[STUB] Options pricing models including Black-Scholes, binomial trees, Monte Carlo, implied volatility surfaces, and Greeks for crypto options
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill options-pricing
> Status: STUB — This skill provides a basic Black-Scholes implementation and an overview of planned capabilities. Full implementation is awaiting community contribution.
Options pricing is the quantitative foundation of derivatives trading. For crypto markets, options on BTC and ETH trade actively on Deribit, Lyra, and Aevo, while Solana options are emerging on platforms like Zeta Markets and PsyOptions. Understanding pricing models, implied volatility surfaces, and Greeks is essential for hedging, volatility trading, and constructing structured products.
This skill is informational and analytical only. It does not provide financial advice or trading recommendations.
This stub includes a working Black-Scholes calculator with Greeks computation and a basic implied volatility solver. See scripts/black_scholes.py for the implementation.
import math
from scipy.stats import norm
def black_scholes_call(S: float, K: float, T: float, r: float, sigma: float) -> float:
"""Price a European call option using Black-Scholes.
Args:
S: Current underlying price.
K: Strike price.
T: Time to expiration in years.
r: Risk-free rate (annualized).
sigma: Volatility (annualized).
Returns:
Theoretical call option price.
"""
d1 = (math.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * math.sqrt(T))
d2 = d1 - sigma * math.sqrt(T)
return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
Run the demo:
python scripts/black_scholes.py --demo
When fully implemented, this skill will cover:
| Model | Option Style | Use Case |
|-------|-------------|----------|
| Black-Scholes | European | Vanilla calls/puts, quick Greeks |
| Binomial Tree | American | Early exercise, dividend-paying assets |
| Monte Carlo | Exotic | Path-dependent, barrier, Asian options |
| Black-76 | Futures | Futures options on crypto perpetuals |
| Greek | Measures | Formula Basis |
|-------|----------|---------------|
| Delta | Price sensitivity to underlying | dC/dS |
| Gamma | Delta sensitivity to underlying | d²C/dS² |
| Theta | Time decay per day | dC/dT |
| Vega | Sensitivity to volatility | dC/dσ |
| Rho | Sensitivity to interest rates | dC/dr |
| Platform | Chain | Assets | Style |
|----------|-------|--------|-------|
| Deribit | Off-chain | BTC, ETH | European |
| Lyra | Optimism/Arbitrum | ETH, BTC | European |
| Aevo | Ethereum L2 | BTC, ETH, alts | European |
| Zeta Markets | Solana | SOL, BTC | European |
| PsyOptions | Solana | SOL, various | American |
# Core (for full implementation)
uv pip install numpy scipy
# Optional (for visualization)
uv pip install matplotlib
The included scripts/black_scholes.py uses only the Python standard library (math module) and runs without any dependencies.
Compute delta-neutral hedge ratios for crypto spot positions using options. Calculate the number of put contracts needed to protect a portfolio against downside moves.
Compare implied volatility to realized volatility to identify over/underpriced options. When IV significantly exceeds realized vol, selling premium may be favorable (and vice versa).
Price structured products that combine options at different strikes and expirations. Analyze payoff profiles and breakeven points before execution.
Use Greeks to understand portfolio-level exposure to price moves (delta), acceleration (gamma), time decay (theta), and volatility changes (vega).
Call price:
C = S * N(d1) - K * e^(-rT) * N(d2)
Put price:
P = K * e^(-rT) * N(-d2) - S * N(-d1)
Where:
d1 = [ln(S/K) + (r + σ²/2) * T] / (σ * √T)
d2 = d1 - σ * √T
Put-call parity:
C - P = S - K * e^(-rT)
| File | Description |
|------|-------------|
| references/planned_features.md | Planned features, formulas, data sources, and implementation priorities |
| scripts/black_scholes.py | Black-Scholes calculator with Greeks and implied vol solver |
This skill is a stub awaiting full implementation. To contribute:
See references/planned_features.md for the full feature list and implementation priorities.
*This skill provides analytical tools and mathematical models for informational purposes only. It does not constitute financial advice. Options trading involves substantial risk of loss.*
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Take agiprolabs/options-pricing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, uv.
Without those the skill loads but fails at the first command.