How far back does the history go?
Insider transactions to 2006, 13F holdings to 2013, Form 144 proposed sales to 2022, and IPO records to 1993. Enough to test across multiple market cycles.
Insider, 13F, and Form 144 signals with real history, delivered as normalized JSON that feeds straight into your research pipeline.
Today
The signals are public, but they live in filings. Someone on the team maintains a parser, or the desk rations a terminal that will not feed a factor library.
With RyxelData
Normalized JSON with history across cycles. Filter at the API, load the slice, run the backtest. The parser is our problem.
Insider and ownership signals are public information, but they arrive as filings. Form 4s, 13Fs, and Form 144s are compliance documents first, and turning them into a clean time series takes a parsing pipeline most funds would rather not maintain.
The vendors who already solved this sell terminals. A terminal is a screen for a human, priced per seat. It doesn't feed a factor library, and it doesn't scale to a research team that wants the data in Python.
So the choice becomes: build the scraper, or ration the terminal. Neither is research.
FORM 4
Open-market buys and sells, coded (P, S, M, A, F, G), with the position after the trade. Enough history to test through 2008 and 2020.
13F
Position-level holdings plus issuer and manager aggregates. Measure how crowded a name is without scanning every filing.
FORM 144
Proposed sales land before the Form 4. Pair the two and you can separate announced selling from completed trades.
IPO
Offer price, size, underwriters, and lockups as fields. Use them as features, not as prospectus homework.
RyxelData normalizes the filings into queryable JSON. Insider transactions back to 2006, 13F positions back to 2013, Form 144 proposed sales back to 2022, and IPO records back to 1993.
Filters run at the API layer: ticker, transaction code, insider role, value thresholds, date ranges. Your pipeline pulls the slice it needs instead of bulk-downloading the universe and filtering locally.
Schemas are stable and typed, with an OpenAPI spec you can generate clients from. When you return to a strategy six months later, the fields mean what they meant.
Research time goes to the signal. Backtests run across full market cycles, including 2008 and 2020, on a consistent record.
Form 144 intent paired with Form 4 execution separates announced selling from completed trades. Most feeds flatten that distinction; the filings don't.
And the access model matches the work: per dataset, sized to the signals you are actually testing.
If the work lives in Python, a factor library, or a research database, a screen is the wrong interface. RyxelData sits next to your backtester and your production jobs.
Insider data to 2006, 13F to 2013, IPOs to 1993. Test the signal through more than one regime.
Ticker, transaction code, role, value, security type, date range. Pull the slice. Do not download the universe.
OpenAPI 3.1 and typed fields. When you reopen a strategy in six months, the columns still mean what they meant.
Insider transactions to 2006, 13F holdings to 2013, Form 144 proposed sales to 2022, and IPO records to 1993. Enough to test across multiple market cycles.
Insider transactions are parsed within minutes of each EDGAR filing. Form 144 updates daily. 13F is quarterly by regulation, processed within days of the filing deadline.
Yes. Ticker, transaction code, insider role, transaction value, security type, and date ranges are query parameters, so you pull signal instead of bulk noise.
JSON over REST, documented by a public OpenAPI 3.1 spec, plus a typed TypeScript SDK if your stack calls for it.
Share your use case and the datasets you need — we'll set up access for your team and one API key that covers it all.