analisi mercato/simulazioni.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas_datareader import data\n",
    "import datetime as dt\n",
    "import pandas_datareader.data as web\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### importazione dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_data(name,source='yahoo'):    \n",
    "    start = dt.datetime(2009, 1, 1, 0, 0)\n",
    "    end = dt.datetime.now()\n",
    "    df = web.DataReader(name, source, start, end)\n",
    "    #Symbol = 'High'\n",
    "    df.reset_index(inplace=True)\n",
    "    df.set_index(\"Date\", inplace=True)\n",
    "    df = df.drop(['Low','Open','Adj Close','High','Volume'],axis='columns')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QUI VENGONO IMPORTATO GLI ETF, SPECIFICANDO IL LORO TICKER.\n",
    "# IN QUESTO ESEMPIO 1) AZIONARIO GLOBALE, 2) AZIONARIO EMERGENTI, 3) BOND EUROPEI, 4) BOND SOCIETARI, 5) ORO\n",
    "# PUOI CAMBIARE ETF CAMBIANDO I TICKERS, DOPO IMPORT_DATA\n",
    "\n",
    "\n",
    "df=import_data(['IWDE.MI','IEEM.MI','XGLE.MI','IBCX.MI','XAD1.MI']) \n",
    "#df=import_data(['SWDA.MI','IEEM.MI','IEAG.MI','IBCX.MI','XAD1.MI'])\n",
    "df.columns=['Msci','Emergenti','EUBond','CorporateBond','Gold']\n",
    "\n",
    "df=df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "0.75\n"
     ]
    }
   ],
   "source": [
    "df_sub=df[df.index>='2012']\n",
    "\n",
    "dfsub2=df_sub.iloc[np.arange(0,254*9,254)]\n",
    "\n",
    "dfsub2_pct=dfsub2.pct_change().iloc[1:,:]\n",
    "\n",
    "print(np.mean(dfsub2_pct['Msci']>dfsub2_pct['Emergenti']))\n",
    "print(np.mean(dfsub2_pct['EUBond']>dfsub2_pct['CorporateBond']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# NORMALIZZAZIONE AL SOLO FINE ILLUSTRATIVO\n",
    "\n",
    "normalizzazione_ETF=np.array(df[df.index=='2012-01-02'])\n",
    "\n",
    "dfnorm=df/normalizzazione_ETF\n",
    "\n",
    "dfnorm.plot()\n",
    "plt.xlim('2012','2020-04-15')\n",
    "plt.grid()\n",
    "plt.savefig('grafico_etf.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### massimo drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.795621352102083 35\n"
     ]
    },
    {
     "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": [
    "# DEFINIZIONE DELLA FUNZIONE PER IL CALCOLO DEL MASSIMO DRAWDOWN\n",
    "\n",
    "rand_sequence=np.random.normal(3/12,5/np.sqrt(12),size=12*10)\n",
    "rend_cumulativo=np.cumprod(1+rand_sequence/100)*100\n",
    "\n",
    "\n",
    "def drawdown(array):\n",
    "    massimo=array[0]\n",
    "    lista=[]\n",
    "    for i in range(len(array)):\n",
    "        if array[i]>massimo:\n",
    "            massimo=array[i]\n",
    "            lista.append(massimo)\n",
    "        else:\n",
    "            lista.append(massimo)\n",
    "     \n",
    "    lista=np.array(lista)\n",
    "    \n",
    "    drawdown_parz=(lista-array)/lista*100\n",
    "    #print(np.max(drawdown_parz),np.argmax(drawdown_parz))\n",
    "    \n",
    "    return (np.max(drawdown_parz),np.argmax(drawdown_parz),lista)\n",
    "\n",
    "\n",
    "massimo_esp=drawdown(rend_cumulativo)\n",
    "\n",
    "print(massimo_esp[0],massimo_esp[1])\n",
    "\n",
    "plt.plot(rend_cumulativo)\n",
    "plt.plot(massimo_esp[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simulazione benchmark, senza ribilanciamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2data=pd.read_csv('p2-rendimento.csv')\n",
    "p2data.index=pd.to_datetime(p2data.index,format='%d.%m.%Y')\n",
    "p2data['Data']=p2data['Data']-3.59\n",
    "\n",
    "p2data['Rendimento']=100000*(1+p2data['Data']/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Massimo drawdown  10.606815636416387 %\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydZ3gV1daA35VGCgkkAUIgpAgBAgTS6F06KN2CSJMi2Hv7vIpYruXaUBQbRfSCBQW8ighKkU5CEyFUE6ohDQgpkLK/HzPEE0hCIDnnJLDf55nnnNmzy5pDmDV77bXXEqUUGo1Go9FUNhzsLYBGo9FoNMWhFZRGo9FoKiVaQWk0Go2mUqIVlEaj0WgqJVpBaTQajaZSohWURqPRaColWkFprilExE1EfhCR0yLyjb3lqShEJEFEepZwbY6IvGRjeaaKyBe2HPOi8W1+zxrboxWUxi6IyB0iEisiZ0XkhIgsFZFOFdD1cMAP8FVK3VIB/RXB3g9mjeZ6Qisojc0RkUeAd4BXMJRJIPABMKgCug8C9iml8koY26kCxtDYERFxtLcMGtugFZTGpohIDWAacK9S6julVKZSKlcp9YNS6nGzTjUReUdEjpvHOyJSzbzWTUSOisijInLSnH2NM6+9ADwH3GbOzMaLyFgRWScib4tIGjBVRBxE5FkRSTT7+NyUCxEJFhElImNE5LCIpIjI/5nX+gLPWPS/wyyvJyJLRCRNRA6IyMRS7t9NRN40xz4tImtFxM28NlBE/hSRUyKySkTCruCnrSUiy0UkQ0RWi0iQxZhNzWtpIrJXRG61uDZHRGaIyI9m200i0tDienOLtkki8ozFmC7mb5dhyh1j0S5BRB4XkZ0ikikin4mInzlTzhCRFSLibVH/GxH52/xN1ohI84tk/FBEfhKRTKD7Rb+pp4isFJHpIiJX8JtpKjtKKX3ow2YH0BfIA5xKqTMN2AjUAWoD64EXzWvdzPbTAGegP5AFeJvXpwJfWPQ11qx/P+AEuAF3AQeAG4DqwHfAPLN+MKCAT8y6rYBzQFhx/ZtlqzFmgK5ABJAM9Cjh3mYAq4D6gCPQAagGNAYygV7mfT1hyuhitksAepbQ5xwgA+hi9vUusNa85gEcAcaZ9x8FpADNLdqmAW3M618CC8xrnsAJ4FHz3jyBtha/Q475+zsC/wY2WsiUYP4b+pn3ehLYCkSaMv4GPG9R/y6z/2oYs+vtF93faaAjxku1q1n2EuALbAZesvfftj4q/rC7APq4vg5gJPD3ZeocBPpbnPcBEszv3YBsLBSc+fBrZ34vokAwFNThi/r/FbjH4rwJkGs+oIMxFFSAxfXNwO0l9N8AyAc8Lcr+Dcwp5r4cTNlbFXPtX8DXF9U9BnQzzxMoXUEtsDivbsrUALgN+P2i+h9dUA5m208trvUH4s3vI4BtJYw5FVhhcd4MyLY4TwBGWpwvBD60OL8fWFRC3zXNf4MaFjJ+Xsw9zwJ2AY/b++9aH9Y5tIlPY2tSMcxRpa0F1QMSLc4TzbLCPlTRNaYsjIdySRwpQ/9OGG/7F/i7jP3XA9KUUhkX9Ve/mLq1MN7+D5bQT6FMSqkCU+7i+imOwntUSp3FmBXVw1iTa2uaDU+JyCmMl4S6Fm1LutcGJchaUjvXi/5dkyy+ZxdzXh2MNSUReVVEDorIGQzlBsbvdcn9WTAAY5Y7sxQZNVUYraA0tmYDhmlocCl1jmM8WC8QaJZdLReH7C+u/zyKPkCvpC8fEfG8qL9jxbRNwbj3hsVcKyKTuZbSoIR+iqOBRdvqgI/Z5xFgtVKqpsVRXSk1pQx9HilB1ormDgwHmZ5ADYxZLIDlelJxaRc+AX4GfhIRD2sKqLEPWkFpbIpS6jSGI8MMERksIu4i4iwi/UTkdbPafOBZEaktIrXM+hXp2j0feFhEQsyH+SvAV6oEz7+LSAKCRcTBvJ8jGGtk/xYRVxFpCYzHWMspgjkrmgW8ZTpWOIpIe9MB5GtggIj0EBFnjHWfc2bfZaG/iHQSERfgRWCTKdv/gMYiMsr8nZ1FpHUZHTD+B9QVkYfEcFzxFJG2ZZTnSvDEuNdUwB3j36Os3AfsBf53wdlEc+2gFZTG5iil3gIeAZ7FcCg4gvGgWWRWeQmIBXYCf2AsrlfkpsxZwDxgDfAXxqzm/jK2vbD5N1VEtprfR2C89R8HvsdY31leQvvHMO5pC4YZ7jXAQSm1F7gTeA9jpnUzcLNS6nwZ5fov8LzZZzSGGQ/T9NgbuN2U729zzGqX69Bs28uU5W9gPxd50FUQn2OYN48BuzGcK8qEUkoBkzD+hhaLiKsV5NPYCTH+fTUajUajqVzoGZRGo9FoKiVaQWk0Go2mUqIVlEaj0WgqJVpBaTQajaZSogNnmtSqVUsFBwfbWwyNRqOp9MTFxaUopWpbexytoEyCg4OJjY21txgajUZT6RGRxMvXKj/axKfRaDSaSolWUBqNRqOplGgFpdFoNJpKiV6D0mgqmNzcXI4ePUpOTo69RdFoyoWrqysBAQE4OzvbZXytoDSaCubo0aN4enoSHByMTvCqqaoopUhNTeXo0aOEhITYRQarmfhEZJaZTnuXRZmPmT56v/npbZaLma75gJkiOsqizRiz/n4RGWNRHi0if5htClM9lzSGRmMrcnJy8PX11cpJU6UREXx9fe1qCbDmGtQcjPTeljwF/KqUCsXIavqUWd4PCDWPScCHYCgbjAjNbTFSUj9voXA+NOteaNf3MmOUjg6aq6lAtHLSXAvY++/YagpKKbUGI/S/JYOAueb3ufyTtG4QRkpnpZTaCNQUEX+MVN/LlVJpSql0YDnQ17zmpZTaYIbb//yivoobo1ROJyWy7kAKOrq7RqPRVA5s7cXnp5Q6AWB+1jHL61M0pfNRs6y08qPFlJc2xiWIyCQRiRWR2BoF6XwyaybDPlzPyr0ntaLSVGkcHR2JiIigVatWREVFsX59WfMelo1Vq1Zx0003VWifJVG9evVSrx85coTu3bsTFhZG8+bNeffddwuvTZ06lfr16xMREUFERAQ//fRTsX3cdttthXWCg4OJiIgAICEhATc3t8JrkydPrrgb01yWyuIkUdw8Ul1F+RWhlPoY+BggJqi6mln9U245Hca42adoGVCD+7o3omeYHw4O2lyjqVq4ubmxfft2AJYtW8bTTz/N6tWr7SzVlaGUKtOLopOTE2+++SZRUVFkZGQQHR1Nr169aNasGQAPP/wwjz32WKl9fPXVV4XfH330UWrUqFF43rBhw8LfUmNbbD2DSjLNc5ifJ83yo0ADi3oBGNk/SysPKKa8tDFKxzsY14IcFtf/gteGNudUVi6T5sXRf/rv/LzrxJXco0ZTqThz5gze3v/4Cr3xxhu0bt2ali1b8vzzzwPGTCEsLIyJEyfSvHlzevfuTXZ2NgAHDhygZ8+ehbOxgwcPAnD27FmGDx9O06ZNGTlyZKEyCQ4O5plnnqF9+/bExMSwdetW+vTpQ8OGDZk5c2Zh2x49ehAVFUV4eDiLFy8uIsc999xDVFQUR478Y0BJSUmhffv2/Pjjj0Xuz9/fn6gow6/K09OTsLAwjh07dlW/lVKKr7/+mhEjRlxVe03FYusZ1BJgDPCq+bnYovw+EVmA4RBxWil1QkSWAa9YOEb0Bp5WSqWJSIaItAM2AaMxUmWXNkbpOLlC31dw+N/D3NboB4Y9ei9Ldhzn/ZUHmPzFVt4bEcnNreqV9/411xkv/PAnu4+fqdA+m9Xz4vmbm5daJzs7m4iICHJycjhx4gS//fYbAL/88gv79+9n8+bNKKUYOHAga9asITAwkP379zN//nw++eQTbr31VhYuXMidd97JyJEjeeqppxgyZAg5OTkUFBRw5MgRtm3bxp9//km9evXo2LEj69ato1OnTgA0aNCADRs28PDDDzN27FjWrVtHTk4OzZs3Z/Lkybi6uvL999/j5eVFSkoK7dq1Y+DAgQDs3buX2bNn88EHHxTeT1JSEgMHDuSll16iV69eJd53QkIC27Zto23btoVl77//Pp9//jkxMTG8+eabRZT1xfz+++/4+fkRGhpaWPbXX38RGRmJl5cXL730Ep07dy71t9dUHNZ0M58PbACaiMhRERmPoTR6ich+oJd5DvATcAg4AHwC3AOglEoDXgS2mMc0swxgCvCp2eYgsNQsL2mMyxM9DpreBCtewClpJ0OjAlj2UBciA2vyzHd/cCQt66p+C43G1lww8cXHx/Pzzz8zevRolFL88ssv/PLLL0RGRhIVFUV8fDz79+8HICQkpHDtJTo6moSEBDIyMjh27BhDhgwBjI2b7u7uALRp04aAgAAcHByIiIggISGhcPwLyiY8PJy2bdvi6elJ7dq1cXV15dSpUyileOaZZ2jZsiU9e/bk2LFjJCUlARAUFES7du0K+8rNzaVHjx68/vrrpSqns2fPMmzYMN555x28vLwAmDJlCgcPHmT79u34+/vz6KOPlvq7zZ8/v8jsyd/fn8OHD7Nt2zbeeust7rjjDs6cqdgXDk3JWG0GpZQqaY7co5i6Cri3hH5mAbOKKY8FWhRTnlrcGGVCBAa+Bx92hIXjYdJqnKtVZ/rtkfR/93ceWLCNr+9uj7OjjhClKRuXm+nYgvbt25OSkkJycjJKKZ5++mnuvvvuInUSEhKoVq1a4bmjoyPZ2dmlrgFdXD8vL++Saw4ODkXqOTg4kJeXx5dffklycjJxcXE4OzsTHBxcuN/Gw8OjyDhOTk5ER0ezbNkyunbtWqwsubm5DBs2jJEjRzJ06NDCcj8/v8LvEydOLHTsGDduHNu2baNevXqFjhN5eXl89913xMXFFbmPC/JHR0fTsGFD9u3bR0xMTIm/i6bi0E/ai3H3gaEfQ+pB+PlJABr4uPPK0HC2HT7Fuyv221nA8qOU4nRWrvZUvE6Ij48nPz8fX19f+vTpw6xZszh79iwAx44d4+TJkpdpvby8CAgIYNGiRQCcO3eOrKzyWxJOnz5NnTp1cHZ2ZuXKlSQmlpy9QUSYNWsW8fHxvPrqpQYRpRTjx48nLCyMRx55pMi1Eyf+WT/+/vvvadHCeKedPXs227dvL+LVt2LFCpo2bUpAwD/L28nJyeTn5wNw6NAh9u/fzw033HB1N625YiqLF1/lIqQzdH4Efn8TGvaAFkO5uVU9ft+fzIxVB+jYqBbtG/raW8qrIic3n8lfxLFqbzLVqzkR5OtuHh4Em58htTzw83K1t6iacnBhDQqMB/jcuXNxdHSkd+/e7Nmzh/bt2wOGC/cXX3yBo6NjiX3NmzePu+++m+eeew5nZ2e++eabcss3cuRIbr75ZmJiYoiIiKBp06al1nd0dGTBggXcfPPNeHl5cc899xReW7duHfPmzSM8PLzwnl955RX69+/PE088wfbt2xERgoOD+eijj0ocY8GCBZc4R6xZs4bnnnsOJycnHB0dmTlzJj4+PuW4c82VIPot2iAmJkYVSViYnwuz+kLKfpiyFmoGknU+j5umryXzfB5LH+yCj4eL/QS+CnJy85n4eSxrD6QwoVMIufmKxNRMElOzOJyWRV7BP38L93ZvyON9Sn9oaIpnz549hIWF2VsMjaZCKO7vWUTilFJWt3PqGVRJODrDsE9hZmdYOAHu/A73atWZPiKSoR+s54lvd/LJ6Gi7hwIpK5bK6bVhLbk1pkGR63n5BZw4nUNCaibfxB5lxsqDtKhXg37h/naSWKPRXO/oNajS8AmBm9+BI5vh465wYgct6tfgyX5NWbEniXkbbZL1uNxkn89nwlxDOb1ejHICcHJ0oIGPO51Da/PGLS1p1aAmj3+7kwMnz9pBYo1Go9EK6vKED4cxS+B8JnzaEzZ8wF0dgujepDYv/biH+L8rt8tp9vl8Jny+hXUHU3hjeCtuKUY5XUw1J0c+HBmFi5MDk7+II/Nc3mXbaDQaTUWjFVRZCOkCk9dBo56w7Glk/m38p389arg5c/9/t5F9Pt/eEhZL9vl8xs/dwvqDqfxneCuGRwdcvpFJvZpuvDcikkPJZ3ly4U7t8afRaGyOVlBlxcMXbv8v9P8PHFqN77wbmdX5LPtPnuXFH3fbW7pLuKCcNhxK5c1bWjHsCpTTBTo2qsVjfZrwv50nmLUuoeKF1Gg0mlLQCupKEIE2E2Hib+DmTfhvY5kfspSvNx3i511/21s6wHCG+HVPEqNnbWLjoVTeurUVQ6OuXDldYErXhvRq5se/f9rD5r8uzp6i0Wg01kMrqKuhbguYuBKix9L+xDzmeH3MU9/t5MTpbLuIk5xxjq+3HGHi57FETPuF8XNj2XMig7dvi2BI5NUrJzA2Sb55aysCvN24979bOXnGftk1NWVHRBg1alTheV5eHrVr1y6MpLBkyZJiN71ai1OnThWJrVca06dPJywsjJEjR17VWBfScxw/fpzhw4eXud327dtp3749zZs3p2XLlkUinFsyduxY3N3dycjIKCx78MEHERFSUlKuSuYrJT4+noiICCIjIwuD916LaDfzq8XF3fDw86pHp5Uv076gAw9/5cmXE9rhaOX0HEop9p88y/LdSazYk8T2I6dQCurXdOO2mAb0bOZH2xBfXJwq5v3Dy9WZmaOiGTJjPff9dxtfTmyrwz1Vcjw8PNi1axfZ2dm4ubmxfPly6tevX3h94MCBhfHyrE1+fn6hgrLcYFsSH3zwAUuXLiUkJKRc49arV49vv/22zPXd3d35/PPPCQ0N5fjx40RHR9OnTx9q1qx5Sd1GjRqxePFi7rzzTgoKCli5cmWR39faLFq0iEGDBvHCCy+UuU1+fn6pG7IrI/opU146PgS1m/Ifjy/Yceg4M1db520mN7+A9QdSmPbDbrq+sYreb6/hjWV7yS9QPNyzMT890Jm1T3bnhUEt6Bxau8KU0wWa1vXi1WHhbE5I4+Uf92iniSpAv379ClNTXBwEdc6cOdx3330AJCYm0qNHD1q2bEmPHj04fPjwJX1NnTqVUaNGceONNxIaGsonn3wCGC9Ljz/+OC1atCA8PLxw1rFq1Sq6d+/OHXfcQXh4OE899RQHDx4kIiKCxx9/vMR0G5MnT+bQoUMMHDiQt99+m7S0NAYPHkzLli1p164dO3fuBIwQRL169SIqKoq7776boKCgS2YvCQkJhaGNcnJyGDduHOHh4URGRrJy5cpL7rFx48aFUczr1atHnTp1SE5OLva3HTFiRJF77dixI05O/7zvv/XWW7Ro0YIWLVrwzjvvFMpTUkqTgwcP0rdvX6Kjo+ncuTPx8fFkZGQQEhJCbm4uYKRNCQ4OZvHixbzzzjt8+umndO/eHYDBgwcTHR1N8+bN+fjjjwvlqF69Os899xxt27Zlw4YNZUqFUpnQM6jy4uQCA97CY05/3vVfzpTlbnRo6EtkYMkh/cvK6excVu9LZsXuJFbuPUlGTh4uTg50alSLyV0b0iOsjk1DEg2KqM+2w6eYsz6BpDM5vDqsJTXcnG02fpVk6VPw9x8V22fdcOh3efPc7bffzrRp07jpppvYuXMnd911F7///vsl9e677z5Gjx7NmDFjmDVrFg888EBh7D1Ldu7cycaNG8nMzCQyMpIBAwawYcMGtm/fzo4dO0hJSaF169Z06dIFgM2bN7Nr1y5CQkJISEhg165dhYn/8vLyik23MXPmTH7++WdWrlxJrVq1uP/++4mMjGTRokX89ttvjB49mu3bt/PCCy9w44038vTTT/Pzzz8XeSgXx4wZMwD4448/iI+Pp3fv3uzbtw9X1+L//2zevJnz58/TsGHDYq+HhoayePFi0tPTmT9/PnfeeSdLlxoJFeLi4pg9ezabNm1CKUXbtm3p2rUr3t7eJaY0mTRpEjNnziQ0NJRNmzZxzz338Ntvv9GtWzd+/PFHBg8ezIIFCxg2bBiDBg1i27ZtVK9evTAR46xZs/Dx8SE7O5vWrVszbNgwfH19yczMpEWLFkybNq1Q9sulQqlMaAVVEQR3hIg76bVzAe2rt+XBBdv58YFOeLpe+cP7SFoWy3cn8Wt8EpsOpZFXoPD1cKFv87r0bOZH59BauLvY75/tuZuaUbeGK/9ZtpedR39n+ohIooPKr4w1FU/Lli1JSEhg/vz59O/fv8R6GzZs4LvvvgNg1KhRPPHEE8XWGzRoEG5ubri5udG9e3c2b97M2rVrGTFiBI6Ojvj5+dG1a1e2bNmCl5cXbdq0KdFMdyHdxpo1a3BwcChMt1G3bt0i9dauXcvChQsBuPHGG0lNTeX06dOsXbuW77//HoC+ffuWmuPpQj/3338/AE2bNiUoKIh9+/bRsmXLS+qeOHGCUaNGMXfuXBwcSrZEDB06lAULFrBp06YiMf7Wrl3LkCFDCqOyDx06lN9//52BAwcWm9Lk7NmzrF+/nltuuaWwj3PnzgEwYcIEXn/9dQYPHszs2bMLZ64XM3369MLf48iRI+zfvx9fX18cHR0ZNmxYkbqWqVDOnj2Lp6cnnp6ehalQijNp2gutoCqKXtOQvT/xoecXRBx5iOcX/8lbt0VctllBgWLH0VOs2JPEit0n2ZtkLLyG1qnOxC430DOsDhENvK2+rlVWHByEyV0b0jbEh/vnb+PWjzbwSK/GTOnaEIdKImOlogwzHWsycOBAHnvsMVatWkVqamqZ2pQUvuvichEp1dR7cdoMS0pLt2FJcf1fbtziKGv9M2fOMGDAAF566aUiOamK4/bbbycqKooxY8YUUWRXkqIkOzubgoICatasWWxa+Y4dO5KQkMDq1avJz88vNFlasmrVKlasWMGGDRtwd3enW7duhb+lq6vrJetOl0uFUpmwyxqUiDwoIrtE5E8RecgsixCRjSKyXURiRaSNWS4iMl1EDojIThGJsuhnjIjsN48xFuXRIvKH2Wa62CJgnocv9H4Rz5OxzGwez3fbjrFoW/Fpp7PP57N8dxJPfruTNq/8ypAP1jNz9SG8PZx5dkAYqx7rxvJHuvJk36ZEB/lUGuVkSWSgNz892Jm+LeryxrK9jJ61mZMZ2sOvsnHXXXfx3HPPER4eXmKdDh06sGDBAsBQHBey4l7M4sWLycnJITU1lVWrVhWa87766ivy8/NJTk5mzZo1tGnT5pK2np6eRbzeyppuo0uXLnz55ZeA8SCuVasWXl5edOrUia+//howsgSnp6eX+jtY9rNv3z4OHz5MkyZNitQ5f/48Q4YMYfTo0UVmMyURGBjIyy+/fInjR5cuXVi0aBFZWVlkZmby/fffl5qF18vLi5CQkMIo8UopduzYUXh99OjRjBgxgnHjxhXb/vTp03h7e+Pu7k58fDwbN268rOxVBZvPoESkBTARaAOcB34WkR+B14EXlFJLRaS/ed4N6AeEmkdb4EOgrYj4AM8DMYAC4kRkiVIq3awzCdiIka23L/9k3LUere6AbV/S89gMbmzwAc8u2kVUoDeBvu6cPJPDr/En+XVPEr/vT+FcXgGe1Zzo2qQ2PcP86NakNjXdq1Z0dC9XZ94fEUnnRrWY+sOf9H/3d968NYKujWvbWzSNSUBAAA8++GCpdaZPn85dd93FG2+8Qe3atZk9e3ax9dq0acOAAQM4fPgw//rXv6hXrx5Dhgxhw4YNtGrVChHh9ddfp27dusTHxxdp6+vrS8eOHWnRogX9+vXjySefLFO6jalTpzJu3DhatmyJu7s7c+fOBeD5558vdFTo2rUr/v7+eHp6lniP99xzD5MnTyY8PBwnJyfmzJlTZPYA8PXXX7NmzRpSU1OZM2cOYDiTXDDJFcfFiR8BoqKiGDt2bKGinjBhApGRkUUyDl/Ml19+yZQpU3jppZfIzc3l9ttvp1WrVoCRmuTZZ5+9JBXIBfr27cvMmTNp2bIlTZo0uezMryph83QbInIL0EcpNcE8/xdwDiML7iyl1FciMgK4WSl1h4h8BKxSSs036+/FUFzdgG5KqbvN8o+AVeaxUinV1CwfYVmvJC5Jt3G1nNwDMzuR2WQY7fYMw7+GK24uTuw4cgowXMF7NfOjZ5gfbUJ8Ktzbzl7sS8rgvv9uZV/SWe7ucgOP9m5yzdzblXItptuYOnVqkUV5e3Pu3DkcHR1xcnJiw4YNTJkypVgT2bXAt99+y+LFi5k3b55dxr/e0m3sAl4WEV8gG+gPxAIPActE5D8YpscOZv36wBGL9kfNstLKjxZTbhvqhEGHB/BY+xafdBnAyBX5tKhfg0d7NaZnMz+a1vWsMik6roTGfp4sua8TL/5vNx+tOcTGv9J47/ZIAn3d7S2a5hrk8OHD3HrrrRQUFODi4lKi80BV5/7772fp0qVFMv9eT9hcQSml9ojIa8By4CywA8gDpgAPK6UWisitwGdAT6C4p7m6ivJLEJFJGKZAAgMDr/BOSqHL47DrW9rtfpn4qWtwdrk+stO6Ojvy8pBwOjWqxRMLdzJg+u+8MjScm1vVs7domnIydepUe4tQhNDQULZt22ZvMazOe++9Z28R7IpdbDBKqc+UUlFKqS5AGrAfGAN8Z1b5BmONCowZkGWOiADg+GXKA4opL06Oj5VSMUqpmNq1K3DdxMUd+r8JKXtx3vh+xfVbRegX7s9PD3Qm1K8698/fxlMLd1baiO/WQm9k1lwL2Pvv2F5efHXMz0BgKDAfQ4l0NavciKG0AJYAo01vvnbAaaXUCWAZ0FtEvEXEG+gNLDOvZYhIO9N7bzSw2Fb3Vkjj3hA2ENa8AWeK1Y/XNA183Pnq7vbc060hX8Ue4eb311b63FkVhaurK6mpqXb/z63RlAelFKmpqSVuZrYF9toHtdBcg8oF7lVKpYvIROBdEXECcjBNbxheeP2BA0AWMA5AKZUmIi8CW8x605RSF8JtTwHmAG4Y3nvW9+Arjhv/BXuWQPyPRhT06wxnRwee6NuUDg1r8dBX2xn0/jr+dVMzRrYNvCbX4S4QEBDA0aNHSwyTo9FUFVxdXQkIKF/A6fJgcy++ykqFefFdzPRI8GkId5Y9aOW1SHLGOR79Zgdr9iXTr0VdXh3akhruOkySRlMVsZUX3/XpB2xLGveFv9YYKeOvY2p7VmPO2NY8078py3cn0X/678Ql6vxSGo2mZLSCsjahvSH/nKGkroSjxa8AACAASURBVHMcHIRJXRry7ZQOODjArR9tZMbKA+QX6Fm8RqO5FK2grE1QR3CpDvuW2VuSSkNEg5r8+EBn+hWGSdqkEyFqNJpL0ArK2ji5QMPusP8X0Ot9hXi5OvPeiEheGxZOXGI6/d79nVV7T9pbLI1GU4nQCsoWhPaBM8cgaZe9JalUiAi3tQ7kh/s6Uat6NcbO3sIrP+3hfF6BvUXTaDSVAK2gbEFob+NTm/mKJdTPk8X3deTOdoF8vOYQt8xcz+HULHuLpdFo7IxWULbA0w/qRRpmPk2xuDo78tLgcD4cGcVfKZn0n/47S3ZcfxucNRrNP2gFZStC+8CRzZBZtqRx1yv9wv356cHONParzgPzt/HktzvJOl96ErVzeflsOJhKQsr17cqv0Vxr6Iy6tqJxb1j9KhxYAa1us7c0lZoAbyNM0jsr9vHBqoPEHU7n/TsiaVrXCzBCsCSkZrFmXzKr9yWz4WAq2bn5hPl7sfTBkhPDaTSaqoVWULbCPxI86sD+ZVpBlQFnRwce7/NPmKSB76/j3m6NSD6bw5p9KRxOM9aognzdGR4dQE5uPt/EHeVoehYB3jrFh0ZzLaAVlK1wcDCcJeJ/gPw8cNQ/fVno2KgWSx/szKNf7+DtFftwd3GkQ0NfJnQOoUtobYJreQDwV0om38Qd5dc9JxnTIdi+Qms0mgpBPyVtSePesP0LOLIJgjvaW5oqQ63q1Zg9tjWHUjJp4ONGNSfHS+qE1PLghtoerNiTpBWURnONoJ0kbMkN3cHB2TDzaa4IBwehUZ3qxSqnC/QK82PjoVQycnJtKJlGo7EWWkHZElcvCOoA+7S7uTXoEeZHbr5izb4Ue4ui0WgqAK2gbE3jPpC8B9IT7S1JySTvg7zz9pbiiokKrElNd2d+3ZNkb1E0Gk0FoBWUrQntY3xW1k27f3wLM9rADw/aW5IrxsnRgRub1OG3vSfJy9fhkjSaqo69Ur4/KCK7RORPEXnIovx+Edlrlr9uUf60iBwwr/WxKO9rlh0QkacsykNEZJOI7BeRr0TExXZ3dxlqNQKfGypn2KN9y+D7uw1T5I7/wuFN9pboiukR5seprFy2Hj5lb1E0Gk05sbmCEpEWwESgDdAKuElEQkWkOzAIaKmUag78x6zfDLgdaA70BT4QEUcRcQRmAP2AZsAIsy7Aa8DbSqlQIB0Yb7MbLAuVMYlhwjr4ejT4tYB7N4NXffjpUSjIt7dkV0SXxrVwdhRt5tNorgHsMYMKAzYqpbKUUnnAamAIMAV4VSl1DkApdSH3wiBggVLqnFLqL+AAhnJrAxxQSh1SSp0HFgCDRESAG4ELOdbnAoNtdG9lo7IlMTy+HebfDjUD4c7vwLMu9H4J/v4D4mbbW7orwtPVmXY3+LJcKyiNpspjDwW1C+giIr4i4g70BxoAjYHOpmlutYi0NuvXB45YtD9qlpVU7gucMpWfZXnlwdpJDAsKYNdCSDlw+brJ++CLoeBaE0YtAg9fo7z5EAjpAr++WOXiB/YM8+NQciaHks/aWxSNRlMObK6glFJ7MExwy4GfgR1AHsamYW+gHfA48LU5G5LiurmK8ksQkUkiEisiscnJyVd6K1ePNZMYKgW/PAvf3gXvR8OXtxjx/wqKcRo4dQTmDQFxgNGLoIaFHheBfm/A+bPw27SKldHK9AirA8Cve3QCRI2mKmMXJwml1GdKqSilVBcgDdiPMdP5ThlsBgqAWmZ5A4vmAcDxUspTgJoi4nRReXFyfKyUilFKxdSuXbvibrAsWCuJ4dq3YOMMiLkLuj1tmO++GAYzWsOmj+FchlHvbDLMG2ycj/oefBte2ledptB2MsTNhWNbK1ZOKxLg7U7Tup6s0GY+jaZKYy8vvjrmZyAwFJgPLMJYO0JEGgMuGMpmCXC7iFQTkRAgFNgMbAFCTY89FwxHiiVKKQWsBIabw40BFtvq3spMYRLDnyuuz7g58Os0CL8V+r8J3Z6Ch/+EoZ+Aaw1Y+ji8GQZLn4IvhsDpYzDya6gbXnKfXZ8Ej9rw0+PFz8IqKT3D/IhNTOdUVtXbz6XRaAzstQ9qoYjsBn4A7lVKpQOzgBtEZBeGw8MYczb1J/A1sBvDJHivUirfXGO6D1gG7AG+NusCPAk8IiIHMNakPrPlzZUJTz8IaA2/vQyzB8DmTyDj76vvb/cS+N/D0KgXDP7ACE4Lhjmx5a0w8TeY8Cs06QdbPoWT8XD7FxDYrvR+Xb2g94twLBa2f3n18tmYHmF1yC9QrNprQ9OtRqOpUERV9BpIFSUmJkbFxsbadtAzx41Zz+7FkBwPCAS2h2aDoNlA8KpXtn4OrYYvh4N/hLGW5OJRev2MJMg5BbWblK1/pWBWX0g9APfHgpt32drZkYICRZtXfqXdDT68f0eUvcXRaK4pRCROKRVj7XF0JAl74lUPuj8D926CezYZa0Y5p+DnJ+GtMPisN2yYAaePltzH8W2w4A7waQh3fHV55QTG7K2sygkMh4n+b0B2Gqz8d9nb2REHB6FH0zqs3pvM+byqY5rUaDT/oBVUZaFOU+j2JNyzAe7dAt2fhfNZsOwZeLs5fNID1r9XNIZfygH4Yji4+cCo78Ddx3ry+bc0HC+2fAJ/V7Bjh5Xo2cyPjHN5bElIs7coGo3mKtAmPhO7mPjKQsoB2LMY/lwEf+80yupFQdhNEDsHcrNg/C/Fe+FVNFlp8F40+DaCO781HC8qMdnn84mY9gsj2gQydWBze4uj0VwzaBOfxqBWI+j8KEz+HR7YBj1fAJThrZedDncutI1yAmOG1u91w2FiRjvYW4EeiFbAzcWRTo1q8Wt8EvpFTKOpemgFVZXwuQE6PQSTVsGDO2HKOqgXYVsZWt4C41eAW02Yfxt8Ox4yK2/+pR5hfhxJy2Zfko4qodFUNbSCqqp4BxmHPQiIhkmrodszhgfi+61h5zcVHxWjArgQVUJv2tVoqh5aQWmuDicXw6nj7jXgEwLfTTACzp4+Zm/JiuDn5UrLgBo6urlGUwXRCkpTPvyawfjl0OcVYz/WjLYQO6tSRZ3o0dSPbUdOkXL2nL1F0Wg0V4BWUJry4+AI7e81XOTrRxoRLebeDKkH7S0ZAD2b1UEpWL5bz6I0mqqEVlCaisMnBEYvgZunGy7xH3aAde9Cft7l21qRZv5eBPm689MfJ+wqh0ajuTK0gtJULCIQPcaIjtHwRlj+HHzW066be0WE/uH+rD+YSlqmDh6r0VQVtILSWAevenD7f2H4bCPv1MddjcC4efZZBxoQ7k9+geKXP8sRkFej0dgUraA01kMEWgyF+7ZAi+Gw5nWY2RmObLa5KM3reRHo486P2syn0VQZtILSWB93Hxj6EYz8Fs5nGkFwlz5lfLcRlma+dG3m02iqBFpBaWxHaC/D06/1eNj0IXzQDg6utNnwN7U0zXy7tZlPo6kKlKigRGS6iEwxvztblE8Wkem2EE5zDeLqBQPehLE/gYOzkXZ+8b1GXEEr84+ZTysojaYqUNoMqr1S6kPz+xoRaQ+glJoJdCzPoCLyoIjsEpE/ReShi649JiJKRGqZ52IqywMislNEoizqjhGR/eYxxqI8WkT+MNtMFxEpj7waKxDc0Ygl2Olh2D7f2OC75werDllo5juQos18Gk0VoDQF5XRBSQCuwAwAEakDOF7tgCLSApgItAFaATeJSKh5rQHQCzhs0aQfEGoek4APzbo+wPNAW7Ov50XkQqrXD826F9r1vVp5NVbE2Q16ToWJv4JHHfjqTlj1qlWHHBDuT54282k0VYLSFNQ0YJ2IPAl4Ap7m97XmtaslDNiolMpSSuUBq4Eh5rW3gScAy6ijg4DPlcFGoKaI+AN9gOVKqTSlVDqwHOhrXvNSSm1QRo6Fz4HB5ZBXY23qRcKkldC4H2z4wEjUaCVa1PeigY+bNvNpNFWAEhWUUup7YCDgBtQyDzdgoFLqu3KMuQvoIiK+IuIO9AcaiMhA4JhSasdF9esDRyzOj5plpZUfLab8EkRkkojEikhscnJyOW5JU24cnY1wSedOGxHSrYSlme9UljbzaTSVGafSLiql9gJTReSMef5WeQdUSu0RkdcwZjxngR1AHvB/QO9imhS3fqSuorw4WT4GPgYjo+5lhddYl+BO4NMQts6FiBFWG2ZAuD8frT7EL38mcWvrBlYbR1N+lFLsOnaG/+08zvI9SdTyqEZ0sDcxQd5EB3lT093F3iJqrEipCuoCFaGYLurvM+AzABF5BUgCRgI7TH+GAGCriLTBmAFZPkUCgONmebeLyleZ5QHF1NdUdkQgajSseB6S90HtxlYZJrx+DQK83fjxjxNaQVVS9iVl8MOO4/yw4zgJqVk4OQjtG/pyJiePT9Yc4sMC430ytE51YoK9iQ7yISbImyBfd7RP1LVDmRRURSMidZRSJ0UkEBiK4TH4rsX1BCBGKZUiIkuA+0RkAYZDxGml1AkRWQa8YuEY0Rt4WimVJiIZItIO2ASMBt6z4e1pykPEHfDbi8Ysqs/LVhlCRBgQ7s9na//iVNZ5/RZeSTidncu8DQn8sOMEe5MycBBo39CXyV0b0qd5Xbw9jH+n7PP5bD9yirjENGIT0/nfzhPM32xY+2tVr0ZMkLeptLxpXq8GLk56u2dVxS4KClgoIr5ALnCv6eRQEj9hrFMdALKAcQCmInoR2GLWm6aUSjO/TwHmYKyZLTUPTVWgeh1o0g92zIcez4FTNasM0z/cn4/WHOKX3UncGqNnUZWBfy3axZIdx2kd7M20Qc3p18Kf2p6X/vu7uTjSvqEv7Rv6AlBQoNh3MoPYhHTiEtOJTUzjZzPmYjUnB1o1qEnrYG9ignyICvSmhrvzJX1qKieiLpOm29xDNBK4QSk1zZz11FVK2T6gmhWJiYlRsbGx9hZDA7B/BXw5zAg022KoVYZQStH59ZU0qlOdOePaWGUMTdk5nZVL61dWMKJ1A14Y1KLc/Z08k0NsYjqxCYbC+vP4GfJNs2Bjv+qFJsHWwT408HHTZsErRETilFIx1h6nLDOoD4AC4EYM9/IMYCHQ2opyaa5nGnaHGg1g6+dWU1AXvPlmrf2L01m5+q3azizZcYzzeQXcUkGz2TpervQP96d/uD8AWefzDLNgQrphFtxxnPmbje2WtT2rFTpdxAT70LyeF86O2ixYGSiLgmqrlIoSkW0ASql0EdFGe431cHCEyDth1b8hPQG8g60yzIBwfz5ec4hlu//WZj47803cUcL8vWhRv4ZV+nd3caJDw1p0aGjEHsgvUOxLyiA2MZ24BGMta+kuwyzo6uxARIOaxAT5EB3sbZgF3fQLjD0oi4LKFRFHTFdtEamNMaPSaKxH5J2w+jXYOg96/MsqQ7QMMLz5fvrjhFZQdiT+7zPsPHqa525qZrMxHR2EMH8vwvy9GNUuCICkMznEJqSzJSGNuMR0Plx9kPyVChEY0z6Y529upk2BNqYsCmo68D1QR0ReBoYDz1pVKo2mRgA06gnbv4RuT4NjxfvzXDDzzV6nzXz25JvYozg7CoMji91PbzP8vFwZ0NKfAS0Ns2DmuTx2HDnFd9uOMWd9Ag1rezCqfbBdZbzeuKyhVSn1JUb4oX8DJ4DBSqlvrC2YRkPUaMg4AQeWW22I/uH+5Obr2Hz2Ije/gEXbjtGjqR8+HpVr5cCjmhMdGtXi9WEtubFpHV74YTeb/0q7fENNhVFaug2fCwdwEpgP/BdIMss0GuvSuK8RRDZurtWGaBVQg/o13fhu6zEu59GqqXh+iz9JauZ5bokJuHxlO+HgILx9WwQNfNy558s4TpzOtrdI1w2lzaDigFjzM+6ic+2PrbE+js4QORL2L4Mz1gkGIiKM6xjMhkOpvLo03ipjaErmm9ij1PasRtfGte0tSqnUcHPm41HRZJ/PZ/K8OHJy8+0t0nVBacFiQ5RSN5ifIRed32BLITXXMZGjQBUYa1FWYnynEEa3D+KjNYf4aPVBq42jKUpyxjlW7j3J0Mj6OFUBt+5QP0/eui2CHUdP8+yiXXrGbQPKtPJshhMKxcgLBYBSao21hNJoCvFtCMGdDW++To+CQ8U/yESEqTc3Jy3zPP9eGo+3h4v26rMBi7YdI79AVWrz3sX0aV6XB25sxPTfDhBevwZjOgTbWySboJQiPSuXhNRMElMzbTbuZRWUiEwAHsQIurodaAdswNi4q9FYn+ixsHA8/LUKGlrnz87BQXjr1ghOZ+fy9Hd/4O3uQq9mflYZS2M88L6JO0JEg5o0quNpb3GuiId6NubP42d48X+7aVrXk7Y3+NpbpApBKcXJjHMkpGSSmJZFYmomCanGZ2JKFhnn8mwuU1lCHf2BETVio1IqQkSaAi8opW6zhYC2Qoc6qsTk5sBbTSGkK9w6F7LSIDkeTu6B5L3G9+S9hlv64BnlGirzXB53fLqJ+BNn+PyuNtfMw6eysePIKQbNWMfLQ1owsm2QvcW5Ys7k5DL4/XWczs7lh/s7Ua+mm71FKhP5BYrjp7I5nJZlzoaySEjJ5HBaFompWWRbrK05OggNvN0I9PUg2NedoMJPd0L9vGwS6qgsCmqLUqq1iGzHiCpxTkS2K6UirC2cLdEKqpKz9CnY/DG4+0CmRXJJZw+o3cT4fnwbPLi93JEn0jLPc8vM9ZzMOMdXk9rTrJ5XufrTXMqzi/7gm9ijbHm2J16uVXP/2YGTGQyesZ4Abzd6N6+Ls4Pg5OiAs6PgZPG9sZ8nEQ1q2myTb25+AUfTsw0FlGLMgi4opCNpWeTm//PMd3FyINDHvYgCuqCQ6tV0KzHkU2WKxXdURGoCi4DlIpKOzq+ksTXtpkDKPvDyh9phULupoZhqBBh5pE4fhXfCIXYW9JpWrqF8PFz4fHxbhn+4njGzN7NwcgcCfd0r6EY0Obn5LNl+nL4t6lZZ5QTQqI4n79wWwcNfbWf6r/tLrduqQU3u6hhM/3D/Conzl5Obbygdc/ZTOBtKzeT4qZzCwLgA7i6OBPl60MTPk97N6hJkzoKCfT2o6+WKg0PljY5x2RlUkcoiXYEawFKlVK7VpLIDegZ1DbBgJCSuh0f2gLPr5etfhgMnMxg+cwM13Jz5ZnJ76niWv08NLNlxnAfmb+OL8W3pFFrL3uJUCAUFirwCRX6BIreggLx8RV5+AefzC1gZf5LZ6xI4lJJJXS9XRrUP4o42gYX5rUoiIyeXRIvZT2KK8Xk4LYsTp3OK1PVydSKklgdBvh6mAvrHLFerukuFz95sNYMqi4lvnlJq1OXKqjpaQV0DHFoFnw+CwR8aiQ8rgG2H07njk02E1PJgwd3tqvQbf2Vh1GebOJScye9PdK/Ub+8VSUGBYtW+k8xam8DaAym4OjswNCqAO9oEklegDIeElCwS04yZUGJqJilnzxfpo1b1akVmP5aKyNZJNyuTgtqqlIqyOHcE/lBK2S6yow3QCuoaQCmY0QZcqsOklRXW7ep9yUyYu4WoQG/m3tUGV2fHCuv7euP4qWw6vvYb93dvxCO9m9hbHLuw9+8MZq39i++3GylGLPGv4WqhgDwKFVKQrwfVq9krv+yl2H0NSkSeBp4B3ETkzIVi4DzwcXkGFZEHgYlmf58opd4RkTeAm83+DwLjlFKnLGQZD+QDDyillpnlfYF3AUfgU6XUq2Z5CLAA8AG2AqOUUkVfRzTXHiLQegIsfQKOxUH96Arptmvj2vznllY89NV2Hpi/jQ9GRlWJjaWVke+2HkUpGB59/e4za1LXk9eGt+SJvk34dc9JvD1cCPZ1p4GPu375uYjSIkn8WynlCbyhlPIyD0+llK9S6umrHVBEWmAopzZAK+AmEQkFlgMtlFItgX3A02b9ZsDtQHOgL/CBiDiaM7kZQD+gGTDCrAvwGvC2UioUSMdQbprrgVa3G559Wz6r0G4HRdTn+Zua8cvuJP7vex1F4GrIL1B8G3eUtiE+2ukE8K1ejVtbN6BXMz9C/Ty1ciqG0oLFNjW/fiMiURcf5RgzDGNPVZZSKg9YDQxRSv1ingNsxNgYDDAIWKCUOqeU+gs4gKHc2gAHlFKHzNnRAmCQmaL+RuBbs/1cYHA55NVUJVxrQKvbYNdCY79UBTK2YwgP3NiIr2KP8PqyvRXa97XOmZxcJszdQkJqFne2q3r7njT2oTSj5iPAJODNYq4prj6SxC7gZRHxBbKB/lwafPYu4Cvze30MhXWBo2YZwJGLytsCvsApC2VnWb8IIjIJ4x4JDAy8mnvRVEZaTzTczbfNg44PVmjXD/dqTGrmeT5cdRBfDxcmdNZhKS9HQkomEz6PJSElk5eHtODmVvXsLZKmilCiglJKTTI/u1fkgEqpPSLyGoZJ7yywAyiMoSEi/2eeX4gOWpybj6L42Z8qpX5xsnyMuZ4WExOjbTbXCn7NIKijYeZrf5+RQr6CEBGmDWpBetZ5XvpxD97uLgyLrjqx5GzNugMp3PPlVhwEvpjQlnY6MofmCrjsSq+IuIrIIyLynYgsFJGHRKRcG0KUUp8ppaKUUl2ANGC/OdYY4CZgpPrHyH8UsFxRDcDYKFxSeQpQU0ScLirXXE+0ngCnEuHAigrv2tHMD9SxkS9PLNzJb/FJFT5GVUcpxZx1fzF61mbqermy5L5OWjlprpiyuCJ9juGg8B7wPoZDwrzyDCoidczPQGAoMN/0yHsSGKiUyrKovgS4XUSqmd55ocBmYAsQKiIhIuKC4UixxFRsKzFS0wOMARaXR15NFSTsZqheFzZ/YpXuqzk58tGoGJrX8+KeL7cSm6AzrV7gfF4Bz3z/B1N/2E33JnVYeE8HGvhopwjNlVMWBdVEKTVeKbXSPCYBjcs57kIR2Q38ANyrlErHUH6eGOGUtovITACl1J/A18Bu4Gezfr65xnQfsAzYA3xt1gVD0T0iIgcw1qQq1qVLU/lxdDaioB9YAWmHrDJE9WpOzB7bmno13Lhrzhbi/z5z+UbXOEfTs7jzs03M33yEe7s35ONR0ZVq/46malGWjbpzgJlKqY3meVtgjFLqHuuLZzv0Rt1rkDMn4J0W0HYy9HnZasMcTc9i+IcbKFCKhVOun9lCfoEi/u8zbE1MJzYxndiEdI6dyqaakwOvD2/JoIhifZM01wCVKZLEHqAJcNgsCsSYsRQAyty3VOXRCuoa5esxRgikR/aAi/UUx76kDG6ZuQFvd2e+ndKBWtWrWW0se3H2XB7bDqcTl2gc2w6f4qyZI6iOZzVigr2JDvLhxqZ1CKnlYWdpNdbE7pEkLOhrbSE0GqvRZiLsXmTsi4qyXvjIxn6ezBrbmpGfbmTs7M3Mn9gOzyoct08pxbFT2cSZM6O4xHTi/z5DgTICdjSt68XgyHrEBPkQHeRNgLebzdJJaK4fyhzN3HRssEz5friU6lUOPYO6RlEKPmgPTi4wabXxdLUiK/eeZOLcWFoH+zB7XOsqEx0gN7+A3cfPFM6OYhPTSDpzDgAPF0ciA72JCvImJsibyMCaVVr5aspPpZlBichAjM269YCTQBCGia+5dUXTaCoAEWgzAX58FI5vrbD4fCXRvUmdwrh9Dy3YzoyRUThWwojdp7Ny2XrYUERxiensOHK6MJtq/ZputA3xJSbYm6hAb5rW9dSxBzV2oSwmvheBdsAKpVSkiHQHRlhXLI2mAmk+1FBQB1daXUEBDI6sT1rmeab9bzfPLvqDV4aE29X8pZQiMTWL2MR04hLTiE1IZ//Js4Cxp6uZvxe3tW5griF541+jaqQv11z7lEVB5SqlUkXEQUQclFIrzUgQGk3VwN3HyMB7eIPNhryrUwhpmed5f+UBfD2q8Vgf26aWOJKWxdJdJ4hNSGfr4fTC3EKerk5EB3kzsFU9ooO9iWhQE3cX7QauqZyU5S/zlIhUB9YAX4rISSxCE2k0VYLA9oajREF+hYY+Ko1Hextx+95feQAfDxfu6hRik3HP5eUz7MP1nMw4R5CvO10a1y50ZgitU/26SRKoqfqURUENwgjq+jAwEiPl+zRrCqXRVDhBHSBuNiT9Cf622RkhIrw0uAXpprnPx8OFwZHW3xv0484TnMw4x6yxMdzY1M/q42k01qK0dBuNRKSjUipTKVWglMpTSs0FtgM1bSeiRlMBBLY3Pm1o5gNjjeed2yNof4Mvj32zg5V7T1p1PKUUs9cl0KhOdbo3qWPVsTQaa1Oaa847QEYx5VnmNY2m6lCzAdRoAInrbT60q7MjH4+Opqm/J1O+iCMuMd1qY209fIo/jp1mTIdgvS9JU+UpTUEFK6V2XlyolIoFgq0mkUZjLQLbGTMoO2TD9XR1Zs64NtT1cuWuOVvYl1Tcu1/5mbM+AU9XJ4bawJSo0Vib0hRUaSk1tB+qpuoR2B7OJlkteOzlqFW9GvPGt6WakwOjP9vM0fSsyze6Av4+ncPSP05wW0wDPHSAVs01QGkKaouITLy4UETGA3HWE0mjsRJBHYxPG69DWdLAx53Px7ch63weoz/bTOrZcxXW9xcbE8lXijEdgiusT43GnpSmoB4CxonIKhF50zxWAxOAis2jrdHYglpNwM3brgoKjDh2s8a25vjpbO6as4Xc/IJy95mTm89/Nx+mZ5jfdRNNXXPtU6KCUkolKaU6AC8ACebxglKqvVLqb9uIp9FUIA4Ohpkv0b4KCiAm2Ie3bo1gx9HTzF2fUO7+fthxnLTM84zTsyfNNcRlA2yZSQrfM4/fbCGURmM1AttD2kHIsH+a9v7h/nRvUpt3V+wnOePqTX0XXMub+HnSvqFOq665drBLBEgReVBEdonInyLykFnmIyLLRWS/+eltlouITBeRAyKyU0SiLPoZY9bfLyJjLMqjReQPs8100f62mgvYaT9USfzrpmbk5OXz+s/xV93HloR0dp84w9iO2rVcc21hcwUlIi2AiUCb/2/vzMOrKLP8/zlJWGVH9rCp0MgiDoTVXRpQxF1b3ACXRnFp+bfwlwAAHZBJREFUm2mdsV3GddqesUdH/dkobqCt4i6oIKINOoIsQUEEFHEBgqBRVtmTnN8fpyIXSUJCklt1K+fzPPXcum/VrXu+dW/Vqfd9z3teoDswVEQ6ADcC76lqB+C94D3AyUCHYBkFjA2O0wi4DegTHOu2QqcW7DMq4XM+p5VjtOgOGbUi46AOaVKHS49uz0sLcli4euMBHWP87G+oX6saZ/gMtk7MCKMGdTgwR1W3qWoe8D5wJpZSaUKwzwTgjGD9dOBpNeYADUSkBTAYmK6q61V1AzAdOCnYVk9VP1Kb7OrphGM5VZ2M6pCZFcqA3eK49sQONK1bg9smfUZBQdnGaK3ZuJ1pS75nWO/W1KqeGnNPOU5pCcNBfQYcKyKNRaQ2MARoDTRT1bUAwWthnpZWwOqEz+cEZSWV5xRRvg8iMkpEskUkOzc3t9zCnBShbX/4/jPYsTlsSwCoUyODPw/pxKKcTbz8cc7+P5DAMx+tRFW5uG/bSrLOccIj6Q5KVZcB/4XVeN4GFlFydvSiGtX1AMqLsmWcqmapalaTJk1KtNuJEW36gRZAzrywLfmFM45sRc+2Dfnvtz9n847dpfrM9l35TJy/isFdmpPZ0EPLnfgRSpCEqj6hqj1U9VhgPfAl8H3QPEfwWphVMwerYRWSCXy3n/LMIsodx8jsBZIeiXDzQkSEO07rwk9bd/HAu1+W6jOTFq5h47bdjPTQciemhBXF1zR4bQOcBTwPTAYKI/FGAJOC9cnA8CCary+wKWgCnAYMEpGGQXDEIGBasG2LiPQNoveGJxzLcaBGHZtyIyKBEoV0bVWfYb3aMGH2t3y5n1x9qsr42d9yeIt69G7fKEkWOk5yCcVBAa+IyFLgDeDqIMjhr8BAEfkSGBi8B5gCfA2sAB4DrgJQ1fXYdPTzg+XOoAxgNPB48JmvgKnJEOWkEG36Q0425FVcqqGK4PpBHaldPZ073liKFpPUdtVP27hn6ud8vm4Ll3houRNjQskoqarHFFH2EzCgiHIFri7mOE8CTxZRng10Lb+lTmxp2w/mPAzffWJZziNC4zo1+NOg33Db5CVMW/I9J3VtDsC2XXlMWbyOl7JXM/eb9YjAoM7NOK17y5AtdpzKw1MeO1WTxAG7EXJQABf2acNzc1dx91tLqVcrg9c/WcNbn65l66582jWuzfWDOnJWj0xaNvBJBZx44w7KqZocdDAc3NECJY4eE7Y1e5GRnsbtp3Xh/MfmcMFjc6ldPZ1TurXg3KzW9GrX0Jv0nCqDOyin6tKmHyx9HQoKLJFshOh3aGPuOasb1dLTOLlrc5/fyamSROuqdJxk0qYf7NgEPywN25IiOb93G87pmenOyamyuINyqi5to5U41nGcvXEH5VRdGrSFui3dQTlORHEH5VRdRKwWtfIjKGbMkeM44eEOyqnatOkHW76DjSvDtsRxnF/hDsqp2rTtb68RysvnOI7hDsqp2jQ5HGrWh1XRmR/KcRzD41edqk1aGrQ7Bj55FjblQNdz4PCh5rQcxwkVr0E5ztD74ajr4KevYNJVcO9hMPFC+OxV2LUtbOscp8oixWVMrmpkZWVpdnZ22GY4YaIKaxbA4pdhyavw8/dQ7SDoNMRqVoeeaFPGO04VR0QWqGpWpX+POyjDHZSzFwX5sHKWOaulk2DHRqjZADqfZs6q3dGQlh62lY4TCu6gkow7KKdY8nbB1zPMWX3+FuzeCnWaQ5czoevZkJllY6ocp4qQLAflQRKOsz8yqkPHwbbs2gZfTjNnlf0kzB1rGSm6ng3dzoFmXcK21nFiQ1hTvo8RkSUi8pmIPC8iNUVkgIh8LCILReRDETks2LeGiLwgIitEZK6ItEs4zp+D8i9EZHBC+UlB2QoRuTH5Cp3YUr221ZyGPQs3fAlnjIXGh8GsB2Bsf3i4L7x/rwVcOI5TLpLexCcirYAPgc6qul1EXsSmdb8JOF1Vl4nIVUBvVR0ZrB+hqleKyDDgTFU9T0Q6A88DvYGWwLtAx+BrlmPTxudg08Gfr6olpqz2Jj6nXPycC8smweJX9oypatnDala9LoNqPrmgEx+S1cQXVph5BlBLRDKA2sB3gAL1gu31gzKA04EJwfrLwACxGdtOByaq6k5V/QZYgTmr3sAKVf1aVXcBE4N9HafyqNMEel0Ol06FMUtg4F2g+fDOzTD9trCtc5yUJOkOSlXXAH8DVgFrgU2q+g5wOTBFRHKAi4G/Bh9pBawOPpsHbAIaJ5YH5ARlxZXvg4iMEpFsEcnOzc2tGIGOUz8TjvoDXPEBdD8fFj5r8045jlMmku6gRKQhVqNpjzXNHSQiFwFjgCGqmgk8BdxX+JEiDqMHUL5voeo4Vc1S1awmTZqUTYjjlIY+V8Kuny1TheM4ZSKMJr7fAt+oaq6q7gZeBY4Cuqvq3GCfF4Agiyc5QGuAoEmwPrA+sTwgE2sWLK7ccZJPyyMtY/q8R21sleM4pSYMB7UK6CsitYO+pAHAUqC+iBQGOQwElgXrk4ERwfo5wD/VIjsmA8OCKL/2QAdgHhYU0UFE2otIdWBYsK/jhEOfK2DDt7B8WtiWOE5KkfRxUKo6V0ReBj4G8oBPgHFYzecVESkANgCXBh95AnhGRFZgNadhwXGWBBGAS4PjXK2q+QAicg0wDUgHnlTVJcnS5zj70OlUqJdpY6Y6DQnbGsdJGTyTRICHmTuVyof3w7u3w+iPoFnnsK1xnHIR9zBzx6la9BgBGbVg7iNhW+I45WP7xqR9lTsox0kGtRtB9/Pg0xdg2/qwrXGcAyMnGx49Jmlf5w7KcZJF7ysgbwcsGB+2JY5TNgoKYPZD8OTgYgbtVA7uoBwnWTTrDO2Pg/mPQ/7usK1xnNKx9Sd4fhi8cwv85mS48oOkfbU7KMdJJn1Hw+Y1sOyNsC1xnP2zcjY8crRNN3PyvfC7Z6BWw6R9vTsox0kmHQZDw/Yw99GwLXGc4ikogA/+BuOHQrWacNl06DMq6fOe+XxQjpNM0tJs4O7bN8J3n0DLfwnbIqeqsSkHXrsStv1ks0TXamCvNevvWV/+ttWaup4NQ/8Xatbb/3ErAXdQjpNsjrwA/nk3zHkEzvKalJNENqyECadaqHj7YyyJ8cbVsGOxle3aYvtl1IRTH4Qew0OdLdodlOMkm5r14cgLbUbegXdC3WZhW+RUBX76CiacZsmLR0wquvaen2dOK71aaLWmRLwPynHCoM8VUJBnTsqJLhtW2m+0cnbYlpSP3OUw/hTI2w4j3yy+aTk9Aw5qHAnnBF6DcpxwaHwodBgE2U/A0WOsI9opnl3bYMr1sGXdnn6S/b3WqFv25qn8PMiZb30wy6dB7rI927IusxpvjToVq62y+X4pPB3M2TryLWh6eLj2lAF3UI4TFv2ushvHpy9AzxH737+qogpvXAeLX7LpSzZ8Y/0lOzbZrMXFIenWnJrY+V/ca95OWPEurJgO2zdAWga07Q89LoZDTrBJJz96GL56D07/O7Q7Knn6y8O6xfYfS68OI96AgzuEbVGZ8GSxAZ4s1kk6qjDueOsTuHoepKWHbVE0mfsoTP03OOFmOO7f9pSrws4tsGNj4LA27nFc+5QV8VqQt/f31G5stdqOg+HQE82xJbJyNrw+2pr9+l4FA26FarUqX/+BsuZjeOZMqF4HRky2WnsFkaxksV6DcpywEIGjroOXL4EvpsDhp4ZtUfRY+RFMuwk6ngzHXL/3NhHrK6lZDxq0KdtxVWH3tgRnlQ/NupT8kNC2P1w5C969DeY8DF++A2c+ApmVfp8unvzdVuPb9lOwrLfXrbmWmqhWAxjxJjRsG56N5cBrUAFeg3JCIT8P/l9PqH0wXP5uqCG9kWPLOnj0WKh+EPx+ht1so8JXM2DytZYVpP+10Ov30KD1/j9XEns5m/V7nM729Xu//2V9PezcVPzxmnaBC1+E+pnls6sIvAblOFWB9Ay7wb31J2tCSpW+jcombxe8OMKa8C5+PVrOCeDQE2D0bKvdzXrAlhZHQqeh0OkUC0Qo7mFj81pYPQdWzYXvPoaffzAntKMEZ1PtIGuCrN3IXhu1D943ttRDheuF22s1ikXgTSg1KBEZA1yO5cVdDFwC7ATuBs4F8oGxqvpgMC38A8AQYBswUlU/Do4zArglOOzdqjohKO8JjAdqAVOA63Q/Qr0G5YTG7u1wf1do1dOeeB2YcgPMGwdnPwHdzgnbmpL5cQV8/qYtOfOtrNEh5qg6DbVowlVzbFk9Bzausn0yalm4d/1We5xKoYOJuLNJVg0q6Q5KRFoBHwKdVXV7MG37FECAEzAHVCAiTVX1BxEZAlyLOag+wAOq2kdEGgHZQBbm6BYAPVV1g4jMA64D5gTHflBVp5ZklzsoJ1Te/2+Y8Z8+4y7AohfgtVHQ92o46S9hW1M2tqyz/sRlb8I3H0BBQtb6Os2gTV9o3Rfa9IHmR9iA2BQk7k18GUAtEdkN1Aa+w2pPF6hqAYCq/hDsezrwdFADmiMiDUSkBXA8MF1V1wOIyHTgJBGZCdRT1Y+C8qeBM4ASHZTjhEqvy21a+NkPWsd7VWXtpxZS3vZoGHhH2NaUnbrNIetSW3ZsstD1/Dxo3RsatvM+xjKS9EwSqroG+BuwClgLbFLVd4BDgfNEJFtEpopIYcB+K2B1wiFygrKSynOKKN8HERkVfF92bm5u+cU5zoFSu5FNC7/4JcuNVtUoyIcN38ILF1mfyrlPpWzt4hdq1rdkq93Psz4jd05lJuk1KBFpiNWK2gMbgZdE5CKgBrBDVbNE5CzgSeAYrOnv1+gBlO9bqDoOGAfWxFdGKY5TsfS72vpd5oyNbtPW5u/g2w/h8NMOrF9k9TxY96ll1N60xl4359hxC/IgrRpcMhXqNK14252UI4wmvt8C36hqLoCIvAr0x2o6rwT7vAY8FaznAInxm5lYk2AO1syXWD4zKM8sYn/HiTYNWltAwILxcNwNSZ0YrkR2bYXP34KFz8HXMwGFnrPh1P8t23GWvAYvjbT1tAyo1xLqt7Y+mfqZtrTuDc27VbAAJ1UJw0GtAvqKSG1gOzAAC3bYDJyI1ZyOA5YH+08GrhGRiViQxCZVXSsi04C/BDUygEHAn1V1vYhsEZG+wFxgOPBQkrQ5Tvno/wdLfTT/cTj2hvDsKCiAlbNg0URY+rplu2jQxmza+gMseAo6DLRItdKwcRVMvg5aZcF5z1jAgGfOcPZD0h2Uqs4VkZeBj4E84BOsma0W8GwQgv4zFoYOFoU3BFiBhZlfEhxnvYjcBQRxndxZGDABjGZPmPlUPEDCSRWad4XDBlp6n37XJD+Vzo8r4NOJFkm3aRVUrwtdzoDuF0CbfjbhYt5OS6Mz6Rpo2QPqtSj5mPl58Ooo0AI4+3GrOTlOKfBMEgEeZu5Ehm/+DyYMhVPug16XVf73bVtvzW+LnrdxPJJmCVK7n281pOq19/1M7nLL8tCmL1z0qjmu4pj5V5h5D5z1GBzxu8rT4SSNuIeZO45THO2OtprJ7Ieg58jKaQrL320h0Aufs6kl8ndBk8NtOoluv9t/rahJRwvkeHMMzB1rAR5FsWoOvP9fcMQwd05OmXEH5ThRQwSO/iO8ONwmy+t1ecWEKKvC2kVWU1r8Mmz70XIAZl0GR55vA0fL8j09L4Evp8O7t0O7Y6DFEXtv374RXrnc+q6G3Ft++50qhzsox4kinYZaQMGU6+GLqXDSPdDkNwd2rM1rLfBi0USbgC+9OvzmZOtXOmzAgY83EoHTHoKx/c0RjZq5pzlQFd78I2xZC5e+E5kZWp3Uwh2U40SRtHS49G2Y95j14fy9H/QeBcf/e+nCz3dthc+nwKIgNFwLILM3DL0fupxZcSHsBx0MZ4yFf5wF02+FU/7Hyhc+a/1aA26DzJ4V811OlcODJAI8SMKJLFt/tDx9C8bb7K8n3gw9Rlom9EJ2bbVBsCtn2UDaNQusX6l+G+g+zJYKnLBuH96+yeZIOv8FaHyYBVC06gHDJ3k4eQyJbbLYqOIOyok86xbD23+Gb//P5vrpfy38uNyc0poFlolB0m1a9LZH2eywbY8qOcKuosjbCY+daE16dVvaPEmjZ3lIeUxxB5Vk3EE5KYEqLJsM79xig1/TMmzKhnZHW4LVNn1seocw+OFzGHcc5O2AYc+VfhCvk3J4mLnjOPsiAp1Pt9rR90ugSSeoUSdsq4ymneB3T1t+PXdOTgXgDspxUpFqtSCz0h9gy07HwWFb4MSIpE+34TiO4zilwR2U4ziOE0ncQTmO4ziRxB2U4ziOE0ncQTmO4ziRxB2U4ziOE0ncQTmO4ziRxDNJBIhILrAybDuK4GDgx7CNqEDipgdcU1Unjudqf5raqmqTyjbCHVTEEZHsZKQUSRZx0wOuqaoTx3MVFU3exOc4juNEEndQjuM4TiRxBxV9xoVtQAUTNz3gmqo6cTxXkdDkfVCO4zhOJPEalOM4jhNJ3EE5juM4kcQdlOM4lY6ISNg2OKmHOygn5RGR2gnrKX8jFJEeIlItbDsqEvXO7irPgVyn7qCqCCIySkSuC9ZT/iYOICIXich84D4R+QOk9o1QRC4QkUXAYKAgbHsqAhG5WERmiMi9InJu2PZEHb9O98anfI85IlIT+BNwFVBbRCap6rfhWlV+RORMYBRwPVAfuFBEXlTVdeFaVnaC3+h2YBhwgarOTtgmqeZ0gxtrbeAeoBtwG9AJOE9EvlbVBWHaF0X8Oi0ar0HFFBFJB1DVHUC2qrYCHgPuDtWwclCoKeA44FlVfR/YAmxORecEv/xGPwATgLkiUktEBolI3RR0TmlqbAUWAWeo6gfAZGADUCNUAyOGX6f7OVaK/f+dUiAitwNNgH+q6ivBTaNARA4CFgKjVHVGYXmoxpaSBE0zVPVlETkbu4g/AgYBS7Hklh+o6iNR1yYi1wDvq+ri4P1hwBXAkUBz4AtAgGmqOi7qegBE5CagMfBR8BvVAHYBaaqaLyIzgFtUdVaohkYEv073f516DSpmBH+Q3sB04FoRGQM0AAieah8E7giajlLpT5+o6V+BN4ATsf/wEFUdhI1+v1pEGkRVm4i0FZH3gVuA+wrLVXUFkA0sAQao6jnAWOAqEakfVT0AInKEiMwFugLzgVtFZIiq7gxqU/ki0hzYCXwaqrERwa/T0l2n7qBiRBD5dTTwJ1V9HWv7bwmcX7iPqj4EpANnikgbETklFGNLSRGa/gPTdCmQC7QDvg12/xT4GKiXdENLz3rgWaADUCAiIxO2vQbcqKo/BO+XYppqJdXCspMGPKGqF6jqROAl4FywJr9gn6bAdlXdIiLdROTkkGwNHb9OS3+duoNKUX4d4RNUlXcDy9jzR5+NPdF2E5GOCbv/DXgZ+ADrzI4EZdA0D+iJzVmzDHhcROoAdwRlPxABitAjqroFeCZ4fQS4JiGkfHfQF1HYjn8zFsiUm0SzS6SYyLIvgX8kOKOZgAbvC/sQugDVReRW4Cmi73QrBL9Oy3eduoOKCQlV5TeBNiLSKfjTLAY2Y08ziEhP4FbgUaCzqr4Uhr2lYT+aNgJtgRuxm+Bbwb7nFt7ko0ZhwIOqbg+KJgHLsQv2l+0iMhxr7tsNXKaq+cm3dv8U3qhUdauqbkv4vU4G1qlqQUKQR3+sw7wmcKyqvpp8i0OhDuwVDBGH67Qsmsp1nbqDSjFE5CQRmQTcJSJZCeWFkTNLgVXACABVXQZ0xjqvwZ5aTlfV0aq6LXmWF085NLVT1U3AcOBUVb02CppK0JOW+PQZXNj/AwwWkboi0kFE6gILsAv4jwnOLFSK0qSqmqhJRAqHrXTEblaISNeg7HWgt6reHIXfqDIRo6mIzAQeByh8yEjV67Scmg74OnUHlQIEf46aIjIe61x/AnuKuUxEGgdNR4VP2VuAaUAXEfmDiDTGmom2AqjqalVdnXwVe1MBmtKBTQBBZ/zm5KvYQyn1FAQ39QaFzXpqY4IWYX1TE4BGqrokCJoIlbJqAgqbKn8GWovIP4C/iEhTVX1PVZeGIiTJBLXGHcFyRGF/m4hUS7XrtJByaCrfdaqqvqTIApwJpAfrxwKPJGwT4O/Ak1j7bi+srX8xcHvYtlcVTaXQ8zDmiNoFZWOA1cANYdteAZqaAYdgWTA+Aa4L2/aQzlcaVnP4K3A6FnZfuK1aqv2nw9Tk46AijFhakJbAx6r6YkL5udgf4jPgQ2AqkIeNQh+jqhuC/dKBahqhPpm4aaoAPQOAT1R1fbJtL47yaBKRpsDlmBOLjKbKJOF8zVfVV4KyBtgNexQWVv0OFn7dELiGCP+nIUKawvbMvhT5tCLYk/Us4BwsAmYk0DTYfjyWQiYDuzk8DjRL+Hx62BrirqkC9GSEraGiNVW1pYTz1QjIAv4j2O96rOnujV99PlL/6Shq8lx8EURVVUROwEbdzxCRn7EEogpMUNWZhfuKyGLgGGBr0Fmd2HcTGeKmqQL05IVhd0lUgKaUGFBaURRzvk4CtmM3+ONEZAoWxTYL+Bp+iX6M3H8aoqfJgyQihuwZS5KN3QBQ1bexcOTDZe9xEmDpQ7ZjgyA1ijeJuGmKmx6Ip6bKpITz9QVwBJayajXWRNYFSwR8vIi0iur5iqImd1AhUxiiGTyBkPAjrwDqiki34P37WDbgeiJSXWwag08JxhhE6WksbpripgfiqakyKcP5+gDLkJALXKmqtwX7rweOUtU1STW8BFJBkzuokBCRo0RkAnCLiDTSwgbgPVkF5gH5wEARyVAL0W0F9FTVXdiTzGhVHa57UuOEStw0xU0PxFNTZXIA52sJ5rz/RVV3iEh6ggP4OQwNvyaVNLmDCgEROQSLhpqB/fB3icgQALUR2KiNg5kPHIaNwgZLtrky2D5TI5QVOm6a4qYH4qmpMinn+fo22J5f6ACiQKppcgcVDr2BZao6HouGWQicKiItAETkbhF5Asso8CDQW0QWYIM53wnH5P0SN01x0wPx1FSZxPF8pZQmHweVBETkVOxpJVtV5wRPMc8A56vqKhHpDFwMfI91UF6FhXOuCD5fBwtL3hiOgn2Jm6a46YF4aqpM4ni+Ul2T16AqERFpISJvADdgg9meEpHBqvo1NoHXucGuX2B5rOoDi9WmLVghQVSNqv4clT993DTFTQ/EU1NlEsfzFRdN7qAqlyzgQ1U9VlXvAh7ARmGDjcTvJiJ91CKh1mBZnjfBLynsIxeKSvw0xU0PxFNTZRLH8xULTe6gKhgRGS4ix4tNd/0e8HTC5p+wcSUAc7B8ZfcH1eguwEoRqQ17hXyGTtw0xU0PxFNTZRLH8xVHTZ5JogIQEQGaA89hiTK/An6PJctcK5bxdzfQAqtuo6rrgAdEpC2W36otMFwjkFof4qcpbnognpoqkzierzhq2guNQP6nVF7Yk+W5I/CPYD0DeAh49Vf7vAH8NlhvmrBv3bB1xFlT3PTEVZOfL9f068VrUAeI2ORsdwLpYrmp6mGD21DVPLFswN+JyHGq+r6IVMdGYi8Xkf8EhorI8WrZf7eEJGMv4qYpbnognpoqkzierzhqKg7vgzoAROQ4bJxAQywtyF3Y9NwniEhv+GWCrzsJpvPGproeibUN18WeZjYk1/LiiZumuOmBeGqqTOJ4vuKoqUTCrsKl4oIlUrw44f3fgdHYn2BBUJaGtQ2/CGRiA+SeBo4M2/6qoClueuKqyc+XaypRb9gGpOIC1AZqsKd990LgnmB9IXBtsJ4FTAzb3qqoKW564qrJz5drKmnxJr4DQFW3qepO3ZPJeSDWxgtwCTY9wZvA81h1/JeMwVElbpripgfiqakyieP5iqOmkvAgiXIglq5egWbA5KB4C3AT0BX4RoNU9Bo81kSduGmKmx6Ip6bKJI7nK46aisJrUOWjAKgG/AgcETy53AoUqOqHGqG5X8pA3DTFTQ/EU1NlEsfzFUdN++DJYsuJiPQFZgfLU6r6RMgmlZu4aYqbHoinpsokjucrjpp+jTuociIimVg24PtUdWfY9lQEcdMUNz0QT02VSRzPVxw1/Rp3UI7jOE4k8T4ox3EcJ5K4g3Icx3EiiTsox3EcJ5K4g3Icx3EiiTsox3EcJ5K4g3Icx3EiiTsox3EcJ5L8f/YDo2i+aB2FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sim=df[df.index>='2020-02-22']\n",
    "\n",
    "df_sim=df_sim[['Msci','EUBond']]\n",
    "\n",
    "n_obbligazioni=75000/df_sim.iloc[0,1]\n",
    "n_azioni=25000/df_sim.iloc[0,0]\n",
    "\n",
    "df_sim['Benchmark']=n_azioni*df_sim['Msci']+n_obbligazioni*df_sim['EUBond']\n",
    "\n",
    "print('Massimo drawdown ',drawdown(np.array(df_sim['Benchmark']))[0],'%')\n",
    "\n",
    "plt.plot(df_sim.index,df_sim['Benchmark'],label='Benchmark 25-75')\n",
    "plt.plot(p2data.index,p2data['Rendimento'],label='Mio portafoglio 2 Moneyfarm')\n",
    "plt.xticks(rotation=30)\n",
    "plt.title('Confronto col benchmark')\n",
    "plt.ylabel('Capitale €')\n",
    "plt.xlim('2020-02-25','2020-04-09')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('benchmark.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2623.74771855952"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sim.iloc[-1,2]-100000    #PERDITA ATTUALE DEL BENCHMARK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simulazione portafoglio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SIMULAZIONE PORTAFOGLIO, CON RIBILANCIAMENTO ATTUALE\n",
    "\n",
    "def rendimento_bis(ripartizione,capitale0=100000,inizio='2012',fine='2020-05',nomietf=['Msci','Emergenti','EUBond','CorporateBond','Gold']):\n",
    "    \n",
    "    daysperyear=255\n",
    "    \n",
    "    # acquisizione prezzi\n",
    "    df2=df[(df.index>=inizio) & (df.index<=fine)]\n",
    "    durata=round(len(df2)/daysperyear,1)\n",
    "    titoli=np.array(df2[nomietf])\n",
    "    n_titoli=titoli.shape[1]\n",
    "    \n",
    "    \n",
    "    # ripartizione iniziale\n",
    "    ripartizione=np.array(ripartizione)\n",
    "    cap_rip=ripartizione/100*capitale0\n",
    "    \n",
    "    num_rip=cap_rip/titoli[0,:]\n",
    "    capitale2=np.random.rand(len(df2),n_titoli+2)\n",
    "    \n",
    "    rend_annuo_bar=[]\n",
    "    \n",
    "    kk=-1\n",
    "    \n",
    "    # evoluzione\n",
    "    for i in range(len(df2)):\n",
    "        capitale2[i,0]=round(i/daysperyear,2)\n",
    "        for j in range(1,n_titoli+1):\n",
    "            capitale2[i,j]=num_rip[j-1]*titoli[i,j-1]\n",
    "        \n",
    "        capitale2[i,n_titoli+1]=np.sum(capitale2[i,1:n_titoli+1])\n",
    "        kk=kk+1\n",
    "        \n",
    "    # ribilanciamento\n",
    "        if kk==255:\n",
    "            cap_rip=ripartizione/100*capitale2[i,n_titoli+1]\n",
    "            num_rip=cap_rip/titoli[i,:]\n",
    "            #print(num_rip,capitale2[i,n_titoli+1])\n",
    "            # rendimento annuale nella lista\n",
    "            rannuo_bar=np.round((capitale2[i,n_titoli+1]/capitale2[i-daysperyear,n_titoli+1]-1)*100,2)\n",
    "            #print(capitale2[i,n_titoli+1],capitale2[i-255,n_titoli+1],i,i-255)\n",
    "            rend_annuo_bar.append([df2.index[i].year-1,rannuo_bar])\n",
    "            #print(df2.index[i])\n",
    "            # azzero il contatore kk (responsabile del bilanciamento annuale)\n",
    "            kk=0\n",
    "    \n",
    "    \n",
    "    # rendimento annualizzato\n",
    "    rend_annualizzato=round(((capitale2[i,n_titoli+1]/capitale0)**(1/durata)-1)*100,2)\n",
    "    \n",
    "    array_finale=capitale2[:,n_titoli+1]\n",
    "    \n",
    "    # massima perdita\n",
    "    maxdrowdawn=round(drawdown(array_finale)[0],2)\n",
    "    drowdawn_when=round(drawdown(array_finale)[1]/daysperyear,2)\n",
    "    \n",
    "    return (capitale2,rend_annualizzato,maxdrowdawn,drowdawn_when,np.array(rend_annuo_bar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rendimento  -17.23 % - massima perdita  18.15 % - dopo  0.2 anni\n"
     ]
    },
    {
     "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": [
    "#risiko=50\n",
    "#print([risiko*0.8*3/4,risiko*0.8*1/4,(100-risiko)*3/4,(100-risiko)*1/4,risiko*0.2])\n",
    "#peppe_completo=rendimento_bis([risiko*0.8*3/4,risiko*0.8*1/4,(100-risiko)*3/4,(100-risiko)*1/4,risiko*0.2],inizio='2012',fine='2020-06')\n",
    "\n",
    "peppe_completo=rendimento_bis([45*2/3,45*1/3,50*2/3,50*1/3,5],inizio='2020-01',fine='2021')\n",
    "\n",
    "peppe=peppe_completo[0]\n",
    "print('rendimento ',peppe_completo[1],'% - massima perdita ',peppe_completo[2],'% - dopo ',peppe_completo[3],'anni')\n",
    "\n",
    "xticks_str=[]\n",
    "xticks_num=np.arange(0,9,1)\n",
    "\n",
    "for i in range(len(xticks_num)):\n",
    "    xticks_str.append(str(xticks_num[i]+2012))\n",
    "\n",
    "plt.plot(peppe[:,0],peppe[:,6],label='Totale')\n",
    "plt.plot(peppe[:,0],peppe[:,1],label='Msci',linestyle='--')\n",
    "plt.plot(peppe[:,0],peppe[:,2],label='Emergenti',linestyle='--')\n",
    "plt.plot(peppe[:,0],peppe[:,3],label='EUBond',linestyle='--')\n",
    "plt.plot(peppe[:,0],peppe[:,4],label='CorporateBond',linestyle='--')\n",
    "plt.plot(peppe[:,0],peppe[:,5],label='Gold',linestyle='--')\n",
    "#plt.xticks(xticks_num,xticks_str)\n",
    "#plt.ylim(0,220000)\n",
    "plt.xlabel('Anni')\n",
    "plt.ylabel('Totale €')\n",
    "#plt.ylim(0,500000)\n",
    "#plt.xlim(8,8.18)\n",
    "plt.savefig('output_portfolio/andamento_exempio.png',dpi=300)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.05987697337769687"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultimo=peppe[:,6][-1]\n",
    "massimo=peppe[:,6].max()\n",
    "\n",
    "(ultimo-massimo)/massimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# VISUALIZZAZIONE DEI RENDIMENTI ANNUALI\n",
    "\n",
    "plt.bar(peppe_completo[4][:,0],peppe_completo[4][:,1])\n",
    "plt.xlabel('Anno')\n",
    "plt.ylabel('Rendimento %')\n",
    "plt.savefig('output_portfolio/rendimenti-annuali.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.54"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definition of sharpe ratio\n",
    "\n",
    "def sharpe_ratio(az):\n",
    "    rend_tempo=rendimento_bis([az,0,100-az,0,0],inizio='2012',fine='2020-06')[0]\n",
    "    rend_perc=pd.DataFrame(rend_tempo[:,6]).pct_change().iloc[1:]\n",
    "    sharpe=np.mean(rend_perc)*255/(np.std(rend_perc)*np.sqrt(255))\n",
    "\n",
    "    return round(np.array(sharpe)[0],2)\n",
    "\n",
    "sharpe_ratio(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6038198072059182"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1+rendimento_bis([50,0,50,0,0],inizio='2012',fine='2020-06')[1]/100)**8.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3.0 1.09 8.42\n",
      "10 3.3 1.22 9.34\n",
      "20 3.5 1.2 11.41\n",
      "30 3.7 1.09 13.59\n",
      "40 3.9 0.96 15.84\n",
      "50 4.1 0.85 18.09\n",
      "60 4.2 0.76 20.34\n",
      "70 4.3 0.68 22.59\n",
      "80 4.4 0.63 24.84\n",
      "90 4.5 0.58 27.1\n",
      "100 4.6 0.54 29.35\n"
     ]
    }
   ],
   "source": [
    "# confronto con portafogli Moneyfarm\n",
    "\n",
    "mfarm=[1.7,2.7,3.8,4.5,5.8]\n",
    "kk=0\n",
    "\n",
    "for act in np.arange(0,101,10):\n",
    "    ob=100-act\n",
    "    ac=act-5\n",
    "    rend_act=rendimento_bis([ac*2/3,ac*1/3,ob*2/3,ob*1/3,5],inizio='2012',fine='2020-06')[1:3]\n",
    "    print(act,round(rend_act[0],1),sharpe_ratio(az=act),rend_act[1])\n",
    "    kk=kk+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEMCAYAAAA/Jfb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd5xU1fn/33f67M7Wme29wi7s0kGKoBQFAXtvses39nxNoqZo4s9oEpNootGI3URNovJVBBRQFCnSYZe6vfdep9/fH3d3YWGXXZbZwnLerxevGe49594zs7ufOfM5z3keSZZlBAKBQDC6UA33AAQCgUDgeYS4CwQCwShEiLtAIBCMQoS4CwQCwShEiLtAIBCMQoS4CwQCwShEM1w3tlgscmxs7HDdXiAQCM5Kdu/eXSPLclBf7YZN3GNjY9m1a9dw3V4gEAjOSiRJKuxPO2HLCAQCwShEiLtAIBCMQoS4CwQCwShEiLtAIBCMQoS4CwQCwShEiLtAIBCMQoS4CwQCwRAiyzIul3vQ7zNsce4CgUBwruByuCnNrqcgo5aCzBrGz41g8sUxg3pPIe4CgUAwCLQ12Sk8UENBZi3Fh+pw2FxotCoiUwIJCPMe9PsLcRcIBAIPIMsyNSUtFGYqgl5Z0AQymAL0JM8IJTbNTMSYALQ69ZCMR4i7QCAQDBCn3UXJ0XoKMmspzKyhpd4GQHCsLzOWxxGTZsESaUKSpCEfmxB3gUAgOA1aG2wUdMzOSw7X4XS40ejVRKcEMm2ZmZjxZrz99MM9TCHuAoFAcCpkt0x1cTMFGYqgVxc1A+ATaCBldrhityQHoNaOrOBDIe4CgUBwAg6bi+LDdYp/fqCWtkY7SBAa58d5l8cTm2YhMNx7WOyW/iLEXSAQCIDmOmvX7Lz0aD0upxudQU1Uqpm4dDPR480YTbrhHma/EeIuEAjOSdxumaqCpi5Bry1tAcA3yMj4uRHEppsJS/RHrRlZdkt/6VPcJUl6C1gGVMmyPL6H82OBt4HJwC9kWX7B46MUCAQCD2Bvd1J8uI6CzBoKD9TS3uxAUkmEJfgx68pEYtPN+Id4jWi7pb/0Z+b+DvAy8F4v5+uAh4DLPTQmgUAg8BiN1e2KmGfWUJrVgNslo/fSED3OTGy6mehUMwZv7XAP0+P0Ke6yLG+SJCn2FOergCpJkpZ6cFwCgUAwINwuNxX5x+yW+vJWAAJCvZgwP4rYdDOh8X6o1Gen3dJfhOcuEAjOemxtDooO1VGQUUPhwVpsrU5UKonwZH/GzQknJs2Mf7DXcA9zSBlScZck6R7gHoDo6OihvLVAIBhlNFS2dWwmqqE8uxG3W8Zg0hKbZiE2zUJUaiB647k7fx3SVy7L8uvA6wBTp06Vh/LeAoHg7MblclOe06gIekYNjVXtAJgjvJl4UTSxaRZC4nxRqc7+xVBPcO5+rAkEghGPtcVB4UElTW7RwTrs7U5UGonIMQFMmB9FzHgzvhbjcA9zRNKfUMgPgQsAiyRJJcBTgBZAluXXJEkKBXYBvoBbkqRHgFRZlpsGbdQCgWBUIssy9eXH7JaK3EZkGYy+OhImBRGbbiFybAA6g5iX9kV/omVu6ON8BRDpsREJBIJzCpfDTVl2Q5egN9VYAbBEmZiyJJbYdAvB0T5Iwm45LcTHn+CsQpZlKvObCAz3FrO3sxilkIWSJreoo5CFWqsiamwAky6KITbNjCnAMNzDPKsRfx2Cs4r9Xxez5eMctHo1SdNCSJ0TTnCMz6jYUTiakWWZ2tLWjtjzmq5CFt7+epKnhxCbZiFi7NAVsjgXEOIuOGsoOljL1k9yiEkzY/TRkbWjgkObyzBHmEidE07y9JBRudPwbMXpcFF6tKFL0LsKWcT4MH1ZHLFpFixRw1PI4lxAkuXhiUicOnWqvGvXrmG5t+Dso63JzgdP/4ApwMCVP52MzqDB3u4ka2clhzaXUV3UjFqrImFyEOPmRBCe5D/cQz4naW20UZhZS35GDSVH6nDalUIWUWMDiE23jJhCFmczkiTtlmV5al/txMxdcFaQtaMCW5uTK/43tctr1xk1jJ8bwfi5EVQXNXNoSxlZOyrJ2l7JjEvjmXpJ7PAO+hxAlmWqi5opyKylIKOmq5CFKVBPyswwYtItRCT7o9EKu2WoEeIuGHTsVme3xc+Gyjb8go2n9XU8a0clQdE+mCNMPZ4PivZhXvQYZl2VyLf/PML2z/PwCzaSNDXkjMcv6I7D7qLkcF1X3dDWrkIWvmdNIYtzASHuAo/TWQU+b281efuqqStrZfG940mYFEz+/mrWvJrJpQ9PJColsF/Xq69opbqomdlXJ/bZVqtTM/+WFJrrrHz97mEskSYCQr3P9CWd8zTXWZWqRJm1lBytx+VwozWoiU4NVOyWcco6iGDkIMRd4BHcbpmKvEby9lWTt7ea5lorkgRhif74BRnZ+kkO0almtq3MBaCxqq3f4p61sxIkSJrWv1m4WqviwpvH8sHT2ynPbRTiPgBkt0xlYVOXf15b0lHIwmJg3PnhxKZbCD+LC1mcCwhxF5wRDruLbZ/mkrOnivYmOyqNRFRKIFMviSUu3YLRR0fxoTo+/+s+Pn9pL/UVbQC0NNj6dX1ZlsnaUUnkmIDTWojz9lfaWlscp/+izlHs1s5CFordcnwhi5lXJhCXbhk1hSzOBYS4C86I3WsKyPy2hMQpwcRPCiJmnBndCZn4olIDiRlvpvBALSFxvrTUWWntp7jvXV9EU3U7U5fEnta4tHo1aq2K9mb7afU712iqae/YGVpLaVY9budxhSzSzESPG52FLM4FhLgLBkxjdRt7NxQxZkYoC29PPWXb2Vcn0tZkZ861SXz/7+x+ifuerwrZtjKXxKnBjJlxegujkiRhNGlpFzP3brjdMpV5jV2CXlemFLLwD/Ei/cIoYtPMhCb4oR7lhSzOBYS4CwbM5v/moFarmHlFQp9tA0K9ufbJaQCY/PXUV7adsn3RwVq2rcwlaWowC29PHVDVHKOPjvZmIe62didFnZkVD9RhbXWgUkmEJfkz55pwYsYrdUMFowsh7oIBUVWolDGbeUVCl7/dX7z99ZQcre/1vMvlZvN/s/ELMrLgtoEJO4DRpMXacm7aMj0WsvDWEjPeTEyH3XIuF7I4FxA/XcGAKMisBQlSZ4efdl9vfx32dicOmwut/uTNLQc3lVJf0cYl/5N2RtEYBh9t1wLuUFKe00BguDd6r6Hzql0uNxU5x+yWho5vRoHh3kxcFE1smpmQeD9RyOIcQoi7YEAUHawlJNYXg+n0BczUMdNvbbCdZAdYWx3sWJVPZMd29TPBaNLRPsQz94LMGla/koFWr2bc+eFMWRI7aAuS1laHYrdkKJkVbW0dhSySA0i7IJLYNFHI4lxGiLvgtLG2OqgqaGLKALf3d9o4LT2I+44v8rG3O5lzTdIZh9wZfbQ47W4cdtegZRt0udzsWl1A4pRgAkK92PppLr5BRkLjfNn/TQl5+6pZfE8aQdE+Z3wvWZapr2jrKjPXVcjCR0vcxCDi0ixEpohCFgIF8VsgOG1KjtQjyxCdah5Qf+/jZu7HU1fWyoHvShl3fkSvaQZOB6NJ2THZ3mxHax6cGWxVfhO7OsJBx8wIpb68lSX3phE/KYi0CyL5asUBPvnDbubekDwgC8vlPK6QRUYPhSzSLErKY2G3CE5AiLvgtCk6WIveS0NI7MBmo72J+5ZPstHq1Uy/NO6Mxwh0WUbWFge+gyTu1cXKzk21VkXGxhLCEv2Im6jYSaHxflz75DTWvXmQje8foTKvkfOvT+4ziVZ7i53CzI7olkN1OKxKIYvIjkIWMePN+ASKQhaCUyPEXXBayLJM0aE6IscGDDiKRWfQoDOou+1S7SyAPPvqxK4Z95nSmetkMMMha4qbMfpoufrnU9n2aQ5TlsR2s5OMPjqWPzSRHavy2L22kOriFhbfM75XL7z4cB1r/p6B0+HG209H0jSlkEWkKGQhOE2EuAv6TUV+Iwe+LaW1wTZgS6YTb39918zd5XKz5eMc/EO8SLvAc+V4jR0z98FcVK0pacESacIn0MBFd43vsY1KJXHeZQmExvmx4Z1D/Od3O1l4eyqxad0XjCvyGlnzagZ+wUbm35pCULSoMCUYOGIbmqBfVOY38ckfdpO3r5pxcyNInn5mqXSPF/cD35bSUNnG7KsSPZqIyujTIe6DNHN3udzUlrVgieqfPRWbbuGaJ6bhYzaw+pUMtn+eh9utFMuxtjr44uX9ePvpWf7QRIJjfIWwC84IIe6CfpG1swK1WsWtz87ighvHoDlDi6BT3Ntb7Oxcna/kn0k7s28DJ6IzalCppUHbyFRf3orbKRPUT3EH8AsyctVPpzB2Vhi71hRwZGs5AIUHarG1OVl4e6qoVCTwCELcBX0iyzJ5e6uJSg0cUFx7T3j762lttLP983zsVhezr070+ExVkiQMJu2gzdxrOhZTLVGnF9mj0amZf8tY/EO8yNpZAUBhZg1GHy0hsb4eH6fg3ESI+wjFYXeRtaMCe7tzuIdCVWEzLfU2EiYF4XbLfHOkEpf7zGrvmvz1yG6Zg5tKGX9+OObwMw997AllI9PgiHt1cTManQq/4NPPyyJJEolTginLaqC1wUbRoTpi0iwipFHgMYS4DyO9FSdvqbey8oU9rH/rEP/89TYObSlDPkMxHQgut8yhsibWfZWHpJKITbew5kA5d7yzi5V7S8/o2p3hkHovDdOXx3tiuD1i07VxqOQoxc3FHr92TbGymDrQLf0Jk4ORZdjySQ62NiexHralBOc2IlpmGGipt7FjVR45e6q4/NFJBMcc+yreVNvOJ3/YjcPqYu71yWTtqGTj+0c48F0p51+bRFiiv0fGsPFIFc+tPUyIr4EJkf5MjQ1gfIQfWZXN7C6oZ2dhPXsL62m2OrmrWU9gsBcGby3vbysEYNX+Mq6eMvDIFr9gJRRw2rK4Pq2erPosbE5l8fWb4m/YWraVGJ8YpoZOZUrIFOL94k+ydFrsLfxu++9oawoguDWaTSWbuCnlpgGP90RkWaamuJnk6aEDvoY5whv/EC+yd1aiUkv9rkwlEPQHIe5DiK3dyd6vCtn/dTFuWUatVrH98zyWPzixq832z/Kwtzm5+vGpmCNMjJ8XQfauSrZ9msunL+whaVoIM69IOKNNLO//UMhTnx0g1uJNbYudV7/LJWS9xPx2LUYZVDJMVKmYJulQo0N2y3xvbWNmUT3b8+sI8tGzOaeGulY7gd4Di0k3h5u4/tfTCQw7dQm8/xz9D8/88EzX/9WSmvSgdHZV7mJtwVoAAvQBTAmZwpSQKUwNnYpRY+SRjY+Q35jPXWG/wHHYm/zGPQMaZ2801VixW12n7bcfT6c1s2tNARHJ/iJtgMCjnBO/TbIsD2tYmcvp5uD3pexcXYC1xUHStBDOuyyenN1VbFuZS0VeI6HxftSUNJO1s5LJF8V0bb+XJInkaaHEpQexZ10he9cVkZ9Rw3W/mIb/ALze77Kq+dX/HWDB2GD+esMkvPUaju6p5Ou3DiEbVPhFmbD4GjDo1ag0KtRqiVq7k13787j7vV3o1Cr+cu1Ebn5zO18eqODGGdEDfl/68tl3V+7mue3PMTtiNjeMuQGby8bkkMlYjBZkWaa4uZjdlbvZVbmL3ZW72VC0oauvr86X1xa9hnpPCDsO5HO43rO2TE1xM8AZ54zpFPe4CUGeGJZA0MWoFXdrq4N964soPFhLU3U71/1y+rBkyKsuauarFQdorG4nYow/s65M7LJhxs+LYO/6InZ8kc+S+9LYtjIPvVHD5ItPFkytXs2M5fEkTQ3hw99sJ39fDZMu6p+wupxucvdWUXSgjjcb64jwN/LqzVPQaVTk7avm6zcOYYk0seyBCXj59jwT/9zRyobDlVwxKYLZiWbiLd6s2l/GjTOicbldVLVVUdpSSmlLKWUtZd2eN9ubuX/S/dw49kYkScLhcrC5dDPflXxHq6MVl+zC6XbidDtxyS5cbhcOt4PshmwifCL4w9w/4KvrHkUiSRLRvtFE+0ZzRdIVAJS3lLO7ajd5DXlcnng50b7RHPBR1gbKa6v6/TPrDzUlLUgqicDwMyu+bY4wce2T0zBHiCLeAs8yKsW9vcXO5y/to7a0ldA4X+xWF7l7qvsthp6i+HAda1/LRO+tYdkDE4geF9jtG4TOoGHSomi2rczl9Ye+A2DmlQmnzAMeGOZNQKgXJUfqur2exjYHr23KZWlaGOMj/ABobbRxcFMpB78vo61JifWO0rhYclMSOo2KspwG1r1xkKBoHy57ZOIpbYGfXjyGw+VN3DknDkmSmD/eyPuH3mPRRy9QY8/DKR+LSJGQCPIKItIUyeSQyVS3V/P8juc5WHMQgO9Lv6fB1oCPzgezwYxaUqNWqdGoNGgkTdfzKSFTeHTKoycJe2+EmcJYZlrW7VjnLtXmpnbaHG14aT1Tcai6uJmAUK8+88T0B09kjBQITmTUiXtTTTur/55BY3U7S+9PJ2acmY+e2UFBZv9nuqfC5nTRYnXSYnPSbFX+Od1u0iL88PfS4XS5abI6aStu4YuX9xMQ6sXyByf2Wq1owsIojD5KLLZKLZE2T1mkPFhzkJU5K4kwRZBqTiXFnNIlcpEpgRzeUobL4UatVQKenl51kJV7S3l9Yy5XxFiIa5ChuA1k8I/3ZdzlcXy4MY/EYkhtkji0pYytn+TgYzaw7IH0Pv3eMaE+bHl8PnaXnZXZK1ld9wK6wFZK6qNwt5/HdRMns3hsKhGmCMK8w9Cpj30DcMtuXtrzEm8deAs/vR9zIuawJHYJsyJmoVUNbkGLzl2qRoeJ4uZixgSO8ch1a4qaiRwrFkAFI5dRI+5ut0zmxhJ++DwPCVh2f3rXH1/cBAu71yp+t86opiynkZA439NKxLR3y1cErH8Y3G6a8KLJ7UWdO5QGRzSNsoX/UY/HP9CXqiYbOqebBx0++AUZueJ/J59yJq5Wq0iZdSwVbEVrBS9vf5nPcj9Dp9Jhdx/bXRntE02qOZWxXlNx2oMoy60naqyZrw9XsmdHOT9R+aBqcSJltGJF5oDexV6dk4a6FlRfZiBpGvlJ2DS2r8wFIDjWl8k3BfNl+Wo2FG1gZ8VODGoDAYYAAg2BXY++Ol+a7E2UtpSyu3I37c52JgVP4umZT2NSh7Psr5upLPVn1vypPb5GlaTi0SmPcmvqrfjr/VGrhi4Blpev8qHq5fAlvynfI+Le1mSntdF+RoupAsFgMyrEva6slW/eP0xlfhPR48xccNOYbtEksWkWdq0poPCgUu19z1eF6IwakqaFkDo7rM8ETeXVNQSue5gq6xTKNdNpa/enyWrB4VY8fBXwgM5JkdYL/+RAWvfWYbU5ufzRSaj1avYXN5Ae6XfKe7Q6Wvn7vr/z0ZGPcOPm9nG3c0/6PTjcDg7VHur6l1GdwTeN33Ibz/G7lS9x6bUX8PE7bq5p1RMQqiNuThg1hlJKArIxNR0lojEHWotwddgm/3atYon+VurCCvncaytl35QBEO4dzqUJlyoFIWz11LbXktOQQ721niZ7E746XyxGC5cmXMrcyLnMiZiDSlK+NVySFsYHO4posTkx6Xv/lTIbhz6O28dsQJLAt91CQWOBR65ZU6IspvY3p4xAMByc1eLucrrZ81Uhu9YWoNNrWHh7KsnTQ04S0eAYH7x8dexdV0hdWSvxE4PQ6FUc2VbOwU2lmCNMpMwKY8yM0JNirhvbHex58xG0jdeTbZ2LRqfCHGEiOdKEJdKEJUyP/N4VfO94DFeWBmjDG1jtZcd2sJQ9a+v5Ia+OP1yVzrXTogBwuB2syl3FhsINzI6YTUpgCk9tfYqi5iKWxy/nfyb+DxGmiK4xzI6YzeyI2V3/r2mv4f9K9xHdmMIX725lcuMs1AlOaubu4PWCT6hrqIMKCPMOIykgiYWx80j0TyTQEMiGog1s9P2AAEMAaT5p3BJ0C9NCp5EckNzrh09f0UZL08N4Z2sBXx+u5LKJEb22Gw7UGhU+ZgMhzigKmwo9cs2utAORYuYuGLmcteLe2mhj1V+VRdOkqcHMuTa510gPSSURm2bm0JZyfMwGFtyWgs6gYe51DrJ3VXF4UxGb/5vNDx8f4PLk9wlJDOZg/KOs/KSEuqZsLnOHccA6lxmXxTP54piTdyQm+HG17UUqFqwEScLbT8fu1Qf5+7e56DUq4ize/P7LIyxIsbClcj2v7X+N4uZiLEYL35d+D0CwMZg3LnqDaaHT+nztFqOF8RPi2L1WRSoh7IlYz46gL5COSMyLnMc1Y65hcvBkTLqTxef4D4n+0lcY6ZToAEJ9DXyRUT7ixB2UZF3m8lD2NG71yPVqipvxCTQMWm1UgcATnLXifnhrObWlrSy5L434iX3HCCdND+Xo9koW/Cila/FQL7Uw3vEm46XXqLEEs7r5t6wvvIor6n/C1q+mE+AMJYBoDhBN+rxQpiyO6VnoYmej2vRHwqNUYFAiVX5z6Th8DBrumRuPW3Zx5buvsmzlC7S4y0gJTOHl+S8zN3Iuh2oPsatyF8sTlhNo6P8CXWy6hZ1fFrLHx8nVNyzgQd/riPeP73dkSb9xu6EuDyyJvTZRqSQuSQvjnz8U0mR14GsYWaLnF+yFMcePgsYCj+x5qC5u6dNvd8tuVuWuYlb4LIK8RAy7YOg5a8W96GAtQdE+/RJ2gMgxAdz94lwlX7itGX54Fba+DLZG5PFXoZnxGIYMLdWrinnb9nck2chF8f8mqGkTDfNeJWZBSu+iEDMb5N9D0Q+QfDEAUYFevHDNeNYWrOXtA29jiMihqS2Ex2Y8y20Tlndda5xlHOMs40779Wc57Lzo287z16SzNDHqtPsDUJMNgQmg6iHFkMMKGR8p71FtNtz0MXLiQtxNDTi/XYFNk4K9tAKVwYDa34/lKh8+a6lnw4Fyrpw6tCGnfeEXZERyaHC2y9Raa7EYLX136gW71UlDVVuf+ey/LvqaX275JRGmCFYsWkGU7wB/RgLBADkrxd3W5qAir6nHzT69Ud1sY8X3ecQ27+GKnCcxOhrYbZzNP7yv5fv9obTvygfgqkBv4uuMTIvYQlLbRzD9R/gvuuDUF4+cBiotFGxWxN3ayN6mPB777jGq2qpI9E/kN+c9xzP/0bJ6mw+3Teje3e2W+e/uYj7ZU8qfr51AiKOF5nXrad26FdluV2bPyErysI5kY45WmQdbVMxNqaVNnYZhzBhUXsdiuGWHA3tREba8POx5+djzcnFUVGJMT8d79iw01nxUq+/HHTEb19RHaT+aT/uBA8jtrVBfCHUF4LIj63zBFozjmydwtjyOu62tx7fAAPwTcG7QkhsdiTYyEl1UJNrIKLRRkehiYtDFxKDSD32u8s6dvH7WIAoaC85I3OvKWkHu229/9+C7BHsF0+po5dYvb+W1ha95LAxTIOgPfYq7JElvAcuAKlmWT6ojJilT0JeAS4A24DZZlj2byOMEig/XI7tlYsb3749UlmUe++9+2nM384jmeYplC79R/5xGrzQi/I3cmOJFZICRWIs3c+LNVBc2E2YZA7vcMOeRvm+g84KIKZC/Cdb8lO2Z7/NgeCTBplBeXfgqs8NnI1utMPUgr3y2m6/eqWKmRYO7vQ1nwhge3VyN/5ZvuLZkL9Wv19Hc2qRcNiYGlb8fEpIyu5YkUEnglpHKKpnT3kjd8z9QByBJaCMjkfQ6ZLsDR1kZOI+lC9aEhKCxWKh9+21qV6zoOBoC5AD3A6ANNKCSW0F2g8EHyRQLBl8kZz56fQveF9+CtuIbNM0H0Z13Cbqb/4bssOOqb8BRVsbna3eQn5nFjdFaXGWlNO7fj7up6dj7pNUS/MjDBN5xR5/WiOx2Y8vKAklC5W1CE+CP5OU1IEulM0lZgDWELwu+ZGpozyGb/aG6qO9ImX1V+9hfvZ8nZzzJ9NDp3LP+Hm7/6nb+vuDvTAye2Gs/wchClmVsLhs2lw2r06o8uqzHnh93zOY84VzHsd7OL4tfxo0pNw7q+Pszc38HeBl4r5fzS4Ckjn8zgFc7HgeNwgM1qA1wx94bkPe58dJ6YdQY8dIojz46HyJ9IknyT+L8yPNZubuSouwMvvJ6Aa1/NNE3r+JfAWG9Xj880R/wh4VP9XtMbdEz2LL3dTY6ilkXZGZGpYafNc6HX75J1v6Hcbe1kYLyRvItlB3X9xcdjzWhMWz2T+WqS2diXjgffWLPPndju4O7fruOBy9M5IEJ/lgPHMB65Aj23DxklwtJrcJ3yRL08XHo4hPQxcWhNinb210tLbTv3o3rgzuQA1ORUhejzlmJQVeBRqqC1Mtg5gMQknrshvs/gpX3wg1z4F8vgtkO8lEweQPeaAIC0MfHkRozjidf2ULy1elcM1WxIVyNjdiLS7AXFNC0di1Vf3yB9gMHCP/d71AZT04HYc3KomnVKhpXfYGzoqLbOZWfHxEv/BHT+ef3++cC4GsxIkkw3WsOH2S/xJ3j7yTM1PvP/1TUFDdj8NZiCuj9G8g7B9/BT+/HZQmX4aX14r0l73HPunu4Z/09vHjBi8yKmDWge5/rnCi2x4vl6YptZ/9TCbDNZet7UL2gU+kwaAwY1Ab0Gj16tb7rua/OF6Nm8FOh9CnusixvkiQp9hRNLgPek5Xk5D9IkuQvSVKYLMvlHhpj9/G4ZfIyqyjX7GPpxiYCfYJp1bpp1TbTom2gWeOkQt3O97o6GrzBW+9Pc/Uk7g+uRtVsI+uyP5Nbv5c0tfOUPmiLvYUdFTuYHDwZf4OSZrfB2kBVexU17TXUttdS215LTXsNxXX5yF9/z9TDgZzfInFNg4yhvZE2VqAfMwa/yy9HExaKJiCAYpeOx78uYsGMMdx5QSJP/L8POU9q4JqHb8IeGMOfXtmC9/hU7kiM63VsO/LrcMswM9GCNsSMNiQEnwUL+vX+qU0mTFFAWA1cezekXkrnzL1Xki4CSQVrfgouO8RfCHnfgrUJDMcWcCdE+hHhb2R1ZnmXuKv9/DD6+WEcPw7fpZdQ+8YbVP/5L5Q0NhH56t9R6fU4Kitp+mI1jatWYTtyBNRqvOfMJujhh1EZjbhbW3DV19P42WeUPvMgmSUAACAASURBVPZT4j/9BG1E/6NyOsMhI1TKF88VmSv49cxf97v/8dSUKIupvX2DKG8p55uib7gr7a6uVAcRpgjeXfIu962/j/u/uZ/fn/97Loq9aED3H0kcL7btzvZuAnv8885zPQlwt749iW2nCHtAbPUaPUa1sUexDVIHKWKsMXQ71/m86/gJ57udO65P5x6Q4cQTnnsEcHzKvZKOY4Mi7pvfWoG9JZF5hzMIq6wFantt69Bq2RbnYvOEjbwdBSsCI3BsPCZk0T7RzAqfxeyI2UwPnY6X1gu7y866wnX8adefqGmrZkylhqXlIfjmVWGptKKSQQZ8JfCRIBaJC60SXlY37rAgTAnJaOUqvJ078H5mExp1M3xyF1z1KQTG4Q9M0GXyys5iqgOaWRMygbt+PAtjdAATgMnR/ry7rYAfzYpF3UsRiK25Neg1KiZFDzC3+6HPQOsFiQv7194rEKJmQNE28I2AWQ9A3kYo3QUJ87uaSZLEsvQw3tycT0ObHX+v7qGpkiRhuftuNIFmyn/xC4rvvgfUKtp+2A6yjGFCOiG//CW+SxajMZ+84cln4ULyr7qakp/8hNj330fS9T/dsF+wF7Z6B1dNvIpPsj7hzrQ7u+0l6A8ul5va0lbSLuw9j/2Gog3IyFyeeHm34xajhbcWv8UDXz/ATzf9lGZ7M1clX4UsyxysPUidtQ6n28mUkCn46f1Oa1yddIrt8YJ4otiearbbm93Q7XzH7NZTYmtQHxPNE8VWr9Fj1Bh7FNvehLWn640UsR1qPCHuPSlQj2WDJEm6B7gHIDp6YBEVhbXNaOwNVETW8d197zAxMZhog0Sk3o2v247c1kZdeQ3vfL4Dr8Jc5hcfYG6WC4BWswa1vxm9ty8NXlCsayZb9R8+9fqAd01qTCERFLdXoLU6uKkkkBlHA9CU1+CWimgI98GdFo/By4Repceg0qFX6dBIaiS9Ht8lS/CeORNJpYLSPbDiQqjfC3veU0IJD3wCcx8D4KcXjWFNZjkf7ihmWmwAk6MDul7f7bPjePDDvazJLGf5hPAe34NtubVMjQ1ArxnANn63C458oczGdaeRRCv5YkXcUy+HyOnKTL54RzdxB1iWHs4/NuWx7mBl16atE/G/6kpkh4OKp59GGx2N5cc/xm/5MnSxsaccgi42lrBn/x+ljzxK2S9/Sfjzzyvvdz/wCzKSld/EnePv5NPsT1mRsYKnZz0NKKLYUNlGbWkrcemWrnw9J9JQ0YbL6SboFGGQ3xR9Q6J/ItG+J/9+++p8+ceif/CTb3/C09ueprSllEN1h9hSuqWrjV6tZ1HMIoKMQScJdI+z2SEW205BFWI78vGEuJcAx/8VR9LdUu5CluXXgdcBpk6dOqC6cTf97Cc0/etWjLk/MD8zi3f313SdM+k1RAd6UdOioiVkKi8/ejfp8QG0/e0e2rdvxDdsIW67C3dbG/qaGszVLaQ3duZucQMFx26kqcd75kx8H1iMz4L5qP1PY5YcNhG8g+C730NdrhJJc2R1l7gHeOv42cVjeXJlJvfNS+jW9ZK0MF7ZmMMfvzrKReNCugm4LMscLGviSEUzj12UfJrvXAclO6GlssOOOQ3GXQn7PoTJtyhWTPA4JfTzBMZH+BId6MWqjLJexR0g4Prr8Fm0EHVg4GktkvouXoz9kQKqX3wJjdlCyM9/1q9+/sFe2Nud+GPmmqRr+HLfRha0ZNJWJFGW1dCVNXPhbSmMOa9nP74zh7slsufF1DprHXuq9nB32t29jsOoMfLXC//Kk5ufZEXmCry13jw29TGmhEzB7rKzOm81q/NX43A5ThLHzkc/vR/B6uBu542ak+2GnoS1p+sJsR2deELcPwcekCTpI5SF1MbB8ttB+Wrvt+xZeHkd303+jvy5f6Gwto3C2jaK6tooqG3Fx6DhV8tSldS3bhcm9xZMV82F6/520vXcdjuu6mqc1dU46+qRHQ4kjRqvadNQ+w5wQ5BKBQkLlDhxvyiYdDN8+xw0loKfYgXcMD2K6XGBJAZ3nwWqVRJPXJLCj97awT9/KOLWmTHsLKjj68NVbDhcSWFtGzqNioWpp46z7pWcDSCplfGdDgEx8MCOY/+Pmg4Z/1G+CRyXCEySJJamh/H6prw+KzX1ZL30B/O99+KsrqHu7bfRWCyY77yjzz6dETNfrThAUNlsrm2eQca+arz99USODSA8yZ9NH2ZRX3lyqGdFawUSEpVFTWi0KvxDe/7G813xd7hlNwuiT/3eatVanj//eRZEL2BKyJRum5wmh0zmVzN/1efrEQj6oj+hkB8CFwAWSZJKgKcALYAsy68Ba1DCIHNQQiFvH6zBduEfDbMeRL3pjyT6hJA4/5eQ0oPYtdfDxt9BaxWMv6rHS6l0OlQREae1QNcvkhYp4j7v54pf/e1zcHQNTFdmdZIknSTsncxLDuL8JAt/XneUlzZk0WR1olOrmJlg5q7z41mYEkyY3wBX23M2KHH5xjOsxRo1A3a9CWX7IHJKt1PL0sN49dtcvjpYwQ3TPb+hSZIkQp58AmddLVV//CNqcyD+l19+yj6WSB/UGhUNle3EpJrJ0P7AZ60f8v71bxLjFwPA3nVFNFa3d+tX0lzC0pVLcctulh98AIPRyDVfXI3FaOn6F+wVzHlh5/FN0TeEe4czNnBsn69BrVKzOG7xwN8EgaAP+hMtc0Mf52X6DLcYBM5/DNrqYNvLkL0OLn+tu8gU74R/XQ3WRph0C4xdOrTjG3eFshAZf6ESn25OUqyZ6b1/ZT+eXy1L5aEP95IW4ceClBDOT7LgfYqMi7jdUHUQQsYr9+uJ1hpFjC/8Rc/nT4eE+WAMhJX3wJ3rldfaQWqYL3EWb1ZnlA+KuANIajXhv/89xfUNlP/il2gCAjDNm9dre1OAnrtfnItKLSFJEpPaQ/jok9d4PfN1np3zLKDM7huruov77srduGU39094ANfueKyxVYSbwqltryWvMY+a9hqc7mP7CW5OuXlYSzoKBJ2cvUab1gDL/gy3rAR7K7y5EL7+LThtiqB/cofiDd+3GS57GdRDnO9EpVYEsPMPfexSKPge2hv61T05xIcvH5nLH6+ZwOLxoacWdocVPr4NXpsDhz/vvV3uRkCGxPm9t+kvpiC44UNoKIaPblTe9w4kSWJpWhhbc2uobRn4Il9fqHQ6Il/+G/oxyZQ88ijt+/adsr1ao+oSXovRwnVjruOLvC+6UgH7BXnRWNWGLB9bDsqozsCkNXF95M1gV3Px1Av42/y/8cHSD1h/9Xr23LyH9Vev58kZT7IweiHXjblu0F6vQHA6nL3i3knCfPjxNphwI3z/J1gxHz65W/G3r3oTQk/aVDs8jF0GbqfyLcOTWJvgvcuU8Ea9L+x8o/e2uV8rs+0wD+2SjD4PLv+7EkWzp/set6XpYbhlWHugopfOnkFtMhH9+utogoIovvc+bLlKIRK3zUb9hx/iamnpte/t429Hr9bzWsZrgDJzt1tdWFuOlQzMqMkgzZJGXbHixQedsDNVkiRCvUO5YewN/OXCvxDrF+vhVygQDIyzX9xBycR4+Stww7+htRqyv+rwuqcP98iOETEFTCFKGKKncLvgkzuVePNr3oHZDyspEGqye2jrhpyvlQ9DT1ZCGn8VhE+CHSu68t4AjA31ISFIsWYGG43FQvQbK0Cjoeiuu7EXF1P60MNU/Oa3NH/5Za/9zEYz14+9nrX5a8lryMMvSFnHaOiwZtocbWTVZ5EelE51STOSBIGikLXgLGF0iHsnYxbDj3+Aq9+C8/93uEfTHZUKxlwC2RsUG8UTbHhK+Saw5A+Kxz/pFlBpYPc7ynm3C8r2wuYX4f3LlYXlxNOMkukLSYLp90LNUWXXatdhiaXp4WzPr6Wq2UOv9xTooqOJXvE67qYm8i5ZSst334FKhS0n95T9bh/XMXvf/1pXgrHGamWWfrD2IG7ZTXpQOjXFLfiHep9WaUaBYDgZXeIOysLe+KtAPQITXo5dBo5WyP/uzK9Vtg+2/g2m3QXT7lSO+YRAynLY+0/49y3wxwR4/QLlQ6ClSpnZ9xI1dEaMvxK8LLDj9W6Hl3VYM18NsjXTiSE1lchXXkbl60vo00+hHzOmy6bpjQBDADeOvZEvC76kSlWKpJK6FlX3V+8HIN2STk1x8yk3LwkEI43RJ+4jmbjzQefTszXjtMPhLzrS+/aDyoPK48wTApVm3KcsKJfugTFL4coV8L9H4f4fYNFvQTMIKXc1eph6OxxdC3X5XYeTQ3xIDjGxagismU68zzuPpM3fE3D99egTE7Hl5PTZ57Zxt2HUGHn90D/wCdTTWKXM3DOqM4jxjUHv9KKl3tbr5iWBoD+429tpz8yk4ZNP+1z89wQjcHo7itHolfj3o2tP2vzD2p8qdsot/wcJF/Z9rfp8JQWA3wm7QKPPg8eLQO/Te0jkYDD1Dtj8F2VB9+Jnuw4vTQvnxa+z2JRVzexES6/5cjxJZ0SMPiGBplWrcLW0oDb1Puv2N/hzU8pNrMhcQbLXQuqr1MiyTEZ1BrPCZx2rmRotZu6CvpGdTuyFhdiysrBlZ2PNysKWlY2juLhrXSrg1lswThzc9M9C3IeasUvh4KdKGoDo85Rje94/5pNXZPZT3AvAL7LnEE+Dh0vt9Qff8A5L6H248EnQKQuPV06O4K0t+dz61g6CffR8dM95xAcNjUjqE5XUDva8PIzp6ads+6NxP2Jb2Tb25v1Acs00Zn0wixZni7KY2pHDPUjM3AXHIcsyzvLyYwKenY0tKxt7bi6yoyPiSqVCFxuLISUFv8suRZ+cjCEpCW3U4FfmEuI+1CQt6sg184Ui7jXZsPp/If4CqDoCVYf6d536AgjoPS3wsDD9Xji4UklLMFXZqBwV6MXWx+ezan8Zj3+ayZ6ihiETd12CIu627Jw+xd1P78cHSz/gC8dWir6ycWnkFYSZg1mesJwtWwowBegxmEZWbVjB0OFqaOgm4J2zcvdxobaa0FD0yUmY5sxGn5SEPjkZXXz8sFQfAyHuQ4/BD+LnKf76omc64tJlxRtfeR9UHujfdeoLlOibkUT0eRCariysTrmtyxby1mu4YnIEj3+aSVlD+6mv4UF0UVFIOl2fi6qdSJJEWsIYisjg9qh7CY1XUu/WFDefsvKSYPTgbm/HlpvXJd62rCxsWVk4q6u72qj8/DAkJeF36XL0ycmKkCclDTwX1SAhxH04GLsUvngUyvcpVY5SloMpGELGwfbvweU8dbSPrUWJ5w+IHbIh9wtJghn3wmf3K7tx4+Z2ndJr1AT56IdU3CWNBl1cHLbcvhdVOwkIU+yknD1VhMb7KQWxK9tInBI8WMMUDAOy06nUGO6ahSu+uL2oqMsXl/R69AkJeM8+NhPXJyehCQ4+K1JMCHEfDsZcooj75w+CtQEm36ocDxmnVDqqzYHgUySfaihUHkeauIMSarnuV7D9H93EHSDc30jpEIo7KIuqpxOZ4BdkZNz54ez/upjolEAyvy1BliEyJbDvzoIRhyzLOCsru2bgij/e4YvbO9J9q1ToYmLQjxmD7/LlHUKehC46Gkl99u5rEOI+HPiEKpkZS3YqAh3bIYIh45THygOnFvf6AuVxJIq71ghTfgRbXoKGIiWDZwcR/gaOVDQP6XD0SYk0rVmDs7aW9r17Mc2d22cFp9lXJ1Ga1cAXL+9HlmHeDckddXUFIxlXYyO2rCysnXZKdo7iix9XpF0TEoI+ORnvmTPRJydh6PTFDYZhHPngIMR9uBi7VBH3Sbcou1cBLMnKDtO+FlU7Y8lHorgDTL0TtvwVdr4Ji37TdTjcz8g3R6qQZXnIvtZ2LqrmXnQx7tZWQp58gsBbbz1lH61ezaI7Uln1t/1MWRzD+Hm9l9UTDD1uqxVbbq5iqWQfW9x0VlZ2tVH5+KBPTsZ36SXokxQR1yclofYbWAnDsxEh7sPFhBuh8hBMOS79vUavpAbu3KDUG/UFoPcDY8Cp2w0X/lHKh9eed+GCx5XZPIotY3W4qW9znLKIhycxpqUh6XQYJ6TjKCun8YvVfYo7QHCML3f8YQ7SEMTlC3pGdrmwFxZ1E3BbVpbii3ds9pN0OnSJCXifdx765KSuBU5NSMhZ4YsPJkLchwufELhqxcnHQ8ZB8fZT960vgMDYod2kdLrMuFdJP5z53641hXB/ReTLGtqHTNy1YWGM2bUTSaej9o03qHrhT9iLitD1o4avEPahQZZlnFVVHb54doe1koU9Nw/Z1pEyWqVCFx2NPikJ36VLj4UaRkchaYSM9YR4V0YaIalw4GMlhYChl6+Q9QVKu5FMzGylzur21xXryeVgXPVq9ARS2tCulEAcIjo9dt9LLqHqhT/RtGYNlvvuG7L7C47hamrqmoF3xY1n5+BubOxqowkOVnzxGecdCzVMTBiVvvhgIsR9pBHSkX++4gDEzj75vNutRMuMHWEx7iciSTDjHlj1sJLvPXsdUZv/wnzVw5Q1DO62697QhodjnDyZptWrhbgPMm6bDXtubrft97bsbJwVx5LIqXx8lJn4ksXdffHTKUYv6BUh7iON8MnKY8nOnsW9uVwJlxypi6nHk3YtrH8K1vysa3NWvLp6SGPdT8R32VIqf/sM1qNZGMYkD9s4Rguyy4WjuLibgNuysrAXFh7zxbVadImJeE2fpgh4py8eGnrO++KDiRD3kYYpSEkrULKz5/NH1yiPgfFDN6aBovNS/Patf1VeU1sdya561jUMfn733vBdvJjKZ39H0+rVQtxPA8UXr+6+czM7G1tuLrK14+cpSWijozAkJyuz8Q4h10VHC198GBDv+EgkarpS+EKWuy+a5m6ELx9X8tDEzBmmwZ0mM+6D8v2w8Cn44ifE1lUP+Uam49EEBuI9cyZNa9YQ9OgjYubYA67m5uO23h8TctfxvnhQEPrkZCW18vG+uNE4jCMXHI8Q95FI5DTI+LeyCSggRjlWeQj+c6sSC3/teyOzGElP+EXAjzqKdvtHE1a7b1htGQDfpUspf+IJrPv3D3ra1ZGM227v8sWPbf7Jxll+LP++ymRCn5SEz+LFXTs39UlJaAJGaBiuoIuzRCHOMTprv5bsVMS9qRz+dQ1oveDG//QeRTPSCYgh0PkVVW1WbE4Xes3wbO32WbSQiqeeonH1mnNC3GWXC0dJSfeshtnZ2AsKwOUCOnzxhAS8pk7tEnBDcjKasDDx7eYsRYj7SCR4HGi9oXgHJC+GD6+D9nq4fY2yQehsxT8GjdtGEA1UNtqINnsNyzDUJhOmefNoWruWkJ//bNT4wbIs46yu7ibgtqwsbDk53X3xqCj0yUn4XLSoK0JFFxODpBUpjUcTo+O3erSh1kDEZCWE8OM7lAIeN3wE4Wf5LNNfsZiipGpKGtr6Je4ut8zaA+XMSrB4dOOT79KlNK9fT9uOHXjPmuWx6/YHd3s7jrIynLW1GCdORNVHrpuecLW0dBfwTl+8oaGrjTrIgiEpiYDrruvKaKhPSEDlNTwfqoKhRYj7SCVyGmz+M1RkwNI/QfLFwz2iM6cjiVikVE15PyNmVu4t5bH/7sffS8uTS1K4ZmqkR2wC0wXzUHl707h6tUfF3W23U/ncc6hNJnQJCbibW3CUleEoLVUey8pw1dV1tdclJhD+3PMY08b3ej17fv4xAc/KxpqdhbPsOF/c21vxxRct6opQ0ScLX/xcR4j7SCVmliLusx6CaXcN92g8Q4e4R0nVlDf2vajqdLl5ZWMOySEm/IxafvZJBmH+Bs5PCjrjoagMBnwWLqR53XrcTz01oNlzT7Rs2EDDhx+BWn3Mz9br0YaHow0Px5CaqjyPiABkql74EwXXXYcmJAS1nx+6qEi0EZE4Kis6fPFCcDqVi2u16OPj8Zo8Bf31yeiTEhVfPDxc+OKCkxDiPlJJXAh3rFNm8KMFnRd4B5HQVsuuxr5n7qsyysivaeUft0xhemwgk55Zz9GKZo+IOygbmho/+4zW77/HZ8ECj1yz4bPP0ISFkfDlWhylpah9fVGbzb2Kr2nuXOrefU+Z0dfXY8vNo+W7TV2paX0WLjzmi8fGCl9c0G+EuI9UJAmiZwz3KDyPfwzxthpW9REO6XLL/O2bHMaG+rAoJQRJAm+d2qMx8t7nnYc6IICm1as9Iu7O6mpaN2/BfOedqPR69PF9bzRT+/kR9NCDZ3xvgeBEVMM9AME5RkAM4VRR3sfM/cMdReRVt/LwgiRUKglJkogIMFJS7zlxl7RafBZfTPM3G3G3tp7x9RpXrwaXC7/LLvXA6ASCM0OIu2Bo8Y/G7KyivKF3Ma1psfGHL48wM97M4vGhXccj/I2UelDcAfyWLkW2Wmn+ZuNp93VUVFD5hz+SPXcexff9D/UffoghLQ19R4EQgeBEHC43r36by/7ihr4bnyFC3AVDi38Malx4W6tosTl7bPL82iO0O1w8c/k4xatuVirsRAZ4eTx1gXHyZDShoTStXt3vPtajRyn7+c/JWbiIunffRZ8yFuuhQzgKi/C/8gqPjk8wesgoaeDSl7fw+y+P8OXBir47nCHCcxcMLcdHzDS0kxTi0+30zoI6Pt5dwn3zEkgM9oGsdfDBtXDRM0QEXEJju4NmqwMfg2cWFiWVCt9LLqHu/fdxNTT0mm5WlmXatm2j9q23ad28GcnLi4AbbyDw1h+hi4xAdrmwHT2Kfuwpat8Kzkna7E7+vC6Lt7bkE+Sj5x+3TOHicaF9dzxDhLgLhpbAOACiVZWUNVq7ibvT5eZX/3eAcD8DDy1IBKcNvvw5IMPXvyXlgjQAShvaGRvquagR36WXUPfWWzStW0fAtdeedL5p3TpqXn0N2+HDqC0Wgh55hIDrr+v2QSCp1RhSR3gBFcGQsymrmidXZlJS385NM6L5+ZKx+HpoYtIXwpYRDC1+0cgqLfFSOeUnWCzvbC3gSEUzv14+Di+dBra9AnV5cOUKMPgxbe8TaHF63Hc3pKaii42lafWak87ZcnIofehhZKuV0Gd+S+LXG7Dcd68oKCE4JXWtdn7y733c+tYO9BoV/7l3Js9ekTZkwg5i5i4YatQaCIgjrqqCQ8dFzFQ2WXlxQzYXjAni4nEhUF8Im16Ascsg/VpQa/H6721cqNpLSf0Ejw5JkiR8ly2j5pVXcFRWoQ0J7jrXtncvAFGv/h1dbKxH7ysYfciyzGf7yvjtF4dotjp4aEES91+YMCxJ8sTMXTDkSJZEEjWV3Wbu/2/1YewuN7+5dBySywH/vQ1Uarj4d0qDjvz1Uer6QckH77NoIcgybTu6Fye3ZmSi8vVFGxPj8XsKRhfFdW3c9vZOHvn3PmLMXnzx4Pn8ZFHysGU/FTN3wdATGE+0vL4rHHJLTg2r9pfxyMIkYszesPZxKNuj5K3vzGfvZQaVhgRtM5vr2zw+JG14OADO6ppux9szMjCmpYnt/YJecbll3t6Sz5/WZaGS4DeXjuPm82JQq4b3d0aIu2DoMSeiw4GrvgSb08WvPjtAjNmL++YlwKHPYfurSgWn1MuO9VGpwBRClLPJ4547KEUpJJ0OV11t1zF3Wxu27Gx8Fsz3+P0Eo4NDZU088WkG+0saWTA2mGcuH0+4/8ioRtUvcZckaTHwEqAG3pBl+fkTzscAbwFBQB1wsyzLJR4eq2C0YE4EwNiczxvf55NX3crbt0/D0FwEnz2gFAlf9MzJ/UwhhDQ1DIotI0kSarMZZ+2xjI3WQ4fA7caQlubx+wnObqwOF3/9OpvXN+Xh76XlbzdMYln6yCps0qe4S5KkBl4BFgElwE5Jkj6XZfnQcc1eAN6TZfldSZLmA88BtwzGgAWjgA5xj3CX8dKGbBaPC+XCBD948xqQgGveAU0PWRp9QglszKKmxU673YVR51kvUxMYiLP2mC3TnpEJgDE93aP3EZzdbMut5cmVmeTXtHLNlEh+sTQFfy/P1RrwFP2ZuU8HcmRZzgOQJOkj4DLgeHFPBR7teL4R+D9PDlIwyvAJxan2Is5ZgVqS+PXyVFj3SyjfB9d/cMxn76Gfj2MroMS6JwabPDostcWMq+aYLdOekYE2IgKN2ezR+wjOThrbHDy39jAf7SwmxuzFv+6awexEy3APq1f6Ey0TARQf9/+SjmPHsx+4quP5FYCPJEkn/UVIknSPJEm7JEnaVV1dPZDxCkYDkoQzIJ44qZyHFiQRXvol7HgdZj4AY5f23s8Uit7egBYnxXWeX1TVBJpxHldIw5qRgSFdWDLnOrIssyaznAV//o7/duye/vLhuSNa2KF/4t6TiSSf8P/HgHmSJO0F5gGlwEmJQ2RZfl2W5amyLE8NCvJMTm7B2YkhJIlZAQ3cOx747EElb/3Cp0/dyScEgDB1Iz/k15667QDQmANx1dQotUhra3GUlWFME5bMuUx5Yzt3v7ebH/9rD6F+ej67fzaPLxnrcUtwMOiPLVMCHF+VORIoO76BLMtlwJUAkiSZgKtkWW701CAFoxBzIvpDn8HHP1Li2a9+G9R97N4zKfk45oW72XikiieWpHh0SGqzBdnhwN3Sgj0/HwB9UpJH7yE4O3C7Zf61vZDff3kUp9vNLy5J4fbZsWjUZ8/WoP6I+04gSZKkOJQZ+fXAjcc3kCTJAtTJsuwGnkCJnBEIesecCLK7o/j3v8E/qu8+Poq4zw1z8f6OForr2ogK9FyxZ405EABnTQ32EiXYSxt5ogMpGO1kVzbzxKeZ7Cqs5/wkC89entavYu4jjT4/hmRZdgIPAF8Bh4H/yLJ8UJKk30qS1FmV4ALgqCRJWUAI8OwgjVcwWgjumHXPegjGLO5fnw5xn+SvhEJuPFrl0SGpA5VlIlddHY6SUpCkjlqngnMBm9PFixuyuOSv35NT3cKfrpnAe3dMPyuFHfoZ5y7L8hpgzQnHfn3c84+Bjz07NMGoJmwC3PUNhE/sfx/vIJBUWGggzuLNm5VtXAAAIABJREFUN0equHVmrMeG1DVzr63FUVyMJjjYY4WzBSOb3YV1/PyTTHKqWrhsYji/WpaKxaQf7mGdEWKHqmD4iJxyeu1VakXgmyu4cEww/9xeSJvd+f/bO/O4qsvsj78P+yKiCKiAopiCGggK7ntmLuVYTVlOWtpmi23Tb7KmsnSasqmmmrFmrBkrLZ0WtVwqc5cwU9PABc1dUEFBlMXL+vz++F4UcQHhctfn/Xr5wvv9Pvf5nnPhfu5zz/c85xgVJC2AuznlsTwnh5LMDDxbRVhkXo39km8q5W/f72bOT4cIC/Rl9oQkBkWH1vxEB8Bx7g5oNGCEZgqyGBwTSklZBSl7LZc149G0KQBlOUZYxitci7sz88POLK5/ax1zfjrEhN5tWf5kf6cRdtArd42j0agF5B+le9sg/L3cWZmezZBOzS0ytXh64h4YSOnxY5RlZeEZocXdGcnON/HyNztZmnaMmBYBvH9XVxJaN7W1WRZHi7vGsQhoDke34uXhRr/2IazZnY1SymI1PdyDgzGlbQeltLg7GUopPt98hFeW7sJUVsH/3RDNA/2j8HSg9MarQYu7xrEIaAmFJ6C8jMEdQ/lux3F2HcunU1hji0zvERRE0ZYtAHjpmLvTcOBkIc8tSGPD/hx6tA3i1VtiiQqxbPkKe0OLu8axaNQcUFB4goHRxi7nVelZFhN392bNoKICQK/cnYDS8go+WL+fd1b8hpeHG6/eEsuYxFa42bjWujXQ4q5xLMy57hQcJzSsJV0iAlmVns2jgy2zk7SySJh4euIR6jw311yRX4/kMWVBGruOnWH4tS14eVRnQhv72Nosq+GcwSaN81Ip7rlGeYBBMaFsPZJHbmGJRaZ3N+e6e4aFIW767eGIFJWUMX3JTm5+70dyC4v597huvH9XN5cSdtDirnE0mscaGTPbPgNgcEwoSsEaC+1W9TDvUvVsVYtyCBq7Y+2eEwz9+zr+k3yAsT1a88NTA7ihcwtbm2UTtLhrHAsPL0icAHt/gJx9XBsWSEiAN6vSLSTuwWZx1zVlHIrcwhKe/N827v7vz3h7uPHFpF78ZXQsjX1qKEbnxGhx1zge3SaAmyf8/AFubsKg6BDW7jlBaXlFvaeurC/jpW+mOgRKKRZuzeC6N9ewJPUoj13XnmWP9yOpTZCtTbM5Wtw1jkdAc+h8M2ydC8X5DI5pTr6pjC2HTtV7aq82kbgHBuKbkGABQzUNyZHcIu6evYkn//crbYL9WfpYP566vgPeHvZfa90a6GwZjWPS40FI+xx+nU/fLhPwdBdWp2fTM6p+LfE8goLosPEnCxmpaQjKKxSzfzzAm8v34Cbw8qjO3NUzEncXSG+8GvTKXeOYRCRCWFf4eRaNvNzpGdWMlRaKu2vsl51Hz3Dzez/yl6W76N2uGT88NYC7e7dxLGE/nQn5xxv8MlrcNY5Ljwfh5B7Yv5pB0aHszS5okN6qGttjKi1nxnfp3PTPZI7mneUfdybw4d2JhDXxtbVptaOsBHZ+DXN/D29fCxv+2eCX1OKucVw632yUAN74bwbHGBuOLJU1o7EfUvadZNjb63h/zT5u7RrOiqcGcFOXMIvVE2pQstPh+z/DWx3h8/GQtQP6PgWJ9zb4pXXMXeO4eHgbmTPr/kabYdlEhfizMj2bu3u3sbVlGgtwuqiUvy7bxf82HyGymR+f3deD3tcE29qsminOh+1fGTf8MzYZmV3Rw6HreGg32OhLYAW0uGscm8SJkPwWbPqQwdF/4JMNhygsLsPfW/9pOypKKZalHWfqNzs4VVTCpAHteGJIe3w87TgLRik4/BNsnQM7FkJpEYTEwNBXoMsd4G/9DyX9DtA4No1bQsdRsHUOQ0Y/yIfJFfy49yRDXXRXoqNz7PRZXli0nRW7sokND+SjCUlcGx5oa7MuT34W/DrPWKXn/AZejSD295Aw3rjpb8PQkRZ3jePT40HYsYCkMz8Q4B3B6t3ZWtwdjIoKxdyNh3j9u92UVVTw5xEdmdCnDR72WGu9vMzYIf3LHNjzHahyaN0L+j4JnX4H3vZRSliLu8bxadUDWnbBfdMH9G8/k1Xplm3goWlYfsvKZ8qCNLYcOkW/9sG8MjqW1s38bG3WxeTsM8Iu2+ZBwXHwD4Xej0LCOAi2TFVSS6LFXeP4iED3B+Hrh7n9mgMs3e7Lhn05jnHzzYUpLivnvdX7eG/NXvy9PXjzti7c0jXcvj6USwqNFMZf5sDhFBB3aD8Uuo4zfrrbb+0aUUrZ5MKJiYlq8+bNNrm2xgkpNcHfO1Ea3oOBR+7nZEEx00dfy+2JurqjPbL5YC5TFqSxN7uA0fFhvHBjJ5o18ra1WQZKQeYvsPUTSPsKSvIhqB0k3AXxY8+XnbYRIrJFKZVY0zi9ctc4B54+0PVuPH98myX3TeeRZSf505epRAb50aOeJQk0liPfVMqM79KZ+9Nhwpv4MntCEoOi7aQpSmEOpP7PCL1k7wQPX2MvRddxRkzdnr5R1AIt7hrnIele+PEdmu78hBm3TqHf66s5nFukxd1O+GFnFi8s2k5WvomJfdryx6EdbJ+yWlEO+1cbYZf0pVBRCuHd4Ma34dpbwccy7RttgRZ3jfMQGAEdb4QtH+Of9BQAhcVlNjZKk51v4qVvdrAs7TgxLQL417huxLdqYlujTh2ErZ8aTV/OZIBvEHS/37g52ryTbW2zEFrcNc5Fj4dg59c0+eQ6bnIbRmFxB1tb5LIopfjfpiP8ddkuTGUV/N8N0TzQPwpPW6U3lpogfQn88gkcWAuIsWP0hr9A9Ahjx7MTocVd41xE9oI75yMrp/EPr38y/0RHwP7S1JydAycLeXZBKj/tz6VH2yBevSWWqBAb5X8fSzXi6KmfgykPmrSGQX+GLndCE+e94a7FXeN8RA9HQjvBO3F4F2ba2hqXorS8glnr9vPOyt/w9nDj1VtiGZPYCjdrl+Q9mwdpXxiifuxXcPeGjjcZN0fb9AcXaH6uxV3jnHgHGD9LCm1rhwvx65E8nvkqlfTj+Qy/tgUvj+pMaGMf6xlQUQEH1xuCvmsxlJmgRSwM/5tREsDPtVrvaXHXOCde/gCIFvcGp7C4jDeX7+GjlAOEBHgza1w365Z/OJ1p3BjdNte4UeodaOSkJ4yDsHjr2WFnaHHXOCfuXpThjpRqcW9I1uzO5s8Lt5OZd5a7erbmT8NiaOxjhV2bZSWw51sjhXHfSlAV0KafEUvveBN4OkgTjwZEi7vGORGhWHzxKNPi3hDkFBQzfclOFm07SrsQf76Y1IukNlYIe2SnG2GXX+dD0UkICIN+f4T4P0BQ24a/vgOhxV3jtBS7+eJRrtvuWRKlFAu3ZjJ9yU4Kist47Lr2PDKoHd4eDVhrvTgfti8wRN2GzS8cDS3uGqelxN0PTy3uFuNIbhHPLUxj/W8n6dq6Ca/dGkeH5gENc7FzzS/mmptfFNq8+YWjocVd47SUefjhVXLW1mY4PGXlFXyUcpA3l+/BTWDa7zpzV4/IhklvvGTzi1vtovmFo6HFXeO0lHn44a10zL0+7Dh6milfpZGWeZrrYkKZPvpawppY+GalgzS/cDS0uGuclgoPP/zUSUrLK2y35d1BMZWW887K35i1bj9N/Tz559gERsa2tGytdQdrfuFo1ErcRWQY8A7gDnyolHqt2vnWwMdAE/OYKUqpZRa2VaO5KpSnP36YKCouJ9DP/sS9okJZf+dmLUjZe5LnFqZxMKeI2xMjeG5ER5r4eVlmcgdufuFo1CjuIuIOzASuBzKATSLyjVJqZ5VhzwOfK6XeF5FOwDKgTQPYq9HUHu9G+EsxBSVlBPrZl2hszzzNHz7cyLt3JjCgQ4itzQEgr6iEvy7bxeebM4hs5sdn9/WwTDeryzW/uG6qXTS/cFZqs3LvDuxVSu0HEJH5wO+AquKugMrCx4HAUUsaqdHUCa9G+GHiqJ2V/S0oLmPyvK2cPlvK9szTNhd3pRRL047x0jc7OFVUyqQB7XhiSHt8POuZYuhkzS8cjdqIezhwpMrjDKBHtTEvActFZDLgDwyxiHUaTT1w826EH8UUmkqsds3TZ0sZ8+8NvHJzLN0im150XinFC4u2cyinEC8PNzLzbJvNczTvLC9+vZ0Vu7KJDQ/k44nd6RwWWPcJnbj5haNRG3G/1Mdr9cardwIfKaXeFJFewBwRuVYpVXHBRCIPAA8AtG7d+qJJS0tLycjIwGQy1cp4Td3w8fEhIiICT0/7ClVYGnefRriJ4mxhAWCdbkypGXmkH8/n27RjlxT3L7dksHBrJk8Mac+KXVkcs5G4V1Qo5m48xIxv0ylXiudHduSe3m3wqOuNZxdofuFo1EbcM4CqRY8juDjsci8wDEAptUFEfIBgILvqIKXULGAWGA2yL7pQRgYBAQG0adPGvjqgOxFKKXJycsjIyKBtW+feru3hY2ywMRWdsdo1dx/PB2DL4VMXndubnc+LX++gZ1QQkwe3Z8fRMxzOsf4mqz1Z+Uz5KpVfDufRr30wr4yOpXUzv6ufyMWaXzgatRH3TUB7EWkLZAJ3AGOrjTkMXAd8JCIdAR/gxNUaYzKZtLA3MCJCs2bNOHHiqn89DoennxECKLWiuKebxX175mlMpeXn4tam0nIe/Wwrvl7uvHNHAu5uQngTX37an2M124rLypm5eh/vr9lLI28P3rq9CzcnhF/9+81Fm184GjWKu1KqTEQeBb7HSHP8r1Jqh4hMAzYrpb4B/gh8ICJPYoRs7lFKXbQyrw1a2BseV3mNvXyNlXvp2XyrXXP38Xy8PNwoKasgLfP0uWJa05fsJP14PrMnJNHcXOO8ZaAP+aYy8k2lBDRwJcXNB3N55qtU9p0oZHR8GC/c2Ilmja5iZa2bXzgctcpzN+esL6t27MUq/98J9LGsaRpN/fD2rxT3Aqtcr7xC8Vt2PiNjW7JwayabD54iqU0QS1OP8enGwzzQP4pB0aHnxrc07/Q8dtrUYOJ+xlTK69+lM/enw4Q38WX2hKQLbLgiFRVwKNm4ObrrG5dvfuFo6I/baogI48aNO/e4rKyMkJAQbrzxxque68UXX2TFihUXHPv111+Jjz/fQGDevHn4+flRWloKQFpaGnFxcVd1nXvuuYcvv/zyouNr1qypk93Ogrc5LFNRbJ2V++HcIkylFfSKakZUsD9bDp3iSG4RUxak0qVVE54eGn3B+PAmxgq+oTJmlu84zvVvreWzjYeZ2Kcty5/sXzthP50Ja/8G/0iAj2+CPd8bzS8eWAuTkqHHA1rYHQBdfqAa/v7+bN++nbNnz+Lr68sPP/xAeHh4neaaNm3aRcdiY2M5dOgQ+fn5BAQEkJKSQkxMDFu3bqV79+6kpKTQp0/tvwSVl5fXyTZXQLyMmiQVxdZZue8+bsT2o1sE0C2yKSt2ZTF53lYA/nlnAl4eF66lWgaaV+55ls0Oyz5jYuo3O/h2+3FiWgTw73GJxLdqcuUn6eYXTodeuV+C4cOHs3TpUsBYWd95553nzq1du5b4+Hji4+NJSEggP99YFb7++uvExsbSpUsXpkyZAlx6Re3m5kZSUhIbN24EYMuWLTzyyCOkpKQAkJKSQu/evQFYuXIlCQkJxMbGMnHiRIqLiwFo06YN06ZNo2/fvnzxxRcXzP/dd98RExND3759WbBggaVfGseisuCUlcQ9/Xg+ItChuSHup4pK2XYkjxm3xtEq6OJslNAAb9zdhKMWWrkrpZj/82Gue2stK9Oz+b8bolk8ue+VhT07Hb7/M7zVET4fD1k7jOYXj22De5ZA3O1a2B0Uu125v7x4BzuPWjbLoVNYY6be1LnGcXfccQfTpk3jxhtvJDU1lYkTJ7J+/XoA3njjDWbOnEmfPn0oKCjAx8eHb7/9lkWLFrFx40b8/PzIzc294vy9e/cmJSWFXr164ebmxsCBA3n22Wd54oknSElJYerUqZhMJu655x5WrlxJhw4dGD9+PO+//z5PPPEEYOSqJycnA4agg5FtdP/997Nq1SquueYaxowZU5+Xy/Exr9yVlfqo7j6eT2KTInzfS6DfoLcB+EOP1oyIbXnJ8R7ubjQP8LaIuO8/UcCzC9LYeCCXHm2DePWWWKJCLlNNUTe/cAn0yv0SxMXFcfDgQebNm8eIESMuONenTx+eeuop3n33XfLy8vDw8GDFihVMmDABPz9jdRYUdOV4ZJ8+fUhJSeHnn38mKSmJdu3asXfvXk6cOEFBQQFRUVHs3r2btm3b0qFDBwDuvvtu1q1bd26OSwl3eno6bdu2pX379ogId911V31fCsemskm2lfqo7s7K536v5ZB3iPDCnSx9rC8vj7ryYiKsiS9HT9dd3EvLK5i5ei/D3lnPzmNneO2WWObd3/NiYVcKDm2ARY/AG9Gw+DFD5Ie+An9MhzFzoP31WtidCLtduddmhd2QjBo1iqeffpo1a9aQk3M+F3nKlCmMHDmSZcuW0bNnT1asWIFS6qrSC3v27MmmTZtITk6mV69eAERERDB//vxzIZmaMkn9/f0vedxV0hxrhblJtkdpoSFuv3wCnUeDTz22118GU2k5J0+eYICfEc4j/1ittvG3bOLLr0fy6nTNbUfymPJVKunH8xkR24KXbupMqDnN8hy6+YXLYrfibmsmTpxIYGAgsbGxrFmz5tzxffv2ERsbS2xsLBs2bCA9PZ2hQ4cybdo0xo4dey4sc6XVe0BAAK1ateKjjz46N3evXr14++23efjhhwGIiYnh4MGD7N27l2uuuYY5c+YwYMCAK9ocExPDgQMH2LdvH+3atWPevHn1fh0cGhGK3XxxKysycrMXPwZZ22HE3yx+qb3ZBdzmthrv8kLw9IP8Y7V6XlgTH77fbrqq8r+FxWW8uXwPs1MOEBrgzaxx3RjauUplRd38QoMW98sSERHB448/ftHxt99+m9WrV+Pu7k6nTp0YPnw43t7ebNu2jcTERLy8vBgxYgR//etfrzh/nz59+Prrr2nVytjR16tXL5577rlzK3cfHx9mz57NbbfdRllZGUlJSUyaNOmKc/r4+DBr1ixGjhxJcHAwffv2Zfv27XV8BZyDEjdzH9W8Q8aBzbOh58MQZNnSC+mZuUz0+I6zYb3w9XSDMzWIe1kJrH+Ta7wHU1JewcnCYkIDfK78HGD17myeX7idzLyz3NWzNX8aFkPjyhx53fxCUwWp40bSepOYmKg2b958wbFdu3bRsWNHm9jjarjKa531ahdSS1py/fUjYfnzxs7KTr+DWz+w6HX+M+9z7t19PxW3/Ae3Pd9C5hZ4fNvln/DzB7DsafbEPcPQn7vw9SN96HKFrJacgmKmLdnJ19uO0i7En9dujTN2v+rmFy6HiGxRSiXWNE6v3DVOTbmHL96ms5B3BLwbQ9K9kPw29HnM2G1pIdyPG/nsbpG94NhWIyyj1KVj2sUFsPZ1AEJMB4EufLB+P0M7t2BY5xYX5MMrpVjwSyZ/WbqTguIyHr+uPQ8PjMI761dY/LJufqG5LFrcNU5NmYc/PioflXcICWwFfZ4wQjMrp8Efvqh5glqglCL49HbOeATRuHEYBLQ0tuqb8sD34rK/bHwfCrMhoCWBhfvo3iaI5TuyWJJ6jJsTwnnr9i6ICEdyi3huYRrrfztJ19ZNeH1EBNccWwof6OYXmprR4q5xapSnH/6coOLUEdybtgbfJsaNxRVT4eCP0Kb+JZFO5BfTseI38prG0VgEGpvz2vOPXyzuRbnw47sQPRIat8Qt9XM+n9KT0rIyUuZOY9q2VswOD6S8QvHWD3vwkAo+7JPHdWc/R+Ysg/IS3fxCUyu0uGucGuVptNqT07lspCO5accYmvQA7hv/ZQj8vT/Ue8W75/BR+rod40i4eSdzgFnczxyF0Gr3NZLfMvLLr3sBDiZD8RnIP45n3mEGHHqXRb6NuXNpMXk0YkboZkZUrMZjS6bR/CLpPt38QlNrtLhrnBtvf1rIKdxKSvjhqDcffvoLUcH+zGj7AElpL8PuZRAzsl6XyN1rlJIIam/sWTgX884/fuHA05mwcZZR9zy0IxSaa+qfSIejRsze3z+AhWoanqoEdVqQdoNh2Cu6+YXmqtHirnFqxKsRvmL0UA1o3paZA7vy3pq93LHpGlb6hBO4+Hk8I4fQyLfuwimZWwDwb2tOYKhcuedXa1i25lVAwUCj9hAhMcbPE7vhUAoER+M2biFu3/4JWsQh8WN18wtNndHlB6rh7u5+rjBYfHw8r732GmAU6zp58uS5cZcrp7tmzRoCAwOJj48nLi6OIUOGkJ2dfdG4uvDSSy/xxhtvWGQuV8HN5/yGnSG9kxgZ15Ilk/vy0b29WBg0gaaF+5kx4yXeWr6bnILiOl2jad52sjzCz5fB9fQFnyYXrtxP7IFtn0LivdA00jjmH2LE5LN3wOGfILI3BIbDHZ/CwGe0sGvqhRb3avj6+rJt27Zz/yorPF4N/fr1Y9u2baSmppKUlMTMmTMbwFJNbfCoIu6dO14LGCUa+rUP4cnJT1MY3IUnPb7k36t30nfGatKPX12xupKyCtqV7CYnsFq5jMZhF25kWjXd2Lna74/nj4lAcDTsWmykM0bqfjcay6HFvQFRSpGfn0/TpkbGRG5uLqNHjyYuLo6ePXuSmpoKGCvyiRMnMnDgQKKionj33XfPzfHKK68QHR3NkCFD2L17t038cGSaNDFW0+UevuDX7MKTIviPmE5QWTZr+u/jbGk5P+69up6mq1Z/T0vJxS2i24UnAlqcL0GQucXoZNR7MjQKuXBcSDScNTfTjux9VdfWaK6E/cbcv50Cx9MsO2eLWBj+2hWHnD179oJOSc8+++xVl85dv3498fHx5OTk4O/vf64UwdSpU0lISGDRokWsWrWK8ePHs22bsYsxPT2d1atXk5+fT3R0NA899BCpqanMnz+frVu3UlZWRteuXenWrduVLq2phq+/UbzLvWnkpbNiogZA1CBapM4k2PMNMk/VvkLjiYzf6PbjJE66hdDhugkXngxoadRKB1jxsvHB0uuRiyepjLs3bWOEZDQaC2G/4m4jKsMy1blUtcXLVWDs168fS5YsAWDGjBn86U9/4l//+hfJycl89dVXAAwePJicnBxOnz4NwMiRI/H29sbb25vQ0FCysrJYv349N99887lSwqNGjbKIjy6FuewvgVeIXw+ZiswayOO+35GcF1mraVVRLqUf34K/KiV/zELcGje/cEBASyjIgr0r4MBaGPYaeAdcPFGIufWeDsloLIz9insNK2xr06xZM06dOkVwcDBghFgq/38lRo0axa233gpcuoxv5QeEt/f5bA13d3fKysouOK+pI5Xi3qT15ceEJUDnm7l95zd8n3tTzXOWmjj139toVnKU7xLe53fRXS8eE9DCqMa49GnjgyVx4qXnahEHHj5GHRiNxoLomHstGThwIHPmzAGMvqVz585l0KBBNT4vOTmZdu3aAdC/f38+/fRTwMiqCQ4OpnHjy+8w7N+/PwsXLuTs2bPk5+ezePFiC3jiYpi7MdWYedLvabyViXZ5G648rqKCki/vI+jkZt4OeIqRN/3+0uMahxk/Tx2AQc9dPke9UQg8/ZtRzEyjsSD2u3K3EdVj7sOGDeO1117jhRde4KGHHqJLly4opRg2bNhlOx1VxtyVUgQGBvLhhx8Cxo3TCRMmEBcXh5+fHx9//PEVbenatStjxowhPj6eyMhI+vXrZzlHXYVGoYCcj21fjuD2KIQmJVkUlZTh53WZt8byP+O1ezGvlN3F6LGP4uF+mfVR5UamkBiIq+GejS4hoGkAdMlfF8WlXusTe4x65jWEuEyvtuPrwmvpOnku7ZtfIj6+YSZ8/xz/LRvGiT4v8czwK7x+pjPw32Fww1+MvqQajYXQJX81mkpCOtRqWFlAOGFFJ8nIO3uxuG9fAN8/x2r3Xsz1f5BlQ2qY06cxPJxSR4M1mvqjY+4ajRn3pq0Ik5yL0yEPJsPCBzncqAuTCh/k1Vu74OOpG0lr7Bst7hqNGe9mkWZxLzp/sCgX5o/F1KgVv8t5hFt7XEOPqGaXn0SjsRO0uGs0ZtwCI/CVEvJOVqkJc/gnMJ3m+fL78ApoxpThNdyY1WjsBB1z12gqCYwAoPTUEeNneQVyZBNuuLP4ZEtmjo8934xao7FztLhrNJWYxV3OZLI3u4Cb/pHMLJbTVFpxfVwkQzo1r2ECjcZ+0GGZS5CVlcXYsWOJioqiW7du9OrVi4ULF152/OXK/8LFpYI1doy5RIH/2WNMX7ITDzdFd6+DuEV045WbLddMW6OxBlrcq6GUYvTo0fTv35/9+/ezZcsW5s+fT0ZGhq1N0zQ0/sGUu3nRUk6yds8JpnT3wLu8gE5Jgwn01eEYjWOhxb0aq1atwsvLi0mTJp07FhkZyeTJkzGZTEyYMIHY2FgSEhJYvXr1Rc/Pyclh6NChJCQk8OCDD16ynozGThGhxL8lYZJDy0AfbmthbrISritxahwPu425z/h5Bum56RadMyYohme6P3PFMTt27KBr10sUgoJzTTfS0tJIT09n6NCh7Nmz54IxL7/8Mn379uXFF19k6dKlzJo1yzLGa6yCe9NWRJzJ5tkRHfHKWAZeARBcu01QGo09Ybfibi888sgjJCcn4+XlRUREBJMnTwYgJiaGyMjIi8R93bp1LFiwADDK+FY26tA4Bl5BkcSf2k9ClzDYuAXC4sFNb1jSOB52K+41rbAbis6dO5+ruQ7Gav3kyZMkJiYSHl67Zgq6TK8D0zgcKTgOxflwfDv0ftTWFmk0dULH3KsxePBgTCYT77///rljRUXGjsWqJXv37NnD4cOHiY6OvuD5Vcd8++23nDp1ykqWayxCYASoCvjiHqgohaiayzprNPaIFvdqiAiLFi1i7dq1tG3blu7du3P33XczY8YMHn74YcrLy4mNjWXMmDF89NFHFzSA8obzAAAKz0lEQVTZAKOV3rp16+jatSvLly+ndesrNInQ2B/mXHf2roABU4w2fBqNA1Krkr8iMgx4B3AHPlRKvVbt/N+ByiWOHxCqlGpypTl1yV/bol/ry3DqILzTBbqOh5verbFMsEZjbSxW8ldE3IGZwPVABrBJRL5RSu2sHKOUerLK+MlAQp2s1mhsTdM28OhmCGqnhV3j0NQmLNMd2KuU2q+UKgHmA1fqCXYnMM8Sxmk0NiG4PbjpiKXGsanNX3A4cKTK4wzzsYsQkUigLbCqrgbpTT8Nj36NNRrnpzbifqnvppdThzuAL5VS5ZecSOQBEdksIptPnDhx0XkfHx9ycnK0+DQgSilycnLw8fGxtSkajaYBqU2eewZQtXV8BHD0MmPvAB653ERKqVnALDBuqFY/HxERQUZGBpcSfo3l8PHxISIiwtZmaDSaBqQ24r4JaC8ibYFMDAEfW32QiEQDTYENdTXG09OTtm3b1vXpGo1GozFTY1hGKVUGPAp8D+wCPldK7RCRaSIyqsrQO4H5SsdUNBqNxubUqvyAUmoZsKzasRerPX7JcmZpNBqNpj7ofC+NRqNxQmq1Q7VBLixyAjh0lU8LBlyxrZEr+e1KvlbF1fx2NX8rsYTfkUqpkJoG2Uzc64KIbK7Ntltnw5X8diVfq+Jqfruav5VY028dltFoNBonRIu7RqPROCGOJu6u2rPOlfx2JV+r4mp+u5q/lVjNb4eKuWs0Go2mdjjayl2j0Wg0tUCLuxMjLtLM1VX8rI6r+q2pHS4j7iLiMr5WoktBOD2N4FxDHadHRGrXod7JEJHuItL4ap/n1IInIqNE5Clb22FtRGSkiHwmIlNF5Bpb29NQiMgwEfkamC4iLpEzLQahIrIG+BDgciW2nQURGSIiW4BJtrbFmojIABHZCTwAaHEHEBEPEXkGeBd4Q0TilVIVzr7CEREfEfkX8CJGN6woYJK5oqdTYBY3HxH5CHge+A/GCvZeEQm2qXFWwPxtzGT+Fyciw8H5vpmaf89eIvIe8AYwXSn1QtXztrOu4RERH+BxYJpS6j6lVIb5eK39dqo/iErMlSx3AzHAU8C/zcedeoWjlDJhVO78vVJqMfAq0BVDCJwCZWACvgYGKKW+ARZgZH45/XZ2s4hHANuAKRgf5CilKmxpl6Ux/55LAD9gkVJqkYi4iUiXyvO2tbDBCQdylFLzRcRXRG4RkRDAHWon8k6TCikijwFhwC9Kqc9FxFMpVWo+dwD4s1Lqs6rHnYEqfm9RSn0hIr4YYu6llCoWkR+AZ5RSv9jU0HpS/fdb5fjtGA3ctwPJwPdKqWTbWGl5qvi9SSn1lflYE+C/GF/XZwHLgZVKqd9sZqiFqOLvVqXU/0SkHYaPW4EhGC0/jwFfKaW+t52llqWK35uVUl+KSGuMdqX3AC8AZ4FC4IBS6nkRkRo/4JRSDv0Pow3gk8CPwO8xVq73AKFVxtwMZNraViv5HVJlTCvz+ca2trcB/GxuPj8QiMUoX/0wRhw6xJY2N7DfQUAiMNU87mnzm36x+bGHrW23oL/3ms9NBpYA0UAA8BjwLyDY1nY3kN/3mc+9iRGBGGJ+3BFIBTrVZu5a1XO3Z5RSSkQGAc8rpVaLSAFwA1AOzDGPWSgij4nI00qpN0RkiFJqhS3tri+18RuIA3Yrpc6ISBjGB942G5lcJ67gpwI+VkqtqRwrIqlAX4xVjkNzGb+HYfj2I9BfRJYBkebH+81PdcjQ42X8HSEityul/iEis5VSBQAisg3oCRTZ0mZLcBm/h4vIGOBtjA82D/PYXSKSAnjWZm6HjrlXuYm0GegHoJT6DtgDdDa3/qvkIeB1ETmOEc9yWGrhd2fz+WDAJCKTMTpptao+lz1Tg58dRaRDtafcgBGScmhxv4LfuzE+sOMxwhOblFKdMVpfDhSRcGVe4jkSV/B3F9BVRKIrhd3M9RjC7tD3kq7gdzrQDTiDkTTwlIh0FpEXgGsx+lrXiEOJe2W2S+XNBHX+JtJeIEBEYs2P1wKBGF/hEJF44APgK6CrUupja9pdX+rgt6/58WiM9LFrgGHKuMlqt9TBz8bmjIpx5lV7JDBFOdiN86vwex1GStwJYJJSaqp5fC7QRymVaVXD60g93sd3iMh2jN/zc8rBbiLXwe8opdTrwFzgEYz38W1KqZzaXM8hxF1E+ojIx8DzIhJUuToRkcqvJz9jfB29XkQ8lFI7MVbnlbnPOcDDSqnblFJHrW1/XamH393N5+cA1ymlHrfnN349/OymjIyKI8BDSqnxSqlsW/hQF+rg9w4MYUtQSplExL2KUBRc6hr2hAXex4dwjd/zTqAl0AtAKfUJ8LhS6m6l1LHaXtfuxV1EooD3gNUYf9jTRWQEgDJnvSil9gKbMD7ZppifWoy505NS6ohSKs3KpteLevq933x+gVJqtZVNvyos9Ptdo5T60cqm14t6+n3QfL7cUcIwFvo9b1BKrbey6fXCEu/jqmOvBrsXd4xV6C6l1EcYmQHbgJtEpCWAiPxFRP4DbMHYtNRdjN1suRhxZkelPn4vt43JdcJV/KyOq/mt38dW/j3bXZ67iNyE8Qm3WSn1k/mTbw5wp1LqsIh0AsYBWRg3Ih4GXjR/+iEijTDSwfJs40HdcBW/XcXP6ria367mbyX25LfdrNxFpKWILAb+D2gKzBaRG5RS+4ENwG3mobuBnRg3HNKUUmOVUnvFfOdZKVXgSH8QruK3q/hZHVfz29X8rcQe/bYbcce4aZKslOqvlJoOvIOxAw+MnYexItJDGZkQmUB/pdRpMFKKHO3OeRVcxW9X8bM6rua3q/lbid35bVNxF5HxIjJQRLyBlcAnVU7nYOQzA/yEsf347+avLZ2BQyLiB45XV8NV/HYVP6vjan67mr+V2LvfVt+hKiICtAA+AyqAfcD9GKk+x+R87ZeWGF9vUEodB94RkUiMmhqRwHillMPsUHMVv13Fz+q4mt+u5m8lDuW3sm4dBXfzzw7AXPP/PYB/AAuqjVnM+ZoKoVXGBljTZu239lP77dr+OqrfVlm5i4gHMA1wF6MeRmPMNTCUUmViVEQ7KiIDlFJrRcQLYxfeHhF5BbhRRAYqpU4B+daw2RK4it+u4md1XM1vV/O3Ekf1u8Fj7iIyACOHsynGNtvpQCkwSES6w7nazNOAl81P88GogLcSY+vxEPML4zC4it+u4md1XM1vV/O3Eof22wpfZfoB46o8fg+jiNc9GDXIwfiQaQF8jtGIoDvGzYl4a37t0n5rP7Xf2l9n8dsaL44f4M35WNQfgFfN/98GTDb/PxGYb+tfpvZb+6n91v46g98NHpZRShUppYrV+Up912PEowAmYJRuXYLR83MLOEd/RFfx21X8rI6r+e1q/lbiyH5bLRVSjHKXCmgOfGM+nA88h1Gj+IAyVy5U5o9CZ8BV/HYVP6vjan67mr+VOKLf1tzEVIHRQeQkRtf2JRi9ASuUUsnKjkvS1hNX8dtV/KyOq/ntav5W4nB+W7VwmIj0BFLM/2Yrpf5jtYvbEFfx21X8rI6r+e1q/lbiaH5bW9wjMCqivaWUKrbahW2Mq/jtKn5Wx9X8djV/K3E0v+2u5K9Go9Fo6o89VYXUaDQajYXQ4q7RaDROiBZ3jUajcUK0uGs0Go0TosVdo9FonBAt7hqNRuOEaHHXaDQaJ0SLu0aj0Tgh/w+Q0lVq8NWmHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rendimenti da inizio 2020\n",
    "\n",
    "df_2020=df.loc[df.index>='2020-01-02',:]\n",
    "df_2020=df_2020/df_2020.iloc[0,:]\n",
    "\n",
    "plt.plot(df_2020)\n",
    "plt.legend(['Msci World','EU Bond','Gold'])\n",
    "#plt.xlim('2020-01-02','2020-05-28')\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.73485772082718"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend_2020=(df_2020.iloc[-1,:]-df_2020.iloc[0,:])/df_2020.iloc[0,:]*100\n",
    "rend_2020=rend_2020.values\n",
    "\n",
    "rend_2020\n",
    "\n",
    "np.sum(rend_2020*vec/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "act_grid=np.arange(0,105,10)\n",
    "y_oro5=[]\n",
    "\n",
    "for act in act_grid:\n",
    "    ac=act-5\n",
    "    ob=100-act\n",
    "    if ac>0:\n",
    "        vec=np.array([ac*2/3,ac*1/3,ob*2/3,ob*1/3,5])\n",
    "    else:\n",
    "        vec=np.array([0,0,(ob-5)*2/3,(ob-5)*1/3,5])\n",
    "    rr=np.sum(rend_2020*vec/100)\n",
    "    y_oro5.append(rr)\n",
    "\n",
    "    \n",
    "plt.bar(act_grid/10,y_oro5,label='No oro')\n",
    "plt.xticks(np.arange(0,11),['0%','10%','20%','30%','40%','50%','60%','70%','80%','90%','100%'])\n",
    "#plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel('Quota azionaria %')\n",
    "plt.ylabel('Rendimento %')\n",
    "plt.title('Performance da inizio 2020 ad oggi')\n",
    "plt.savefig('output_portfolio/portfoglio_2020.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "act_grid=np.arange(0,105,10)\n",
    "y_oro5=[]\n",
    "y_oro5_feb20=[]\n",
    "#y_oro20=[]\n",
    "#y_oro50=[]\n",
    "\n",
    "for act in act_grid:\n",
    "    ac=act-5\n",
    "    ob=100-act\n",
    "    if ac>=0:\n",
    "        vec=np.array([ac*2/3,ac*1/3,ob*2/3,ob*1/3,5])\n",
    "    else:\n",
    "        vec=np.array([0,0,(ob-5)*2/3,(ob-5)*1/3,5])\n",
    "    rr=rendimento_bis(vec,inizio='2012',fine='2020-06')[1]\n",
    "    y_oro5.append(rr)\n",
    "    \n",
    "for act in act_grid:\n",
    "    ac=act-5\n",
    "    ob=100-act\n",
    "    if ac>=0:\n",
    "        vec=np.array([ac*2/3,ac*1/3,ob*2/3,ob*1/3,5])\n",
    "    else:\n",
    "        vec=np.array([0,0,(ob-5)*2/3,(ob-5)*1/3,5])\n",
    "    rr=rendimento_bis(vec,inizio='2012',fine='2020-01-01')[1]\n",
    "    y_oro5_feb20.append(rr)\n",
    "    \n",
    "#for act in act_grid:\n",
    "#    rr=rendimento_bis([act*0.8,0,100-act,0,act*0.2],inizio='2012',fine='2020-06')[1]\n",
    "#    y_oro20.append(rr)\n",
    "    \n",
    "#for act in act_grid:\n",
    "#    rr=rendimento_bis([act*0.5,0,100-act,0,act*0.5],inizio='2012',fine='2020-06')[1]\n",
    "#    y_oro50.append(rr)\n",
    " \n",
    "plt.bar(act_grid/10,y_oro5_feb20,label='fino a Gennaio 2020')    \n",
    "plt.bar(act_grid/10,y_oro5,label='fino ad oggi')\n",
    "#plt.bar(act_grid/10,y_oro20,label='Oro = 20% della quota azionaria')\n",
    "#plt.bar(act_grid/10,y_oro50,label='Oro = 50% della quota azionaria')\n",
    "plt.xticks(np.arange(0,11),['0%','10%','20%','30%','40%','50%','60%','70%','80%','90%','100%'])\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.ylim(2,8.5)\n",
    "plt.xlabel('Quota azionaria %')\n",
    "plt.ylabel('Rendimento annualizzato %')\n",
    "plt.title('Performance da Gennaio 2012')\n",
    "plt.savefig('output_portfolio/portfoglio_2012.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0493362276208897"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.47**(1/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rendimenti 2020 delle asset classes  [ -7.64398102 -14.27470172   0.6642363   -2.37635614  12.53304944]\n",
      "rendimento nel 2020 del portafoglio  -2.5 %\n"
     ]
    }
   ],
   "source": [
    "df_2020=df.loc[df.index>='2020-01-02',:]\n",
    "df_2020=df_2020/df_2020.iloc[0,:]\n",
    "rend_2020=(df_2020.iloc[-1,:]-df_2020.iloc[0,:])/df_2020.iloc[0,:]*100\n",
    "rend_2020=rend_2020.values\n",
    "\n",
    "print('rendimenti 2020 delle asset classes ',rend_2020)\n",
    "pesi=[45,0,50,0,5]\n",
    "\n",
    "print('rendimento nel 2020 del portafoglio ',round(np.sum(pesi*rend_2020/100),1),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analisi dei pesi di 5 ETF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#altrigrid=np.arange(0,20,1)\n",
    "\n",
    "#for aa in altrigrid:\n",
    "#    maxperd=rendimento_bis([45-aa,aa,45-aa,aa,10],inizio='2012',fine='2020-05')[2]\n",
    "#    rend=rendimento_bis([45-aa,aa,45-aa,aa,10],inizio='2012',fine='2020-05')[1]\n",
    "#    print(aa,rend,maxperd)\n",
    "\n",
    "\n",
    "def port_2var(action,frac_gold,eta=0,start='2016',end='2020-06'):\n",
    "    v1=round(action*(1-frac_gold)*eta/(eta+1),1)\n",
    "    v2=round(action*(1-frac_gold)/(eta+1),1)\n",
    "    v3=round((100-action)*eta/(eta+1),1)\n",
    "    v4=round((100-action)/(eta+1),1)\n",
    "    v5=round(100-v1-v2-v3-v4,1)\n",
    "    risiko=round((v1+v2+v5),1)\n",
    "    eta=int(100-risiko)\n",
    "    port_res=rendimento_bis([v1,v2,v3,v4,v5],inizio=start,fine=end)\n",
    "    #print('composizione ',v1,v2,v3,v4,v5,'rendimento ',port_res[1],'% massima perdita ',port_res[2],'%')\n",
    "    #print('check 100=',v1+v2+v3+v4+v5)\n",
    "    return risiko,int(eta),v1,v2,v3,v4,v5,port_res[1],port_res[2]\n",
    "\n",
    "port_2var_vec=np.vectorize(port_2var)\n",
    "\n",
    "ac_grid=np.arange(0,101,2)\n",
    "sim0=port_2var_vec(ac_grid,0,eta=10**3)\n",
    "sim1=port_2var_vec(ac_grid,0.2,eta=3.5)\n",
    "\n",
    "plt.plot(sim0[8],sim0[7],label='Oro 0% delle azioni, $\\eta=\\infty$')\n",
    "plt.plot(sim1[8],sim1[7],label='Oro 5% delle azioni, $\\eta=3.5$')\n",
    "plt.xlabel('Max.perdita %')\n",
    "plt.ylabel('Rendimento annuo %')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Risiko</th>\n",
       "      <th>Age</th>\n",
       "      <th>MSCI World</th>\n",
       "      <th>Emerging</th>\n",
       "      <th>EU Bond</th>\n",
       "      <th>Corporate Bond</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Performance</th>\n",
       "      <th>Max loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>76.2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>74.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.89</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>73.1</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.94</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>71.6</td>\n",
       "      <td>20.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.99</td>\n",
       "      <td>8.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>70.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.04</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>68.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.09</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>66.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.14</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>65.3</td>\n",
       "      <td>18.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.18</td>\n",
       "      <td>10.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>63.8</td>\n",
       "      <td>18.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>62.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>10.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>60.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.32</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>59.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.36</td>\n",
       "      <td>11.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>57.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.41</td>\n",
       "      <td>11.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>28.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.45</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>15.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.49</td>\n",
       "      <td>12.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>15.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.53</td>\n",
       "      <td>13.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>14.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.57</td>\n",
       "      <td>13.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>49.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.61</td>\n",
       "      <td>13.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>38.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2.65</td>\n",
       "      <td>14.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>46.7</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>14.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>42.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>45.1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2.72</td>\n",
       "      <td>14.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>43.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.76</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>46.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.79</td>\n",
       "      <td>15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>8.5</td>\n",
       "      <td>40.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>2.83</td>\n",
       "      <td>16.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>16.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>37.3</td>\n",
       "      <td>10.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>2.89</td>\n",
       "      <td>16.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>35.8</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2.92</td>\n",
       "      <td>17.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>9.8</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>58.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>36.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>32.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.98</td>\n",
       "      <td>18.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>10.7</td>\n",
       "      <td>31.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>18.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>62.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.04</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>11.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>3.07</td>\n",
       "      <td>19.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>66.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>11.7</td>\n",
       "      <td>26.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.09</td>\n",
       "      <td>19.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>12.1</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.12</td>\n",
       "      <td>19.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>70.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.14</td>\n",
       "      <td>20.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>12.8</td>\n",
       "      <td>21.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>3.17</td>\n",
       "      <td>20.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>74.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>20.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>14.8</td>\n",
       "      <td>3.19</td>\n",
       "      <td>21.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>47.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>3.21</td>\n",
       "      <td>21.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>78.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>13.9</td>\n",
       "      <td>17.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3.23</td>\n",
       "      <td>21.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>22.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>82.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>3.27</td>\n",
       "      <td>22.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>84.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.8</td>\n",
       "      <td>3.29</td>\n",
       "      <td>22.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>86.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.30</td>\n",
       "      <td>23.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>88.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>54.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.32</td>\n",
       "      <td>23.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.34</td>\n",
       "      <td>24.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>92.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>3.35</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>3.36</td>\n",
       "      <td>24.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>96.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>3.37</td>\n",
       "      <td>25.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>98.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>3.39</td>\n",
       "      <td>25.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Risiko    Age  MSCI World  Emerging  EU Bond  Corporate Bond  Gold  \\\n",
       "0      0.0  100.0         0.0       0.0     77.8            22.2   0.0   \n",
       "1      2.0   98.0         1.2       0.4     76.2            21.8   0.4   \n",
       "2      4.0   96.0         2.5       0.7     74.7            21.3   0.8   \n",
       "3      6.0   94.0         3.7       1.1     73.1            20.9   1.2   \n",
       "4      8.0   92.0         5.0       1.4     71.6            20.4   1.6   \n",
       "5     10.0   90.0         6.2       1.8     70.0            20.0   2.0   \n",
       "6     12.0   88.0         7.5       2.1     68.4            19.6   2.4   \n",
       "7     14.0   86.0         8.7       2.5     66.9            19.1   2.8   \n",
       "8     16.0   84.0        10.0       2.8     65.3            18.7   3.2   \n",
       "9     18.0   82.0        11.2       3.2     63.8            18.2   3.6   \n",
       "10    20.0   80.0        12.4       3.6     62.2            17.8   4.0   \n",
       "11    22.0   78.0        13.7       3.9     60.7            17.3   4.4   \n",
       "12    24.0   76.0        14.9       4.3     59.1            16.9   4.8   \n",
       "13    26.0   74.0        16.2       4.6     57.6            16.4   5.2   \n",
       "14    28.0   72.0        17.4       5.0     56.0            16.0   5.6   \n",
       "15    30.0   70.0        18.7       5.3     54.4            15.6   6.0   \n",
       "16    32.0   68.0        19.9       5.7     52.9            15.1   6.4   \n",
       "17    34.0   66.0        21.2       6.0     51.3            14.7   6.8   \n",
       "18    36.0   64.0        22.4       6.4     49.8            14.2   7.2   \n",
       "19    38.0   62.0        23.6       6.8     48.2            13.8   7.6   \n",
       "20    40.0   60.0        24.9       7.1     46.7            13.3   8.0   \n",
       "21    42.0   58.0        26.1       7.5     45.1            12.9   8.4   \n",
       "22    44.0   56.0        27.4       7.8     43.6            12.4   8.8   \n",
       "23    46.0   54.0        28.6       8.2     42.0            12.0   9.2   \n",
       "24    48.0   52.0        29.9       8.5     40.4            11.6   9.6   \n",
       "25    50.0   50.0        31.1       8.9     38.9            11.1  10.0   \n",
       "26    52.0   48.0        32.4       9.2     37.3            10.7  10.4   \n",
       "27    54.0   46.0        33.6       9.6     35.8            10.2  10.8   \n",
       "28    56.0   44.0        34.8      10.0     34.2             9.8  11.2   \n",
       "29    58.0   42.0        36.1      10.3     32.7             9.3  11.6   \n",
       "30    60.0   40.0        37.3      10.7     31.1             8.9  12.0   \n",
       "31    62.0   38.0        38.6      11.0     29.6             8.4  12.4   \n",
       "32    64.0   36.0        39.8      11.4     28.0             8.0  12.8   \n",
       "33    66.0   34.0        41.1      11.7     26.4             7.6  13.2   \n",
       "34    68.0   32.0        42.3      12.1     24.9             7.1  13.6   \n",
       "35    70.0   30.0        43.6      12.4     23.3             6.7  14.0   \n",
       "36    72.0   28.0        44.8      12.8     21.8             6.2  14.4   \n",
       "37    74.0   26.0        46.0      13.2     20.2             5.8  14.8   \n",
       "38    76.0   24.0        47.3      13.5     18.7             5.3  15.2   \n",
       "39    78.0   22.0        48.5      13.9     17.1             4.9  15.6   \n",
       "40    80.0   20.0        49.8      14.2     15.6             4.4  16.0   \n",
       "41    82.0   18.0        51.0      14.6     14.0             4.0  16.4   \n",
       "42    84.0   16.0        52.3      14.9     12.4             3.6  16.8   \n",
       "43    86.0   14.0        53.5      15.3     10.9             3.1  17.2   \n",
       "44    88.0   12.0        54.8      15.6      9.3             2.7  17.6   \n",
       "45    90.0   10.0        56.0      16.0      7.8             2.2  18.0   \n",
       "46    92.0    8.0        57.2      16.4      6.2             1.8  18.4   \n",
       "47    94.0    6.0        58.5      16.7      4.7             1.3  18.8   \n",
       "48    96.0    4.0        59.7      17.1      3.1             0.9  19.2   \n",
       "49    98.0    2.0        61.0      17.4      1.6             0.4  19.6   \n",
       "50   100.0    0.0        62.2      17.8      0.0             0.0  20.0   \n",
       "\n",
       "    Performance  Max loss  \n",
       "0          1.78      8.05  \n",
       "1          1.83      8.19  \n",
       "2          1.89      8.32  \n",
       "3          1.94      8.46  \n",
       "4          1.99      8.66  \n",
       "5          2.04      9.03  \n",
       "6          2.09      9.40  \n",
       "7          2.14      9.75  \n",
       "8          2.18     10.12  \n",
       "9          2.23     10.48  \n",
       "10         2.28     10.85  \n",
       "11         2.32     11.20  \n",
       "12         2.36     11.57  \n",
       "13         2.41     11.93  \n",
       "14         2.45     12.30  \n",
       "15         2.49     12.69  \n",
       "16         2.53     13.06  \n",
       "17         2.57     13.45  \n",
       "18         2.61     13.82  \n",
       "19         2.65     14.21  \n",
       "20         2.69     14.59  \n",
       "21         2.72     14.97  \n",
       "22         2.76     15.35  \n",
       "23         2.79     15.73  \n",
       "24         2.83     16.12  \n",
       "25         2.86     16.49  \n",
       "26         2.89     16.88  \n",
       "27         2.92     17.25  \n",
       "28         2.95     17.64  \n",
       "29         2.98     18.01  \n",
       "30         3.01     18.40  \n",
       "31         3.04     18.77  \n",
       "32         3.07     19.16  \n",
       "33         3.09     19.54  \n",
       "34         3.12     19.92  \n",
       "35         3.14     20.30  \n",
       "36         3.17     20.68  \n",
       "37         3.19     21.06  \n",
       "38         3.21     21.44  \n",
       "39         3.23     21.82  \n",
       "40         3.25     22.20  \n",
       "41         3.27     22.58  \n",
       "42         3.29     22.96  \n",
       "43         3.30     23.34  \n",
       "44         3.32     23.72  \n",
       "45         3.34     24.10  \n",
       "46         3.35     24.48  \n",
       "47         3.36     24.85  \n",
       "48         3.37     25.24  \n",
       "49         3.39     25.61  \n",
       "50         3.40     25.99  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_composizione=pd.DataFrame(np.transpose(sim1))\n",
    "df_composizione.columns=['Risiko','Age','MSCI World','Emerging','EU Bond','Corporate Bond','Gold','Performance','Max loss']\n",
    "\n",
    "df_composizione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(ac_grid,sim1[8],color='red')\n",
    "plt.xlabel('Quota azionaria %')\n",
    "plt.ylabel('Max perdita %')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analisi rischio rendimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QUESTA FUNZIONE RESTISTUISCE IL RENDIMENTO E LA MASSIMA PERDITA\n",
    "\n",
    "def rischio_rendimento(array,start='2012',end='2021',nomietf=['Msci','Emergenti','EUBond','CorporateBond','Gold']):\n",
    "    risultato=rendimento_bis(array,capitale0=100000,inizio=start,fine=end,nomietf=nomietf)\n",
    "    return (risultato[1],risultato[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.9, 12.15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rischio_rendimento([33,11,33,11,12],start='2012',end='2020-02-22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# genero le combinazioni\n",
    "\n",
    "valori=[]\n",
    "n_val=10    # aumentare per avere maggiori combinazioni tra gli ETF (richiede maggior tempo e l'aumento è esponenziale)\n",
    "\n",
    "for i in range(0,n_val+1):\n",
    "    for j in range(0,n_val+1-i):\n",
    "        for m in range(0,n_val+1-i-j):\n",
    "            for n in range(0,n_val+1-i-j-m):\n",
    "                k=n_val-i-j-m-n\n",
    "                valori.append([i,j,m,n,k])\n",
    "                \n",
    "                               \n",
    "valori=np.array(valori)*(100/n_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.   0.   0.   0. 100.]\n",
      " [  0.   0.   0.  10.  90.]\n",
      " [  0.   0.   0.  20.  80.]\n",
      " ...\n",
      " [ 90.   0.  10.   0.   0.]\n",
      " [ 90.  10.   0.   0.   0.]\n",
      " [100.   0.   0.   0.   0.]]\n",
      "Controllo: deve essere 100 = 100.0\n",
      "1001\n"
     ]
    }
   ],
   "source": [
    "print(valori)\n",
    "\n",
    "print('Controllo: deve essere 100 =',np.mean(np.sum(valori,axis=1)))\n",
    "\n",
    "print(len(valori))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 %\n",
      "19 %\n",
      "29 %\n",
      "39 %\n",
      "49 %\n",
      "59 %\n",
      "69 %\n",
      "79 %\n",
      "89 %\n",
      "99 %\n"
     ]
    }
   ],
   "source": [
    "# analisi rischio rendimento con le combinazioni create in precedenza\n",
    "\n",
    "rr_val=[]\n",
    "costi=0.4    # costi degli ETF\n",
    "counter=0\n",
    "for i in range(len(valori)):\n",
    "    rr_parz=rischio_rendimento(valori[i,:],start='2012',end='2020-05')\n",
    "    r_netto=(rr_parz[1]-0.2)*0.75\n",
    "    rr_val.append([i,rr_parz[0]-costi,rr_parz[1],np.abs(rr_parz[1]/(rr_parz[0]-costi)),valori[i,0],valori[i,1],valori[i,2],valori[i,3],valori[i,4]])\n",
    "    counter=counter+1\n",
    "    if counter==100:\n",
    "        stage=int(i/len(valori)*100)\n",
    "        counter=0\n",
    "        print(stage,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rr=pd.DataFrame(rr_val)\n",
    "df_rr.to_csv('rischio-rendimento.csv',index=False)\n",
    "#df_rr.to_csv('rischio-rendimento-precrisi.csv',index=False)\n",
    "#df_rr=pd.read_csv('rischio-rendimento.csv')\n",
    "df_rr.columns=['ID','rendimento','max perdita','anni per recuperare','Msci','Emergenti','EUBond','CorporateBond','Gold']\n",
    "rr_val=np.array(df_rr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### presentazione dei risultati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "arg_massimo=np.argmin(rr_val[:,3])   #portafoglio col minor tempo di recupero dopo la perdita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                      65.000000\n",
       "rendimento               4.150000\n",
       "max perdita              6.140000\n",
       "anni per recuperare      1.479518\n",
       "Msci                     0.000000\n",
       "Emergenti                0.000000\n",
       "EUBond                 100.000000\n",
       "CorporateBond            0.000000\n",
       "Gold                     0.000000\n",
       "Name: 65, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rr.iloc[arg_massimo,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5272852155516556\n",
      "4.248970767593341\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_rr=df_rr['max perdita']\n",
    "y_rr=df_rr['rendimento']\n",
    "\n",
    "costi_mfarm=0.65\n",
    "\n",
    "#p2_rend=((121/115.6)**(1/5)-1)*100\n",
    "#p4_rend=((136.1/124.3)**(1/5)-1)*100\n",
    "\n",
    "p2_rend=((122.1/100)**(1/8)-1)*100\n",
    "p4_rend=((139.5/100)**(1/8)-1)*100\n",
    "p6_rend=((153.6/100)**(1/8)-1)*100\n",
    "\n",
    "\n",
    "print(p2_rend)\n",
    "print(p4_rend)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x_rr,y_rr,'.')\n",
    "plt.xlabel('Max loss %')\n",
    "plt.ylabel('Annual interest %')\n",
    "plt.plot(10.6,p2_rend,marker='*',color='orange',markersize=11,label='P2')\n",
    "plt.plot(16.9,p4_rend,marker='*',color='red',markersize=11,label='P4')\n",
    "plt.plot(19.6,p6_rend,marker='*',color='pink',markersize=11,label='P6')\n",
    "#plt.plot(15.33,5.09,marker='*',color='cyan',markersize=11,label='My Portfolio')\n",
    "plt.title('Without Moneyfarm fee (from 2012)')\n",
    "plt.legend()\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x_rr,y_rr,'.')\n",
    "plt.xlabel('Max loss %')\n",
    "plt.ylabel('Annual interest %')\n",
    "plt.plot(10.6,p2_rend-costi_mfarm,marker='*',color='orange',markersize=11,label='P2')\n",
    "plt.plot(16.9,p4_rend-costi_mfarm,marker='*',color='red',markersize=11,label='P4')\n",
    "plt.plot(19.6,p6_rend-costi_mfarm,marker='*',color='pink',markersize=11,label='P6')\n",
    "#plt.plot(15.33,5.09-0.07,marker='*',color='cyan',markersize=11,label='My Portfolio')\n",
    "plt.title('With Moneyfarm fee (from 2012)')\n",
    "plt.legend()\n",
    "plt.savefig('output_portfolio/risk-interest.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(x_rr,y_rr,'.')\n",
    "plt.xlabel('Massima perdita %')\n",
    "plt.ylabel('Rendimento annualizzato %')\n",
    "plt.grid()\n",
    "plt.savefig('output_portfolio/risk-interest-general.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imposta una massima perdita tollerabile\n",
    "max_perdita=(4861/26500)*100   \n",
    "\n",
    "#df_sub=df_rr[df_rr['max perdita']<=max_perdita]    #togli qui il commento per impostare una perdita massima\n",
    "df_sub=df_rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andreapalladino/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "The current behaviour of 'Series.argmax' is deprecated, use 'idxmax'\n",
      "instead.\n",
      "The behavior of 'argmax' will be corrected to return the positional\n",
      "maximum in the future. For now, use 'series.values.argmax' or\n",
      "'np.argmax(np.array(values))' to get the position of the maximum\n",
      "row.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                     1000.000000\n",
       "rendimento                6.270000\n",
       "max perdita              30.770000\n",
       "anni per recuperare       4.907496\n",
       "Msci                    100.000000\n",
       "Emergenti                 0.000000\n",
       "EUBond                    0.000000\n",
       "CorporateBond             0.000000\n",
       "Gold                      0.000000\n",
       "Name: 1000, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_max=df_sub['rendimento'].argmax()\n",
    "df_sub.loc[pos_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub_sorted=df_sub.sort_values(by='rendimento',ascending=False)\n",
    "#df_sub_sorted=df_sub.sort_values(by='max perdita',ascending=True)\n",
    "#df_sub_sorted=df_sub.sort_values(by='anni per recuperare',ascending=True)\n",
    "\n",
    "\n",
    "# THIS SHOWS ONLY PORTOFLIO WITH ALL 5 ETFS\n",
    "\n",
    "#mask_subdf=(df_sub_sorted[['Msci','Emergenti','EUBond','CorporateBond','Gold']]!=0).sum(axis=1)==5\n",
    "#mask_subdf=(df_sub_sorted[['Gold']]).sum(axis=1)==5\n",
    "#df_sub_sorted=df_sub_sorted.loc[mask_subdf,:]\n",
    "#df_sub_sorted.iloc[0,[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inserisco la massima perdita del 2020\n",
    "\n",
    "#portafogli_scelti=np.array(df_sub_sorted[['Msci','EUBond','Gold']])\n",
    "#perdita2020=[]\n",
    "\n",
    "#for i in portafogli_scelti:\n",
    "#    perdita2020.append(rischio_rendimento(i,start='2020',end='2020-05')[1])\n",
    "    \n",
    "#df_sub_sorted['max perdita 2020']=perdita2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>ID</th>\n",
       "      <th>rendimento</th>\n",
       "      <th>max perdita</th>\n",
       "      <th>anni per recuperare</th>\n",
       "      <th>Msci</th>\n",
       "      <th>Emergenti</th>\n",
       "      <th>EUBond</th>\n",
       "      <th>CorporateBond</th>\n",
       "      <th>Gold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>1000</td>\n",
       "      <td>6.27</td>\n",
       "      <td>30.77</td>\n",
       "      <td>4.907496</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>6.18</td>\n",
       "      <td>28.17</td>\n",
       "      <td>4.558252</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>991</td>\n",
       "      <td>6.05</td>\n",
       "      <td>25.58</td>\n",
       "      <td>4.228099</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>975</td>\n",
       "      <td>5.90</td>\n",
       "      <td>22.98</td>\n",
       "      <td>3.894915</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>5.80</td>\n",
       "      <td>29.15</td>\n",
       "      <td>5.025862</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>5.74</td>\n",
       "      <td>30.70</td>\n",
       "      <td>5.348432</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>945</td>\n",
       "      <td>5.73</td>\n",
       "      <td>20.39</td>\n",
       "      <td>3.558464</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>990</td>\n",
       "      <td>5.68</td>\n",
       "      <td>26.55</td>\n",
       "      <td>4.674296</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>994</td>\n",
       "      <td>5.64</td>\n",
       "      <td>28.09</td>\n",
       "      <td>4.980496</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>5.60</td>\n",
       "      <td>28.43</td>\n",
       "      <td>5.076786</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>895</td>\n",
       "      <td>5.53</td>\n",
       "      <td>17.79</td>\n",
       "      <td>3.216998</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>974</td>\n",
       "      <td>5.53</td>\n",
       "      <td>23.95</td>\n",
       "      <td>4.330922</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>981</td>\n",
       "      <td>5.51</td>\n",
       "      <td>25.49</td>\n",
       "      <td>4.626134</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>989</td>\n",
       "      <td>5.48</td>\n",
       "      <td>25.83</td>\n",
       "      <td>4.713504</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>944</td>\n",
       "      <td>5.36</td>\n",
       "      <td>21.35</td>\n",
       "      <td>3.983209</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>955</td>\n",
       "      <td>5.36</td>\n",
       "      <td>22.89</td>\n",
       "      <td>4.270522</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>973</td>\n",
       "      <td>5.33</td>\n",
       "      <td>23.24</td>\n",
       "      <td>4.360225</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>988</td>\n",
       "      <td>5.30</td>\n",
       "      <td>27.52</td>\n",
       "      <td>5.192453</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>818</td>\n",
       "      <td>5.30</td>\n",
       "      <td>15.20</td>\n",
       "      <td>2.867925</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>993</td>\n",
       "      <td>5.26</td>\n",
       "      <td>29.07</td>\n",
       "      <td>5.526616</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  rendimento  max perdita  anni per recuperare   Msci  Emergenti  \\\n",
       "1000  1000        6.27        30.77             4.907496  100.0        0.0   \n",
       "998    998        6.18        28.17             4.558252   90.0        0.0   \n",
       "991    991        6.05        25.58             4.228099   80.0        0.0   \n",
       "975    975        5.90        22.98             3.894915   70.0        0.0   \n",
       "997    997        5.80        29.15             5.025862   90.0        0.0   \n",
       "999    999        5.74        30.70             5.348432   90.0       10.0   \n",
       "945    945        5.73        20.39             3.558464   60.0        0.0   \n",
       "990    990        5.68        26.55             4.674296   80.0        0.0   \n",
       "994    994        5.64        28.09             4.980496   80.0       10.0   \n",
       "996    996        5.60        28.43             5.076786   90.0        0.0   \n",
       "895    895        5.53        17.79             3.216998   50.0        0.0   \n",
       "974    974        5.53        23.95             4.330922   70.0        0.0   \n",
       "981    981        5.51        25.49             4.626134   70.0       10.0   \n",
       "989    989        5.48        25.83             4.713504   80.0        0.0   \n",
       "944    944        5.36        21.35             3.983209   60.0        0.0   \n",
       "955    955        5.36        22.89             4.270522   60.0       10.0   \n",
       "973    973        5.33        23.24             4.360225   70.0        0.0   \n",
       "988    988        5.30        27.52             5.192453   80.0        0.0   \n",
       "818    818        5.30        15.20             2.867925   40.0        0.0   \n",
       "993    993        5.26        29.07             5.526616   80.0       10.0   \n",
       "\n",
       "      EUBond  CorporateBond  Gold  \n",
       "1000     0.0            0.0   0.0  \n",
       "998     10.0            0.0   0.0  \n",
       "991     20.0            0.0   0.0  \n",
       "975     30.0            0.0   0.0  \n",
       "997      0.0           10.0   0.0  \n",
       "999      0.0            0.0   0.0  \n",
       "945     40.0            0.0   0.0  \n",
       "990     10.0           10.0   0.0  \n",
       "994     10.0            0.0   0.0  \n",
       "996      0.0            0.0  10.0  \n",
       "895     50.0            0.0   0.0  \n",
       "974     20.0           10.0   0.0  \n",
       "981     20.0            0.0   0.0  \n",
       "989     10.0            0.0  10.0  \n",
       "944     30.0           10.0   0.0  \n",
       "955     30.0            0.0   0.0  \n",
       "973     20.0            0.0  10.0  \n",
       "988      0.0           20.0   0.0  \n",
       "818     60.0            0.0   0.0  \n",
       "993      0.0           10.0   0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostra i portafogli con la massima perdita tollerabile, ordinate per rendimento\n",
    "\n",
    "df_sub_sorted.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### massimo rendimento e minima perdita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxrend_minperd(perdmin,perdmax):\n",
    "    df_rendpend=df_sub_sorted.loc[(df_sub_sorted['max perdita']>=perdmin) &(df_sub_sorted['max perdita']<perdmax)]\n",
    "    rendimento=np.round(df_rendpend.sort_values(by='rendimento',ascending=False).iloc[0,1],2)\n",
    "    portafoglio=np.array(df_rendpend.sort_values(by='rendimento',ascending=False).iloc[0,4:])\n",
    "    return (rendimento,portafoglio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n\\nlista_rend_perd=[]\\n\\n#portafogli_range=np.linspace(6,30,8)\\nportafogli_range=np.linspace(5.46,30.78,8)\\n\\nfor i in range(len(portafogli_range)-1):\\n    #print(i,maxrend_minperd(i,i+1))\\n    peppino=maxrend_minperd(portafogli_range[i],portafogli_range[i+1])\\n    print(np.round(portafogli_range[i],2),'--',np.round(portafogli_range[i+1],2),peppino,rischio_rendimento(peppino[1])[1])\\n    lista_rend_perd.append([i+1,peppino[0],rischio_rendimento(peppino[1])[1]])\\n\\nlista_rend_perd=np.array(lista_rend_perd)\\nlista_rend_perd_gold5=np.array([[ 1        ,  4.41      ],[ 2,  4.83      ],[3,  5.09      ],[4,  5.42      ],\\n[5,  5.7       ],[6,  5.86      ],[7,  6.04      ]])\\n\\nplt.subplot(2,1,1)\\nplt.bar(lista_rend_perd[:,0],lista_rend_perd[:,1],color='green')\\nplt.xlabel('N. Portafoglio')\\nplt.ylabel('Rendimento annualizzato %')\\nplt.ylim(2,9)\\nplt.grid()\\nplt.subplot(2,1,2)\\nplt.bar(lista_rend_perd[:,0],-lista_rend_perd[:,2],color='red')\\n#plt.bar(lista_rend_perd_gold5[:,0],lista_rend_perd_gold5[:,1],label='With gold 5%')\\nplt.xlabel('N. Portafoglio')\\nplt.ylabel('Massima perdita %')\\nplt.grid()\\nplt.ylim(-5,-35)\\n#plt.ylim(3,7)\\n#plt.legend()\\nplt.tight_layout()\\nplt.savefig('output_portfolio/rend-perdita.png',dpi=300)\\nplt.show()\\n\\n\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "\n",
    "lista_rend_perd=[]\n",
    "\n",
    "#portafogli_range=np.linspace(6,30,8)\n",
    "portafogli_range=np.linspace(5.46,30.78,8)\n",
    "\n",
    "for i in range(len(portafogli_range)-1):\n",
    "    #print(i,maxrend_minperd(i,i+1))\n",
    "    peppino=maxrend_minperd(portafogli_range[i],portafogli_range[i+1])\n",
    "    print(np.round(portafogli_range[i],2),'--',np.round(portafogli_range[i+1],2),peppino,rischio_rendimento(peppino[1])[1])\n",
    "    lista_rend_perd.append([i+1,peppino[0],rischio_rendimento(peppino[1])[1]])\n",
    "\n",
    "lista_rend_perd=np.array(lista_rend_perd)\n",
    "lista_rend_perd_gold5=np.array([[ 1        ,  4.41      ],[ 2,  4.83      ],[3,  5.09      ],[4,  5.42      ],\n",
    "[5,  5.7       ],[6,  5.86      ],[7,  6.04      ]])\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.bar(lista_rend_perd[:,0],lista_rend_perd[:,1],color='green')\n",
    "plt.xlabel('N. Portafoglio')\n",
    "plt.ylabel('Rendimento annualizzato %')\n",
    "plt.ylim(2,9)\n",
    "plt.grid()\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(lista_rend_perd[:,0],-lista_rend_perd[:,2],color='red')\n",
    "#plt.bar(lista_rend_perd_gold5[:,0],lista_rend_perd_gold5[:,1],label='With gold 5%')\n",
    "plt.xlabel('N. Portafoglio')\n",
    "plt.ylabel('Massima perdita %')\n",
    "plt.grid()\n",
    "plt.ylim(-5,-35)\n",
    "#plt.ylim(3,7)\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('output_portfolio/rend-perdita.png',dpi=300)\n",
    "plt.show()\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range(15):\n",
    "#    df=import_data(['IWDE.MI','IEEM.MI','XGLE.MI','IBCX.MI','XAD1.MI'])   \n",
    "#    df.columns=['Msci','Emergenti','EUBond','CorporateLargeCup','Gold']\n",
    "#    print(df.iloc[-1,:])\n",
    "#    time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rendimenti pre crisi - perdita durante crisi  (con 20% di quota a rischio (azionaria) in oro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rend_rischio_crisi(x):\n",
    "    costi=0.4\n",
    "    rendimento=round(rischio_rendimento([x*0.8,100-x,x*0.2],start='2012',end='2020-02-22',nomietf=['Msci','EUBond','Gold'])[0]-costi,2)\n",
    "    rischio=rischio_rendimento([x*0.8,100-x,x*0.2],start='2020',end='2020-05',nomietf=['Msci','EUBond','Gold'])[1]\n",
    "    return rendimento,rischio\n",
    "\n",
    "rend_rischio_crisi_vec=np.vectorize(rend_rischio_crisi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgrid=np.arange(0,21,1)\n",
    "ygridpos=rend_rischio_crisi_vec(xgrid*5)[0]\n",
    "ygridneg=rend_rischio_crisi_vec(xgrid*5)[1]\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(2,1,1)\n",
    "plt.bar(xgrid,ygridpos,color='green')\n",
    "plt.xlabel('Quota a rischio %')\n",
    "plt.ylabel('Rend. annualizzato %')\n",
    "plt.xticks(np.arange(0,21,1),np.arange(0,105,5))\n",
    "plt.ylim(3,9)\n",
    "plt.title('Rendimento dal 2012 (crisi esclusa)')\n",
    "plt.grid()\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(xgrid,-ygridneg,color='red')\n",
    "#plt.bar(lista_rend_perd_gold5[:,0],lista_rend_perd_gold5[:,1],label='With gold 5%')\n",
    "plt.xlabel('Quota a rischio %')\n",
    "plt.ylabel('Massima perdita %')\n",
    "plt.title('Perdita durante crisi 2020')\n",
    "plt.grid()\n",
    "plt.xticks(np.arange(0,21,1),np.arange(0,105,5))\n",
    "plt.ylim(-5,-30)\n",
    "#plt.ylim(3,7)\n",
    "#plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('output_portfolio/rend-perdita2.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Quota a rischio %</th>\n",
       "      <th>Quota azionaria %</th>\n",
       "      <th>Oro %</th>\n",
       "      <th>Quota obbligazionaria %</th>\n",
       "      <th>Rendimento dal 2015-03 (crisi esclusa) %</th>\n",
       "      <th>Perdita nel 2020 %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>4.58</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95</td>\n",
       "      <td>4.77</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90</td>\n",
       "      <td>4.96</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85</td>\n",
       "      <td>5.16</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>5.34</td>\n",
       "      <td>9.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>75</td>\n",
       "      <td>5.53</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>70</td>\n",
       "      <td>5.71</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>65</td>\n",
       "      <td>5.90</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>60</td>\n",
       "      <td>6.07</td>\n",
       "      <td>13.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>55</td>\n",
       "      <td>6.25</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50</td>\n",
       "      <td>6.42</td>\n",
       "      <td>15.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>45</td>\n",
       "      <td>6.60</td>\n",
       "      <td>16.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "      <td>48.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40</td>\n",
       "      <td>6.76</td>\n",
       "      <td>17.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "      <td>52.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35</td>\n",
       "      <td>6.93</td>\n",
       "      <td>18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70</td>\n",
       "      <td>56.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30</td>\n",
       "      <td>7.09</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>75</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25</td>\n",
       "      <td>7.26</td>\n",
       "      <td>20.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80</td>\n",
       "      <td>64.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20</td>\n",
       "      <td>7.42</td>\n",
       "      <td>21.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "      <td>68.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15</td>\n",
       "      <td>7.57</td>\n",
       "      <td>22.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90</td>\n",
       "      <td>72.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7.73</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>95</td>\n",
       "      <td>76.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7.88</td>\n",
       "      <td>25.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.03</td>\n",
       "      <td>26.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Quota a rischio %  Quota azionaria %  Oro %  Quota obbligazionaria %  \\\n",
       "0                   0                0.0    0.0                      100   \n",
       "1                   5                4.0    1.0                       95   \n",
       "2                  10                8.0    2.0                       90   \n",
       "3                  15               12.0    3.0                       85   \n",
       "4                  20               16.0    4.0                       80   \n",
       "5                  25               20.0    5.0                       75   \n",
       "6                  30               24.0    6.0                       70   \n",
       "7                  35               28.0    7.0                       65   \n",
       "8                  40               32.0    8.0                       60   \n",
       "9                  45               36.0    9.0                       55   \n",
       "10                 50               40.0   10.0                       50   \n",
       "11                 55               44.0   11.0                       45   \n",
       "12                 60               48.0   12.0                       40   \n",
       "13                 65               52.0   13.0                       35   \n",
       "14                 70               56.0   14.0                       30   \n",
       "15                 75               60.0   15.0                       25   \n",
       "16                 80               64.0   16.0                       20   \n",
       "17                 85               68.0   17.0                       15   \n",
       "18                 90               72.0   18.0                       10   \n",
       "19                 95               76.0   19.0                        5   \n",
       "20                100               80.0   20.0                        0   \n",
       "\n",
       "    Rendimento dal 2015-03 (crisi esclusa) %  Perdita nel 2020 %  \n",
       "0                                       4.58                6.12  \n",
       "1                                       4.77                6.49  \n",
       "2                                       4.96                7.25  \n",
       "3                                       5.16                8.28  \n",
       "4                                       5.34                9.31  \n",
       "5                                       5.53               10.34  \n",
       "6                                       5.71               11.37  \n",
       "7                                       5.90               12.39  \n",
       "8                                       6.07               13.43  \n",
       "9                                       6.25               14.50  \n",
       "10                                      6.42               15.56  \n",
       "11                                      6.60               16.62  \n",
       "12                                      6.76               17.68  \n",
       "13                                      6.93               18.74  \n",
       "14                                      7.09               19.80  \n",
       "15                                      7.26               20.86  \n",
       "16                                      7.42               21.91  \n",
       "17                                      7.57               22.96  \n",
       "18                                      7.73               24.01  \n",
       "19                                      7.88               25.06  \n",
       "20                                      8.03               26.11  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rend=pd.DataFrame(xgrid*5,columns=['Quota a rischio %'])\n",
    "\n",
    "df_rend['Quota azionaria %']=(xgrid*5)*0.8\n",
    "df_rend['Oro %']=xgrid*5*0.2\n",
    "df_rend['Quota obbligazionaria %']=100-xgrid*5\n",
    "\n",
    "df_rend['Rendimento dal 2015-03 (crisi esclusa) %']=ygridpos\n",
    "df_rend['Perdita nel 2020 %']=ygridneg\n",
    "\n",
    "df_rend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}