Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Profitable Strategy Design for Trades on Cryptocurrency Markets with
  Machine Learning Techniques   AI and data driven solutions have been applied to different fields and
achieve outperforming and promising results. In this research work we apply
k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers
for detecting the trend problem of three cryptocurrency markets. Our input data
includes price data and technical indicators. We use these classifiers to
design a strategy to trade in those markets. Our test results on unseen data
are very promising and show a great potential for this approach in helping
investors with an expert system to exploit the market and gain profit. Our
highest profit factor for an unseen 66 day span is 1.60 (60% profit). We also
discuss limitations of these approaches and their potential impact on Efficient
Market Hypothesis.

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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: alg-geom