options-strategy-advisor

You can simulate options profit and loss for major strategies by running theoretical models against historical data. This Skill simulates P/L scenarios for strategies like bull call spreads without requiring real-time market data, relying instead on Python and the FMP API.

Yes, you can analyze pre-earnings volatility strategies by integrating an earnings calendar with options pricing models. This Skill uses the FMP API to fetch historical data and evaluate theoretical pricing for volatility plays before earnings announcements.

You do not need real-time market data to calculate options max loss and profit. This Skill performs risk management and position sizing analysis using theoretical pricing methods and historical data from the FMP API rather than live market feeds.

The Python dependencies required for options strategy simulation are numpy, scipy, and requests. These libraries support the mathematical calculations for the Black-Scholes model and handle API requests to fetch historical data for the analysis.

Black-Scholes pricing provides a foundational theoretical model, whereas other theoretical methods may offer different assumptions for volatility and risk metrics. This Skill uses Black-Scholes alongside other theoretical pricing methods to calculate Greeks and simulate strategy outcomes.