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.

Stop-Loss strategies are often used by investors to combat negative returns by predetermining thresholds at which they should exit trades. Though existing traditional Stop-Loss mechanisms such as Fixed Stop-Loss and Trailing Stop-Loss are empirically proven to have the ability to minimize risks associated with trades, they still face serious challenges when it comes to achieving a balance between risk and return. In this study, we develop a Deep Learning model that combines the concept of Stop-Loss with the capabilities offered by Deep Neural Networks. The architecture is composed of three components where trend detection and price prediction components provide inputs to the stop price prediction component which predicts the variation of stop price. The study focuses on short term trades of minute frequency which involves analysing massive chunks of data that fluctuates rapidly within extremely short time intervals. Despite the advancements that has taken place in the context of Big Data analytics, intraday financial time series analysis has not received much academic attention. The model utilizes convolutional layers to capture spatial features along with Long Short Term Memory networks to capture temporal dependencies in price sequences. The proposed solution gives outstanding results for diverse market conditions and it works specifically well for trades that are downtrending. We evaluate our model for a portfolio that consists of five stock symbols from NASDAQ that are adequately liquid and two cryptocurrencies which are highly circulated. The model delivers a stable outcome across all symbols indicating its ability to be generalized over a range of symbols and its tolerance to diverse market conditions. Results also indicate that Stop-Loss mechanisms possess the potential to work well even in speculative markets such as the cryptocurrency market. The ability to reduce losses without compromising on opportunities to realize profits in dynamic and unstable market conditions is an important property of our Stop-Loss solution.