Inventory Risk Modeling Data for Hedge Funds
Resolved Markets provides hedge funds with real-time orderbook snapshots from Polymarket's prediction markets, capturing continuous bid/ask depth across crypto, sports, economics, and weather categories. With 11.4M+ snapshots from 100+ markets and a 20Hz capture rate for crypto assets, hedge funds gain unprecedented visibility into market sentiment and directional flows before major announcements. The platform's millisecond-precision timestamps and full depth arrays enable sophisticated arbitrage strategies, cross-market correlation analysis, and early detection of significant probability shifts. Access to categories including FOMC meetings, jobs reports, BTC/ETH/SOL price movements, and major sports events provides diverse trading opportunities with lower latency than traditional markets.
Hedge funds use Inventory Risk Modeling Data as alternative data. Resolved Markets captures research-grade prediction-market data with 11.4M+ snapshots across 7 prediction-market categories, providing institutional-grade signal across regime detection, arbitrage backtests, sentiment indices, factor models that's uncorrelated with traditional alpha sources.
Data challenges Hedge Funds run into
Inventory Risk Modeling Data from Resolved Markets is built around the data gaps Hedge Funds hit when they try to work with raw Polymarket feeds.
Delayed market intelligence from fragmented data sources
Traditional prediction market data is often aggregated with significant delays, causing hedge funds to miss critical arbitrage opportunities. Resolved Markets eliminates this lag with 20Hz capture rates for crypto markets and millisecond-precise timestamps, ensuring every bid/ask movement is recorded. This continuous flow of orderbook data reveals market microstructure patterns that aggregated feeds completely miss, giving your fund a measurable edge.
High latency reducing arbitrage window effectiveness
Most market data providers capture snapshots at irregular intervals, creating blind spots where significant moves occur undetected. With 11.4M+ snapshots across 100+ Polymarket contracts, Resolved Markets ensures no price action is missed. Each snapshot includes full bid/ask depth arrays, enabling precise analysis of how orders cascade through the book during volatile periods.
Limited depth data across prediction market categories
Prediction market data is typically fragmented across sports betting, crypto derivatives, and commodity futures platforms. Hedge funds waste resources integrating disparate APIs and handling inconsistent data formats. Resolved Markets consolidates crypto (BTC, ETH, SOL, XRP), sports (NBA, NFL, EPL), economics (FOMC, jobs), and weather markets into a unified API, eliminating integration overhead.
Difficulty tracking sentiment shifts in emerging markets
Early warning indicators of major market moves often hide in orderbook depth changes that happen seconds before news breaks. Institutional players monitor FOMC probability shifts, jobs report expectations, and crypto sentiment before retail awareness. Resolved Markets' WebSocket streaming and historical snapshots enable sophisticated models that detect these patterns, giving hedge funds crucial alpha on event-driven trades.
Built for quantitative work on Inventory Risk Modeling Data
Orderbook-level prediction-market data that doesn't exist anywhere else.
Millisecond-precision arbitrage across Polymarket pairs
Resolved Markets timestamps orderbook changes at millisecond granularity, enabling detection of pricing inefficiencies that exist for mere seconds. When BTC price predictions shift on Polymarket ahead of spot markets, or when election markets reprrice before news, the latency advantage compounds. Your algorithms can identify and execute spread trades before competitors even see the data.
Complete bid/ask depth visibility enabling optimal execution
Unlike aggregated market data, Resolved Markets provides full depth arrays showing every bid and ask level across the entire orderbook. This reveals hidden liquidity, identifies walls set by smart money, and shows exactly where large orders will experience slippage. You can construct precise market impact models and identify optimal entry/exit points for position building.
Event-driven alpha from economics and crypto categories
Polymarket's prediction markets for FOMC meetings, unemployment reports, and economic data release outcomes move hours or days before traditional markets fully price in consensus estimates. Resolved Markets' economics category combined with crypto (BTC/ETH positioning around macro events) creates unique cross-category trading signals. Your quants can build models correlating crypto sentiment with economic expectations.
Scalable data infrastructure for quantitative strategies
The platform's ClickHouse-backed infrastructure handles massive data volumes without degradation. Store and query 11.4M+ snapshots to identify historical patterns, backtest strategies across multiple market regimes, and deploy live algos via the WebSocket API. Your team scales from single-market analysis to portfolio-wide systematic strategies without rebuilding infrastructure.
How Hedge Funds use Inventory Risk Modeling 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.
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.
Inventory Risk Modeling Data ships with
What Hedge Funds build with Inventory Risk Modeling Data
Up and running in minutes
Three steps from signup to live Inventory Risk Modeling Data in your application.
Get Your API Key
Generate a free API key instantly. No credit card. Just click and go.
Sign Up FreeExplore the API
Browse 11 endpoints with live examples. Test requests directly from the docs.
API ReferenceStart Building
Integrate live Inventory Risk Modeling Data into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
Wiring Inventory Risk Modeling Data into your workflow
Hedge funds integrate Inventory Risk Modeling Data via the enterprise API tier for high-throughput access, ClickHouse bulk exports for quant research, and WebSocket streaming for real-time signals. Cross-category coverage in Inventory Risk Modeling Data enables factor construction impossible with single-category data.
- Polygon.io-compatible REST shim
- QuantConnect Lean engine adapter
- Native Zipline bundle for backtesting
Why Hedge Funds pick Inventory Risk Modeling Data
- 20Hz orderbook capture rate in crypto markets eliminates timing delays that cost traditional algorithms
- Unified API across prediction markets, crypto derivatives, sports betting, and economic events creates cross-category alpha opportunities
- Full bid/ask depth arrays enable precise market microstructure analysis impossible with aggregated data
- 11.4M+ historical snapshots with millisecond timestamps provide deep backtesting capability for quantitative strategy development
Why Inventory Risk Modeling Data matters
Inventory Risk Modeling Data matters for hedge funds because it's an uncorrelated, structured, institutional-grade alt-data source. 11.4M+ snapshots across 7 prediction-market categories guarantees the dataset is clean enough for systematic strategies on regime detection, arbitrage backtests, sentiment indices, factor models.
Inventory Risk Modeling Data in context
Hedge funds increasingly treat prediction markets as alternative data, and Inventory Risk Modeling Data is the structured form that makes the integration practical. 11.4M+ snapshots across 7 prediction-market categories on regime detection, arbitrage backtests, sentiment indices, factor models delivers the clean, time-stamped signal that systematic strategies need.
Frequently asked: Inventory Risk Modeling Data for Hedge Funds
-
How does Resolved Markets' orderbook data compare to traditional crypto exchange APIs?
Resolved Markets captures Polymarket prediction market orderbooks specifically, offering unique depth into sentiment around Bitcoin, Ethereum, Solana, and XRP price movements. Unlike spot exchange APIs that show only current prices, our 20Hz snapshot rate reveals how probability expectations evolve ahead of actual price moves. This creates arbitrage opportunities between prediction market sentiment and spot prices, invisible to traditional market data vendors.
-
Can we use Resolved Markets data for event-driven strategies around FOMC and jobs reports?
Yes, the economics category tracks Polymarket contracts predicting FOMC decisions and employment outcomes. Our API provides real-time probability shifts hours before official announcements, shown through bid/ask spread evolution in the orderbook. You can model how market participants collectively price consensus estimates, creating entry signals for both prediction market trades and correlated spot market positions.
-
What latency can we expect from your WebSocket streaming API?
WebSocket streams deliver orderbook updates with millisecond precision, matching the native capture rate (20Hz for crypto, variable for other categories). End-to-end latency from Polymarket to your infrastructure is typically under 500ms, enabling high-frequency arbitrage detection and automated execution integration. Our API supports subscription-based filtering for specific markets or depth levels to optimize bandwidth.
-
How do you handle orderbook data from multiple Polymarket markets simultaneously?
Our infrastructure subscribes to all 100+ tracked Polymarket markets and maintains a complete historical archive via ClickHouse. You can query snapshots for any market, any time window, with full depth data included. The API supports batch queries for efficient portfolio-wide analysis across all tracked contracts, or real-time streams filtered by category (crypto, sports, economics, weather) based on your strategy needs.
-
What competitive advantage does the 30-city weather derivatives data provide?
Weather derivatives on Polymarket represent a largely inefficient market segment where institutional data is scarce. Resolved Markets tracks 30 cities with continuous orderbook snapshots, revealing how sophisticated participants price weather-driven commodity and agricultural risks. Agricultural hedge funds can identify basis trades between weather prediction probabilities and futures markets, while energy traders can gauge heating/cooling demand expectations.
-
How do funds integrate Inventory Risk Modeling Data into existing infrastructure?
REST endpoints integrate with portfolio management systems. WebSocket feeds connect to execution engines. ClickHouse bulk exports feed quantitative research pipelines. Inventory Risk Modeling Data fits into all three paths.
-
What compliance considerations apply?
Inventory Risk Modeling Data is read-only data — no trading execution. Funds use it as alt-data input for research and signal generation, similar to satellite imagery or social media sentiment data.
-
Why do hedge funds use Inventory Risk Modeling Data?
Inventory Risk Modeling Data provides uncorrelated alpha sources and real-time sentiment data for hedging and directional bets. Full orderbook depth supports the kind of statistical analysis that aggregated prices simply cannot.
-
Is there published research using Inventory Risk Modeling Data?
Yes — academic and industry researchers have published work on prediction-market microstructure using Resolved Markets data. The dataset is documented and reproducible, which makes it suitable for peer review.
-
Can Inventory Risk Modeling Data be used in a portfolio context?
Yes. Many funds treat prediction markets as an alternative sleeve and use Inventory Risk Modeling Data as the structured data feed. Risk-parity, factor-tilting, and sentiment-overlay strategies all consume Inventory Risk Modeling Data.