quiz1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "me_m = pd.read_csv(\"data/Portfolios_Formed_on_ME_monthly_EW.csv\",\n",
    "                       header=0, index_col=0, na_values=-99.99)\n",
    "rets = me_m[['Lo 20', 'Hi 20']]\n",
    "rets.columns = ['SmallCap', 'LargeCap']\n",
    "rets = rets/100\n",
    "rets.index = pd.to_datetime(rets.index, format=\"%Y%m\").to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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</th>\n",
       "      <td>-0.0057</td>\n",
       "      <td>0.0333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-08</th>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-09</th>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-10</th>\n",
       "      <td>-0.0329</td>\n",
       "      <td>-0.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926-11</th>\n",
       "      <td>-0.0055</td>\n",
       "      <td>0.0316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SmallCap  LargeCap\n",
       "1926-07   -0.0057    0.0333\n",
       "1926-08    0.0384    0.0233\n",
       "1926-09   -0.0048   -0.0009\n",
       "1926-10   -0.0329   -0.0295\n",
       "1926-11   -0.0055    0.0316"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.151977\n",
       "LargeCap    0.098490\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_obs = rets.shape[0]\n",
    "annualized_rets = (((1 + rets).prod())**(12/number_of_obs)) - 1\n",
    "annualized_rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.336701\n",
       "LargeCap    0.195116\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_volatility = rets.std()*np.sqrt(12)\n",
    "annualized_volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.114426\n",
       "LargeCap    0.062865\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limited_rets = rets[\"1999\":\"2015\"]\n",
    "number_of_obs = limited_rets.shape[0]\n",
    "annualized_rets = (((1 + limited_rets).prod())**(12/number_of_obs)) - 1\n",
    "annualized_rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmallCap    0.228894\n",
       "LargeCap    0.172713\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annualized_volatility = limited_rets.std()*np.sqrt(12)\n",
    "annualized_volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6248092431236988"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_index = 1000*(1+limited_rets[\"SmallCap\"]).cumprod()\n",
    "previous_peaks = wealth_index.cummax()\n",
    "drawdown = (wealth_index - previous_peaks)/previous_peaks\n",
    "drawdown.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009-02', 'M')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5527349978713653"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_index = 1000*(1+limited_rets[\"LargeCap\"]).cumprod()\n",
    "previous_peaks = wealth_index.cummax()\n",
    "drawdown = (wealth_index - previous_peaks)/previous_peaks\n",
    "drawdown.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009-02', 'M')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawdown.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "hfi = pd.read_csv(\"data/edhec-hedgefundindices.csv\",\n",
    "                      header=0, index_col=0, parse_dates=True)\n",
    "hfi = hfi/100\n",
    "hfi.index = hfi.index.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hfi = hfi[\"2009\":\"2018\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fixed Income Arbitrage    0.003321\n",
       "Merger Arbitrage          0.003611\n",
       "Global Macro              0.005075\n",
       "Equity Market Neutral     0.005186\n",
       "Relative Value            0.005711\n",
       "Convertible Arbitrage     0.006681\n",
       "Funds Of Funds            0.007948\n",
       "Distressed Securities     0.009498\n",
       "Event Driven              0.010135\n",
       "CTA Global                0.010996\n",
       "Long/Short Equity         0.012139\n",
       "Emerging Markets          0.015276\n",
       "Short Selling             0.021240\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfi[hfi<0].std(ddof=0).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def skewness(r):\n",
    "    \"\"\"\n",
    "    Alternative to scipy.stats.skew()\n",
    "    Computes the skewness of the supplied Series or DataFrame\n",
    "    Returns a float or a Series\n",
    "    \"\"\"\n",
    "    demeaned_r = r - r.mean()\n",
    "    # use the population standard deviation, so set dof=0\n",
    "    sigma_r = r.std(ddof=0)\n",
    "    exp = (demeaned_r**3).mean()\n",
    "    return exp/sigma_r**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Equity Market Neutral    -0.896327\n",
       "Funds Of Funds           -0.646908\n",
       "Merger Arbitrage         -0.551065\n",
       "Event Driven             -0.488821\n",
       "Long/Short Equity        -0.463703\n",
       "Distressed Securities    -0.254944\n",
       "Emerging Markets          0.033123\n",
       "CTA Global                0.052062\n",
       "Relative Value            0.159953\n",
       "Global Macro              0.348184\n",
       "Short Selling             0.456518\n",
       "Fixed Income Arbitrage    1.121453\n",
       "Convertible Arbitrage     1.305911\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewness(hfi).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kurtosis(r):\n",
    "    \"\"\"\n",
    "    Alternative to scipy.stats.kurtosis()\n",
    "    Computes the kurtosis of the supplied Series or DataFrame\n",
    "    Returns a float or a Series\n",
    "    \"\"\"\n",
    "    demeaned_r = r - r.mean()\n",
    "    # use the population standard deviation, so set dof=0\n",
    "    sigma_r = r.std(ddof=0)\n",
    "    exp = (demeaned_r**4).mean()\n",
    "    return exp/sigma_r**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Convertible Arbitrage</th>\n",
       "      <th>CTA Global</th>\n",
       "      <th>Distressed Securities</th>\n",
       "      <th>Emerging Markets</th>\n",
       "      <th>Equity Market Neutral</th>\n",
       "      <th>Event Driven</th>\n",
       "      <th>Fixed Income Arbitrage</th>\n",
       "      <th>Global Macro</th>\n",
       "      <th>Long/Short Equity</th>\n",
       "      <th>Merger Arbitrage</th>\n",
       "      <th>Relative Value</th>\n",
       "      <th>Short Selling</th>\n",
       "      <th>Funds Of Funds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01</th>\n",
       "      <td>0.0227</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.0427</td>\n",
       "      <td>0.0169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>0.0267</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.0421</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>0.0253</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>-0.1340</td>\n",
       "      <td>0.0666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>0.0243</td>\n",
       "      <td>-0.0138</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0318</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.0104</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>-0.0230</td>\n",
       "      <td>0.0039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>0.0223</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>-0.0101</td>\n",
       "      <td>-0.0541</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>-0.0304</td>\n",
       "      <td>-0.0201</td>\n",
       "      <td>0.0188</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>-0.0269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>-0.0132</td>\n",
       "      <td>-0.0433</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>-0.0034</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>-0.0070</td>\n",
       "      <td>-0.0097</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>-0.0122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Convertible Arbitrage  CTA Global  Distressed Securities  \\\n",
       "date                                                                \n",
       "2000-01                 0.0227      0.0128                 0.0088   \n",
       "2000-02                 0.0267     -0.0022                 0.0421   \n",
       "2000-03                 0.0243     -0.0138                 0.0103   \n",
       "2000-04                 0.0223     -0.0241                -0.0101   \n",
       "2000-05                 0.0149      0.0114                -0.0132   \n",
       "\n",
       "         Emerging Markets  Equity Market Neutral  Event Driven  \\\n",
       "date                                                             \n",
       "2000-01            0.0077                 0.0075        0.0088   \n",
       "2000-02            0.0528                 0.0253        0.0346   \n",
       "2000-03            0.0318                 0.0134        0.0069   \n",
       "2000-04           -0.0541                 0.0168       -0.0059   \n",
       "2000-05           -0.0433                 0.0062       -0.0034   \n",
       "\n",
       "         Fixed Income Arbitrage  Global Macro  Long/Short Equity  \\\n",
       "date                                                               \n",
       "2000-01                  0.0041        0.0021             0.0075   \n",
       "2000-02                  0.0097        0.0408             0.0699   \n",
       "2000-03                 -0.0061       -0.0104             0.0006   \n",
       "2000-04                 -0.0006       -0.0304            -0.0201   \n",
       "2000-05                  0.0107       -0.0070            -0.0097   \n",
       "\n",
       "         Merger Arbitrage  Relative Value  Short Selling  Funds Of Funds  \n",
       "date                                                                      \n",
       "2000-01            0.0143          0.0173         0.0427          0.0169  \n",
       "2000-02            0.0239          0.0185        -0.1340          0.0666  \n",
       "2000-03            0.0131          0.0163        -0.0230          0.0039  \n",
       "2000-04            0.0188          0.0092         0.1028         -0.0269  \n",
       "2000-05            0.0146          0.0080         0.0704         -0.0122  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfi = pd.read_csv(\"data/edhec-hedgefundindices.csv\",\n",
    "                      header=0, index_col=0, parse_dates=True)\n",
    "hfi = hfi/100\n",
    "hfi.index = hfi.index.to_period('M')\n",
    "hfi = hfi[\"2000\":]\n",
    "hfi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CTA Global                 2.845022\n",
       "Global Macro               3.653278\n",
       "Short Selling              4.352758\n",
       "Long/Short Equity          4.472589\n",
       "Merger Arbitrage           4.520785\n",
       "Event Driven               5.543444\n",
       "Emerging Markets           5.941289\n",
       "Distressed Securities      6.231720\n",
       "Funds Of Funds             7.584725\n",
       "Relative Value            12.731096\n",
       "Equity Market Neutral     20.329621\n",
       "Convertible Arbitrage     23.001449\n",
       "Fixed Income Arbitrage    33.378729\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(hfi).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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