Options Strategies: Volatility Trading and Greeks Management

Module: Fintech AI | Difficulty: Advanced

Straddle

Iron Condor

Volatility Arbitrage

Greeks Management

import numpy as np

class OptionsStrategy:
    def __init__(self):
        self.positions = []
    def add_leg(self, option_type, strike, premium, quantity):
        self.positions.append({
            'type': option_type, 'strike': strike,
            'premium': premium, 'quantity': quantity
        })
    def pnl_at_expiry(self, spot):
        total = 0
        for pos in self.positions:
            if pos['type'] == 'call':
                intrinsic = max(0, spot - pos['strike'])
            else:
                intrinsic = max(0, pos['strike'] - spot)
            total += (intrinsic - pos['premium']) * pos['quantity']
        return total
    def max_profit_loss(self):
        strikes = sorted([p['strike'] for p in self.positions])
        pnl_range = [self.pnl_at_expiry(s) for s in np.linspace(strikes[0]-10, strikes[-1]+10, 100)]
        return max(pnl_range), min(pnl_range)

Research Insight: Volatility trading requires understanding the relationship between implied and realized volatility. When implied volatility exceeds realized volatility, selling options is profitable on average, but requires careful risk management to avoid large losses.