2.08    2.18   2.19  -2.78   1.69   1.46   2.62   2.49  -3.65   \n",
       "201812   NaN -14.28  -12.41  -9.76 -14.77 -12.44 -12.22 -11.34  -9.21 -15.31   \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  \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "201808   5.07   5.30   4.70   3.02   5.01   3.50   3.58   2.65   2.34  \n",
       "201809  -3.08  -3.22  -1.23  -2.65  -2.27  -1.31  -0.12  -0.51   0.87  \n",
       "201810 -11.98 -11.89 -11.52 -11.58  -9.23 -10.74  -8.04  -8.28  -6.57  \n",
       "201811  -0.23   1.23   2.30   1.74   1.11   3.54   1.63   2.45   2.53  \n",
       "201812 -13.19 -11.94 -13.13 -12.73 -11.57 -11.87 -10.75  -9.54  -8.90  \n",
       "\n",
       "[1110 rows x 19 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Lo 10</th>\n",
       "      <th>Hi 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192607</th>\n",
       "      <td>-1.45</td>\n",
       "      <td>3.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192608</th>\n",
       "      <td>5.12</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192609</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192610</th>\n",
       "      <td>-4.84</td>\n",
       "      <td>-2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192611</th>\n",
       "      <td>-0.78</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Lo 10', 'Hi 10']\n",
    "returns = returns[columns]\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Lo 10</th>\n",
       "      <th>Hi 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192607</th>\n",
       "      <td>-0.0145</td>\n",
       "      <td>0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192608</th>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.0370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192609</th>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192610</th>\n",
       "      <td>-0.0484</td>\n",
       "      <td>-0.0243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192611</th>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.0270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = returns / 100\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns.columns = ['Small cap', 'Large cap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a7b6d6898>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.106288\n",
       "Large cap    0.053900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "annualized_volatility = returns.std()*np.sqrt(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.368193\n",
       "Large cap    0.186716\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small cap    1.298637\n",
      "Large cap    0.742342\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "n_months = returns.shape[0]\n",
    "return_per_month = (returns + 1).prod() ** (1 / n_months) - 1\n",
    "print(return_per_month*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.167463\n",
       "Large cap    0.092810\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_return = (return_per_month + 1)**12 - 1\n",
    "annualized_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One step formula for annualized return\n",
    "annualized_return = (returns + 1).prod() ** (12 / n_months) - 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.167463\n",
       "Large cap    0.092810\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.454825\n",
       "Large cap    0.497063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_return / annualized_volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Small cap    0.373346\n",
       "Large cap    0.336392\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riskfree_rate = 0.03 # Normally you should take a timeseries of the riskfree rate\n",
    "excess_return = annualized_return - riskfree_rate\n",
    "sharpe_ratio = excess_return / annualized_volatility\n",
    "sharpe_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = pd.read_csv(\"data/Portfolios_Formed_on_ME_monthly_EW.csv\",\n",
    "                        header=0,\n",
    "                        index_col=0,\n",
    "                        parse_dates=True,\n",
    "                        na_values=-99.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>&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",
       "    </tr>\n",
       "    <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",
       "    </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",
       "      <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>201808</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.09</td>\n",
       "      <td>5.05</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.41</td>\n",
       "      <td>5.07</td>\n",
       "      <td>5.30</td>\n",
       "      <td>4.70</td>\n",
       "      <td>3.02</td>\n",
       "      <td>5.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201809</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-2.48</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-3.08</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-2.65</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201810</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.76</td>\n",
       "      <td>-10.88</td>\n",
       "      <td>-7.63</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>-11.74</td>\n",
       "      <td>-10.55</td>\n",
       "      <td>-9.45</td>\n",
       "      <td>-7.41</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-11.98</td>\n",
       "      <td>-11.89</td>\n",
       "      <td>-11.52</td>\n",
       "      <td>-11.58</td>\n",
       "      <td>-9.23</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>-8.28</td>\n",
       "      <td>-6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201811</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>-2.78</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.49</td>\n",
       "      <td>-3.65</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201812</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.28</td>\n",
       "      <td>-12.41</td>\n",
       "      <td>-9.76</td>\n",
       "      <td>-14.77</td>\n",
       "      <td>-12.44</td>\n",
       "      <td>-12.22</td>\n",
       "      <td>-11.34</td>\n",
       "      <td>-9.21</td>\n",
       "      <td>-15.31</td>\n",
       "      <td>-13.19</td>\n",
       "      <td>-11.94</td>\n",
       "      <td>-13.13</td>\n",
       "      <td>-12.73</td>\n",
       "      <td>-11.57</td>\n",
       "      <td>-11.87</td>\n",
       "      <td>-10.75</td>\n",
       "      <td>-9.54</td>\n",
       "      <td>-8.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1110 rows × 19 columns</p>\n",
       "</div>"
      ],
      "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",
       "201808   NaN   3.47    4.04   2.87   3.09   5.05   3.90   3.54   2.49   2.41   \n",
       "201809   NaN  -2.24   -1.85   0.08  -2.04  -2.38  -2.48  -0.74   0.19  -1.68   \n",
       "201810   NaN -10.76  -10.88  -7.63 -10.52 -11.74 -10.55  -9.45  -7.41 -10.02   \n",
       "201811   NaN  -2.08    2.18   2.19  -2.78   1.69   1.46   2.62   2.49  -3.65   \n",
       "201812   NaN -14.28  -12.41  -9.76 -14.77 -12.44 -12.22 -11.34  -9.21 -15.31   \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  \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "201808   5.07   5.30   4.70   3.02   5.01   3.50   3.58   2.65   2.34  \n",
       "201809  -3.08  -3.22  -1.23  -2.65  -2.27  -1.31  -0.12  -0.51   0.87  \n",
       "201810 -11.98 -11.89 -11.52 -11.58  -9.23 -10.74  -8.04  -8.28  -6.57  \n",
       "201811  -0.23   1.23   2.30   1.74   1.11   3.54   1.63   2.45   2.53  \n",
       "201812 -13.19 -11.94 -13.13 -12.73 -11.57 -11.87 -10.75  -9.54  -8.90  \n",
       "\n",
       "[1110 rows x 19 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  }
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