Content
# Congressional Trade Signals
**Do U.S. congressional stock trades beat the market — and can a retail investor act on them?**
An end-to-end, auditable research pipeline that collects public STOCK Act disclosures,
market-adjusts every trade against the S&P 500, detects multi-politician *cluster* buys,
and scores them into **STRONG / WATCH / SKIP** signals — exposed both as a research dataset
and as a live MCP server.
> Research project extending Ziobrowski et al. (2004, 2011) into the post-STOCK Act era.
> Built as a Fellow at [Humanitarians AI](https://github.com/Humanitariansai) under the
> Mycroft verified-intelligence framework. **Research only — not financial advice. No trades are placed.**
---
## The headline finding
In aggregate, congressional BUY trades beat the market by only **+0.24%** after per-trade
SPY adjustment — most members simply ride market beta. The real signal is concentrated in
**cluster buys**: when 2+ politicians independently buy the same ticker within 30 days,
especially in semiconductors and AI infrastructure under active legislative oversight,
those clusters show **+17% to +68% alpha** above SPY.
**The value of the system is the skip rate** — it filters out ~95% of trades as noise.
---
## Architecture
```
INGEST GIGO (validate) TOOL (analyze, read verified only)
┌─────────┐ ┌──────────────────────┐ ┌─────────────────────────────────────┐
│scraper │ → │enricher │ → │cluster_analyzer → langgraph_pipeline │
│(Selenium│ │(yfinance prices) │ │(cluster detect) (5-node multi-agent)│
│Capitol │ │market_adjusted │ │ │
│Trades) │ │(SPY per-trade alpha) │ │ server.py (MCP, 5 tools) │
└─────────┘ └──────────────────────┘ └─────────────────────────────────────┘
data/raw/ ───────────────────────────→ data/verified/ ──→ logs/ + reports/
```
### The LangGraph multi-agent pipeline (`langgraph_pipeline.py`)
```
conformance ──(fail)──→ END hard data gate: never analyze garbage
│
↓
cluster ──→ scorer ──(0 STRONG)──→ report skip the LLM when nothing to explain
│
(STRONG>0)
↓
research (Claude) ──→ report LLM thesis note per strong signal
```
Shared typed state with an **append-only run-log reducer** — every number in the final
report traces back through the graph to its source filing (provenance).
---
## Quick start
```bash
pip install -r requirements.txt
python scraper.py # 1. INGEST — scrape Capitol Trades → data/raw/trades.csv
python enricher.py # 2. GIGO — add yfinance prices (resumable, checkpointed)
python market_adjusted.py # 3. GIGO — per-trade SPY-matched alpha
python cluster_analyzer.py # 4. TOOL — detect clusters + politician sector profiles
python langgraph_pipeline.py --no-llm # 5. TOOL — score signals, write log + report
```
Open `dashboard.html` in any browser for the interactive research dashboard.
### As an MCP server (Claude Desktop)
`server.py` exposes 5 natural-language tools: `get_price_history`, `get_recent_trades`,
`get_buy_signals`, `get_stock_activity`, `get_politician_activity`.
---
## Methodology highlights
- **Per-trade market adjustment** — each trade's return has SPY's return over the *identical*
30-day window subtracted. Stricter than the aggregate benchmarking in prior literature.
- **Returns measured from disclosure date**, not transaction date — the moment a retail
investor could realistically have known. No look-ahead.
- **Cluster = temporal co-occurrence**, not ML clustering: 2+ distinct politicians buying the
same ticker in a 30-day sliding window.
- **Two-factor score** = cluster size × max buy-conviction ratio (BCR), validated against
out-of-sample market-adjusted alpha.
---
## Repo layout
| Path | What |
|------|------|
| `scraper.py` | Selenium scraper for Capitol Trades (checkpoint/resume) |
| `enricher.py` | yfinance price enrichment (incremental, resumable) |
| `market_adjusted.py` | per-trade SPY alpha |
| `cluster_analyzer.py` | cluster detection + politician sector profiles |
| `langgraph_pipeline.py` | 5-node LangGraph multi-agent signal pipeline |
| `server.py` | FastMCP server (5 tools) |
| `dashboard.html` | interactive research dashboard (Chart.js) |
| `PAPER_DRAFT.md` | working paper |
| `recipes/`, `DATA_CONTRACT.md`, `logs/RUN_LOG.md` | Mycroft framework compliance |
---
## Author
**Ameya Deshmukh** — [@Ameya-Deshmukh26](https://github.com/Ameya-Deshmukh26)
deshmukh.amey@northeastern.edu · Northeastern University
## License
MIT — see [LICENSE](LICENSE).
*Data sourced from public STOCK Act disclosures via [Capitol Trades](https://www.capitoltrades.com)
and price data via Yahoo Finance. This project is for research and educational purposes only
and does not constitute financial advice.*
