markov-regime

Name: markov-regime
Availability: InStock
Author: danielorzeata83-a11y

Classify market regimes and forecast probabilities using Markov models.

Updated May 31, 2026

Search Tags:#market analysis #forecasting #backtesting #volatility #markov regime

Ready to run

npx skills add https://github.com/danielorzeata83-a11y/Abc --skill markov-regime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: markov-regime
Source: https://github.com/danielorzeata83-a11y/Abc/tree/main/.claude/skills/markov-regime
Command: npx skills add https://github.com/danielorzeata83-a11y/Abc --skill markov-regime

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, hmmlearn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill identifies the market regime (bull, sideways, bear) for a given symbol using the Markov regime trading method, providing insights into the current market state.

Core Features & Use Cases

Market Regime Detection: Classify the market regime as bull, sideways, or bear based on historical data.
Forecasting: Predict future market regime probabilities.
Volatility Analysis: Assess the volatility level of the market.
Backtesting: Validate the model's performance using walk-forward backtesting.
Use Case: Use this Skill to analyze the market regime for a stock like AAPL or NVDA, understand the current trend, and make informed trading decisions.

Quick Start

Use the markov-regime skill to analyze the market regime for the symbol AAPL.

Frequently Asked Questions about markov-regime

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

FAQPage Schema

How do I detect market regimes like bull, sideways, or bear for a specific stock?▼

Market regime detection classifies a symbol into bull, sideways, or bear states using the Markov regime trading method. This Skill analyzes historical data to identify the current market trend and provide insights for informed trading decisions.

Can I forecast future market regime probabilities using historical stock data?▼

You can forecast future market regime probabilities by applying Markov regime models to historical stock data. The Skill calculates upcoming regime likelihoods and pairs them with volatility analysis to assess expected market risk.

Do I need Python and hmmlearn to run Markov regime backtesting?▼

You need Python with numpy, pandas, and hmmlearn installed to run Markov regime backtesting. These libraries provide the necessary computational foundation for regime classification, volatility analysis, and walk-forward validation.

What's the best way to validate a market regime classification model?▼

The best way to validate a market regime classification model is through walk-forward backtesting. This Skill tests the Markov regime method against historical data to confirm the accuracy of regime predictions and volatility analysis.

How does Markov regime analysis handle market volatility for trading signals?▼

Markov regime analysis handles market volatility by assessing historical price fluctuations to classify the current market state. The Skill evaluates volatility levels alongside regime probabilities to generate informed trading decision insights.