stock-screening

Name: stock-screening
Availability: InStock
Author: nima

Filter large financial datasets into shortlists using mechanism-based screening criteria.

25|3|Updated Jul 14, 2026

Search Tags:#data analysis #finance #screening #backtesting #bias #investing

Ready to run

npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill stock-screening

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stock-screening
Source: https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/finance/stock-screening
Command: npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill stock-screening

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the common failure of investment screening, where users inadvertently create overfitted, biased, or non-actionable lists of stocks that fail to perform in real-world conditions.

Core Features & Use Cases

Systematic Screen Design: Learn to build filters based on economic mechanisms rather than backtest-fitted noise.
Bias Mitigation: Identify and remove survivorship and look-ahead biases that invalidate historical performance data.
Use Case: When you need to narrow a large market universe into a manageable shortlist of 10-40 candidates for manual fundamental analysis, this skill ensures your criteria are defensible and statistically sound.

Quick Start

Use the stock-screening skill to evaluate my current criteria for identifying high-momentum, profitable companies while ensuring I avoid survivorship bias.

Frequently Asked Questions about stock-screening

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

FAQPage Schema

How do I build a stock screen that avoids survivorship bias and look-ahead bias in backtesting?▼

To avoid survivorship and look-ahead bias in stock screening, you must use point-in-time data integrity and logically validate financial factors against actual historical conditions. This ensures your backtesting relies on chronologically accurate information rather than future data leaks.

What is the best way to filter a large market universe into a manageable shortlist for fundamental analysis?▼

The best way to filter a market universe into a shortlist of 10-40 candidates is by applying mechanism-based screening criteria. This grounds your stock filtering in defensible economic logic rather than statistically overfitted market noise.

Why does my investment screening strategy fail in real-world conditions despite strong historical backtesting?▼

Investment screening strategies often fail in real-world conditions due to overfitting. When screens are fitted to historical noise rather than underlying economic mechanisms, they lack statistical soundness and cannot generalize to live market environments.

How do I validate financial factors to ensure my stock screening criteria are statistically sound?▼

Validating financial factors requires applying logical validation of economic mechanisms and ensuring point-in-time data integrity. This rigorously tests your screening criteria against defensible market logic to prevent overfitting and confirm statistical reliability.

Can I use economic mechanism-based screening to narrow down high-momentum profitable companies?▼

Yes, you can use mechanism-based screening to identify high-momentum, profitable companies. By filtering large financial datasets using defensible economic logic rather than backtest-fitted noise, you generate an actionable shortlist for manual analysis.

When should I not use backtest-fitted noise for investment screening?▼

You should not use backtest-fitted noise for investment screening when narrowing a large market universe into actionable shortlists. Relying on overfitted criteria instead of mechanism-based economic logic invalidates historical performance and leads to poor real-world results.