MPT/EffFrontOpt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "from tqdm import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = [\"SAF.PA\", \"ATO.PA\", \"MC.PA\", \"AIR.PA\", \"RNO.PA\", \"HO.PA\", \"ENGI.PA\", \"CS.PA\", \"ENR.DE\", \"TOT\", \"CA.PA\", \"SU.PA\", \"BN.PA\", \"SIEGY\", \"AI.PA\", \"ML.PA\", \"AM.PA\", \"ORA.PA\", \"^FCHI\"]\n",
    "period = \"2y\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  19 of 19 completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\D580656\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AI.PA</th>\n",
       "      <th>AIR.PA</th>\n",
       "      <th>AM.PA</th>\n",
       "      <th>ATO.PA</th>\n",
       "      <th>BN.PA</th>\n",
       "      <th>CA.PA</th>\n",
       "      <th>CS.PA</th>\n",
       "      <th>ENGI.PA</th>\n",
       "      <th>ENR.DE</th>\n",
       "      <th>HO.PA</th>\n",
       "      <th>MC.PA</th>\n",
       "      <th>ML.PA</th>\n",
       "      <th>ORA.PA</th>\n",
       "      <th>RNO.PA</th>\n",
       "      <th>SAF.PA</th>\n",
       "      <th>SIEGY</th>\n",
       "      <th>SU.PA</th>\n",
       "      <th>TOT</th>\n",
       "      <th>CAC40</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",
       "      <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>2020-12-29</th>\n",
       "      <td>135.850006</td>\n",
       "      <td>93.070000</td>\n",
       "      <td>907.5</td>\n",
       "      <td>75.760002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>14.345</td>\n",
       "      <td>19.736000</td>\n",
       "      <td>12.755</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>76.320000</td>\n",
       "      <td>512.799988</td>\n",
       "      <td>106.300003</td>\n",
       "      <td>9.826</td>\n",
       "      <td>36.255001</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>71.760002</td>\n",
       "      <td>120.650002</td>\n",
       "      <td>42.512001</td>\n",
       "      <td>55.234154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>135.300003</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>895.0</td>\n",
       "      <td>75.339996</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>14.245</td>\n",
       "      <td>19.676001</td>\n",
       "      <td>12.695</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>75.440002</td>\n",
       "      <td>513.099976</td>\n",
       "      <td>106.050003</td>\n",
       "      <td>9.804</td>\n",
       "      <td>35.945000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>71.779999</td>\n",
       "      <td>120.849998</td>\n",
       "      <td>42.669998</td>\n",
       "      <td>55.112305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>134.250000</td>\n",
       "      <td>89.779999</td>\n",
       "      <td>897.0</td>\n",
       "      <td>74.779999</td>\n",
       "      <td>53.759998</td>\n",
       "      <td>14.030</td>\n",
       "      <td>19.511999</td>\n",
       "      <td>12.520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.900002</td>\n",
       "      <td>510.899994</td>\n",
       "      <td>104.949997</td>\n",
       "      <td>9.734</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>115.949997</td>\n",
       "      <td>71.790001</td>\n",
       "      <td>118.300003</td>\n",
       "      <td>41.910000</td>\n",
       "      <td>54.639864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>136.449997</td>\n",
       "      <td>89.889999</td>\n",
       "      <td>886.0</td>\n",
       "      <td>76.120003</td>\n",
       "      <td>54.400002</td>\n",
       "      <td>14.490</td>\n",
       "      <td>19.438000</td>\n",
       "      <td>12.805</td>\n",
       "      <td>30.129999</td>\n",
       "      <td>75.180000</td>\n",
       "      <td>512.099976</td>\n",
       "      <td>106.750000</td>\n",
       "      <td>9.950</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>116.150002</td>\n",
       "      <td>71.820000</td>\n",
       "      <td>121.750000</td>\n",
       "      <td>42.380001</td>\n",
       "      <td>55.009448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>134.850006</td>\n",
       "      <td>88.970001</td>\n",
       "      <td>899.0</td>\n",
       "      <td>75.839996</td>\n",
       "      <td>54.759998</td>\n",
       "      <td>14.925</td>\n",
       "      <td>19.320000</td>\n",
       "      <td>12.635</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>76.199997</td>\n",
       "      <td>507.200012</td>\n",
       "      <td>104.800003</td>\n",
       "      <td>9.878</td>\n",
       "      <td>36.200001</td>\n",
       "      <td>115.699997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.449997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.636120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AI.PA     AIR.PA  AM.PA     ATO.PA      BN.PA   CA.PA  \\\n",
       "Date                                                                     \n",
       "2020-12-29  135.850006  93.070000  907.5  75.760002  54.540001  14.345   \n",
       "2020-12-30  135.300003  91.250000  895.0  75.339996  54.240002  14.245   \n",
       "2020-12-31  134.250000  89.779999  897.0  74.779999  53.759998  14.030   \n",
       "2021-01-04  136.449997  89.889999  886.0  76.120003  54.400002  14.490   \n",
       "2021-01-05  134.850006  88.970001  899.0  75.839996  54.759998  14.925   \n",
       "\n",
       "                CS.PA  ENGI.PA     ENR.DE      HO.PA       MC.PA       ML.PA  \\\n",
       "Date                                                                           \n",
       "2020-12-29  19.736000   12.755  29.900000  76.320000  512.799988  106.300003   \n",
       "2020-12-30  19.676001   12.695  30.000000  75.440002  513.099976  106.050003   \n",
       "2020-12-31  19.511999   12.520        NaN  74.900002  510.899994  104.949997   \n",
       "2021-01-04  19.438000   12.805  30.129999  75.180000  512.099976  106.750000   \n",
       "2021-01-05  19.320000   12.635  30.080000  76.199997  507.200012  104.800003   \n",
       "\n",
       "            ORA.PA     RNO.PA      SAF.PA      SIEGY       SU.PA        TOT  \\\n",
       "Date                                                                          \n",
       "2020-12-29   9.826  36.255001  120.500000  71.760002  120.650002  42.512001   \n",
       "2020-12-30   9.804  35.945000  119.000000  71.779999  120.849998  42.669998   \n",
       "2020-12-31   9.734  35.759998  115.949997  71.790001  118.300003  41.910000   \n",
       "2021-01-04   9.950  35.759998  116.150002  71.820000  121.750000  42.380001   \n",
       "2021-01-05   9.878  36.200001  115.699997        NaN  119.449997        NaN   \n",
       "\n",
       "                CAC40  \n",
       "Date                   \n",
       "2020-12-29  55.234154  \n",
       "2020-12-30  55.112305  \n",
       "2020-12-31  54.639864  \n",
       "2021-01-04  55.009448  \n",
       "2021-01-05  54.636120  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohlc = yf.download(tickers, period=period)\n",
    "prices = ohlc[\"Adj Close\"]\n",
    "prices[\"CAC40\"] = prices[\"^FCHI\"] / 101.6\n",
    "del prices[\"^FCHI\"]\n",
    "prices.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected returns and risk models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pypfopt\n",
    "from pypfopt import risk_models, expected_returns\n",
    "from pypfopt import plotting\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = expected_returns.capm_return(prices)\n",
    "S = risk_models.semicovariance(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu.plot.barh(figsize=(10,5));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_covariance(S, plot_correlation=True);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Min volatility with a transaction cost objective\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3 * prices.iloc[-2][\"ATO.PA\"] + \\ #ATOS SE\n",
    "#0*prices.iloc[-2][\"MC.PA\"] + \\#LVMH\n",
    "#6 * prices.iloc[-2][\"AIR.PA\"] + \\ #AIRBUS\n",
    "#6 * prices.iloc[-2][\"RNO.PA\"] +\\ #RENAULT\n",
    "#10* prices.iloc[-2][\"HO.PA\"] +\\ #THALES\n",
    "#20* prices.iloc[-2][\"ENGI.PA\"]+\\ #ENGIE\n",
    "#5 * prices.iloc[-2][\"CS.PA\"] +\\ #AXA\n",
    "#1 * prices.iloc[-2][\"ENR.DE\"]+\\ #SIEMENS ENERGY\n",
    "#4 * prices.iloc[-2][\"TOT\"]  + \\#TOTAL SE\n",
    "#25* prices.iloc[-2][\"CA.PA\"] +\\ #CARREFOUR\n",
    "#5 * prices.iloc[-2][\"SU.PA\"] +\\ #SCHNEIDER ELECTRIC SE\n",
    "#6 * prices.iloc[-2][\"BN.PA\"] +\\ #DANONE\n",
    "#3 * prices.iloc[-2][\"SIEGY\"] +\\ #SIEMENS AG\n",
    "#10* prices.iloc[-2][\"AI.PA\"] +\\ #AIR LIQUIDE\n",
    "#5 * prices.iloc[-2][\"ML.PA\"] +\\ #MICHELIN\n",
    "#1 * prices.iloc[-2][\"AM.PA\"] +\\ #DASSAULT AVIATION\n",
    "#25* prices.iloc[-2][\"ORA.PA\"] +\\ #ORANGE\n",
    "#20* prices.iloc[-2][\"CAC40\"] #CAC40\n",
    "\n",
    "PEA = 3 * prices.iloc[-2][\"ATO.PA\"] + 0*prices.iloc[-2][\"MC.PA\"] + 6 * prices.iloc[-2][\"AIR.PA\"] + 6 * prices.iloc[-2][\"RNO.PA\"] + 10* prices.iloc[-2][\"HO.PA\"] + 20* prices.iloc[-2][\"ENGI.PA\"]+ 5 * prices.iloc[-2][\"CS.PA\"] + 1 * prices.iloc[-2][\"ENR.DE\"]+ 4 * prices.iloc[-2][\"TOT\"]  + 25* prices.iloc[-2][\"CA.PA\"] + 5 * prices.iloc[-2][\"SU.PA\"] + 6 * prices.iloc[-2][\"BN.PA\"] + 3 * prices.iloc[-2][\"SIEGY\"] + 10* prices.iloc[-2][\"AI.PA\"] + 5 * prices.iloc[-2][\"ML.PA\"] + 1 * prices.iloc[-2][\"AM.PA\"] + 25* prices.iloc[-2][\"ORA.PA\"] + 20* prices.iloc[-2][\"CAC40\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7787096401202643,\n",
       " 0.006370043696598125,\n",
       " 0.0,\n",
       " 0.015044750481829183,\n",
       " 0.005985095735133051,\n",
       " 0.020971267725277545,\n",
       " 0.007143843802395262,\n",
       " 0.002711090014826391,\n",
       " 0.0008404685772784219,\n",
       " 0.004728716919336356,\n",
       " 0.010104870421183316,\n",
       " 0.01698092468201787,\n",
       " 0.009104844306654031,\n",
       " 0.006010201259040771,\n",
       " 0.03806237570496376,\n",
       " 0.01488881897170766,\n",
       " 0.024714742124464614,\n",
       " 0.0069388171395163645,\n",
       " 0.030689488317513048]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pretend that you started with a default-weight allocation\n",
    "\n",
    "PEG = 27916\n",
    "\n",
    "initial_weights = {\n",
    "    \"SAF.PA\" : PEG/(PEA+PEG), \n",
    "    \"ATO.PA\" : 3 * prices.iloc[-2][\"ATO.PA\"] / (PEA+PEG), \n",
    "    \"MC.PA\"  :  0*   prices.iloc[-2][\"MC.PA\"] / (PEA+PEG), \n",
    "    \"AIR.PA\" : 6 * prices.iloc[-2][\"AIR.PA\"] / (PEA+PEG), \n",
    "    \"RNO.PA\" : 6 * prices.iloc[-2][\"RNO.PA\"] / (PEA+PEG), \n",
    "    \"HO.PA\"  : 10* prices.iloc[-2][\"HO.PA\"] / (PEA+PEG), \n",
    "    \"ENGI.PA\": 20* prices.iloc[-2][\"ENGI.PA\"] / (PEA+PEG), \n",
    "    \"CS.PA\"  : 5 * prices.iloc[-2][\"CS.PA\"] / (PEA+PEG), \n",
    "    \"ENR.DE\" : 1 * prices.iloc[-2][\"ENR.DE\"] / (PEA+PEG), \n",
    "    \"TOT\"    : 4 * prices.iloc[-2][\"TOT\"] / (PEA+PEG), \n",
    "    \"CA.PA\"  : 25* prices.iloc[-2][\"CA.PA\"] / (PEA+PEG), \n",
    "    \"SU.PA\"  : 5 * prices.iloc[-2][\"SU.PA\"] / (PEA+PEG), \n",
    "    \"BN.PA\"  : 6 * prices.iloc[-2][\"BN.PA\"] / (PEA+PEG), \n",
    "    \"SIEGY\"  : 3 * prices.iloc[-2][\"SIEGY\"] / (PEA+PEG), \n",
    "    \"AI.PA\"  : 10* prices.iloc[-2][\"AI.PA\"] / (PEA+PEG), \n",
    "    \"ML.PA\"  : 5 * prices.iloc[-2][\"ML.PA\"] / (PEA+PEG), \n",
    "    \"AM.PA\"  : 1 * prices.iloc[-2][\"AM.PA\"] / (PEA+PEG), \n",
    "    \"ORA.PA\" : 25* prices.iloc[-2][\"ORA.PA\"] / (PEA+PEG), \n",
    "    \"CAC40\"  : 20* prices.iloc[-2][\"CAC40\"] / (PEA+PEG)\n",
    "}\n",
    "\n",
    "initial_weights = [x for k,x in initial_weights.items()]\n",
    "initial_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.41886),\n",
       "             ('AIR.PA', 0.0),\n",
       "             ('AM.PA', 0.0),\n",
       "             ('ATO.PA', 0.01504),\n",
       "             ('BN.PA', 0.00599),\n",
       "             ('CA.PA', 0.02097),\n",
       "             ('CS.PA', 0.0),\n",
       "             ('ENGI.PA', 0.00271),\n",
       "             ('ENR.DE', 0.46482),\n",
       "             ('HO.PA', 0.00473),\n",
       "             ('MC.PA', 0.0101),\n",
       "             ('ML.PA', 0.01698),\n",
       "             ('ORA.PA', 0.0091),\n",
       "             ('RNO.PA', 0.0),\n",
       "             ('SAF.PA', 0.0),\n",
       "             ('SIEGY', 0.0),\n",
       "             ('SU.PA', 0.0),\n",
       "             ('TOT', 0.0),\n",
       "             ('CAC40', 0.03069)])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pypfopt import EfficientFrontier, objective_functions\n",
    "\n",
    "ef = EfficientFrontier(mu, S)\n",
    "\n",
    "# 1% broker commission\n",
    "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.01)\n",
    "ef.min_volatility()\n",
    "weights = ef.clean_weights()\n",
    "weights\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "comparaison avec un cout de transaction à 0.1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.08985),\n",
       "             ('AIR.PA', 0.0),\n",
       "             ('AM.PA', 0.0),\n",
       "             ('ATO.PA', 0.0),\n",
       "             ('BN.PA', 0.00599),\n",
       "             ('CA.PA', 0.08715),\n",
       "             ('CS.PA', 0.0),\n",
       "             ('ENGI.PA', 0.0),\n",
       "             ('ENR.DE', 0.77541),\n",
       "             ('HO.PA', 0.00473),\n",
       "             ('MC.PA', 0.0101),\n",
       "             ('ML.PA', 0.01698),\n",
       "             ('ORA.PA', 0.00979),\n",
       "             ('RNO.PA', 0.0),\n",
       "             ('SAF.PA', 0.0),\n",
       "             ('SIEGY', 0.0),\n",
       "             ('SU.PA', 0.0),\n",
       "             ('TOT', 0.0),\n",
       "             ('CAC40', 0.0)])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ef = EfficientFrontier(mu, S)\n",
    "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n",
    "ef.min_volatility()\n",
    "weights = ef.clean_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ajout d'un régularisation L2 afin d'éviter que certains poids ne soient réduit à 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.05889),\n",
       "             ('AIR.PA', 0.0359),\n",
       "             ('AM.PA', 0.05079),\n",
       "             ('ATO.PA', 0.05511),\n",
       "             ('BN.PA', 0.06268),\n",
       "             ('CA.PA', 0.0611),\n",
       "             ('CS.PA', 0.04857),\n",
       "             ('ENGI.PA', 0.05185),\n",
       "             ('ENR.DE', 0.0779),\n",
       "             ('HO.PA', 0.05216),\n",
       "             ('MC.PA', 0.05584),\n",
       "             ('ML.PA', 0.05357),\n",
       "             ('ORA.PA', 0.06079),\n",
       "             ('RNO.PA', 0.03446),\n",
       "             ('SAF.PA', 0.03801),\n",
       "             ('SIEGY', 0.05071),\n",
       "             ('SU.PA', 0.05245),\n",
       "             ('TOT', 0.04606),\n",
       "             ('CAC40', 0.05316)])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ef = EfficientFrontier(mu, S)\n",
    "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n",
    "ef.add_objective(objective_functions.L2_reg)\n",
    "ef.min_volatility()\n",
    "weights = ef.clean_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour réduire l'effet de la régularisation il est possible de réduire le gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.23339),\n",
       "             ('AIR.PA', 0.00637),\n",
       "             ('AM.PA', 0.00053),\n",
       "             ('ATO.PA', 0.01593),\n",
       "             ('BN.PA', 0.09562),\n",
       "             ('CA.PA', 0.08689),\n",
       "             ('CS.PA', 0.00714),\n",
       "             ('ENGI.PA', 0.00271),\n",
       "             ('ENR.DE', 0.29218),\n",
       "             ('HO.PA', 0.01757),\n",
       "             ('MC.PA', 0.03615),\n",
       "             ('ML.PA', 0.01852),\n",
       "             ('ORA.PA', 0.08224),\n",
       "             ('RNO.PA', 0.0),\n",
       "             ('SAF.PA', 0.02753),\n",
       "             ('SIEGY', 0.01489),\n",
       "             ('SU.PA', 0.02471),\n",
       "             ('TOT', 0.00694),\n",
       "             ('CAC40', 0.03069)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef = EfficientFrontier(mu, S)\n",
    "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.01)\n",
    "ef.add_objective(objective_functions.L2_reg, gamma=0.05)  # default is 1\n",
    "ef.min_volatility()\n",
    "weights = ef.clean_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected annual return: 7.8%\n",
      "Annual volatility: 10.6%\n",
      "Sharpe Ratio: 0.55\n"
     ]
    }
   ],
   "source": [
    "ef.portfolio_performance(verbose=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(weights).plot.pie(figsize=(10,10));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom convex objectives\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cvxpy as cp\n",
    "\n",
    "# Note: functions are minimised. If you want to maximise an objective, stick a minus sign in it.\n",
    "def logarithmic_barrier_objective(w, cov_matrix, k=0.1):\n",
    "    log_sum = cp.sum(cp.log(w))\n",
    "    var = cp.quad_form(w, cov_matrix)\n",
    "    return var - k * log_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.05899),\n",
       "             ('AIR.PA', 0.02017),\n",
       "             ('AM.PA', 0.03662),\n",
       "             ('ATO.PA', 0.04582),\n",
       "             ('BN.PA', 0.09292),\n",
       "             ('CA.PA', 0.07905),\n",
       "             ('CS.PA', 0.03211),\n",
       "             ('ENGI.PA', 0.03727),\n",
       "             ('ENR.DE', 0.2),\n",
       "             ('HO.PA', 0.04042),\n",
       "             ('MC.PA', 0.05097),\n",
       "             ('ML.PA', 0.04264),\n",
       "             ('ORA.PA', 0.07649),\n",
       "             ('RNO.PA', 0.01858),\n",
       "             ('SAF.PA', 0.02104),\n",
       "             ('SIEGY', 0.03679),\n",
       "             ('SU.PA', 0.0393),\n",
       "             ('TOT', 0.02934),\n",
       "             ('CAC40', 0.04149)])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.2))\n",
    "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n",
    "weights = ef.clean_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected annual return: 8.9%\n",
      "Annual volatility: 12.8%\n",
      "Sharpe Ratio: 0.54\n"
     ]
    }
   ],
   "source": [
    "ef.portfolio_performance(verbose=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.04051),\n",
       "             ('AIR.PA', 0.01971),\n",
       "             ('AM.PA', 0.03068),\n",
       "             ('ATO.PA', 0.03484),\n",
       "             ('BN.PA', 0.05181),\n",
       "             ('CA.PA', 0.05007),\n",
       "             ('CS.PA', 0.02777),\n",
       "             ('ENGI.PA', 0.03115),\n",
       "             ('ENR.DE', 0.40185),\n",
       "             ('HO.PA', 0.03227),\n",
       "             ('MC.PA', 0.03767),\n",
       "             ('ML.PA', 0.03403),\n",
       "             ('ORA.PA', 0.04749),\n",
       "             ('RNO.PA', 0.01863),\n",
       "             ('SAF.PA', 0.02042),\n",
       "             ('SIEGY', 0.02994),\n",
       "             ('SU.PA', 0.03218),\n",
       "             ('TOT', 0.0258),\n",
       "             ('CAC40', 0.03318)])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.9))\n",
    "\n",
    "#SAF_index = ef.tickers.index(\"SAF.PA\")  # get the index of TOTAL\n",
    "#ef.add_constraint(lambda w: w[SAF_index] >= 0.7787096401202643)\n",
    "\n",
    "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n",
    "weights = ef.clean_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected annual return: 8.6%\n",
      "Annual volatility: 10.4%\n",
      "Sharpe Ratio: 0.63\n"
     ]
    }
   ],
   "source": [
    "ef.portfolio_performance(verbose=True);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('AI.PA', 0.04051),\n",
       "             ('AIR.PA', 0.01971),\n",
       "             ('AM.PA', 0.03068),\n",
       "             ('ATO.PA', 0.03484),\n",
       "             ('BN.PA', 0.05181),\n",
       "             ('CA.PA', 0.05007),\n",
       "             ('CS.PA', 0.02777),\n",
       "             ('ENGI.PA', 0.03115),\n",
       "             ('ENR.DE', 0.40185),\n",
       "             ('HO.PA', 0.03227),\n",
       "             ('MC.PA', 0.03767),\n",
       "             ('ML.PA', 0.03403),\n",
       "             ('ORA.PA', 0.04749),\n",
       "             ('RNO.PA', 0.01863),\n",
       "             ('SAF.PA', 0.02042),\n",
       "             ('SIEGY', 0.02994),\n",
       "             ('SU.PA', 0.03218),\n",
       "             ('TOT', 0.0258),\n",
       "             ('CAC40', 0.03318)])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pypfopt import DiscreteAllocation\n",
    "\n",
    "da = DiscreteAllocation(weights, prices.iloc[-2], total_portfolio_value=1200)\n",
    "alloc, leftover = da.lp_portfolio()\n",
    "print(f\"Leftover: ${leftover:.2f}\")\n",
    "alloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'alloc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-87735d1a90ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0malloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'alloc' is not defined"
     ]
    }
   ],
   "source": [
    "alloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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