Behavioural Pattern Detection

Equinox AI identifies subtle and recurring behaviours across on-chain transactions and off-chain actions. This includes wallet behaviour loops, KOL influence cycles, wash trading, hidden exits, post-sell callouts, and group-based coordination. Our models do not rely on basic wallet-to-wallet flows—they focus on timeline, frequency, correlation, and repetition.

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Detection Categories

• Cross-Platform Behaviour: Identifies sequences like Telegram join → Tweet → Swap → Exit. • Social Echo Patterns: Detects identical narratives across multiple KOL handles and groups. • Liquidity Shuffling: Flags wallets rerouting ETH or tokens across chains or mixers before buys/sells. • Event Sniping: Catches those who repeatedly buy just before contract events or narrative shifts. • Delayed Exit Indicators: Marks wallets who shill days after they've sold.

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Timeline Scoring

Input: Wallet A
→ TX pattern detected across 4 tokens
→ Telegram post timeline aligned 3/4 times
→ X call aligned 2/4 times
→ Average lag: 3.1 minutes
→ Flag: Medium Risk | Coordination Score: 87/100

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ML Scoring Matrix (Simplified)

Factor	Weight	Notes
Cross-platform pattern	30%	Synced behaviour across platforms
Repetition score	25%	Recurrence of actions
Time correlation window	20%	Delay between post/call and TX
Volume vs influence ratio	15%	Volume moved vs social push size
Alias cluster density	10%	Same users across other flagged IDs

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Alert Logic Example

{
  "wallet": "0x123...def",
  "detected": true,
  "risk_level": "High",
  "alerts": [
    "Echo Narrative Detected",
    "Pre-snipe Detected",
    "Alias Overlap in 3 Groups"
  ],
  "score": 94.2
}

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Real Use Cases

• Traders who repeatedly dump tokens within minutes of posting bullish tweets.

• Influencers calling tokens they've already exited, tracked by bridge + swap logs.

• Groups using common timing to rotate buys and coordinate exits.

• Teams with recycled social aliases across multiple failed projects.

These behaviours are scored continuously—not as one-time flags—and used to build behavioural fingerprints for each wallet and identity.

Equinox treats behavioural analysis as an evolving science. Scores are recalibrated regularly based on macro trends, cluster accuracy, and volume of signal data.