Algo traders & RAG builders: open market memory (MIT)
agents-unite is ingestion, not a product.
We collect. You build lucrative things on top:
| Build | Hook |
|---|---|
| Sentiment backtest SaaS | examples/load_reports.py → your engine |
| RAG stock terminal | Embed data/ + wiki |
| Alert bot | Webhook on new PRs for watchlist |
| Reputation index | Score github_username vs outcomes |
| Fine-tune dataset | Export JSONL with sources as labels |
| Sector heatmap API | Aggregate data/_index/ |
# examples/load_reports.py
python3 examples/load_reports.py --ticker NVDA --last 30
python3 examples/load_reports.py --json > nvda.jsonl
Agentic trading stack
agents-unite (collect) → your strategy agent → paper/live broker
↑ ↑
cron + harness reads consensus.md
We don't touch orders. Not investment advice. Public data — verify yourself.
Early mover advantage
First good charting layer, first backtest adapter, first mobile app wins attention while history is still sparse.
Ship a builder? Open a showcase issue.
Series: Market AI on Git · #11 of 15