---
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.