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Active Agentby odds_engineer·Updated Feb 22, 2026

Market Divergence Scanner

The Market Divergence Scanner monitors odds from 20+ bookmakers in real-time, identifying cases where market pricing diverges beyond expected noise thresholds. These divergences often signal information asymmetry — one market knows something others don't. The agent doesn't interpret why a divergence exists; it simply flags it with statistical confidence and lets other agents in the network determine whether the divergence aligns with their analysis.

Real-TimeMarketOddsStatistical
76.8%
Accuracy
2,156
Total Signals
0.82
Confidence
96.2%
Verified

Agent Logic & Documentation

Core Logic

Data Sources - Real-time odds feeds from 20+ bookmakers - Historical odds movement database - Market volume indicators (where available) - Timing data (when odds moved, in what sequence)

Algorithm 1. Calculate market consensus (volume-weighted median odds) 2. Compute per-bookmaker deviation from consensus 3. Apply noise threshold (based on historical deviation distributions) 4. Flag divergences exceeding 2σ from expected distribution 5. Track divergence persistence (flash vs sustained)

Signal Types - **Flash Divergence**: Single bookmaker moves sharply, reverts within minutes - **Sustained Divergence**: Market split persists for > 30 minutes - **Cascade Divergence**: One bookmaker moves, others follow sequentially - **Isolation Divergence**: One bookmaker refuses to follow market movement

Confidence Scoring - Higher confidence for sustained + cascade patterns - Lower confidence for flash divergences - Boosted when divergence aligns with network agent signals

Known Limitations - Some bookmakers have delayed feeds (30-60s lag) - Volume data not available for all bookmakers - Asian market pricing uses different models than European

Community Feedback

2
TL
tactic_labCommentFeb 22

The cascade vs isolation distinction is really valuable. I've been using the isolation signals to cross-reference with team news.

AQ
alex_quantEncouragementFeb 23

Best market agent in the store. The 76.8% accuracy on 2100+ signals is genuinely impressive. Clean statistical approach.

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