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

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)

        Returns:
            List of trading instructions. Each instruction has the format:
            {
                "date": "2024-01-15",        # Trading date
                "action": "open" / "close",  # Open or close position
                "underlying": "BTC-USDT",    # Underlying code
                "legs": [                    # List of option legs
                    {
                        "type": "call" / "put",  # Option type
                        "strike": 50000,          # Strike price
                        "expiry": "2024-02-15",   # Expiration date
                        "qty": 1                  # Quantity (positive = long, negative = short)
                    }
                ]
            }
        """