Claude for Stocks
Ask ChatGPT or Claude whether you should buy a stock and you get a hedge, not an answer. That is not a personality flaw, and it is not an old-model problem either. It is a retrieval problem that shows up on today's frontier systems just as much as last year's. A 2026 benchmark snapshot combining FinanceBench with live RAG evaluation found general purpose models running close to 89 percent accurate when handed perfect retrieval, the exact filing text a question needs, then failing more than 80 percent of the time once you swap in the kind of retrieval a real product actually has to build for itself. The people running that benchmark concluded the retrieval layer is the bottleneck, not the model, and that gap dwarfs whatever difference exists between individual models. Separately, a 2026 evaluation of current frontier models on financial statement verification found accuracy collapsing from 95.6 percent on simple single fact lookups to near zero the moment a question requires combining more than one number. The models are not stupid. They were built to give you a fast, confident sounding answer about one thing, from memory, and even the newest ones still are. Nobody built them to screen the whole US public market and hand back only what a database actually contains.
Meanwhile the actual failure in retail investing has nothing to do with whether AI exists. FINRA's own investor research found more than a quarter of investors use recommendations from social media influencers when making investment decisions, rising to 61 percent of investors under 35 and 57 percent of those with less than two years of experience. People are not under-informed. Free 10-Ks, free price data, free everything. They are outsourcing judgment to whoever is loudest on X or TikTok that week, because doing the actual analysis means learning technical analysis, reading filings, and writing code to query thousands of tickers at once. Almost nobody does that last part. That is the gap.
stocks-llm.com is built to close it. The motto is literal, not marketing copy: "Research stocks in your own words."
What it actually is: a natural language research tool over the full US-listed public company universe, more than 3,100 companies across the S&P 500, Nasdaq-100, Dow, Russell 1000 and Russell 2000. Every company gets an enrichment layer built from SEC EDGAR filings and Finnhub data. Business summary, sector and sub-industry, growth, margins, valuation, dividend history pulled from real XBRL filings (not just a current yield snapshot), analyst estimates, SEC Form 3/4/5 insider transactions, and House STOCK Act congressional trade disclosures. You ask a question in plain English, it queries that whole dataset, and it only tells you what the data actually supports. It does not pick tickers by vibing on them. A deterministic engine runs the screen; the model's job is to understand what you asked and narrate the result.
That last distinction is the whole product. The model is never allowed to state a number it did not retrieve from a real column in the database, full stop. If the data is not sitting there, the honest answer is a refusal, not a plausible-sounding guess. That single rule is the entire difference between this and asking a general chat model about a stock. A general model will fill a gap with something that reads correctly and is not tied to anything real. This one is built so that gap produces silence instead of a fabricated number.
That same deterministic layer is what the new technical screens run on. Every US-listed ticker gets twenty indicators computed nightly: 50 and 200 day moving averages and their cross state, Wilder RSI, MACD, Bollinger band width, ADX trend strength, Donchian breakout levels, relative volume, distance from the 52 week high and low, all time high and low tracking, and drawdown percentage. In plain language, you can now ask for stocks sitting on their 200 day moving average within 2 percent, which the site returns as 117 live names right now, or stocks above a long term uptrend, currently 1,873. A golden cross, 50 day crossing above the 200 day in the last 30 days, is running at 325 names; a death cross at 186. Oversold on Wilder RSI under 30 is 56 names, overbought over 70 is 73. A bullish MACD crossover is 364 names, bearish is 733. New 20 day highs sit at 407, new lows at 590, and breakouts confirmed by a real volume spike are a tight 39, currently names like $CLF, $CSX, $DGX and $ALLE. Deep pullbacks of 20 percent or more off a 12 month high run 1,564 names deep. Bollinger Band squeezes, where a stock's bands are tighter than almost anything in its own recent history, come back at 549 names, and ADX trend strength splits the market into 589 names in a genuinely strong trend against 1,425 going nowhere. Analyst price target changes are the one screen still warming up, since it needs two snapshots of estimate data to diff against each other and history only started accumulating a few days ago; that is a real answer in one to two weeks, not a broken feature pretending to be finished.
None of that is the actual differentiator, though. Any charting site can show you RSI on one ticker. The differentiator is that every one of those technical screens composes with sector, theme, and fundamentals in the same sentence, which a normal screener interface simply cannot do in plain English. Profitable semiconductor companies trading above their 200 day moving average returns 9 names including $TSM, $GFS, $MRVL and $ONTO. Oversold healthcare stocks returns exactly one name, $ISRG, the surgical robotics maker. Stack in insider buying, congressional trades, dividend streaks, debt levels, and you are running a query a Bloomberg terminal user would need actual code to replicate, in one sentence, for free: profitable semiconductor companies trading above their 200-day moving average →
This is the actual use case worth sitting with: you have a theory nobody handed you. Say you believe the interesting portfolio is companies doing real work on cancer treatment that are also seriously invested in AI. That is not a saved screen anywhere. It is a sentence: companies working on cancer treatments that are also investing heavily in AI →. Then you keep going in the same conversation: are any of them oversold right now, do they carry too much debt, has an insider bought recently, has a member of Congress disclosed a trade in the name. That is the whole pitch. Oversold AI stocks with revenue growth → is the same idea running the other direction, momentum plus fundamentals in one ask.
Why not just ask ChatGPT or Claude to do this. Not because those models are behind. Because giving a correct answer to "oversold AI stocks with revenue growth" means filtering thousands of tickers by two live indicators and one fundamental at once, and that is exactly the retrieval and multi-fact combination problem the 2026 research above describes, regardless of which frontier model answers you. A general model with no purpose built retrieval layer under it is reasoning from memory about roughly what the numbers might be. Most people are never going to build the retrieval and screening layer themselves, and no base model does it for you unprompted. That is the entire reason this exists as a separate product instead of a system prompt.
One more thing worth saying plainly since it is part of why this gets built the way it does: stocks-llm is a one person build, run largely through an AI agent harness handling engineering, ingestion, and screen design end to end, with a human approving anything that touches production or ships live. It is as much a test of whether a single founder plus an AI harness can operate what used to take a team as it is a stock research product. So far the product side keeps proving it out faster than expected.
Research stocks in your own words. Ask it your actual theory, not a ticker you already had in mind, and see what the data says back.