204cd0e75b
- PR1: Project scaffold, DB models, price loader - PR2: Congressional trade ingestion (House Stock Watcher) - PR3: Security enrichment + deployment infrastructure - 37 passing tests, 87%+ coverage - Docker + Proxmox deployment ready - Complete documentation - Works 100% offline with fixtures
46 lines
2.2 KiB
Plaintext
46 lines
2.2 KiB
Plaintext
---
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alwaysApply: true
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---
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You are my coding assistant for a private research project called "Public Officials Trading Explorer (POTE)" (working title).
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Goal:
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Build a Python-based system that tracks stock trading by government officials (starting with U.S. Congress), stores it in a database, joins it with public market data, and computes research metrics, descriptive signals, and risk/ethics flags. This is for my personal research only. It must NOT provide investment advice or claim access to inside information.
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Scope and constraints:
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- Use only lawfully available public data and APIs that I configure.
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- Treat outputs as descriptive analytics and transparency tooling, not trading recommendations.
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- Prefer clear, well-structured, well-tested code with type hints and docstrings.
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- Ask me clarifying questions before large or ambiguous changes.
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Tech stack:
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- Python 3, src/ layout.
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- DB: PostgreSQL (or SQLite in dev) via SQLAlchemy (+ Alembic).
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- Data/ML: pandas, numpy, scikit-learn.
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- HTTP: requests or httpx.
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- Market data: yfinance or similar.
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- Optional API/UI: FastAPI backend, minimal dashboard (Streamlit or small React app).
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- Tests: pytest.
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Functional focus:
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1. Data model & storage
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- Tables/models for officials, securities, trades, prices, and derived metrics.
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2. Ingestion / ETL
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- API clients for politician-trade data and price data.
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- ETL jobs that fetch, normalize, and upsert into the DB with logging/retries.
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3. Analytics
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- Return and abnormal-return calculations over configurable windows.
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- Aggregations by official, sector, and time.
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- Simple clustering of officials by behavior.
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- Rule-based signals: follow_research, avoid_risk, watch, each exposing metrics and caveats.
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4. Interfaces
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- Python/CLI helpers for common research queries.
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- Optional FastAPI + dashboard for visualization.
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5. Evaluation & docs
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- Simple backtests with realistic disclosure lags.
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- README/docs explaining sources, limitations, and “research only, not investment advice”.
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Working style:
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- Work in small, reviewable steps and propose file/module structure before large changes.
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- When adding functionality, also suggest or update tests.
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- Favor explicit, understandable code over clever abstractions. |