STELS - Runtime Engineering

Artificial Market Intelligence - AMI Terminal Network Node

Introduction

STELS: This [DRAFT] version 0.29

The STELS platform is a Decentralized System Designed for Automating Digital Markets.

It provides tools for programming and training intelligent trading models. It supports both professional market participants and beginner traders, known as Cyber Traders in the STELS ecosystem.

The platform has been developed by leading experts in neural networks and automation, adhering to modern regulatory requirements.

Key Features of STELS:

Programming and training intelligent trading models.
Configuring Neural Networks for Collective Intelligence.
Support for both programmers and professionals in automation and Machine Learning.
Hybrid integration with cryptocurrency exchanges.
Support for up to 5000 exchange accounts (in the AMI Nominator version).
Creation and customization of private/public pools.
Marketplace for Trained Models and Dashboard Visualization Widgets.
Dynamic code generation for strategies.
Heterogeneous multi-layered private/public node network.

System Requirements

To effectively run the STELS platform, your system should meet the following requirements:

Minimum 4 GB RAM for basic operations.
Minimum 4-core processor for handling multiple threads efficiently.
Operating System: Linux, macOS, or Windows.
High-speed internet connection to ensure seamless data transfers and interaction with exchanges.
Support for CPU, GPU, and NPU:
- CPU: Required for core processing.
- GPU: Significantly accelerates model training and decision-making processes.
- NPU: Currently in experimental stages, benchmarks will be released soon.
SSD storage for fast data access and improved performance.
At least 600 GB bandwidth for network-intensive operations, especially when interacting with multiple exchanges and handling large amounts of data.

Note: The more computational resources available, the faster data processing, neural network training (RNN), and the runtime execution of trading operations will be. Utilizing GPUs can substantially boost training speed and decision-making, while NPU support is under testing and will be optimized based on upcoming benchmarks.

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Last updated 5 months ago

Introduction

STELS: This [DRAFT] version 0.29

The STELS platform is a Decentralized System Designed for Automating Digital Markets.

It provides tools for programming and training intelligent trading models. It supports both professional market participants and beginner traders, known as Cyber Traders in the STELS ecosystem.

The platform has been developed by leading experts in neural networks and automation, adhering to modern regulatory requirements.

Key Features of STELS:

Programming and training intelligent trading models.

Configuring Neural Networks for Collective Intelligence.

Support for both programmers and professionals in automation and Machine Learning.

Hybrid integration with cryptocurrency exchanges.

Support for up to 5000 exchange accounts (in the AMI Nominator version).

Creation and customization of private/public pools.

Marketplace for Trained Models and Dashboard Visualization Widgets.

Dynamic code generation for strategies.

Heterogeneous multi-layered private/public node network.

System Requirements

To effectively run the STELS platform, your system should meet the following requirements:

Minimum 4 GB RAM for basic operations.

Minimum 4-core processor for handling multiple threads efficiently.

Operating System: Linux, macOS, or Windows.

High-speed internet connection to ensure seamless data transfers and interaction with exchanges.

Support for CPU, GPU, and NPU:

CPU: Required for core processing.
GPU: Significantly accelerates model training and decision-making processes.
NPU: Currently in experimental stages, benchmarks will be released soon.

SSD storage for fast data access and improved performance.

At least 600 GB bandwidth for network-intensive operations, especially when interacting with multiple exchanges and handling large amounts of data.

Note: The more computational resources available, the faster data processing, neural network training (RNN), and the runtime execution of trading operations will be. Utilizing GPUs can substantially boost training speed and decision-making, while NPU support is under testing and will be optimized based on upcoming benchmarks.