Calculate theoretical prices and Greeks for European and American options.
npx skills add https://github.com/gauss314/skills --skill option-pricing-gauss314
Please help me install this Agent Skill. Skill: option-pricing Source: https://github.com/gauss314/skills/tree/main/skills/option-pricing Command: npx skills add https://github.com/gauss314/skills --skill option-pricing-gauss314
This skill solves the need for fast, accurate, and transparent option pricing in backtesting environments, eliminating the reliance on heavy, opaque external libraries.
Use the option-pricing skill to calculate the Black-Scholes price for a call option with a spot price of 100, strike of 100, 0.25 years to maturity, 5% risk-free rate, and 20% volatility.
High-intent search queries and answers about installing and using this skill.
Option pricing supports American and European exercise styles using Black-Scholes, binomial trees, Monte Carlo, and stochastic volatility models. It calculates theoretical prices and Greeks using vectorized NumPy routines for high-throughput performance.
The best way to run high-throughput option pricing for backtesting is using vectorized NumPy routines. This approach achieves up to 800,000 operations per second for closed-form models, maintaining sub-millisecond latency per option for massive quantitative research workflows.
NumPy supports vectorized Monte Carlo simulations and stochastic volatility models. The implementation uses flat, vectorized Python code to calculate theoretical prices for Heston and Bates models, enabling fast and transparent pricing without heavy external libraries.
You can use binomial CRR and trinomial tree methods for American option pricing without external libraries. The implementation relies solely on NumPy, providing transparent calculations for early exercise scenarios in backtesting environments.
You should use Longstaff-Schwartz over Black-Scholes when pricing American options requiring early exercise valuation. Black-Scholes handles European options via closed-form solutions, while Longstaff-Schwartz uses Monte Carlo simulation for American option path-dependent pricing.
Volatility surface calibration requires stochastic volatility models like Heston because they capture the volatility smile and term structure dynamics that flat-volatility models miss. The Heston and Bates models parameterize volatility randomness to fit observed market option prices.
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