ABSTRACT
The development of the fintech industry has transformed cryptocurrencies into intangible assets and opened many opportunities in the fields of financial research and quantitative markets. Cryptocurrencies are a type of electronic currency used to conduct transactions in the financial system. In addition to the technical analysis that a trader typically does, it has been established over time that market mood is extremely important in determining market conditions. This document provides a method for estimating cryptocurrency prices based on historical data and user sentiment. To achieve this, a long short-term memory (LSTM) model and sentiment analysis of tweets were used. Furthermore, it was supported by the outcomes, as the LSTM model demonstrated a precision of 69.32%, which is respectable when it comes to the forecasting of financially risky assets like bitcoin. The final accuracy attained was 70%, indicating that the model will accurately recommend buying or selling in about 3 out of every 4 scenarios that it is presented with. Traders can achieve a high alpha with a risk reward ration of 1:2 to benefit from this research finding and can combine the findings with technical indicators to produce better trades. This research has a very large application in the field of quant trading. Findings in this research can be used to build multiple models with multiple attributes which will improve the overall accuracy and precision of trades.
