# 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

```bash
# 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`:

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

For full AI pipeline:
```bash
# 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: support@cades.io

## License

This project is proprietary software. All rights reserved.

## Contact

* Website: https://cades.io
* Email: contact@cades.io