AlphAI

I will use this tool for my website, thank you !

I built AlphaAI because every trading bot and agent project I worked on started the same way: crawl news, dedupe it, figure out which tickers it touches, guess how much it matters. That pipeline is expensive to build and boring to maintain, so I turned it into a service. AlphaAI ingests GDELT, SEC EDGAR and publisher RSS, and every story comes out with per-ticker impact analysis, a category and a 1-10 relevance score. You consume it as plain JSON over REST or as an MCP server that Claude Desktop or Cursor can use directly. The free tier (20 requests per minute, 100 per day, no card) is enough to build and test against. Happy to answer questions about the scoring or the SEC filings pipeline.

The MCP server integration is a game-changer for AI agents. Having pre-scored financial news with per-ticker impact directly in Claude Desktop or Cursor eliminates the friction of building custom news ingestion pipelines. The relevance scoring removes noise - agents only see signals that matter rather than parsing the entire financial news firehose. SEC Form 4 insider data coming through structured within 6 minutes is killer for compliance monitoring. Free tier with no card is the right move for indie developers testing trading strategies.

The 6-minute turnaround on scored SEC Form 4 events is the standout feature here — most retail-facing tools that surface insider trades just re-post the raw EDGAR filing with no context, so having impact/category/relevance already attached at ingest is genuinely useful for cluster-buy detection. The MCP + REST dual surface is also a smart hedge, since it works whether you're building a bot or just querying it ad hoc from Claude Desktop. How far back does the historical Form 4 data go for backtesting relevance scores against actual price moves?

You list dedupe as one of the four things that pushed you to build this, but I can't find anywhere how you actually do it. Asking because I run a news pipeline myself and dedup is the part that never generalizes cleanly. GDELT plus publisher RSS means one Reuters story comes back at you dozens of times, reworded every time. Headline hashing dies on day one. What I landed on is two passes: shingles + Jaccard first, because it's nearly free and kills reposts and syndication, then embeddings + cosine for the paraphrases. The lexical pass scores a flat zero on "Central bank holds rate at 16%" vs "Regulator keeps key rate unchanged at 16 percent", so the second pass isn't optional. Two things I'd genuinely like to know: 1. Do you dedupe against a rolling window or against all history? Yesterday's Fed decision and next quarter's Fed decision are different stories. If you compare against everything forever, legitimately recurring events get silently suppressed, and for a financial feed that seems like the expensive kind of bug. 2. Are thresholds per-source or global? A wire feed tolerates a much lower semantic threshold than an opinion blog. Mine had to be tuned per feed and I never found a universal number. Separate one on the relevance score: is it a classifier or an LLM call? If it's a model, a version bump quietly reshuffles what "7" means, and somebody's bot is filtering on >= 7 without knowing the scale moved. Do you pin the model, or re-score history when it changes?