create_dataset_manual.py

import pandas as pd

def calculate_risk(current_ratio, quick_ratio, payout_ratio):
    return (payout_ratio * 10) - (current_ratio + quick_ratio)

def calculate_return(annualized_growth_rate, div_yld, price, div_cash_flow_period):
    total_cash_flow = 0
    div_amt = price * div_yld     # annual
    for _ in range(div_cash_flow_period):
        total_cash_flow += div_amt
        div_amt *= (1 + annualized_growth_rate)
    
    return total_cash_flow / price      # % return

def generate_pdf_from_data(data, div_cash_flow_period):
    results = {
        'ticker': [],
        'expected_return': [],
        'risk': []
    }
    for ticker in data:
        results["ticker"].append(ticker)
        results["expected_return"].append(
            calculate_return(data[ticker]["annualDividendGrowth"], data[ticker]["annualDividend"], data[ticker]["price"], div_cash_flow_period)
        )
        results["risk"].append(calculate_risk(data[ticker]["currentRatio"], data[ticker]["quickRatio"], data[ticker]["payoutRatio"]))

    pd.DataFrame.from_dict(results).to_csv("./mpt2.csv", index=False)    

if __name__ == '__main__':
    div_cash_flow_period = 5
    data = {
        "AAPL": {
            "price": 145.54,
            "annualDividend": .92,
            "annualDividendGrowth": .10,   # 10 %
            "payoutRatio": .152,           # 15.2 %
            "currentRatio": 1.1,
            "quickRatio": 1.0
        }#,
        #...
    }
    generate_pdf_from_data(data, div_cash_flow_period)