Options Strategy

Purpose

Backtesting of option portfolio strategies. Starting from the underlying price, the engine synthesizes theoretical option prices with the Black-Scholes model, then simulates PnL, Greeks exposure, and expiration exercise for multi-leg option portfolios.

Applicable scenarios:

• Hedging strategies (covered call, protective put)
• Volatility trading (straddle, strangle)
• Spread strategies (iron condor, butterfly, calendar spread)
• Option pricing analysis and Greeks sensitivity research

Supported Strategy Types

Strategy	Structure	Applicable Market View
Covered Call	Hold underlying + short call	Mildly bullish, collect premium
Protective Put	Hold underlying + long put	Bullish but wants downside protection
Straddle	Buy same-strike call + put	Expect large movement, direction uncertain
Strangle	Buy different-strike call + put	Expect large movement, lower cost
Iron Condor	Sell put spread + sell call spread	Range-bound market, collect premium
Butterfly	Buy low call + sell 2 middle calls + buy high call	Expect narrow-range movement
Calendar Spread	Sell near-month + buy far-month at same strike	Exploit differences in time decay

OptionsSignalEngine Interface

Write the strategy in code/signal_engine.py, with class name SignalEngine, implementing the generate method:

class SignalEngine:
    """Option strategy signal engine."""

    def generate(self, data_map: dict) -> list:
        """Generate option trading instructions.

        Args:
            data_map: code -> DataFrame (columns: open, high, low, close, volume)