Queue Position Analytics for Financial Analysts
Financial analysts harness Resolved Markets to incorporate prediction market data into investment theses, risk models, and portfolio construction frameworks. The platform provides continuous orderbook snapshots from 100+ Polymarket prediction markets spanning crypto volatility, macroeconomic indicators (FOMC decisions, jobs reports, inflation), sports outcomes, and weather events—all captured at high frequency with millisecond precision. With 11.4M+ historical snapshots and full bid/ask depth arrays, analysts construct market expectations curves, measure sentiment changes, and quantify disagreement through spread analysis. The combination of professional-grade data infrastructure and no-credit-card-required free tier enables rigorous research without barriers, supporting fundamental analysis, macro forecasting, correlation studies, and portfolio hedging strategies grounded in real prediction market behavior.
Financial analysts use Queue Position Analytics as a leading-indicator alt-data source. Resolved Markets captures orderbook microstructure signals from Polymarket with tick-level features extracted from full bid/ask depth, surfacing implied probabilities on spread compression, depth imbalance, queue position, quote flicker that often move before traditional indicators.
Data challenges Financial Analysts run into
Queue Position Analytics from Resolved Markets is built around the data gaps Financial Analysts hit when they try to work with raw Polymarket feeds.
Limited access to market expectations data beyond traditional forecasts and surveys
Traditional financial analysis relies on surveys, historical data, and analyst forecasts that reflect past conditions and periodic sampling. Prediction markets offer forward-looking, real-money views updated continuously, but accessing this data typically requires direct market participation or expensive data providers. Resolved Markets solves this by providing complete orderbook data from Polymarket's 100+ markets through a unified API. Track FOMC decision expectations, jobs report surprises, inflation sentiment, and economic policy predictions as they evolve—this real-time market-implied data improves macro forecasting and risk assessment significantly.
Inability to quantify market disagreement and uncertainty systematically
Understanding market agreement/disagreement on outcomes is crucial for portfolio risk management and hedging decisions. Bid/ask spreads directly reflect this disagreement—when traders strongly agree about outcomes, spreads compress; when uncertainty rises, spreads widen dramatically. Resolved Markets captures these spread dynamics across economic prediction markets (FOMC, jobs, inflation), crypto markets (BTC/ETH volatility), and sports markets. Analysts can construct disagreement indices that predict volatility spikes, identify consensus shifts before they become obvious, and position portfolios accordingly.
Fragmented data sources preventing unified macro sentiment analysis
Macro analysis requires simultaneous access to multiple market expectations—how do traders expect FOMC to act? What are jobs report expectations? How much inflation do markets expect? These datasets traditionally live in different systems with different formats, timestamps, and update frequencies. Resolved Markets unifies prediction market access through a single API with consistent formatting, standardized timestamps, and complete depth information. Build comprehensive macro sentiment dashboards that correlate economic expectations across decision types, time horizons, and market participants simultaneously.
Difficulty identifying correlations between prediction markets and asset prices
Correlation breakdown between prediction markets and real markets (spot prices, yields, volatility indices) often precedes broader market dislocations and provides early warning signals. Traditional data providers don't offer the granular orderbook data needed to detect these correlations at short timescales. Resolved Markets' millisecond-precision timestamps and complete orderbook depth enable analysts to construct correlation models with sub-second resolution, identify when prediction market expectations diverge from asset price moves, and time hedges around these divergence periods.
Built for quantitative work on Queue Position Analytics
Orderbook-level prediction-market data that doesn't exist anywhere else.
Incorporate real-money market expectations into fundamental analysis and forecasting
Prediction markets aggregate knowledge from thousands of traders with direct financial stakes in outcomes. Unlike surveys or analyst estimates, these markets reflect beliefs that traders have actually bet money on. Resolved Markets gives analysts direct access to this collective intelligence through continuous orderbook data on FOMC decisions, jobs reports, inflation expectations, and economic policy. Build investment theses grounded in what traders actually believe will happen, not what they say will happen in surveys. This real-money sentiment data improves forecast accuracy and enables earlier detection of consensus shifts.
Measure macro sentiment and disagreement through prediction market spread analysis
Disagreement metrics derived from prediction market spreads predict volatility and uncertainty periods. When bid/ask spreads in FOMC decision markets widen sharply, traders expect significant uncertainty; when spreads compress as the decision date approaches, consensus has formed. Resolved Markets enables construction of disagreement indices across multiple economic dimensions simultaneously—create composite sentiment scores that incorporate FOMC expectations, jobs report uncertainty, inflation disagreement, and macro growth expectations. These indices predict market volatility better than traditional measures and enable proactive portfolio adjustments.
Build superior risk models using prediction market correlation data
Modern portfolio construction requires understanding how assets behave under different macro scenarios. Resolved Markets enables scenario-based analysis: when FOMC decision probabilities shift toward higher rates (visible in orderbook spread changes), how do equities, bonds, and crypto react? Build scenario tables correlating prediction market expectations with historical returns across assets. Test portfolio resilience against macro scenarios that traders are currently pricing in. This grounds portfolio construction in real market expectations rather than theoretical scenarios, improving risk management.
Identify hedging opportunities through prediction market sentiment shifts
Hedging decisions benefit from real-time sentiment shifts in prediction markets. When economic uncertainty rises sharply (visible in widening spreads), consider increasing defensive positions or hedging duration risk. When prediction markets price in stronger job growth, consider cyclical positioning. Resolved Markets' WebSocket API enables real-time sentiment monitoring—trigger hedging decisions when prediction markets shift significantly, align portfolio positioning with actual market expectations. This dynamic approach to hedging responds to current conditions rather than static historical correlations.
How Financial Analysts use Queue Position Analytics
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.
Queue Position Analytics ships with
What Financial Analysts build with Queue Position Analytics
Up and running in minutes
Three steps from signup to live Queue Position Analytics 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 Queue Position Analytics 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/categoriesWiring Queue Position Analytics into your workflow
Financial analysts pull Queue Position Analytics via REST for ad-hoc analysis and CSV exports for spreadsheet workflows. Larger studies move into ClickHouse via the CLI bulk-download path.
- Reference implementation of VPIN in the Python SDK
- PyTorch Geometric example for orderbook GNNs
Why Financial Analysts pick Queue Position Analytics
- Real-time market expectations on FOMC decisions, employment, inflation, and economic policy from Polymarket's prediction markets with complete orderbook depth and millisecond timestamps
- 11.4M+ historical snapshots enabling fundamental analysis, scenario testing, and correlation studies grounded in actual trader behavior across market cycles
- Unified API access to 100+ prediction markets across crypto, macro, sports, and weather categories eliminating fragmented data sources and enabling comprehensive sentiment analysis
- Free tier with no credit card required making prediction market research accessible to independent analysts and small firms without expensive data subscriptions
Why Queue Position Analytics matters
Queue Position Analytics matters for financial analysts because Polymarket prices outcomes faster than surveys. tick-level features extracted from full bid/ask depth on spread compression, depth imbalance, queue position, quote flicker delivers a structured leading-indicator feed analysts can ship to clients.
Queue Position Analytics in context
Financial analysts are adding alt-data to their toolkit, and prediction markets are an obvious source. Queue Position Analytics from Resolved Markets makes that integration practical with tick-level features extracted from full bid/ask depth and structured exports.
Frequently asked: Queue Position Analytics for Financial Analysts
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How do I use Resolved Markets to forecast Federal Reserve decisions and policy outcomes?
Resolved Markets tracks FOMC decision prediction markets that show trader expectations about interest rate decisions weeks in advance. Monitor orderbook depth and bid/ask prices to track how market expectations evolve toward announcement dates. When spreads widen, traders are uncertain; when they compress, consensus forms. Historical analysis of these expectations against actual FOMC outcomes reveals whether markets consistently under/over-estimate likelihood of particular outcomes. Use this knowledge to build forecasts that adjust based on real-time prediction market sentiment, outperforming models based solely on Fed statements and economic data.
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Can I use bid/ask spreads from prediction markets to predict economic volatility?
Yes. Bid/ask spread width in prediction markets directly correlates with expected volatility and disagreement among traders. When spreads in FOMC decision markets widen dramatically in days before policy meetings, traders expect significant volatility. When jobs report prediction markets show narrow spreads, traders expect mild surprises. Construct volatility prediction models based on spread dynamics across economic markets (FOMC, employment, inflation, GDP). These prediction market-derived indicators often outperform traditional volatility proxies, enabling better portfolio positioning and hedging decisions ahead of economic releases.
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How can I correlate prediction market expectations with equity, bond, and crypto market movements?
Resolved Markets provides millisecond-precision orderbook data that enables precise correlation analysis with other asset prices. Build datasets that match prediction market snapshots to contemporary price data from equities, bonds, and cryptocurrencies. Test correlation strength between BTC volatility prediction markets and crypto prices, FOMC expectations and Treasury yields, jobs report sentiment and equity indices. Identify correlation breakdown periods that precede broader market dislocations. Use these relationships to construct hedging strategies and portfolio rebalancing signals that respond when prediction market expectations diverge from current asset pricing.
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What historical data is available for backtesting macro forecasting models?
Resolved Markets stores 11.4M+ snapshots with complete orderbook state—bid prices, bid volumes, ask prices, ask volumes, and millisecond timestamps. Download historical snapshots for FOMC markets, jobs report markets, inflation expectation markets, and economic policy markets across multiple years. Reconstruct historical market expectations curves, backtest your forecasting models against actual trader behavior, and measure prediction accuracy. Test whether incorporating prediction market expectations improves your baseline forecasts. This rigorous historical validation ensures your models will perform reliably in live deployment.
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How do I monitor prediction market sentiment in real-time during economic releases and policy announcements?
Subscribe to our WebSocket API for your target prediction markets—FOMC decisions, employment reports, inflation data, or policy announcements. Receive continuous orderbook updates with millisecond timestamps throughout the event window. Watch bid/ask prices and spread width shift in real-time as traders react to economic releases and policy statements. This real-time sentiment feed enables you to update portfolio positions, trigger hedging decisions, and monitor whether market expectations aligned with actual outcomes. The millisecond precision reveals how quickly traders repriced expectations after new information arrived.
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Is Queue Position Analytics a leading or lagging indicator?
Frequently leading. Polymarket prediction markets price outcomes ahead of surveys and polls, and Queue Position Analytics captures that signal at tick-level features extracted from full bid/ask depth.
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How is Queue Position Analytics delivered to analyst workflows?
REST endpoints for ad-hoc queries, CSV exports for spreadsheet workflows, and ClickHouse for analytical pipelines. Queue Position Analytics fits into every standard analyst toolset.
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How do financial analysts use Queue Position Analytics?
Analysts treat Queue Position Analytics as alt-data alongside traditional indicators. Bid/ask depth reveals conviction, not just point probabilities — useful for client reports, sentiment indices, and event-study work.
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Does Queue Position Analytics include derived features or just raw orderbook?
Both. Queue Position Analytics 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.
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How do I compute VPIN from Queue Position Analytics?
Bucket trades by volume from the Queue Position Analytics 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.