31aReuniao.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução à análise de risco"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valor Esperado da Utilidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: 0.0\n",
      "b: 1.0\n",
      "valor_esperado_funcao_utilidade: 0.7071067811865476\n",
      "\n",
      "a: 0.0\n",
      "b: 2.0\n",
      "valor_esperado_funcao_utilidade: 1.0\n",
      "\n",
      "a: 0.0\n",
      "b: 3.0\n",
      "valor_esperado_funcao_utilidade: 1.224744871391589\n",
      "\n",
      "a: 0.0\n",
      "b: 4.0\n",
      "valor_esperado_funcao_utilidade: 1.4142135623730951\n",
      "\n",
      "a: 0.0\n",
      "b: 5.0\n",
      "valor_esperado_funcao_utilidade: 1.5811388300841898\n",
      "\n",
      "a: 0.0\n",
      "b: 6.0\n",
      "valor_esperado_funcao_utilidade: 1.7320508075688772\n",
      "\n",
      "a: 0.0\n",
      "b: 7.0\n",
      "valor_esperado_funcao_utilidade: 1.8708286933869707\n",
      "\n",
      "a: 0.0\n",
      "b: 8.0\n",
      "valor_esperado_funcao_utilidade: 2.0\n",
      "\n",
      "a: 0.0\n",
      "b: 9.0\n",
      "valor_esperado_funcao_utilidade: 2.1213203435596424\n",
      "\n",
      "a: 0.0\n",
      "b: 10.0\n",
      "valor_esperado_funcao_utilidade: 2.23606797749979\n",
      "\n",
      "a: 5.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 2.5\n",
      "\n",
      "a: 6.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 2.7386127875258306\n",
      "\n",
      "a: 7.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 2.958039891549808\n",
      "\n",
      "a: 8.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 3.1622776601683795\n",
      "\n",
      "a: 9.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 3.3541019662496847\n",
      "\n",
      "a: 10.0\n",
      "b: 0.0\n",
      "valor_esperado_funcao_utilidade: 3.5355339059327378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# https://colab.research.google.com/drive/1FUei2qXoBaxWI4kr7FqRzgUDETVqDzvS?usp=sharing\n",
    "import numpy as np\n",
    "\n",
    "# quantidade de ações\n",
    "vet_a = np.linspace(0,10,num = 11)\n",
    "vet_b = np.linspace(0,10,num = 11)\n",
    "\n",
    "# custos das ações\n",
    "custo_a = 10\n",
    "custo_b = 10\n",
    "\n",
    "# resolvendo por força bruta\n",
    "flag  = 0\n",
    "for a in vet_a: # para cada valor de vet_a faça...\n",
    "    for b in vet_b: #para cada valor de vet_b faça...\n",
    "\n",
    "        # só executa o cálculo de a restrição de custo for atendida\n",
    "        if (a * custo_a) + (b * custo_b) <= 100:\n",
    "            # cálculo cenário 1\n",
    "            rendimento_cenario_1 = (a * 15) + (b * 5) - (a * custo_a) - (b * custo_b)\n",
    "            if rendimento_cenario_1 >= 0: # se o rendimento do cenário for menor que 1, não calcula\n",
    "                funcao_utilidade_cenario_1 = np.sqrt(rendimento_cenario_1)\n",
    "            else:\n",
    "                funcao_utilidade_cenario_1 = 0\n",
    "\n",
    "            # cálculo cenário 2\n",
    "            rendimento_cenario_2 = (a * 5) + (b * 12) - (a * custo_a) - (b * custo_b)\n",
    "            if rendimento_cenario_2 >= 0: # se o rendimento do cenário for menor que 1, não calcula\n",
    "                funcao_utilidade_cenario_2 = np.sqrt(rendimento_cenario_2)\n",
    "            else:\n",
    "                funcao_utilidade_cenario_2 = 0\n",
    "\n",
    "            # cálculo do valor esperado\n",
    "            valor_esperado_funcao_utilidade = (0.5 * funcao_utilidade_cenario_1) + \\\n",
    "                (0.5 * funcao_utilidade_cenario_2)\n",
    "\n",
    "            # só imprime o valor esperado se ele for maior que o valor esperado anterior\n",
    "            if valor_esperado_funcao_utilidade > flag:\n",
    "                print(\"a:\", a)\n",
    "                print(\"b:\", b)\n",
    "                print(\"valor_esperado_funcao_utilidade:\", valor_esperado_funcao_utilidade)\n",
    "                print()\n",
    "                flag = valor_esperado_funcao_utilidade\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otimização de Portfólio de Markowitz - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Return_Micro = -0.0030\n",
      "Return_Dell = 0.0097\n",
      "Return_GE = 0.0035\n",
      "\n",
      "Standard_Deviation_Micro = 0.0406\n",
      "Standard_Deviation_Dell = 0.0839\n",
      "Standard_Deviation_GE = 0.0714\n"
     ]
    }
   ],
   "source": [
    "# Three securities were selected: Microsoft, Dell, and General Electric (G.E.).\n",
    "# The monthly returns from investing in each security are shown\n",
    "# \n",
    "# Plot the “expected return” versus “standard deviation” (aka risk/return graph) \n",
    "# from each of three securities (Microsoft, Dell, and General Electric (G.E.) \n",
    "# and a portfolio composed of 50% Dell and 50% G.E.\n",
    "#\n",
    "# Finally plot risk/return graph for 4000 Portfolios.\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# The monthly returns of 3 securities were selected: \n",
    "# Microsoft, Dell, and General Electric (G.E.)\n",
    "list1 = [[-0.66, -2.88, 10.11],\n",
    "[-3.55, 20.29, 4.57],\n",
    "[-4.48, -8.34, -4.16],\n",
    "[2.09, 6.62, 2.00],\n",
    "[-2.89, 3.94, -3.96],\n",
    "[3.96, 3.67, -3.21],\n",
    "[5.38, -2.58, -5.04],\n",
    "[-2.34, -8.47, -8.93],\n",
    "[-6.43, -4.88, -5.76],\n",
    "[6.99, 11.81, 9.79],\n",
    "[-3.19, -0.32, -4.79],\n",
    "[1.49, -7.17, 13.64]]\n",
    "\n",
    "data_np = np.array(list1) # transforma a lista de listas em uma matriz numpy\n",
    "data_1 = data_np / 100 # corrige os valores percentuais para decimais\n",
    "\n",
    "# cálculo do valor médio do retorno\n",
    "Return_Micro = np.mean(data_1[:,0])\n",
    "Return_Dell = np.mean(data_1[:,1])\n",
    "Return_GE = np.mean(data_1[:,2])\n",
    "\n",
    "# cálculo do desvio padrão\n",
    "std_Micro = np.std(data_1[:,0])\n",
    "std_Dell = np.std(data_1[:,1])\n",
    "std_GE = np.std(data_1[:,2])\n",
    "\n",
    "# imprime os retornos médios\n",
    "print()\n",
    "print('Return_Micro = {:.4f}'.format(Return_Micro))\n",
    "print('Return_Dell = {:.4f}'.format(Return_Dell))\n",
    "print('Return_GE = {:.4f}'.format(Return_GE))\n",
    "\n",
    "# imprime os devios padrão\n",
    "print()\n",
    "print('Standard_Deviation_Micro = {:.4f}'.format(std_Micro))\n",
    "print('Standard_Deviation_Dell = {:.4f}'.format(std_Dell))\n",
    "print('Standard_Deviation_GE = {:.4f}'.format(std_GE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [std_Micro,std_Dell,std_GE] # vetor do desvio padrão\n",
    "y = [Return_Micro,Return_Dell,Return_GE] # vetor do retorno esperado\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.title('Risco vs. Retorno')\n",
    "plt.xlabel('Desvio padrão')\n",
    "plt.ylabel('Valor esperado do retorno')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Return_Carteira_1: 0.0066\n",
      "std_Carteira_1: 0.0627\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Carteira 50% DELL + 50% GE\n",
    "Carteira_1 = np.ones(12)\n",
    "\n",
    "for mes in range(12):\n",
    "    Carteira_1[mes] = (data_1[mes,1] + data_1[mes,2]) / 2 # metade do orçamento está em DELL(1) e metade em GE(2)\n",
    "\n",
    "\n",
    "Return_Carteira_1 = np.mean(Carteira_1)\n",
    "std_Carteira_1 = np.std(Carteira_1)\n",
    "\n",
    "x = [std_Micro,std_Dell,std_GE,std_Carteira_1] # vetor do desvio padrão\n",
    "y = [Return_Micro,Return_Dell,Return_GE,Return_Carteira_1] # vetor do retorno esperado\n",
    "\n",
    "print(\"Return_Carteira_1: {:.4f}\".format(Return_Carteira_1))\n",
    "print(\"std_Carteira_1: {:.4f}\".format(std_Carteira_1))\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.title('Risco vs. Retorno')\n",
    "plt.xlabel('Desvio padrão')\n",
    "plt.ylabel('Valor esperado do retorno')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carteiras: 4095\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Código do Marcelo Bacchetti\n",
    "\n",
    "def desvio_carteira(carteira, matriz_cov):\n",
    "    varianca_carteira = 0\n",
    "    for i in range(3):\n",
    "        for j in range(3):\n",
    "            var2 = carteira[j] * carteira[i] * matriz_cov[i, j]\n",
    "            varianca_carteira = var2 + varianca_carteira\n",
    "    return np.sqrt(varianca_carteira)\n",
    "\n",
    "portifolios = []\n",
    "n = 90\n",
    "for a in np.linspace(0, 1, n):\n",
    "    for b in np.linspace(0, 1, n):\n",
    "        c = 1 - (a+b)\n",
    "        if c >= 0:\n",
    "            portifolios.append((a, b, c))\n",
    "\n",
    "matriz_cov = np.cov(data_1, rowvar=False)\n",
    "\n",
    "plan = []\n",
    "for p in portifolios:\n",
    "    dev = desvio_carteira(p, matriz_cov)\n",
    "    ret = (p * data_1.mean(0)).sum()\n",
    "    plan.append((dev, ret))\n",
    "\n",
    "print(f\"Carteiras: {len(plan)}\")\n",
    "plt.scatter(*zip(*plan), s=0.3);"
   ]
  }
 ],
 "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": "3.10.8 (main, Oct 19 2022, 07:46:20) [GCC]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}