README.md

---
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

  # --- Source code (src/) ---
  # config.py and temporal_data.py live at the top of the package; the data-prep
  # scripts, node_features.py, and feature_lookups.py live in src/data_prep/.
  - 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
  - config_name: src_build_campaign_events
    data_files:
      - split: train
        path: src/data_prep/build_campaign_events.py
  - config_name: src_build_geographical_edges
    data_files:
      - split: train
        path: src/data_prep/build_geographical_edges.py
  - config_name: src_build_lobbying_events
    data_files:
      - split: train
        path: src/data_prep/build_lobbying_events.py
  - config_name: src_node_features
    data_files:
      - split: train
        path: src/data_prep/node_features.py
  - config_name: src_feature_lookups
    data_files:
      - split: train
        path: src/data_prep/feature_lookups.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, 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:

```bash
# 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:
$$x' = \text{sign}(x) \times \log(1 + |x|)$$

### 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.