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Machine learning models show promise for Bitcoin trading after costs

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning models show promise for Bitcoin trading after costs

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

  1. arXiv cs.LG TIER_1 English(EN) · Andrei Bysik, Robert \'Slepaczuk ·

    Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting

    arXiv:2606.00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs. Using approximately 70,000 hourly observations from 2…

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