benroodman/HillStreetSample

This sample was made by sampling 1,000 rows from every table of HillStreet.

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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, a relational tabular database accessed via Hugging Face configurations, and the source code used to build everything from the raw tables.

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 — one annual shard per active year.
• 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. Structural edges additionally carry a last_seen timestamp (most recent interaction) alongside t (the start of the relationship), so recency/days-since features can be computed at load time.
• ID Mappings: src_id_map.npy (Legislator Bioguide IDs → row index) and dst_id_map.npy (Company Tickers → row index). These define the global node ordering the static node tensors are aligned to.
• Node Features: node_features_static.pt (the five static node tensors) plus node_features_meta.json (dimensions and categorical vocabularies). Produced by Phase 3 of the pipeline and aligned to the ID maps above. With the default configuration flags, legislator features combine a trading-performance summary, chamber/party/leadership indicators, DW-NOMINATE ideology coordinates (evaluated as of the snapshot date), and committee-membership indicators; company features encode the SIC industry division. Integer category indices for legislator state and company sector/industry are also provided for use as learned embeddings. Note that Census district employment enters the graph as geo edge weights (not node features), and SEC fiscal facts are shipped as a raw table that can be enabled as company node features via a configuration flag (off by default).

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.

3. Source Code (src/)

The complete pipeline that turns the raw tables into the processed edge tables and graph objects is included as src_* configurations. The package is laid out as:

src/
├── __init__.py
├── config.py              # central paths + feature flags
├── temporal_data.py       # pipeline orchestrator (Phases 1–4)
└── data_prep/
    ├── __init__.py
    ├── build_lobbying_events.py
    ├── build_campaign_events.py
    ├── build_geographical_edges.py
    ├── node_features.py
    └── feature_lookups.py

• config.py — Resolves the project root and centralizes every input/output path and the feature flags (which structural channels and node-feature blocks are enabled). The data-prep scripts import it via from src import config.
• build_lobbying_events.py — Maps lobbying clients to tickers (via NAICS→SIC→ticker crosswalks) and links them to sponsoring legislators, writing data/processed/events_lobbying.csv.
• build_campaign_events.py — Aggregates corporate PAC and 527 contributions above a conviction threshold, maps donors to legislators, and writes data/processed/events_campaign_finance.csv.
• build_geographical_edges.py — Builds industrial-constituency edges from Census County Business Patterns district data (top industries per district by employment), writing data/processed/events_geographical_industry.csv.
• temporal_data.py — The orchestrator. Ingests the trade table and the three processed event tables, broadcasts sector-level structural events to individual tickers, collapses repeated structural pairs into single weighted edges (keeping both first-seen time and last_seen), writes the per-channel edge parquets, and shards the unified edge stream into annual PyTorch Geometric TemporalData objects with global node-id maps. It then invokes Phase 3 to build the aligned static node tensors.
• node_features.py — Phase 3. Once temporal_data.py has written src_id_map.npy / dst_id_map.npy, build_node_features() assembles the five static node tensors (legislator features, legislator state embedding index, company features, company sector and industry embedding indices) aligned to those maps, saving node_features_static.pt and node_features_meta.json. It is called automatically by temporal_data.py (skip with --skip_node_features); it is not run standalone.
• feature_lookups.py — Helper module imported by node_features.py. Provides the as-of-date lookup classes (TermLookup, PoliticianBioLookup, IdeologyLookup, CommitteeLookup, CompanySICLookup, CompanyFinancialsLookup) that resolve a legislator's or company's attributes from the raw tables (congress terms, DW-NOMINATE ideology, committee assignments, company SIC, SEC financials) at a given snapshot date. It is not executed directly.

Reproduction Pipeline

All commands are run from the repository root. The three builders are independent of one another and produce the processed event tables that temporal_data.py consumes; run them first, then the orchestrator:

# 1. Build the three processed structural-event tables (any order)
python src/data_prep/build_lobbying_events.py
python src/data_prep/build_campaign_events.py
python src/data_prep/build_geographical_edges.py

# 2. Build edge parquets, annual PyG shards, node-id maps, and aligned node features
#    (Phase 3 node features run automatically at the end)
python src/temporal_data.py

temporal_data.py expects the trade table at data/processed/ml_dataset_continuous.csv. It does not call the three build scripts itself — it reads their CSV outputs — but it does invoke node_features.py (which imports feature_lookups.py) as its final phase, so both must be present.

Feature Engineering & Normalization

To stabilize variance in graph training, continuous features are transformed using signed log-scaling: $$x^{\mathrm{\prime }}=\text{sign}(x)\times \log (1+\mathrm{\mid }x\mathrm{\mid })x\text{'}\; =\; \backslash text\{sign\}(x)\; \backslash times\; \backslash log(1\; +\; |x|)$$

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.

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.