Abstract
The cryptocurrency and stock markets are dynamic environments that attract traders, seeking to enhance their investment returns. In cryptocurrency trading, there is a pullback in investors from trading due to recent market crashes, losses, and bankruptcies. For anticipating future market behavior, algorithmic trading has gained popularity due to its ability to provide consistent and accurate price and volatility predictions. Specifically, the bottom turning points of the market are where an investor can use to enter the market. Hence, identifying market turning points, particularly market bottoms, is vital in timing trading strategies for a maximum profit. This study introduces a novel and ground-breaking approach to market forecasting that focuses on identifying market bottoms, particularly in the domain of cryptocurrency trading. The study utilizes a Wasserstein Generative Adversarial Network (WGAN) with Gated Recurrent Unit (GRU) to identify future market trends effectively. A classifier is added into the model as a substantial contribution to forecast future market bottoms by utilizing hidden WGAN features. The research findings indicate that the combination of the price prediction and bottom classification models provides outperforming results in terms of prediction accuracy. In addition, the suitability of the proposed solution for locating stock market bottoms has been evaluated.