  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.
