{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ratio de Sharpe et valeurs du portefeuille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creéer un Portefeuille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = pd.to_datetime('2012-01-01')\n",
    "end = pd.to_datetime('2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "quandl.ApiConfig.api_key = \"GpVgeVazag5mR7JaB3Gn\"\n",
    "# Nous avons pris un tas d'actions technologiques pour notre portefeuille\n",
    "aapl = quandl.get('WIKI/AAPL.11',start_date=start,end_date=end)\n",
    "cisco = quandl.get('WIKI/CSCO.11',start_date=start,end_date=end)\n",
    "ibm = quandl.get('WIKI/IBM.11',start_date=start,end_date=end)\n",
    "amzn = quandl.get('WIKI/AMZN.11',start_date=start,end_date=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternatives\n",
    "# aapl = pd.read_csv('AAPL_CLOSE',index_col='Date',parse_dates=True)\n",
    "# cisco = pd.read_csv('CISCO_CLOSE',index_col='Date',parse_dates=True)\n",
    "# ibm = pd.read_csv('IBM_CLOSE',index_col='Date',parse_dates=True)\n",
    "# amzn = pd.read_csv('AMZN_CLOSE',index_col='Date',parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl.to_csv('AAPL_CLOSE')\n",
    "cisco.to_csv('CISCO_CLOSE')\n",
    "ibm.to_csv('IBM_CLOSE')\n",
    "amzn.to_csv('AMZN_CLOSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normaliser les Prix\n",
    "\n",
    "C'est la même chose que les rendements quotidiens cumulatifs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.848786580038"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exemple\n",
    "aapl.iloc[0]['Adj. Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stock_df in (aapl,cisco,ibm,amzn):\n",
    "    stock_df['Normed Return'] = stock_df['Adj. Close']/stock_df.iloc[0]['Adj. Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Normed Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>52.848787</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>53.132802</td>\n",
       "      <td>1.005374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>53.722681</td>\n",
       "      <td>1.016536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>54.284287</td>\n",
       "      <td>1.027162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>54.198183</td>\n",
       "      <td>1.025533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close  Normed Return\n",
       "Date                                 \n",
       "2012-01-03   52.848787       1.000000\n",
       "2012-01-04   53.132802       1.005374\n",
       "2012-01-05   53.722681       1.016536\n",
       "2012-01-06   54.284287       1.027162\n",
       "2012-01-09   54.198183       1.025533"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Adj. Close</th>\n",
       "      <th>Normed Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>115.080808</td>\n",
       "      <td>2.177549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>115.811668</td>\n",
       "      <td>2.191378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>115.317843</td>\n",
       "      <td>2.182034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>115.288214</td>\n",
       "      <td>2.181473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>114.389454</td>\n",
       "      <td>2.164467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close  Normed Return\n",
       "Date                                 \n",
       "2016-12-23  115.080808       2.177549\n",
       "2016-12-27  115.811668       2.191378\n",
       "2016-12-28  115.317843       2.182034\n",
       "2016-12-29  115.288214       2.181473\n",
       "2016-12-30  114.389454       2.164467"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Répartitions\n",
    "\n",
    "Imaginons que nous ayons les répartitions suivantes pour l'ensemble de notre portefeuille :\n",
    "\n",
    "* 30% d'Apple\n",
    "* 20% de Cisco\n",
    "* 40% d'Amazon\n",
    "* 10% d'IBM\n",
    "\n",
    "Faisons en sorte que ces valeurs soient reflétées en multipliant notre Rendement Standard par notre répartition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stock_df,allo in zip([aapl,cisco,amzn,ibm],[.3,.2,.4,.1]):\n",
    "    stock_df['Allocation'] = stock_df['Normed Return']*allo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Normed Return</th>\n",
       "      <th>Allocation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>52.848787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>53.132802</td>\n",
       "      <td>1.005374</td>\n",
       "      <td>0.301612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>53.722681</td>\n",
       "      <td>1.016536</td>\n",
       "      <td>0.304961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>54.284287</td>\n",
       "      <td>1.027162</td>\n",
       "      <td>0.308149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>54.198183</td>\n",
       "      <td>1.025533</td>\n",
       "      <td>0.307660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close  Normed Return  Allocation\n",
       "Date                                             \n",
       "2012-01-03   52.848787       1.000000    0.300000\n",
       "2012-01-04   53.132802       1.005374    0.301612\n",
       "2012-01-05   53.722681       1.016536    0.304961\n",
       "2012-01-06   54.284287       1.027162    0.308149\n",
       "2012-01-09   54.198183       1.025533    0.307660"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investissement\n",
    "\n",
    "Imaginons qu'on ait investi un million de dollars dans ce portefeuille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stock_df in [aapl,cisco,ibm,amzn]:\n",
    "    stock_df['Position Values'] = stock_df['Allocation']*1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valeur totale du Portefeuille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_val = pd.concat([aapl['Position Values'],cisco['Position Values'],ibm['Position Values'],amzn['Position Values']],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position Values</th>\n",
       "      <th>Position Values</th>\n",
       "      <th>Position Values</th>\n",
       "      <th>Position Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>301612.236461</td>\n",
       "      <td>203864.734300</td>\n",
       "      <td>99592.055824</td>\n",
       "      <td>396603.921131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>304960.727573</td>\n",
       "      <td>203113.258186</td>\n",
       "      <td>99119.699410</td>\n",
       "      <td>396827.347372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>308148.724558</td>\n",
       "      <td>202361.782072</td>\n",
       "      <td>97981.749866</td>\n",
       "      <td>407998.659443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>307659.946988</td>\n",
       "      <td>203650.026838</td>\n",
       "      <td>97471.819646</td>\n",
       "      <td>398949.896665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position Values  Position Values  Position Values  Position Values\n",
       "Date                                                                          \n",
       "2012-01-03    300000.000000    200000.000000    100000.000000    400000.000000\n",
       "2012-01-04    301612.236461    203864.734300     99592.055824    396603.921131\n",
       "2012-01-05    304960.727573    203113.258186     99119.699410    396827.347372\n",
       "2012-01-06    308148.724558    202361.782072     97981.749866    407998.659443\n",
       "2012-01-09    307659.946988    203650.026838     97471.819646    398949.896665"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_val.columns = ['AAPL Pos','CISCO Pos','IBM Pos','AMZN Pos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL Pos</th>\n",
       "      <th>CISCO Pos</th>\n",
       "      <th>IBM Pos</th>\n",
       "      <th>AMZN Pos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>301612.236461</td>\n",
       "      <td>203864.734300</td>\n",
       "      <td>99592.055824</td>\n",
       "      <td>396603.921131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>304960.727573</td>\n",
       "      <td>203113.258186</td>\n",
       "      <td>99119.699410</td>\n",
       "      <td>396827.347372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>308148.724558</td>\n",
       "      <td>202361.782072</td>\n",
       "      <td>97981.749866</td>\n",
       "      <td>407998.659443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>307659.946988</td>\n",
       "      <td>203650.026838</td>\n",
       "      <td>97471.819646</td>\n",
       "      <td>398949.896665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL Pos      CISCO Pos        IBM Pos       AMZN Pos\n",
       "Date                                                                  \n",
       "2012-01-03  300000.000000  200000.000000  100000.000000  400000.000000\n",
       "2012-01-04  301612.236461  203864.734300   99592.055824  396603.921131\n",
       "2012-01-05  304960.727573  203113.258186   99119.699410  396827.347372\n",
       "2012-01-06  308148.724558  202361.782072   97981.749866  407998.659443\n",
       "2012-01-09  307659.946988  203650.026838   97471.819646  398949.896665"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_val['Total Pos'] = portfolio_val.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL Pos</th>\n",
       "      <th>CISCO Pos</th>\n",
       "      <th>IBM Pos</th>\n",
       "      <th>AMZN Pos</th>\n",
       "      <th>Total Pos</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>300000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>400000.000000</td>\n",
       "      <td>1.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>301612.236461</td>\n",
       "      <td>203864.734300</td>\n",
       "      <td>99592.055824</td>\n",
       "      <td>396603.921131</td>\n",
       "      <td>1.001673e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>304960.727573</td>\n",
       "      <td>203113.258186</td>\n",
       "      <td>99119.699410</td>\n",
       "      <td>396827.347372</td>\n",
       "      <td>1.004021e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>308148.724558</td>\n",
       "      <td>202361.782072</td>\n",
       "      <td>97981.749866</td>\n",
       "      <td>407998.659443</td>\n",
       "      <td>1.016491e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>307659.946988</td>\n",
       "      <td>203650.026838</td>\n",
       "      <td>97471.819646</td>\n",
       "      <td>398949.896665</td>\n",
       "      <td>1.007732e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL Pos      CISCO Pos        IBM Pos       AMZN Pos  \\\n",
       "Date                                                                     \n",
       "2012-01-03  300000.000000  200000.000000  100000.000000  400000.000000   \n",
       "2012-01-04  301612.236461  203864.734300   99592.055824  396603.921131   \n",
       "2012-01-05  304960.727573  203113.258186   99119.699410  396827.347372   \n",
       "2012-01-06  308148.724558  202361.782072   97981.749866  407998.659443   \n",
       "2012-01-09  307659.946988  203650.026838   97471.819646  398949.896665   \n",
       "\n",
       "               Total Pos  \n",
       "Date                      \n",
       "2012-01-03  1.000000e+06  \n",
       "2012-01-04  1.001673e+06  \n",
       "2012-01-05  1.004021e+06  \n",
       "2012-01-06  1.016491e+06  \n",
       "2012-01-09  1.007732e+06  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Portfolio Value')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_val['Total Pos'].plot(figsize=(10,8))\n",
    "plt.title('Total Portfolio Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120afb7d0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "portfolio_val.drop('Total Pos',axis=1).plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>AAPL Pos</th>\n",
       "      <th>CISCO Pos</th>\n",
       "      <th>IBM Pos</th>\n",
       "      <th>AMZN Pos</th>\n",
       "      <th>Total Pos</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>653264.617079</td>\n",
       "      <td>377469.015679</td>\n",
       "      <td>101839.988903</td>\n",
       "      <td>1.699358e+06</td>\n",
       "      <td>2.831931e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>657413.396830</td>\n",
       "      <td>379323.596496</td>\n",
       "      <td>102102.667778</td>\n",
       "      <td>1.723510e+06</td>\n",
       "      <td>2.862350e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>654610.167268</td>\n",
       "      <td>376108.989746</td>\n",
       "      <td>101522.330729</td>\n",
       "      <td>1.725141e+06</td>\n",
       "      <td>2.857383e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>654441.973495</td>\n",
       "      <td>376603.544631</td>\n",
       "      <td>101772.791982</td>\n",
       "      <td>1.709546e+06</td>\n",
       "      <td>2.842364e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>649340.095692</td>\n",
       "      <td>373636.215323</td>\n",
       "      <td>101400.154508</td>\n",
       "      <td>1.675406e+06</td>\n",
       "      <td>2.799783e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AAPL Pos      CISCO Pos        IBM Pos      AMZN Pos  \\\n",
       "Date                                                                    \n",
       "2016-12-23  653264.617079  377469.015679  101839.988903  1.699358e+06   \n",
       "2016-12-27  657413.396830  379323.596496  102102.667778  1.723510e+06   \n",
       "2016-12-28  654610.167268  376108.989746  101522.330729  1.725141e+06   \n",
       "2016-12-29  654441.973495  376603.544631  101772.791982  1.709546e+06   \n",
       "2016-12-30  649340.095692  373636.215323  101400.154508  1.675406e+06   \n",
       "\n",
       "               Total Pos  \n",
       "Date                      \n",
       "2016-12-23  2.831931e+06  \n",
       "2016-12-27  2.862350e+06  \n",
       "2016-12-28  2.857383e+06  \n",
       "2016-12-29  2.842364e+06  \n",
       "2016-12-30  2.799783e+06  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistiques du Portefeuille\n",
    "### Rendements Quotidiens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_val['Daily Return'] = portfolio_val['Total Pos'].pct_change(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rendement Cumulatif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notre rendement était de 179.97828220000912 %!\n"
     ]
    }
   ],
   "source": [
    "cum_ret = 100 * (portfolio_val['Total Pos'][-1]/portfolio_val['Total Pos'][0] -1 )\n",
    "print('Notre rendement était de {} %!'.format(cum_ret))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rendement Quotidien Moyen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0009023579071769599"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Daily Return'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecart-Type du Rendement Quotidien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012896893878458078"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Daily Return'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120d99750>"
      ]
     },
     "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": [
    "portfolio_val['Daily Return'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ratio de Sharpe\n",
    "\n",
    "Le ratio de Sharpe est une mesure permettant de calculer le rendement ajusté en fonction du risque, et ce ratio est devenu la norme de l'industrie pour de tels calculs. \n",
    "\n",
    "Le ratio de Sharpe mesure la rentabilité excédentaire par rapport au taux de l'argent sans risque d'un portefeuille d'actifs divisé par l'écart type de cette rentabilité. C'est donc une mesure de la rentabilité marginale par unité de risque. Il permet de mesurer les performances de gérants pratiquant des politiques de risque différentes.\n",
    "\n",
    "Ratio de Sharpe = (Rendement moyen du portefeuille - Taux sans risque)/Écart type du rendement du portefeuille\n",
    "\n",
    "Le rapport de Sharpe original\n",
    "\n",
    "Ratio de Sharpe Annuel = K-value * SR\n",
    "\n",
    "K-values pour divers taux d'échantillonnage:\n",
    "\n",
    "* Daily = sqrt(252)\n",
    "* Weekly = sqrt(52)\n",
    "* Monthly = sqrt(12)\n",
    "\n",
    "Aux USA, on utilise un taux sans risque très bas (le taux que vous obtiendriez si vous placez votre argent dans une banque, il est actuellement très bas aux USA, disons juste son rendement de ~0%). Si vous êtes dans un pays différent avec des taux plus élevés pour votre devise de trading, vous pouvez utiliser cette astuce pour convertir un taux annuel avec un taux quotidien :\n",
    "\n",
    "daily_rate = ((1.0 + yearly_rate)**(1/252))-1\n",
    "\n",
    "D'autres valeurs que les gens utilisent sont des choses comme le bon du Trésor à 3 mois: [LIBOR](http://www.investopedia.com/terms/l/libor.asp) ou [WIkipédia LIBOR](https://fr.wikipedia.org/wiki/Libor)\n",
    "\n",
    "Plus de lectures sur le Sharpe Ratio (SR): https://educationfinance.ca/investissements/ratio-de-sharpe/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "SR = portfolio_val['Daily Return'].mean()/portfolio_val['Daily Return'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0699670723571809"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ASR = (252**0.5)*SR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1106928405221748"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ASR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012896893878458078"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Daily Return'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0009023579071769599"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_val['Daily Return'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22c095d0>"
      ]
     },
     "execution_count": 35,
     "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": [
    "portfolio_val['Daily Return'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22cc2610>"
      ]
     },
     "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": [
    "aapl['Adj. Close'].pct_change(1).plot(kind='kde')\n",
    "ibm['Adj. Close'].pct_change(1).plot(kind='kde')\n",
    "amzn['Adj. Close'].pct_change(1).plot(kind='kde')\n",
    "cisco['Adj. Close'].pct_change(1).plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.699606293110037"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.sqrt(252)* (np.mean(.001-0.0002)/.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bon Travail!"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}