Algorithmic crypto trading using information-driven bars, triple barrier labeling and deep learning.
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| Název: | Algorithmic crypto trading using information-driven bars, triple barrier labeling and deep learning. |
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| Autoři: | Grądzki, Przemysław, Wójcik, Piotr, Lessmann, Stefan |
| Zdroj: | Financial Innovation; 12/15/2025, Vol. 11 Issue 1, p1-43, 43p |
| Témata: | CRYPTOCURRENCIES, ALGORITHMIC trading (Securities), DEEP learning, STATISTICAL sampling, TRANSFORMER models, CUSUM technique |
| Abstrakt: | This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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