Options analytics agent guide
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文，并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill options-analytics-agent-guideAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
What its author says it does
Copied from the file, not written here
AI agent for options pricing, Greeks, and strategy analysis
SKILL.md
3.1 KB, as published. Nobody here has run it
Options Analytics Agent Guide
Overview
An AI agent for options pricing, risk analysis, and strategy evaluation. It combines Black-Scholes and binomial models, Greeks calculations, implied volatility surfaces, and portfolio risk analytics into a conversational interface. Researchers and quantitative analysts can query options data, price exotic derivatives, and evaluate trading strategies through natural language.
Core Capabilities
from options_agent import OptionsAgent
agent = OptionsAgent(llm_provider="anthropic")
# Price an option
result = agent.price(
option_type="call",
strike=100,
spot=105,
expiry_days=30,
risk_free_rate=0.05,
volatility=0.20,
model="black_scholes",
)
print(f"Price: ${result.price:.2f}")
print(f"Delta: {result.delta:.4f}")
print(f"Gamma: {result.gamma:.4f}")
print(f"Theta: {result.theta:.4f}")
print(f"Vega: {result.vega:.4f}")
print(f"Rho: {result.rho:.4f}")
Greeks Analysis
# Full Greeks surface
surface = agent.greeks_surface(
strike=100,
spot_range=(80, 120),
expiry_range=(7, 90), # days
volatility=0.25,
)
surface.plot_delta_surface("delta_surface.png")
surface.plot_gamma_surface("gamma_surface.png")
surface.plot_theta_decay("theta_decay.png")
Strategy Evaluation
# Evaluate an options strategy
strategy = agent.evaluate_strategy(
legs=[
{"type": "call", "strike": 100, "action": "buy", "qty": 1},
{"type": "call", "strike": 110, "action": "sell", "qty": 1},
],
spot=105,
expiry_days=30,
volatility=0.20,
)
print(f"Strategy: {strategy.name}") # Bull Call Spread
print(f"Max profit: ${strategy.max_profit:.2f}")
print(f"Max loss: ${strategy.max_loss:.2f}")
print(f"Breakeven: ${strategy.breakeven:.2f}")
strategy.plot_payoff("payoff.png")
strategy.plot_pnl_scenarios("scenarios.png")
Implied Volatility
# Calculate implied volatility
iv = agent.implied_volatility(
market_price=5.50,
option_type="call",
strike=100,
spot=105,
expiry_days=30,
risk_free_rate=0.05,
)
print(f"Implied volatility: {iv:.2%}")
# Volatility smile/surface
vol_surface = agent.volatility_surface(
ticker="SPY",
date="2025-03-10",
)
vol_surface.plot("vol_surface.png")
Use Cases
- Options pricing: Black-Scholes and numerical methods
- Risk management: Greeks and portfolio risk metrics
- Strategy analysis: P&L profiles and breakeven analysis
- Volatility analysis: IV surfaces and skew analysis
- Education: Interactive derivatives teaching tool
References
- Options Analytics Agent
- QuantLib — Quantitative finance library