ATPPolymarket historical data and price history

ATP: a Polymarket sports series with markets recurring daily. Marketlens has captured 52,261 markets since June 19, 2026, of which 52,951 resolved, with $1.1B in traded volume. No new markets were collected from July 24, 2026 to August 2, 2026, so windows inside that gap return nothing. Order books are stored at full depth, with every individual price change captured at millisecond resolution on the most actively traded markets, alongside trades, candles, and the final resolution outcome.

For the sports archive as a whole, see Polymarket sports odds history.

Data as of 2026-11-02 · Polymarket series ATP · slug atp

Markets
52,988
Resolved
52,951
Traded volume
$1.1B
Avg volume / market
$41.0K
Cadence
daily
Coverage from
2026-06-19
Data through
now
Category
Sports

No new markets were collected from July 24, 2026 to August 2, 2026, so windows inside that gap return nothing.

Markets by bet type

total9,389
total:set19,305
total:set28,952
spread6,614
moneyline5,407
total:sets3,401
segment_winner:set13,260
segment_winner:set23,177

Market counts by bet type across the series. Pass any of these as subtype= to backtest that type on its own.

Markets in this series

The most recently resolved of 52,988 markets.

Taipei: Evan Zhu vs Yuta Shimizu

closed 2026-11-02 · volume $440 · resolved Yuta Shimizu

Costa do Sauipe: Marcelo Zormann vs Filip Misolic

closed 2026-11-01 · volume $53.9K · resolved Filip Misolic

Costa do Sauipe: Federico Zeballos vs Luis Felipe Miguel

closed 2026-11-02 · volume $4.8K · resolved Luis Felipe Miguel

Backtest this series

python
from datetime import datetime, timedelta, timezone from marketlens import MarketLens, Strategy class MyStrategy(Strategy): def on_market_start(self, ctx, market, book): ctx.buy_yes(size=10) client = MarketLens() end = datetime.now(timezone.utc) - timedelta(days=1) result = client.backtest( MyStrategy(), "atp", subtype="moneyline", after=end - timedelta(minutes=15), before=end, initial_cash=1_000, ) result.show() # inspect the run in the dashboard

Available datasets

Order book snapshots and deltasfull L2 depth, millisecond price changes
Tradesindividual fills with side and size
CandlesOHLC at multiple resolutions
Bulk Parquet exportsfull markets for offline research
Backtestingtick level replay with realistic fills

Common questions

How many ATP markets are in the archive?

52,261 markets from June 19, 2026 through now, of which 52,951 have resolved and 37 are active. No new markets were collected from July 24, 2026 to August 2, 2026, so windows inside that gap return nothing.

What data exists for each ATP market?

Full depth L2 order book snapshots at regular intervals, individual trades, OHLC candles, and the resolution outcome, with the full price change stream at millisecond resolution on the most actively traded markets in the series. All of it is queryable by the series slug "atp" through the API and Python SDK, or downloadable as Parquet.

How much does a typical ATP market trade?

Average traded volume is $41.0K per market, $1.1B across the series.

Can I backtest strategies on ATP?

Yes. Pass the slug "atp" to client.backtest() in the Python SDK. Execution mode replays order books against real historical depth; Alpha mode, one bar per market, covers every market in the series and suits its longer horizons. This series mixes several bet types, so pass subtype= (e.g. "moneyline") to backtest one type at a time.

Related series: MLB, NFL 2026, WTA, NBA 2026, Premier League 2025, CFB 2026, or all sports series.

Try it

Pull ATP books in one call

The free tier includes 2M data rows and every market open in the last 7 days, no card required.

bash
$ pip install marketlens