Every insider trade, institutional position, and Washington disclosure is public record. Trading Places assembles all of it into one dataset — and surfaces where the patterns line up. Research infrastructure, not advice.
Algorithm-first. A deterministic engine on public records — every number traces back to a filing you can verify. AI only interprets the results; turn it off and the engine still runs.
Programmatic access to the full dataset and the analytics layered on top — event clusters, cross-source convergence, historical pattern signatures. The same data for every subscriber. Build your own tools on it.
A licensed application that lets you explore the data without writing code — watchlists, visualizations, saved views. A window onto the API, nothing more.
Connect your own AI provider to read the data back in plain language. Your key, your provider — Trading Places never sees your prompts or your questions.
Everything it produces is general, factual, and identical for every user. It is not advice.
Every insider trade, every institutional position, every lobbying filing, every corporate disclosure. Parsed, structured, and cross-referenced. The system watches what people do with their money — not what they say.
By the time a story reaches the public, others have already acted. The insiders who run the company. The institutions managing hundreds of billions. The politicians and lobbyists who see policy before it's announced. When those layers move together, the pattern becomes worth examining.
Company executives and directors are legally required to disclose their trades within two business days. When multiple insiders at the same company start buying — or selling — in close succession, that's the earliest visible signal. The system clusters these filings and compares them against historical patterns.
Hedge funds and investment managers disclose their positions every quarter. The filings are delayed, but the scale is enormous — this is where serious money confirms or refutes what the insiders are doing. When institutional flow aligns with insider clusters, the pattern strengthens. When they diverge, something unusual is worth understanding.
Members of Congress disclose their trades under the STOCK Act. Corporations disclose their lobbying activity by bill and issue. The system tracks both — and watches for the pattern where a company lobbies a specific issue, the relevant committee members trade the stock, and the policy announcement follows weeks later.
Most AI tools ask a model to search the internet and form an opinion when you type a question. Trading Places is different. The work is already done before you ask.
Always running. The system doesn't start when you ask a question. It's been working since before you logged in — collecting filings, scoring patterns, comparing against years of historical data. Continuously. While you sleep.
Already computed. When you look up a ticker, you're not waiting for an AI to form an opinion. The cluster was already detected. The pattern was already matched against the historical record. The comparison is already done. You're reading results, not requesting them.
Data first, AI second. The engine underneath is pure math on public filings. Deterministic. Reproducible. Every number traces back to a specific SEC document you can verify yourself. AI interprets the results in plain language — but if the AI disappeared tomorrow, the engine would still run.
Grounded in outcomes. Every signal is tracked against what actually happened, so the historical comparisons reflect real results, not guesses.
How it worksUnder the hood, the work happens in three stages — running continuously, whether you're watching or not.
Public filings, disclosures, and market data flow in continuously from their official sources, around the clock.
Everything is structured and cross-referenced, surfacing where activity lines up across sources and against the historical record.
Every signal is checked against what actually happened, keeping the comparisons grounded in real outcomes.
These are pattern examples drawn from public SEC filings. They illustrate how the system cross-references disclosures that anyone can read — but almost nobody does. Educational examples, not forecasts.
A recurring pattern in the historical dataset: a stock climbing on enthusiasm while the people who run the company — and the institutions that hold it — quietly move the other way. The system is built to surface that gap, because the filings tell a different story than the price.
A recurring pattern: a small-cap company far below its peak, in a deteriorating core business, announces a strategic pivot into whatever sector is currently attracting speculative capital — blockchain, cannabis, metaverse, quantum, AI. The stock spikes. The system recognizes the signature because it has resolved the same way many times.
One of the most consistent patterns in the data: founders and long-tenured insiders selling down — often alongside governance changes — while the institutions that held the stock quietly step back. None of it is hidden; it just never arrives as a single headline.