politician-trade-score 2025.1.4

Score stocks based on US politician trade disclosures (Capitol Trades).

politician-trade-score

Score stocks based on US politician trade disclosures (Capitol Trades).

This tool scrapes public politician trade data and computes a score for each ticker based on buy/sell activity and recency, helping identify potential signals.

---

Installation

pip install politician-trade-score

Install (editable)

pip install -e .

On Windows PowerShell

If PowerShell blocks virtual-environment activation, use this one-time PATH update to point pip and poltrade to your venv:

# from your project root
py -3.13 -m venv .venv
$env:Path = "$PWD\.venv\Scripts;$env:Path"
where python
where pip
pip install politician-trade-score

Then run the CLI.

CLI usage

Fetch recent trades and score tickers:

poltrade --tickers AAPL TSLA --pages 50 --hold-length 180 --buy-weight 0.8 --details

• --tickers - One or more tickers.
• --pages - Number of pages to fetch from Capitol Trades (default 303 in the original).
• --hold-length - Lookback window in days for the date filter (default 180 in the original).
• --buy-weight - Weight on buy-ratio in final score. Recency weight is 1 - buy-weight.
• --details - Print the intermediate details shown by the original script.

You can also pass explicit start and end dates (YYYY-MM-DD) instead of --hold-length:

poltrade --tickers NVDA --start-date 2025-06-01 --end-date 2025-09-01

Library usage

from politician_trade_score.data import gather_data
from politician_trade_score.score import PoliticianTradeScorer

data = gather_data()
scorer = PoliticianTradeScorer(buy_ratio_weight=0.8, recency_weight=0.2)
score = scorer.final_score(data, "AAPL", details=True, tutorial=True)

Notes

• The gather_data function relies on pandas.read_html to scrape capitoltrades.com pages.