arXiv Machine Learning June 2, 2026 By Andrei Bysik, Robert \'Slepaczuk

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 20

CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.

By Andrei Neagu, Eeham Khan, Leila Kosseim

llms agents reinforcement-learning

arXiv AI

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MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

arXiv:2608. 17342v1 Announce Type: cross Abstract: Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies.

By Bowen Liu, Mingming Sun

benchmarks

arXiv Machine Learning

Jul 28

Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

arXiv:2607. 23370v1 Announce Type: new Abstract: Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance.

By Muhammad Abdullah Haroon

rag multimodal benchmarks

arXiv Machine Learning

Jun 4

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.

By Damian Lebied\'z, Robert \'Slepaczuk

agents reinforcement-learning

arXiv Machine Learning

Jul 21

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets

arXiv:2607. 17555v1 Announce Type: new Abstract: Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance.

By Maorufa Zaman, Haris Md Sahed

Hugging Face Trending Papers

Jul 20

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets

Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations.

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CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets

Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets