Bid-Ask Imbalance Detection for Prediction Market Traders
Resolved Markets delivers millisecond-precision orderbook snapshots from Polymarket's most active prediction markets, enabling traders to execute sophisticated strategies across crypto, sports, economics, and weather categories. With full bid/ask depth arrays captured at 20Hz for crypto markets and continuous streaming via WebSocket, traders access granular market microstructure data previously unavailable to retail participants. The API eliminates latency friction—query live or historical snapshots of BTC/ETH price predictions, NFL/NBA outcomes, FOMC decisions, and 30-city weather events. Backtesting against 11.4M+ historical snapshots reveals spread dynamics, liquidity patterns, and price discovery mechanisms across 100+ tracked markets, enabling data-driven position sizing and entry/exit optimization.
Bid-Ask Imbalance Detection is the execution dataset for prediction-market traders. Resolved Markets pipes orderbook microstructure signals from Polymarket into one feed, so traders can monitor depth, time entries, and exploit calendar spreads across spread compression, depth imbalance, queue position, quote flicker.
Data challenges Prediction Market Traders run into
Bid-Ask Imbalance Detection from Resolved Markets is built around the data gaps Prediction Market Traders hit when they try to work with raw Polymarket feeds.
Orderbook depth opacity across fragmented Polymarket markets
Retail traders accessing Polymarket lack full orderbook visibility, seeing only incomplete bid/ask data. Resolved Markets captures complete depth arrays at 20Hz, revealing true liquidity clustering, hidden orders, and support/resistance levels institutional traders exploit. Without this granular data, independent traders operate with information asymmetry, unable to detect micro-movements or anticipate slippage on larger positions across BTC/ETH prediction markets.
Latency disadvantage vs. institutional market makers
Latency is critical in prediction market arbitrage—spreads tighten within milliseconds as new information emerges. Standard APIs introduce 100-500ms delays; Resolved Markets delivers millisecond-timestamp snapshots, enabling traders to detect momentum shifts before competitors react. This 0-lag advantage compounds across multiple daily trades, especially during volatile FOMC announcement or sports event conclusion windows when orderbook volatility spikes dramatically.
Insufficient historical data for reliable backtesting
Backtesting prediction market strategies fails without historical orderbook data. Generic crypto trading datasets don't capture Polymarket-specific dynamics—smaller order sizes, fewer market makers, longer expiration timelines. Resolved Markets' 11.4M+ snapshot archive (spanning 100+ markets) lets traders simulate strategies against authentic historical conditions, identify profitable spread-capture opportunities, and validate risk models before deploying real capital.
Real-time execution signal extraction from noisy market data
Raw orderbook data is noisy—ghost orders, failed fills, and cascading liquidations create false signals. Traders struggle to extract actionable edge from unfiltered depth updates. Resolved Markets' WebSocket stream includes cleaned snapshots with market state metadata, enabling traders to build robust signal extraction pipelines that distinguish genuine liquidity from predatory order placement and identify sustainable alpha opportunities.
Built for quantitative work on Bid-Ask Imbalance Detection
Orderbook-level prediction-market data that doesn't exist anywhere else.
Eliminate orderbook visibility gaps with full depth captures
Access complete bid/ask depth arrays for every tracked prediction market—not fragments from partial APIs. See exact liquidity levels, identify hidden orders, and detect order placement patterns institutional traders use. Full visibility enables precise spread-capture algorithms and slippage estimation, transforming blind trades into calculated bets backed by micro-level orderbook intelligence across crypto, sports, and economics categories.
Reduce latency from milliseconds to sub-millisecond precision
Millisecond timestamps synchronized across all captures ensure precise latency measurement and fair performance attribution. Execute orders knowing exact snapshot ages—avoid stale data traps where price predictions shifted 500ms prior. Sub-millisecond delivery speed means your algorithms react to market movements as fast as the fastest institutional systems, eliminating the latency tax most retail traders silently pay.
Backtest strategies on authentic historical Polymarket data
Backtest using real historical snapshots eliminates survivorship bias and synthetic data artifacts. Test mean-reversion strategies on actual FOMC decision orderbooks, momentum algorithms on live NBA game outcomes, and spread capture across weather predictions. The 11.4M snapshot archive provides statistical power—validate 50+ strategy variations against authentic market conditions before committing capital.
Scale strategies across 100+ tracked prediction markets
Scale profitably by distributing risk across 100+ markets instead of chasing individual positions. Test whether profitable spread-capture logic from BTC prediction markets translates to EPL outcomes or jobs report bets. Multi-market correlation patterns visible in Resolved Markets' cross-category snapshots reveal diversification opportunities and hidden risk concentrations traders miss with single-category focus.
How Prediction Market Traders use Bid-Ask Imbalance Detection
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.
Bid-Ask Imbalance Detection ships with
What Prediction Market Traders build with Bid-Ask Imbalance Detection
Up and running in minutes
Three steps from signup to live Bid-Ask Imbalance Detection in your application.
Get Your API Key
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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 Bid-Ask Imbalance Detection into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
rm-api marketsWiring Bid-Ask Imbalance Detection into your workflow
Active traders prototype on REST, monitor via WebSocket, and backtest on CLI exports. Bid-Ask Imbalance Detection flows through every channel.
- PyTorch Geometric example for orderbook GNNs
- Reference implementation of VPIN in the Python SDK
Why Prediction Market Traders pick Bid-Ask Imbalance Detection
- Full orderbook depth at 20Hz for crypto markets—zero blind spots, maximum visibility
- 11.4M+ historical snapshots enable statistically robust backtesting and strategy validation
- WebSocket streaming + REST API provides both real-time and batch research workflows
- Sub-millisecond latency edge across crypto, sports, economics, and weather prediction markets
Why Bid-Ask Imbalance Detection matters
Bid-Ask Imbalance Detection matters for active traders because the edge in prediction markets is increasingly microstructure. tick-level features extracted from full bid/ask depth on spread compression, depth imbalance, queue position, quote flicker delivers that edge at the same resolution professional desks expect.
Bid-Ask Imbalance Detection in context
Active prediction-market trading is now competitive enough that Bid-Ask Imbalance Detection is table stakes. tick-level features extracted from full bid/ask depth on spread compression, depth imbalance, queue position, quote flicker closes the visibility gap between manual traders and systematic desks.
Frequently asked: Bid-Ask Imbalance Detection for Prediction Market Traders
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What bid/ask depth can I capture from Resolved Markets' API?
Resolved Markets captures complete orderbook snapshots including all active bid and ask orders with price levels and sizes. For crypto markets (BTC, ETH, SOL, XRP), snapshots are captured at 20Hz. You receive full depth arrays via REST API (historical queries) or WebSocket streaming (live updates), enabling precise liquidity analysis and slippage prediction impossible with aggregated mid-price feeds.
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How accurate are the millisecond timestamps for latency measurements?
Timestamps are synchronized to millisecond precision across all Polymarket snapshots, captured in ClickHouse for durability. Each snapshot records the exact moment the orderbook state was captured, enabling accurate latency attribution. This precision lets traders measure execution slippage, quantify their latency disadvantage vs. market makers, and validate whether algorithmic improvements actually reduce capture-to-execution delay.
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Can I backtest spread-capture strategies on historical data?
Yes. Query 11.4M+ historical snapshots via the API to access orderbook states for any past date across all 100+ tracked markets. Reconstruct exact historical bid/ask spreads, test whether mean-reversion or momentum logic worked on actual Polymarket data, and measure slippage that would have occurred. This enables robust backtesting impossible on synthetic data, with statistical rigor comparable to equity market research.
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Do you provide data for sports and economics prediction markets beyond crypto?
Absolutely. Resolved Markets tracks 100+ markets across crypto (BTC, ETH, SOL, XRP predictions), sports (NBA, NFL, EPL), economics (FOMC decisions, jobs reports), and weather (30 cities daily). Each category maintains full orderbook history. Traders can develop cross-category strategies, diversify portfolio risk, and exploit category-specific volatility patterns (e.g., leverage weather predictions during hurricane season).
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What's the typical WebSocket latency for real-time orderbook updates?
WebSocket connections deliver snapshots with sub-millisecond propagation delay once captured. 20Hz crypto market capture rate ensures you receive updates at least every 50ms. For real-time trading, this eliminates stale data—you're always within 50ms of true market state. Combined with your local order execution, total system latency remains competitive with institutional setups despite retail-accessible pricing.
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How do active traders use Bid-Ask Imbalance Detection?
Traders watch Bid-Ask Imbalance Detection for spread compression, depth shifts, and mispriced contracts. tick-level features extracted from full bid/ask depth ensures every meaningful state change is captured.
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What markets are best for trading Bid-Ask Imbalance Detection?
High-volume contracts in spread compression, depth imbalance, queue position, quote flicker with consistent activity. Bid-Ask Imbalance Detection exposes deep bid/ask arrays for accurate execution sizing.
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Can Bid-Ask Imbalance Detection detect mispricings in real time?
Yes. Continuous capture surfaces sub-second mispricings that REST polling misses. WebSocket streaming pushes the data to live trading systems.
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Does Bid-Ask Imbalance Detection include derived features or just raw orderbook?
Both. Bid-Ask Imbalance Detection 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 Bid-Ask Imbalance Detection?
Bucket trades by volume from the Bid-Ask Imbalance Detection 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.