This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
Study high-frequency trading patterns in cryptocurrencies.
problem Understanding automated trading algorithms in cryptocurrency markets.
method Analyzes intraday trading data of cryptocurrencies, focusing on returns, volumes, and volatility.
result Provides insights into predictability of economic value in cryptocurrency markets.
RL enhances cryptocurrency trading profits.
problem Enhancing cryptocurrency trading profits through dynamic scaling.
method Combining RL with pair trading, using new reward shaping and observation/action spaces.
result RL-based trading achieved 9.94% to 31.53% annualized profits, vs. 8.33% for traditional methods.
Novel OTT method for cryptocurrency trading offers high annualized profit.
problem Quantifying and exploiting trading opportunities in cryptocurrency markets.
method Bi-objective convex optimization for balancing profit and risk.
result Annualized profit of 15.49% in cryptocurrency market from 2020 to 2022.
Develops an LLM-based agent for superior cryptocurrency trading.
problem Lack of LLMs in cryptocurrency trading due to its unique data types.
method Combines on-chain and off-chain data analysis with a reflective mechanism.
result Demonstrates superior performance in maximizing returns compared to traditional strategies.
Novel pairs trading strategy for cointegrated cryptocurrencies using copulas.
problem Identifying profitable trading opportunities in cointegrated cryptocurrency pairs.
method Linear and non-linear cointegration tests, correlation coefficient, copula families, back-testing.
result The strategy outperforms buy-and-hold trading strategies in profitability and risk-adjusted returns.
Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryp…
Study uses RNN for real-time crypto price prediction and trading optimization.
problem High volatility in cryptocurrency markets makes traditional forecasting models unreliable.
method Data collection, preprocessing, model refinement, and backtesting.
result Improved accuracy in real-time crypto price prediction and optimized trading strategies.
The paper tackles backtest overfitting in cryptocurrency trading using deep reinforcement learning.
problem Backtest overfitting in deep reinforcement learning for cryptocurrency trading.
method Formulated hypothesis test for overfitting detection, trained agents, estimated overfitting probability, and rejected overfitted agents.
result Less overfitted deep reinforcement learning agents outperformed more overfitted agents and market benchmarks.
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
problem The influence of Twitter on cryptocurrency pump-and-dump events.
method Analysis of abnormal returns, trading volume, and tweet activity.
result Investors relying on Twitter information sell later, leading to significant losses.
New blockchain metrics improve cryptocurrency trading and prediction.
problem Improving trading and prediction in the volatile cryptocurrency market.
method Developed blockchain metrics based on public data from Bitcoin mining nodes.
result Blockchain metrics provide statistical advantage in trading Bitcoin assets.
The paper analyzes cryptocurrency trading networks using pairwise and high-order dependencies.
problem Understanding information flows and dependencies in cryptocurrency markets.
method Defined a cryptocurrency trading network using weekly log returns, analyzed using Granger causality and O-information.
result High-order dependencies reveal that stable coins play a major role in high-order effects.
An ensemble method enhances cryptocurrency trading strategies using deep reinforcement learning.
problem Improving generalization performance in stochastic cryptocurrency trading environments.
method Model selection and mixture distribution policy to ensemble deep reinforcement learning models.
result Improved out-of-sample performance compared to benchmarks.
Deep learning predicts cryptocurrency price movements from trade data.
problem Predicting short-term price changes in cryptocurrencies.
method Long Short-term Memory Network (LSTM) trained on trade-by-trade data.
result Optimal LSTM model achieves over 60% accuracy on out-of-sample test periods.
Neural nets analyze crypto markets for multi-timeframe trading.
problem High-frequency trading in cryptocurrency markets.
method Multi-timeframe trend analysis and high-frequency direction prediction networks.
result Positive risk-adjusted returns through machine learning.
Study compares price patterns of cryptocurrencies and stocks using machine learning.
problem Investor behavior in cryptocurrencies vs. stocks.
method Machine learning models (LR, RF, SVM) classify price time series of cryptocurrencies and stocks.
result Cryptocurrencies and stocks have distinct price patterns, explained by various statistical features.
Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.
problem Predicting returns in cryptocurrency futures markets using quarter-hour market bursts.
method Analysis of trade data and Autocorrelation Map to identify and quantify algorithmic trading activity.
result Quarter-hour market bursts are associated with algorithmic trading and can predict returns.
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between N…
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
This paper proposes a trading strategy using TD3 for stock and cryptocurrency markets.
problem Predicting price movements in financial markets using historical data.
method Twin-Delayed DDPG (TD3) for continuous action space in algorithmic trading.
result The proposed strategy improves trading performance based on Return and Sharpe ratio metrics.
CTBench benchmarks cryptocurrency time series generation for trading applications.
problem Lack of comprehensive benchmarks for cryptocurrency time series generation.
method Developed a comprehensive benchmark extsf{CTBench} with 13 metrics across 5 dimensions.
result Uncovered trade-offs between statistical fidelity and real-world profitability.
Study shows multifractality emerging in decentralized cryptocurrency trading.
problem Understanding financial dynamics in decentralized cryptocurrency markets.
method Multifractal Detrended Fluctuation Analysis (MFDFA) on tick-by-tick transaction data.
result Multifractality is emerging in decentralized cryptocurrency trading, with larger fluctuations dominating.
Deep RL strategies outperform traditional methods in cryptocurrency trading.
problem Designing profitable trading strategies for cryptocurrency markets.
method Applied Proximal Policy Optimization, Soft Actor-Critic, and Generative Adversarial Imitation Learning to a Gym environment based on cryptocurrency markets.
result Highest gain of 4850 US dollars per 10000 US dollars investment on unseen data.
Study on cryptocurrency trading patterns using multifractal analysis.
problem Lack of systematic study on temporal structure of cryptocurrency trading.
method Multifractal detrended cross-correlation analysis of price returns, trades, and volume.
result All analyzed quantities exhibit multifractal structure, both univariate and bivariate.
The study examines cryptocurrency market activity, revealing multifractal inter-transaction times and challenging traditional statistical models.
problem Analyzing long-range autocorrelations and multifractality in cryptocurrency market activity.
method Analysis of tick-by-tick data from multiple cryptocurrency trading platforms, focusing on inter-transaction times, transaction volumes, and volatility.
result Inter-transaction times exhibit multifractality, indicating periods of increased market activity are more complex than quiet periods.
The study uses machine learning to predict cryptocurrency market trends and design profitable trading strategies.
problem Predicting cryptocurrency market trends for profitable trading.
method Applied k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends.
result High profit factor of 1.60 for unseen data, showing promising results.
Paper uses SAC and DDPG to optimize cryptocurrency portfolios.
problem Adapting to volatile and nonlinear cryptocurrency markets.
method Reinforcement learning with SAC and DDPG algorithms.
result SAC and DDPG outperform traditional strategies in cryptocurrency markets.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
problem Understanding the dynamics of cryptocurrency markets.
method Evolutionary finance concepts applied to toy models of cryptocurrency data.
result Survival of the fittest in cryptofinance is explained through scaling laws.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15…
Study improves cryptocurrency volatility forecasting using multiple data sources.
problem Improving accuracy of predicting cryptocurrency volatility.
method Developed CoMForE, a multimodal AdaBoost-LSTM ensemble model.
result Significantly improved cryptocurrency volatility forecasting (19.29% improvement).
At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its h…
Study compares statistical and machine learning models for detecting crypto trading anomalies.
problem Detecting outliers in cryptocurrency limit order books for market dynamics analysis.
method Comprehensive comparative analysis of 13 diverse models using a unified testing environment.
result Empirical Covariance (EC) model outperforms standard Buy-and-Hold by 6.70%.
Study evaluates 41 ML models for Bitcoin trading performance.
problem Predicting Bitcoin prices for algorithmic trading.
method Examined 21 classifiers and 20 regressors under various market conditions.
result Certain models like Random Forest and Stochastic Gradient Descent outperform others in profit and risk management.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
problem Predicting price movements of cryptocurrencies using deep learning.
method Used deep learning algorithms (MLP, CNN, LSTM, ALSTM) on hourly and daily data of Bitcoin and Ethereum.
result Unrestricted model with trading and social media indicators outperforms restricted model.
MFIN networks improve crypto trading with multiple features.
problem Selecting and processing multiple features for effective trading.
method End-to-end framework using Multi-Factor Inception Networks (MFINs).
result MFINs learn uncorrelated, higher-Sharpe strategies not captured by traditional factors.
This study compares Bitcoin and Litecoin using cryptocurrency metrics and trading strategies.
problem Valuation and trading strategies for cryptocurrencies.
method Metrics like UTXO, STXO, WAL, CDD, and trading strategies based on PU ratio.
result Bitcoin's superior store-of-value proposition compared to Litecoin validated.
Optimizes cryptocurrency trading pairs for efficiency and decentralization.
problem Finding optimal trading pairs among many cryptocurrencies without direct volume data.
method Two-stage process: 1) Fill missing values using eigenvalue decomposition with regularization, 2) Optimize pairs using branch and bound with pruning.
result Optimal trading pairs lead to more decentralized markets and better liquidity.
Study predicts cryptocurrency price movements using Twitter sentiment analysis.
problem Predicting short-term price movements of cryptocurrencies.
method Conditional examination of return and excess return rates following tweet publication.
result Statistically significant increases in return rates within the first three minutes after tweet publication.
New trading strategy beats traditional grid in crypto markets.
problem Low expected return of traditional grid trading strategy.
method Dynamic Grid Trading (DGT) strategy that adapts to market conditions.
result DGT strategy outperforms traditional grid and buy-and-hold strategies.
Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the 2010 flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow…
The study identifies key factors affecting cryptocurrency prices, including market beta, trading volume, and volatility.
problem Understanding the factors influencing cryptocurrency prices and their dynamics over time.
method ARDL technique and error-correction models applied to weekly data of Bitcoin, Ethereum, Dash, Litecoin, and Monero from 2010-2018.
result Cryptomarket-related factors are significant determinants of cryptocurrency prices in both short- and long-run, while attractiveness matters only in the long-run.
Study detects unusual trading patterns on crypto exchanges using complexity measures.
problem Detecting artificial trading activity on cryptocurrency exchanges.
method Complexity and statistical-structure measures derived from high-frequency trade-level data.
result Unusual trading patterns detected on Bitget for BTC and ETH after mid-May 2025.
This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.
problem Wash trading in cryptocurrency markets to inflate volume and manipulate market conditions.
method Analysis of 18 million Mt. Gox transactions, exogenous demand shock study.
result Wash trading intensifies in low legitimate trading volume and responds to demand shocks, indicating strategic behavior.
Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large num…
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Unified asymptotic theory and tests for ACD models reveal infinite-mean durations in cryptocurrency trading.
problem Challenges in asymptotic theory for ACD models, especially for integrated ACD.
method Unified asymptotic theory for quasi-maximum likelihood estimator, hypothesis testing framework.
result Infinite-mean durations in cryptocurrency trading, rejected integrated ACD hypothesis.
Cross-correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015, through March 31, 2019, are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others la…
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …