Running the Alphapoly Pipeline

Running the Alphapoly Pipeline

The pipeline fetches Polymarket events, groups related markets, extracts logical implications via LLM, and builds hedged portfolios. Output lands in data/_live/.

Quick Run

make pipeline         # Incremental (new groups only, uses cache)
make pipeline-full    # Full reprocess (resets all state)

With Model Overrides

Run from the backend/ directory:

uv run python -c "
from core.runner import run
run(
    implications_model='openai/gpt-4o-mini',
    validation_model='openai/gpt-4o',
)"

Pipeline Steps

1. Fetch events from Polymarket
2. Build market groups
3. Detect new groups (incremental check)
4. Extract implications (LLM, cached)
5. Expand to market-level pairs
6. Validate pairs (LLM, cached)
7. Build portfolios with tier metrics
8. Export to data/_live/

Price updates happen separately via the WebSocket service when the backend is running.

Seed Data

make export-seed    # Save current state as seed
make import-seed    # Reset DB and import seed (resets state)

Verify

After running, confirm the pipeline produced output:

• Check data/_live/ for output files
• Or start the backend (make backend) and hit GET /data/portfolios to see generated portfolios

Troubleshooting

• Crashes on startup → verify .env has all three required vars: OPENROUTER_API_KEY, IMPLICATIONS_MODEL, VALIDATION_MODEL
• LLM errors → check OPENROUTER_API_KEY is valid; try a different model via override
• Stale state → make pipeline-full to reprocess everything
• Partial outputs → check data/_live/ then re-run make pipeline to resume incrementally