options-strategy-advisor
Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.
npx skills add BaggaT236/AI-Trading-Skills --skill options-strategy-advisor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Options Strategy Advisor ## Overview This skill provides comprehensive options strategy analysis and education using theoretical pricing models. It helps traders understand, analyze, and simulate options strategies without requiring real-time market data subscriptions. **Core Capabilities:** - **Black-Scholes Pricing**: Theoretical option prices and Greeks calculation - **Strategy Simulation**: P/L analysis for major options strategies - **Earnings Strategies**: Pre-earnings volatility plays integrated with Earnings Calendar - **Risk Management**: Position sizing, Greeks exposure, max loss/profit analysis - **Educational Focus**: Detailed explanations of strategies and risk metrics **Data Sources:** - FMP API: Stock prices, historical volatility, dividends, earnings dates - User Input: Implied volatility (IV), risk-free rate - Theoretical Models: Black-Scholes for pricing and Greeks ## Prerequisites **Required:** - Python 3.9+ with `numpy`, `scipy`, `requests` **Optional:** - FMP API key (for real-time stock prices and historical volatility) - Set via `FMP_API_KEY` environment variable or `--api-key` argument - Without API key: Use manual inputs for stock price and volatility **I
- Overview
- Prerequisites
- When to Use This Skill
- Supported Strategies
- Income Strategies
- Protection Strategies
- Directional Strategies
- Volatility Strategies
- Range-Bound Strategies
- Advanced Strategies
- Analysis Workflow
- Step 1: Gather Input Data
- Step 2: Calculate Historical Volatility (if IV not provided)
- Step 3: Price Options Using Black-Scholes
pip install numpy scipy requests Basic call option pricing (no API key needed) python3 scripts/black_scholes.py With FMP API key for real-time data python3 scripts/black_scholes.py --ticker AAPL --api-key $FMP_API_KEY Custom option parameters python3 scripts/black_scholes.py --stock-price 180 --strike 185 --days 30 --volatility 0.25 Put option analysis python3 scripts/black_scholes.py --stock-price 180 --strike 175 --days 30 --option-type put
What does the options-strategy-advisor skill do?
Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill options-strategy-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.