Quants: 1B Rows of Prediction Market Data for Reproducible Backtests

The fastest route to complete historical prediction-market coverage is a unified, cross-venue data API rather than stitching together separate Polymarket and Kalshi integrations. A single schema across venues, bulk export support, and a multi-year historical horizon save weeks of pipeline engineering. Developers can start by requesting API access through a provider’s developer landing page and pulling a test export before committing to a subscription tier.

TL;DR:

• Cross-venue unified data APIs significantly reduce integration time and provide a comprehensive historical dataset for prediction markets.

• A complete dataset should include trade records, orderbook snapshots, multi-resolution OHLCV candles, and detailed metadata on settlement and activity streams.

• Five or more years of data are necessary to reliably backtest strategies and capture rare tail events like elections or unexpected market shocks.

• Data quality issues such as schema changes, venue migrations, and indexing gaps require layered validation and provenance tracking for accurate analysis.

• Top file format choice for large-scale datasets is Parquet, which offers efficient compression and flexible, column-specific querying.

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Table of Contents

• What a Complete Historical Prediction Market Dataset Contains

• Authentication, Endpoints, and Data Access Patterns

• Historical Data Specifics: Formats, Exports, and Reproducible Workflows

• Data Quality, Gaps, and Provenance: How Coverage Breaks

• How to Integrate Historical Data Into Backtests and Quant Workflows

• Developer Quickstart and Code Examples

• Assymetrix Evidence: Dataset Scale, Provenance, and API Capabilities

• Timestamp Synchronization and Time Zone Handling

• Handling Market Anomalies and Outliers in Historical Data

• What Actually Matters When Building on This Data

• Getting Started With the Assymetrix Data API

• Sources

• FAQ

What a Complete Historical Prediction Market Dataset Contains

Backtesting a strategy on six months of Polymarket data will tell you almost nothing about how that strategy performs during a contested election or a Fed rate decision. Those events are rare, and a shallow dataset simply never contains one. A dataset built for serious quant work needs several distinct layers, not just closing prices.

• Trade-level records: every executed fill, with price, size, side, wallet address, and timestamp, forming the raw ledger that everything else gets built from.

• Orderbook snapshots: periodic captures of bid/ask depth, essential for modeling slippage and liquidity rather than assuming instant fills at the last trade price.

• OHLCV candles at multiple resolutions: 1-second, 1-minute, and daily bars, so the same underlying data supports both microstructure research and long-horizon strategy testing.

• Resolution and outcome metadata: how and when a market actually settled, which matters enormously, for prediction markets, since payout structure differs from continuous asset pricing.

• Wallet and activity streams: address-level trading history, the raw material for identifying which wallets consistently trade ahead of resolution.

Orderbook snapshots let you model realistic execution costs instead of a fantasy fill price. Wallet trails are what make Smart Money tracking possible in the first place, since flagging a skilled trader requires a full history of their positions, not just their most recent bet.

On history length: A multi-year history is generally recommended for anything resembling robust backtesting or ML training. Prediction markets are event-driven and thin on tail scenarios. A model trained on eighteen months of data has likely never seen a genuine market shock, and research on high-frequency prediction market benchmarks treats continuous market-implied probabilities as a distinct forecasting signal precisely because they compress information that surveys and one-off polls cannot capture over time.

Authentication, Endpoints, and Data Access Patterns

Most prediction-market data providers mix authentication models depending on data sensitivity. Basic market metadata, like series names, active events, and current prices, is often available through unauthenticated public endpoints. Kalshi’s own quickstart demonstrates this pattern directly, offering unauthenticated access to series and market data before any credential exchange happens. Deeper historical exports, wallet-level data, and bulk downloads typically sit behind API keys or OAuth flows, since these carry real commercial cost to serve at scale.

A typical integration touches several endpoint categories in this rough order:

1. Market and series metadata — discover active and historical markets by category, venue, or ticker.

2. Trade and price endpoints — pull tick-level fills or aggregated OHLCV bars for a given market and window.

3. Orderbook endpoints — retrieve current or historical book snapshots at a chosen depth.

4. Resolution endpoints — fetch settlement outcome, timestamp, and payout structure.

5. Wallet and activity endpoints — query address-level trading history across venues.

6. Bulk historical export — request a compressed archive or streaming feed for a large date range instead of paginating live.

Pagination and rate limits deserve real planning, not an afterthought. Historical endpoints frequently mirror the access tier of the live streaming feed, and Interactive Brokers documents this pattern explicitly, noting that if you lack live market-data permissions, historical slices for that same data may simply not return. Build exponential backoff into every retry loop, cache cursor positions so a failed page fetch doesn’t force a full restart, and check documented rate limits before running a wide backfill across hundreds of markets in parallel. Modern providers like Alpaca structure their historical endpoints around clear base-URL and sandbox conventions, which is worth studying even outside the prediction-market space, since it shows how a well-designed historical API separates test traffic from production billing.

Pro Tip: Always hit the sandbox or test endpoint first with a narrow date range before requesting a full historical backfill. Catching a schema mismatch on 500 rows costs you a minute; catching it after downloading 200 million rows costs you an afternoon.

Historical Data Specifics: Formats, Exports, and Reproducible Workflows

Format choice determines whether your backtests run in minutes or hours. Parquet is the standard for anything beyond a few million rows. It stores data in columnar form, compresses far better than CSV, and lets query engines like pyarrow or duckdb read only the columns a given script actually needs instead of parsing every field on every row.

Schema normalization matters just as much as file format. Polymarket, Kalshi, and Limitless each expose slightly different field names and timestamp conventions for what is functionally the same event: a trade execution. A dataset that maps all three into one consistent schema, with shared column names for price, size, side, venue, and market ID, saves a meaningful chunk of the engineering time most teams spend before they can run a single query. Community projects like the prediction-market-analysis repository demonstrate this pattern directly, shipping Parquet schemas and venue-specific indexers alongside a pre-collected dataset for exactly this purpose.

Bulk delivery generally comes in one of two shapes: pre-bundled compressed archives delivered through object storage like S3 or Cloudflare R2, or incremental exports that only ship rows changed since your last checkpoint. The same repository references a compressed archive delivered via Cloudflare R2 as one working example of this delivery pattern, useful groundwork before touching a commercial vendor’s export tooling.

A widely referenced open dataset built on this kind of pipeline spans thousands of markets across multiple years, enough scale to run calibration curves and volume-by-category breakdowns that a few months of data simply can’t support.

Snapshot and replay strategy depends on what you’re testing. Strategy backtests generally work fine on 1-minute or 5-minute orderbook snapshots. Market microstructure research, especially anything examining how quickly a book absorbs a large order, usually needs sub-second event data reconstructed from the raw trade and order-update stream rather than pre-aggregated snapshots. A practical engineering pattern here: keep the raw event stream immutable, build a normalized Parquet layer on top for fast queries, and precompute daily snapshot tables at the resolutions you’ll query most often, typically 1-second, 1-minute, and 5-minute.

A reproducible workflow, end to end, looks like this: ingest raw events into immutable storage, normalize into a shared Parquet schema, generate resolution-specific snapshot tables, then run backtests against the snapshot layer while keeping a pointer back to the raw events for any anomaly you need to investigate.

Data Quality, Gaps, and Provenance: How Coverage Breaks

Historical prediction-market data breaks in ways that historical equities data mostly doesn’t. Venues shut down, migrate infrastructure, or fork their market schema without warning. A market that resolved in 2023 might carry different field names than one that resolved last month on the same venue.

Common causes of gaps include:

• Venue shutdowns or migrations that orphan historical archives entirely.

• Schema changes where a venue adds or renames fields mid-history, breaking naive parsers.

• Resolution disputes where an outcome gets contested or amended after initial settlement.

• On-chain indexing gaps, particularly for venues built on smart contracts, where a node provider’s outage can silently drop a window of events.

Detecting these gaps before they corrupt a backtest requires layered validation: schema checks on every ingest batch, checksums on bulk file transfers, and lineage metadata that records which indexer version and timestamp produced each row. Without lineage tracking, a silent gap from 2022 can sit in your dataset for years before someone notices a suspiciously flat volume curve.

Mitigation usually comes down to three habits. Run progressive reindexing on a schedule so any single missed window gets backfilled automatically. Cross-reference the same market across data sources when a venue supports it, since a resolution discrepancy between two independent pulls is often the first sign of a data quality problem. And checkpoint every long-running ingest job so a crash halfway through a year-long backfill doesn’t force a restart from zero.

Pro Tip: Treat resolution metadata as suspect until confirmed by at least two independent signals, on-chain settlement plus venue-reported outcome, whichever combination your data source supports. A silently unresolved or reversed market can quietly wreck an otherwise clean backtest.

How to Integrate Historical Data Into Backtests and Quant Workflows

The first architectural decision is event-time versus wall-clock backtesting. Event-time replay processes data in the order events actually occurred, which matters enormously, for prediction markets, where a resolution or a large trade can trigger an immediate repricing. Wall-clock backtests, which sample state at fixed intervals, are simpler to build but will miss fast-moving information cascades that event-time replay catches naturally.

Multi-resolution data needs careful resampling and alignment. Mixing 1-second orderbook snapshots with daily OHLCV bars in the same model requires explicit forward-fill or interpolation rules, and those rules should match how the strategy will actually see data live, not just what’s convenient in a notebook.

A practical backtest integration follows this sequence:

1. Load normalized Parquet data for the target market set and date range.

2. Reconstruct orderbook state at the chosen replay frequency from the raw event stream.

3. Build a replay engine that models realistic latency between signal generation and order placement.

4. Run the strategy logic against replayed state, logging every simulated fill.

5. Score results against calibration and outcome metrics, not just raw return.

Evaluation metrics for binary outcome markets differ from continuous asset backtests, and are widely discussed in AI football predictions contexts where continuous probability outputs improve forecasting. Brier score and log-loss both measure calibration, meaning how well your predicted probabilities matched actual outcome frequencies, which matters more here than simple hit rate. A strategy that’s right 70% of the time but consistently overconfident on the other 30% will look great on accuracy and terrible on calibration, and calibration is usually the better predictor of real-world edge.

• Track Brier score and log-loss across the full holdout period, not just aggregate accuracy.

• Segment performance by market category, since a strategy calibrated well on politics markets may be miscalibrated on sports or economic indicators.

• Model latency explicitly in the replay engine rather than assuming instant execution at the snapshot price.

Developer Quickstart and Code Examples

A minimal working pipeline, from API key to a runnable replay loop, takes five steps.

• Obtain an API key from the provider’s developer dashboard and confirm which endpoints your tier includes.

• Request a historical export for a defined market set and date range, specifying resolution if the API supports multiple granularities.

• Download the resulting archive, checking file integrity against any provided checksum before processing.

• Load the data with pyarrow or pandas, using pyarrow.parquet.read_table() for large files to avoid loading the full dataset into memory at once.

• Run a small replay loop that iterates chronologically through events, updating a simple position tracker as a sanity check before building full strategy logic on top.

For scale beyond a single machine’s memory, dask handles out-of-core Parquet processing with a pandas-like interface, and joblib parallelizes independent per-market backtests across CPU cores without much added complexity. A Python-focused developer guide covers the full code path from authentication through a working replay script if you want a concrete starting template rather than building the pipeline from scratch.

Pro Tip: *Checkpoint your download progress by market ID, not by byte offset.

Error handling matters more here than in most API integrations, since a historical backfill job might run for hours. Wrap every network call in retry logic with exponential backoff, log the specific market and date range on every failure, and write completed batches to disk immediately rather than holding results in memory until the full job finishes.

Assymetrix Evidence: Dataset Scale, Provenance, and API Capabilities

Assymetrix built its Data API around a specific problem: prediction-market data lives in fragmented, venue-specific silos with no centralized historical archive, forcing every team to build the same ingestion pipeline from scratch. The intelligence layer aggregates Polymarket, Kalshi, and Limitless into one normalized schema, backed by roughly 1.5 terabytes of historical data spanning close to one billion rows of trading activity.

What that scale actually includes:

• Indexed on-chain and off-chain trading events across all three supported venues, dating back to each venue’s operational start.

• Price snapshots numbering in the hundreds of millions, supporting orderbook reconstruction at multiple replay resolutions.

• Wallet-level activity streams that power Smart Money wallet tracking and Trader Skill Scoring.

• Cross-venue market divergence and arbitrage signal endpoints built directly on top of the unified schema.

Developer documentation, bulk export options, and backtesting examples using the full snapshot history live at the Assymetrix Data API guide, including a worked example of backtesting a strategy against 200 million-plus price snapshots.

Timestamp Synchronization and Time Zone Handling

Every prediction-market venue timestamps events differently, and reconciling that across a multi-venue dataset is one of the more common sources of silent backtest error. On-chain venues typically record block timestamps in UTC by default, since blockchain infrastructure has no concept of local time. Centralized venues like Kalshi often timestamp at the application layer, which can introduce small discrepancies between when an order was placed and when it was recorded, particularly under load.

The practical rule: normalize everything to UTC at ingest time, store the original venue timestamp alongside the normalized one, and never perform time zone conversion downstream in analysis code. Doing conversion at the analysis stage instead of at ingest is a common source of duplicated bugs, since every notebook and script that touches the data has to get the conversion right independently instead of once.

Daylight saving transitions cause a specific, underappreciated bug: a naive resampling function that assumes fixed-width days will silently misalign candles twice a year in any dataset that stores local time instead of UTC. If your OHLCV candles ever show a suspicious one-hour gap or overlap in March or November, check whether the underlying timestamps are UTC or local before assuming it’s a data quality issue elsewhere in the pipeline.

For cross-venue analysis specifically, latency between when an event actually occurred and when it was indexed matters more than raw timestamp precision. A venue with a five-second indexing delay will make a strategy backtest on the raw timestamp look faster than it could actually execute live, so any latency-sensitive research should model indexing delay explicitly rather than trusting the recorded timestamp as ground truth.

Handling Market Anomalies and Outliers in Historical Data

The challenge is distinguishing a real information shock from a data artifact, like a stale orderbook snapshot or a single wash-trade-style fill that briefly distorted the last-trade price.

A few detection heuristics help separate the two. Real information-driven moves typically show sustained volume alongside the price shift, since genuine repricing draws in multiple independent traders responding to the same news. A price spike on a single trade with no follow-through volume, especially in a thin market with low open interest, is more likely a data artifact or a single actor testing the book than a real shift in consensus probability.

Thin markets deserve extra scrutiny generally. A market with only a handful of active wallets can show wild price swings from a single large order that would barely register in a deep, liquid market. Segment your outlier detection thresholds by market liquidity rather than applying one flat threshold across your whole dataset, since a 20-point move in a thin niche market and the same move in a heavily traded election market carry very different informational weight.

When a resolution genuinely gets disputed or reversed after initial settlement, flag that market explicitly in your dataset rather than silently updating the historical record. Backtests run before and after a resolution reversal can produce materially different results, and losing track of which version of “ground truth” a given backtest used is a reproducibility problem worth avoiding.

What Actually Matters When Building on This Data

Most guides to prediction-market data treat the API integration as the hard part. It isn’t. Any competent developer can authenticate against a REST endpoint and paginate through results in an afternoon. The genuinely hard part, and the part conventional advice mostly skips, is building a pipeline that stays correct as venues change their schemas underneath you.

Deep historical coverage gets treated as a nice-to-have, something you add once the core pipeline works. That’s backwards. A backtest built on eighteen months of data will pass every sanity check you run against it and still fail the first time it meets a real market shock, because it has never seen one. Five-plus years of history isn’t about having more rows. It’s about having actually witnessed the tail events that determine whether a strategy survives contact with reality.

If there’s one priority worth putting ahead of everything else in this guide, it’s provenance tracking. Know exactly which indexer version, timestamp, and venue produced every row in your dataset before you trust a single backtest result built on top of it. Everything else, format choice, replay frequency, evaluation metrics, is secondary to knowing your data is actually what it claims to be.

— Dean

Getting Started With the Assymetrix Data API

Stitching together separate Polymarket and Kalshi integrations, each with its own schema, rate limits, and historical quirks, typically costs a small engineering team weeks before a single backtest runs. Assymetrix collapses that into one integration: a unified schema across Polymarket, Kalshi, and Limitless, built on close to a billion rows of historical trading activity.

Developer and paid tiers on the Assymetrix Data API generally include programmatic access to historical and live endpoints, bulk export delivery, and options for S3-based data delivery at scale. Research and academic access paths exist for teams working on non-commercial studies of market microstructure or forecasting accuracy, a natural fit if your work touches prediction market accuracy research rather than live trading. Commercial licensing scales with usage for teams building trading bots, AI agents, or institutional analytics products on top of the feed.

Request API access through the Assymetrix Data API developer docs and pull a test export before committing to a paid tier.

Sources

• prediction-market-analysis (GitHub)

• Prediction markets paper (Jared Dean Katz)

• TWS API v9.72+: Historical Market Data (Interactive Brokers)

• Historical API (Alpaca)

FAQ

What Data Does a Historical Prediction Market API Provide?

A complete historical prediction market API provides trade-level records, orderbook snapshots, OHLCV candles at multiple resolutions, resolution and outcome metadata, and wallet-level activity streams across venues.

How Many Years of Historical Data Do I Need for Backtesting?

Five years or more is the practical minimum for robust backtesting and ML training, since prediction markets are event-driven and shorter windows rarely contain the tail events, like elections or major policy shifts, that determine real strategy performance.

Why Is Historical Prediction Market Data Hard to Find?

Coverage breaks due to venue shutdowns, schema changes between and within venues, resolution disputes, and gaps in on-chain indexing, and no single centralized archive exists across Polymarket, Kalshi, and Limitless.

Does Assymetrix Offer a Historical Prediction Market Data API?

Yes. The Assymetrix Data API unifies historical and real-time data across Polymarket, Kalshi, and Limitless into one schema, built on close to one billion rows of trading activity, with bulk export and developer access tiers.

What File Format Is Best for Storing Prediction Market Data?

Parquet is the standard choice for large-scale datasets, since its columnar structure compresses efficiently and lets query engines like pyarrow read only the needed columns instead of parsing full rows.