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canslim-screener

Scores stocks using eight metrics to identify outperformers.

2.6k|600|Updated Oct 19, 2025
Search Tags:#data analytics#ranking#market direction#stock screening#canslim#institutional ownership
One-click install
npx skills add https://github.com/tradermonty/claude-trading-skills --skill canslim-screener
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: canslim-screener
Source: https://github.com/tradermonty/claude-trading-skills/tree/main/skills/canslim-screener
Command: npx skills add https://github.com/tradermonty/claude-trading-skills --skill canslim-screener

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, beautifulsoup4, lxml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This CANSLIM screener helps investors identify high-conviction growth stocks by applying William O'Neil's CANSLIM framework, filtering for earnings momentum, growth consistency, price action near highs, and market direction.

Core Features & Use Cases

  • Phase 2 enhancements: adds S (Supply/Demand) and I (Institutional) signals to improve filtering; Phase 3 will include L (Leadership) for full CANSLIM.
  • Automated ranking: computes composite CANSLIM scores, identifies the weakest component, and outputs ranked top candidates.
  • Report generation: produces machine-readable JSON and human-friendly Markdown reports for quick review and portfolio planning.
  • Use cases include screening a 40-stock universe, backtesting across bull/bear markets, and building execution-ready watchlists.

Quick Start

To run the canslim-screener, install Python 3.7+, provide a Financial Modeling Prep API key, and execute the screening script. Then review the generated JSON/Markdown reports for top CANSLIM candidates.

Frequently Asked Questions about canslim-screener

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

FAQPage Schema
How do I screen stocks using the CANSLIM methodology in Python?▼

To screen stocks using the CANSLIM methodology, this tool analyzes earnings momentum, growth consistency, price action, and institutional ownership to identify high-conviction growth candidates. It computes composite scores and outputs ranked reports in JSON and Markdown formats.

What data do I need to rank CANSLIM stocks and generate a watchlist?▼

Ranking CANSLIM stocks requires a Financial Modeling Prep API key and Python 3.7+ with libraries like requests, beautifulsoup4, and lxml. The screener processes market data to output automated rankings and execution-ready watchlists for a default 40-stock universe.

Does the CANSLIM screener analyze institutional ownership and supply demand?▼

Yes, the CANSLIM screener analyzes institutional ownership and supply demand. Phase 2 enhancements specifically add S (Supply/Demand) and I (Institutional) signals to improve filtering, while Phase 3 will include L (Leadership) for full CANSLIM coverage.

Can I backtest CANSLIM stock screening strategies across different markets?▼

Yes, you can backtest CANSLIM stock screening strategies across bull and bear markets. The automated ranking computes composite scores, identifies the weakest component, and produces machine-readable JSON outputs suitable for backtesting analysis.

How does automated CANSLIM scoring identify the weakest stock components?▼

Automated CANSLIM scoring evaluates C, A, N, S, I, and M components to calculate a composite score. During the ranking process, it explicitly identifies the weakest component for each stock to help investors quickly evaluate high-conviction growth candidates.

What is the best way to identify high-conviction growth stocks near price highs?▼

The best way to identify high-conviction growth stocks near price highs is applying William O'Neil's CANSLIM framework. This screener filters for earnings momentum, growth consistency, and price action, ranking candidates by composite CANSLIM scores.

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