002-session-risk-adjusted-returns.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d1845f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64185a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = pd.read_csv('data/sample_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bfead1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = prices.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "846e3bb3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.024041</td>\n",
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       "      <th>12</th>\n",
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       "        BLUE    ORANGE\n",
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   "source": [
    "returns"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "832ab906",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = returns.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "755043f1",
   "metadata": {},
   "outputs": [
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       "        BLUE    ORANGE\n",
       "1   0.023621  0.039662\n",
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    "returns"
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   "cell_type": "code",
   "execution_count": 9,
   "id": "8b0bc53d",
   "metadata": {},
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    {
     "data": {
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       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
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     "metadata": {},
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   "source": [
    "returns.std()"
   ]
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   "execution_count": 10,
   "id": "048c4bf0",
   "metadata": {},
   "outputs": [
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       "      <td>0.014041</td>\n",
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       "      <th>12</th>\n",
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      "text/plain": [
       "        BLUE    ORANGE\n",
       "1   0.013621  0.029662\n",
       "2  -0.031807 -0.043638\n",
       "3  -0.041763  0.072232\n",
       "4   0.024477  0.034544\n",
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       "11  0.002162  0.014041\n",
       "12  0.011149 -0.065623"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "deviations = returns - returns.mean()\n",
    "deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0552e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "squared_deviations = deviations ** 2\n",
    "variance = squared_deviations.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b82d928",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8c151560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volatility = np.sqrt(variance)\n",
    "volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92d77105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 2)"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "returns.shape"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e698cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_observations = returns.shape[0]\n",
    "variance = squared_deviations.sum() / (number_of_observations - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f0f34178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.023977\n",
       "ORANGE    0.079601\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volatility = np.sqrt(variance)\n",
    "volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab44ecaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLUE      0.083060\n",
       "ORANGE    0.275747\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "returns.std() * np.sqrt(12)"
   ]
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   "execution_count": 27,
   "id": "888cb3ec",
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       "      <td>-3.06</td>\n",
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       "      <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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aeafabb2",
   "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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Lo 10', 'Hi 10']\n",
    "returns = returns[columns]\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "09e6e22f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = returns / 100\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21ee0939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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>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": [
       "        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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.columns = ['SmallCap', 'LargeCap']\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4905a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "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": [
    "returns.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "be37c2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "580917ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.106288\n",
       "LargeCap    0.053900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d24f915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.368193\n",
       "LargeCap    0.186716\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_vol = returns.std() * np.sqrt(12)\n",
    "annualized_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "216736a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.012986\n",
       "LargeCap    0.007423\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "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": 42,
   "id": "b59b85cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.167463\n",
       "LargeCap    0.092810\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_return = (return_per_month + 1) ** 12 - 1\n",
    "annualized_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95269a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.454825\n",
       "LargeCap    0.497063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_return / annualized_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "88bf8ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.373346\n",
       "LargeCap    0.336392\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "riskfree_rate = 0.03 #just to illustration, in reality need to calculate from US Treasures data over the period\n",
    "excess_return = annualized_return - riskfree_rate\n",
    "sharpe_ratio = excess_return / annualized_vol\n",
    "sharpe_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35538dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}