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Cryptocurrency Trading: A Comprehensive Survey

Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, David Martinez-Regoband, Fan Wu

arXiv:2003.11352v5q-fin.TRq-fin.CP

TL;DR

Cryptocurrency trading research spans a rapidly emerging field whose asset behavior remains distinct from traditional assets and insufficiently understood. The paper surveys 146 studies, analyzes their research distributions, datasets, trends, and technologies, and identifies open research opportunities. Its conclusion is a comprehensive overview of the field alongside future directions, with machine-learning validation and market-condition analysis highlighted among the research considerations.

  • Problem

    Cryptocurrencies have distinct and still incompletely understood asset behavior, motivating a synthesis of research on trading platforms, signals, strategies, and risk management.

  • Method

    The paper conducts a comprehensive survey of cryptocurrency trading research, analyzing papers, research distributions, datasets, technologies, trends, and opportunities.

  • Results

    146 papers were collected across six cryptocurrency trading research areas.

  • Takeaways & Limitations

    The survey presents a nomenclature and current state of the art while identifying challenges and promising future research directions in cryptocurrency trading.

  • Takeaways & Limitations

    The survey notes that existing work mainly contrasts long- and short-term trading, leaving areas such as signal extraction, portfolio management, crashes, and derivative pricing open.

Abstract

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In recent years, the tendency of the number of financial institutions including 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 conditions, 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.

1 Introduction

The paper surveys cryptocurrency trading research across disciplines, organizing 146 papers by trading aspects, research properties, technologies, datasets, trends, and opportunities. It frames cryptocurrency trading as an emerging research area with most publications appearing since 2018.

  • The survey defines cryptocurrency trading as studies aimed at facilitating and building strategies to trade cryptocurrencies.
  • Over 85% of cryptocurrency trading papers appeared since 2018, marking the field’s emergence as a new financial-trading research area.
  • The literature is organized into six areas: trading software systems, systematic trading, emergent technologies, portfolios and assets, market conditions, and miscellaneous research.
  • 146 papers were surveyed across cryptocurrency trading research, spanning finance, economics, artificial intelligence, and computer science.
  • The paper analyzes research distributions, datasets, trends, and technologies while identifying challenges and promising directions for future cryptocurrency trading research.

2 Cryptocurrency Trading

This section introduces cryptocurrency trading through its enabling blockchain technology, cryptocurrency markets, and trading strategies.

  • The section provides an introduction to cryptocurrency trading by discussing blockchain, cryptocurrency markets, and cryptocurrency trading strategies.

2.1 Blockchain

Blockchain is presented as the distributed-ledger infrastructure supporting cryptocurrency transactions. Transactions are signed, broadcast, verified, grouped into blocks, and confirmed as subsequent blocks are added.

  • Blockchain is an immutable, timestamped series of cryptographically linked data records managed by a distributed cluster rather than one entity.
  • A Bitcoin transaction is signed with the sender’s private key, broadcast to the peer-to-peer network, and checked using the signer’s public key.
  • Valid transactions are added to the chain, and later blocks provide confirmations based on the number of blocks added afterward.
  • Miners confirm transactions by combining transactions with the preceding block’s hash, storing the derived hash in the current block, and broadcasting the result.

2.2 Introduction of cryptocurrency market

Cryptocurrencies are decentralized digital assets secured by cryptography and blockchain technology, with rapidly expanding and volatile markets served by exchanges that facilitate trading.

  • Cryptocurrencies use cryptographic functions and blockchain technology to provide decentralization, transparency, and immutability.
  • Cryptocurrencies are described as the first pure digital assets, beginning with Bitcoin in 2009.
  • 4,950 cryptocurrencies and 20,325 cryptocurrency markets existed on December 20, 2019, with market capitalization around $190 billion.
  • The market experienced exponential growth in 2017, a large bubble burst in early 2018, dramatic growth in 2020, and historically high volatility in 2021.
  • Bitcoin and Ethereum accounted for the majority of total cryptocurrency market capitalization on September 14, 2021.
  • Cryptocurrency exchanges let customers trade cryptocurrencies as market makers using bid-ask spreads or matching platforms charging fees.

2.3 Cryptocurrency Trading

Cryptocurrency trading is buying and selling cryptocurrencies for profit, conducted through varied transaction modes in a market offering continuous access but also scalability, cybersecurity, and regulatory challenges.

  • Cryptocurrency trading is the act of buying and selling cryptocurrencies with the intention of making a profit.
  • The trading concept comprises the traded cryptocurrency, transaction operation mode, and trading strategy.
  • Cryptocurrency trading offers rapid intraday price fluctuations that can create earning opportunities but also increase risk.
  • The cryptocurrency market operates 24 hours a day, 7 days a week because it is decentralized.
  • Scalability constraints have caused transaction backlogs, including a multi-day backlog in March 2020 affecting transfers to exchanges.
  • Cryptocurrency trading faces cybersecurity breaches and diverse regulatory systems whose associated risks remain partly unknown.

3 Cryptocurrency Trading Strategy

The survey organizes cryptocurrency trading strategies across technical, fundamental, programmatic, systematic, econometric, machine-learning, portfolio, and market-condition research, emphasizing prediction, automation, and risk control.

  • Trading strategy categories: Cryptocurrency trading strategies are broadly divided into technical and fundamental approaches, with programmatic trading receiving increasing attention.
  • Trading strategy categories: Programmatic trading uses exchange trading activity to make buying or selling decisions, and its use is favored by cryptocurrency market characteristics such as arbitrage opportunities and high fluctuation.
  • Trading systems: Cryptocurrency trading systems use pre-programmed principles and procedures to execute trades and address manipulation, cybercrime, and transaction delays.
  • Systematic trading: Systematic trading defines trading goals, risk controls, and rules, including technical analysis, pairs trading, high-frequency trading, and trend tracking.
  • Econometrics and machine learning: Econometric research applies time-series models such as GARCH and BEKK to evaluate cryptocurrency fluctuations.
  • Econometrics and machine learning: Machine learning strategies combine input features with an objective function and require validation because generalization error is important in financial applications.
  • Portfolios and market conditions: Portfolio research uses diversification and strategic asset allocation to seek higher returns for a given risk level, while market-condition research examines bubbles and extreme volatility.

4 Paper Collection and Review Schema

The survey uses a bottom-up review schema covering cryptocurrency trading systems, predictive models, signals, trading methods, market conditions, portfolios, and risk management. Papers were collected through keyword searches and snowballing, yielding 146 studies whose publication venues, research distributions, datasets, trends, and future opportunities were analyzed.

  • Review approach: The review follows a bottom-up structure from trading systems and predictive models to trading signals, technical methods, market conditions, portfolios, and risk management.The paper organizes collected research across six angles and analyzes properties and technologies.
  • Review approach: The collection criteria required papers to discuss cryptocurrency trading, target trading efficiency or accuracy, or compare cryptocurrency trading approaches.These criteria define the scope of the paper collection.
  • Paper collection: The survey is broader than earlier brief surveys because it includes newer cryptocurrency trading research in a fast-moving field.Earlier work covered cryptocurrencies, cryptocurrency systems, or trading opportunities more narrowly.
  • Paper collection: Keyword searches and snowballing resulted in 146 papers across six cryptocurrency trading research areas.The searches used Google Scholar and arXiv, with snowballing continued until closure.
  • Collection analysis: 48.63% of collected papers appeared in Finance and Economics venues, while the remainder spanned intelligent engineering, AI, physics, other venues, and arXiv.The venue distribution indicates finance is the largest publication area, alongside substantial disciplinary diversity.
  • Collection analysis: The review analyzes research properties, technologies, datasets, research timelines, and opportunities for future cryptocurrency trading research.Its organization is presented through the review schema and subsequent statistical analysis.

5 Cryptocurrency Trading Software Systems

The survey covers cryptocurrency trading software ranging from exchange APIs and backtesting platforms to real-time, automated, technical-analysis, trend-following, and arbitrage systems. Reported examples show a trade-off between high-return, high-risk trend trading and lower-risk arbitrage, while both can capture alpha.

  • Trading platforms: Existing cryptocurrency trading software includes proprietary and open-source platforms for exchange access, strategy creation, backtesting, execution, data management, and risk controls.Examples include Capfolio, 3 Commas, CCXT, StockSharp, and Freqtrade.
  • Trading platforms: CCXT provides a unified API, normalized data options, and access to many cryptocurrency exchange markets for strategy development and cross-exchange analysis.It supports public and private APIs and can facilitate arbitrage analysis.
  • Trading platforms: CryptoSignal tracks more than 500 coins and generates technical-analysis alerts through channels including email, Slack, and Telegram.Its technical indicators include momentum, RSI, Ichimoku Cloud, and MACD.
  • Real-time systems: Real-time cryptocurrency trading systems connect clients, servers, databases, and market-data APIs to support buying, selling, and storage of balances, trades, and order-book information.One reported experiment tested such a system using Coinmarket API data.
  • Trend-following systems: 114.41% average net profit margin and 52.75% average profitability were reported for Extended Turtle Trading, compared with 18.59% and 35.94% for Original Turtle Trading.The comparison used eight prominent cryptocurrencies over nearly one year, with 41 and 87 trades respectively.
  • Arbitrage systems: Arbitrage software identified a BTG-BTC signal with profit up to 495.44% across Cryptopia and Binance exchanges.The system searched 787 cryptocurrencies across seven exchanges and listed ten signals from 186 found opportunities.
  • System characteristics: Turtle trading produced high returns with high risk, whereas arbitrage produced lower revenue with lower risk; both systems performed well in capturing alpha.The survey presents this as a contrast in profit and risk behavior.

6 Systematic Trading

Systematic cryptocurrency trading research examines technical patterns, indicator-based rules, genetic programming, market efficiency, pairs trading, and informed trading. Reported findings include excess returns, long-term memory, time-varying inefficiency, mean-reversion profits, and order-flow evidence around large events.

  • Technical Analysis: Five classical technical strategies use chart patterns and indicators to generate cryptocurrency trading signals, including breakouts, reversals, and bottom selection.Examples include Turtle Soup, Nem, Amazing Gann Box, Busted Double Top, and Bottom Rotation Trading.
  • Technical Analysis: Genetic programming successfully found attractive cryptocurrency technical patterns using more than 12 indicators and measures of gain, matching, market pressure, and diversity.Moving Average and Stochastic oscillator indicators were among those used.
  • Technical Analysis: 8.76% annualised excess return was produced by technical trading rules excluding Bitcoin after controlling for average market returns.The study used daily data for the 11 most traded cryptocurrencies from 2016 to 2018 and also suggested market inefficiency.
  • Technical Analysis: All examined cryptocurrency markets showed long-term memory and multiple fractals, while market inefficiency varied over time.The analysis used Hurst exponents, time-rolling MF-DFA, and quantile regression; high liquidity with low volatility was linked to arbitrage opportunities.
  • Pairs Trading: Pairs trading applies mean-reversion by first identifying stable long-run pairs and then estimating their equilibrium relationship.The reviewed approaches include cointegration, stochastic control, dynamic weights, and time-varying volatility.
  • Pairs Trading: 3% monthly profit was achieved in Miroslav’s cryptocurrency pairs-trading experiments.Other reviewed work found 31 significantly cointegrated pairs and attributed profitability to arbitrage opportunities.
  • Other systematic methods: Order-size imbalances in buyer- and seller-initiated Bitcoin orders provided evidence of informed trading before large positive or negative events.The study used USD/BTC exchange-rate trading data and a volume-imbalance-inspired indicator.

7 Emergent Trading Technologies

The survey covers econometric and machine-learning technologies for cryptocurrency trading, spanning volatility modeling, prediction, trading signals, and market analysis. Statistical methods remain prominent, while deep-learning models are widely applied to return and price prediction.

  • Econometrics on cryptocurrency: Econometric studies apply GARCH, BEKK, copula-based causality, long-memory, and regime-switching methods to cryptocurrency returns, volatility, dependence, and risk.These approaches examine volatility components, causal dependence, structural changes, volatility persistence, spillovers, and Value-at-Risk forecasting.
  • Econometrics on cryptocurrency: MSGARCH models clearly outperform single-regime GARCH for Value-at-Risk forecasting.
  • Machine learning technology: Deep-learning research commonly uses CNN, RNN, GRU, MLP, and LSTM architectures, with LSTM/RNN-based methods especially prevalent in cryptocurrency trading.The survey distinguishes classification, clustering, regression, reinforcement learning, and deep-learning applications.
  • Trading signals: Technical indicators, price, volume, news, social media, and community activity are used to generate cryptocurrency trading signals and predict market movements.ANNs predict intraday up-or-down trends from 15-minute price and volume data, while sentiment models use news, Twitter, Google Trends, and Telegram data.
  • Research on Machine Learning Models: Seq2seq improves over ARIMA for Bitcoin-USD prediction but performs very poorly in extreme cases.
  • Deep Learning Algorithms: CNN-LSTM achieves the highest prediction accuracy among the compared MLP, CNN, RNN, LSTM, and CNN-RNN models.

8 Portfolio, Cryptocurrency Assets and Market Condition Research

Research on crypto-asset portfolios examines connectedness, diversification, pricing, hedging, risk measurement, bubbles, crashes, and extreme market conditions. Findings document both portfolio benefits and substantial dependence, volatility, and tail-risk concerns.

  • Market connectedness: Bitcoin was dominant in the studied correlation dynamics, with exchange-rate movements at least as influential as US-dollar dynamics.
  • Crypto-asset Portfolio Research: Combining cryptocurrencies expands the set of low-risk investment opportunities, while crypto-assets can also increase portfolio risk exposure.
  • Risk management: Vine-copula and robust-volatility methods estimate cryptocurrency portfolio VaR and Expected Shortfall, while Cauchy-distribution methods provide analytical VaR measures.
  • Bubbles and extreme conditions: Higher volatility and trading volume are positively associated with cryptocurrency bubble presence.
  • Bubbles and extreme conditions: Negative news during the 2017 boom period for Litecoin and Ripple incurred a risk premium that could explain cryptocurrency returns during the 2018 crash.
  • Extreme condition: Cryptocurrencies exhibit heavier tails and asymmetric extreme dependence between returns and trading volumes.
  • Extreme condition: After the 2017/18 crash, Bitcoin exhibited greater shock persistence and evidence of non-mean reversion, implying chances of further price falls.

9 Others related to Cryptocurrency Trading

Other cryptocurrency-trading research addresses market behavior, herding, stablecoin-related price dynamics, regulation, benchmarks, data mining, exchanges, and practical trading processes. These studies extend beyond predictive models and portfolio construction.

  • Market behaviour: Both examined studies found significant evidence of herding in the cryptocurrency market.
  • Market behaviour: Blockchain analysis found Tether purchases timed after market downturns and followed by sizeable increases in Bitcoin prices.
  • Other research: The surveyed literature also includes benchmarks, regulatory frameworks, data mining, efficient-market analysis, decentralized exchanges, and artificial financial markets.
  • Regulatory mechanisms: Regulatory-event analysis found no systematic evidence that regulatory measures caused traders to flee or enter affected regional jurisdictions.
  • Trading processes: Introductory studies explain cryptocurrency and blockchain basics, profitable-trend identification, exchange platforms such as GDAX and Coinbase, and crypto-wallet use.

10 Summary Analysis of Literature Review

The survey analyzes research chronology, topical distribution, methods, technologies, and datasets across cryptocurrency-trading literature. The literature is recent, return-prediction focused, statistically dominated, and increasingly attentive to machine learning.

  • Research distribution among properties: Return prediction accounts for 37.67% of papers, while bubbles and extreme conditions and pair or portfolio relationships occupy another roughly one-third.
  • Research distribution among categories and technologies: 102 of 146 papers (69.86%) cover statistical methods or machine-learning categories; among these, 88 use statistical methods and 13.72% study machine learning.
  • Research distribution among categories and technologies: Basic regression and time-series analysis are the most commonly used statistical methods.
  • Research distribution among categories and technologies: Machine-learning papers account for 13.7% of the total, with LSTM, RNN, and GRU methods the most popular within that subfield.
  • Datasets: Representative datasets include exchange price, volume, and order-level data; sentiment datasets combine market data with labeled media or Internet data.
  • Datasets: Less than half of cryptocurrency papers published since January 2017 employ correct data.

11 Opportunities in Cryptocurrency Trading

Future cryptocurrency-trading research opportunities span richer sentiment inputs, broader empirical settings, market relationships, bubble dynamics, and new modelling approaches.

  • Sentiment-based research: Sentiment research could expand media inputs, strengthen text preprocessing and labelling, extend holding periods, incorporate transaction fees, and examine opinion dynamics and user reputation.The survey identifies video sources, more robust preprocessing, neural-network label training, longer holding periods, transaction fees, opinion dynamics, and user reputation as possible directions.
  • Long-and-short term trading research: Long- and short-horizon trading differ in risk control, trend tracing, market noise, and transaction-time pressures.The survey also points to trading signals, time-series research, portfolio management, crash relationships, and cryptocurrency derivative pricing as open topics.
  • Correlation between cryptocurrency and others: Further research could examine cryptocurrency correlations with other assets, including principal components and relationships during financial collapse.The survey states that cryptocurrency–asset correlations still require further research.
  • Bubbles and crash research: Bubble and crash research could connect bubble formation to financial collapse and aftermath, while applying microeconomic, physical, industrial, and supply-and-demand models.Examples include coherent formation-to-burst analysis, microeconomic bubble theory, Omori law, and supply-and-demand simulations.
  • Game theory and agent-based analysis: Game theory and agent-based modelling are identified as promising approaches for cryptocurrency-market trading research.The survey frames these methods as established research directions in traditional financial markets that could be applied to cryptocurrency trading.
  • Public nature of Blockchain technology: Blockchain transaction networks and user identification may provide new features for price prediction and improve understanding of cryptocurrency financial bubbles.The survey links growing attention to transaction-network formation, prices, and user identification with these potential applications.
  • Balance between the opening of trading research literature and the fading of alphas: Cryptocurrency research must account for a possible tension between open publication of trading findings and diminishing market alphas as investors learn about mispricing.The survey suggests exploring pricing methods that incorporate real-time market changes and increasing proportions of informed traders.

12 Conclusions

The survey provides a comprehensive overview of cryptocurrency-trading research and synthesizes its literature, datasets, distributions, trends, and opportunities. It is intended as a quick resource for finance researchers and quantitative traders and as a stimulus for further work.

  • 12 Conclusions: The paper surveys 146 cryptocurrency-trading papers and analyses research distributions, datasets, trends, and future opportunities.It presents a nomenclature and current state of the art across properties and categories or technologies.
  • 12 Conclusions: The survey offers academics and quantitative traders a quick way to become familiar with cryptocurrency-trading literature.It is also intended to motivate contributions to pressing problems identified by the survey.
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