canslim-screener

Screen stocks using the CANSLIM methodology by analyzing 7 key components: current earnings, annual growth, newness, supply/demand, leadership, institutional sponsorship, and market direction. Generate composite scores (0-100) and actionable buy/watchlist/avoid recommendations based on quantitative thresholds.

The CANSLIM stock screening approach identifies market leaders by evaluating 7 components of William O'Neil's methodology to find growth stocks. It analyzes historical earnings, volume momentum, relative strength leadership, institutional sponsorship, and market direction to produce objective scores for investment recommendations.

Yes, you need an FMP API key to fetch US stock market data for CANSLIM stock screening. The skill requires this API key along with Python libraries like requests, beautifulsoup4, and lxml to retrieve financial data and perform the fundamental and technical analysis.

Get actionable investment recommendations from CANSLIM composite scores by analyzing the 0-100 rating alongside the weakest component analysis. The skill delivers clear buy, watchlist, or avoid guidance based on quantitative thresholds and current market conditions to identify high-probability growth stocks.

Limitations of CANSLIM screening include its strict focus on growth stock leaders and dependence on accurate historical earnings, volume, and institutional data. The methodology requires a clear market direction indicator to function properly, meaning it may generate avoid recommendations during broader market downtrends.

Yes, you can use Python with BeautifulSoup and lxml for fundamental and technical analysis in stock screening. This skill leverages these libraries alongside requests to fetch and parse financial data, applying William O'Neil's 7-component CANSLIM framework to identify top growth stocks.