YBLL: FactSet Workshop #1

fetch_data.py

import argparse


def main():
    # Instantiate the parser
    parser = argparse.ArgumentParser(description='Retrieves a given datapoint from input PSE:TICKER. Supports timeseries data.')

    # This is an example of configuring positional input arguments to your script
    parser.add_argument('ticker', type=str, help='PSE Ticker')
    parser.add_argument('field', type=str, help='Fieldname')

    # This makes it so that this is an optional argument
    parser.add_argument('date', type=str, nargs='?', help='YYYYMMDD date input for timeseries datapoints.')

    # We start parsing the user input arguments and store them in a dict-like structure
    args = parser.parse_args()

    print("Ticker: {}".format(args.ticker))
    print(f"Fieldname: {args.field}")

    if args.date:
        print(f"Date: {args.date}")
    else:
        print("No date supplied.")
    
    # We are just printing stuff for now...
    # You may add extra validations to ensure inputs are correct such as enforcing the date format, etc.
    # The next logical problem to solve is parsing the CSV file. Let's try to solve this problem first. :-)
        
    # CODE GOES HERE...

if __name__ == "__main__":
    main()

fetch_data_v2.py

import argparse
import csv


def extract_sector_and_subsector(input_string: str) -> (str, str):
    """Extracts the sector and subsector from the input string."""
    sector, subsector = input_string.split("|")
    return sector, subsector


def main():
    # Instantiate the parser
    parser = argparse.ArgumentParser(description='Retrieves a given datapoint from input PSE:TICKER. Supports timeseries data.')

    # This is an example of configuring positional input arguments to your script
    parser.add_argument('ticker', type=str, help='PSE Ticker')
    parser.add_argument('field', type=str, help='Fieldname')

    # This makes it so that this is an optional argument
    parser.add_argument('date', type=str, nargs='?', help='YYYYMMDD date input for timeseries datapoints.')

    # We start parsing the user input arguments and store them in a dict-like structure
    args = parser.parse_args()

    # Let's use a dict data structure to store our data in-memory
    # Remember though that in a real-world scenario, you would want to use a database
    # to store your data. This is just a simple example.
    data = {}

    # Now, let's open the CSV file and read the data into our data structure
    # Assume the filename is hardcoded for this exercise
    with open('pse_data_01052024.csv', 'r') as csv_file:
        reader = csv.reader(csv_file)
        for index, row in enumerate(reader):
            # Condition # 1. Skip header row
            if index == 0:
                continue
            # Condition # 2. Skip rows that start with "<" because we don't need them
            if row[0].startswith("<"):
                continue
            
            # Now we start extracting the elements for every data row
            key = row[0]
            raw_date = row[1]
            company_name = row[2]
            company_price = row[3]
            company_shares = float(row[4])
            net_foreign = row[5]
            sector_subsector = row[6]

            if key not in data:
                data[key] = {}
            data[key]["NAME"] = company_name

            # This is an example of unpacking a Tuple with 2 elements, we directly store them in our dict
            data[key]["SECTOR"], data[key]["SUBSECTOR"] = extract_sector_and_subsector(sector_subsector)

            # At this point, we're able to store facts or non-timeseries data into our dict.
            # Now, try storing timeseries data:
            # You would need a second key to hold the date, and then a third key to hold the fieldname.

            # Example:
            
            # data[key][date]["PRICE"] = *insert variable or value here*
            # data[key][date]["SHARES"] = *insert variable or value here*

            # Make sure date is in YYYYMMDD format!

            # You would also need to convert net foreign from USD to PHP.
            # This is where you can use the API I shared to get the exchange rate.
            # Try creating a function to convert it for you. :)
    
    result = ''
    if args.date:
        result = data[args.ticker][args.date][args.field]
    else:
        result = data[args.ticker][args.field]
    print(result)

if __name__ == "__main__":
    main()