Monte Carlo Simulation/MC Predicting Stock Prices - Part II _IEX.ipynb

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   "source": [
    "## Monte Carlo - Forecasting Stock Prices - Part II "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Suggested Answers follow (usually there are multiple ways to solve a problem in Python).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecasting Future Stock Prices – continued:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5y\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd  \n",
    "from pandas_datareader import data as wb  \n",
    "import matplotlib.pyplot as plt  \n",
    "from scipy.stats import norm\n",
    "%matplotlib inline\n",
    "\n",
    "ticker = 'MSFT' \n",
    "data = pd.DataFrame()\n",
    "data[ticker] = wb.DataReader(ticker, data_source='iex', start='2015-1-1')['close']\n",
    "\n",
    "log_returns = np.log(1 + data.pct_change())\n",
    "u = log_returns.mean()\n",
    "var = log_returns.var()\n",
    "drift = u - (0.5 * var)\n",
    "stdev = log_returns.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "******"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use “.values” to transform the *drift* and the *stdev* objects into arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(drift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00086773])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drift.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01458333])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdev.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecast future stock prices for every trading day a year ahead. So, assign 250 to “t_intervals”. <br />\n",
    "Let’s examine 10 possible outcomes. Bind “iterations” to the value of 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_intervals = 250\n",
    "iterations = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the formula we have provided and calculate daily returns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "daily\\_returns = exp({drift} + {stdev} * z), \n",
    "$$ \n",
    "<br \\>\n",
    "$$\n",
    "where\\  z = norm.ppf(np.random.rand(t\\_intervals, iterations)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.01095699, 0.9769792 , 0.99202127, ..., 0.9886795 , 0.99794908,\n",
       "        0.97714619],\n",
       "       [1.01842063, 1.00408016, 1.00199587, ..., 0.97127252, 1.0149269 ,\n",
       "        1.00687163],\n",
       "       [0.98575652, 1.01999794, 1.00000545, ..., 0.99449251, 1.01975812,\n",
       "        1.01872293],\n",
       "       ...,\n",
       "       [1.00375482, 0.99546051, 1.00401341, ..., 1.01439301, 1.01589728,\n",
       "        0.98207802],\n",
       "       [0.98553824, 0.98907875, 1.0176363 , ..., 1.00112249, 0.98386497,\n",
       "        0.99711455],\n",
       "       [0.98769115, 1.01029332, 0.99612666, ..., 0.98857682, 0.99242259,\n",
       "        0.9834048 ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, iterations)))\n",
    "daily_returns"
   ]
  }
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