Neural Network-Based Algorithmic Trading Systems: Multi-Timeframe Analysis and High-Frequency Execution in Cryptocurrency Markets
By: Wěi Zhāng
Potential Business Impact:
Makes computers trade money super fast.
Plain English Summary
Imagine you could automatically buy and sell digital money like Bitcoin at the perfect moments to make a profit, even faster than a human can react. This new system uses smart computer programs that analyze lots of information about the market all at once, from price changes to how many people are buying or selling. This means it can make smart trading decisions in fractions of a second, potentially leading to more money for investors and a more efficient digital currency market.
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.
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