Cryptocurrency trading: a comprehensive survey

被引:183
作者
Fang, Fan [1 ,2 ]
Ventre, Carmine [1 ]
Basios, Michail [2 ]
Kanthan, Leslie [2 ]
Martinez-Rego, David [2 ]
Wu, Fan [2 ]
Li, Lingbo [2 ]
机构
[1] Kings Coll London, Dept Informat, Fac Nat Math & Engn Sci, London WC2R 2LS, England
[2] Turing Intelligence Technol Ltd, London, England
关键词
Trading; Cryptocurrency; Machine learning; Econometrics; NEURAL-NETWORKS; BITCOIN; VOLATILITY; RETURN; PORTFOLIOS; BUBBLES; DIVERSIFICATION; CONNECTEDNESS; PERSISTENCE; DOLLAR;
D O I
10.1186/s40854-021-00321-6
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
引用
收藏
页数:59
相关论文
共 302 条
[1]   ChainNet: Learning on Blockchain Graphs with Topological Features [J].
Abay, Nazmiye Ceren ;
Akcora, Cuneyt Gurcan ;
Gel, Yulia R. ;
Islambekov, Umar D. ;
Kantarcioglu, Murat ;
Tian, Yahui ;
Thuraisingham, Bhavani .
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), 2019, :952-957
[2]  
Adeyanju C., 2019, WHAT CRYPTO EXCHANGE
[3]  
Ahamad S., 2013, P 4 INT C ADV COMP S, P42
[4]   Why cryptocurrency markets are inefficient: The impact of liquidity and volatility [J].
Al-Yahyaee, Khamis Hamed ;
Mensi, Walid ;
Ko, Hee-Un ;
Yoon, Seong-Min ;
Kang, Sang Hoon .
NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE, 2020, 52
[5]   Anticipating Cryptocurrency Prices Using Machine Learning [J].
Alessandretti, Laura ;
ElBahrawy, Abeer ;
Aiello, Luca Maria ;
Baronchelli, Andrea .
COMPLEXITY, 2018,
[6]   A critical investigation of cryptocurrency data and analysis [J].
Alexander, C. ;
Dakos, M. .
QUANTITATIVE FINANCE, 2020, 20 (02) :173-188
[7]  
[Anonymous], 2021, FORBES MORE 600 MILL
[8]  
[Anonymous], 2020, COINBASE OUR PATH LI
[9]  
[Anonymous], 2020, BLACKBIRD BLACKBIRD
[10]  
[Anonymous], 2020, CBOE CBOE PRODUCTS