BTC$80,471.01 ETH$2,319.15 SOL$93.66 XRP$1.43 SPX18 markets Elon71 markets NBA64 markets NFL46 markets EPL18 markets FOMC12 markets Weather44 cities Hyperliquid4 perps
Live Polymarket Feed · 171 active markets

Crypto Traders: Get Liquidity Profiling Data at 20Hz

Backtest Polymarket strategies with Liquidity Profiling Data data — Liquidity Profiling Data: continuous Polymarket capture for crypto trading desks. ClickHouse-ready, WebSocket streaming.

Depth Chart Liquidity Profiling Data
Mid: 0.5450 BIDS ASKS
Bids Asks
171 Live Markets
793.2M Snapshots Captured
20 Hz Capture Rate
7 Categories

Liquidity Profiling Data for Crypto Traders

Resolved Markets delivers real-time orderbook snapshots from Polymarket's crypto prediction markets at 20Hz capture rates, enabling crypto traders to identify mispricing across BTC, ETH, SOL, and XRP up/down markets before they move. Access full bid/ask depth arrays with millisecond-precision timestamps, allowing you to analyze market microstructure and detect arbitrage opportunities between prediction markets and spot/derivatives exchanges. The platform tracks 11.4M+ snapshots across 100+ markets with WebSocket streaming integration, eliminating delays in your algorithmic trading workflows and providing the competitive edge needed in fast-moving prediction markets.

Resolved Markets ships Liquidity Profiling Data for crypto traders working on the orderbook microstructure signal layer. Continuous snapshots, full bid/ask depth, and millisecond timestamps mean you can see spread compression, depth imbalance, queue position, quote flicker the moment a quote moves — not 1-2 seconds later.

Live snapshot: Resolved Markets is currently tracking 171 active Polymarket contracts and has captured 793.2M orderbook snapshots. Latest update: 2026-05-09 03:14:12.061.

Data challenges Crypto Traders run into

Liquidity Profiling Data from Resolved Markets is built around the data gaps Crypto Traders hit when they try to work with raw Polymarket feeds.

01

Slow orderbook data access creates arbitrage lag

Traditional crypto exchanges provide orderbook updates at variable intervals, often 1-5 second delays. Prediction markets on Polymarket move rapidly with bid/ask spreads that shift within milliseconds. Without 20Hz capture rates and low-latency WebSocket feeds, traders miss alpha-generating opportunities before the market reprices. Resolved Markets' continuous snapshots ensure you never miss critical microstructure signals.

02

Fragmented data sources across exchanges

Crypto traders monitor spot markets, derivatives, and prediction markets separately, manually reconciling data from multiple APIs. This fragmentation creates blind spots where cross-market arbitrage opportunities go undetected. Resolved Markets aggregates BTC, ETH, SOL, and XRP prediction market orderbooks in one unified platform with consistent formatting and timestamps, enabling holistic market analysis without data integration overhead.

03

Missing high-frequency capture for fast markets

Most prediction market data providers capture snapshots every 5-60 seconds, missing the high-frequency dynamics crucial for algorithmic trading. Polymarket crypto markets experience bid/ask shifts at sub-second intervals, especially during volatile news events. Resolved Markets' 20Hz sampling rate captures the full granularity of order flow changes, revealing market microstructure patterns invisible at lower frequencies.

04

Lack of unified crypto prediction market data

Cryptocurrency prediction markets remain fragmented across platforms with inconsistent data formats and update frequencies. Traders building crypto-focused strategies need standardized, continuously-updated orderbook depth from Polymarket's largest crypto markets. Resolved Markets consolidates 100+ crypto-related prediction markets with uniform data schemas, millisecond timestamps, and full bid/ask depth arrays, enabling seamless integration into trading systems.

Built for quantitative work on Liquidity Profiling Data

Orderbook-level prediction-market data that doesn't exist anywhere else.

01

Detect sub-second arbitrage opportunities

With 20Hz orderbook snapshots and millisecond timestamps, you can identify moments when prediction market prices diverge from rational valuations faster than competitors. Full bid/ask depth arrays reveal liquidity layers and hidden orders, exposing inefficiencies that exist for only fractions of a second. Resolved Markets' WebSocket streaming pushes updates directly to your algorithms, eliminating poll latency and enabling millisecond-scale trade execution before prices adjust.

02

Optimize trade execution across prediction markets

Prediction markets often show better informed trading activity ahead of spot market moves, especially in crypto. Resolved Markets' continuous orderbook data lets you quantify order flow toxicity, estimate intent from bid/ask clustering, and optimize execution timing across prediction and spot markets. Historical snapshots in ClickHouse storage enable rapid backtesting of execution strategies against months of real orderbook data.

03

Monitor market microstructure in real-time

Most market participants analyze only the best bid/ask, missing crucial information in deeper orderbook layers. Resolved Markets provides full depth arrays showing all resting orders, allowing you to model market impact, detect spoofing patterns, and gauge institutional positioning. Real-time microstructure analytics help you identify when large orders are being accumulated or unwound, giving you directional alpha.

04

Automate trading strategies with reliable feeds

Manual API integration with Polymarket and other sources introduces operational risk and code maintenance burden. Resolved Markets offers production-ready REST and WebSocket APIs with automatic retry logic, connection pooling, and guaranteed data continuity. Deploy trading strategies immediately without worrying about data reliability—our free tier requires no credit card, letting you validate your approach risk-free before committing to infrastructure.

Research Applications
Spread analysis and market making simulation
Liquidity depth profiling across categories
Implied probability vs realized outcomes
Market microstructure and order flow analysis
Weather derivative research across 44 cities
Cross-category correlation studies

How Crypto Traders use Liquidity Profiling Data

1
Identify market maker activity through repeated quote refreshes inside Liquidity Profiling Data
2
Measure prediction-market efficiency around CPI releases by replaying Liquidity Profiling Data at the moment of print
3
Compute VPIN order flow toxicity from Liquidity Profiling Data for high-frequency strategy filtering
4
Build a queue-position model on Liquidity Profiling Data to estimate fill probability at each price level
5
Detect quote-flickering with millisecond-level diff analysis on Liquidity Profiling Data

Seven categories, hundreds of markets

Prediction markets across crypto, sports, economics, weather, and more — live and historical orderbook data, all queryable through one API.

16 markets

Crypto

BTC, ETH, SOL, XRP — up/down markets every 5m to 1d.

18 markets

Equities

S&P 500 (SPX) daily open — up or down predictions.

71 markets

Social

Elon Musk tweet counts — weekly prediction ranges.

64 markets

Sports

NBA, NFL, EPL — game outcomes and season predictions.

12 markets

Economics

Fed decisions, jobs reports — FOMC meetings and macro data.

78 markets

Weather

44 cities daily — temperature, hurricanes, Arctic ice.

4 pairs

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.

polymarket.snapshots_hf 793.2M rows
SideBidSizeAskSizeSpread
UP0.54001,2400.55001,1001.00%
UP0.53009800.56001,4503.00%
UP0.52001,5600.57008905.00%
UP0.51002,1000.58002,3007.00%
UP0.50001,8000.59001,7009.00%
UP0.49003,2000.60003,10011.00%
Schema 14 columns
cryptoLowCardinality(String)BTC
timeframeLowCardinality(String)5m
token_sideEnum8('UP','DOWN')UP
timestampDateTime64(3)2026-05-09 03:14:12.061
crypto_priceFloat64$80,471.01
best_bidFloat640.5400
best_askFloat640.5500
mid_priceFloat640.5450
spreadFloat640.0100
bidsArray(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.

7
Categories
Crypto Sports Economics Weather
171
Active Markets
BTC ETH SOL XRP + sports, econ, weather
44
Weather Cities
Daily prediction-market capture across global cities.
20 Hz
Capture Rate
Crypto 20 Hz Sports 2 Hz Econ 1 Hz

Liquidity Profiling Data ships with

20Hz crypto orderbook snapshots
WebSocket streaming for real-time bid/ask depth
Cross-exchange arbitrage detection tools
Millisecond-precision timestamp data
REST API for historical orderbook analysis
Market microstructure analytics dashboard

What Crypto Traders build with Liquidity Profiling Data

Microstructure analysis around major crypto news events
Funding-rate correlation work comparing prediction-market premium with perp funding
Anomaly detection on orderbook shape via autoencoders
Order flow toxicity scoring (VPIN, BVC)
Microprice estimation from depth-weighted bid/ask

Up and running in minutes

Three steps from signup to live Liquidity Profiling Data in your application.

1

Get Your API Key

Generate a free API key instantly. No credit card. Just click and go.

Sign Up Free
2

Explore the API

Browse 11 endpoints with live examples. Test requests directly from the docs.

API Reference
3

Start Building

Integrate live Liquidity Profiling Data into your research pipeline, trading bot, or analytics platform.

fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
1
Sign up at resolvedmarkets.com and generate a free API key
2
Query the live Liquidity Profiling Data feed: curl -H 'X-API-Key: rm_xxx' https://api.resolvedmarkets.com/v1/markets/live
3
Pull a BTC orderbook snapshot: curl -H 'X-API-Key: rm_xxx' 'https://api.resolvedmarkets.com/api/snapshot?crypto=BTC&timeframe=1h&includebook=true'
4
Stream 20Hz updates over WebSocket for live signal generation
5
Backfill historical data with the CLI: rm-api download --crypto BTC --timeframe 1h --days 30

Wiring Liquidity Profiling Data into your workflow

Crypto traders integrate Resolved Markets Liquidity Profiling Data via four channels: REST for backtesting, WebSocket for live signals, the CLI for bulk exports, and the MCP server for AI agents. All four return the same 20Hz Liquidity Profiling Data, so production systems and research notebooks share one source of truth.

  • PyTorch Geometric example for orderbook GNNs
  • Reference implementation of VPIN in the Python SDK

Why Crypto Traders pick Liquidity Profiling Data

  • Only prediction market API capturing crypto orderbooks at 20Hz with full bid/ask depth and millisecond timestamps
  • Real-time WebSocket streaming eliminates latency between price movement and your algorithm execution
  • Historical ClickHouse-backed data enables rigorous backtesting of orderbook-based strategies across 11.4M+ snapshots
  • Free tier access with no credit card required—validate arbitrage opportunities before scaling production deployments

Why Liquidity Profiling Data matters

Liquidity Profiling Data matters for crypto traders because spot prices are no longer enough. Polymarket prediction markets price expectations directly — and tick-level features extracted from full bid/ask depth from Resolved Markets means crypto traders can read those expectations the same way they read order book data on a centralized exchange.

Liquidity Profiling Data in context

Liquidity Profiling Data sits at the center of the orderbook microstructure signal layer. Crypto traders increasingly treat Polymarket as a leading indicator for spot moves — and Liquidity Profiling Data is the format that lets them act on it. With tick-level features extracted from full bid/ask depth, every quote shift in spread compression, depth imbalance, queue position, quote flicker is captured and time-stamped, so trading desks can model order flow at the same resolution they use for spot exchanges.

Frequently asked: Liquidity Profiling Data for Crypto Traders

  • How does 20Hz orderbook capture compare to manual Polymarket API polling?

    Polymarket's public REST API typically updates every 1-2 seconds and requires continuous polling, introducing network latency and rate limits. Resolved Markets continuously captures orderbooks at 20Hz (every 50ms), with millisecond timestamps and WebSocket push delivery, ensuring you never miss rapid bid/ask shifts. This 10-40x frequency advantage is critical for algorithmic trading where market conditions change within seconds.

  • Can I use Resolved Markets data to detect spread arbitrage between crypto prediction markets and spot exchanges?

    Yes—that's a primary use case. Our REST API lets you query historical orderbook depth across BTC, ETH, SOL, and XRP prediction markets, while WebSocket streaming provides real-time bid/ask for live strategy execution. By correlating prediction market prices against spot/futures data, you can identify moments when prediction prices lag the underlying asset, enabling profitable cross-market arbitrage with minimal slippage.

  • What orderbook data depth is available, and how far back does historical data extend?

    Resolved Markets captures full bid/ask depth arrays (all resting orders, not just the best level) with millisecond timestamps. Historical snapshots extend back months, stored in ClickHouse for efficient time-range queries. You can analyze order accumulation patterns, estimate market impact, and backtest strategies against complete microstructure data without sampling bias.

  • Does WebSocket streaming cover all crypto prediction markets, or just major ones like BTC/ETH?

    WebSocket streams cover 100+ prediction markets across all categories, including BTC, ETH, SOL, XRP and lower-volume altcoin markets. Subscribe to specific market symbols to receive 20Hz updates only for the markets relevant to your strategy, reducing bandwidth consumption while maintaining coverage. Custom filtering ensures your algorithms process only actionable orderbook changes.

  • How reliable is the orderbook data for live trading, and what's your data accuracy guarantee?

    Resolved Markets continuously validates orderbook snapshots against Polymarket's canonical state and provides millisecond-precision timestamps for event correlation. WebSocket connections include automatic reconnection with gap-fill logic—if a connection drops, we immediately backfill missing snapshots so your algorithms never trade on stale data. Free tier includes uptime SLA monitoring; production accounts get dedicated support for zero-downtime deployments.

  • How do crypto traders use Liquidity Profiling Data from Resolved Markets?

    Crypto traders pipe Liquidity Profiling Data into trading systems via WebSocket or REST to detect arbitrage between Polymarket prediction markets and spot venues. tick-level features extracted from full bid/ask depth ensures no quote shift is missed across spread compression, depth imbalance, queue position, quote flicker.

  • Which crypto markets are covered for Liquidity Profiling Data?

    Resolved Markets captures orderbook microstructure signals from Polymarket including spread compression, depth imbalance, queue position, quote flicker. All 100+ active crypto contracts on Polymarket are continuously sampled.

  • What's the latency on Liquidity Profiling Data for live crypto trading?

    WebSocket streaming pushes snapshots in well under a second. REST queries serve historical Liquidity Profiling Data from ClickHouse in under 200ms — fast enough for production crypto trading systems.

  • Does Liquidity Profiling Data include derived features or just raw orderbook?

    Both. Liquidity Profiling Data ships raw bid/ask arrays plus derived best_bid, best_ask, mid_price, and spread columns. You can compute additional features (depth imbalance, queue position, VPIN) from the raw arrays.

  • How do I compute VPIN from Liquidity Profiling Data?

    Bucket trades by volume from the Liquidity Profiling Data time series, then compute the absolute difference between buy-side and sell-side volume per bucket. VPIN is the moving average of those differences. Most quant teams ship a 50-line Python implementation.

Related orderbook datasets

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