hypothesis-test

Perform statistical hypothesis testing on PAM trading signals to validate alpha evidence.

Updated Feb 27, 2026

Search Tags:#data-analysis #hypothesis #p-value #research-reproducibility #statistical-testing #alpha-signals #pam-database

Ready to run

npx skills add https://github.com/danny0926/NLP-data-for-trading --skill hypothesis-test-danny0926

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hypothesis-test
Source: https://github.com/danny0926/NLP-data-for-trading/tree/main/.claude/skills/hypothesis-test
Command: npx skills add https://github.com/danny0926/NLP-data-for-trading --skill hypothesis-test-danny0926

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill provides a structured, reproducible workflow to validate trading signals through formal hypothesis testing on PAM data, ensuring that alpha signals are backed by statistical evidence.

Core Features & Use Cases

Hypothesis formulation and test selection
Data-driven validation of alpha factors across PAM tables
Structured, audit-friendly reporting with effect sizes and confidence intervals
Use Case: validating a new factor across Senate vs House data

Quick Start

Run the hypothesis-test workflow on your PAM dataset to validate a factor.

Frequently Asked Questions about hypothesis-test

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

FAQPage Schema

How do I validate alpha trading signals with hypothesis testing?▼

To validate alpha trading signals with hypothesis testing, you apply a structured workflow that formulates hypotheses, queries PAM data, calculates effect sizes, and generates reproducible reports to ensure signals are backed by statistical evidence.

What is statistical hypothesis testing for alpha factors?▼

Statistical hypothesis testing for alpha factors is a formal process to determine whether a trading signal has significant predictive power. It enforces a complete workflow from hypothesis design to effect size reporting to validate signal robustness across data.

Can I compare Senate and House trading performance using statistical testing?▼

Yes, you can compare Senate and House trading performance using statistical testing by applying a rigorous hypothesis framework to PAM data. This allows you to validate performance differences and explore signal robustness across different legislative datasets.

Does this hypothesis testing workflow work with numpy and scipy?▼

Yes, the hypothesis testing workflow works with numpy and scipy as its core dependencies. These libraries provide the computational foundation for executing statistical tests and calculating p-values on PAM trading signal data.

How do I ensure research reproducibility when validating PAM signals?▼

To ensure research reproducibility when validating PAM signals, you must enforce a complete workflow that includes hypothesis formulation, test design, data queries, and structured reporting of effect sizes and confidence intervals for audit-friendly outputs.

What are the limitations of statistical testing for alpha signals?▼

Limitations of statistical testing for alpha signals include the necessity of a complete workflow from design to reporting to avoid invalid conclusions. Testing without structured outputs and effect size calculations may compromise the reproducibility and validation of the factor.