LaunchKit · 2026
Back to Skills

options-spread-conviction-engine

Multi-regime options spread analysis engine with quantitative rigor.

0
1.6k downloads
by @adamnaghs

Setup & Installation

openclaw skills install @adamnaghs/options-spread-conviction-engine

Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:

npx clawhub install options-spread-conviction-engine

Version History

v2.2.1Feb 14, 2026 - Version note by skill developer:

Major upgrade introducing quantitative regime detection, volatility forecasting, robust position sizing, and backtesting. - Added VIX-based regime detection to adapt strategies to market conditions. - Integrated GARCH volatility forecasting for improved IV and risk estimation. - Introduced drawdown-constrained Kelly position sizing for smarter, risk-aware contract sizing. - Implemented walk-forward backtesting tools to validate scoring and position sizing models. - New scripts and tests: regime detection, volatility forecasting, enhanced Kelly sizing, integration and backtest validators. - Updated documentation to reflect quantitative enhancements and methodology changes.

What This Skill Does

Options spread analysis engine that scores seven strategies across vertical spreads and multi-leg structures. Uses VIX-based regime detection, GARCH volatility forecasting, and drawdown-constrained Kelly sizing to output conviction tiers. Covers bull put, bear call, iron condor, butterfly, and calendar spreads from a single CLI.

Combines regime-aware scoring, GARCH volatility forecasting, and drawdown-constrained Kelly sizing in one CLI tool, where most retail scanners provide only raw IV rank or single-indicator signals.

When to use it

  • Screening SPY iron condor entries during high-IV environments
  • Sizing bull put spread contracts on a small options account
  • Detecting inverted IV term structure for calendar spread opportunities
  • Scanning Nasdaq 100 tickers for EXECUTE-tier butterfly setups
  • Walk-forward backtesting a strategy's conviction scores on historical data

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Score SPY for an iron condor setup

AGENT
  1. 1Fetch SPY price history and options chain data from Yahoo Finance
  2. 2Calculate IV rank proxy using Bollinger Bandwidth percentile over 252 trading days
  3. 3Score RSI neutrality, ADX range-bound condition, price centering, and MACD flatness
  4. 4Detect current VIX regime and apply regime-adjusted weights to component scores
  5. 5Output conviction tier, all four strike prices, max profit zone, and wing width
OUTPUT

SPY iron condor score of 31.8/100 (WAIT tier), strikes BUY 680P / SELL 685P / SELL 695C / BUY 700C, max profit zone $685-$695, wing width $5.00

Requirements

Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.

yahoo-finance2 (npm package)