Predicting the trend of Bitcoin, a highly volatile cryptocurrency, remains a challenging task. Accurate forecasting holds immense potential for investors and market participants dealing with High Frequency Trading systems. The purpose of this study is to demonstrate the significance of using a systematic approach toward selecting informative observations for enhancing Bitcoin minute trend prediction. While a multitude of data collection methods exist, a crucial barrier remains: efficiently selecting the most informative data for building powerful prediction models. This study tackles this challenge head-on by introducing the Separation Index, a groundbreaking tool for fast and effective data (feature) subset selection. The Separation Index operates by measuring the improvement in class separability (i.e. upward vs. downward trends) with each added feature set. This innovative metric guides the creation of a highly informative dataset, maximizing the model's ability to differentiate between price movements. Our research demonstrates the effectiveness of this approach, achieving unprecedented accuracy in minute-scale Bitcoin trend prediction, surpassing the performance of previous studies. This significant advancement paves the way for a new era of data-driven decision-making in the dynamic world of cryptocurrency markets.

翻译：预测比特币这一高度波动的加密货币趋势仍是一项具有挑战性的任务。对于处理高频交易系统的投资者和市场参与者而言，精确预测蕴含着巨大潜力。本研究旨在证明采用系统化方法选择信息性观测值对于提升比特币分钟趋势预测的重要性。尽管存在多种数据采集方法，但一个关键障碍依然存在：如何高效选择最具信息量的数据以构建强大的预测模型。本研究通过引入分离指数这一开创性工具，直接应对这一挑战，实现快速有效的数据（特征）子集选择。分离指数通过度量每增加一个特征集时类别可分性（即上涨与下跌趋势）的提升程度来运作。这一创新指标指导构建高信息量的数据集，从而最大化模型区分价格走势的能力。我们的研究证明了该方法的有效性，在分钟级比特币趋势预测中达到了前所未有的准确度，超越了以往研究的性能。这一重要进展为加密货币市场动态世界中数据驱动决策的新时代铺平了道路。