high_dividend_yield.py

import requests
import pandas as pd
from config import financial_model_prep
import sys
import os
parent_dir = os.path.dirname(os.getcwd())
sys.path.append(parent_dir)
import tickers as ti

# Create an instance of the financial model prep class for API access
demo = financial_model_prep()

# Read ticker symbols from a saved pickle file
symbols = ti.tickers_sp500()

# Initialize a dictionary to store dividend yield data for each company
yield_dict = {}

# Loop through each ticker symbol in the list
for company in symbols:
    try:
        # Fetch company data from FinancialModelingPrep API using the ticker symbol
        companydata = requests.get(f'https://fmpcloud.io/api/v3/profile/{company}?apikey={demo}')
        companydata = companydata.json()  # Convert response to JSON format
        
        # Extract relevant data for dividend calculation
        latest_Annual_Dividend = companydata[0]['lastDiv']
        price = companydata[0]['price']
        market_Capitalization = companydata[0]['mktCap']
        name = companydata[0]['companyName']
        exchange = companydata[0]['exchange']

        # Calculate the dividend yield
        dividend_yield = latest_Annual_Dividend / price

        # Store the extracted data in the yield_dict dictionary
        yield_dict[company] = {
            'dividend_yield': dividend_yield,
            'latest_price': price,
            'latest_dividend': latest_Annual_Dividend,
            'market_cap': market_Capitalization / 1000000,  # Convert market cap to millions
            'company_name': name,
            'exchange': exchange
        }

    except Exception as e:
        # Skip to the next ticker if there's an error with the current one
        print(f"Error processing {company}: {e}")
        continue

# Convert the yield_dict dictionary to a pandas DataFrame
yield_dataframe = pd.DataFrame.from_dict(yield_dict, orient='index')

# Sort the DataFrame by dividend yield in descending order
yield_dataframe = yield_dataframe.sort_values('dividend_yield', ascending=False)

# Display the sorted DataFrame
print(yield_dataframe)