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We may divide the input into several groups of features, for example, those based on Economic indicators such as, gross domestic product indicator, interest rates, etc. The objective function defines the fitness criteria one uses to judge if the Machine Learning model has learnt the task at hand.
Typical predictive models try to anticipate numeric e. The machine learning model is trained by using historic input data sometimes called in-sample to generalise patterns therein to unseen out-of-sample data to approximately achieve the goal defined by the objective function. Clearly, in the case of trading, the goal is to infer trading signals from market indicators which help to anticipate asset future returns.
Generalisation error is a pervasive concern in the application of Machine Learning to real applications, and of utmost importance in Financial applications. We need to use statistical approaches, such as cross validation, to validate the model before we actually use it to make predictions. The process of using machine learning technology to predict cryptocurrency is shown in Fig. Depending on the formulation of the main learning loop, we can classify Machine Learning approaches into three categories: Supervised learning, Unsupervised learning and Reinforcement learning.
We list a general comparison IntelliPaat among these three machine learning methods in Table 2. Supervised learning is used to derive a predictive function from labeled training data. Labeled training data means that each training instance includes inputs and expected outputs. Usually, these expected outputs are produced by a supervisor and represent the expected behaviour of the model.
The most used labels in trading are derived from in sample future returns of assets. Unsupervised learning tries to infer structure from unlabeled training data and it can be used during exploratory data analysis to discover hidden patterns or to group data according to any pre-defined similarity metrics. Reinforcement learning utilises software agents trained to maximise a utility function, which defines their objective; this is flexible enough to allow agents to exchange short term returns for future ones.
In the financial sector, some trading challenges can be expressed as a game in which an agent aims at maximising the return at the end of the period. Further concrete examples are shown in a later section. Portfolio theory advocates diversification of investments to maximize returns for a given level of risk by allocating assets strategically.
The celebrated mean-variance optimisation is a prominent example of this approach Markowitz Generally, crypto asset denotes a digital asset i. There are some common ways to build a diversified portfolio in crypto assets. The first method is to diversify across markets, which is to mix a wide variety of investments within a portfolio of the cryptocurrency market. The second method is to consider the industry sector, which is to avoid investing too much money in any one category.
Diversified investment of portfolio in the cryptocurrency market includes portfolio across cryptocurrencies Liu and portfolio across the global market including stocks and futures Kajtazi and Moro Market condition research appears especially important for cryptocurrencies. A financial bubble is a significant increase in the price of an asset without changes in its intrinsic value Brunnermeier and Oehmke ; Kou et al.
In , Bitcoin faced a collapse in its value. This significant fluctuation inspired researchers to study bubbles and extreme conditions in cryptocurrency trading. Some experts believe that the extreme volatility of exchange rates means that cryptocurrency exposure should be kept at a low percentage of your portfolio. In any case, bubbles and crash analysis is an important researching area in cryptocurrency trading. The section introduces the scope and approach of our paper collection, a basic analysis, and the structure of our survey.
We adopt a bottom-up approach to the research in cryptocurrency trading, starting from the systems up to risk management techniques. For the underlying trading system, the focus is on the optimisation of trading platforms structure and improvements of computer science technologies. At a higher level, researchers focus on the design of models to predict return or volatility in cryptocurrency markets.
These techniques become useful to the generation of trading signals. Bubbles and extreme conditions are hot topics in cryptocurrency trading because, as discussed above, these markets have shown to be highly volatile whilst volatility went down after crashes. Portfolio and cryptocurrency asset management are effective methods to control risk. We group these two areas in risk management research. Other papers included in this survey include topics like pricing rules, dynamic market analysis, regulatory implications, and so on.
Table 3 shows the general scope of cryptocurrency trading included in this survey. Since many trading strategies and methods in cryptocurrency trading are closely related to stock trading, some researchers migrate or use the research results for the latter to the former.
When conducting this research, we only consider those papers whose research focuses on cryptocurrency markets or a comparison of trading in those and other financial markets. Specifically, we apply the following criteria when collecting papers related to cryptocurrency trading:. The paper introduces or discusses the general idea of cryptocurrency trading or one of the related aspects of cryptocurrency trading.
The paper proposes an approach, study or framework that targets optimised efficiency or accuracy of cryptocurrency trading. Some researchers gave a brief survey of cryptocurrency Ahamad et al. These surveys are rather limited in scope as compared to ours, which also includes a discussion on the latest papers in the area; we want to remark that this is a fast-moving research field. To collect the papers in different areas or platforms, we used keyword searches on Google Scholar and arXiv, two of the most popular scientific databases.
We also choose other public repositories like SSRN but we find that almost all academic papers in these platforms can also be retrieved via Google Scholar; consequently, in our statistical analysis, we count those as Google Scholar hits. We choose arXiv as another source since it allows this survey to be contemporary with all the most recent findings in the area. The interested reader is warned that these papers have not undergone formal peer review. The keywords used for searching and collecting are listed below.
We conducted 6 searches across the two repositories until July 1, To ensure high coverage, we adopted the so-called snowballing Wohlin method on each paper found through these keywords. We checked papers added from snowballing methods that satisfy the criteria introduced above until we reached closure. Table 4 shows the details of the results from our paper collection.
Keyword searches and snowballing resulted in papers across the six research areas of interest in " Survey scope " section. Figure 7 shows the distribution of papers published at different research sites. Among all the papers, The distribution of different venues shows that cryptocurrency trading is mostly published in Finance and Economics venues, but with a wide diversity otherwise.
We discuss the contributions of the collected papers and a statistical analysis of these papers in the remainder of the paper, according to Table 5. The papers in our collection are organised and presented from six angles. We introduce the work about several different cryptocurrency trading software systems in " Cryptocurrency trading software systems " section.
In " Emergent trading technologies " section, we introduce some emergent trading technologies including econometrics on cryptocurrencies, machine learning technologies and other emergent trading technologies in the cryptocurrency market.
Section 8 introduces research on cryptocurrency pairs and related factors and crypto-asset portfolios research. In " Bubbles and crash analysis " and " Extreme condition " sections we discuss cryptocurrency market condition research, including bubbles, crash analysis, and extreme conditions. We would like to emphasize that the six headings above focus on a particular aspect of cryptocurrency trading; we give a complete organisation of the papers collected under each heading.
This implies that those papers covering more than one aspect will be discussed in different sections, once from each angle. We analyse and compare the number of research papers on different cryptocurrency trading properties and technologies in " Summary analysis of literature review " section, where we also summarise the datasets and the timeline of research in cryptocurrency trading.
We build upon this review to conclude in " Opportunities in cryptocurrency trading " section with some opportunities for future research. Table 6 compares the cryptocurrency trading systems existing in the market. Capfolio is a proprietary payable cryptocurrency trading system which is a professional analysis platform and has an advanced backtesting engine Capfolio It supports five different cryptocurrency exchanges.
Twelve different cryptocurrency exchanges are compatible with this system. Any trader or developer can create a trading strategy based on this data and access public transactions through the APIs Ccxt The CCXT library is used to connect and trade with cryptocurrency exchanges and payment processing services worldwide.
It provides quick access to market data for storage, analysis, visualisation, indicator development, algorithmic trading, strategy backtesting, automated code generation and related software engineering. It is designed for coders, skilled traders, data scientists and financial analysts to build trading algorithms.
Current CCXT features include:. It can generate market-neutral strategies that do not transfer funds between exchanges Blackbird The motivation behind Blackbird is to naturally profit from these temporary price differences between different exchanges while being market neutral. Unlike other Bitcoin arbitrage systems, Blackbird does not sell but actually short sells Bitcoin on the short exchange.
This feature offers two important advantages. Firstly, the strategy is always market agnostic: fluctuations rising or falling in the Bitcoin market will not affect the strategy returns. This eliminates the huge risks of this strategy. Buy and sell transactions are conducted in parallel on two different exchanges. There is no need to deal with transmission delays. StockSharp is an open-source trading platform for trading at any market of the world including 48 cryptocurrency exchanges Stocksharp It has a free C library and free trading charting application.
Manual or automatic trading algorithmic trading robot, regular or HFT can be run on this platform. StockSharp consists of five components that offer different features:. Shell - ready-made graphics framework that can be changed according to needs and has a fully open source in C ;. Any trading strategies can be created in S.
Freqtrade is a free and open-source cryptocurrency trading robot system written in Python. It is designed to support all major exchanges and is controlled by telegram. It contains backtesting, mapping and money management tools, and strategy optimization through machine learning Fretrade Freqtrade has the following features:. Strategy optimization through machine learning: Use machine learning to optimize your trading strategy parameters with real trading data;. Marginal Position Size: Calculates winning rate, risk-return ratio, optimal stop loss and adjusts position size, and then trades positions for each specific market;.
CryptoSignal is a professional technical analysis cryptocurrency trading system Cryptosignal The system gives alerts including Email, Slack, Telegram, etc. CryptoSignal has two primary features. First of all, it offers modular code for easy implementation of trading strategies; Secondly, it is easy to install with Docker.
This trading system can place or cancel orders through supported cryptocurrency exchanges in less than a few milliseconds. Moreover, it provides a charting system that can visualise the trading account status including trades completed, target position for fiat currency, etc. Catalyst is an analysis and visualization of the cryptocurrency trading system Catalyst It makes trading strategies easy to express and backtest them on historical data daily and minute resolution , providing analysis and insights into the performance of specific strategies.
Catalyst allows users to share and organise data and build profitable, data-driven investment strategies. Catalyst not only supports the trading execution but also offers historical price data of all crypto assets from minute to daily resolution. Catalyst also has backtesting and real-time trading capabilities, which enables users to seamlessly transit between the two different trading modes.
Lastly, Catalyst integrates statistics and machine learning libraries such as matplotlib, scipy, statsmodels and sklearn to support the development, analysis and visualization of the latest trading systems. Users can test the strategy in sandbox environment simulation. If simulation mode is enabled, a fake balance for each coin must be specified for each exchange. Bauriya et al. A real-time cryptocurrency trading system is composed of clients, servers and databases.
The server collects cryptocurrency market data by creating a script that uses the Coinmarket API. Finally, the database collects balances, trades and order book information from the server. The authors tested the system with an experiment that demonstrates user-friendly and secure experiences for traders in the cryptocurrency exchange platform. The original Turtle Trading system is a trend following trading system developed in the s. The idea is to generate buy and sell signals on stock for short-term and long-term breakouts and its cut-loss condition which is measured by Average true range ATR Kamrat et al.
The trading system will adjust the size of assets based on their volatility. Essentially, if a turtle accumulates a position in a highly volatile market, it will be offset by a low volatility position. Extended Turtle Trading system is improved with smaller time interval spans and introduces a new rule by using exponential moving average EMA.
The author of Kamrat et al. Through the experiment, Original Turtle Trading System achieved an Extended Turtle Trading System achieved Arbitrage trading aims to spot the differences in price that can occur when there are discrepancies in the levels of supply and demand across multiple exchanges.
As a result, a trader could realise a quick and low-risk profit by buying from one exchange and selling at a higher price on a different exchange. Arbitrage trading signals are caught by automated trading software. The technical differences between data sources impose a server process to be organised for each data source.
Relational databases and SQL are reliable solution due to the large amounts of relational data. The author used the system to catch arbitrage opportunities on 25 May among cryptocurrencies on 7 different exchanges. Arbitrage Trading Software System introduced in that paper presented general principles and implementation of arbitrage trading system in the cryptocurrency market. Real-time trading systems use real-time functions to collect data and generate trading algorithms. Turtle trading system and arbitrage trading system have shown a sharp contrast in their profit and risk behaviour.
Using Turtle trading system in cryptocurrency markets got high returns with high risk. Arbitrage trading system is inferior in terms of revenue but also has a lower risk. One feature that turtle trading system and arbitrage trading system have in common is they performed well in capturing alpha. Many researchers have focused on technical indicators patterns analysis for trading on cryptocurrency markets.
Table 7 shows the comparison among these five classical technical trading strategies using technical indicators. This strategy is a kind of chart trading pattern. Technical analysis tools such as candlestick and box charts with Fibonacci Retracement based on golden ratio are used in this technical analysis.
Fibonacci Retracement uses horizontal lines to indicate where possible support and resistance levels are in the market. This strategy used a price chart pattern and box chart as technical analysis tools. 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. But this time feels different. It feels like a bubble. We also began to see a robust supply response. Bubbles are complex dynamics. What they all have in common, however, is they require emotion to truly go parabolic. Moreover, the less we understand the object of the bubble, the greater the scope for greed and FOMO to fill in the blanks.
His views were especially prescient. He told Bloomberg this month that he made a profit twice due to this canny call. Gox was the go-to service for handling transactions. But it was still early enough for people to believe that the blockchain system was still getting all the technical kinks out. This, once again, sent shockwaves through the community—but also had the unfortunate impact of normalizing these types of hacks for some people.
At the end of and beginning of , more people—especially those in the mainstream finance world—were paying attention to bitcoin and cryptocurrency trading. This happened right around the time that bitcoin slipped from its peak value, and it certainly seemed to accelerate its drop. According to Stephen Innes, the head of Asian trading for the foreign exchange Oanda, hacks were the first element to have a chilling effect on crypto.
Over the course of a few months, China, Japan, and South Korea all announced different measures to better regulate crypto-trading. The world was watching to see if this new technology would hit the mainstream—and government crackdowns following gigantic hacks helped poison the public perception.
Beyond the clampdown by some governments, what bitcoin really needed to achieve sustained success was overall mainstream acceptance. While some financial institutions announced projects exploring blockchain-based solutions, many others balked. JPMorgan CEO Jamie Dimon, for instance, made multiple comments throughout the year expressing his general antipathy for cryptocurrency.
One theory that the U. Justice Department is reportedly looking into is that the digital coin Tether which is supposedly pegged to the U. This theory stems from an academic paper , which cast Tether in a very damning light. And it also led many to believe that the initial bitcoin craze was manufactured and destined to bust. This would be a path for more mainstream people in finance to dabble with blockchain; it would allow investors to dip their toes in bitcoin without owning the actual asset.
Not only that, but it would make bitcoin available on the most prominent financial markets. The U. Securities and Exchange Commission SEC , however, has yet to allow such a fund to exist—mostly because it is unable to monitor crypto-transactions in order to avoid market manipulation. The inability to get SEC approval really held back bitcoin and cryptocurrencies in general.
Blockchains are decentralized, and democratic systems require buy-in from participants in order to keep the engines running. In , this became apparent with the DAO hack. But DAO users had to agree to this change, and there were dissenters. Though the hard fork was approved, it created two active blockchains with two different sets of rules. Ultimately, this hack—coupled with the inability to deal with it—caused the DAO to end in This year we saw a similar fight break out—this time over bitcoin cash.
This coin, mind you, is not bitcoin, though it is built on the same architecture. It was created by a group of miners who disagreed with some of the fundamentals of the initial bitcoin system, and so they forked a new blockchain and went their own way.
In terms of market capitalization, bitcoin cash has always been one of the top cryptocurrencies—in the ranks of Ethereum and XRP. This past autumn, the bitcoin cash community—which was created due to a technical disagreement with the larger bitcoin sector—started a civil war.
Essentially, bitcoin cash developers had diverging views on the software update for the system, and so they decided to implement another hard fork. This created two new bitcoin cash sects. Internally, the fork caused a lot of strife; one of the most popular bitcoin alternatives was unable to reach a consensus, and instead had to create two different paths that would essentially go to war with each other. When the hard fork arrived—and participants had to choose which path to take—the entire cryptocurrency market dropped.
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