Vantex
LIVE
SCANNING 2,412 markets
PIPELINE 83ms
UPTIME 99.7%
NYC · EWR
initializing vantage scan…

Prediction market data
turned into
mathematical trades.

● data pipeline 83ms
Markets analyzed
2,412
Polymarket CLOB · real-time
Signal accuracy (7d)
73.4%
Verified on-chain · 14d backtest
Models running
6
OB depth · divergence · path · surface
Signal Feed — Live
14:31:02 BTC·UP·5m OB imbalance flagged: bid wall $0.512 / ask $0.496 → Δ1.6¢
14:31:02 ETH·UP·15m venue divergence: PM $0.623 vs Kalshi $0.604 → Δ1.9¢
14:31:01 SOL·DOWN·5m graph solver: 3-hop path A→B→C found → +2.8¢ edge
14:31:01 TRUMP·WINS·2028 pair imbalance: YES $0.341 / NO $0.665 → Δ0.6¢
14:31:01 BTC·UP·1h signal decayed (spread closed before confirm)
14:31:00 FED·RATE·JUL venue divergence: PM $0.782 vs Kalshi $0.798 → Δ1.6¢
Mathematical Models
Order Book

OB Depth Analysis

Real-time L2 orderbook modeling. Detects bid/ask imbalances and support/resistance zones before they resolve. Probability-weighted edge estimates on every signal — you decide which ones to trade.
Refresh rate83ms
Signal ratio1 in 14
Cross-Venue

Venue Divergence

Tracks 180+ matched event pairs across Polymarket and Kalshi. When same-event prices diverge beyond model threshold, you get the signal — you decide the execution and timing.
Avg divergence3.4¢
Pairs tracked180+
Graph Theory

Path Solver

Multi-hop market graphs modeled as a directional network. Identifies payout-guaranteed paths through complementary markets. Up to 5 hops deep, solved in under 50ms.
Paths surfaced112/min
Max depth5 hops
AI Pipeline — Models that learn, signals that sharpen
Model Activity — Live
14:31:02 threshold-optimizer tightened DIVERGENCE_MIN 0.8¢ → 0.6¢ (24h precision gain)
14:30:47 signal-auditor verified 1,847 signals vs on-chain outcomes — 73.4% accuracy
14:29:18 noise-filter suppressed 3 low-confidence signals (volatility artifact)
14:27:55 volatility-model adjusted BTC·5m probability weights (IV spike +12%)
14:25:12 pair-discovery added 14 new matched pairs across venues
14:23:44 latency-monitor data pipeline: 81ms e2e (baseline 83ms, within σ)
14:22:09 data-integrity replayed 24h feed: 0 gaps detected
14:20:31 backtest-runner 14-day replay, path-solver → precision 71.2%, sharpe 1.84
14:18:56 edge-validator pruned 7 pairs below confidence threshold
14:16:02 model-weighter recalibrated ensemble: OB 0.38 / Div 0.35 / Path 0.27
How the models improve
Rolling Backtest Engine
● Active — 14-day window, replayed every 4h
Every model is replayed against 14 days of L2 orderbook and settlement data. Parameters are hill-climbed against actual outcomes. Precision improvements deploy automatically; regressions roll back. You always trade against the best-performing model version.
Signal Accuracy Audit
● Active — last sweep 14:30 UTC
Every signal is tracked to its on-chain result. False positives trigger model weight adjustments. The pipeline self-audits across 8 dimensions: precision, recall, timeliness, edge decay, venue drift, fill probability, volatility adjustment, and noise ratio.
Market Structure Awareness
● Active — 2,412 markets monitored
Models adapt as the market evolves. New markets ingested and classified within 60 seconds. Liquidity regime changes trigger parameter recalibration. When a venue adds a matching event, divergence models update automatically.

Data into trades. Math into edge.

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