Abstract:

Cryptocurrency has become an increasingly popular investment asset due to its decentralized nature and potential for high returns. However, the volatility and unpredictability of cryptocurrency prices present a challenge for investors in making informed decisions. In this research paper, we propose the use of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models to predict cryptocurrency prices and the development of a Django website for simulating cryptocurrency trading. We compare the performance of the two models and evaluate their accuracy in predicting cryptocurrency prices. The proposed website will allow users to simulate cryptocurrency trading based on the predicted prices generated by the ANN and LSTM models. The website will provide a simulated portfolio of various cryptocurrencies, enable users to visualize the performance of their portfolio over time, and make transactions based on the predicted prices. The website will also provide updates on cryptocurrency prices and enable users to test their investment strategies without risking actual funds. This research contributes to the existing literature on cryptocurrency trading and prediction and provides valuable insights into the performance of the ANN and LSTM models.

Description:

ANN, LSTM, Cryptocurrency price prediction, Django application