Data Analysis

Advanced analytics and pattern recognition for Solana wallets

Objective

Perform comprehensive data analysis on Solana wallet activity to extract actionable insights and identify behavioral patterns.

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Analysis Capabilities

Solar Sentra provides AI-powered analysis tools that process millions of transactions to uncover:

• Trading patterns and strategies
• Whale movements and accumulation
• Network effects and wallet clusters
• Anomaly detection for fraud prevention
• Portfolio optimization opportunities

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Transaction Pattern Analysis

Identify recurring patterns in wallet behavior.

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import { SolarSentra } from '@solar-sentra/sdk';

const client = new SolarSentra({ apiKey: process.env.API_KEY });

async function analyzePatterns(walletAddress: string) {
  const analysis = await client.analytics.patternRecognition(
    walletAddress,
    {
      timeRange: '90d',
      minConfidence: 0.75,
      patterns: ['accumulation', 'distribution', 'swing_trading']
    }
  );

  return analysis;
}

const result = await analyzePatterns('7xKXtg2CW87...');
console.log(result);
// {
//   detectedPatterns: [
//     {
//       type: 'accumulation',
//       confidence: 0.89,
//       startDate: '2025-08-01',
//       tokens: ['SOL', 'JUP'],
//       averageSize: 150.5
//     }
//   ],
//   tradingStyle: 'long_term_holder',
//   riskProfile: 'moderate'
// }

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Wallet Clustering

Group wallets with similar behavioral characteristics.

Clustering Algorithm

Solar Sentra uses a proprietary multi-dimensional clustering algorithm based on:

• Transaction frequency distribution
• Token preference vectors
• Time-of-day activity patterns
• Network connectivity graphs
• Average holding periods

Implementation

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from solar_sentra import SolarSentra

client = SolarSentra(api_key="your_key")

# Analyze wallet cluster
cluster_data = client.analytics.get_wallet_cluster(
    wallet="7xKXtg2CW87...",
    depth=2  # Degrees of separation
)

print(f"Cluster size: {cluster_data.size}")
print(f"Similarity score: {cluster_data.cohesion}")

for similar_wallet in cluster_data.wallets[:10]:
    print(f"{similar_wallet.address}: {similar_wallet.similarity}%")

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Visualization Engine

Generate charts and graphs from wallet data.

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const visualization = await client.analytics.generateChart({
  walletAddress: '7xKXtg2CW87...',
  chartType: 'balance_over_time',
  timeRange: '30d',
  format: 'png',
  width: 1200,
  height: 600
});

// Returns base64-encoded image
fs.writeFileSync('balance_chart.png', visualization.image, 'base64');

Available Chart Types:

• balance_over_time - Historical balance tracking
• transaction_heatmap - Activity by day/hour
• token_allocation_pie - Portfolio distribution
• pnl_waterfall - Profit and loss breakdown
• network_graph - Wallet relationship visualization

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Statistical Analysis

Compute statistical metrics across transaction history.

Metric	Description	Formula
Sharpe Ratio	Risk-adjusted returns	(R - Rf) / σ
Win Rate	Profitable transactions %	wins / total_trades
Average Holding Time	Mean token hold duration	Σ(sell_time - buy_time) / n
Transaction Velocity	Trades per day	total_trades / days
Diversification Index	Portfolio concentration	1 - Σ(weight²)

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const stats = await client.analytics.getStatistics(walletAddress);

console.log({
  sharpeRatio: stats.sharpeRatio.toFixed(2),
  winRate: `${(stats.winRate * 100).toFixed(1)}%`,
  avgHoldingTime: `${stats.avgHoldingTime}h`,
  txVelocity: stats.txVelocity.toFixed(1),
  diversificationIndex: stats.diversificationIndex.toFixed(3)
});

---

Machine Learning Predictions

Next Transaction Probability

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prediction = client.ml.predict_next_transaction(
    wallet="7xKXtg2CW87...",
    features=["time_of_day", "day_of_week", "recent_activity"]
)

print(f"Probability: {prediction.probability}%")
print(f"Estimated time: {prediction.estimated_hours}h")
print(f"Likely action: {prediction.action}")
print(f"Confidence: {prediction.confidence}")

Price Impact Analysis

Estimate how wallet transactions affect token prices.

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const impact = await client.analytics.estimatePriceImpact({
  wallet: '7xKXtg2CW87...',
  token: 'BONK',
  action: 'sell',
  amount: 1000000
});

// {
//   estimatedSlippage: 0.034,
//   priceImpact: 0.012,
//   optimalSplit: [250000, 250000, 250000, 250000],
//   executionStrategy: 'TWAP'
// }

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Real-World Applications

Hedge Fund Analytics

Monitor multiple wallets simultaneously and aggregate performance metrics for institutional reporting.

Compliance Monitoring

Flag suspicious patterns matching known fraud signatures or money laundering indicators.

Portfolio Optimization

Identify underperforming assets and suggest rebalancing strategies based on historical performance.

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Performance Benchmarks

Operation	Time (p95)	Data Points
Pattern recognition	850ms	10,000 txs
Clustering analysis	1.2s	500 wallets
Chart generation	320ms	30d data
Statistical compute	180ms	Full history
ML prediction	420ms	90d training

All analysis operations are cached for 15 minutes to optimize performance and reduce API costs.