Affiliation:
1. Vellore Institute of Technology, Chennai, India
Abstract
The cryptocurrency market is highly unpredictable, trading values for some tokens can encounter a swift spike up or downturn in a subject of minutes. As a consequence, traders are encountering hardship regarding all the negotiating price movements unless they are observing them manually. Our system aids to advance trading practices. Our system has IoT foundation. An Autonomous Crypto-stock alert system is a significant method, which shall help an investor to deal with various problems faced by them and would help them to be very observant on stock percentages. The device sends you updates straight from a crypto API. You can get alerts of all schemes and Stock or filter also sets alerts for a precise Coin/asset. We are working to accomplish this by using Bolt IOT & Ubuntu Terminal. This research paper allows us to integrate with the Bolt cloud and WIFI module. We will be using LED & Buzzers for the alerts with integration of Live API From Crypto compares website in Ubuntu Interface for conveying the message to users through our system for any target price fluctuations or an irregularity occurs.
Reference10 articles.
1. Y. Abu-Mostafa, “Learning from hints,” Journal of Complexity, Academic Press, vol. 10, pp. 165–178, 1994.
2. H. Akaike, “Fitting autoregressive models for prediction,” Ann. Inst. Statist. Math., vol. 21, pp. 243–247, 1969.
3. E. Fama, “Efficient capital markets: II,” Journal of Finance, vol. 46, no. 5, pp. 1575–1618, 1991.
4. A. Hoptroff, “The principles and practice of time series forecasting and business modeling using neural nets,” Neural Computing and Applications, vol. 1, no. 1, pp. 59–66, 1993.
5. W. Hsu, L. Hsu, and M. Tenorio, “Feature subset selection with application to financial prediction tasks,” in Financial Applications and Neural Networks, edited by A. Refenes, Cambridge Press, 1993.
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