scarface-mean-reversion

Automate mean reversion trading with standard deviation bands and RSI indicators.

Updated Jan 24, 2026

Search Tags:#strategy #finance #trading #statistical #quantitative #mean reversion

Ready to run

npx skills add https://github.com/copyleftdev/sk1llz --skill scarface-mean-reversion

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scarface-mean-reversion
Source: https://github.com/copyleftdev/sk1llz/tree/main/domains/trading/mean-reversion
Command: npx skills add https://github.com/copyleftdev/sk1llz --skill scarface-mean-reversion

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the identification and execution of mean reversion trading setups, leveraging statistical probabilities to capture price reversals at market extremes.

Core Features & Use Cases

Statistical Edge Identification: Pinpoints trading opportunities based on standard deviation bands and RSI extremes.
Scaled Entry & Exit Logic: Implements a systematic approach to entering and exiting positions in thirds for optimal risk management.
Backtesting Framework: Provides tools to rigorously test the mean reversion strategy on historical data.
Use Case: Identify and automatically trade stocks that have moved 2-3 standard deviations away from their mean, with RSI confirming overbought or oversold conditions, and scale into positions with defined risk.

Quick Start

Use the scarface-mean-reversion skill to scan for long setups in AAPL and MSFT with a 20-period lookback.

Frequently Asked Questions about scarface-mean-reversion

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

FAQPage Schema

How do I identify mean reversion trading opportunities using standard deviation and RSI?▼

Mean reversion trading opportunities are identified by calculating standard deviation bands to find price extremes and confirming overbought or oversold conditions with RSI indicators. This Skill automates that statistical edge detection process for systematic execution.

Can I backtest a mean reversion strategy on historical stock data using Python?▼

Yes, you can backtest mean reversion strategies on historical data using Python. This Skill includes a backtesting framework that leverages pandas, numpy, and scipy to rigorously simulate and validate trading strategies against past market behavior.

Do I need pandas and scikit-learn to automate scaled entry and exit logic for trading?▼

You need pandas, numpy, scipy, and scikit-learn installed to run this mean reversion strategy. These Python libraries provide the statistical analysis and machine learning foundation required to compute position sizing and automate scaled entry and exit logic.

What is the best way to automate position sizing based on defined risk for mean reversion?▼

The best way to automate position sizing is by implementing a systematic approach that scales into positions in thirds. This Skill handles risk-defined position sizing automatically when entering trades at statistical extremes like 2-3 standard deviations from the mean.

How does scaled entry in thirds work for mean reversion trading setups?▼

Scaled entry in thirds divides your total position into three incremental orders placed as the asset moves further into statistical extremes. This systematic risk management approach optimizes entry averages when trading mean reversion setups at standard deviation bands.

Why does my mean reversion backtest require scipy and scikit-learn dependencies?▼

Mean reversion backtesting requires scipy and scikit-learn because statistical analysis and quantitative modeling are computationally intensive. These dependencies provide the mathematical functions needed to calculate standard deviation bands and validate trading simulations accurately.