options-strategy

Name: options-strategy
Availability: InStock
Author: OpenSucker

A toolbelt for designing, testing, and evaluating options trading strategies and risk metrics in-house.

15|2|Updated May 1, 2026

Search Tags:#strategy #derivatives #backtesting #options #greeks #black-scholes

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill options-strategy-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: options-strategy
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/options-strategy
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill options-strategy-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to build, analyze, and backtest complex options strategies using theoretical models and historical data, simplifying options trading analysis.

Core Features & Use Cases

Strategy Synthesis: Generate option positions such as spreads, straddles, and butterflies based on underlying prices.
Pricing and Greeks Calculation: Use Black-Scholes model to assess option premiums and sensitivities.
Scenario Analysis: Simulate PnL, Greeks exposure, and expiration outcomes for multi-leg options portfolios, suitable for risk management and strategy optimization.
Use Case: An options trader can evaluate the profitability and risks of a calendar spread on BTC options by providing market data and strategy parameters.

Quick Start

Write your options trading strategy in code/signal_engine.py, then run the backtest with your specified configuration to analyze performance and risks.

Frequently Asked Questions about options-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I backtest a multi-leg options strategy using historical data?▼

You can backtest options strategies by writing your trading logic in a signal engine file and running it with specified configurations against historical data to analyze performance and risks.

How does Black-Scholes calculate option premiums and Greeks?▼

The Black-Scholes model calculates option premiums and Greeks sensitivities to simulate PnL, risk exposure, and expiration outcomes for multi-leg portfolios within the framework.

Can I simulate scenario analysis for a calendar spread on BTC options?▼

Yes, scenario analysis simulates PnL, Greeks exposure, and expiration outcomes for multi-leg options portfolios, allowing you to evaluate complex strategies like a calendar spread on BTC options.

Do I need a specific Python environment to run options pricing and backtesting?▼

Yes, running options pricing and backtesting requires a Python environment with relevant libraries installed to support theoretical models and historical data processing.

What is the best way to generate option positions like spreads and butterflies?▼

Strategy synthesis generates option positions such as spreads, straddles, and butterflies based on underlying prices to simplify building complex options trading analysis.