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options-strategy

Simulate options portfolio backtests with Black-Scholes pricing and multi-leg strategies.

Updated Apr 9, 2026
Search Tags:#backtesting#options#trading#volatility#greeks#multi-leg#black-scholes
One-click install
npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill options-strategy-jacobhsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: options-strategy
Source: https://github.com/JacobHsu/vibe-trading-agent/tree/main/agent/src/skills/options-strategy
Command: npx skills add https://github.com/JacobHsu/vibe-trading-agent --skill options-strategy-jacobhsu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the need to manually price and simulate options portfolios by generating synthetic Black-Scholes valuations based on underlying price series, so traders can assess Greeks and capital outcomes before deploying real capital.

Core Features & Use Cases

  • Black-Scholes Pricing Engine: Synthesizes call and put values for any strike/expiry pair with configurable risk-free rate, historical volatility source, and contract multiplier so deep Greeks and synthetic Greeks flows are available on demand.
  • Multi-leg Strategy Support: Drives covered calls, protective puts, straddles, strangles, iron condors, butterflies, and calendar spreads with a consistent instruction format that lists legs, expiry, strike, and quantity.
  • Configurable Backtests: Reads config.json targeting engine "options" with codes, date range, cash, and commission inputs, then outputs equity, metrics, trades, greeks, and raw OHLCV artifacts for both crypto and equity universes.
  • Use Case: Use the signal engine to test a volatility trade on BTC-USDT by opening a straddle on an upcoming expiry, then study the greeks.csv file to decide whether to roll or close the position.

Quick Start

Ask the agent to run an options-strategy backtest on BTC-USDT straddle using the historical volatility source.

Frequently Asked Questions about options-strategy

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

FAQPage Schema
How do I backtest multi-leg options strategies like iron condors and straddles?▼

To backtest multi-leg options strategies, you simulate synthetic Black-Scholes valuations from underlying OHLCV price histories. This computes theoretical pricing, Greeks, and PnL for constructs like iron condors and straddles before deploying capital.

Can I simulate options pricing for cryptocurrency markets using historical volatility?▼

Yes, you can simulate options pricing for cryptocurrency markets by configuring the IV source to use historical volatility. The engine synthesizes call and put values for crypto pairs like BTC-USDT from underlying price series.

What's the best way to calculate Greeks for a covered call backtest?▼

The best way to calculate Greeks for a covered call backtest is using a synthetic Black-Scholes pricing engine. It generates deep Greeks and PnL outcomes on demand by reading underlying price histories and options config parameters.

How do I configure a config.json file to run an options backtest?▼

To configure a config.json file for an options backtest, specify the engine as "options" and include codes, start and end dates, initial cash, and commission. Add options_config parameters for risk-free rate, IV source, and contract multiplier.

Does the Black-Scholes pricing engine support calendar spreads and butterflies?▼

Yes, the Black-Scholes pricing engine supports calendar spreads and butterflies through a consistent instruction format. You list legs, expiry, strike, and quantity to drive these multi-leg strategy constructs.

Why do I need a data_map of OHLCV series for options backtesting?▼

You need a data_map of OHLCV series because the backtest simulates options portfolios from underlying price histories. The OHLCV data drives the synthetic Black-Scholes valuations that compute theoretical pricing and Greeks.

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