FBA_FML_AIforTrading Term1_Lesson 7 Quiz1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using what know about the DataFrame shift Function, implement the function. <br>\n",
    "Once you successfully implemented the quiz, you can can continue to the next concept in the classroom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_returns(close):\n",
    "    \"\"\"\n",
    "    Compute returns for each ticker and date in close.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    close : DataFrame\n",
    "        Close prices for each ticker and date\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    returns : DataFrame\n",
    "        Returns for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    \n",
    "    return None\n",
    "\n",
    "\n",
    "quiz_tests.test_calculate_returns(calculate_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_returns(close):\n",
    "    \"\"\"\n",
    "    Compute returns for each ticker and date in close.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    close : DataFrame\n",
    "        Close prices for each ticker and date\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    returns : DataFrame\n",
    "        Returns for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    \n",
    "    return (close - close.shift(1))/close.shift(1)\n",
    "\n",
    "\n",
    "quiz_tests.test_calculate_returns(calculate_returns)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}