Dataset
congress-trading
Normalized stock-trade disclosures filed by U.S. House and Senate members under the STOCK Act, regenerated on a schedule by the repository's own pipeline rather than curated manually.
Data preview
Dataset card
The congress-trading dataset, published by ZipLime, aggregates STOCK Act disclosures from members of the U.S. House and Senate into a normalized, append-only table rebuilt on a schedule. It contains roughly 775,000 rows of securities transactions with disclosure-lag metadata designed for point-in-time backtesting.
What this dataset contains
The dataset covers transaction reports filed by U.S. members of Congress under the STOCK Act, normalized into a single schema and rebuilt automatically from source documents. It flags cases where the recorded transaction date appears to precede the notification date as date_quality = "notify_before_tx", and provides both raw (`trades`) and transaction-date-aligned (`features`) configurations. Approximately 237 of ~178k transaction rows fall back to the statutory 45-day disclosure limit when the original date is missing or implausible.
Structure and fields
The disclosed columns in this dataset focus on legislator attributes: bioguide_id, committee_id, rank, title, and party. The broader schema described in manifest.json adds transaction-level fields including chamber, transaction_type, asset_ticker, amount_low, amount_high, disclosure_lag_days, knowledge_date, knowledge_estimated, and event_date. Data is delivered in Parquet format with large_string and int64 dtypes, supporting loaders such as pandas, polars, and the Hugging Face datasets library.
How analysts use it
Investment analysts use this dataset to study the relationship between congressional trading activity and subsequent stock returns, taking care to key on the disclosure filing date rather than transaction_date to avoid look-ahead bias in backtests. The chamber and party fields support segmentation by House versus Senate or by political affiliation, while amount_low and amount_high capture the disclosed transaction ranges. The 30–45 day disclosure lag is a central consideration for any strategy derived from the data.
Documentation & references 1
Related datasets
Browse moreParsed eligibility criteria from 13,229 ClinicalTrials.gov studies representing the candidate pool gathered by two first-stage retrievers during TREC Clinical Trials 2021–2023, formatted as typed entity-relation graphs for reranking models.
The clinical-trials-patient-graphs dataset is a small tabular collection of patient-level clinical records distributed in Parquet format by the 2001jdev publisher. It organizes entities and relations extracted from trial data into structured rows spanning 2021 through 2023.
This dataset is a parsed snapshot of ClinicalTrials.gov records, distributed in Parquet format on the Hugging Face Hub under an unspecified license. It contains roughly 52,000 trial entries drawn from public registry filings.
clinical-trials-trec-qrels is a tabular relevance-judgment file distributed by the 2001jdev user on the Hugging Face Hub. It maps clinical-trial topic identifiers to NCT registry IDs with graded relevance scores.
The clinical-trials-trec-topics dataset is a small tabular collection of clinical-trial topic records distributed in Parquet format on the Hugging Face Hub. It appears to be derived from TREC clinical-trial retrieval topics, with rows partitioned by year.
SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
Discussion
Questions or observations about this dataset? Join the conversation on X.
No comments yet. Start the conversation on X.