ETH Bid-Ask Spreads 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.
Quantitative researchers use ETH Bid-Ask Spreads from Resolved Markets to study the crypto orderbook ecosystem at tick resolution. With 11.4M+ snapshots, 20Hz capture against live crypto spot prices, and a 14-column ClickHouse schema, the dataset supports rigorous market microstructure work on BTC up/down tokens, ETH 1h settlements, SOL daily markets that simply isn't possible with aggregated OHLC sources.
Data challenges Quantitative Researchers run into
ETH Bid-Ask Spreads 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 ETH Bid-Ask Spreads
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 ETH Bid-Ask Spreads
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.
ETH Bid-Ask Spreads ships with
What Quantitative Researchers build with ETH Bid-Ask Spreads
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Three steps from signup to live ETH Bid-Ask Spreads 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 ETH Bid-Ask Spreads 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 ETH Bid-Ask Spreads into your workflow
Quantitative researchers typically access ETH Bid-Ask Spreads 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 ETH Bid-Ask Spreads when validating hypotheses in real time.
npm install resolved-markets— TypeScript SDK with full type definitions for the 14-column schemacargo add resolved-markets— Rust crate for ultra-low-latency consumers- Native ClickHouse connector via JDBC/ODBC for direct SQL queries against historical ETH Bid-Ask Spreads
Why Quantitative Researchers pick ETH Bid-Ask Spreads
- 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 ETH Bid-Ask Spreads matters
ETH Bid-Ask Spreads matters for quant research because it provides 20Hz capture against live crypto spot prices on BTC up/down tokens, ETH 1h settlements, SOL daily markets. That's the level of resolution required for proper microstructure work — and it's exactly what aggregated public datasets fail to deliver.
ETH Bid-Ask Spreads in context
The Resolved Markets dataset behind ETH Bid-Ask Spreads is built for quant researchers: continuous capture, ClickHouse-native columnar storage, and 11.4M+ snapshots. That makes ETH Bid-Ask Spreads suitable for the kind of empirical microstructure work that until recently could only happen on equity tape data.
Frequently asked: ETH Bid-Ask Spreads 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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What research questions does ETH Bid-Ask Spreads enable?
Market microstructure analysis, volatility forecasting, liquidity dynamics, cross-market correlations, and ML model validation across the crypto orderbook ecosystem. ETH Bid-Ask Spreads pairs especially well with information-velocity and price-discovery studies.
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How is ETH Bid-Ask Spreads different from existing prediction-market datasets?
Most public prediction-market datasets capture only final outcomes or hourly OHLC. ETH Bid-Ask Spreads from Resolved Markets is continuous, with full bid/ask arrays and 20Hz capture against live crypto spot prices. That makes it usable for true microstructure research.
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Can ETH Bid-Ask Spreads be exported for R, Python, or MATLAB?
Yes. The REST API returns JSON that maps directly into pandas. The CLI supports bulk CSV export. ClickHouse native queries return columnar data optimized for analytical workloads. ETH Bid-Ask Spreads works in every standard statistical environment.
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Can I run ETH Bid-Ask Spreads alongside my Hyperliquid feed?
Yes, that's a common setup. Hyperliquid provides 1Hz perp orderbook snapshots and Resolved Markets provides 20Hz Polymarket prediction-market snapshots. Most quant teams subscribe to both and join on timestamp for cross-venue analysis.
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Can I use ETH Bid-Ask Spreads with TradingView, Plotly, or matplotlib?
Yes. ETH Bid-Ask Spreads ships as JSON over REST and WebSocket, so any charting library that consumes time-series data works out of the box. The CLI exports CSV directly into pandas + matplotlib pipelines, and the WebSocket stream plugs into TradingView Pine Script via a custom datafeed adapter.