cades 1.0.0

Advanced AI system for detecting anomalous patterns in cryptocurrency markets

CADES (Crypto Anomaly Detection Engine System)

CADES is an advanced AI system for detecting anomalous patterns in cryptocurrency markets through on-chain data analysis and social sentiment monitoring. It provides real-time analysis and early warning systems for potential market manipulations.

Core Features

• Real-time blockchain monitoring
• Social sentiment analysis
• Pattern recognition system
• Anomaly detection engine
• Risk scoring system

Basic System Requirements

Hardware Requirements

• CPU: 8+ cores (AMD Ryzen 7/Intel i7 or better)
• RAM: 32GB minimum
• Storage: 1TB NVMe SSD
• Network: 1Gbps stable connection

Software Requirements

• Operating System: Ubuntu 22.04+ / Debian 11+
• Python 3.9 or higher
• Docker 24.0+
• NGINX 1.18+
• PostgreSQL 14+

Advanced AI Features

CADES includes advanced AI capabilities for deep pattern analysis and prediction. These features require additional resources.

Note: Advanced AI features are optional. You can run basic anomaly detection without enabling the full AI pipeline.

Additional Hardware Requirements for AI Pipeline

• CPU: 16+ cores recommended
• RAM: 64GB minimum
• Storage: Additional 1TB NVMe SSD
• GPU: NVIDIA RTX 4080 16GB or better
• Network: 10Gbps connection recommended

AI Specifications

• Models: BERT, LSTM, Custom Transformers
• Quantization: FP16 precision
• Memory Usage: ~40GB RAM when fully active
• Disk Space: ~100GB for model files
• Processing Time: Sub-second for basic analysis
• Context Window: 8192 tokens
• Data Sources: On-chain + 5 social platforms

Setup Guide

1. Basic Installation

# Clone the repository
git clone https://github.com/joengo72/crypto-anomaly-detection-engine-system.git
cd crypto-anomaly-detection-engine-system

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

2. Configure Your System Create a configuration file at config/development.yml:

# Basic configuration
server:
  host: "0.0.0.0"
  port: 8000
  workers: 4
  
database:
  url: "postgresql://user:pass@localhost:5432/cades"
  pool_size: 20

blockchain:
  rpc_endpoint: "your-solana-rpc"
  update_interval: 60

# AI pipeline configuration (optional)
ai_pipeline:
  enabled: false  # Set to true for full AI capabilities
  model_path: "./models/"
  batch_size: 32
  use_gpu: true
  memory_limit: 40GB

3. Start the System

For basic monitoring:

# Start the basic system
python src/api/routes.py --config config/development.yml

For full AI pipeline:

# Download AI models (approximately 100GB)
python scripts/download_models.py

# Start with full AI capabilities
python src/api/routes.py --config config/development.yml --enable-ai

Performance Optimization

Basic System Optimization

• Monitor database performance
• Adjust worker processes
• Configure connection pooling
• Optimize blockchain RPC calls

AI Pipeline Optimization

• Use GPU acceleration when available
• Adjust batch sizes based on memory
• Enable model quantization
• Configure processing queues

Troubleshooting

1. Basic System Issues

   • Check database connectivity
   • Verify RPC endpoint access
   • Monitor system resources
   • Check log files in logs/

2. AI Pipeline Issues

   • Insufficient GPU memory: Reduce batch size
   • High CPU usage: Adjust worker count
   • Slow processing: Check network bandwidth
   • Model loading errors: Verify model files

Support

For technical support:

• Review documentation: https://cades.gitbook.io/docs
• Submit issues on GitHub: https://github.com/joengo72/crypto-anomaly-detection-engine-system/issues
• Contact technical team: [e-mail]

License

This project is proprietary software. All rights reserved.

Contact

• Website: https://cades.io
• Email: [e-mail]