01 GettingStarted.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GettingStarted.ipynb\n",
    "\n",
    "In this code, we will run basic Python code to make sure Python is properly installed at your device.\n",
    "\n",
    "Code for the Chicago Booth course on Quantitative Portfolio Management by Ralph S.J. Koijen and (current and former) teaching assistants Sangmin Oh, Federico Mainardi, and Laurenz de Rosa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting fastparquet\n",
      "  Downloading fastparquet-2024.11.0-cp312-cp312-macosx_11_0_arm64.whl.metadata (4.2 kB)\n",
      "Requirement already satisfied: pandas>=1.5.0 in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from fastparquet) (2.2.3)\n",
      "Requirement already satisfied: numpy in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from fastparquet) (1.26.4)\n",
      "Collecting cramjam>=2.3 (from fastparquet)\n",
      "  Downloading cramjam-2.9.1-cp312-cp312-macosx_11_0_arm64.whl.metadata (4.9 kB)\n",
      "Collecting fsspec (from fastparquet)\n",
      "  Using cached fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n",
      "Requirement already satisfied: packaging in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from fastparquet) (23.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/qpm/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.17.0)\n",
      "Downloading fastparquet-2024.11.0-cp312-cp312-macosx_11_0_arm64.whl (685 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m685.4/685.4 kB\u001b[0m \u001b[31m325.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading cramjam-2.9.1-cp312-cp312-macosx_11_0_arm64.whl (1.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m594.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hUsing cached fsspec-2024.12.0-py3-none-any.whl (183 kB)\n",
      "Installing collected packages: fsspec, cramjam, fastparquet\n",
      "Successfully installed cramjam-2.9.1 fastparquet-2024.11.0 fsspec-2024.12.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install fastparquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Packages successfully imported..!\n"
     ]
    }
   ],
   "source": [
    "import wrds\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "print('Packages successfully imported..!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing Packages\n",
    "\n",
    "This notebook tests that your Python installation works. \n",
    "\n",
    "- You can download the data from: https://www.dropbox.com/scl/fo/ze2tut4prlptuyhuyofx4/AI7wV4q-6e-KDBSnRhyJptI?rlkey=93yaxhwrk0w8drx5d7o71cvow&dl=0. \n",
    "- Please copy the file `GettingStartedSample.parquet` to a local folder. \n",
    "\n",
    "Now we're ready to start! The goal is not to understand the code yet (we will by the end of the course); just to see that you Python installation works.\n",
    "\n",
    "Every box that has \"[]:\" in front of it needs to be run. To run it, put your cursor in the box and then push the \"play button\" on top. Alternatively, you can press \"shift+enter.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get started, make sure to enter the path to your directory that contains the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "_DATA_DIR = 'Data'      # <-- Edit this line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GettingStarted.ipynb\n",
    "\n",
    "This script is intended to make sure that Python 3 is installed properly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Loading Data\n",
    "\n",
    "We will use datasets with `.parquet` format.\n",
    "\n",
    "For importing `.parquet` files, we will use the `read_parquet` function available in the `pandas` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            permno       prc          comnam\n",
      "date                                        \n",
      "2001-01-01   84788  0.865625  AMAZON COM INC\n",
      "2001-02-01   84788  0.509375  AMAZON COM INC\n",
      "2001-03-01   84788  0.511500  AMAZON COM INC\n",
      "2001-04-01   84788  0.789000  AMAZON COM INC\n",
      "2001-05-01   84788  0.834500  AMAZON COM INC\n",
      "\n",
      ".dta file successfully loaded!\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_parquet('%s/GettingStartedSample.parquet' %(_DATA_DIR)).set_index('date')\n",
    "print(df.head(5))\n",
    "print('\\n.dta file successfully loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout the course, you will be able use data already prepared for you. Alternatively, you may also decide to download and construct datasets directly from WRDS. This choice is advisable for those of you that would like to keep using these scripts in the future. In order to download and construct data directly from WRDS, we proceed in three steps:\n",
    "1. Make sure to have a WRDS account with associated username and password.\n",
    "3. Once you complete step 1., you can run the cell below.\n",
    "4. A message will appear in the cell output. This is Python asking to confirm your username and password.\n",
    "5. Type your username and press Enter. Type your password and press Enter one more time.\n",
    "5. As a last step, Python will ask you to confirm the creation of a web connection. You can confirm by typing the word y.\n",
    "\n",
    "Congratulations! You are now ready to download data directly from the web."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WRDS recommends setting up a .pgpass file.\n",
      "Created .pgpass file successfully.\n",
      "You can create this file yourself at any time with the create_pgpass_file() function.\n",
      "Loading library list...\n",
      "Done\n",
      "            permno          comnam       prc\n",
      "date                                        \n",
      "2001-01-31   84788  AMAZON COM INC  0.865625\n",
      "2001-02-28   84788  AMAZON COM INC  0.509375\n",
      "2001-03-30   84788  AMAZON COM INC  0.511500\n",
      "2001-04-30   84788  AMAZON COM INC  0.789000\n",
      "2001-05-31   84788  AMAZON COM INC  0.834500\n",
      "\n",
      ".dta file successfully loaded!\n"
     ]
    }
   ],
   "source": [
    "import_data = True         # <-- Edit this line\n",
    "\n",
    "if import_data == True:\n",
    "    \n",
    "    # Choose start and end dates\n",
    "    start_date = '2001-01-01'\n",
    "    end_date = '2024-09-30'\n",
    "\n",
    "    # Establish connection with wrds\n",
    "    db = wrds.Connection()\n",
    "\n",
    "    # Define your SQL statement\n",
    "    sql_statement = \"\"\"\n",
    "    SELECT a.permno, b.ticker, a.date, a.prc, a.cfacshr, b.comnam\n",
    "    FROM crsp_m_stock.msf as a\n",
    "    LEFT JOIN crsp_m_stock.msenames as b\n",
    "    ON a.permno=b.permno AND b.namedt<=a.date AND a.date<=b.nameendt\n",
    "    WHERE a.date >= '{}' AND a.date <= '{}' AND b.ticker = 'AMZN'\n",
    "    \"\"\"\n",
    "\n",
    "    # Perform the query\n",
    "    df = db.raw_sql(sql_statement.format(start_date, end_date))\n",
    "    \n",
    "    # Reformat date\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    \n",
    "    # Restrict to one specific stock: AMAZON\n",
    "    #df = df[df['permno'] == 84788]\n",
    "    \n",
    "    # Adjust prices for stock splits, issuance etc.\n",
    "    df['prc'] = df['prc']/df['cfacshr']\n",
    "    \n",
    "    # Minor adjustments\n",
    "    df = df[['permno','date','comnam','prc']].set_index('date') # Restrict attention to variables of interest: PERMNO, Date, Company Name and Price\n",
    "\n",
    "    # Close the connection\n",
    "    db.close()\n",
    "    \n",
    "    print(df.head(5))\n",
    "    print('\\n.dta file successfully loaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Plotting Data\n",
    "\n",
    "We will create a basic plot to test the functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plot successfully plotted!\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 6))\n",
    "plt.plot(df[['prc']], color = 'maroon', linestyle = '--')\n",
    "plt.show()\n",
    "plt.close()\n",
    "print('\\nPlot successfully plotted!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}