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canslim-screener

Screen US stocks using CANSLIM methodology with FMP API and Finviz data.

2|Updated Apr 6, 2026
Search Tags:#fundamental analysis#growth stocks#stock screening#CANSLIM#price momentum
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill canslim-screener-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: canslim-screener
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/canslim-screener
Command: npx skills add https://github.com/k1064190/stock-expectation --skill canslim-screener-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a systematic approach to screen and analyze US stocks using the CANSLIM methodology, helping users identify growth stocks with strong fundamentals and price momentum.

Core Features & Use Cases

  • CANSLIM Stock Screening: Screen US stocks based on the CANSLIM methodology's 7 key components: Current Earnings, Annual Growth, Newness, Supply/Demand, Leadership, Institutional Sponsorship, and Market Direction.

  • Data Fetching: Fetches fundamental and price data for the stock universe using the FMP API and Finviz web scraping.

  • Composite Scoring: Scores each stock based on the weighted sum of the 7 CANSLIM components.

  • Ranking and Reporting: Generates a ranked list of stocks and actionable reports.

  • Use Case: A user wants to identify multi-bagger candidates with strong fundamentals and momentum. They can use this Skill to screen a universe of stocks and generate a report with the top candidates ranked by composite score.

Quick Start

Use the canslim-screener skill to screen stocks in the Technology sector.

Frequently Asked Questions about canslim-screener

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

FAQPage Schema
How do I screen US stocks using the CANSLIM methodology for growth investing?▼

Screen US stocks using the CANSLIM methodology by evaluating seven components: Current Earnings, Annual Growth, Newness, Supply/Demand, Leadership, Institutional Sponsorship, and Market Direction. The Skill fetches fundamental and price data to generate a ranked list of growth stock candidates.

How do I identify multi-bagger stocks with strong fundamentals and price momentum?▼

Identify multi-bagger candidates by running a composite scoring system on a stock universe. This Skill calculates a weighted sum of the seven CANSLIM components to generate actionable reports ranking top growth stocks with strong momentum.

Do I need an FMP API key to screen stocks based on CANSLIM criteria?▼

Yes, you need a Financialmodelingprep (FMP) API key. The Skill requires this key alongside Python dependencies to fetch fundamental financial data for the stock universe before applying the CANSLIM screening and scoring logic.

Can I use Finviz web scraping to analyze institutional ownership for stock screening?▼

Yes, this Skill incorporates Finviz web scraping specifically to handle institutional ownership and market trend analysis. It combines this scraped data with FMP API fundamental data to complete the CANSLIM evaluation.

What Python dependencies are required to run CANSLIM stock screening?▼

You need the requests, beautifulsoup4, lxml, and financialmodelingprep Python packages. These dependencies support web scraping Finviz data and fetching fundamental data from the FMP API to execute the CANSLIM screening workflow.

What are the limitations of using automated CANSLIM screeners for fundamental analysis?▼

Automated CANSLIM screeners rely on accurate data fetching from FMP and Finviz. Limitations include potential web scraping blocks from Finviz and API rate limits from FMP, which can restrict the size of the stock universe analyzed during screening.

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