CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction
CryptoMamba is a novel Mamba-based state space model that improves Bitcoin price prediction and enhances trading performance over traditional methods.
What it examines
The study tackles Bitcoin price prediction challenges by introducing CryptoMamba, a novel Mamba-based state space model architecture designed to capture long-range dependencies and market volatility. Incorporating trading volume and advanced trading algorithms, CryptoMamba outperforms traditional methods like ARIMA, GARCH, and LSTMs, offering improved predictive accuracy and robust market insights.
What it concludes
CryptoMamba predicts Bitcoin prices effectively using a Mamba-based state space model that captures long-term dependencies and volatility. It outperforms LSTMs and S-Mamba in accuracy and trading returns. The approach applies to other financial assets, and future work may extend its use to multi-asset portfolios with enhanced risk management.
Evidence objects
CryptoMamba introduces a novel Mamba-based model that predicts Bitcoin prices, outperforming traditional approaches such as LSTM, Bi-LSTM, GRU, and earlier state space models while naturally adapting during abrupt regime shifts.
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Integrating trading volume data, CryptoMamba employs a hierarchical architecture featuring multiple C-Blocks, CMBlocks, and an innovative final Merge block, significantly enhancing prediction performance and generalization across diverse, volatile market conditions.
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Efficiently using $136k$ parameters, the model remarkably achieves top regression scores and trading success with Vanilla and Smart algorithms, while pioneering context-aware input-dependent dynamics and advocating enhanced risk management integration.
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Utilizing a novel Mamba-based state space model, the paper transforms Bitcoin price prediction by addressing non-linear dynamics and regime shifts. Its originality integrates forecasting with practical trading algorithms, while its novelty adapts techniques from NLP and computer vision, rendering it a compelling contribution to academic research and quantitative finance applications.
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Raw abstract and provenance
- … model specifically designed for time series forecasting. S-… for high-dimensional time series forecasting tasks. These … for financial applications like Bitcoin price forecasting. …
Source row: 485 · abstract type: snippet