Classification/Retrieving_Data.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Webscraping and Downloading the Fundamental Data\n",
    "From Stockpup.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import time\n",
    "import random\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from bs4 import BeautifulSoup as bs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BeautifulSoup to Navigate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "page = requests.get(\"http://www.stockpup.com/data/\")\n",
    "soup = bs(page.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "links = soup.find('div', class_='col-md-4 col-md-offset-1').find_all('p')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating through the Links found on the Website"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making a dictionary of all the stocks and their respective fundamental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "website = \"http://www.stockpup.com\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iterating through the list of links and stocks to create a dictionary full of DataFrames for each stock.\n",
    "\n",
    "As well as tickers and their corresponding company names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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      "\n"
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   ],
   "source": [
    "stocks_df = {}\n",
    "tickers = {}\n",
    "\n",
    "# Randomizing the clicks/downloads\n",
    "seq = [i/10 for i in range(2,12)]\n",
    "\n",
    "for i in tqdm(range(len(links))):\n",
    "    if links[i].find('b').text in stocks_df.keys():\n",
    "        continue\n",
    "    else:\n",
    "        stocks_df[links[i].find('b').text] = pd.read_csv(website+links[i].find('a', href=True).nextSibling.nextSibling['href'])\n",
    "        tickers[links[i].find('b').text] = links[i].text.split(\":\")[0]\n",
    "\n",
    "    time.sleep(random.choice(seq))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pickling the Dictionary of DataFrame for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"stockpup.pkl\", 'wb') as fp:\n",
    "    pickle.dump(stocks_df, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stock tickers with related company name\n",
    "with open(\"tickers.pkl\", 'wb') as fp:\n",
    "    pickle.dump(tickers, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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