Same event priced on both Polymarket and Kalshi. Compare order flow, spread, and price discovery across venues with provider-canonical identifiers (Polymarket condition IDs, Kalshi event/series tickers).
Academic7research themes5data areasPolymarket + Kalshi
Prediction-market data for academic research
0xinsider runs a unified data layer across Polymarket and Kalshi. Provider-canonical identifiers, full trade and position lineage, and orderbook depth — already collected and deduplicated. If you are a researcher working on prediction markets, we want to make that substrate easy for you to use.
Research themes
7 themesIdentify traders active on both Polymarket and Kalshi, quantify migration, study how cross-venue exposure shapes position sizing and outcomes.
Lead-lag dynamics between prediction markets, traditional finance, and news cycles. Sub-second trade timestamps and full orderbook snapshots support event-study designs.
Polymarket CLOB vs Kalshi orderbook microstructure: spread persistence, depth recovery after large fills, market-maker inventory patterns.
Position sizing, holding period, post-resolution realized P&L by trader cohort. Per-trader equity curves and grade history available across the universe of tracked wallets.
Resolved political markets with full price history from open to settlement. Calibration plots, Brier scores, and timing analysis against polling and other forecasts.
Orderbook depth, spread behavior, and fill quality across venues. Compare resting liquidity in CLOB vs orderbook regimes.
Featured themes
2 picksSame event priced on both Polymarket and Kalshi. Compare order flow, spread, and price discovery across venues with provider-canonical identifiers (Polymarket condition IDs, Kalshi event/series tickers).
Resolved political markets with full price history from open to settlement. Calibration plots, Brier scores, and timing analysis against polling and other forecasts.
Research questions
7 questionsAreas we are happy to scope a collaboration around. Pick one or propose your own — the data substrate is the same either way.
Cross-market behavior
Same event priced on both Polymarket and Kalshi. Compare order flow, spread, and price discovery across venues with provider-canonical identifiers (Polymarket condition IDs, Kalshi event/series tickers).
Trader overlap and migration between venues
Identify traders active on both Polymarket and Kalshi, quantify migration, study how cross-venue exposure shapes position sizing and outcomes.
Information flow and price discovery
Lead-lag dynamics between prediction markets, traditional finance, and news cycles. Sub-second trade timestamps and full orderbook snapshots support event-study designs.
Market efficiency
Polymarket CLOB vs Kalshi orderbook microstructure: spread persistence, depth recovery after large fills, market-maker inventory patterns.
Whale and smart-money behavior
Position sizing, holding period, post-resolution realized P&L by trader cohort. Per-trader equity curves and grade history available across the universe of tracked wallets.
Election forecasting accuracy and calibration
Resolved political markets with full price history from open to settlement. Calibration plots, Brier scores, and timing analysis against polling and other forecasts.
Liquidity dynamics
Orderbook depth, spread behavior, and fill quality across venues. Compare resting liquidity in CLOB vs orderbook regimes.
Data and analytics surface
5 areasWhere a provider exposes a canonical field (Polymarket /positions cashPnl, Gamma outcomes, Kalshi event_ticker), we serve that field. Local heuristics stay off the primary path.
Positions and trades
Per-trader positions and realized P&L lineage on Polymarket (entry, merges, splits, redeems). Trade-level fills with maker/taker, size, price, and timestamp.
DatasetsMarket metadata and resolved outcomes
Unified market identifiers across Polymarket and Kalshi. Open/close timestamps, resolution status, category labels, structured event and series identifiers.
DatasetsOrderbook and microstructure
Best bid/ask snapshots and price history at minute granularity across CLOB and Kalshi orderbooks; suitable for spread, depth, and microstructure work.
DatasetsTrader analytics
Grades (S to F), per-trader P&L history, win rates, strategy breakdowns, and activity heatmaps. Leaderboards filtered by category, platform, and time window.
LeaderboardProgrammatic access
REST API and MCP server expose the same canonical data layer that powers the product. Useful for replicable pipelines and bulk export to research environments.
DevelopersHow to engage
5 inputsThe engagement model is conversational, not a self-serve tier. We are happy to discuss bulk exports, ad-hoc queries, and citation terms with research groups working on questions we find genuinely interesting. Treat a first email as a peer message to another technical team — include:
- Your institution and research group, with a short description of the work.
- The research question and the time window of data you expect to need.
- The data fields and granularity you care about (trades, positions, orderbook, resolved outcomes, trader analytics).
- Whether you need a one-time bulk export, ongoing access, or ad-hoc queries.
- Citation and acknowledgment terms you typically use with data providers.
How to cite 0xinsider
CitationWhen 0xinsider data appears in a published paper, working paper, or thesis, the preferred acknowledgment is the URL 0xinsider.com/academic and a short attribution line such as:
Prediction-market trade, position, and orderbook data sourced from 0xinsider (0xinsider.com), which provides unified coverage of Polymarket and Kalshi.
For specific data-set versions or query snapshots, include the access date. We are happy to provide formal citation strings on request.
Reach out and let us know what you need.
Send a short email describing the work and the data fields you need. We will reply with what is straightforward, what needs a bulk export, and citation terms.
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