| --- |
| dataset_info: |
| - config_name: processed_events_campaign_finance |
| data_files: |
| - split: train |
| path: data/processed/events_campaign_finance.csv |
| - config_name: processed_events_geographical_industry |
| data_files: |
| - split: train |
| path: data/processed/events_geographical_industry.csv |
| - config_name: processed_events_lobbying |
| data_files: |
| - split: train |
| path: data/processed/events_lobbying.csv |
|
|
| - config_name: raw_527_committees |
| data_files: |
| - split: train |
| path: data/raw/527_data_open_secrets/cmtes527.csv |
| - config_name: raw_527_expenditures |
| data_files: |
| - split: train |
| path: data/raw/527_data_open_secrets/expends527.csv |
| - config_name: raw_527_receipts |
| data_files: |
| - split: train |
| path: data/raw/527_data_open_secrets/rcpts527.csv |
|
|
| - config_name: raw_voteview_members |
| data_files: |
| - split: train |
| path: data/raw/HSall_members_VoteView.csv |
| - config_name: raw_voteview_rollcalls |
| data_files: |
| - split: train |
| path: data/raw/HSall_rollcalls.csv |
| - config_name: raw_voteview_votes |
| data_files: |
| - split: train |
| path: data/raw/HSall_votes.csv |
| - config_name: raw_voteview_ideology |
| data_files: |
| - split: train |
| path: data/raw/ideology_scores_quarterly_VoteView.csv |
|
|
| - config_name: raw_cf_candidates |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/cands*.csv |
| - config_name: raw_cf_committees |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/cmtes*.csv |
| - config_name: raw_cf_expenditures |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/expenditures*.csv |
| - config_name: raw_cf_individuals |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/indivs*.csv |
| - config_name: raw_cf_pac_other |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/pac_other*.csv |
| - config_name: raw_cf_pacs |
| data_files: |
| - split: train |
| path: data/raw/campaign_finance_open_secrets/pacs*.csv |
|
|
| - config_name: raw_committee_assignments |
| data_files: |
| - split: train |
| path: data/raw/committee_assignments.csv |
| - config_name: raw_company_sic |
| data_files: |
| - split: train |
| path: data/raw/company_sic_data.csv |
| - config_name: raw_congress_terms |
| data_files: |
| - split: train |
| path: data/raw/congress_terms_all_github.csv |
| - config_name: raw_sec_financials |
| data_files: |
| - split: train |
| path: data/raw/sec_quarterly_financials.csv |
|
|
| - config_name: raw_district_industries_estimates |
| data_files: |
| - split: train |
| path: data/raw/district_industries/*_CB_estimates.csv |
| - config_name: raw_district_industries_surveys |
| data_files: |
| - split: train |
| path: data/raw/district_industries/*_CB_survey.csv |
| - config_name: raw_district_industries_dates |
| data_files: |
| - split: train |
| path: data/raw/district_industries/survey_release_dates.csv |
|
|
| - config_name: raw_naics_2012_crosswalk |
| data_files: |
| - split: train |
| path: data/raw/industry_codes_NAICS/2012-NAICS-to-SIC-Crosswalk.csv |
| - config_name: raw_naics_2013_mappings |
| data_files: |
| - split: train |
| path: data/raw/industry_codes_NAICS/2013-CAT_to_SIC_to_NAICS_mappings.csv |
| - config_name: raw_naics_2017_crosswalk |
| data_files: |
| - split: train |
| path: data/raw/industry_codes_NAICS/2017-NAICS-to-SIC-Crosswalk.csv |
| - config_name: raw_naics_2022_crosswalk |
| data_files: |
| - split: train |
| path: data/raw/industry_codes_NAICS/2022-NAICS-to-SIC-Crosswalk.csv |
| - config_name: raw_naics_effective_dates |
| data_files: |
| - split: train |
| path: data/raw/industry_codes_NAICS/classification_effective_dates.csv |
|
|
| - config_name: raw_lobbyview_bills |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/bills.csv |
| - config_name: raw_lobbyview_clients |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/clients.csv |
| - config_name: raw_lobbyview_issue_text |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/issue_text.csv |
| - config_name: raw_lobbyview_issues |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/issues.csv |
| - config_name: raw_lobbyview_network |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/network.csv |
| - config_name: raw_lobbyview_reports |
| data_files: |
| - split: train |
| path: data/raw/lobbying_data_lobbyview/reports.csv |
|
|
| - config_name: src_build_campaign_events |
| data_files: |
| - split: train |
| path: src/build_campaign_events.py |
| - config_name: src_build_geographical_edges |
| data_files: |
| - split: train |
| path: src/build_geographical_edges.py |
| - config_name: src_build_lobbying_events |
| data_files: |
| - split: train |
| path: src/build_lobbying_events.py |
| - config_name: src_config |
| data_files: |
| - split: train |
| path: src/config.py |
| - config_name: src_temporal_data |
| data_files: |
| - split: train |
| path: src/temporal_data.py |
| |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - graph-ml |
| - tabular-classification |
| tags: |
| - finance |
| - politics |
| - legal |
| - gnn |
| - temporal-graph |
| pretty_name: "HillStreet: Relational Congressional Trading Dataset" |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # **This sample was made by sampling 1,000 rows from every table of HillStreet.** |
|
|
| --- |
|
|
| ## HillStreet: A Relational Dataset for Evaluating Information Channels in Congressional Trading |
|
|
| **HillStreet** is a large-scale, longitudinal dataset and multimodal dynamic graph formalizing the intersection of Capitol Hill and Wall Street. It spans **13.5 years** of mandatory STOCK Act disclosures (July 2012–December 2025), unifying the congressional trading ecosystem into a single, machine-learning-ready framework. |
|
|
| ### Dataset Summary |
| The dataset represents the relationship between **1,137 legislators** and **6,825 companies**. By framing congressional trading as a **dynamic bipartite graph**, HillStreet allows researchers to treat trade signal validation as an edge classification task. |
|
|
| - **Nodes:** Legislators (session-specific) and Publicly Traded Companies. |
| - **Target Edges:** Individual stock trades. |
| - **Structural Edges:** Lobbying records, campaign finance (PAC/527) contributions, and geographical/industrial-constituency alignments. |
|
|
| ### Dataset Structure |
|
|
| HillStreet is divided into pre-built graph objects for deep learning and a relational tabular database accessed via Hugging Face configurations. |
|
|
| #### 1. Dynamic Graph Objects (`.pt` & `.npy`) |
| For immediate use in Graph Neural Networks (GNNs) and Temporal Graph Networks (TGNs), the core of HillStreet consists of annual **PyTorch Geometric Temporal** objects. |
| - **Graph Files:** `hillstreet_temporal_graph_YEAR.pt` |
| - **Temporal Integrity:** Every node feature and edge is instantiated based on its **public disclosure date**, not its reference date, ensuring a look-ahead-bias-free environment for backtesting. |
| - **Node Features:** Includes rolling DW-NOMINATE scores, SEC fiscal facts, and Census district employment statistics. |
| - **ID Mappings:** `src_id_map.npy` (Legislator Bioguide IDs) and `dst_id_map.npy` (Company Tickers). |
|
|
| #### 2. Relational Tables (Hugging Face Configs) |
| For researchers using flat-feature models (XGBoost, LightGBM) or custom graph builders, the structural connective tissue is provided as multiple dataset configurations. You can load these individually using the Hugging Face `datasets` library (e.g., `load_dataset("benroodman/HillStreet", "processed_events_lobbying")`). |
|
|
| **Processed Edge Tables:** |
| - `processed_events_lobbying`: Mappings of legislative activity to corporate nodes. |
| - `processed_events_campaign_finance`: Itemized PAC/527 donations broadcasted to corporate sectors. |
| - `processed_events_geographical_industry`: Industrial-constituency edges linking legislators to companies in their districts. |
|
|
| **Raw Source Tables:** |
| The raw, underlying tables are also available as distinct configurations for custom aggregations and feature engineering: |
| - **Campaign Finance:** `raw_cf_*` and `raw_527_*` configs. |
| - **Legislator Data:** `raw_voteview_*` configs and `raw_committee_assignments`. |
| - **Corporate & Industry:** `raw_sec_financials`, `raw_naics_*` crosswalks, and `raw_district_industries_*` configs. |
| - **Lobbying:** `raw_lobbyview_*` configs. |
|
|
| ### Feature Engineering & Normalization |
| To stabilize variance in graph training, continuous features are transformed using signed log-scaling: |
| $$x' = \text{sign}(x) \times \log(1 + |x|)$$ |
|
|
| ### Intended Use |
| - **Trade Signal Validation:** Determining if a trade constitutes a meaningful price signal based on political context. |
| - **Graph Representation Learning:** A benchmark for GNNs and TGNs. |
|
|
| > [!WARNING] |
| > **Non-Intended Use:** This dataset is for research purposes only. It is not designed for legal determinations of insider trading nor for real-time automated trading. |
