Intelligent Resource Allocation in Cryptocurrency Trading Systems Through Attention-Based Volatility Filtering

Abstract

The continuous operation of cryptocurrency markets generates massive data streams that challenge real-time trading systems. Traditional approaches process every price update equally, leading to substantial computational waste during routine market periods. This study introduces an attention-based mechanism that intelligently filters market activity, triggering predictions only when volatility exceeds dynamically adjusted thresholds. We implemented this approach within a Bidirectional Long Short-Term Memory framework and tested it across Bitcoin, Ethereum, XRP, Cardano, and Solana over a 24-hour monitoring period following extensive training on historical data from 2022 to 2025. Our findings demonstrate that selective processing reduces computational requirements by approximately 72% while maintaining prediction accuracy within 0.1 percentage points of continuous processing approaches. The system generated predictions during only 28% of monitored periods on average, yet achieved Root Mean Square Errors ranging from 0.8% to 2.1% across different cryptocurrencies. Confidence scoring proved well-calibrated, with predicted confidence levels matching actual accuracy within 1.2 percentage points. Notably, the system correctly identified stable market conditions, issuing "Hold" recommendations with 99% confidence when price movements fell within normal variance bands. Alert delivery consistently occurred within 10 seconds of significant market events, enabling timely trading decisions. The dynamic threshold adjustment successfully adapted to varying volatility regimes, preventing false triggers during high-volatility periods while maintaining sensitivity during stable conditions. These results suggest that attention-based filtering offers a practical solution for multi-cryptocurrency monitoring on standard computing hardware.