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regime

Classify market regimes and output structured trading signals from ticker or CSV data.

443|212|Updated May 19, 2026
Search Tags:#forecasting#trading#risk#market#regime#markov
One-click install
npx skills add https://github.com/jackson-video-resources/markov-hedge-fund-method --skill regime
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regime
Source: https://github.com/jackson-video-resources/markov-hedge-fund-method/tree/main/skills/regime
Command: npx skills add https://github.com/jackson-video-resources/markov-hedge-fund-method --skill regime

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Detect the market regime (Bull / Bear / Sideways) for any asset and turn it into a tradeable signal or a risk filter. Use this whenever the user wants regime detection, a regime-aware confirmation on an existing strategy, a regime risk gate, regime-based position sizing, a Markov transition matrix, n-step regime forecasting, a stationary regime mix, or a no-lookahead walk-forward regime backtest — on a ticker (via yfinance) or on the user's own CSV price series. Composes into any existing trading agent or strategy without rewriting it. Framework by Roan (@RohOnChain).

Core Features & Use Cases

  • Regime detection and labeling for an asset, classifying days as Bull, Bear, or Sideways.
  • Builds a 3×3 transition matrix and performs n-step regime forecasting to project regime dynamics.
  • Emits a tradeable signal (bull_prob − bear_prob) and supports regime-based risk gating and position sizing.
  • Optional HMM support via hmmlearn with graceful degradation if compilation fails.

Quick Start

Invoke the regime analysis with a ticker or CSV, for example uv run ${CLAUDE_PLUGIN_ROOT}/scripts/markov_regime.py --ticker BTC-USD --json.

Frequently Asked Questions about regime

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

FAQPage Schema
How do I detect market regimes for a specific stock ticker?▼

You can detect market regimes by passing a stock ticker to the script, which fetches price data via yfinance and classifies the current state as Bull, Bear, or Sideways. It outputs a tradeable signal and transition matrix.

Can I use my own CSV price data for regime classification?▼

Yes, the regime classifier accepts user-provided CSV price series as an alternative to tickers. It processes the custom data to identify the current market regime and generates a structured JSON signal for trading decisions.

What is a Markov transition matrix used for in market regime forecasting?▼

A Markov transition matrix projects regime dynamics by calculating the probability of shifting between Bull, Bear, and Sideways states. It enables n-step regime forecasting to anticipate future market conditions and guide position sizing.

How do I add a regime-aware risk gate to an existing trading strategy?▼

You can slot the regime detector into an existing strategy as a confirmation layer or risk gate. It emits a tradeable signal based on bull and bear probabilities, allowing you to filter trades without rewriting your framework.

Does the regime detector require hmmlearn to function properly?▼

No, the regime detector features graceful degradation if the hmmlearn dependency fails to compile. It still performs core Markov regime classification and outputs walk-forward backtest metrics without the optional HMM support.

Does the regime backtest suffer from lookahead bias?▼

No, the regime backtest is deterministic and explicitly designed with no-lookahead logic. It provides walk-forward metrics to ensure that regime signals are based strictly on historical data available at each point.

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