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Use Polymarket’s current Python SDK
Polymarket describes its unified client as the “Official Python SDK for Polymarket.” It documents the polymarket-client package, with synchronous PublicClient and asynchronous AsyncPublicClient options. For a small, scheduled CSV export, the synchronous client is a straightforward starting point; async is useful when collecting many markets concurrently or integrating with an asynchronous application.
Avoid older tutorials built around py-clob-client. Polymarket’s legacy repository says it was archived on May 25, 2026, and warns that the client is no longer functional and should not be used for new or existing integrations. The notice applies to that client, not to Polymarket’s APIs generally.
The API and SDK can change. Pin the package version in your project and check the current SDK documentation for method signatures before relying on a script. The example below shows the export pattern without inventing a complete response payload or unverified method calls.
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Find the market and its outcome token
In Polymarket’s data model, an event can contain one or more markets. Each market represents a tradable question, and its outcomes—such as YES and NO—have separate token IDs. Price and order-book requests need the token ID for the exact outcome you want to read.
Use the official SDK’s public discovery methods to find an event or market by ID, slug, or Polymarket URL, or to list and filter events or markets. The public discovery workflow does not require authentication. For direct API access, keep the documented services distinct: Gamma API examples handle event and market discovery, while CLOB API examples handle market-data reads. The official market-data overview describes the objects and discovery options.
- Install the current SDK package,
polymarket-client, following the official SDK instructions. - Create a
PublicClientfor a synchronous export, or anAsyncPublicClientif your application is asynchronous. - Use public discovery to retrieve the event or market. If an event contains multiple markets, identify the individual question rather than treating the whole event as one market.
- Inspect that market’s outcomes and token IDs. Record the selected outcome label alongside its token ID.
- Use the SDK’s documented public methods to read the selected token’s price, book, or relevant market and trade activity data.
- Normalize the response into rows with identifiers and a UTC retrieval timestamp, then write them with Python’s
csvmodule or a dataframe library.
The public market-data workflow is separate from authenticated account or trading workflows. A read-only export does not need a wallet private key.
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Choose what “odds” means before exporting
A price read is a quote for a particular outcome token at the time of retrieval, not a timeless forecast or a guarantee of what will happen. Label the price measure in your CSV: last trade, best bid, best ask, or midpoint are different values and should not all be called simply “the odds.” Polymarket’s CLOB market-data documentation describes price and book reads, including midpoint and spread methods.
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Every quote row should retain the retrieval time, market identity, token ID, outcome label, and metric name. This lets you distinguish, for example, a YES midpoint from a NO last trade and makes clear that the result is a point-in-time snapshot that can become stale immediately.
Export order-book depth as price-size levels
An order book contains resting bids and asks, each represented by a price and size. Polymarket documents bids as ascending and asks as descending, so the best quote on each side is the final entry in that side’s array. The response also includes state metadata such as a hash; comparing it with a previous response can show whether the book changed.
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For a full-depth export, write one row per level to a separate long-form CSV. Include retrieved_at_utc, market_id, token_id, outcome, side (bid or ask), level, price, and size. Keep all levels if you need depth analysis. If you export only best bid and ask, or calculate a spread, name that reduction explicitly. Polymarket defines spread as best ask minus best bid.
For comparisons between markets or outcomes, use the same retrieval time or sampling window, comparable questions and outcome sides, and the same price measure. If comparing liquidity, state which visible price levels and sizes you included.
Define volume by source, scope and time window
“Volume” can refer to a published market-level measure or a total you calculate from individual matched trades; these are not interchangeable by default. State whether a value applies to one market or an event, its units, its time window, and whether it is a source-provided field or your own aggregation.
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The official analytics documentation describes recent matched trades with side, price, size, outcome, wallet, and timestamp, sorted newest first. A list of recent trades is not itself a precomputed volume total. To derive one, document the records included, filters, time window, and aggregation rule, and retain the underlying records if reproducibility matters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Structure the CSV so the snapshot stays interpretable
For quotes and market-level measures, a useful flat-file schema is:
retrieved_at_utcevent_id,market_id, andmarket_slugcondition_id, when availabletoken_idandoutcomemetricandprice- The relevant volume field, with its unit and window or aggregation rule
Use a separate long-form file for order-book levels so each row has one side and one price-size pair. These schemas are practical recommendations, not Polymarket-mandated formats. A CSV writer can emit rows once the SDK response objects have been normalized; consult the SDK’s live documentation for the current method names and response fields:
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import csv
from datetime import datetime, timezone
# Install polymarket-client and initialize PublicClient using
# the current instructions in Polymarket's official SDK documentation.
# Discover a market, select its outcome token ID, then use the
# SDK's documented public methods to fetch price and/or book data.
retrieved_at_utc = datetime.now(timezone.utc).isoformat()
# Populate these rows from the normalized SDK response.
quote_rows = [
{
"retrieved_at_utc": retrieved_at_utc,
"event_id": "...",
"market_id": "...",
"market_slug": "...",
"token_id": "...",
"outcome": "...",
"metric": "...",
"price": "...",
}
]
with open("polymarket_quotes.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.DictWriter(file, fieldnames=quote_rows[0].keys())
writer.writeheader()
writer.writerows(quote_rows)
The ellipses mark values to supply from the market and response you actually retrieve; they are not API fields or a ready-to-run payload. Check current signatures and response shapes in the official SDK repository and market-data documentation before adapting the pattern.
Keep comparisons fair and exports auditable
When comparing outcomes, align the retrieval time or measurement window, use equivalent market questions and the same outcome side, and compare the same price metric. Include spread and visible depth at stated levels where liquidity matters. For volume, keep the scope, units, period, and aggregation method consistent; an event-wide measure should not be compared with a single-market value as if their scopes matched.
Timestamp every read and retain the market and token identifiers. That gives each row enough context to interpret later, while recognizing that API snapshots do not establish a permanent price or a real-world probability. The official documentation and SDK repositories cited here were accessed October 4, 2026; recheck them when implementing because their interfaces and guidance can change.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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