Methodology

The Distributed is a one-stop financial intelligence dashboard that unifies mainstream market data (live prices, sector heatmaps, earnings calendars, fundamentals) with alternative-data signals the rest of the market usually pays for: insider trades from SEC Form 4 filings, Reddit mention volume and sentiment, coordinated-activity flagging, and cross-ticker correlation analysis.

Each surface in the product answers a specific question:

• Markets (home)— what's notable today across every signal we have, on one page.
• Insider— who's buying or selling their own company's stock, with cluster detection for unusual conviction.
• Earnings — what reports are coming up and what the actuals were, sourced direct from SEC XBRL.
• Screener — combine signals (Reddit volume + insider activity + upcoming earnings) to surface tickers worth a closer look.
• Correlations— which tickers' Reddit mentions move together, plus a search to filter the full correlation set.
• Asset profile pages (/stocks/AAPL, /etfs/SPY, /commodities/oil, /macro/cpi) — every signal we have for one asset, in one place.

We don't hide methodology. Every metric on the site links back to this page, and every signal explains its source, its window, and its known limitations.

Insider transactions come directly from SEC EDGAR Form 4. We pull the raw filing index, parse the Form 4 XML, and store one row per transaction. There is no third-party data vendor in this path.

The transaction code surfaced in the table is the literal SEC code (e.g. P for open-market purchase, S for sale, A for award). Cluster buys are computed in a Postgres function: cluster_buys_last_7d groups by ticker, requires at least two distinct insider names with buys in the trailing 7 days, and aggregates total purchase value.

Latency: filings appear here within ~10 minutes of EDGAR acceptance. There is a structural ~2-day lag from trade date to filing imposed by Section 16 — that is a regulator window, not a pipeline delay.

Calendar dates are sourced from SEC 8-K filings in which the issuer announces an upcoming earnings call. Where multiple 8-Ks reference the same fiscal period, the most recently filed prevails.

Actuals(EPS, revenue, segment data) are extracted from the company's 10-Q / 10-K XBRL tags once the period closes. We never approximate from press releases — if a number is missing it is because the issuer has not yet filed.

We do not show analyst estimates. Estimate data is licensed by a small number of vendors at high cost, the consensus methodology varies between vendors, and the estimates themselves create a strong anchoring bias when reading actuals. Showing them would force us into a licensing model that contradicts the sources-direct philosophy of the rest of the product. Surprise calculations relative to estimate are out of scope until we can source estimate data with provenance we trust.

The Reddit data layer reads from a fine-tuned NER pipeline that classifies each post and comment for stock tickers, companies, ETFs, commodities, and macro indicators. Source subreddits include r/wallstreetbets, r/stocks, r/investing, r/options, r/etfs, and r/commodities.

Mention ratio (the “↗ 3.2x” chip): today's mention count divided by the trailing 30-day baselinemean of daily mention counts for that ticker. We exclude today's value from the baseline so a quiet ticker that just woke up does not deflate its own denominator. Tickers with no mentions in the prior 30 days return no ratio (we will not invent a baseline of 1).

A ratio at or above 1.5x is rendered as trending; at or above 2x we additionally outline the chip in the brand accent to visually separate genuine spikes from drift.

Sentiment uses the FinBERT ONNX INT8 quantized model, scored per mention. Known limitation: WSB sarcasm degrades classifier accuracy by ~20 percentage points relative to formal news copy. We surface the raw label distribution so users can judge whether a flip is a real mood shift or a meme cycle.

All correlation coefficients on this site are Pearson correlation over a 30-day rolling window of daily values, unless the chart caption states otherwise. We use Pearson rather than Spearman because most pairs we plot (price vs mentions, mentions vs sentiment) approximate continuous distributions over 30 samples; rank correlation throws away too much information.

Sample size disclosure:any reported r value with n < 20 daily observations is rendered with a warning glyph and the chart caption explicitly names the sample size. We do not display r values for n < 10 — there is not enough data for the statistic to be meaningful, and showing it would be misleading. Confidence intervals on r are computed via Fisher's z-transform.