Python Developer: Historical Data Scraper for Polymarket

I am looking for an experienced Python developer to build a robust data extraction tool for Polymarket. The goal is to collect high-resolution historical price data for the recurring 15-minute SOL Up/Down events.

Polymarket "recurring" events are technically a series of individual markets, each with a unique ID. To succeed, you might likely need to use standard endpoints or query the Polymarket Subgraph (The Graph/Goldsky) or iterate through individual trade events to reconstruct a sub-1-minute price history.

Requirements

• Historical Extraction: Scrape the last 30 days of data for both "Yes" and "No" shares across all 15-minute intervals in the specified series.
• High Granularity: The final output must be a timeseries with the highest possible resolution (sub-1-minute or trade-by-trade).
• Ongoing Collection: The script must also function as a recurring tool that can be run daily/hourly to append new data to the existing dataset without duplicates.
• Output: A clean CSV/Excel file containing Timestamp, Market_ID, Yes_Price, No_Price, and Volume.

Skills Required

• Python (Pandas, Requests)
• Web3 / Blockchain Data (The Graph, GraphQL, Subgraphs)
• Polymarket CLOB API experience (Highly Preferred)
• Data Cleaning & ETL
• Async Programming (to handle high-volume trade data)

Project Type

One-time project (with potential for ongoing maintenance/updates).

Deliverables / Milestones

• Phase 1 (Proof of Concept): A sample CSV showing 1 hour of sub-1-minute data for a resolved 15m SOL market.
• Phase 2 (Full History): The complete 30-day historical dataset in CSV format.
• Phase 3 (Script Delivery): The Python source code with a README on how to run it for future data collection.

Specifically looking for developers who have worked with Polymarket, UMA, or DeFi subgraphs before.

Questions for Applicants

• Have you worked with the Polymarket CLOB API or their Subgraphs before?
• How do you plan to bypass the 12-hour granularity limit for resolved historical markets?
• What is your approach to handling the "recurring" nature of these 15m events, given each has a different ID?
• Can you provide a brief example of a similar data scraping project you've completed?

Additional Details / Notes

I am open to using tools like Dune Analytics or Goldsky if they provide a more efficient path to the data, provided the final output is a CSV generated via a script I can run locally.

Contract duration of 1 to 3 months.

Mandatory skills: Python, Data Extraction, polymarket, subgraph, API, Python Script