Samfisheryu/polymarket_data

Polymarket Resolved Events — Crowd-Belief & Volume Trajectories

~29,600 fully-resolved Polymarket events, each with its complete daily crowd-belief probability trajectory, daily trading volume, and ground-truth outcome — crawled directly from the Polymarket APIs.

The data is provided as-is from the crawl: every event whose full tradeable lifetime falls inside the collection window is included, with no quality/liquidity/signal selection applied. You can apply your own filtering downstream.

What the dataset contains

• One file, full.jsonl — JSON Lines, one event per line (~29,600 lines).
• Every event is resolved (ground_truth_status == "resolved"): the outcome is known, so it can be used for supervised forecasting and backtesting.
• Each record bundles four things for one event:
  1. Event metadata — title, description, dates, tags, volume.
  2. Per-option markets — the underlying binary YES/NO books (1 for a binary event, K for a multi-outcome event).
  3. Daily crowd-belief trajectory — the market-implied probability for each day of the event's tradeable life.
  4. Daily trading volume — trades, share volume, and notional per day, aligned to the same daily grid.

Coverage

• Time window: events whose entire tradeable lifetime — market open (start_date) through on-chain resolution (closed_time) — falls inside [2025-06-01, 2026-06-01) (UTC).
• Event types:
  • binary — a single YES/NO market.
  • multi_neg_risk — a multi-outcome event modeled as K mutually-exclusive, linked binary YES/NO books ("neg-risk").
• Domains: primarily sports, games, and weather (soccer, cricket, daily temperature), plus politics, finance, and others. See each record's tags. The domain mix reflects Polymarket's activity and is not balanced.

How it was collected

Crawled from the public Polymarket APIs in four stages:

1. Metadata — GET gamma-api.polymarket.com/events (closed=true). Enumerate resolved events; derive ground truth from each market's final outcomePrices (YES wins if the final price ≥ 0.99).
2. Window-fit — keep only events whose full lifetime (start_date → closed_time) lies inside the collection window.
3. Crowd belief — GET clob.polymarket.com/prices-history (fidelity=60), re-aggregated to UTC days, to build each option's daily probability series.
4. Daily volume — Goldsky Polymarket "orderbook-subgraph" GraphQL (fallback: data-api.polymarket.com/trades), aggregated per day and aligned to the belief grid.

Record schema

Each line is one event. Fields:

Event metadata

Field	Type	Meaning
event_id	str	Polymarket event id
slug, title, body	str	Identifiers / description
creation_date	str (ISO)	DB-insert moment (before publish; not trading open)
start_date	str (ISO)	Order book open — left edge of the trajectory
end_date	str (ISO)	Resolution deadline (often much later than actual resolution)
close_date / closed_time	str (ISO)	Actual on-chain resolution — right edge of the trajectory
resolution_date	str (ISO)	Gamma endDate
active, closed, archived	bool	Lifecycle flags
neg_risk	bool	true ⇒ multi-outcome (K linked binary books)
total_volume	float	Total traded volume (USDC), from Gamma metadata
tags	list	Domain/category tags
category	str	Category
num_markets	int	Number of option markets (1 = binary)

Date note: markets often settle well before end_date, so use closed_time — not end_date — as the true right edge of a trajectory to avoid look-ahead leakage.

Ground truth

Field	Type	Meaning
ground_truth_status	str	"resolved" for every record here
resolved_label	str	Winning outcome label (or "yes"/"no" for binary)
winner_market_index	int	Index into markets[] of the winning option

Per-option markets — markets (list)

One entry for a binary event; one per option for a multi-outcome event. Each object: market_id, question, label, condition_id, outcomes (["Yes","No"]), outcome_prices_final, clob_token_ids, yes_token_id, yes_outcome_index, yes_resolved (1/0/None), closed, active, archived, umaResolutionStatus, volume, end_date, created_at, start_date, closed_time.

Daily crowd-belief trajectory

Field	Type	Meaning
belief_kind	str	"binary" or "multi_neg_risk"
daily_index	list[str]	ISO UTC day midnights — the time axis all series align to
probability_start_date, probability_end_date	str	Span of the series
raw_yes_history	dict	{label: [p_yes per day]} — daily-avg YES price ∈ [0,1]
raw_no_history	dict	{label: [1 - p_yes per day]} (binary only)
normalized_history	dict	{label: [p per day]} — per-day normalized K-way distribution (multi only)
daily_probability_sum	list	Σ_k YES_k(t) per day (multi only; the normalizer)
missingness	dict	Per-option {days_total, days_with_value, days_missing}

Multi-outcome note: per-option YES prices do not sum to 1 (each YES book has one-sided liquidity). The implied distribution per day is p_i / Σ_k p_k — that's what normalized_history already stores.

Days inside the active lifetime with no trade tick are explicit null in the belief series (the grid spans the true tradeable lifetime, not just observed ticks).

Daily trading volume

Field	Type	Meaning
daily_volume_by_market	dict	{market_id: [{date, trades, share_volume, notional}]}
daily_volume	list	Event-level daily volume (sum across markets)
winner_daily_volume	list	The winning market's own daily volume series
total_volume_metadata	float	Gamma's reported market.volume (USDC)
daily_volume_meta	dict	Method used, per-market stats, truncation flags, window
diagnostics	dict	Coverage ratios (ratio_notional_to_metadata ≈ 1.0 ⇒ complete)

Per-day volume metrics: trades (fill count), share_volume (conditional tokens transacted), notional (USDC that changed hands). All are densified onto daily_index (days with no trades → 0). Note the asymmetry: missing belief days are null; missing volume days are 0.

Quick start

import json

records = [json.loads(line) for line in open("full.jsonl")]
print(len(records), "events")

ev = records[0]
print(ev["title"], "->", ev["resolved_label"])

# Reconstruct the winner's probability trajectory
days = ev["daily_index"]
if ev["belief_kind"] == "binary":
    label = next(iter(ev["raw_yes_history"]))
    p_yes = ev["raw_yes_history"][label]
    p_winner = p_yes if ev["resolved_label"] == "yes" else [
        1 - p if p is not None else None for p in p_yes
    ]
else:  # multi_neg_risk
    p_winner = ev["normalized_history"][ev["resolved_label"]]

for d, p in zip(days, p_winner):
    print(d, p)

With the datasets library:

from datasets import load_dataset
ds = load_dataset("<your-username>/<dataset-name>", data_files="full.jsonl", split="train")

License & attribution

Data derived from the public Polymarket Gamma / CLOB APIs and the Goldsky-hosted Polymarket subgraph. Please respect Polymarket's terms of service.