Institutional Use Cases
Built for systematic desks, market makers, and quantitative quants.
Prediction market order books normalized into standard equities-style Level 2 feeds. Explore four real-world quantitative architectures powered by NaxosData.
Cross-Venue Arbitrage Bot Operators
Backtesting and modeling dual-leg prediction market arbitrage across Polymarket, Kalshi, Limitless, PredictIt, Drift BET, and Smarkets. NaxosData provides the dual-timestamped millisecond tick archives required to model asymmetric execution latency, shadow liquidity depletion, net-of-fee curves, emergency Leg-1 scratch unwinds, and local backtesting validation.
Key Architectural Pillars
- Pre-matched pair_id mapping across venues with instant spread calculations
- Dual microsecond timestamping (exchange_ts_ms vs. ingest_ts_ms) for queue delay modeling
- Resting L2 depth rungs to simulate full cost-to-sweep slippage and unhedged Leg-2 risk
- Settlement Fungibility Scoring (0–100) to protect against toxic oracle divergence
async def evaluate_cross_venue_arb(tick):
# Normalized USD bids/asks provided by NaxosData
poly_bid = tick["polymarket"]["best_bid"]
kalshi_ask = tick["kalshi"]["best_ask"]
spread_bps = (poly_bid - kalshi_ask) * 10000
if spread_bps > 150 and tick["fungibility_score"] >= 95:
# Model 4-stage latency & sweep depth
fill_estimate = simulate_l2_sweep(
kalshi_asks=tick["kalshi"]["asks"],
notional_usd=5000,
latency_ms=35.0
)
if fill_estimate["slippage_bps"] < 15:
await execute_simultaneous_ioc(poly_bid, kalshi_ask)High-Frequency Market Makers
Passive liquidity provision on prediction markets requires continuous risk adjustment around fast-moving underlying asset spot prices. Market makers utilize NaxosData's tick archives and pre-calculated 1h VWAP metrics to model queue priority, queue cancellations, order book imbalance ratios, and adverse selection risk on short-horizon 5m/15m/1h crypto strike ladders and sports/macro contracts.
Key Architectural Pillars
- Historical queue position simulation via lossless sequence-numbered tick streams
- Pre-computed order book imbalance ratios and trailing VWAP benchmarks
- Adverse selection modeling around spot Oracle updates (Pyth, Chainlink, Binance)
- WebSocket streaming feed (wss://stream.naxosdata.com/v1/l2) for sub-millisecond updates
import naxosdata as nd
# Calculate 1-minute order book imbalance & adverse selection
client = nd.Client(api_key="nx_live_...")
timeseries = client.orderbooks.timeseries(
venue="POLYMARKET",
market_id="poly-btc-dec-2026",
metric="imbalance",
resolution="1m"
)
# Stream L2 order book deltas in real time
for update in client.stream.orderbooks(["poly-btc-dec-2026"]):
imbalance = update.bids[0].size / (update.bids[0].size + update.asks[0].size)
if imbalance > 0.72:
adjust_inventory_quotes(skew="LONG")Macro & Event-Driven Desks
Systematic macro funds and event-driven desks extract real-time implied probability distributions directly from prediction market order books. Instead of viewing isolated contract bucket prices, quants utilize NaxosData's standardized metadata and resolution catalog to construct clean probability term structures, expected values, and dispersion parameters around scheduled central bank rate decisions, CPI prints, nonfarm payrolls, and elections.
Key Architectural Pillars
- Complete historical event series with plain-English question descriptions and rules
- Resolution Oracle provenance catalog (BLS official releases, FOMC wires, UMA disputes)
- Full historical resolution status (YES, NO, VOID) for calibration testing
- Implied density reconstruction from multi-bucket contract strikes
import naxosdata as nd
# Query synchronized macro markets via NaxosData Tick Engine
client = nd.Client(api_key="nx_live_...")
macro_markets = client.markets.list(category="Macro", status="ACTIVE")
for market in macro_markets:
# Retrieve exact reconstructed L2 book at historical FOMC release
snapshot = client.orderbooks.snapshot(
venue=market.venue,
market_id=market.id,
timestamp_ms=1730000000300
)
print(f"{market.title}: Best Bid {snapshot.best_bid} ({snapshot.spread_bps} bps)")Quantitative & Academic Researchers
Academic institutions and quantitative research labs analyzing market microstructure, information aggregation, and forecasting accuracy require deep historical tick datasets. The NaxosData Historical Archive and REST API enable researchers to stream, filter, and export scoped datasets (CSV, JSON, Parquet) across billions of historical ticks with millisecond precision.
Key Architectural Pillars
- Millisecond L2 Reconstruction: Deterministic full order books reconstructed at any millisecond timestamp
- Lossless sequence-numbered tick streams with microsecond exchange & ingest timestamps
- Scoped Historical Exports in CSV, JSONL, and Apache Parquet formats
- Self-serve Hobbyist ($0/mo) and Quant Pro ($30/mo) tiers with instant API key access
import naxosdata as nd
client = nd.Client(api_key="nx_live_...")
# Reconstruct Level 2 order book snapshot at an exact historical millisecond
snapshot = client.orderbooks.snapshot(
venue="KALSHI",
market_id="kalshi-fed-nov-2026",
timestamp_ms=1730000000150
)
# Export scoped time window to Parquet for local backtesting
export_job = client.exports.create(
pair_id="PAIR_FED_NOV26_25BPS",
format="parquet",
start_ts="2026-10-24T00:00:00Z",
end_ts="2026-10-25T00:00:00Z"
)
df = export_job.to_dataframe()Immediate Deployment
Power your algorithms with NaxosData.
Provision an API key in 30 seconds. Start on the free Hobbyist tier or scale to Quant Pro for 90 days millisecond L2 history & scoped exports.