OptionsMaxPainCalc.py

'''
FILE: OptionsMaxPainCalc.py
'''
import pandas as pd
import yfinance as yf
import datetime
import matplotlib.pyplot as plt


def options_chain(tk, expiry):
    '''
    Get's the option chain for a given symbol and expiry date and add it to panda df
    Credit: https://medium.com/@txlian13/webscrapping-options-data-with-python-and-yfinance-e4deb0124613
    '''
    # Get options exp
    options = pd.DataFrame()
    opt = tk.option_chain(expiry.strip())
    opt = pd.DataFrame().append(opt.calls).append(opt.puts)
    opt['expirationDate'] = expiry
    options = options.append(opt, ignore_index=True)
    
    # Add 1 day to get the correct expiration date
    options['expirationDate'] = pd.to_datetime(options['expirationDate']) + datetime.timedelta(days = 1)
    options['dte'] = (options['expirationDate'] - datetime.datetime.today()).dt.days / 365
    
    # Boolean column if the option is a CALL
    options['CALL'] = options['contractSymbol'].str[4:].apply(
        lambda x: "C" in x)
    
    options[['bid', 'ask', 'strike']] = options[['bid', 'ask', 'strike']].apply(pd.to_numeric)
    
    # Drop unnecessary and meaningless columns
    options = options.drop(columns = ['contractSize', 'currency', 'change', 'percentChange', 'lastTradeDate', 'lastPrice', 'contractSymbol', 'bid', 'ask', 'impliedVolatility', 'inTheMoney', 'dte'])
    
    return options
    
def total_loss_on_strike(chain, expiry_price):
    '''
    Get's the total loss at the given strike price
    '''    
    # call options with strike price below the expiry price -> loss for option writers
    callChain = chain.loc[chain['CALL'] == True]
    callChain = callChain.dropna()       
    in_money_calls = callChain[callChain['strike'] < expiry_price][["openInterest", "strike"]]
    in_money_calls["CLoss"] = (expiry_price - in_money_calls['strike'])*in_money_calls["openInterest"]

    # get puts n drop null values
    putChain = chain.loc[chain['CALL'] == False]
    putChain = putChain.dropna()    
    
    # put options with strike price above the expiry price -> loss for option writers
    in_money_puts = putChain[putChain['strike'] > expiry_price][["openInterest", "strike"]]
    in_money_puts["PLoss"] = (in_money_puts['strike'] - expiry_price)*in_money_puts["openInterest"]
    total_loss = in_money_calls["CLoss"].sum() + in_money_puts["PLoss"].sum()

    return total_loss


def getInput():
    '''
    gets the user input on symbol and expiry
    '''
    symbol = input("Enter the Symbol: " )
    
    tk = yf.Ticker(symbol)
    # Expiration dates
    exps = tk.options   
    print("Expiry dates:")        
    for i in exps:
        print(f"{i}", end=", ")
  
    
    expiry = input("\nEnter the Expiry data [format: YYYY-MM-DD]: " )
    return tk, expiry, symbol

def main():
    '''
    Get's the symbol and expiry date n plot the data
    '''
    # gets the user input on symbol and expiry
    tk, expiry, symbol = getInput()
    chain = options_chain(tk, expiry)
    
    strikes = chain.get(['strike']).values.tolist()
    losses = [total_loss_on_strike(chain, strike[0]) for strike in strikes] 
    
    # max pain min loss to option writers/sellers at strike price
    flat_strikes = [item for sublist in strikes for item in sublist]
    point = losses.index(min(losses))
    max_pain = flat_strikes[point]
    buffer = 3
    bufferHigh = max_pain + (max_pain * (buffer/100))
    bufferLow = max_pain - (max_pain * (buffer/100))
    print(f"Maximum Pain: {bufferLow} < {max_pain} < {bufferHigh}")
    
    # calc put to call ratio
    callChain = chain.loc[chain['CALL'] == True]
    putChain = chain.loc[chain['CALL'] == False]
    pcr = putChain["volume"].sum() / callChain["volume"].sum()
    print("Put to call ratio:", round(pcr,2))
    
    # get the cummulated losses
    total = {}
    for i in range(len(flat_strikes)):
        if flat_strikes[i] not in total: total[flat_strikes[i]] = losses[i]
        else: total[flat_strikes[i]] += losses[i]
            
    
    # plot
    keys = set(list(total.keys()))
    plt.bar(list(keys), list(total.values()), width=1)
    plt.xlabel('Strike Price')
    plt.title(f'{symbol.upper()} Max Pain')    


if __name__ == "__main__":
    main()