With the worldwide spread of cryptocurrencies, data has become richer and more heterogeneous than before and, as a result, poses a new problem related to prediction and decision making. Although there has been recent increase in acceptance of deep learning (DL) technologies, the current analytical models appear to have limited correspondence with cryptocurrency markets. This work directly addresses price forecasting, fraud detection, sentiment analysis, and risk management, thus illustrating the applicability of the current technologies. We implement complex and novel deep learning techniques such as long short-term memory, gated recurrent unit, Bidirectional-LSTM to explore the temporal and spatial behaviour of data associated with cryptocurrencies. These models enable accurate predictions of price and market trends along with reinforcement strategies to refine trading strategies and generative models to assess the market and project what the future might require. This is used to help investors and traders spot patterns in the buying and selling of various crypto currencies, these models might have far-reaching effects on the economy.

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