StockNews.AI monitors curated financial sources – Reuters, CNBC, Benzinga and similar – and turns raw articles into structured data: sentiment scores, importance ratings, ticker tags, and a price-at-signal timestamp, aimed at both human researchers and AI agents that would otherwise have to parse the raw articles themselves. Free access covers testing and learning with delayed data and smaller limits; paid plans are for live workflows and higher usage, priced on request.
The framing is exactly right and the site says so directly: ‘StockNews.AI organizes public market information for research. It does not execute trades or provide personalized financial advice.’ Scores are described as research aids, not forecasts. Against the nine investment-scam funnels rejected earlier in this catalogue and the more aggressively marketed backtested-performance products elsewhere, a product that sells structured news data and states plainly what it does not do is the honest version of the finance-and-AI category. Pre-structuring news for an AI agent to consume – sentiment and ticker tags already extracted – is also a genuinely useful piece of infrastructure as more trading research gets delegated to agents.
A sentiment score is still a summary of how an article was written, not a prediction of what a stock will do, and ‘importance rating’ is a judgement call the system is making on your behalf about what matters – worth checking against your own read of a story before treating either number as settled. No pricing figures are published for the paid tier.
