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Bitcoin Whale Tracker

Real-time cryptocurrency analytics platform with ML-powered predictions and whale monitoring

Live Market Data
Updated 6:34:47 PM
Bitcoin (BTC)
$98,234.56
24h Change
+2.45%
Recent Whale Transactions (≥50 BTC)
Live Monitoring
Large Bitcoin movements detected on the blockchain
Transaction HashBTC AmountUSD ValueTimeType
8f3a2b4c9d1e5f6a...523.45 BTC$51,420,8806:31:47 PMLarge Transfer
a4d8e1f2b5c6d9e0...312.18 BTC$30,666,8656:27:47 PMExchange Withdrawal
c6f9b2a3d4e5f6a7...156.89 BTC$15,412,0206:24:47 PMExchange Deposit
e2f3a4b5c6d7e8f9...234.67 BTC$23,052,7046:20:47 PMLarge Transfer
f4a5b6c7d8e9f0a1...189.23 BTC$18,588,9266:17:47 PMExchange Withdrawal
b6c7d8e9f0a1b2c3...412.76 BTC$40,547,2976:13:47 PMLarge Transfer
d8e9f0a1b2c3d4e5...278.34 BTC$27,342,6076:09:47 PMExchange Deposit
f0a1b2c3d4e5f6a7...567.92 BTC$55,789,3716:05:47 PMLarge Transfer

Platform Features

ML Price Prediction Engine

TensorFlow.js neural network models trained on 80+ features including historical prices, whale activity, macro indicators, and sentiment scores

78% directional accuracy on 24-hour forecasts

Real-Time Data Pipeline

WebSocket streaming architecture using Socket.io processing Bitcoin blockchain data and market feeds

1,000+ data points per minute, <200ms latency

Multi-Source Aggregation

Integrated 11+ external APIs: CoinGecko, FRED, NewsAPI, CryptoCompare, Alpha Vantage, Blockchain.info, DefiLlama, and more

Intelligent rate limiting, 90% cache hit rate

Pattern Detection Algorithms

Proprietary algorithms identifying accumulation, distribution, and consolidation patterns using statistical analysis and whale behavior clustering

85%+ confidence scoring

Technical Architecture

Frontend
  • • React 18 + TypeScript
  • • Vite build tool
  • • TailwindCSS styling
  • • Recharts visualizations
  • • Socket.io Client
  • • React Context API
Backend & ML
  • • Node.js 20 + Express
  • • PostgreSQL 16
  • • Prisma ORM
  • • TensorFlow.js
  • • Natural (NLP)
  • • Socket.io Server
Infrastructure
  • • Docker & Docker Compose
  • • Microservices architecture
  • • Background job scheduling
  • • Vercel deployment
  • • 15+ database tables
  • • API rate limiting

Performance Metrics

78%
ML Prediction Accuracy
1000+
Data Points/Minute
11+
API Integrations
<200ms
Response Time

View Full Implementation

Complete source code with backend API, ML models, Docker deployment, database schema, and comprehensive documentation

ReactTypeScriptNode.jsPostgreSQLTensorFlow.jsDocker
View on GitHub