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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. Regulatory authority and supported currencies of listed exchanges are collected from official websites or blogs.
Cryptocurrency trading is the act of buying and selling of cryptocurrencies with the intention of making a profit. The definition of cryptocurrency trading can be broken down into three aspects: object, operation mode and trading strategy.
A trading strategy in cryptocurrency trading, formulated by an investor, is an algorithm that defines a set of predefined rules to buy and sell on cryptocurrency markets.
Drastic fluctuations The volatility of cryptocurrencies are often likely to attract speculative interest and investors. The rapid fluctuations of intraday prices can provide traders with great money-earning opportunities, but it also includes more risk.
Unlike buying and selling stocks and commodities, the cryptocurrency market is not traded physically from a single location. Cryptocurrency transactions can take place between individuals, in different venues across the world.
With increasing concerns over identity theft and privacy, cryptocurrencies can thus provide users with some advantages regarding privacy. Different exchanges have specific Know-Your-Customer KYC measures for identifying users or customers Adeyanju Peer-to-peer transactions One of the biggest benefits of cryptocurrencies is that they do not involve financial institution intermediaries.
As mentioned above, this can reduce transaction costs. Moreover, this feature might appeal to users who distrust traditional systems. Over-the-counter OTC cryptocurrency markets offer, in this context, peer-to-peer transactions on the Blockchain. Cryptocurrencies may also include a partial ownership interest in physical assets such as artwork or real estate. Scalability problem Before the massive expansion of the technology infrastructure, the number of transactions and the speed of transactions cannot compete with traditional currency trading.
Scalability issues led to a multi-day trading backlog in March , affecting traders looking to move cryptocurrencies from their personal wallets to exchanges Forbes Cybersecurity issues As a digital technology, cryptocurrencies are subject to cyber security breaches and can fall into the hands of hackers.
Mitigating this situation requires ongoing maintenance of the security infrastructure and the use of enhanced cyber security measures that go beyond those used in traditional banking Kou et al. Regulations Authorities around the world face challenging questions about the nature and regulation of cryptocurrency as some parts of the system and its associated risks are largely unknown. There are currently three types of regulatory systems used to control digital currencies, they include: closed system for the Chinese market, open and liberal for the Swiss market,and open and strict system for the US market UKTN At the same time, we notice that some countries such as India is not at par in using the cryptocurrency.
This thing is not regulated. Cryptocurrency trading strategy is the main focus of this survey. There are many trading strategies, which can be broadly divided into two main categories: technical and fundamental. Technical and fundamental trading are two main trading analysis thoughts when it comes to analyzing the financial markets. Most traders use these two analysis methods or both Oberlechner Cryptocurrency trading can draw on the experience of stock market trading in most scenarios.
So we divide trading strategies into two main categories: technical and fundamental trading. They are similar in the sense that they both rely on quantifiable information that can be backtested against historical data to verify their performance. In recent years, a third kind of trading strategy, which we call programmatic trading, has received increasing attention. Such a trading strategy is similar to a technical trading strategy because it uses trading activity information on the exchange to make buying or selling decisions.
programmatic traders build trading strategies with quantitative data, which is mainly derived from price, volume, technical indicators or ratios to take advantage of inefficiencies in the market and are executed automatically by trading software. Cryptocurrency market is different from traditional markets as there are more arbitrage opportunities, higher fluctuation and transparency. Due to these characteristics, most traders and analysts prefer using programmatic trading in cryptocurrency markets.
Software trading systems allow international transactions, process customer accounts and information, and accept and execute transaction orders Calo and Johnson A cryptocurrency trading system is a set of principles and procedures that are pre-programmed to allow trade between cryptocurrencies and between fiat currencies and cryptocurrencies. Cryptocurrency trading systems are built to overcome price manipulation, cybercriminal activities and transaction delays Bauriya et al.
When developing a cryptocurrency trading system, we must consider the capital market, base asset, investment plan and strategies Molina Strategies are the most important part of an effective cryptocurrency trading system and they will be introduced below.
There exist several cryptocurrency trading systems that are available commercially, for example, Capfolio, 3Commas, CCXT, Freqtrade and Ctubio. From these cryptocurrency trading systems, investors can obtain professional trading strategy support, fairness and transparency from the professional third-party consulting companies and fast customer services.
Systematic trading is a way to define trading goals, risk controls and rules. In general, systematic trading includes high frequency trading and slower investment types like systematic trend tracking.
In this survey, we divide systematic cryptocurrency trading into technical analysis, pairs trading and others. Technical analysis in cryptocurrency trading is the act of using historical patterns of transaction data to assist a trader in assessing current and projecting future market conditions for the purpose of making profitable trades.
Price and volume charts summarise all trading activity made by market participants in an exchange and affect their decisions. Some experiments showed that the use of specific technical trading rules allows generating excess returns, which is useful to cryptocurrency traders and investors in making optimal trading and investment decisions Gerritsen et al.
Pairs trading is a systematic trading strategy that considers two similar assets with slightly different spreads. If the spread widens, short the high cryptocurrencies and buy the low cryptocurrencies. When the spread narrows again to a certain equilibrium value, a profit is generated Elliott et al. Papers shown in this section involve the analysis and comparison of technical indicators, pairs and informed trading, amongst other strategies.
Tools for building automated trading systems in cryptocurrency market are those emergent trading strategies for cryptocurrency. These include strategies that are based on econometrics and machine learning technologies.
Econometric methods apply a combination of statistical and economic theories to estimate economic variables and predict their values Vogelvang Statistical models use mathematical equations to encode information extracted from the data Kaufman In some cases, statistical modeling techniques can quickly provide sufficiently accurate models Ben-Akiva et al. Other methods might be used, such as sentiment-based prediction and long-and-short-term volatility classification based prediction Chang et al.
The prediction of volatility can be used to judge the price fluctuation of cryptocurrencies, which is also valuable for the pricing of cryptocurrency-related derivatives Kat and Heynen When studying cryptocurrency trading using econometrics, researchers apply statistical models on time-series data like generalised autoregressive conditional heteroskedasticity GARCH and BEKK named after Baba, Engle, Kraft and Kroner, Engle and Kroner models to evaluate the fluctuation of cryptocurrencies Caporin and McAleer A linear statistical model is a method to evaluate the linear relationship between prices and an explanatory variable Neter et al.
When there exists more than one explanatory variable, we can model the linear relationship between explanatory independent and response dependent variables with multiple linear models. The common linear statistical model used in the time-series analysis is the autoregressive moving average ARMA model Choi Machine learning is an efficient tool for developing Bitcoin and other cryptocurrency trading strategies McNally et al.
From the most basic perspective, Machine Learning relies on the definition of two main components: input features and objective function. The definition of Input Features data sources is where knowledge of fundamental and technical analysis comes into play. 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.
and other Seasonal indicators time of day, day of the week, 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.
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.
The sampling error, taking design effects from weighting into consideration, is ±3. This means that 95 times out of , the results will be within 3.
The sampling error for unweighted subgroups is larger: for the 1, registered voters, the sampling error is ±4. For the sampling errors of additional subgroups, please see the table at the end of this section.
Sampling error is only one type of error to which surveys are subject. Results may also be affected by factors such as question wording, question order, and survey timing. We present results for five geographic regions, accounting for approximately 90 percent of the state population. Residents of other geographic areas are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately.
We also present results for congressional districts currently held by Democrats or Republicans, based on residential zip code and party of the local US House member. We compare the opinions of those who report they are registered Democrats, registered Republicans, and no party preference or decline-to-state or independent voters; the results for those who say they are registered to vote in other parties are not large enough for separate analysis.
We also analyze the responses of likely voters—so designated per their responses to survey questions about voter registration, previous election participation, intentions to vote this year, attention to election news, and current interest in politics. The percentages presented in the report tables and in the questionnaire may not add to due to rounding. Additional details about our methodology can be found at www. pdf and are available upon request through surveys ppic.
October 14—23, 1, California adult residents; 1, California likely voters English, Spanish. Margin of error ±3. Percentages may not add up to due to rounding. Overall, do you approve or disapprove of the way that Gavin Newsom is handling his job as governor of California? Overall, do you approve or disapprove of the way that the California Legislature is handling its job?
Do you think things in California are generally going in the right direction or the wrong direction? Thinking about your own personal finances—would you say that you and your family are financially better off, worse off, or just about the same as a year ago?
Next, some people are registered to vote and others are not. Are you absolutely certain that you are registered to vote in California? Are you registered as a Democrat, a Republican, another party, or are you registered as a decline-to-state or independent voter? Would you call yourself a strong Republican or not a very strong Republican? Do you think of yourself as closer to the Republican Party or Democratic Party?
Which one of the seven state propositions on the November 8 ballot are you most interested in? Initiative Constitutional Amendment and Statute. It allows in-person sports betting at racetracks and tribal casinos, and requires that racetracks and casinos that offer sports betting to make certain payments to the state—such as to support state regulatory costs. The fiscal impact is increased state revenues, possibly reaching tens of millions of dollars annually.
Some of these revenues would support increased state regulatory and enforcement costs that could reach the low tens of millions of dollars annually. If the election were held today, would you vote yes or no on Proposition 26? Initiative Constitutional Amendment. It allows Indian tribes and affiliated businesses to operate online and mobile sports wagering outside tribal lands.
It directs revenues to regulatory costs, homelessness programs, and nonparticipating tribes. Some revenues would support state regulatory costs, possibly reaching the mid-tens of millions of dollars annually.
If the election were held today, would you vote yes or no on Proposition 27? Initiative Statute. It allocates tax revenues to zero-emission vehicle purchase incentives, vehicle charging stations, and wildfire prevention.
If the election were held today, would you vote yes or no on Proposition 30? Do you agree or disagree with these statements? Overall, do you approve or disapprove of the way that Joe Biden is handling his job as president?
Overall, do you approve or disapprove of the way Alex Padilla is handling his job as US Senator? Overall, do you approve or disapprove of the way Dianne Feinstein is handling her job as US Senator? Overall, do you approve or disapprove of the way the US Congress is handling its job? Do you think things in the United States are generally going in the right direction or the wrong direction?
How satisfied are you with the way democracy is working in the United States? Are you very satisfied, somewhat satisfied, not too satisfied, or not at all satisfied? These days, do you feel [rotate]  optimistic [or]  pessimistic that Americans of different political views can still come together and work out their differences?
What is your opinion with regard to race relations in the United States today? Would you say things are [rotate 1 and 2]  better ,  worse , or about the same than they were a year ago?
When it comes to racial discrimination, which do you think is the bigger problem for the country today—[rotate]  People seeing racial discrimination where it really does NOT exist [or]  People NOT seeing racial discrimination where it really DOES exist? Next, Next, would you consider yourself to be politically: [read list, rotate order top to bottom]. Generally speaking, how much interest would you say you have in politics—a great deal, a fair amount, only a little, or none? Mark Baldassare is president and CEO of the Public Policy Institute of California, where he holds the Arjay and Frances Fearing Miller Chair in Public Policy.
He is a leading expert on public opinion and survey methodology, and has directed the PPIC Statewide Survey since He is an authority on elections, voter behavior, and political and fiscal reform, and the author of ten books and numerous publications. Before joining PPIC, he was a professor of urban and regional planning in the School of Social Ecology at the University of California, Irvine, where he held the Johnson Chair in Civic Governance.
He has conducted surveys for the Los Angeles Times , the San Francisco Chronicle , and the California Business Roundtable. He holds a PhD in sociology from the University of California, Berkeley. Dean Bonner is associate survey director and research fellow at PPIC, where he coauthors the PPIC Statewide Survey—a large-scale public opinion project designed to develop an in-depth profile of the social, economic, and political attitudes at work in California elections and policymaking.
He has expertise in public opinion and survey research, political attitudes and participation, and voting behavior. Before joining PPIC, he taught political science at Tulane University and was a research associate at the University of New Orleans Survey Research Center.
He holds a PhD and MA in political science from the University of New Orleans. Rachel Lawler is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team. In that role, she led and contributed to a variety of quantitative and qualitative studies for both government and corporate clients.
She holds an MA in American politics and foreign policy from the University College Dublin and a BA in political science from Chapman University.
Deja Thomas is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team. Prior to joining PPIC, she was a research assistant with the social and demographic trends team at the Pew Research Center. In that role, she contributed to a variety of national quantitative and qualitative survey studies. She holds a BA in psychology from the University of Hawaiʻi at Mānoa.
This survey was supported with funding from the Arjay and Frances F. Ruben Barrales Senior Vice President, External Relations Wells Fargo. Mollyann Brodie Executive Vice President and Chief Operating Officer Henry J. Kaiser Family Foundation. Bruce E. Cain Director Bill Lane Center for the American West Stanford University. Jon Cohen Chief Research Officer and Senior Vice President, Strategic Partnerships and Business Development Momentive-AI.
Joshua J. Dyck Co-Director Center for Public Opinion University of Massachusetts, Lowell. Lisa García Bedolla Vice Provost for Graduate Studies and Dean of the Graduate Division University of California, Berkeley.
Russell Hancock President and CEO Joint Venture Silicon Valley. Sherry Bebitch Jeffe Professor Sol Price School of Public Policy University of Southern California. Carol S. Larson President Emeritus The David and Lucile Packard Foundation.
Lisa Pitney Vice President of Government Relations The Walt Disney Company. Robert K. Ross, MD President and CEO The California Endowment. Most Reverend Jaime Soto Bishop of Sacramento Roman Catholic Diocese of Sacramento.
Helen Iris Torres CEO Hispanas Organized for Political Equality. David C. Wilson, PhD Dean and Professor Richard and Rhoda Goldman School of Public Policy University of California, Berkeley. Chet Hewitt, Chair President and CEO Sierra Health Foundation.
Mark Baldassare President and CEO Public Policy Institute of California. Ophelia Basgal Affiliate Terner Center for Housing Innovation University of California, Berkeley. Louise Henry Bryson Chair Emerita, Board of Trustees J.
Paul Getty Trust. Sandra Celedon President and CEO Fresno Building Healthy Communities. Marisa Chun Judge, Superior Court of California, County of San Francisco. Steven A. Leon E. Panetta Chairman The Panetta Institute for Public Policy.
Cassandra Walker Pye President Lucas Public Affairs. Gaddi H. Vasquez Retired Senior Vice President, Government Affairs Edison International Southern California Edison. The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research. PPIC is a public charity. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office.
Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source. Research publications reflect the views of the authors and do not necessarily reflect the views of our funders or of the staff, officers, advisory councils, or board of directors of the Public Policy Institute of California.
PPIC Water Policy Center. PPIC Statewide Survey. PPIC Higher Education Center.
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