Historical Tick Data Archives for Quantitative Researchers
Resolved Markets provides quantitative researchers with unprecedented access to 11.4M+ prediction market orderbook snapshots, enabling rigorous analysis of market microstructure, price discovery mechanisms, and information cascades across crypto, sports, economics, and weather categories. The platform's ClickHouse-backed historical storage allows rapid queries across months of full bid/ask depth data with millisecond timestamps, supporting hypothesis testing without the sampling bias of traditional prediction market datasets. Researchers benefit from WebSocket streaming for live capture and REST APIs for batch analysis, enabling both real-time market observations and deep historical investigations into how information propagates through Polymarket.
Historical Tick Data Archives is structured for quant research from day one. Resolved Markets captures the Resolved Markets capture and delivery stack with 20Hz continuous snapshots delivered via REST, WebSocket, CLI, and MCP, then ships the result as a 14-column schema that maps directly into pandas, R, and ClickHouse. Quant researchers can study WebSocket streams, REST snapshots, CLI bulk exports, MCP servers without rebuilding the data layer.
Data challenges Quantitative Researchers run into
Historical Tick Data Archives from Resolved Markets is built around the data gaps Quantitative Researchers hit when they try to work with raw Polymarket feeds.
Prediction market datasets typically lack orderbook depth information
Published prediction market datasets (Manifold, CPMM datasets) provide only aggregate trade volumes or final outcomes, omitting the rich orderbook structure that reveals price discovery mechanisms. Researchers cannot analyze bid/ask spreads, order accumulation patterns, or market depth changes—the very microstructure that reveals when informed traders enter markets. Resolved Markets captures complete depth arrays, enabling investigation of information asymmetry and market efficiency questions that remain unanswerable with standard datasets.
Millisecond-precision timestamps unavailable from alternative sources
Academic research on prediction markets requires precise timing to correlate information arrivals with price movements. Standard prediction market APIs provide second-level or day-level timestamps, obscuring the millisecond-scale price discovery process. Resolved Markets' millisecond-precision timestamps enable sophisticated event-study methodologies, allowing researchers to measure how quickly Polymarket incorporates external information (news, sports outcomes, economic reports) into prediction prices.
Cross-category market analysis requires manual data fusion
Prediction market dynamics vary dramatically across crypto, sports, economics, and weather—but assembling a unified dataset requires independently scraping each category or using fragmented APIs. Manual data fusion introduces inconsistencies in timestamp precision, orderbook representation, and market coverage. Resolved Markets provides standardized orderbook snapshots across all 100+ tracked markets, eliminating data integration work and enabling cross-category analysis of market efficiency and herding behavior.
Real-time and historical data require separate infrastructure
Traditional research workflows separate real-time observation (for validating hypotheses on live markets) from historical analysis (for deep investigations). This split requires maintaining two separate data pipelines. Resolved Markets unifies real-time WebSocket streams and historical batch queries into a single API, allowing researchers to seamlessly transition from exploratory analysis of live markets to rigorous backtesting against months of historical orderbook data.
Built for quantitative work on Historical Tick Data Archives
Orderbook-level prediction-market data that doesn't exist anywhere else.
Investigate price discovery mechanisms at millisecond resolution
Price discovery happens at the orderbook level—before trades execute, bids and asks reveal trader intentions. With full depth arrays and millisecond timestamps, you can track how orders accumulate before price moves, quantify bid/ask asymmetry changes, and measure how quickly informed orders push prices toward fair value. Analyze moments when sudden order cancellations precede price reversals, revealing strategic order placement and information cascades invisible in trade-level data.
Analyze orderbook microstructure across 100+ markets simultaneously
Prediction markets exhibit distinct microstructure across categories: crypto markets show high-frequency trading dynamics, sports markets experience demand surges near game time, economics markets react to scheduled data releases. Resolved Markets captures cross-category orderbook data with consistent timestamping, enabling comparative studies of information flow and market maturity. Quantify whether crypto prediction markets are more efficient than sports or economics markets by analyzing order speed, spread compression, and depth changes.
Test market efficiency hypotheses with unbiased historical data
Published datasets introduce sampling bias—researchers often access only final outcomes or aggregate statistics, not the continuous orderbook snapshots that define true price paths. Resolved Markets' 11.4M+ snapshots covering months of data eliminate survivor bias and provide the complete information set available to market participants at any moment. Historical queries directly reveal when arbitrage opportunities existed, whether informed traders exploited them, and how quickly prices converged to fundamental values.
Publish reproducible research with timestamped, auditable datasets
Reproducible research requires datasets that can be audited and independently verified. Resolved Markets provides timestamped, immutable orderbook snapshots stored in ClickHouse with query-transparent access—you can export exact data used in analyses, enabling other researchers to validate findings or extend work. REST API batch exports include snapshot checksums, ensuring data integrity across research teams and publications.
How Quantitative Researchers use Historical Tick Data Archives
Seven categories, hundreds of markets
Prediction markets across crypto, sports, economics, weather, and more — live and historical orderbook data, all queryable through one API.
Crypto
BTC, ETH, SOL, XRP — up/down markets every 5m to 1d.
Equities
S&P 500 (SPX) daily open — up or down predictions.
Social
Elon Musk tweet counts — weekly prediction ranges.
Sports
NBA, NFL, EPL — game outcomes and season predictions.
Economics
Fed decisions, jobs reports — FOMC meetings and macro data.
Weather
44 cities daily — temperature, hurricanes, Arctic ice.
Hyperliquid
BTC, ETH, SOL, XRP perp orderbooks — 1/sec sampling.
Tick-level orderbook snapshots
Every snapshot includes full bid/ask depth, mid prices, spreads, and crypto spot price.
| Side | Bid | Size | Ask | Size | Spread |
|---|---|---|---|---|---|
| UP | 0.5400 | 1,240 | 0.5500 | 1,100 | 1.00% |
| UP | 0.5300 | 980 | 0.5600 | 1,450 | 3.00% |
| UP | 0.5200 | 1,560 | 0.5700 | 890 | 5.00% |
| UP | 0.5100 | 2,100 | 0.5800 | 2,300 | 7.00% |
| UP | 0.5000 | 1,800 | 0.5900 | 1,700 | 9.00% |
| UP | 0.4900 | 3,200 | 0.6000 | 3,100 | 11.00% |
cryptoLowCardinality(String)BTCtimeframeLowCardinality(String)5mtoken_sideEnum8('UP','DOWN')UPtimestampDateTime64(3)2026-05-09 03:14:12.061crypto_priceFloat64$80,471.01best_bidFloat640.5400best_askFloat640.5500mid_priceFloat640.5450spreadFloat640.0100bidsArray(Tuple(F64,F64))[(0.54,1240),...]asksArray(Tuple(F64,F64))[(0.55,1100),...]Comprehensive market coverage
Prediction markets across multiple categories, captured continuously with high-frequency precision.
Historical Tick Data Archives ships with
What Quantitative Researchers build with Historical Tick Data Archives
Up and running in minutes
Three steps from signup to live Historical Tick Data Archives in your application.
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Sign Up FreeExplore the API
Browse 11 endpoints with live examples. Test requests directly from the docs.
API ReferenceStart Building
Integrate live Historical Tick Data Archives into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
curl -H 'X-API-Key: rm_xxx' https://api.resolvedmarkets.com/v1/categoriesrm-api download --crypto BTC --days 90 --format csvWiring Historical Tick Data Archives into your workflow
Quantitative researchers typically access Historical Tick Data Archives through the REST API for exploratory work, then switch to ClickHouse bulk exports for large-scale studies. The 14-column schema maps directly to pandas DataFrames and R data.frames. WebSocket streaming supports live observation of Historical Tick Data Archives when validating hypotheses in real time.
- MCP server for Claude Desktop and Cursor
- Modal cron template for scheduled Historical Tick Data Archives jobs
- Cloudflare Workers starter template for Historical Tick Data Archives
Why Quantitative Researchers pick Historical Tick Data Archives
- Only academic-grade dataset capturing complete orderbook depth from 11.4M+ Polymarket snapshots with millisecond timestamps
- ClickHouse-backed historical storage enables rapid hypothesis testing across months of data without sampling bias
- Cross-category coverage (crypto, sports, economics, weather) reveals comparative market microstructure insights unavailable elsewhere
- Unified real-time and historical APIs eliminate infrastructure fragmentation—observe live markets while backtesting against complete historical orderbooks
Why Historical Tick Data Archives matters
Quant researchers reach for Historical Tick Data Archives because Resolved Markets ships it as a reproducible, documented dataset. Methodology, schema, and timestamps are all published, so studies built on Historical Tick Data Archives can be replicated by other teams without infrastructure overhead.
Historical Tick Data Archives in context
The Resolved Markets dataset behind Historical Tick Data Archives is built for quant researchers: continuous capture, ClickHouse-native columnar storage, and 11.4M+ snapshots. That makes Historical Tick Data Archives suitable for the kind of empirical microstructure work that until recently could only happen on equity tape data.
Frequently asked: Historical Tick Data Archives for Quantitative Researchers
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How comprehensive is the orderbook depth data—do you capture all orders or just top-N levels?
Resolved Markets captures complete bid/ask depth arrays showing all resting orders at each price level, not just the top 5 or top 20 levels. This enables full microstructure analysis including visualization of iceberg orders, detection of spoofing (fake orders designed to manipulate prices), and measurement of true market liquidity across all price levels. Each snapshot includes full depth with millisecond precision.
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Can I query historical data for specific time windows, or must I download all 11.4M snapshots?
Resolved Markets provides REST API query functions supporting time-range filtering, market symbol filtering, and orderbook metric calculations (spreads, depth, order counts). Query a single sports market during game hours, or aggregate across all crypto markets during a specific 24-hour window. ClickHouse backend enables sub-second query response times even against months of historical data—you're not limited to batch downloads.
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Are timestamp precision and consistency guaranteed across all markets and the full dataset?
Yes—all 11.4M snapshots include millisecond-precision timestamps captured at the moment of orderbook state validation. Timestamp precision is consistent across crypto (20Hz), sports (regular intervals), economics, and weather categories. Data lineage is fully documented, enabling you to cite exact data provenance in publications and validate timestamp accuracy against Polymarket's canonical time sources.
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How can I use Resolved Markets for publication-quality research if it's a commercial API?
Academic researchers receive free tier access with unlimited query depth, exportable datasets, and versioned snapshots for reproducibility. Export your research dataset with query parameters and snapshot checksums, enabling other researchers to independently validate findings against the same data. We document our data collection and validation methodology in a public technical paper, meeting standards for academic transparency.
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Can I analyze how different information sources (news, sports outcomes, economic data) affect prediction market orderbooks?
Yes—combine Resolved Markets' millisecond-precision orderbook timestamps with external event data (news APIs, sports schedules, economic calendars). Correlate sudden spread compression or order cancellations with external information arrivals to measure information velocity. Our REST API supports datetime range queries enabling event-study methodologies—identify the exact moment markets absorb information by analyzing orderbook changes in millisecond windows around known event times.
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How do quant researchers ingest Historical Tick Data Archives?
Resolved Markets ships Historical Tick Data Archives as a 14-column ClickHouse schema. Bid/ask arrays, depth values, and DateTime64(3) timestamps map directly into pandas DataFrames or R tibbles for downstream microstructure work.
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What research questions does Historical Tick Data Archives enable?
Market microstructure analysis, volatility forecasting, liquidity dynamics, cross-market correlations, and ML model validation across the prediction-market data pipeline. Historical Tick Data Archives pairs especially well with information-velocity and price-discovery studies.
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How is Historical Tick Data Archives different from existing prediction-market datasets?
Most public prediction-market datasets capture only final outcomes or hourly OHLC. Historical Tick Data Archives from Resolved Markets is continuous, with full bid/ask arrays and 20Hz continuous snapshots delivered via REST, WebSocket, CLI, and MCP. That makes it usable for true microstructure research.
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Does Historical Tick Data Archives have an MCP server?
Yes — `npm install -g resolved-markets-mcp` installs an MCP server that exposes Historical Tick Data Archives as function calls. Drop it into your Claude Desktop or Cursor MCP config and your agents can query live prediction-market data on demand.
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Can I run Historical Tick Data Archives in a serverless environment?
Yes. The REST API works from any HTTP client, including Cloudflare Workers, AWS Lambda, Vercel functions, and Modal containers. WebSocket streams work in long-running environments like Fly.io or Railway.