advanced-math-trading/information-causal

Quantify mutual information, transfer entropy, and causal relationships in trading signals.

Updated Aug 27, 2026

Search Tags:#signals #information-theory #causal-inference #bayesian #transfer-entropy

Ready to run

npx skills add https://github.com/keith-mvs/ordinis --skill advanced-math-trading-information-causal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: advanced-math-trading/information-causal
Source: https://github.com/keith-mvs/ordinis/tree/main/docs/knowledge-base/domains/skills/advanced-math-trading/information-causal
Command: npx skills add https://github.com/keith-mvs/ordinis --skill advanced-math-trading-information-causal

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill explores information theory, causal inference, and Bayesian conditioning for trading signals.

Core Features & Use Cases

Mutual Information & Transfer Entropy: Feature relevance for signals
Causal Validation: Granger-causality and DAG-based checks
Bayesian Conditioning: Update beliefs with new data

Quick Start

Example: "Assess MI between price and a candidate indicator and perform a basic causal test."

Frequently Asked Questions about advanced-math-trading/information-causal

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

FAQPage Schema

How do I measure mutual information between trading signals and price movements?▼

Mutual information quantifies how much one signal reduces uncertainty about another. This Skill computes MI between price and candidate indicators using information theory, helping you identify which signals contain predictive relevance for trading decisions.

Can I validate causal relationships in trading signals before live trading?▼

Yes. This Skill performs Granger-causality and DAG-based causal checks to confirm that signals causally influence price rather than merely correlating with it, reducing false signal discovery in backtesting and live scenarios.

What's the difference between correlation and causal inference for trading indicators?▼

Correlation only measures co-movement; causal inference determines whether one variable actually drives another. This Skill uses transfer entropy and Granger tests to distinguish causality from spurious correlation, strengthening signal validation.

How do I update trading beliefs with new market data using Bayesian methods?▼

Bayesian conditioning updates prior probabilities as new evidence arrives. This Skill implements conditional probability updates, letting you refine signal confidence incrementally as fresh data arrives during backtesting or live trading.

Do I need advanced statistical knowledge to assess feature relevance in signals?▼

This Skill wraps information-theoretic and causal inference methods, but understanding mutual information, transfer entropy, and Granger causality concepts helps interpret results. The dependencies are numpy, pandas, and scipy.

Can I use transfer entropy to detect lead-lag relationships between multiple trading signals?▼

Transfer entropy measures directed information flow between time series, revealing which signals lead others. This Skill estimates TE to identify predictive signal ordering and validate causal chains in multi-signal strategies.