Spread Compression Signals for Data Scientists
Data scientists leverage Resolved Markets to build predictive models using 11.4M+ orderbook snapshots from Polymarket across crypto, sports, economics, and weather categories. The platform provides raw bid/ask depth arrays with millisecond timestamps—ideal for feature engineering, time-series analysis, and market microstructure modeling. With continuous 20Hz capture rates for crypto markets and comprehensive coverage of 100+ prediction markets, data scientists can train models on real market behavior patterns, sentiment evolution, and price discovery mechanisms. The unified API and historical data storage enable reproducible research, backtesting frameworks, and deployment of models via WebSocket streaming for live predictions.
Data scientists building ML models on prediction markets need Spread Compression Signals. Resolved Markets ships orderbook microstructure signals from Polymarket as a 14-column ClickHouse schema, with bid/ask arrays, depth values, and millisecond timestamps optimized for feature engineering on spread compression, depth imbalance, queue position, quote flicker.
Data challenges Data Scientists run into
Spread Compression Signals from Resolved Markets is built around the data gaps Data Scientists hit when they try to work with raw Polymarket feeds.
Fragmented data sources requiring extensive ETL and normalization
Building prediction models requires consolidating data from sports betting APIs, crypto exchanges, economics calendars, and weather databases. Each source has different schemas, timestamps, data quality standards, and update frequencies. Data scientists waste weeks building ETL pipelines just to get consistent data for model training. Resolved Markets eliminates this integration burden by providing all four categories through a single, normalized API with consistent timestamp precision and schema.
Insufficient orderbook depth granularity for sophisticated microstructure models
Most market data providers deliver only OHLCV candles—open, high, low, close, volume. This completely discards orderbook microstructure where the signal lives. Sophisticated traders and algorithms exploit bid/ask spreads, depth clustering, and order book imbalances minutes before price moves. Resolved Markets provides full depth arrays showing every bid and ask level, enabling feature engineering on fundamental market structure rather than derived price metrics.
Limited historical data windows for training robust prediction models
Historical prediction market data is nearly impossible to acquire at scale. Most platforms don't archive snapshots, leaving data scientists with limited training windows of days or weeks. Resolved Markets maintains 11.4M+ snapshots across 100+ markets with millisecond precision. This depth enables training time-series models on diverse market regimes, economic cycles, election outcomes, and sports season progressions—impossible with limited data.
High operational overhead managing real-time data pipelines
Real-time data pipelines are operationally complex: maintaining WebSocket connections, handling reconnection logic, buffering, deduplication, and writing to analytical databases. Building this infrastructure takes months and requires dedicated engineering. Resolved Markets abstracts this complexity through simple API endpoints and WebSocket subscriptions, letting data scientists focus on modeling rather than infrastructure.
Built for quantitative work on Spread Compression Signals
Orderbook-level prediction-market data that doesn't exist anywhere else.
Millisecond-precision timestamps enable accurate microstructure feature engineering
Every orderbook update is timestamped to the millisecond, enabling precise sequence analysis and event-driven modeling. You can engineer features like 'time_to_next_large_buy_order', 'depth_concentration_ratio', and 'spread_evolution_velocity'—metrics that predict price moves seconds or minutes ahead. These ultra-precise timestamps turn raw orderbook data into predictive signals for microsecond-scale market efficiency models.
11.4M+ snapshots provide deep historical windows for robust model training
The 11.4M+ snapshot archive spans months of continuous Polymarket evolution. Your models can train on diverse market conditions: pre-election volatility (prediction markets repricing as new polls emerge), FOMC uncertainty (hourly probability shifts as economic data releases), sports event outcomes (live match developments changing contract prices), and crypto volatility (correlation with macro sentiment). This breadth prevents overfitting to narrow market regimes.
Full bid/ask depth enables advanced market structure analysis impossible with price data alone
Orderbook depth reveals market participant composition and conviction. When large bids appear at favorable odds, orders are building conviction. When depth clusters at certain levels, smart money is defending support/resistance. When spreads widen dramatically, information asymmetry is high. Resolved Markets' full depth arrays let you engineer these structural features directly, rather than inferring them from price changes that may have already occurred.
Unified API across 4 market categories enables cross-domain transfer learning
Training a single-category model (just crypto, or just sports) limits generalization. Resolved Markets' unified API across crypto, sports, economics, and weather enables transfer learning: patterns in how BTC price predictions reprices ahead of US macro data might apply to EPL match predictions. Cross-category feature spaces create richer representations, improving model robustness when deploying to new markets.
How Data Scientists use Spread Compression Signals
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.
Spread Compression Signals ships with
What Data Scientists build with Spread Compression Signals
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Three steps from signup to live Spread Compression Signals in your application.
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Browse 11 endpoints with live examples. Test requests directly from the docs.
API ReferenceStart Building
Integrate live Spread Compression Signals 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/api/snapshot?crypto=BTC&timeframe=1h&includebook=true'pd.json_normalize() on the responserm-api download --crypto BTC --days 30 --format csvWiring Spread Compression Signals into your workflow
Data scientists integrate Spread Compression Signals via REST for exploratory work in Jupyter, bulk CSV exports for training pipelines, and WebSocket streaming for inference. The 14-column ClickHouse schema maps directly to pandas DataFrames.
- PyTorch Geometric example for orderbook GNNs
- Reference implementation of VPIN in the Python SDK
Why Data Scientists pick Spread Compression Signals
- 11.4M+ millisecond-timestamped snapshots provide unprecedented depth for training time-series prediction models across market regimes
- Full bid/ask depth arrays enable microstructure-based feature engineering impossible with aggregated price data
- Unified API across crypto, sports, economics, and weather enables transfer learning and cross-domain model development
- WebSocket streaming API enables seamless deployment of trained models into production for live market probability predictions
Why Spread Compression Signals matters
Spread Compression Signals matters for data science because it's structured. Most prediction-market data needs hours of cleanup; Spread Compression Signals ships as a schema-aligned dataset with tick-level features extracted from full bid/ask depth ready for ML pipelines on spread compression, depth imbalance, queue position, quote flicker.
Spread Compression Signals in context
ML pipelines on prediction markets used to fight raw exchange data. Spread Compression Signals from Resolved Markets removes that friction: schema, timestamps, and bid/ask arrays are already aligned for ingestion into pandas, ClickHouse, or any modern feature store.
Frequently asked: Spread Compression Signals for Data Scientists
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What features can we engineer from Resolved Markets orderbook data?
The full bid/ask depth enables dozens of microstructure features: bid-ask spread evolution, depth concentration ratios, order book imbalance (total_bid_quantity vs total_ask_quantity), volume-weighted midpoint shifts, time-to-best-execution, depth clustering entropy, and inter-arrival times between large orders. With millisecond timestamps, you can calculate volatility measures at sub-second timescales. These features capture market sentiment and conviction far better than price-only inputs.
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Can we use historical snapshots for backtesting prediction models?
Yes, our full historical archive of 11.4M+ snapshots enables authentic backtesting. You can train models on snapshots from period A, validate on period B, and backtest on period C with zero look-ahead bias. Each snapshot includes the exact timestamp and full orderbook state, enabling realistic simulation of your model's performance. Export snapshots in JSON or Parquet format for efficient processing in your training pipeline.
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How do we handle missing data or gaps in the snapshot stream?
Our capture process is continuous at 20Hz for crypto and variable intervals for other categories. Gaps occur only during platform maintenance (announced in advance). We provide metadata with each snapshot indicating the time since the last capture, enabling you to detect and interpolate over gaps. For production models, our WebSocket API guarantees delivery of every update; client-side buffering prevents data loss due to network transients.
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Can we build models predicting Polymarket price movements before crypto spot markets move?
Yes, this is a primary use case. Polymarket prediction contracts for BTC and ETH price direction often reprices minutes before spot price changes, as sophisticated traders discover new information. Train models on orderbook features from prediction markets to predict subsequent spot price direction. The unified API makes it simple to correlate prediction market orderbook evolution with spot price candles from any exchange, enabling cross-market alpha research.
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What's the best way to handle the scale of 11.4M+ snapshots in training pipelines?
Export snapshots to Parquet format for efficient storage and query. Our API supports time-range and market-range filtering to limit export scope. Use distributed computing frameworks (Spark, Dask, Ray) to parallelize feature engineering across snapshot partitions. For live training, subscribe to WebSocket streams for specific markets rather than querying entire historical datasets. This hybrid approach—historical exports for model development, streaming for live updates—optimizes both training speed and inference latency.
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What ML projects use Spread Compression Signals?
Price prediction, sentiment classification, liquidity forecasting, anomaly detection, cross-market correlation, and outcome probability estimation. Spread Compression Signals is rich enough for sequence models and statistical pipelines alike.
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How big is the dataset behind Spread Compression Signals?
11.4M+ snapshots across 100+ markets and 7 categories. Each snapshot includes full bid/ask arrays with millisecond timestamps — enough for deep learning and statistical modeling.
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Can data scientists access live Spread Compression Signals for inference?
Yes. WebSocket streaming pushes sub-second updates for real-time inference. The MCP server exposes Spread Compression Signals as function calls for AI agents.
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How do I compute VPIN from Spread Compression Signals?
Bucket trades by volume from the Spread Compression Signals 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.
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Does Spread Compression Signals include derived features or just raw orderbook?
Both. Spread Compression Signals 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.