python-pour-finance/09-Python-Finance-Fondamentaux/.ipynb_checkpoints/02-Optimisation-Portfolio-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“Modern Portfolio Theory (MPT), a hypothesis put forth by Harry Markowitz in his paper “Portfolio Selection,” (published in 1952 by the Journal of Finance) is an investment theory based on the idea that risk-averse investors can construct portfolios to optimize or maximize expected return based on a given level of market risk, emphasizing that risk is an inherent part of higher reward. It is one of the most important and influential economic theories dealing with finance and investment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Simulation for Optimization Search\n",
    "\n",
    "\n",
    "We could randomly try to find the optimal portfolio balance using Monte Carlo simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Download and get Daily Returns\n",
    "aapl = pd.read_csv('AAPL_CLOSE',index_col='Date',parse_dates=True)\n",
    "cisco = pd.read_csv('CISCO_CLOSE',index_col='Date',parse_dates=True)\n",
    "ibm = pd.read_csv('IBM_CLOSE',index_col='Date',parse_dates=True)\n",
    "amzn = pd.read_csv('AMZN_CLOSE',index_col='Date',parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stocks = pd.concat([aapl,cisco,ibm,amzn],axis=1)\n",
    "stocks.columns = ['aapl','cisco','ibm','amzn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>53.063218</td>\n",
       "      <td>15.752778</td>\n",
       "      <td>160.830881</td>\n",
       "      <td>179.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>53.348386</td>\n",
       "      <td>16.057180</td>\n",
       "      <td>160.174781</td>\n",
       "      <td>177.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>53.940658</td>\n",
       "      <td>15.997991</td>\n",
       "      <td>159.415086</td>\n",
       "      <td>177.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>54.504543</td>\n",
       "      <td>15.938801</td>\n",
       "      <td>157.584912</td>\n",
       "      <td>182.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>54.418089</td>\n",
       "      <td>16.040268</td>\n",
       "      <td>156.764786</td>\n",
       "      <td>178.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 aapl      cisco         ibm    amzn\n",
       "Date                                                \n",
       "2012-01-03  53.063218  15.752778  160.830881  179.03\n",
       "2012-01-04  53.348386  16.057180  160.174781  177.51\n",
       "2012-01-05  53.940658  15.997991  159.415086  177.61\n",
       "2012-01-06  54.504543  15.938801  157.584912  182.61\n",
       "2012-01-09  54.418089  16.040268  156.764786  178.56"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aapl     0.000750\n",
       "cisco    0.000599\n",
       "ibm      0.000081\n",
       "amzn     0.001328\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_daily_ret = stocks.pct_change(1).mean()\n",
    "mean_daily_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aapl</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301990</td>\n",
       "      <td>0.297498</td>\n",
       "      <td>0.235487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cisco</th>\n",
       "      <td>0.301990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.424672</td>\n",
       "      <td>0.284470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ibm</th>\n",
       "      <td>0.297498</td>\n",
       "      <td>0.424672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amzn</th>\n",
       "      <td>0.235487</td>\n",
       "      <td>0.284470</td>\n",
       "      <td>0.258492</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           aapl     cisco       ibm      amzn\n",
       "aapl   1.000000  0.301990  0.297498  0.235487\n",
       "cisco  0.301990  1.000000  0.424672  0.284470\n",
       "ibm    0.297498  0.424672  1.000000  0.258492\n",
       "amzn   0.235487  0.284470  0.258492  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.pct_change(1).corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating Thousands of Possible Allocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>53.063218</td>\n",
       "      <td>15.752778</td>\n",
       "      <td>160.830881</td>\n",
       "      <td>179.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>53.348386</td>\n",
       "      <td>16.057180</td>\n",
       "      <td>160.174781</td>\n",
       "      <td>177.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>53.940658</td>\n",
       "      <td>15.997991</td>\n",
       "      <td>159.415086</td>\n",
       "      <td>177.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>54.504543</td>\n",
       "      <td>15.938801</td>\n",
       "      <td>157.584912</td>\n",
       "      <td>182.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>54.418089</td>\n",
       "      <td>16.040268</td>\n",
       "      <td>156.764786</td>\n",
       "      <td>178.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 aapl      cisco         ibm    amzn\n",
       "Date                                                \n",
       "2012-01-03  53.063218  15.752778  160.830881  179.03\n",
       "2012-01-04  53.348386  16.057180  160.174781  177.51\n",
       "2012-01-05  53.940658  15.997991  159.415086  177.61\n",
       "2012-01-06  54.504543  15.938801  157.584912  182.61\n",
       "2012-01-09  54.418089  16.040268  156.764786  178.56"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2258d9c4668>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkY2dyfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2258d9c4eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_normed = stocks/stocks.iloc[0]\n",
    "stock_normed.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>0.005374</td>\n",
       "      <td>0.019324</td>\n",
       "      <td>-0.004079</td>\n",
       "      <td>-0.008490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>0.011102</td>\n",
       "      <td>-0.003686</td>\n",
       "      <td>-0.004743</td>\n",
       "      <td>0.000563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>0.010454</td>\n",
       "      <td>-0.003700</td>\n",
       "      <td>-0.011481</td>\n",
       "      <td>0.028152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-0.001586</td>\n",
       "      <td>0.006366</td>\n",
       "      <td>-0.005204</td>\n",
       "      <td>-0.022178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                aapl     cisco       ibm      amzn\n",
       "Date                                              \n",
       "2012-01-03       NaN       NaN       NaN       NaN\n",
       "2012-01-04  0.005374  0.019324 -0.004079 -0.008490\n",
       "2012-01-05  0.011102 -0.003686 -0.004743  0.000563\n",
       "2012-01-06  0.010454 -0.003700 -0.011481  0.028152\n",
       "2012-01-09 -0.001586  0.006366 -0.005204 -0.022178"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily_ret = stocks.pct_change(1)\n",
    "stock_daily_ret.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Returns vs Arithmetic Returns\n",
    "\n",
    "We will now switch over to using log returns instead of arithmetic returns, for many of our use cases they are almost the same,but most technical analyses require detrending/normalizing the time series and using log returns is a nice way to do that.\n",
    "Log returns are convenient to work with in many of the algorithms we will encounter.\n",
    "\n",
    "For a full analysis of why we use log returns, check [this great article](https://quantivity.wordpress.com/2011/02/21/why-log-returns/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>0.005360</td>\n",
       "      <td>0.019139</td>\n",
       "      <td>-0.004088</td>\n",
       "      <td>-0.008526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>0.011041</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>-0.004754</td>\n",
       "      <td>0.000563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>0.010400</td>\n",
       "      <td>-0.003707</td>\n",
       "      <td>-0.011547</td>\n",
       "      <td>0.027763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-0.001587</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>-0.005218</td>\n",
       "      <td>-0.022428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                aapl     cisco       ibm      amzn\n",
       "Date                                              \n",
       "2012-01-03       NaN       NaN       NaN       NaN\n",
       "2012-01-04  0.005360  0.019139 -0.004088 -0.008526\n",
       "2012-01-05  0.011041 -0.003693 -0.004754  0.000563\n",
       "2012-01-06  0.010400 -0.003707 -0.011547  0.027763\n",
       "2012-01-09 -0.001587  0.006346 -0.005218 -0.022428"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_ret = np.log(stocks/stocks.shift(1))\n",
    "log_ret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGoCAYAAABbkkSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2258e3f8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_ret.hist(bins=100,figsize=(12,6));\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aapl</th>\n",
       "      <td>1257.0</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>0.016466</td>\n",
       "      <td>-0.131875</td>\n",
       "      <td>-0.007358</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.009724</td>\n",
       "      <td>0.085022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cisco</th>\n",
       "      <td>1257.0</td>\n",
       "      <td>0.000497</td>\n",
       "      <td>0.014279</td>\n",
       "      <td>-0.116091</td>\n",
       "      <td>-0.006240</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.007634</td>\n",
       "      <td>0.118862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ibm</th>\n",
       "      <td>1257.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.011819</td>\n",
       "      <td>-0.086419</td>\n",
       "      <td>-0.005873</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.049130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amzn</th>\n",
       "      <td>1257.0</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.019362</td>\n",
       "      <td>-0.116503</td>\n",
       "      <td>-0.008534</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.011407</td>\n",
       "      <td>0.146225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       std       min       25%       50%       75%  \\\n",
       "aapl   1257.0  0.000614  0.016466 -0.131875 -0.007358  0.000455  0.009724   \n",
       "cisco  1257.0  0.000497  0.014279 -0.116091 -0.006240  0.000213  0.007634   \n",
       "ibm    1257.0  0.000011  0.011819 -0.086419 -0.005873  0.000049  0.006477   \n",
       "amzn   1257.0  0.001139  0.019362 -0.116503 -0.008534  0.000563  0.011407   \n",
       "\n",
       "            max  \n",
       "aapl   0.085022  \n",
       "cisco  0.118862  \n",
       "ibm    0.049130  \n",
       "amzn   0.146225  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_ret.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aapl     0.154803\n",
       "cisco    0.125291\n",
       "ibm      0.002788\n",
       "amzn     0.287153\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_ret.mean() * 252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aapl</th>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cisco</th>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ibm</th>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amzn</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           aapl     cisco       ibm      amzn\n",
       "aapl   0.000271  0.000071  0.000057  0.000075\n",
       "cisco  0.000071  0.000204  0.000072  0.000079\n",
       "ibm    0.000057  0.000072  0.000140  0.000059\n",
       "amzn   0.000075  0.000079  0.000059  0.000375"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute pairwise covariance of columns\n",
    "log_ret.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aapl</th>\n",
       "      <th>cisco</th>\n",
       "      <th>ibm</th>\n",
       "      <th>amzn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aapl</th>\n",
       "      <td>0.068326</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.014464</td>\n",
       "      <td>0.018986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cisco</th>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.051381</td>\n",
       "      <td>0.018029</td>\n",
       "      <td>0.019956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ibm</th>\n",
       "      <td>0.014464</td>\n",
       "      <td>0.018029</td>\n",
       "      <td>0.035203</td>\n",
       "      <td>0.014939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amzn</th>\n",
       "      <td>0.018986</td>\n",
       "      <td>0.019956</td>\n",
       "      <td>0.014939</td>\n",
       "      <td>0.094470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           aapl     cisco       ibm      amzn\n",
       "aapl   0.068326  0.017854  0.014464  0.018986\n",
       "cisco  0.017854  0.051381  0.018029  0.019956\n",
       "ibm    0.014464  0.018029  0.035203  0.014939\n",
       "amzn   0.018986  0.019956  0.014939  0.094470"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_ret.cov()*252 # multiply by days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Run for Some Random Allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stocks\n",
      "Index(['aapl', 'cisco', 'ibm', 'amzn'], dtype='object')\n",
      "\n",
      "\n",
      "Creating Random Weights\n",
      "[ 0.51639863  0.57066759  0.02847423  0.17152166]\n",
      "\n",
      "\n",
      "Rebalance to sum to 1.0\n",
      "[ 0.40122278  0.44338777  0.02212343  0.13326603]\n",
      "\n",
      "\n",
      "Expected Portfolio Return\n",
      "0.15599272049632004\n",
      "\n",
      "\n",
      "Expected Volatility\n",
      "0.185026495659\n",
      "\n",
      "\n",
      "Sharpe Ratio\n",
      "0.843083148393\n"
     ]
    }
   ],
   "source": [
    "# Set seed (optional)\n",
    "np.random.seed(101)\n",
    "\n",
    "# Stock Columns\n",
    "print('Stocks')\n",
    "print(stocks.columns)\n",
    "print('\\n')\n",
    "\n",
    "# Create Random Weights\n",
    "print('Creating Random Weights')\n",
    "weights = np.array(np.random.random(4))\n",
    "print(weights)\n",
    "print('\\n')\n",
    "\n",
    "# Rebalance Weights\n",
    "print('Rebalance to sum to 1.0')\n",
    "weights = weights / np.sum(weights)\n",
    "print(weights)\n",
    "print('\\n')\n",
    "\n",
    "# Expected Return\n",
    "print('Expected Portfolio Return')\n",
    "exp_ret = np.sum(log_ret.mean() * weights) *252\n",
    "print(exp_ret)\n",
    "print('\\n')\n",
    "\n",
    "# Expected Variance\n",
    "print('Expected Volatility')\n",
    "exp_vol = np.sqrt(np.dot(weights.T, np.dot(log_ret.cov() * 252, weights)))\n",
    "print(exp_vol)\n",
    "print('\\n')\n",
    "\n",
    "# Sharpe Ratio\n",
    "SR = exp_ret/exp_vol\n",
    "print('Sharpe Ratio')\n",
    "print(SR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now we can just run this many times over!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_ports = 15000\n",
    "\n",
    "all_weights = np.zeros((num_ports,len(stocks.columns)))\n",
    "ret_arr = np.zeros(num_ports)\n",
    "vol_arr = np.zeros(num_ports)\n",
    "sharpe_arr = np.zeros(num_ports)\n",
    "\n",
    "for ind in range(num_ports):\n",
    "\n",
    "    # Create Random Weights\n",
    "    weights = np.array(np.random.random(4))\n",
    "\n",
    "    # Rebalance Weights\n",
    "    weights = weights / np.sum(weights)\n",
    "    \n",
    "    # Save Weights\n",
    "    all_weights[ind,:] = weights\n",
    "\n",
    "    # Expected Return\n",
    "    ret_arr[ind] = np.sum((log_ret.mean() * weights) *252)\n",
    "\n",
    "    # Expected Variance\n",
    "    vol_arr[ind] = np.sqrt(np.dot(weights.T, np.dot(log_ret.cov() * 252, weights)))\n",
    "\n",
    "    # Sharpe Ratio\n",
    "    sharpe_arr[ind] = ret_arr[ind]/vol_arr[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0303260551271067"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe_arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1419"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe_arr.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.26188068,  0.20759516,  0.00110226,  0.5294219 ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weights[1419,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_sr_ret = ret_arr[1419]\n",
    "max_sr_vol = vol_arr[1419]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2258e24ac50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAHjCAYAAAAJ5iYqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2258da3b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(vol_arr,ret_arr,c=sharpe_arr,cmap='plasma')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Return')\n",
    "\n",
    "# Add red dot for max SR\n",
    "plt.scatter(max_sr_vol,max_sr_ret,c='red',s=50,edgecolors='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical Optimization\n",
    "\n",
    "There are much better ways to find good allocation weights than just guess and check! We can use optimization functions to find the ideal weights mathematically!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functionalize Return and SR operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_ret_vol_sr(weights):\n",
    "    \"\"\"\n",
    "    Takes in weights, returns array or return,volatility, sharpe ratio\n",
    "    \"\"\"\n",
    "    weights = np.array(weights)\n",
    "    ret = np.sum(log_ret.mean() * weights) * 252\n",
    "    vol = np.sqrt(np.dot(weights.T, np.dot(log_ret.cov() * 252, weights)))\n",
    "    sr = ret/vol\n",
    "    return np.array([ret,vol,sr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fully understand all the parameters, check out:\n",
    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function minimize in module scipy.optimize._minimize:\n",
      "\n",
      "minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)\n",
      "    Minimization of scalar function of one or more variables.\n",
      "    \n",
      "    In general, the optimization problems are of the form::\n",
      "    \n",
      "        minimize f(x) subject to\n",
      "    \n",
      "        g_i(x) >= 0,  i = 1,...,m\n",
      "        h_j(x)  = 0,  j = 1,...,p\n",
      "    \n",
      "    where x is a vector of one or more variables.\n",
      "    ``g_i(x)`` are the inequality constraints.\n",
      "    ``h_j(x)`` are the equality constrains.\n",
      "    \n",
      "    Optionally, the lower and upper bounds for each element in x can also be\n",
      "    specified using the `bounds` argument.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    fun : callable\n",
      "        Objective function.\n",
      "    x0 : ndarray\n",
      "        Initial guess.\n",
      "    args : tuple, optional\n",
      "        Extra arguments passed to the objective function and its\n",
      "        derivatives (Jacobian, Hessian).\n",
      "    method : str or callable, optional\n",
      "        Type of solver.  Should be one of\n",
      "    \n",
      "            - 'Nelder-Mead' :ref:`(see here) <optimize.minimize-neldermead>`\n",
      "            - 'Powell'      :ref:`(see here) <optimize.minimize-powell>`\n",
      "            - 'CG'          :ref:`(see here) <optimize.minimize-cg>`\n",
      "            - 'BFGS'        :ref:`(see here) <optimize.minimize-bfgs>`\n",
      "            - 'Newton-CG'   :ref:`(see here) <optimize.minimize-newtoncg>`\n",
      "            - 'L-BFGS-B'    :ref:`(see here) <optimize.minimize-lbfgsb>`\n",
      "            - 'TNC'         :ref:`(see here) <optimize.minimize-tnc>`\n",
      "            - 'COBYLA'      :ref:`(see here) <optimize.minimize-cobyla>`\n",
      "            - 'SLSQP'       :ref:`(see here) <optimize.minimize-slsqp>`\n",
      "            - 'dogleg'      :ref:`(see here) <optimize.minimize-dogleg>`\n",
      "            - 'trust-ncg'   :ref:`(see here) <optimize.minimize-trustncg>`\n",
      "            - custom - a callable object (added in version 0.14.0),\n",
      "              see below for description.\n",
      "    \n",
      "        If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,\n",
      "        depending if the problem has constraints or bounds.\n",
      "    jac : bool or callable, optional\n",
      "        Jacobian (gradient) of objective function. Only for CG, BFGS,\n",
      "        Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.\n",
      "        If `jac` is a Boolean and is True, `fun` is assumed to return the\n",
      "        gradient along with the objective function. If False, the\n",
      "        gradient will be estimated numerically.\n",
      "        `jac` can also be a callable returning the gradient of the\n",
      "        objective. In this case, it must accept the same arguments as `fun`.\n",
      "    hess, hessp : callable, optional\n",
      "        Hessian (matrix of second-order derivatives) of objective function or\n",
      "        Hessian of objective function times an arbitrary vector p.  Only for\n",
      "        Newton-CG, dogleg, trust-ncg.\n",
      "        Only one of `hessp` or `hess` needs to be given.  If `hess` is\n",
      "        provided, then `hessp` will be ignored.  If neither `hess` nor\n",
      "        `hessp` is provided, then the Hessian product will be approximated\n",
      "        using finite differences on `jac`. `hessp` must compute the Hessian\n",
      "        times an arbitrary vector.\n",
      "    bounds : sequence, optional\n",
      "        Bounds for variables (only for L-BFGS-B, TNC and SLSQP).\n",
      "        ``(min, max)`` pairs for each element in ``x``, defining\n",
      "        the bounds on that parameter. Use None for one of ``min`` or\n",
      "        ``max`` when there is no bound in that direction.\n",
      "    constraints : dict or sequence of dict, optional\n",
      "        Constraints definition (only for COBYLA and SLSQP).\n",
      "        Each constraint is defined in a dictionary with fields:\n",
      "    \n",
      "            type : str\n",
      "                Constraint type: 'eq' for equality, 'ineq' for inequality.\n",
      "            fun : callable\n",
      "                The function defining the constraint.\n",
      "            jac : callable, optional\n",
      "                The Jacobian of `fun` (only for SLSQP).\n",
      "            args : sequence, optional\n",
      "                Extra arguments to be passed to the function and Jacobian.\n",
      "    \n",
      "        Equality constraint means that the constraint function result is to\n",
      "        be zero whereas inequality means that it is to be non-negative.\n",
      "        Note that COBYLA only supports inequality constraints.\n",
      "    tol : float, optional\n",
      "        Tolerance for termination. For detailed control, use solver-specific\n",
      "        options.\n",
      "    options : dict, optional\n",
      "        A dictionary of solver options. All methods accept the following\n",
      "        generic options:\n",
      "    \n",
      "            maxiter : int\n",
      "                Maximum number of iterations to perform.\n",
      "            disp : bool\n",
      "                Set to True to print convergence messages.\n",
      "    \n",
      "        For method-specific options, see :func:`show_options()`.\n",
      "    callback : callable, optional\n",
      "        Called after each iteration, as ``callback(xk)``, where ``xk`` is the\n",
      "        current parameter vector.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    res : OptimizeResult\n",
      "        The optimization result represented as a ``OptimizeResult`` object.\n",
      "        Important attributes are: ``x`` the solution array, ``success`` a\n",
      "        Boolean flag indicating if the optimizer exited successfully and\n",
      "        ``message`` which describes the cause of the termination. See\n",
      "        `OptimizeResult` for a description of other attributes.\n",
      "    \n",
      "    \n",
      "    See also\n",
      "    --------\n",
      "    minimize_scalar : Interface to minimization algorithms for scalar\n",
      "        univariate functions\n",
      "    show_options : Additional options accepted by the solvers\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    This section describes the available solvers that can be selected by the\n",
      "    'method' parameter. The default method is *BFGS*.\n",
      "    \n",
      "    **Unconstrained minimization**\n",
      "    \n",
      "    Method :ref:`Nelder-Mead <optimize.minimize-neldermead>` uses the\n",
      "    Simplex algorithm [1]_, [2]_. This algorithm is robust in many\n",
      "    applications. However, if numerical computation of derivative can be\n",
      "    trusted, other algorithms using the first and/or second derivatives\n",
      "    information might be preferred for their better performance in\n",
      "    general.\n",
      "    \n",
      "    Method :ref:`Powell <optimize.minimize-powell>` is a modification\n",
      "    of Powell's method [3]_, [4]_ which is a conjugate direction\n",
      "    method. It performs sequential one-dimensional minimizations along\n",
      "    each vector of the directions set (`direc` field in `options` and\n",
      "    `info`), which is updated at each iteration of the main\n",
      "    minimization loop. The function need not be differentiable, and no\n",
      "    derivatives are taken.\n",
      "    \n",
      "    Method :ref:`CG <optimize.minimize-cg>` uses a nonlinear conjugate\n",
      "    gradient algorithm by Polak and Ribiere, a variant of the\n",
      "    Fletcher-Reeves method described in [5]_ pp.  120-122. Only the\n",
      "    first derivatives are used.\n",
      "    \n",
      "    Method :ref:`BFGS <optimize.minimize-bfgs>` uses the quasi-Newton\n",
      "    method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) [5]_\n",
      "    pp. 136. It uses the first derivatives only. BFGS has proven good\n",
      "    performance even for non-smooth optimizations. This method also\n",
      "    returns an approximation of the Hessian inverse, stored as\n",
      "    `hess_inv` in the OptimizeResult object.\n",
      "    \n",
      "    Method :ref:`Newton-CG <optimize.minimize-newtoncg>` uses a\n",
      "    Newton-CG algorithm [5]_ pp. 168 (also known as the truncated\n",
      "    Newton method). It uses a CG method to the compute the search\n",
      "    direction. See also *TNC* method for a box-constrained\n",
      "    minimization with a similar algorithm.\n",
      "    \n",
      "    Method :ref:`dogleg <optimize.minimize-dogleg>` uses the dog-leg\n",
      "    trust-region algorithm [5]_ for unconstrained minimization. This\n",
      "    algorithm requires the gradient and Hessian; furthermore the\n",
      "    Hessian is required to be positive definite.\n",
      "    \n",
      "    Method :ref:`trust-ncg <optimize.minimize-trustncg>` uses the\n",
      "    Newton conjugate gradient trust-region algorithm [5]_ for\n",
      "    unconstrained minimization. This algorithm requires the gradient\n",
      "    and either the Hessian or a function that computes the product of\n",
      "    the Hessian with a given vector.\n",
      "    \n",
      "    **Constrained minimization**\n",
      "    \n",
      "    Method :ref:`L-BFGS-B <optimize.minimize-lbfgsb>` uses the L-BFGS-B\n",
      "    algorithm [6]_, [7]_ for bound constrained minimization.\n",
      "    \n",
      "    Method :ref:`TNC <optimize.minimize-tnc>` uses a truncated Newton\n",
      "    algorithm [5]_, [8]_ to minimize a function with variables subject\n",
      "    to bounds. This algorithm uses gradient information; it is also\n",
      "    called Newton Conjugate-Gradient. It differs from the *Newton-CG*\n",
      "    method described above as it wraps a C implementation and allows\n",
      "    each variable to be given upper and lower bounds.\n",
      "    \n",
      "    Method :ref:`COBYLA <optimize.minimize-cobyla>` uses the\n",
      "    Constrained Optimization BY Linear Approximation (COBYLA) method\n",
      "    [9]_, [10]_, [11]_. The algorithm is based on linear\n",
      "    approximations to the objective function and each constraint. The\n",
      "    method wraps a FORTRAN implementation of the algorithm. The\n",
      "    constraints functions 'fun' may return either a single number\n",
      "    or an array or list of numbers.\n",
      "    \n",
      "    Method :ref:`SLSQP <optimize.minimize-slsqp>` uses Sequential\n",
      "    Least SQuares Programming to minimize a function of several\n",
      "    variables with any combination of bounds, equality and inequality\n",
      "    constraints. The method wraps the SLSQP Optimization subroutine\n",
      "    originally implemented by Dieter Kraft [12]_. Note that the\n",
      "    wrapper handles infinite values in bounds by converting them into\n",
      "    large floating values.\n",
      "    \n",
      "    **Custom minimizers**\n",
      "    \n",
      "    It may be useful to pass a custom minimization method, for example\n",
      "    when using a frontend to this method such as `scipy.optimize.basinhopping`\n",
      "    or a different library.  You can simply pass a callable as the ``method``\n",
      "    parameter.\n",
      "    \n",
      "    The callable is called as ``method(fun, x0, args, **kwargs, **options)``\n",
      "    where ``kwargs`` corresponds to any other parameters passed to `minimize`\n",
      "    (such as `callback`, `hess`, etc.), except the `options` dict, which has\n",
      "    its contents also passed as `method` parameters pair by pair.  Also, if\n",
      "    `jac` has been passed as a bool type, `jac` and `fun` are mangled so that\n",
      "    `fun` returns just the function values and `jac` is converted to a function\n",
      "    returning the Jacobian.  The method shall return an ``OptimizeResult``\n",
      "    object.\n",
      "    \n",
      "    The provided `method` callable must be able to accept (and possibly ignore)\n",
      "    arbitrary parameters; the set of parameters accepted by `minimize` may\n",
      "    expand in future versions and then these parameters will be passed to\n",
      "    the method.  You can find an example in the scipy.optimize tutorial.\n",
      "    \n",
      "    .. versionadded:: 0.11.0\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function\n",
      "        Minimization. The Computer Journal 7: 308-13.\n",
      "    .. [2] Wright M H. 1996. Direct search methods: Once scorned, now\n",
      "        respectable, in Numerical Analysis 1995: Proceedings of the 1995\n",
      "        Dundee Biennial Conference in Numerical Analysis (Eds. D F\n",
      "        Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.\n",
      "        191-208.\n",
      "    .. [3] Powell, M J D. 1964. An efficient method for finding the minimum of\n",
      "       a function of several variables without calculating derivatives. The\n",
      "       Computer Journal 7: 155-162.\n",
      "    .. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.\n",
      "       Numerical Recipes (any edition), Cambridge University Press.\n",
      "    .. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.\n",
      "       Springer New York.\n",
      "    .. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory\n",
      "       Algorithm for Bound Constrained Optimization. SIAM Journal on\n",
      "       Scientific and Statistical Computing 16 (5): 1190-1208.\n",
      "    .. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm\n",
      "       778: L-BFGS-B, FORTRAN routines for large scale bound constrained\n",
      "       optimization. ACM Transactions on Mathematical Software 23 (4):\n",
      "       550-560.\n",
      "    .. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.\n",
      "       1984. SIAM Journal of Numerical Analysis 21: 770-778.\n",
      "    .. [9] Powell, M J D. A direct search optimization method that models\n",
      "       the objective and constraint functions by linear interpolation.\n",
      "       1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez\n",
      "       and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.\n",
      "    .. [10] Powell M J D. Direct search algorithms for optimization\n",
      "       calculations. 1998. Acta Numerica 7: 287-336.\n",
      "    .. [11] Powell M J D. A view of algorithms for optimization without\n",
      "       derivatives. 2007.Cambridge University Technical Report DAMTP\n",
      "       2007/NA03\n",
      "    .. [12] Kraft, D. A software package for sequential quadratic\n",
      "       programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace\n",
      "       Center -- Institute for Flight Mechanics, Koln, Germany.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Let us consider the problem of minimizing the Rosenbrock function. This\n",
      "    function (and its respective derivatives) is implemented in `rosen`\n",
      "    (resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.\n",
      "    \n",
      "    >>> from scipy.optimize import minimize, rosen, rosen_der\n",
      "    \n",
      "    A simple application of the *Nelder-Mead* method is:\n",
      "    \n",
      "    >>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]\n",
      "    >>> res = minimize(rosen, x0, method='Nelder-Mead', tol=1e-6)\n",
      "    >>> res.x\n",
      "    array([ 1.,  1.,  1.,  1.,  1.])\n",
      "    \n",
      "    Now using the *BFGS* algorithm, using the first derivative and a few\n",
      "    options:\n",
      "    \n",
      "    >>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,\n",
      "    ...                options={'gtol': 1e-6, 'disp': True})\n",
      "    Optimization terminated successfully.\n",
      "             Current function value: 0.000000\n",
      "             Iterations: 26\n",
      "             Function evaluations: 31\n",
      "             Gradient evaluations: 31\n",
      "    >>> res.x\n",
      "    array([ 1.,  1.,  1.,  1.,  1.])\n",
      "    >>> print(res.message)\n",
      "    Optimization terminated successfully.\n",
      "    >>> res.hess_inv\n",
      "    array([[ 0.00749589,  0.01255155,  0.02396251,  0.04750988,  0.09495377],  # may vary\n",
      "           [ 0.01255155,  0.02510441,  0.04794055,  0.09502834,  0.18996269],\n",
      "           [ 0.02396251,  0.04794055,  0.09631614,  0.19092151,  0.38165151],\n",
      "           [ 0.04750988,  0.09502834,  0.19092151,  0.38341252,  0.7664427 ],\n",
      "           [ 0.09495377,  0.18996269,  0.38165151,  0.7664427,   1.53713523]])\n",
      "    \n",
      "    \n",
      "    Next, consider a minimization problem with several constraints (namely\n",
      "    Example 16.4 from [5]_). The objective function is:\n",
      "    \n",
      "    >>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2\n",
      "    \n",
      "    There are three constraints defined as:\n",
      "    \n",
      "    >>> cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},\n",
      "    ...         {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},\n",
      "    ...         {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})\n",
      "    \n",
      "    And variables must be positive, hence the following bounds:\n",
      "    \n",
      "    >>> bnds = ((0, None), (0, None))\n",
      "    \n",
      "    The optimization problem is solved using the SLSQP method as:\n",
      "    \n",
      "    >>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,\n",
      "    ...                constraints=cons)\n",
      "    \n",
      "    It should converge to the theoretical solution (1.4 ,1.7).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(minimize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimization works as a minimization function, since we actually want to maximize the Sharpe Ratio, we will need to turn it negative so we can minimize the negative sharpe (same as maximizing the postive sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def neg_sharpe(weights):\n",
    "    return  get_ret_vol_sr(weights)[2] * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Contraints\n",
    "def check_sum(weights):\n",
    "    '''\n",
    "    Returns 0 if sum of weights is 1.0\n",
    "    '''\n",
    "    return np.sum(weights) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# By convention of minimize function it should be a function that returns zero for conditions\n",
    "cons = ({'type':'eq','fun': check_sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 0-1 bounds for each weight\n",
    "bounds = ((0, 1), (0, 1), (0, 1), (0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initial Guess (equal distribution)\n",
    "init_guess = [0.25,0.25,0.25,0.25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sequential Least SQuares Programming (SLSQP).\n",
    "opt_results = minimize(neg_sharpe,init_guess,method='SLSQP',bounds=bounds,constraints=cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: -1.030716870334955\n",
       "     jac: array([  5.64455986e-05,   4.18275595e-05,   3.39921728e-01,\n",
       "        -4.45097685e-05])\n",
       " message: 'Optimization terminated successfully.'\n",
       "    nfev: 42\n",
       "     nit: 7\n",
       "    njev: 7\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([  2.66289778e-01,   2.04189819e-01,   9.24621165e-17,\n",
       "         5.29520404e-01])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.66289778e-01,   2.04189819e-01,   9.24621165e-17,\n",
       "         5.29520404e-01])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_results.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.21885915,  0.21233683,  1.03071687])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ret_vol_sr(opt_results.x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All Optimal Portfolios (Efficient Frontier)\n",
    "\n",
    "The efficient frontier is the set of optimal portfolios that offers the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. Portfolios that lie below the efficient frontier are sub-optimal, because they do not provide enough return for the level of risk. Portfolios that cluster to the right of the efficient frontier are also sub-optimal, because they have a higher level of risk for the defined rate of return.\n",
    "\n",
    "Efficient Frontier http://www.investopedia.com/terms/e/efficientfrontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Our returns go from 0 to somewhere along 0.3\n",
    "# Create a linspace number of points to calculate x on\n",
    "frontier_y = np.linspace(0,0.3,100) # Change 100 to a lower number for slower computers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def minimize_volatility(weights):\n",
    "    return  get_ret_vol_sr(weights)[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frontier_volatility = []\n",
    "\n",
    "for possible_return in frontier_y:\n",
    "    # function for return\n",
    "    cons = ({'type':'eq','fun': check_sum},\n",
    "            {'type':'eq','fun': lambda w: get_ret_vol_sr(w)[0] - possible_return})\n",
    "    \n",
    "    result = minimize(minimize_volatility,init_guess,method='SLSQP',bounds=bounds,constraints=cons)\n",
    "    \n",
    "    frontier_volatility.append(result['fun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2258fdefeb8>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAHjCAYAAAAJ5iYqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8pS0ioiIiEiXp6RVRERERLq8/w+AcPBht1x4wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2258fd7b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(vol_arr,ret_arr,c=sharpe_arr,cmap='plasma')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Return')\n",
    "\n",
    "\n",
    "\n",
    "# Add frontier line\n",
    "plt.plot(frontier_volatility,frontier_y,'g--',linewidth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}