Machine learning models show promise for Bitcoin trading after costs

A new research paper explores the use of machine learning models, including XGBoost, LSTM, and iTransformer, for predicting Bitcoin returns. The study found that while these models can generate positive gross trading performance, profitability is lost once transaction costs are considered. However, a cost-aware execution filter that only trades when forecast magnitude exceeds a threshold restored profitability in some configurations, with one XGBoost strategy yielding over 65% annualized returns. AI

IMPACT Demonstrates potential for ML models in financial markets, but highlights transaction costs as a key challenge for profitability.

RANK_REASON Academic paper detailing a new methodology for financial forecasting. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Bitcoin
• BTC-USDT
• iTransformer
• LSTM
• Robert Ślepaczuk
• XGBoost

• paper
• other

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