WOLVO - Intelligence that hunts insights

The Intelligence Core is Wolvo's real-time signal processing engine. It ingests data streams, detects patterns, identifies anomalies, and generates actionable insights that can trigger automated workflows.

Key Capability: Process thousands of signals per second with sub-50ms latency for real-time decision making.

Signal Processing

Signals are the fundamental data units in Wolvo. They can come from various sources:

On-chain Events

Transactions, token transfers, program invocations.

External APIs

Price feeds, market data, social signals.

Custom Sources

Your own data streams via webhooks.

Signal Subscription

// Subscribe to signals
client.signals.subscribe({
  sources: ['solana-transactions', 'price-feeds'],
  filters: {
    minValue: 1000,
    tokens: ['SOL', 'USDC']
  }
})

// Handle incoming signals
client.on('signal', (signal) => {
  console.log('Type:', signal.type)
  console.log('Source:', signal.source)
  console.log('Data:', signal.payload)
  console.log('Timestamp:', signal.timestamp)
})

Pattern Detection

Wolvo can detect various patterns in your signal streams:

Frequency Patterns

Detect repeated occurrences within time windows (e.g., 10+ transactions in 1 minute).

Threshold Patterns

Trigger when values cross specified thresholds (e.g., volume > $1M).

Sequence Patterns

Identify specific sequences of events (e.g., deposit → swap → withdraw).

Correlation Patterns

Find relationships between different signals (e.g., price vs volume correlation).

Pattern Configuration

const pattern = await client.patterns.create({
  name: 'High Volume Alert',
  type: 'threshold',
  signal: 'trading-volume',
  condition: {
    field: 'volume_usd',
    operator: 'gt',
    value: 1000000
  },
  window: '5m',
  cooldown: '15m'  // Prevent alert spam
})

Anomaly Tracking

The anomaly detection system uses statistical models to identify unusual behavior:

Detection Methods

Z-Score:Standard deviation from historical mean.

IQR:Interquartile range for outlier detection.

Moving Average:Deviation from rolling averages.

Custom ML:Plug in your own ML models.

Anomaly Detector

const detector = await client.anomalies.create({
  name: 'Unusual Activity Detector',
  signal: 'transaction-count',
  method: 'zscore',
  threshold: 3.0,        // 3 standard deviations
  baseline: '7d',        // 7-day baseline
  sensitivity: 'high'
})

client.on('anomaly', (event) => {
  console.log('Anomaly detected:', event.type)
  console.log('Severity:', event.severity)
  console.log('Details:', event.details)
})

Custom Analyzers

Build custom analyzers to process signals with your own logic:

Custom Analyzer

const analyzer = await client.analyzers.create({
  name: 'Whale Tracker',
  handler: async (signal, context) => {
    // Custom logic
    if (signal.value > 100000) {
      return {
        insight: 'whale_detected',
        confidence: 0.95,
        data: {
          wallet: signal.from,
          amount: signal.value,
          token: signal.token
        }
      }
    }
    return null
  }
})

// Insights from custom analyzers trigger workflows
client.on('insight', (insight) => {
  console.log('Custom insight:', insight)
})