Mean Reversion Backtesting 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.
Mean Reversion Backtesting is the execution dataset for prediction-market traders. Resolved Markets pipes research-grade prediction-market data into one feed, so traders can monitor depth, time entries, and exploit calendar spreads across regime detection, arbitrage backtests, sentiment indices, factor models.
Data challenges Prediction Market Traders run into
Mean Reversion Backtesting 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 Mean Reversion Backtesting
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 Mean Reversion Backtesting
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.
Mean Reversion Backtesting ships with
What Prediction Market Traders build with Mean Reversion Backtesting
Up and running in minutes
Three steps from signup to live Mean Reversion Backtesting 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 Mean Reversion Backtesting into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
rm-api marketsWiring Mean Reversion Backtesting into your workflow
Active traders prototype on REST, monitor via WebSocket, and backtest on CLI exports. Mean Reversion Backtesting flows through every channel.
- QuantConnect Lean engine adapter
- Native Zipline bundle for backtesting
- Polygon.io-compatible REST shim
Why Prediction Market Traders pick Mean Reversion Backtesting
- 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 Mean Reversion Backtesting matters
Mean Reversion Backtesting matters for active traders because the edge in prediction markets is increasingly microstructure. 11.4M+ snapshots across 7 prediction-market categories on regime detection, arbitrage backtests, sentiment indices, factor models delivers that edge at the same resolution professional desks expect.
Mean Reversion Backtesting in context
Active prediction-market trading is now competitive enough that Mean Reversion Backtesting is table stakes. 11.4M+ snapshots across 7 prediction-market categories on regime detection, arbitrage backtests, sentiment indices, factor models closes the visibility gap between manual traders and systematic desks.
Frequently asked: Mean Reversion Backtesting 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 is Mean Reversion Backtesting different from polling Polymarket?
Polling at 1-2s misses 10-40x more state changes. Mean Reversion Backtesting runs continuously and is streamed to clients, so prediction-market traders see every quote shift the moment it happens.
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How do active traders use Mean Reversion Backtesting?
Traders watch Mean Reversion Backtesting for spread compression, depth shifts, and mispriced contracts. 11.4M+ snapshots across 7 prediction-market categories ensures every meaningful state change is captured.
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What markets are best for trading Mean Reversion Backtesting?
High-volume contracts in regime detection, arbitrage backtests, sentiment indices, factor models with consistent activity. Mean Reversion Backtesting exposes deep bid/ask arrays for accurate execution sizing.
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Can Mean Reversion Backtesting be used in a portfolio context?
Yes. Many funds treat prediction markets as an alternative sleeve and use Mean Reversion Backtesting as the structured data feed. Risk-parity, factor-tilting, and sentiment-overlay strategies all consume Mean Reversion Backtesting.
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Is there published research using Mean Reversion Backtesting?
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.