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    Intelligent price alert system for digital assets - cryptocurrencies

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    Authors
    Chhem, Sronglong
    Anjum, Ashiq cc
    Arshad, Bilal
    Affiliation
    University of Derby
    Issue Date
    2019-12
    
    Metadata
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    Abstract
    Cryptocurrency market is very volatile, trading prices for some tokens can experience a sudden spike up or downturn in a matter of minutes. As a result, traders are facing difficulty following with all the trading price movements unless they are monitoring them manually. Hence, we propose a real-time alert system for monitoring those trading prices, sending notifications to users if any target prices match or an anomaly occurs. We adopt a streaming platform as the backbone of our system. It can handle thousands of messages per second with low latency rate at an average of 19 seconds on our testing environment. Long-Short-Term-Memory (LSTM) model is used as an anomaly detector. We compare the impact of five different data normalisation approaches with LSTM model on Bitcoin price dataset. The result shows that decimal scaling produces only Mean Absolute Percentage Error (MAPE) of 8.4 per cent prediction error rate on daily price data, which is the best performance achieved compared to other observed methods. However, with one-minute price dataset, our model produces higher prediction error making it impractical to distinguish between normal and anomaly points of price movement.
    Citation
    Chhem, S., Anjum, A. and Arshad, B., (2019). 'Intelligent Price Alert System for Digital Assets-Cryptocurrencies'. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 109-115.
    Publisher
    ACM Press
    Journal
    UCC '19 Companion: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion
    URI
    http://hdl.handle.net/10545/625448
    DOI
    10.1145/3368235.3368874
    Additional Links
    https://dl.acm.org/doi/abs/10.1145/3368235.3368874
    Type
    Meetings and Proceedings
    Language
    en
    ISSN
    9781450370448
    ae974a485f413a2113503eed53cd6c53
    10.1145/3368235.3368874
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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