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Cboe bitcoin etc delay

cboe bitcoin etc delay

Whether it's a new futures contract for the evolving cryptocurrency market, tracking emerging benchmarks like Quotes are delayed by at least 10 minutes. The VanEck Ethereum Trust would list shares on the Cboe BZX The SEC delayed a decision on whether to greenlight VanEck's bitcoin ETF. cboe bitcoin margin-Fed Chair Jerome Powell Could 'Slow Crypto Down' in His Second Term, Warns Billionaire Mike Novogratz – Economics Bitcoin News. 1060 POWER CONSUMPTION ETHEREUM

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Blow to the death, not death, achievement method secrets also by war are burnt out, plus two jin metaphysics prevailing after, daxing, Buddhism and Taoism in the martial way unexpectedly broke inheritance, are now old cheng jing, wulin now is the most outstanding achievement method, but is "page break up, can have a few power. Ha and Moon investigated using genetic programming GP to find attractive technical patterns in the cryptocurrency market. Over 12 technical indicators including Moving Average MA and Stochastic oscillator were used in experiments; adjusted gain, match count, relative market pressure and diversity measures have been used to quantify the attractiveness of technical patterns.

With extended experiments, the GP system is shown to find successfully attractive technical patterns, which are useful for portfolio optimization. Hudson and Urquhart applied almost 15, to technical trading rules classified into MA rules, filter rules, support resistance rules, oscillator rules and channel breakout rules. This comprehensive study found that technical trading rules provide investors with significant predictive power and profitability.

Corbet et al. By using one-minute dollar-denominated Bitcoin close-price data, the backtest showed variable-length moving average VMA rule performs best considering it generates the most useful signals in high frequency trading. Grobys et al. The results showed that, excluding Bitcoin, technical trading rules produced an annualised excess return of 8.

The analysis also suggests that cryptocurrency markets are inefficient. Al-Yahyaee et al. The results showed that all markets provide evidence of long-term memory properties and multiple fractals. Furthermore, the inefficiency of cryptocurrency markets is time-varying.

The researchers concluded that high liquidity with low volatility facilitates arbitrage opportunities for active traders. Pairs trading is a trading strategy that attempts to exploit the mean-reversion between the prices of certain securities. Miroslav Fil investigated the applicability of standard pairs trading approaches on cryptocurrency data with the benchmarks of Gatev et al.

The pairs trading strategy is constructed in two steps. Firstly, suitable pairs with a stable long-run relationship are identified. Secondly, the long-run equilibrium is calculated and pairs trading strategy is defined by the spread based on the values. The research also extended intra-day pairs trading using high frequency data. Broek van den Broek and Sharif applied pairs trading based on cointegration in cryptocurrency trading and 31 pairs were found to be significantly cointegrated within sector and cross-sector.

By selecting four pairs and testing over a day trading period, the pairs trading strategy got its profitability from arbitrage opportunities, which rejected the Efficient-market hypothesis EMH for the cryptocurrency market. Lintilhac and Tourin proposed an optimal dynamic pair trading strategy model for a portfolio of assets. The experiment used stochastic control techniques to calculate optimal portfolio weights and correlated the results with several other strategies commonly used by practitioners including static dual-threshold strategies.

Li and Tourin proposed a pairwise trading model incorporating time-varying volatility with constant elasticity of variance type. The experiment calculated the best pair strategy by using a finite difference method and estimated parameters by generalised moment method.

Other systematic trading methods in cryptocurrency trading mainly include informed trading. The evidence of informed trading in the Bitcoin market suggests that investors profit on their private information when they get information before it is widely available. Copula-quantile causality analysis and Granger-causality analysis are methods to investigate causality in cryptocurrency trading analysis. Bouri et al. The experiment constructed two tests of CGCD using copula functions.

The parametric test employed six parametric copula functions to discover dependency density between variables. The performance matrix of these functions varies with independent copula density. The study provided significant evidence of Granger causality from trading volume to the returns of seven large cryptocurrencies on both left and right tails.

The results showed that permanent shocks are more important in explaining Granger causality whereas transient shocks dominate the causality of smaller cryptocurrencies in the long term. Badenhorst et al. The result shows spot trading volumes have a significant positive effect on price volatility while the relationship between cryptocurrency volatility and the derivative market is uncertain. The results showed increased cryptocurrency market consolidation despite significant price declined in Furthermore, measurement of trading volume and uncertainty are key determinants of integration.

Conrad et al. The technical details of this model decomposed the conditional variance into the low-frequency and high-frequency components. Ardia et al. Moreover, a Bayesian method was used for estimating model parameters and calculating VaR prediction.

Troster et al. The results also illustrated the importance of modeling excess kurtosis for Bitcoin returns. Results showed cryptocurrency returns are strongly characterised by the presence of jumps as well as structural breaks except the Dash market. The research indicated the importance of jumps in cryptocurrency volatility and structural breakthroughs.

The results showed that there is no causal relationship between global stock market and gold returns on bitcoin returns, but a causal relationship between ripple returns on bitcoin prices is found. Some researchers focused on long memory methods for volatility in cryptocurrency markets. Long memory methods focused on long-range dependence and significant long-term correlations among fluctuations on markets.

Chaim and Laurini estimated a multivariate stochastic volatility model with discontinuous jumps in cryptocurrency markets. The results showed that permanent volatility appears to be driven by major market developments and popular interest levels. Caporale et al. The results of the study indicated that the market is persistent there is a positive correlation between its past and future values and that its level changes over time.

Khuntia and Pattanayak applied the adaptive market hypothesis AMH in the predictability of Bitcoin evolving returns. Gradojevic and Tsiakas examined volatility cascades across multiple trading ranges in the cryptocurrency market. Using a wavelet Hidden Markov Tree model, authors estimated the transition probability of propagating high or low volatility at one time scale range to high or low volatility at the next time scale. The results showed that the volatility cascade tends to be symmetrical when moving from long to short term.

In contrast, when moving from short to long term, the volatility cascade is very asymmetric. Nikolova et al. The authors used the FD4 method to calculate the Hurst index of a volatility series and describe explicit criteria for determining the existence of fixed size volatility clusters by calculation. Ma et al. At the same time, the occurrence of jumps significantly increases the persistence of high volatility and switches between high and low volatility.

Katsiampa et al. More specifically, the BEKK-MGARCH methodology also captured cross-market effects of shocks and volatility, which are also known as shock transmission effects and volatility spillover effects. The experiment found evidence of bi-directional shock transmission effects between Bitcoin and both Ether and Litcoin.

In particular, bi-directional shock spillover effects are identified between three pairs Bitcoin, Ether and Litcoin and time-varying conditional correlations exist with positive correlations mostly prevailing. In , Katsiampa further researched an asymmetric diagonal BEKK model to examine conditional variances of five cryptocurrencies that are significantly affected by both previous squared errors and past conditional volatility.

The experiment tested the null hypothesis of the unit root against the stationarity hypothesis. Moreover, volatility co-movements among cryptocurrency pairs are also tested by the multivariate GARCH model. The results confirmed the non-normality and heteroskedasticity of price returns in cryptocurrency markets. A rolling window approach is used in these experiments. Wavelet time-scale persistence analysis is also applied in the prediction and research of volatility in cryptocurrency markets Omane-Adjepong et al.

The results showed that information efficiency efficiency and volatility persistence in the cryptocurrency market are highly sensitive to time scales, measures of returns and volatility, and institutional changes. Omane-Adjepong et al. Zhang and Li examined how to price exceptional volatility in a cross-section of cryptocurrency returns. Using portfolio-level analysis and Fama-MacBeth regression analysis, the authors demonstrated that idiosyncratic volatility is positively correlated with expected returns on cryptocurrencies.

As we have previously stated, Machine learning technology constructs computer algorithms that automatically improve themselves by finding patterns in existing data without explicit instructions Holmes et al. The rapid development of machine learning in recent years has promoted its application to cryptocurrency trading, especially in the prediction of cryptocurrency returns. Some ML algorithms solve both classification and regression problems from a methodological point of view. For clearer classification, we focus on the application of these ML algorithms in cryptocurrency trading.

For example, Decision Tree DT can solve both classification and regression problems. But in cryptocurrency trading, researchers focus more on using DT in solving classification problems. Several machine learning technologies are applied in cryptocurrency trading. We distinguish these by the objective set to the algorithm: classification, clustering, regression, reinforcement learning.

We have separated a section specifically on deep learning due to its intrinsic variation of techniques and wide adoption. Classification algorithms Classification in machine learning has the objective of categorising incoming objects into different categories as needed, where we can assign labels to each category e.

Naive Bayes NB Rish et al. SVM is a supervised learning model that aims at achieving high margin classifiers connecting to learning bounds theory Zemmal et al. SVMs assign new examples to one category or another, making it a non-probabilistic binary linear classifier Wang , although some corrections can make a probabilistic interpretation of their output Keerthi et al. KNN is a memory-based or lazy learning algorithm, where the function is only approximated locally, and all calculations are being postponed to inference time Wang DT is a decision support tool algorithm that uses a tree-like decision graph or model to segment input patterns into regions to then assign an associated label to each region Friedl and Brodley ; Fang et al.

RF is an ensemble learning method. The algorithm operates by constructing a large number of decision trees during training and outputting the average consensus as predicted class in the case of classification or mean prediction value in the case of regression Liaw and Wiener GB produces a prediction model in the form of an ensemble of weak prediction models Friedman et al.

Clustering algorithms Clustering is a machine learning technique that involves grouping data points in a way that each group shows some regularity Jianliang et al. K-Means is a vector quantization used for clustering analysis in data mining. K-Means is one of the most used clustering algorithms used in cryptocurrency trading according to the papers we collected.

Clustering algorithms have been successfully applied in many financial applications, such as fraud detection, rejection inference and credit assessment. Automated detection clusters are critical as they help to understand sub-patterns of data that can be used to infer user behaviour and identify potential risks Li et al. Regression algorithms We have defined regression as any statistical technique that aims at estimating a continuous value Kutner et al.

Linear Regression LR and Scatterplot Smoothing are common techniques used in solving regression problems in cryptocurrency trading. LR is a linear method used to model the relationship between a scalar response or dependent variable and one or more explanatory variables or independent variables Kutner et al. Scatterplot Smoothing is a technology to fit functions through scatter plots to best represent relationships between variables Friedman and Tibshirani Deep learning algorithms are currently the basis for many modern artificial intelligence applications Sze et al.

Convolutional neural networks CNNs Lawrence et al. A CNN is a specific type of neural network layer commonly used for supervised learning. CNNs have found their best success in image processing and natural language processing problems. An attempt to use CNNs in cryptocurrency can be shown in Kalchbrenner et al.

An RNN is a type of artificial neural network in which connections between nodes form a directed graph with possible loops. This structure of RNNs makes them suitable for processing time-series data Mikolov et al. They face nevertheless for the vanishing gradients problem Pascanu et al. LSTM Cheng et al.

LSTMs have shown to be superior to nongated RNNs on financial time-series problems because they have the ability to selectively remember patterns for a long time. A GRU Chung et al. Another deep learning technology used in cryptocurrency trading is Seq2seq, which is a specific implementation of the Encoder-Decoder architecture Xu et al. Seq2seq was first aimed at solving natural language processing problems but has been also applied it in cryptocurrency trend predictions in Sriram et al.

Reinforcement learning algorithms Reinforcement learning RL is an area of machine learning leveraging the idea that software agents act in the environment to maximize a cumulative reward Sutton and Barto Deep Q learning uses neural networks to approximate Q-value functions.

A state is given as input, and Q values for all possible actions are generated as outputs Gu et al. DBM is a type of binary paired Markov random field undirected probability graphical model with multiple layers of hidden random variables Salakhutdinov and Hinton It is a network of randomly coupled random binary units. In the development of machine learning trading signals, technical indicators have usually been used as input features.

Nakano et al. The experiment obtained medium frequency price and volume data time interval of data is 15min of Bitcoin from a cryptocurrency exchange. An ANN predicts the price trends up and down in the next period from the input data.

Their numerical experiments contain different research aspects including base ANN research, effects of different layers, effects of different activation functions, different outputs, different inputs and effects of additional technical indicators. The results have shown that the use of various technical indicators possibly prevents over-fitting in the classification of non-stationary financial time-series data, which enhances trading performance compared to the primitive technical trading strategy.

Buy-and-Hold is the benchmark strategy in this experiment. Some classification and regression machine learning models are applied in cryptocurrency trading by predicting price trends. Most researchers have focused on the comparison of different classification and regression machine learning methods. Sun et al. The experiment collected data from API in cryptocurrency exchanges and selected 5-min frequency data for backtesting.

The results showed that the performances are proportional to the amount of data more data, more accurate and the factors used in the RF model appear to have different importance. Minute-level data is collected when utilising a forward fill imputation method to replace the NULL value i. Different periods and RF trees are tested in the experiments. The results showed that RF is effective despite multicollinearity occurring in ML features, the lack of model identification also potentially leading to model identification issues; this research also attempted to create an HFT strategy for Bitcoin using RF.

Slepaczuk and Zenkova investigated the profitability of an algorithmic trading strategy based on training an SVM model to identify cryptocurrencies with high or low predicted returns. There are other 4 benchmark strategies in this research. The authors observed that SVM needs a large number of parameters and so is very prone to overfitting, which caused its bad performance. Barnwal et al. A discriminative classifier directly models the relationship between unknown and known data, while generative classifiers model the prediction indirectly through the data generation distribution Ng and Jordan Technical indicators including trend, momentum, volume and volatility, are collected as features of the model.

The authors discussed how different classifiers and features affect the prediction. Attanasio et al. Madan et al. Daily data, min data and s data are used in the experiments. Considering predictive trading, min data helped show clearer trends in the experiment compared to second backtesting.

The results showed that SVM achieved the highest accuracy of Different deep learning models have been used in finding patterns of price movements in cryptocurrency markets. Zhengyang et al. The findings show that the future state of a time series for cryptocurrencies is highly dependent on its historic evolution. Kwon et al. This model outperforms the GB model in terms of F1-score.

In particular, the experiments showed that LSTM is more suitable when classifying cryptocurrency data with high volatility. Alessandretti et al. The relative importance of the features in both models are compared and an optimised portfolio composition based on geometric mean return and Sharpe ratio is discussed in this paper.

Rane and Dhage described classical time series prediction methods and machine learning algorithms used for predicting Bitcoin price. Rebane et al. The result showed that the seq2seq model exhibited demonstrable improvement over the ARIMA model for Bitcoin-USD prediction but the seq2seq model showed very poor performance in extreme cases. Similar models were also compared by Stuerner who explored the superiority of automated investment approach in trend following and technical analysis in cryptocurrency trading.

Persson et al. The RNN with ten hidden layers is optimised for the setting and the neural network augmented by VAR allows the network to be shallower, quicker and to have a better prediction than an RNN. This research is an attempt at optimisation of model design and applying to the prediction on cryptocurrency returns. Deep Neural Network architectures play important roles in forecasting. In this subsection, we describe the cutting edge Deep Neural Network researches in cryptocurrency trading.

Recent studies show the productivity of using models based on such architectures for modeling and forecasting financial time series, including cryptocurrencies. Livieris et al. The first component of the model consists of a convolutional layer and a pooling layer, where complex mathematical operations are performed to develop the features of the input data. The second component uses the generated LSTM and the features of the dense layer.

The results show that due to the sensitivity of the various hyperparameters of the proposed CNN-LSTM and its high complexity, additional optimisation configurations and major feature engineering have the potential to further improve the predictive power.

More Intelligent Evolutionary Optimisation IEO for hyperparameter optimisation is core problem when tuning the overall optimization process of machine learning models Huan et al. Lu et al. Fang et al. This research improved and verified the view of Sirignano and Cont that universal models have better performance than currency-pair specific models for cryptocurrency markets.

Yao et al. The experimental results showed that the model performs well for a certain size of dataset. The proposed integrated model is evaluated using a state-of-the-art deep learning model as a component learner, which consists of a combination of LSTM, bidirectional LSTM and convolutional layers.

Sentiment analysis, a popular research topic in the age of social media, has also been adopted to improve predictions for cryptocurrency trading. This data source typically has to be combined with Machine Learning for the generation of trading signals.

Lamon et al. By this approach, the prediction on price is replaced with positive and negative sentiment. Weights are taken in positive and negative words in the cryptocurrency market. Smuts conducted a similar binary sentiment-based price prediction method with an LSTM model using Google Trends and Telegram sentiment.

Nasir et al. The experiment employed a rich set of established empirical approaches including VAR framework, copulas approach and non-parametric drawings of time series. The results found that Google searches exert significant influence on Bitcoin returns, especially in the short-term intervals.

Kristoufek discussed positive and negative feedback on Google trends or daily views on Wikipedia. The author mentioned different methods including Cointegration, Vector autoregression and Vector error-correction model to find causal relationships between prices and searched terms in the cryptocurrency market.

The results indicated that search trends and cryptocurrency prices are connected. There is also a clear asymmetry between the effects of increased interest in currencies above or below their trend values from the experiment. Kim et al. After crawling comments and replies in online communities, authors tagged the extent of positive and negative topics. Then the relationship between price and the number of transactions of cryptocurrency is tested according to comments and replies to selected data.

At last, a prediction model using machine learning based on selected data is created to predict fluctuations in the cryptocurrency market. The results show the amount of accumulated data and animated community activities exerted a direct effect on fluctuation in the price and volume of a cryptocurrency. Phillips and Gorse applied dynamic topic modeling and Hawkes model to decipher relationships between topics and cryptocurrency price movements. The authors used Latent Dirichlet allocation LDA model for topic modeling, which assumes each document contains multiple topics to different extents.

The experiment showed that particular topics tend to precede certain types of price movements in the cryptocurrency market and the authors proposed the relationships could be built into real-time cryptocurrency trading. Li et al. Values of weighted and unweighted sentiment indices are calculated on an hourly basis by summing weights of coinciding tweets, which makes us compare this index to ZCL price data.

The model achieved a Pearson correlation of 0. Flori relied on a Bayesian framework that combines market-neutral information with subjective beliefs to construct diversified investment strategies in the Bitcoin market.

The result shows that news and media attention seem to contribute to influence the demand for Bitcoin and enlarge the perimeter of the potential investors, probably stimulating price euphoria and upwards-downwards market dynamics.

Bouri and Gupta compared the ability of newspaper-based metrics and internet search-based uncertainty metrics in predicting bitcoin returns. The predictive power of Internet-based economic uncertainty-related query indices is statistically stronger than that of newspapers in predicting bitcoin returns.

Similarly, Colianni et al. Colianni et al. Garcia and Schweitzer applied multidimensional analysis and impulse analysis in social signals of sentiment effects and algorithmic trading of Bitcoin. The results verified the long-standing assumption that transaction-based social media sentiment has the potential to generate a positive return on investment.

Zamuda et al. The perspective is rationalized based on the elastic demand for computing resources of the cloud infrastructure. Bartolucci et al. Sentiment, politeness, emotions analysis of GitHub comments are applied in Ethereum and Bitcoin markets. The results showed that these metrics have predictive power on cryptocurrency prices. Deep reinforcement algorithms bypass prediction and go straight to market management actions to achieve high cumulated profit Henderson et al.

Bu and Cho proposed a combination of double Q-network and unsupervised pre-training using DBM to generate and enhance the optimal Q-function in cryptocurrency trading. The trading model contains agents in series in the form of two neural networks, unsupervised learning modules and environments.

The input market state connects an encoding network which includes spectral feature extraction convolution-pooling module and temporal feature extraction LSTM module. A double-Q network follows the encoding network and actions are generated from this network.

Juchli applied two implementations of reinforcement learning agents, a Q-Learning agent, which serves as the learner when no market variables are provided, and a DQN agent which was developed to handle the features previously mentioned. The DQN agent was backtested under the application of two different neural network architectures. Lucarelli and Borrotti focused on improving automated cryptocurrency trading with a deep reinforcement learning approach.

Double and Dueling double deep Q-learning networks are compared for 4 years. By setting rewards functions as Sharpe ratio and profit, the double Q-learning method demonstrated to be the most profitable approach in trading cryptocurrency. Sattarov et al. The model proposed by the authors helped traders to correctly choose one of the following three actions: buy, sell and hold stocks and get advice on the correct option.

Experiments applying BTC via deep reinforcement learning showed that investors made a net profit of Koker and Koutmos pointed out direct reinforcement DR based model for active trading. Within the model, the authors attempt to estimate the parameters of the non-linear autoregressive model to achieve maximum risk-adjusted returns.

The results provide some preliminary evidence that cryptocurrency prices may not follow a purely random wandering process. Atsalakis et al. The proposed methodology outperforms two other computational intelligence models, the first being developed with a simpler neuro-fuzzy approach, and the second being developed with artificial neural networks.

According to the signals of the proposed model, the investment return obtained through trading simulation is This application is proposed for the first time in the forecasting of Bitcoin price movements. Topological data analysis is applied to forecasting price trends of cryptocurrency markets in Kim et al. The approach is to harness topological features of attractors of dynamical systems for arbitrary temporal data. The results showed that the method can effectively separate important topological patterns and sampling noise like bid-ask bounces, discreteness of price changes, differences in trade sizes or informational content of price changes, etc.

Kurbucz designed a complex method consisting of single-hidden layer feedforward neural networks SLFNs to 1 determine the predictive power of the most frequent edges of the transaction network a public ledger that records all Bitcoin transactions on the future price of Bitcoin; and, 2 to provide an efficient technique for applying this untapped dataset in day trading.

The research found a significantly high accuracy It is worth noting that, Kondor et al. Abay et al. The results showed that standard graph features such as the degree distribution of transaction graphs may not be sufficient to capture network dynamics and their potential impact on Bitcoin price fluctuations. The experiment examined the long-memory and market efficiency characteristics in cryptocurrency markets using daily data for more than two years.

In general, experiments indicated that heterogeneous memory behaviour existed in eight cryptocurrency markets using daily data over the full-time period and across scales August 25, to March 13, Ji et al. Furthermore, the regression model is used to identify drivers of various cryptocurrency integration levels.

Further analysis revealed that the relationship between each cryptocurrency in terms of return and volatility is not necessarily due to its market size. Omane-Adjepong and Alagidede explored market coherence and volatility causal linkages of seven leading cryptocurrencies. Wavelet-based methods are used to examine market connectedness.

Parametric and nonparametric tests are employed to investigate directions of volatility spillovers of the assets. Experiments revealed from diversification benefits to linkages of connectedness and volatility in cryptocurrency markets. More results underscore the importance of the jump in trading volume for the formation of cryptocurrency leapfrogging.

The corresponding dynamics mainly involve one of the leading eigenvalues of the correlation matrix, while the others are mainly consistent with the eigenvalues of the Wishart random matrix. Some researchers explored the relationship between cryptocurrency and different factors, including futures, gold, etc. Hale et al. Specifically, the authors pointed out that the rapid rise and subsequent decline in prices after the introduction of futures is consistent with trading behaviour in the cryptocurrency market.

Kristjanpoller et al. The results of multiple fractal asymmetric de-trending cross-correlation analysis show evidence of significant persistence and asymmetric multiplicity in the cross-correlation between most cryptocurrency pairs and ETF pairs.

Bai and Robinson studied a trading algorithm for foreign exchange on a cryptocurrency Market using the Automated Triangular Arbitrage method. Implementing a pricing strategy, implementing trading algorithms and developing a given trading simulation are three problems solved by this research.

Kang et al. DCC-GARCH model Engle is used to estimate the time-varying correlation between Bitcoin and gold futures by modeling the variance and the co-variance but also this two flexibility. Wavelet coherence method focused more on co-movement between Bitcoin and gold futures. From experiments, the wavelet coherence results indicated volatility persistence, causality and phase difference between Bitcoin and gold. Qiao et al. The authors then tested the hedging effect of bitcoin on others at different time frequencies by risk reduction and downside risk reduction.

The empirical results provide evidence of linkage and hedging effects. The experiments showed that Bitcoin, gold and the US dollar have similarities with the variables of the GARCH model, have similar hedging capabilities and react symmetrically to good and bad news.

The authors observed that Bitcoin can combine some advantages of commodities and currencies in financial markets to be a tool for portfolio management. Baur et al. They noticed that Bitcoin excess returns and volatility resemble a rather highly speculative asset with respect to gold or the US dollar. In particular, the results showed that Bitcoin is a strong hedge and safe haven for energy commodities. Kakushadze proposed factor models for the cross-section of daily cryptoasset returns and provided source code for data downloads, computing risk factors and backtesting for all cryptocurrencies and a host of various other digital assets.

The results showed that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting. Beneki et al. The results indicated a volatility transaction from Ethereum to Bitcoin, which implied possible profitable trading strategies on the cryptocurrency derivatives market.

Caporale and Plastun examined the week effect in cryptocurrency markets and explored the feasibility of this indicator in trading practice. Student t -test, ANOVA, Kruskal-Wallis and Mann-Whitney tests were carried out for cryptocurrency data in order to compare time periods that may be characterised by anomalies with other time periods. When an anomaly is detected, an algorithm was established to exploit profit opportunities MetaTrader terminal in MQL4 is mentioned in this research.

The results showed evidence of anomaly abnormal positive returns on Mondays in the Bitcoin market by backtesting in A number of special research methods have proven to be relevant to cryptocurrency pairs, which is reflected in cryptocurrency trading. Delfabbro et al. Decisions are often based on limited information, short-term profit motives, and highly volatile and uncertain outcomes.

The authors examined whether gambling and problem gambling are reliable predictors of reported cryptocurrency trading strength. Results showed that problem gambling scores PGSI and engaging in stock trading were significantly correlated with measures of cryptocurrency trading intensity based on time spent per day, number of trades and level of expenditure.

In further research, Delfabbro et al. There are some similarities noted between online sports betting and day trading, but there are also some important differences. Cheng and Yen investigated whether the economic policy uncertainty EPU index provided by Baker et al. Leirvik analysed the relationship between the particular volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalisation.

The results showed that in general there is a positive correlation between the volatility of liquidity and the returns of large-cap cryptocurrencies. For the most liquid and popular cryptocurrencies, this effect does not exist: Bitcoin. Moreover, the liquidity of cryptocurrencies increases over time, but varies greatly over time.

Some researchers applied portfolio theory for crypto assets. Brauneis and Mestel applied the Markowitz mean-variance framework in order to assess the risk-return benefits of cryptocurrency portfolios. Castro et al. Experiments showed crypto-assets improves the return of the portfolios, but on the other hand, also increase the risk exposure.

Bedi and Nashier examined diversification capabilities of Bitcoin for a global portfolio spread across six asset classes from the standpoint of investors dealing in five major fiat currencies, namely US Dollar, Great Britain Pound, Euro, Japanese Yen and Chinese Yuan.

They employed modified Conditional Value-at-Risk and standard deviation as measures of risk to perform portfolio optimisations across three asset allocation strategies and provided insights into the sharp disparity in Bitcoin trading volumes across national currencies from a portfolio theory perspective. Similar research has been done by Antipova , which explored the possibility of establishing and optimizing a global portfolio by diversifying investments using one or more cryptocurrencies, and assessing returns to investors in terms of risks and returns.

Fantazzini and Zimin proposed a set of models that can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously estimate their credit risk using the Zero Price Probability ZPP model. Using a connectivity metric based on the actual daily volatility of the bitcoin price, they found that Coinbase is undoubtedly the market leader, while Binance performance is surprisingly weak.

The results also suggested that safer asset extraction is more important for volatility linkages between Bitcoin exchanges relative to trading volumes. Fasanya et al. The results showed that there is a significant difference between the behaviour of cryptocurrency portfolio returns and the volatility spillover index over time. Given the spillover index, the authors found evidence of interdependence between cryptocurrency portfolios, with the spillover index showing an increased degree of integration between cryptocurrency portfolios.

The proposed algorithm displayed good performance in estimating both VaR and ES. Hrytsiuk et al. As a result of the optimisation, the sets of optimal cryptocurrency portfolios were built in their experiments. Jiang and Liang proposed a two-hidden-layer CNN that takes the historical price of a group of cryptocurrency assets as an input and outputs the weight of the group of cryptocurrency assets. This research focused on portfolio research in cryptocurrency assets using emerging technologies like CNN.

Training is conducted in an intensive manner to maximise cumulative returns, which is considered a reward function of the CNN network. Estalayo et al. Technical rationale and details were given on the design of a stacked DL recurrent neural network, and how its predictive power can be exploited for yielding accurate ex-ante estimates of the return and risk of the portfolio.

Results obtained for a set of experiments carried out with real cryptocurrency data have verified the superior performance of their designed deep learning model with respect to other regression techniques. Bubbles and crash analysis is an important researching area in cryptocurrency trading. Phillips and Yu proposed a methodology to test for the presence of cryptocurrency bubble Cheung et al.

The research concluded that there is no clear evidence of a persistent bubble in cryptocurrency markets including Bitcoin or Ethereum. GSADF is used to identify multiple explosiveness periods and logistic regression is employed to uncover evidence of co-explosivity across cryptocurrencies. The results showed that the likelihood of explosive periods in one cryptocurrency generally depends on the presence of explosivity in other cryptocurrencies and points toward a contemporaneous co-explosivity that does not necessarily depend on the size of each cryptocurrency.

Extended research by Phillips et al. The evaluation includes multiple bubble periods in all cryptocurrencies. The result shows that higher volatility and trading volume is positively associated with the presence of bubbles across cryptocurrencies. In terms of bubble prediction, the authors found the probit model to perform better than the linear models.

Considering HMM and SIR method, an epidemic detection mechanism is used in social media to predict cryptocurrency price bubbles, which classify bubbles through epidemic and non-epidemic labels. Experiments have demonstrated a strong relationship between Reddit usage and cryptocurrency prices. This work also provides some empirical evidence that bubbles mirror the social epidemic-like spread of an investment idea. Caporale and Plastun examined the price overreactions in the case of cryptocurrency trading.

The results also showed that the overreaction detected in the cryptocurrency market would not give available profit opportunities possibly due to transaction costs that cannot be considered as evidence of the EMH. Chaim and Laurini analysed the high unconditional volatility of cryptocurrency from a standard log-normal stochastic volatility model to discontinuous jumps of volatility and returns.

The experiment indicated the importance of incorporating permanent jumps to volatility in cryptocurrency markets. Cross et al. A generalized time-varying asset pricing model approach is proposed. The results showed that the negative news impact of the boom period in for LiteCoin and Ripple, which incurred a risk premium for investors, could explain the returns of cryptocurrencies during the crash.

Differently from traditional fiat currencies, cryptocurrencies are risky and exhibit heavier tail behaviour. Evidence of asymmetric return-volume relationship in the cryptocurrency market was also found by the experiment, as a result of discrepancies in the correlation between positive and negative return exceedances across all the cryptocurrencies.

There has been a price crash in late to early in cryptocurrency Yaya et al. Yaya et al. The result showed that higher persistence of shocks is expected after the crash due to speculations in the mind of cryptocurrency traders, and more evidence of non-mean reversions, implying chances of further price fall in cryptocurrencies. Manahov obtained millisecond data for major cryptocurrencies as well as the cryptocurrency indices Cryptocurrency IndeX CRIX and Cryptocurrencies Index 30 CCI30 to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during extreme price movements EPM.

Millisecond data was obtained for major cryptocurrencies as well as the cryptocurrency indices CRIX and CCI30 to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during EPM. Shahzad et al. The experiment used daily data and combines LASSO techniques with quantile regression within a network analysis framework.

The main results showed that the interdependence of the tails is higher than the median, especially in the right tail. Fluctuations in market, size and momentum drive return connectivity and clustering coefficients under both normal and extreme market conditions. Chan et al. Experiments with extreme value theory methods highlight how these results can help traders and practitioners who rely on technical indicators in their trading strategies - especially in times of extreme market volatility or irrational market booms.

Some other research papers related to cryptocurrency trading treat distributed in market behaviour, regulatory mechanisms and benchmarks. Krafft et al. Then they highlighted the potential social and economic impact of human-computer interaction in digital agency design.

Yang, on the other hand, applied behavioural theories of asset pricing anomalies in testing 20 market anomalies using cryptocurrency trading data. The results showed that anomaly research focused more on the role of speculators, which gave a new idea to research the momentum and reversal in the cryptocurrency market. Specifically, the model reproduced the unit root attributes of the price series, the fat tail phenomenon, the volatility clustering of price returns, the generation of Bitcoins, hashing power and power consumption.

Leclair applied herding methods of Hwang and Salmon in estimating the market herd dynamics in the CAPM framework. Both their findings showed significant evidence of market herding in the cryptocurrency market. King and Koutmos examined the extent to which herding and feedback trading behaviour drive the price dynamics of nine major cryptocurrencies. In November , Griffin et al. Using algorithms to analyse Blockchain data, they found that purchases with Tether are timed following market downturns and result in sizeable increases in Bitcoin prices.

By mapping the blockchains of Bitcoin and Tether, they were able to establish that one large player on Bitfinex uses Tether to purchase large amounts of Bitcoin when prices are falling and following the prod of Tether. More researches involved benchmark and development in cryptocurrency market Hileman and Rauchs ; Zhou and Kalev , regulatory framework analysis Shanaev et al. Hileman and Rauchs segmented the cryptocurrency industry into four key sectors: exchanges, wallets, payments and mining.

They gave a benchmarking study of individuals, data, regulation, compliance practices, costs of firms and a global map of mining in the cryptocurrency market in Zhou and Kalev discussed the status and future of computer trading in the largest group of Asia-Pacific economies and then considered algorithmic and high frequency trading in cryptocurrency markets as well. Shanaev et al. Feinstein and Werbach collected raw data on global cryptocurrency regulations and used them to empirically test the trading activity of many exchanges against key regulatory announcements.

Patil et al. They used the comparison of uncertainty quantification methods and opinion mining to analyse current market conditions. Sigaki et al. As a result, the cryptocurrency market showed significant compliance with efficient market assumptions. Aspris et al. The study demonstrated the significant differences in the listing and trading characteristics of these tokens compared to their centralised equivalents.

Cocco et al. The proposed simulator is able to reproduce some real statistical properties of price returns observed in the Bitcoin real market. Marko Ogorevc considered the future use of cryptocurrencies as money based on the long-term value of cryptocurrencies. Gandal and Halaburda analysed the influence of network effect on the competition of new cryptocurrency markets. Bariviera and Merediz-Sola gave a survey based on hybrid analysis, which proposed a methodological hybrid method for a comprehensive literature review and provided the latest technology in the cryptocurrency economics literature.

There also exists some research and papers introducing the basic process and rules of cryptocurrency trading including findings of Hansel , Kate , Garza , Ward and Fantazzini Hansel introduced the basics of cryptocurrency, Bitcoin and Blockchain, ways to identify the profitable trends in the market, ways to use Altcoin trading platforms such as GDAX and Coinbase, methods of using a crypto wallet to store and protect the coins in their book.

Kate set six steps to show how to start an investment without any technical skills in the cryptocurrency market. This book is an entry-level trading manual for starters learning cryptocurrency trading. Garza simulated an automatic cryptocurrency trading system, which helps investors limit systemic risks and improve market returns. This paper is an example to start designing an automatic cryptocurrency trading system.

Ward discussed algorithmic cryptocurrency trading using several general algorithms, and modifications thereof including adjusting the parameters used in each strategy, as well as mixing multiple strategies or dynamically changing between strategies. This paper is an example to start algorithmic trading in cryptocurrency market. Fantazzini introduced the R packages Bitcoin-Finance and bubble, including financial analysis of cryptocurrency markets including Bitcoin.

This section analyses the timeline, the research distribution among technology and methods, the research distribution among properties. It also summarises the datasets that have been used in cryptocurrency trading research. Figure 8 shows several major events in cryptocurrency trading.

The timeline contains milestone events in cryptocurrency trading and important scientific breakthroughs in this area. As early as , Satoshi Nakamoto proposed and invented the first decentralised cryptocurrency, Nakamoto It is considered to be the start of cryptocurrency. In , the first cryptocurrency exchange was founded, which means cryptocurrency would not be an OTC market but traded on exchanges based on an auction market system. In , Lee and Yang firstly proposed to check causality from copula-based causality in the quantile method from trading volumes of seven major cryptocurrencies to returns and volatility.

In , Cheah and Fry discussed the bubble and speculation of Bitcoin and cryptocurrencies. From late to , machine learning and deep learning technology were applied in the prediction of cryptocurrency return. In , Jiang and Liang used double Q-network and pre-trained it using DBM for the prediction of cryptocurrencies portfolio weights. From to , several research directions including cross asset portfolios Bedi and Nashier ; Castro et al. In , more regulation issues were put out the stage.

On 18 May , China banned financial institutions and payment companies from providing services related to cryptocurrency transactions, which led to a sharp drop in the price of bitcoin Reuters We counted the number of papers covering different aspects of cryptocurrency trading. Figure 9 shows the result. The attributes in the legend are ranked according to the number of papers that specifically test the attribute. Over one-third Another one-third of papers focus on researching bubbles and extreme conditions and the relationship between pairs and portfolios in cryptocurrency trading.

The remaining researching topics prediction of volatility, trading system, technical trading and others have roughly one-third share. This section introduces and compares categories and technologies in cryptocurrency trading. When papers cover multiple technologies or compare different methods, we draw statistics from different technical perspectives. Among all the papers, papers These papers basically research technical-level cryptocurrency trading including mathematical modeling and statistics.

Other papers related to trading systems on pure technical indicators and introducing the industry and its history are not included in this analysis. Among all papers, 88 papers It is interesting to mention that, there are 17 papers More specifically, Bach and Kasper , Alessandretti et al. Table 8 shows the results of search hits in all trading areas not limited to cryptocurrencies. From the table, we can see that most research findings focused on statistical methods in trading, which means most of the research on traditional markets still focused on using statistical methods for trading.

But we observed that machine learning in trading had a higher degree of attention. It might because the traditional technical and fundamental have been arbitraged, so the market has moved in recent years to find new anomalies to exploit.

Meanwhile, the results also showed there exist many opportunities for research in the widely studied areas of machine learning applied to trade in cryptocurrency markets cf. As from Fig. Others include industry, market data and research analysis in cryptocurrency market. The figure shows that basic Regression methods and time-series analysis are the most commonly used methods in this area.

Papers using machine learning account for Tables 9 — 11 show the details for some representative datasets used in cryptocurrency trading research. Table 9 shows the market datasets. They mostly include price, trading volume, order-level information, collected from cryptocurrency exchanges. Table 10 shows the sentiment-based data. Table 11 gives two examples of datasets used in the collected papers that are not covered in the first two tables. We also present how the dataset has been used i.

Alexander and Dakos made an investigation of cryptocurrency data as well. They summarised data collected from published and SSRN discussion papers about cryptocurrencies and analysed their data quality. They found that less than half the cryptocurrency papers published since January employ correct data. Sentiment-based research As discussed above, there is a substantial body of work, which uses natural language processing technology, for sentiment analysis with the ultimate goal of using news and media contents to improve the performance of cryptocurrency trading strategies.

Possible research directions may lie in a larger volume of media input e. Long-and-short term trading research There are significant differences between long and short time horizons in cryptocurrency trading. In long-term trading, investors might obtain greater profits but have more possibilities to control risk when managing a position for weeks or months.

It is mandatory to control for risk on long term strategies due to the increase in the holding period, directly proportional to the risk incurred by the trader. On the other hand, the longer the horizon, the higher the risk and the most important the risk control.

The shorter the horizon, the higher the cost and the lower the risk, so cost takes over the design of a strategy. In short-term trading, automated algorithmic trading can be applied when holding periods are less than a week. Researchers can differentiate between long-term and short-term trading in cryptocurrency trading by applying wavelet technology analysing bubble regimes Phillips and Gorse and considering price explosiveness Bouri et al.

The existing work is mainly about showing the differences between long and short-term cryptocurrency trading. Long-term trading means less time would cost in trend tracing and simple technical indicators in market analysis. Short-term trading can limit overall risk because small positions are used in every transaction.

But market noise interference and short transaction time might cause some stress in short term trading. It might also be interesting to explore the extraction of trading signals, time series research, application to portfolio management, the relationship between a huge market crash and small price drop, derivative pricing in cryptocurrency market, etc. Correlation between cryptocurrency and others By the effects of monetary policy and business cycles that are not controlled by the central bank, cryptocurrency is always negatively correlated with overall financial market trends.

There have been some studies discussing correlations between cryptocurrencies and other financial markets Kang et al. Considering the characteristics of cryptocurrency, the correlation between cryptocurrency and other assets still requires further research. Possible breakthroughs might be achieved with principal component analysis, the relationship between cryptocurrency and other currencies in extreme conditions i. Bubbles and crash research. To discuss the high volatility and return of cryptocurrencies, current research has focussed on bubbles of cryptocurrency markets Cheung et al.

Additional research for bubbles and crashes in cryptocurrency trading could include a connection between the process of bubble generation and financial collapse and conducting a coherent analysis coherent process analysis from the formation of bubbles to aftermath analysis of bubble burst , analysis of bubble theory by Microeconomics, trying other physical or industrial models in analysing bubbles in cryptocurrency market i.

Game theory and agent-based analysis Applying game theory or agent-based modelling in trading is a hot research direction in the traditional financial market. It might also be interesting to apply this method to trading in cryptocurrency markets.

With an in-depth understanding of these networks, we may identify new features in price prediction and may be closer to understanding financial bubbles in cryptocurrency trading. Balance between the opening of trading research literature and the fading of alphas Mclean et al. McLean and Pontiff pointed out that investors learn about mispricing in stock markets from academic publications. Similarly, cryptocurrency market predictability could also be affected by research papers in the area.

A possible attempt is to try new pricing methods applying real-time market changes. Considering the proportion of informed traders increasing in the cryptocurrency market in the pricing process is another breaking point looking for a balance between alpha trading and trading research literature.

We provided a comprehensive overview and analysis of the research work on cryptocurrency trading. This survey presented a nomenclature of the definitions and current state of the art. The paper provides a comprehensive survey of cryptocurrency trading papers and analyses the research distribution that characterise the cryptocurrency trading literature. We further summarised the datasets used for experiments and analysed the research trends and opportunities in cryptocurrency trading.

Future research directions and opportunities are discussed in " Opportunities in cryptocurrency trading " section. We expect this survey to be beneficial to academics e. The survey represents a quick way to get familiar with the literature on cryptocurrency trading and can motivate more researchers to contribute to the pressing problems in the area, for example along the lines we have identified.

Adeyanju, C. Alexander C, Dakos M A critical investigation of cryptocurrency data and analysis. Quantitative Finance 20 2 — Google Scholar. Antipova V Building and testing global investment portfolios using alternative asset classes. Financ Res Lett — Int Rev Financ Anal Eur J Oper Res 2 — Authority, F. Badenhorst JJ et al Effect of bitcoin spot and derivative trading volumes on price volatility. PhD thesis, University of Pretoria. Bai S, Robinson F Automated triangular arbitrage:: A trading algorithm for foreign exchange on a cryptocurrency market.

Q J Econ 4 — Bedi P, Nashier T On the investment credentials of bitcoin: a cross-currency perspective. Res Int Bus Financ Bell T Bitcoin trading agents. University of Southampton. Mark Lett 13 3 — Res Int Bus Financ — Bitfinex: Bitfinex markets. Bitfinex: Bitfinex terms of service.

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