Inside the SimianX AI News Feed for Stocks and Crypto

Inside the SimianX AI News Feed for Stocks and Crypto

Markets do not move on facts. They move on news — and there is far too much of it. Hundreds of outlets publish thousands of stock and crypto headlines every single day, and by the time a human has skimmed even a fraction, the move is already priced in. The hard part was never finding the news. It was reading all of it, in real time, and knowing in the same breath which headlines actually matter, which ticker they touch, and whether they are bullish or bearish. That is exactly the job the SimianX AI news feed was built to do — and the fastest way to watch it work is to join our Discord, where every tagged headline lands live.

This is an introduction to how that feed works: where the news comes from, how AI reads and labels every article, how stock and crypto are handled separately, and where it is all heading — including the in-house model we are building directly on top of the news stream.

One feed, every market, every minute

The SimianX feed continuously ingests news across roughly 160 sources, split across both asset classes, and re-checks all of them every single minute. There is no batch delay and no "end of day" digest — when a story breaks, it is in the pipeline within seconds.

On the stock side, the feed pulls from the outlets you already trust: household names like Bloomberg, CNBC, the Wall Street Journal, MarketWatch, Yahoo Finance, and Seeking Alpha, alongside the primary sources that move markets the hardest — SEC press releases and Federal Reserve communications. On the crypto side, it covers more than a hundred dedicated outlets, from CoinDesk and Cointelegraph to Decrypt and The Defiant. Stock and crypto are not blended into one mush; each is its own clearly separated stream, because the two markets speak different languages.

The point of casting this wide is coverage. A single outlet misses things; a hundred and sixty of them, cross-checked, rarely do. But breadth alone is just more noise — which is where the intelligence layer comes in.

Every headline, read and tagged by AI

The difference between a news reader and a news aggregator is judgment. SimianX runs every single article through a frontier AI model the moment it arrives, and that model does four concrete things to each one:

1. Resolves the affected symbols. It reads the actual article — not just the headline — and extracts the specific tickers or coins genuinely discussed. A piece that says "$NVDA guidance" tags NVDA; a Bitcoin ETF story tags BTC. Crucially, it verifies upstream tags against the text and discards anything not actually covered, so a passing mention of "the tech sector" never gets mislabeled as a specific stock.
2. Scores the sentiment. Each article is labeled bullish, bearish, or neutral, relative to the symbols it affects — the same vocabulary a trader would use.
3. Rates the impact. Not all news is equal. The model assigns a quality score — high for the urgent stuff (earnings, M&A, FDA decisions, guidance changes), down through medium and low, to very low for spam and pump-and-dump filler. Low-value noise is filtered out so it never reaches you.
4. Flags market-wide events. When a story is about the whole market — an S&P 500 move, a Fed decision, a broad crypto selloff — it is marked global rather than pinned to one symbol, so it surfaces everywhere it is relevant.

The result is that every headline arrives pre-labeled with what it is about, how much it matters, and which way it leans. That is the difference between drowning in a firehose and reading a curated, scored, symbol-aware stream.

Stock and crypto, routed intelligently

Because every article is resolved to specific symbols, the feed can route news exactly where it belongs. Pull up a stock and you see the news tagged to that ticker; open a coin and you see the headlines that move that asset. Macro and market-wide stories flow to everyone.

This is why the feed is not a standalone page you have to remember to check — it is woven into the places you are already looking. Inside the stock command room, the relevant headlines stream alongside the live AI analysis for the names you are watching. Inside the crypto live sessions, the same thing happens for coins. The news, the AI sentiment, and the multi-model analysis you can compare on the crypto leaderboard all sit in one view, so context never lives in a different tab.

See it live on Discord

You do not need to be logged into a trading session to experience the feed. The single easiest way to see exactly what SimianX is reading — and how it is labeling it — is on our community Discord.

Every article the AI processes is pushed to Discord as a clean, structured card: the headline and link, the AI sentiment (bullish / bearish / neutral), the AI quality rating, and the related tickers or coins. Stock and crypto each have their own channel, and the low-value spam is filtered out before it ever posts. It is, in effect, a live window into the intelligence layer — a rolling, AI-annotated tape of everything moving across both markets.

If you read nothing else here, do this: join the SimianX Discord and watch a few hours of tagged headlines roll past. It is the clearest possible demonstration of what the feed is, and it is free.

The disruptive part: a model that learns from the news

Tagging news with AI is powerful, but it is the beginning, not the end. The genuinely disruptive work is what we are building on top of the stream.

For years, the conventional wisdom has been that news is too messy, too unstructured, and too fast to turn into a reliable trading signal. We disagree — and we are building our own model to prove it. Every article that flows through the feed is not just tagged and forgotten; it becomes a labeled data point in a growing archive that pairs what was reported with what the market actually did next. A clean, symbol-resolved, sentiment-scored history of market-moving news, aligned against real price outcomes, is a training set almost nobody else has — because almost nobody else has been quietly structuring the firehose this whole time.

That is the foundation for an in-house model that learns the relationship between narrative and price directly from the news itself: which kinds of headlines actually precede moves, how fast that edge decays, and how sentiment on one asset bleeds into another. It is a fundamentally different approach from a pure price-and-indicator model, because it reads the why behind a move, not just the what. This is early, ambitious work — but it is exactly the kind of thing that becomes possible only after you have spent a long time doing the unglamorous part right: reading every headline, every minute, and labeling it honestly.

The moat here is not a clever algorithm anyone could copy in a weekend; it is the dataset. A model is only as good as what it learns from, and a years-deep, symbol-resolved record of news paired with outcomes compounds quietly the longer the feed runs. Every minute the system reads the market, that archive grows — which means the edge is designed to widen over time, not decay. That is what makes this disruptive rather than incremental: it turns the one resource the market produces in infinite supply, news, into proprietary fuel.

One headline, end to end

It helps to follow a single story through the system. Say an earnings update on a major chipmaker hits a financial wire at 4:01 p.m. Here is what happens, in order, in a matter of seconds:

1. Ingest. The feed is re-checking that source on its one-minute cycle, so the article is pulled almost immediately, de-duplicated against everything already seen, and queued.
2. Read. A frontier AI model reads the full body, not just the headline. It identifies that the piece is specifically about NVDA, confirms the ticker is genuinely the subject rather than a passing reference, and ignores the unrelated names mentioned in the boilerplate.
3. Tag. It scores the article bullish, rates the impact high because it is a guidance beat, and resolves the related symbol to the stock itself. A market-wide line about semiconductor demand would additionally be flagged as relevant beyond the single name.
4. Route and push. The tagged article instantly streams into the stock command room for anyone watching that ticker, and a structured card lands in the Discord stock channel with the sentiment, the quality rating, and the symbol attached.

A reader who only saw the raw headline would still be parsing it. A SimianX user already knows it is a high-impact, bullish, NVDA-specific event — and so does every downstream agent and autopilot. That compression of headline → meaning is the entire product, and it happens the same way for thousands of articles a day across both markets.

Who it is for, and how to use it

You do not have to be a quant to get value from the feed. A few concrete ways people use it:

• As a real-time alert layer. Watch the Discord channel for your tickers and let the AI quality rating tell you when a headline is worth your attention versus when it is noise.
• As a sentiment gut-check. Before acting on a name, glance at how the recent flow leans — bullish, bearish, or mixed — across the outlets covering it.
• As context for the AI analysis. When the multi-model analysis on a stock or coin shifts, the tagged news in the same view usually explains why.
• As an automation trigger. Pair the feed's signals with a SimianX autopilot so a high-impact, bearish headline on a position you hold can prompt a rules-based response instead of a panicked one.

FAQ

How many news sources does SimianX cover?

Roughly 160 across both asset classes — over a hundred dedicated crypto outlets plus mainstream financial media and primary sources like the SEC and the Federal Reserve on the stock side — all re-checked every minute.

How does the AI tag each news article?

A frontier model reads the full article and extracts the specific tickers or coins discussed, scores the sentiment as bullish, bearish, or neutral, rates the impact from high to very low, and flags market-wide stories as global. Low-value spam is filtered out automatically.

Does the feed cover both stocks and crypto?

Yes. Stock news and crypto news are ingested and tagged as two separate streams with their own rules, then routed to the specific symbols — or to the whole market — that each story affects.

How do I see the news feed?

The quickest way is to join the SimianX Discord, where every AI-tagged article posts live with its sentiment, quality rating, and related symbols. The feed also streams inside the stock and crypto live sessions.

Is SimianX building its own model from the news?

Yes. Beyond tagging, SimianX is constructing a proprietary model trained on a symbol-resolved, sentiment-scored archive of market-moving news aligned to real price outcomes — learning the relationship between narrative and price directly. It is early, ambitious, and central to where the platform is going.

Related reading

• Which AI Model Is the Best Trader? — how the models that read this feed stack up
• The S&P 500 Sector Rotation Playbook — where news sentiment fits into a signal model
• Crypto leaderboard — live multi-model analysis
• SimianX autopilots — turn signals into automated action

The news will never stop coming, and it will never get quieter. The only durable edge is to read all of it, instantly, and to know in the same moment what each headline means and how much it matters. That is what the SimianX AI news feed does today — across stocks and crypto, every minute — and it is the foundation for the model we are building to turn the world's market news into a signal of its own. Come see it live on Discord, and follow @SimianXai for what comes next.