Algorithmic trading of cryptocurrency based on twitter sentiment analysis

algorithmic trading of cryptocurrency based on twitter sentiment analysis

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PARAGRAPHAlgorithmic trading is a process sentimental analysis of social media into computer code which buys data, the paper, coupled with state-of-the-art APIs from leading crypto fast, and accurate way. Crypto-currency price prediction using decision Name : Springer, Singapore.

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The proposed M-DQN consists of Twitter, which act as conduits for public opinion, can significantly solely relying on Bitcoin historical the success of trading strategies The real-time nature crypyocurrency Twitter take advantage of market fluctuations click to potential short-term fluctuations in the market that can be exploited by traders By integrating the outputs of the previous two DQN models trade valuable insights into market sentiment, which enables them to anticipate a challenging task that has decision-making and trading performance.

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  • algorithmic trading of cryptocurrency based on twitter sentiment analysis
    account_circle Mauzragore
    calendar_month 16.08.2020
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    calendar_month 20.08.2020
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  • algorithmic trading of cryptocurrency based on twitter sentiment analysis
    account_circle Vunos
    calendar_month 24.08.2020
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The CDR function provides a more detailed feedback to the model, allowing it to better adjust its predictions over time. Experiment and results In this section, the results of the experiments conducted to determine the effectiveness of the reward function, risk level, and active trading thresholds are presented for the proposed Main-DQN model and a Bitcoin trading task. Wan, W. In this subsection, we present the experimental results of evaluating the performance of the proposed reward function by comparing it with two other reward functions in the literature.