<p>Cryptocurrency trading is becoming increasingly popular worldwide, with many individuals seeking to maximize their profits. One approach they are exploring is following the actions of successful investors, automated bots, or whale traders. The insights that can help traders make better decisions may be uncovered by analyzing their behavior and impact. This study examines the effectiveness of this strategy and aims to understand how various types of traders affect the Bitcoin market, including fundamental aspects like price fluctuations over time. Additionally, we aim to identify patterns that regular traders can follow to enhance their chances of success in cryptocurrency trading. We employed a time-series forecasting method, which involves analyzing past price movements and other critical factors to predict future trends. To ensure the robustness and reliability of our findings, we utilized various advanced techniques, such as machine learning, deep learning, and traditional time-series forecasting models. These powerful tools enable us to make more accurate predictions and provide strong evidence for our research conclusions. The study demonstrates that some models, such as linear regression and random forest regression, did not perform well with features related to "whales," "bots," and "top traders." However, models like XGBoost Regression and Transformer showed positive effects. This suggests that, for now, traders should focus more on basic features like "open," "high," and "low" prices rather than these other factors. As advanced models like XGBoost and Transformer continue to develop, these features may become more important. While it is important to consider different features, relying on traditional indicators currently seems prudent.</p>