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.
on the next level above predictive models, researchers discuss technical trading methods to trade in real cryptocurrency markets. 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. The table is sorted based on URL types GitHub or Official website and GitHub stars if appropriate.
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. Secondly, this strategy does not require transferring funds USD or BTC between Bitcoin exchanges. 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 ;. API - a free C library for programmers using Visual Studio.
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 Investors can track over coins of Bittrex, Bitfinex, GDAX, Gemini and more. Automated technical analysis includes momentum, RSI, Ichimoku Cloud, MACD, etc. 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. Golang Crypto Trading Bot is a Go based cryptocurrency trading system Golang 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 This research showed how Extended Turtle Trading System compared can improve over Original Turtle Trading System in trading cryptocurrencies.
Christian Păuna introduced arbitrage trading systems for cryptocurrencies. 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. The research paper Păuna listed the best ten trading signals made by this system from available found signals. 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 approach of the experiment extended the Copula-Granger-causality in distribution CGCD method of Lee and Yang in 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.
Several econometrics methods in time-series research, such as GARCH and BEKK, have been used in the literature on cryptocurrency trading. 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. The results showed that MSGARCH models clearly outperform single-regime GARCH for Value-at-Risk forecasting.
Troster et al. The results also illustrated the importance of modeling excess kurtosis for Bitcoin returns. Charles and Darné studied four cryptocurrency markets including Bitcoin, Dash, Litecoin and Ripple. Results showed cryptocurrency returns are strongly characterised by the presence of jumps as well as structural breaks except the Dash market.
Four GARCH-type models i. The research indicated the importance of jumps in cryptocurrency volatility and structural breakthroughs. Autoregressive-moving-average model with exogenous inputs model ARMAX , GARCH, VAR and Granger causality tests are used in the experiments. 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. The consistent test of Domínguez and Lobato , generalized spectral GS of Escanciano and Velasco are applied in capturing time-varying linear and nonlinear dependence in bitcoin 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. The results showed that the proposed new MRS-MIDAS model exhibits statistically significant improvements in predicting the RV of Bitcoin.
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.
Once stationarity is ensured, ARCH LM is tested for ARCH effects to examine the requirement of volatility modeling in return series. 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. Hultman set out to examine GARCH 1,1 , bivariate-BEKK 1,1 and a standard stochastic model to forecast the volatility of Bitcoin.
A rolling window approach is used in these experiments. Mean absolute error MAE , Mean squared error MSE and Root-mean-square deviation RMSE are three loss criteria adopted to evaluate the degree of error between predicted and true values.
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 Deep learning is a modern take on artificial neural networks ANNs Zhang et al.
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 DQN Gu et al.
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. Data is preprocessed to construct a training dataset that contains a matrix of technical patterns including EMA, Emerging Markets Small Cap EMSD , relative strength index RSI , etc.
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. Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years".
It was apparently these kinds of agreements that Xbox's Phil Spencer had in mind opens in new tab when he spoke to Sony bosses in January and confirmed Microsoft's "intent to honor all existing agreements upon acquisition of Activision Blizzard".
Unfortunately, the footnote ends there, so there's not much in the way of detail about what these restrictions are or how long they'd remain in effect in a potential post-acquisition world. Given COD's continued non-appearance on Game Pass, you've got to imagine the restrictions are fairly significant if they're not an outright block on COD coming to the service.
Either way, the simple fact that Microsoft is apparently willing to maintain any restrictions on its own ability to put first-party games on Game Pass is rather remarkable, given that making Game Pass more appealing is one of the reasons for its acquisition spree.
The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA. While it's absolutely reasonable to worry about a world in which more and more properties are concentrated in the hands of singular, giant megacorps, it does look a bit odd if you're complaining about losing access to games while stopping them from joining competing services.
We'll find out if the CMA agrees when it completes its in-depth, "Phase 2" investigation opens in new tab into the Activision Blizzard acquisition, which is some way off yet. For now, we'll have to content ourselves with poring over these kinds of corporate submissions for more interesting tidbits like this one. So far, we've already learned that Microsoft privately has a gloomy forecast for the future of cloud gaming opens in new tab , and that the company thinks Sony shouldn't worry so much since, hey, future COD games might be as underwhelming as Vanguard opens in new tab.
i Maturities for which rates are solved directly are referred to as "pillar points", these correspond to the input instrument maturities; other rates are interpolated , often using Hermitic splines. ii The objective function : prices must be "exactly" returned, as described. iii The penalty function will weigh: that forward rates are positive to be arbitrage free and curve "smoothness" ; both, in turn, a function of the interpolation method.
v All that need be stored are the solved spot rates for the pillars, and the interpolation rule. A CSA could allow for collateral, and hence interest payments on that collateral, in any currency. It is built by solving for observed mark-to-market cross-currency swap rates , where the local -IBOR is swapped for USD LIBOR with USD collateral as underpin. The latest, pre-solved USD-LIBOR-curve is therefore an external element of the curve-set, and the basis-curve is then solved in the "third stage".
Each currency's curve-set will thus include a local-currency discount-curve and its USD discounting basis-curve. As required, a third-currency discount curve — i. for local trades collateralized in a currency other than local or USD or any other combination — can then be constructed from the local-currency basis-curve and third-currency basis-curve, combined via an arbitrage relationship known here as "FX Forward Invariance".
LIBOR is due to be phased out by the end of , with replacements including SOFR and TONAR. With the coexistence of "old" and "new" rates in the market, multi-curve and OIS curve "management" is necessary, with changes required to incorporate new discounting and compounding conventions, while the underlying logic is unaffected; see. The complexities of modern curvesets mean that there may not be discount factors available for a specific -IBOR index curve. These curves are known as 'forecast only' curves and only contain the information of a forecast -IBOR index rate for any future date.
Some designs constructed with a discount based methodology mean forecast -IBOR index rates are implied by the discount factors inherent to that curve:. In both cases, the PV of a general swap can be expressed exactly with the following intuitive formula:. This shows that the PV of an IRS is roughly linear in the swap par rate though small non-linearities arise from the co-dependency of the swap rate with the discount factors in the Annuity sum. During the life of the swap the same valuation technique is used, but since, over time, both the discounting factors and the forward rates change, the PV of the swap will deviate from its initial value.
Therefore, the swap will be an asset to one party and a liability to the other. The way these changes in value are reported is the subject of IAS 39 for jurisdictions following IFRS , and FAS for U. Swaps are marked to market by debt security traders to visualize their inventory at a certain time. Interest rate swaps expose users to many different types of financial risk.
The value of an interest rate swap will change as market interest rates rise and fall. In market terminology this is often referred to as delta risk. Interest rate swaps also exhibit gamma risk whereby their delta risk increases or decreases as market interest rates fluctuate.
See Greeks finance , Value at risk Computation methods , Value at risk VaR risk management. Other specific types of market risk that interest rate swaps have exposure to are basis risks —where various IBOR tenor indexes can deviate from one another—and reset risks - where the publication of specific tenor IBOR indexes are subject to daily fluctuation.
Uncollateralised interest rate swaps—those executed bilaterally without a CSA in place—expose the trading counterparties to funding risks and credit risks. Funding risks because the value of the swap might deviate to become so negative that it is unaffordable and cannot be funded.
Credit risks because the respective counterparty, for whom the value of the swap is positive, will be concerned about the opposing counterparty defaulting on its obligations. Collateralised interest rate swaps, on the other hand, expose the users to collateral risks: here, depending upon the terms of the CSA, the type of posted collateral that is permitted might become more or less expensive due to other extraneous market movements. Credit and funding risks still exist for collateralised trades but to a much lesser extent.
Regardless, due to regulations set out in the Basel III Regulatory Frameworks, trading interest rate derivatives commands a capital usage. The consequence of this is that, dependent upon their specific nature, interest rate swaps might command more capital usage, and this can deviate with market movements.
Thus capital risks are another concern for users. Given these concerns, banks will typically calculate a credit valuation adjustment , as well as other x-valuation adjustments , which then incorporate these risks into the instrument value. Reputation risks also exist. The mis-selling of swaps, over-exposure of municipalities to derivative contracts, and IBOR manipulation are examples of high-profile cases where trading interest rate swaps has led to a loss of reputation and fines by regulators.
Hedging interest rate swaps can be complicated and relies on numerical processes of well designed risk models to suggest reliable benchmark trades that mitigate all market risks; although, see the discussion above re hedging in a multi-curve environment. The other, aforementioned risks must be hedged using other systematic processes. ICE Swap rate  replaced the rate formerly known as ISDAFIX in Swap Rate benchmark rates are calculated using eligible prices and volumes for specified interest rate derivative products.
Multiple, randomised snapshots of market data are taken during a short window before calculation. This enhances the benchmark's robustness and reliability by protecting against attempted manipulation and temporary aberrations in the underlying market. The market-making of IRSs is an involved process involving multiple tasks; curve construction with reference to interbank markets, individual derivative contract pricing, risk management of credit, cash and capital. The cross disciplines required include quantitative analysis and mathematical expertise, disciplined and organized approach towards profits and losses, and coherent psychological and subjective assessment of financial market information and price-taker analysis.
The time sensitive nature of markets also creates a pressurized environment. Many tools and techniques have been designed to improve efficiency of market-making in a drive to efficiency and consistency. In June the Audit Commission was tipped off by someone working on the swaps desk of Goldman Sachs that the London Borough of Hammersmith and Fulham had a massive exposure to interest rate swaps.
When the commission contacted the council, the chief executive told them not to worry as "everybody knows that interest rates are going to fall"; the treasurer thought the interest rate swaps were a "nice little earner".
The Commission's Controller, Howard Davies , realised that the council had put all of its positions on interest rates going down and ordered an investigation.
Financial Innovation volume 8 , Article number: 13 Cite this article. Metrics details. In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated.
Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management.
This paper provides a comprehensive survey of cryptocurrency trading research, by covering research papers on various aspects of cryptocurrency trading e. Cryptocurrencies have experienced broad market acceptance and fast development despite their recent conception. Many hedge funds and asset managers have begun to include cryptocurrency-related assets into their portfolios and trading strategies.
The academic community has similarly spent considerable efforts in researching cryptocurrency trading. This paper seeks to provide a comprehensive survey of the research on cryptocurrency trading, by which we mean any study aimed at facilitating and building strategies to trade cryptocurrencies. As an emerging market and research direction, cryptocurrencies and cryptocurrency trading have seen considerable progress and a notable upturn in interest and activity Farell From Fig.
The sampling interval of this survey is from to June Cryptocurrency trading software systems i. Systematic trading including technical analysis, pairs trading and other systematic trading methods;.
Emergent trading technologies including econometric methods, machine learning technology and other emergent trading methods;. Portfolio and cryptocurrency assets including research among cryptocurrency co-movements and crypto-asset portfolio research;.
Market condition research including bubbles Flood et al. In this survey we aim at compiling the most relevant research in these areas and extract a set of descriptive indicators that can give an idea of the level of maturity research in this area has achieved. The distribution among properties defines the classification of research objectives and content.
The distribution among technologies defines the classification of methods or technological approaches to the study of cryptocurrency trading. Moreover, We identify datasets and opportunities potential research directions that have appeared in the cryptocurrency trading area. To ensure that our survey is self-contained, we aim to provide sufficient material to adequately guide financial trading researchers who are interested in cryptocurrency trading.
There has been related work that discussed or partially surveyed the literature related to cryptocurrency trading. Kyriazis investigated the efficiency and profitable trading opportunities in the cryptocurrency market.
Ahamad et al. Mukhopadhyay et al. Merediz-Solà and Bariviera performed a bibliometric analysis of bitcoin literature. To the best of our knowledge, no previous work has provided a comprehensive survey particularly focused on cryptocurrency trading. Definition This paper defines cryptocurrency trading and categorises it into: cryptocurrency markets, cryptocurrency trading models, and cryptocurrency trading strategies. The core content of this survey is trading strategies for cryptocurrencies while we cover all aspects of it.
Multidisciplinary survey The paper provides a comprehensive survey of cryptocurrency trading papers, across different academic disciplines such as finance and economics, artificial intelligence and computer science. Some papers may cover multiple aspects and will be surveyed for each category. Analysis The paper analyses the research distribution, datasets and trends that characterise the cryptocurrency trading literature. Horizons The paper identifies challenges, promising research directions in cryptocurrency trading, aimed to promote and facilitate further research.
Figure 2 depicts the paper structure, which is informed by the review schema adopted. More details about this can be found in " Paper collection and review schema " section. This section provides an introduction to cryptocurrency trading. We will discuss Blockchain , as the enabling technology, cryptocurrency markets and cryptocurrency trading strategies.
Blockchain is a digital ledger of economic transactions that can be used to record not just financial transactions, but any object with an intrinsic value Tapscott and Tapscott In its simplest form, a Blockchain is a series of immutable data records with timestamps, which are managed by a cluster of machines that do not belong to any single entity.
Each of these data block s is protected by cryptographic principle and bound to each other in a chain cf. Cryptocurrencies like Bitcoin are conducted on a peer-to-peer network structure. Each peer has a complete history of all transactions, thus recording the balance of each account.
This is basic public-key cryptography, but also the building block on which cryptocurrencies are based. After being signed, the transaction is broadcast on the network. For example, if a transaction is contained in block and the length of the blockchain is blocks, it means that the transaction has 5 confirmations — Johar Confirmation is a critical concept in cryptocurrencies; only miners can confirm transactions.
Miners add blocks to the Blockchain; they retrieve transactions in the previous block and combine it with the hash of the preceding block to obtain its hash, and then store the derived hash into the current block.
Miners in Blockchain accept transactions, mark them as legitimate and broadcast them across the network. After the miner confirms the transaction, each node must add it to its database. In layman terms, it has become part of the Blockchain and miners undertake this work to obtain cryptocurrency tokens, such as Bitcoin. In contrast to Blockchain, cryptocurrencies are related to the use of tokens based on distributed ledger technology.
Any transaction involving purchase, sale, investment, etc. involves a Blockchain native token or sub-token. Blockchain is a platform that drives cryptocurrency and is a technology that acts as a distributed ledger for the network.
The network creates a means of transaction and enables the transfer of value and information. Cryptocurrencies are the tokens used in these networks to send value and pay for these transactions. They can be thought of as tools on the Blockchain, and in some cases can also function as resources or utilities. In other instances, they are used to digitise the value of assets.
In summary, cryptocurrencies are part of an ecosystem based on Blockchain technology. Cryptocurrency is a decentralised medium of exchange which uses cryptographic functions to conduct financial transactions Doran Cryptocurrencies leverage the Blockchain technology to gain decentralisation, transparency, and immutability Meunier In the above, we have discussed how Blockchain technology is implemented for cryptocurrencies.
In general, the security of cryptocurrencies is built on cryptography, neither by people nor on trust Narayanan et al. Elliptic curve cryptography is a type of public-key cryptography that relies on mathematics to ensure the security of transactions.
When someone attempts to circumvent the aforesaid encryption scheme by brute force, it takes them one-tenth the age of the universe to find a value match when trying billion possibilities every second Grayblock Regarding its use as a currency, cryptocurrency has properties similar to fiat currencies. It has a controlled supply.
Most cryptocurrencies limit the availability of their currency volumes. for Bitcoin, the supply will decrease over time and will reach its final quantity sometime around All cryptocurrencies control the supply of tokens through a timetable encoded in the Blockchain.
One of the most important features of cryptocurrencies is the exclusion of financial institution intermediaries Harwick With cryptocurrencies, even if part of the network is compromised, the rest will continue to be able to verify transactions correctly.
Cryptocurrencies also have the important feature of not being controlled by any central authority Rose : the decentralised nature of the Blockchain ensures cryptocurrencies are theoretically immune to government control and interference.
The pure digital asset is anything that exists in a digital format and carries with it the right to use it. As of December 20, , there exist cryptocurrencies and 20, cryptocurrency markets; the market cap is around billion dollars CoinMaketCap Figure 4 shows historical data on global market capitalisation and h trading volume TradingView The total market cap is calculated by aggregating the dollar market cap of all cryptocurrencies. From the figure, we can observe how cryptocurrencies experience exponential growth in and a large bubble burst in early In the wake of the pandemic, cryptocurrencies raised dramatically in value in In , the market value of cryptocurrencies has been very volatile but consistently at historically high levels.
Total market capitalization and volume of cryptocurrency market, USD TradingView There are three mainstream cryptocurrencies Council : Bitcoin BTC , Ethereum ETH , and Litecoin LTC. Bitcoin was created in and garnered massive popularity. a financial institution. A very important feature of Ethereum is the ability to create new tokens on the Ethereum Blockchain. The Ethereum network went live on July 30, , and pre-mined 72 million Ethereum. Litecoin is a peer-to-peer cryptocurrency created by Charlie Lee.
It was created according to the Bitcoin protocol, but it uses a different hashing algorithm. Litecoin uses a memory-intensive proof-of-work algorithm, Scrypt. Figure 5 shows percentages of total cryptocurrency market capitalisation; Bitcoin and Ethereum account for the majority of the total market capitalisation data collected on 14 September Percentage of Total Market Capitalisation Coinmarketcap A cryptocurrency exchange or digital currency exchange DCE is a business that allows customers to trade cryptocurrencies.
Cryptocurrency exchanges can be market makers, usually using the bid-ask spread as a commission for services, or as a matching platform, by simply charging fees.
A cryptocurrency exchange or digital currency exchange DCE is a place that allows customers to trade cryptocurrencies. Cryptocurrency exchanges can be market makers usually using the bid-ask spread as a commission for services or a matching platform simply charging fees.
Chicago Mercantile Exchange CME , Chicago Board Options Exchange CBOE as well as BAKKT backed by New York Stock Exchange are regulated cryptocurrency exchanges.
WebThe Black–Scholes / ˌ b l æ k ˈ ʃ oʊ l z / or Black–Scholes–Merton model is a mathematical model for the dynamics of a financial market containing derivative investment instruments. From the parabolic partial differential equation in the model, known as the Black–Scholes equation, one can deduce the Black–Scholes formula, which gives a theoretical estimate Web12/10/ · Microsoft pleaded for its deal on the day of the Phase 2 decision last month, but now the gloves are well and truly off. Microsoft describes the CMA’s concerns as “misplaced” and says that Web21/10/ · A footnote in Microsoft's submission to the UK's Competition and Markets Authority (CMA) has let slip the reason behind Call of Duty's absence from the Xbox Game Pass library: Sony and WebAn interest rate swap's (IRS's) effective description is a derivative contract, agreed between two counterparties, which specifies the nature of an exchange of payments benchmarked against an interest rate blogger.com most common IRS is a fixed for floating swap, whereby one party will make payments to the other based on an initially agreed WebThe High Reward / Risk Alternative. If you accept more risk, products like binary options and CFDs can return close to % on a single successful trade with top broker Pocket blogger.com products can be used on the forex markets for 24/6 access and results are achieved in minutes rather than hours blogger.com allows expert authors in hundreds of niche fields to get massive levels of exposure in exchange for the submission of their quality original articles ... read more
Furthermore, a popular asset such as Bitcoin is so new that tax laws have not yet fully caught up — is it a currency or a commodity? Namespaces Article Talk. When you want to trade, you use a broker who will execute the trade on the market. The next day it closes below the 5 SMA again. 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.Since the option value whether put or call is increasing in this parameter, it can be inverted to produce a " volatility surface " that is then used to calibrate other models, e. One Greek, "gamma" as well as others not listed here is a partial derivative of another Greek, "delta" in this case, trading binary options strategies parameters pdf. The perspective is rationalized based on the elastic demand for computing resources of the cloud infrastructure. The parametric test employed six parametric copula functions to discover dependency density between variables. Explicit modeling: this feature means that, rather than assuming a volatility a priori and computing prices from it, one can use the model to solve for volatility, which gives the implied volatility of an option at given prices, durations and exercise prices.