markov-regime-features

Diagnose constant Markov regime features in RL observations using InferenceObservationBuilder.

3|Updated Dec 10, 2025
One-click install
npx skills add https://github.com/smith6jt-cop/Skills_Registry --skill markov-regime-features
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: markov-regime-features
Source: https://github.com/smith6jt-cop/Skills_Registry/tree/main/plugins/trading/markov-regime-features/skills/markov-regime-features
Command: npx skills add https://github.com/smith6jt-cop/Skills_Registry --skill markov-regime-features

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

RL observation plots showed constant Markov regime features due to uniform priors or stale state; this Skill guides you to implement stateful Markov tracking to produce dynamic regime probabilities.

Core Features & Use Cases

  • Use InferenceObservationBuilder for stateful Markov tracking across price history.
  • Build observations so vol_probs and trend_probs evolve with data.
  • Visualize regime evolution and diagnose stationary priors.

Quick Start

Create an InferenceObservationBuilder(window=100, use_gpu_markov=True) and call build(prices=..., high=..., low=...). Access vol_probs and trend_probs to inspect regime probabilities.

Frequently Asked Questions about markov-regime-features

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

FAQPage Schema
How do I fix constant Markov regime features in RL observations?▼

Markov regime features remain uniform when using default priors without stateful tracking. Use InferenceObservationBuilder with window=100 and use_gpu_markov=True to maintain Markov state across price history, allowing vol_prob and trend_prob to evolve dynamically instead of staying frozen.

What causes uniform volatility and trend probabilities in RL observation heatmaps?▼

Uniform probabilities indicate stale or missing Markov state. The Markov regime inference requires stateful tracking that updates vol_prob_low/med/high and trend_prob_down/neutral/up based on incoming price data; without it, priors never change.

How do I implement stateful Markov tracking for price history?▼

Create an InferenceObservationBuilder, configure it with window=100 and use_gpu_markov=True for GPU acceleration, then call build() with prices, high, and low arrays. The builder maintains state across calls and outputs evolving vol_probs and trend_probs reflecting current regime estimates.

Can I visualize how Markov regime probabilities change over time?▼

Yes. After building observations with InferenceObservationBuilder, inspect vol_probs and trend_probs across your observation sequence to see regime evolution. This reveals whether probabilities adapt to price volatility and trend changes or remain stationary.

Do I need GPU acceleration for Markov regime inference?▼

GPU acceleration is optional but recommended for performance. Set use_gpu_markov=True in InferenceObservationBuilder to enable it; this accelerates stateful inference across large price histories, though CPU execution is supported.

What window size should I use for Markov state tracking?▼

Window=100 is the recommended starting point for InferenceObservationBuilder; it balances Markov state freshness with sufficient historical context to estimate volatility and trend regimes from price movements.