Crypto Enforcement Action Database for Market Research

Build a crypto enforcement action database around agency publications, action stages, allegations, and respondent-level records without converting charges into findings of liability.

A useful crypto enforcement action database normalizes the public records of the SEC, CFTC, and DOJ while preserving the legal distinctions among them. Alphanume's Crypto Enforcement dataset covers digital-asset actions from 2024 forward. It separates civil complaints, administrative proceedings, settled orders, criminal indictments, pleas, verdicts, and other actions, then records the allegation categories and document evidence.

The Crypto Enforcement documentation defines the agency sources, fields, enums, and update behavior. The database says what an agency announced and what the document alleges or resolves. It does not label a respondent liable before the public action does, and it does not collapse enforcement, rulemaking, sanctions, settlement, and judgment into one undifferentiated event.

Choose the correct unit of analysis

Each source action is identified by action_key, but the served rows are respondent-level. An action naming three defendants produces three rows with the same action key and different respondent indexes. That design supports party research, yet it requires action-level de-duplication for counts and monetary totals.

respondent_key lowercases names and removes punctuation and corporate suffixes. It is a name-string normalization, not entity resolution. Two parties with the same name can share a key, while a renamed party may split across keys. Review the underlying names before publishing respondent-level histories.

Retrieve actions by agency and type

Use published_date as the structural public date. The separately extracted action_date may describe the date of a complaint, order, plea, or other underlying act and carries a precision enum. For a market-timing study, the agency publication is usually the observable event. For legal-process duration, use action dates only when their precision supports the calculation.

GET /v1/crypto/enforcement
date_gte=2024-01-01
date_lte=2025-12-31

group action counts by source_agency, action_type, action_key
group respondent counts by source_agency, action_type, record_id

retain published_date, action_date, action_date_precision,
       respondent_name, allegation_categories, settled_flag,
       monetary_relief_usd, monetary_relief_basis, document_url

Pagination requires the complete three-part cursor: published date, action key, and respondent index. Preserve all three values from next_cursor. A partial cursor is invalid, and a page loop that ignores the respondent index can skip or repeat members of a multi-party action.

Normalize categories without erasing legal stages

Agency and action-type fields make cross-source comparisons possible, but the research table should retain source_index. An SEC litigation release, an SEC administrative proceeding, and a DOJ press announcement are different instruments. Likewise, a criminal indictment and a plea can be successive stages rather than independent evidence about distinct conduct.

• Count actions by distinct action_key and respondents by record_id.
• Keep all allegation categories instead of reducing every case to the first one.
• Preserve the statutes and asset strings as document literals when auditing claims.
• Use document_url to confirm the wording behind any legal characterization.

Follow-up fields link successive actions by the same agency and normalized respondent key. They do not link matters across agencies. A related SEC case and DOJ case can therefore appear as separate chains. Treat cross-agency matching as a reviewed research layer, not as a relationship asserted by the source data.

Asset analysis needs similar restraint. The assets_involved field preserves document literals, so Bitcoin, bitcoin, and BTC can appear as distinct strings. Normalize them in a separate mapping only after retaining the original values. An empty list means the source did not name a specific asset, not that the matter had no connection to digital assets.

Avoid double counts and premature conclusions

The clearest numerical failure mode is summing monetary_relief_usd across respondent rows. The amount repeats on every respondent in an action. De-duplicate distinct action-key and amount pairs, then retain monetary_relief_basis because the total can combine penalties, disgorgement, restitution, forfeiture, and interest. Those components are not economically interchangeable.

The clearest interpretive failure is writing that a party committed misconduct when the record only announces allegations. Label the row by its actual stage. A settled order can support settlement language, a plea can support plea language, and a complaint supports only that charges were filed. Coverage begins in 2024, so the data are not an all-time history of crypto enforcement.

Produce one reproducible agency table

As a concrete next action, retrieve one completed year, save every response page, and create two tables. The first has one row per action with agency, source index, type, publication date, allegations, relief basis, and source URL. The second has one row per respondent and links back by action key. Reconcile action and respondent counts, audit several multi-party releases, and report unknown settlement states separately. That structure answers who acted, what was announced, and against whom without overstating the legal outcome.