NaxosData Solutions & Architecture
Data, normalization, resolution rules and backtesting.
Four specialized technical modules enabling algorithmic arbitrage bots, quantitative market makers, and institutional research desks to interact with prediction markets as clean financial order books.
PAIR_FED_NOV26_25BPSFungibility: 98/100Continuous Tick-Level L2 Capture & Millisecond Reconstruction
Prediction market venues do not provide resting order book historical depth. Our pipeline maintains 24/7 asynchronous WebSocket listeners capturing every price level insert, cancellation, and fill (seq_num, exchange_ts_ms, ingest_ts_ms) paired with continuous Millisecond L2 Reconstruction.
Archived continuously with sub-millisecond fidelity in the NaxosData Historical Archive.
Dual Timestamping
Both exchange_ts_ms and wire ingress ingest_ts_ms recorded to model matching engine queues and transport delay.
Millisecond L2
Deterministic order book reconstruction allows backtesters and quantitative models to inspect full book depth at any millisecond timestamp.
Historical Archive
Institutional depth. High-throughput historical tick extraction and scoped export slices (CSV, JSON, Parquet) with zero rate degradation.
REST & Streaming
Sub-millisecond WebSockets and low-latency REST endpoints optimized for quantitative execution and backtesting systems.
Cross-Venue Symbology & Canonical pair_id Matching
We link Polymarket condition_id & token_id, Kalshi ticker & series, Limitless Base CLOB slugs, PredictIt contract IDs, Drift BET market accounts, and Smarkets contract IDs under a single unified pair_id.
In parallel, our Rust normalization pipeline inverts Kalshi's native reciprocal binary bids (Ask_YES = 1.00 - Bid_NO) into standard USD bid and ask depth ladders in under 400 microseconds.
BUY_ASK / SELL_BID without calculating reciprocal complement contracts at runtime.Unified Symbology
Map disparate exchange identifiers (hex token IDs, ticker symbols, slugs) to deterministic cross-venue event keys.
Reciprocal Math
Deterministic transformation of binary complement bids into unified USD ask ladders with 100% mathematical fidelity.
NegRisk Normalization
Handles Polymarket NegRisk multi-outcome mutually exclusive sets, ensuring sum-to-1.00 cross-outcome invariants.
Zero Schema Drift
All 6 exchanges produce identical Apache Arrow schemas, letting your models run across venues without venue-specific if-branches.
Settlement Provenance & Fungibility Scoring
Arbitrage bots frequently lose money not on market moves, but because contract settlement rules diverge. Polymarket may rely on an UMA Optimistic Oracle dispute, while Kalshi requires an official CFTC/BLS statistical bulletin.
NaxosData stores complete market descriptions, category tags, resolution oracles, cutoff timestamps, and historical final outcomes (YES, NO, VOID), scoring every pair (0–100) on rule equivalence.
Sub-score verification: Both contracts adjudicate based on the official target range announced in the FOMC policy statement. Basis risk is evaluated as minimal (0.2%).
Oracle Auditing
Continuous categorization of decentralized oracles (UMA, Pyth) vs. regulated regulatory filings and wire feeds.
Cutoff Validation
Detects timezone mismatches and discrete resolution cutoff discrepancies before capital is locked.
Historical Outcomes
Parquet catalog includes historical resolution status (YES, NO, VOID) for post-mortem backtesting.
Canonical Linking
Join orderbooks with metadata seamlessly via canonical pair_id links.
Event-Driven Backtesting & Derived Liquidity Metrics
Order book archives pre-calculate 1h/24h notional volume, VWAP, and depth scores directly from resting L2 fills. Connect directly into Python backtesting pipelines or discrete event-driven simulation engines.
Accurately simulate 4-stage execution latency (35ms ± 15ms), shadow liquidity depletion, Rung-2 escape sweeps, and cross-chain USDC rebalancing between Polygon and Base.
VWAP Curves
Pre-calculated trailing 1h and 24h VWAP columns embedded in every tick row to measure market impact and price discovery.
Latency Modeling
Simulate 4-stage execution latency (signal, socket, queue, match) to observe realistic fill rates under heavy market volatility.
Rung-2 Sweeps
Model partial order fills that sweep through depth rungs, computing effective execution price impact in basis points.
Python & Polars
Stream historical tick windows directly into Python and Polars backtesters, validating strategies across millions of ticks in seconds.
Self-Serve Quantitative Infrastructure
Start backtesting prediction market data today.
Explore the web terminal for free with 3-day lookback data, or deploy Quant Pro ($30/mo) for 90 days millisecond L2 history & scoped exports.