How to Automate Polymarket Research

The stack mirrors the data libraries ranking: DuckDB or Polars for processing, Parquet for storage, n8n or GitHub Actions for scheduling, and BigQuery or S3 when it leaves the laptop.

Automation without versioning is a rumor machine: the honest pipeline commits the query, the data fingerprint, and the parameters each run, so a change in result has a cause.

A nightly job pulls newly resolved 5-minute markets, appends them to the Parquet lake, and recomputes the corpus statistics (median rest at the bound, final-second single-sided rate). Each morning the study is current and the raw frames are still on disk.

That is the exact shape of the measured facts this site publishes — reproducible by anyone who runs the same scheduled pull.

Automation compounds both diligence and mistakes: a pipeline that re-resolves slugs nightly inherits every upstream label change, and the size of a vintage label is only visible to a study that versions it.

Automation cadence follows the claim, not the tool: a handoff alert needs a scheduled pull at minute granularity, a fill audit needs the 250ms frames, and a weekly synopsis needs nothing below daily. The cheapest honest schedule is the coarsest one that still answers the question.

Between the plan windows (3 free days, 30 to 120 paid) and the API's per-key rate limits, the automation design question is really a retention question: what you will need in a month must be pulled into your own store now.

• R guide
• n8n
• BigQuery