Neural Network-Based Algorithmic Trading Systems: Multi-Timeframe Analysis and High-Frequency Execution in Cryptocurrency Markets

TL;DR

This paper presents a neural network-based trading system for cryptocurrencies that combines multi-timeframe analysis and high-frequency prediction to achieve positive risk-adjusted returns by integrating diverse data sources for rapid decision-making.

Contribution

It introduces a novel neural network framework that effectively captures cross-timeframe relationships and integrates multiple data sources for high-frequency cryptocurrency trading.

Findings

Achieved positive risk-adjusted returns in cryptocurrency trading.

Effectively captures cross-timeframe relationships with neural networks.

Enables sub-second trading decisions with statistical confidence.

Abstract

This paper explores neural network-based approaches for algorithmic trading in cryptocurrency markets. Our approach combines multi-timeframe trend analysis with high-frequency direction prediction networks, achieving positive risk-adjusted returns through statistical modeling and systematic market exploitation. The system integrates diverse data sources including market data, on-chain metrics, and orderbook dynamics, translating these into unified buy/sell pressure signals. We demonstrate how machine learning models can effectively capture cross-timeframe relationships, enabling sub-second trading decisions with statistical confidence.