Predictions you can audit.

The 90-second version

Every weeknight at 8 PM ET, a Python pipeline runs on GitHub's servers and acts like a research analyst: it visits ~10 data sources for each of our 50 stocks, scores everything, and writes the result to the database. In the morning, the app just reads those pre-computed rows. The signals you see were computed once overnight — not when you opened the page.

What gets pulled per stock

• Prices & technicals — one year of daily OHLCV bars from Yahoo Finance, then RSI / MACD / moving averages computed locally.
• News headlines— up to ~20 articles per stock from Massive (formerly Polygon.io). Each article ships with Polygon's ticker-specific relevance + sentiment metadata, which we use to drop passing mentions before they hit the pipeline.
• Analyst ratings — buy/hold/sell consensus plus price targets from Finnhub.
• Insider activity — Form 4 transactions plus 8-K material events straight from SEC EDGAR (free, government source).
• Social mentions — StockTwits bullish %, r/wallstreetbets attention, Reddit mention deltas.
• Macro context — VIX level and sector ETF performance from FRED.

Two AI models do the parts math can't

• FinBERTreads each news article and decides if it's positive / neutral / negative. We use it instead of keyword matching because financial language depends on context — “raised guidance” is bullish, but “raised concerns”is bearish. Its continuous score is then blended with Polygon's categorical per-ticker sentiment so the final number captures both how the article reads overall and how it pertains to this specific stock.
• Llama-3 / Mistralturns the math (the four bucket scores) into the one-sentence English explanation under the verdict chip — “Bullish — driven by strong analyst upgrades and rising technical momentum.” It also writes each article's TL;DR, seeded with Polygon's ticker-specific reasoning so the summary stays focused on this stock instead of the broader article.

FinBERT runs locally on the pipeline runner — model and tokenizer load once, inference happens in-process, no network round-trips per article. The Llama / Mistral call still goes over HuggingFace's inference API because 7B-parameter models don't fit on a free CI runner. If the LLM call fails or times out, the pipeline degrades gracefully: verdict reasoning falls back to a rule-based template. A shared circuit breaker bounds the cost of an HF outage to a handful of calls before the rest of the run short-circuits to the fallback path. The numerical signal is never blocked by an LLM hiccup.

The audit trail is public

Every signal carries its sources — which fetchers contributed, when the data was fetched, whether they agreed or disagreed. This is the moat against the “robo-advisor” framing: data is shown to you, not interpreted for you. The code that does all of this is open source — pipeline/.

The DOWN threshold

combined = 0.30·Tech + 0.25·Sent + 0.30·Prof + 0.15·Soc

if combined > +0.15   →  UP
if combined < -0.15   →  DOWN
otherwise             →  NEUTRAL

You need the weighted sum below -0.15 for a DOWN call. Practically that means at least two buckets meaningfully bearish, or one bucket very deeply bearish.

Why your feed probably leans UP

If you picked mega-caps (AAPL, NVDA, MSFT, GOOGL, META…), the bullish bias is structural — not a bug. Mega-caps have a different signal distribution than the broader market:

• Professional (30% weight): Analysts rate mega-caps Buy/Hold roughly 85% of the time. Sell ratings are vanishingly rare.
• Sentiment (25% weight): Coverage is mostly favorable — earnings beats, AI hype, expansion stories. Major negative coverage requires a real catalyst.
• Social (15% weight): High retail attention typically comes with bullish framing.
• Technical (30% weight): The only bucket that swings both ways equally — but one bearish technical contribution alone caps at -0.30, not enough to push past the threshold.

That's why COIN with Technical -0.50 still shows NEUTRAL when Professional +0.58 + Social +0.42 neutralize the bearish technical. The math is doing exactly what it should.

Real-world triggers for DOWN verdicts

• Day after a bad earnings miss — Sentiment crashes, Technical follows, often Professional downgrades pile on within 24-48 hours
• Analyst downgrade wave — Professional is the biggest single weight; a 30% drop here moves the needle hard
• Regulatory bombshell — FTC suit, FDA rejection, DOJ probe — flips Sentiment + Professional in one news cycle
• Insider sell cluster — multiple Form 4 filings within a few days flips Professional to negative even before analysts react
• Broad macro selloff — Technical breaks down across the board, VIX spikes; mega-caps less affected than small-caps

Want to see DOWN verdicts in your feed?

1. Add some volatile or struggling tickers to your watchlist alongside the mega-caps — names like RIVN, LCID, GME, AFRM often skew bearish on technical setups.
2. Watch your watchlist during earnings season — post-miss days are when the system genuinely calls DOWN.
3. Don't expect daily DOWN calls on diversified mega-cap watchlists. When DOWN does show up there, it's a stronger signal precisely becauseit's rare.