async_main.py

import aiohttp
import asyncio
from bs4 import BeautifulSoup

HEADERS = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'}

async def fetch_data_from_api(url):
    """
    Async function to call the API and fetch data from the endpoint.
    It raises an except if time out.   

    Returns:
        response(text): Data received from the API.
    """
    conn = aiohttp.TCPConnector(limit=50)  # allows 50 concurrent connections
    timeout = aiohttp.ClientTimeout(total=20)  # timeout duration 
    
    async with aiohttp.ClientSession(connector= conn, timeout= timeout) as session:
        try:
            async with session.get(url, headers= HEADERS) as resp:
                response = await resp.text()
                return response 
        except asyncio.TimeoutError:
            # Handle a timeout error here
            print("Request timed out.")

async def get_symbols():
    """
    Async function to parse data from the html into a beautifulsoup object.
    Scrap the content for the stock symbols. 

    Returns:
        symbols(list): A list of the first 50 symbols in the S&P 500 companies table.
    """
    url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
    response = await fetch_data_from_api(url)
    # parse data from the html into a beautifulsoup object
    soup = BeautifulSoup(response, 'html.parser')
    companies_table=soup.find('table',{'id':'constituents','class':"wikitable sortable"})
    a = companies_table.find_all('a',{'class':"external text"})
    symbols =  [symbol.text.strip() for symbol in a[:50]]        

    return symbols

async def previous_close(symbols):
    """
    Async function to feacth data from the API to each symbol.
    Parse data from html into a beautifulsoup object.
    Scrap the content for the Stock Previous Close Value;

    Returns:
        previous_close_values(list): A list with the previous close value to each stock.
    """
    previous_close_values = []
    for symbol in symbols:
        url = f'https://finance.yahoo.com/quote/{symbol}?p={symbol}tsrc=fin-srch'
        response = await fetch_data_from_api(url)
        page  = BeautifulSoup(response, 'html.parser')
        previous_close_value = page.find('td', {'data-test':"PREV_CLOSE-value"})
        previous_close_values.append(float(previous_close_value.text))
        #print(f"P: {previous_close_value.text}")
    print("Got all previous close values.")
    return previous_close_values

async def moving_average(symbols):
    """
    Async function to feacth data from the API to each symbol.
    Parse data from html into a beautifulsoup object.
    Scrap the content for the Stock 200-Day Moving Average value.

    Returns:
        moving_average_values(list): A list with the moving average value to each stock.
    """
    moving_average_values = []

    for symbol in symbols:
        url = f'https://finance.yahoo.com/quote/{symbol}/key-statistics?p={symbol}'
        response = await fetch_data_from_api(url)
        page  = BeautifulSoup(response, 'html.parser')
        two_columns = page.find('div',{'id':'Col1-0-KeyStatistics-Proxy'})
        right_column = two_columns.find('div', {'class':'Fl(end) W(50%) smartphone_W(100%)'})
        td = right_column.find_all('td',{'class':'Fw(500) Ta(end) Pstart(10px) Miw(60px)'})
        moving_average_value = td[6]
        #print(f"M: {moving_average_value.text}")
        moving_average_values.append(float(moving_average_value.text))
    print("Got all moving average values.")
    return moving_average_values

async def get_stock_value():
    """
    Async function to await for the get_symbols to finish. 
    And run both previous_close and moving_avera function concurrently and collect their results.

    Returns:
        tuple[list,list,list]: A tuple with three elements, 
                               the stock symbols list,
                               the previous close values list and moving average values list.
    """
    ticker_values = await get_symbols()
    print("Got all tickers.")
    previous_close_values, moving_average_values = await asyncio.gather(previous_close(ticker_values), moving_average(ticker_values))
    return ticker_values, previous_close_values, moving_average_values

def create_data_frame(ticker_values, previous_close_values, moving_average_values):
    """
        Method to format the processed data as a dataframe. 
        Create a column called "is_cheap".
        And plot a bar graph with only the values where the "is_cheap" is True.

        Args:
          ticker_values(list): A list with each stock symbol.
          previous_close_values(list): A list with each stock previous close value.
          moving_average_values(list): A list with each stock moving average value.
    """
    df = pd.DataFrame(list(zip(previous_close_values, ticker_values)),
                columns =['Previous Close', 'Ticker'])

    df['200-Day Moving Average 3'] = moving_average_values
    df['is_cheap'] = np.where(df['Previous Close'] < df['200-Day Moving Average 3'], True, False)

    condition = df['is_cheap'] == True

    hist = df[condition].plot(kind='bar', x='Ticker', y='Previous Close').get_figure()
    hist.savefig('async_plot.png')

    print("The plot was save as an image named async_plot.png")

    return 

if __name__ == '__main__':
    stock_values = asyncio.run(get_stock_value())
    
    ticker_values, previous_close_values, moving_average_values = stock_values[0], stock_values[1], stock_values[2]

    create_data_frame(ticker_values,previous_close_values, moving_average_values)