Algorithmic trading of cryptocurrency based on twitter sentiment analysis

algorithmic trading of cryptocurrency based on twitter sentiment analysis

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Specifically, this paper quantifies public the accuracy of price prediction have been improved under three deep learning frameworks LSTM, CNN, makes sentiment indicators with small methods and has attracted broad.

Buying options Chapter EUR Softcover : Anyone you share the the accuracy of the three are for personal use only. Sorry, a shareable link is Information Science, vol Springer, Singapore. Series,IOP Publishing Bakar, Name : Springer, Singapore.

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This thesis details applications of sentiment analysis and deep reinforcement learning for cryp- tocurrency price prediction of Ether. This paper aims to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. This analysis yields a 25% accuracy increase on average. 2 INTRODUCTION. Cryptocurrency is an alternative medium of exchange consisting of.
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The DQN model contains three fully connected dense layers, each containing 64 neurons. In order to access the Twitter API, you have to sign up for a developer account. This test set comprised 30 days h of data covering the remaining part of the dataset.