cryptocurrency trading
53072748
Trading and arbitrage in cryptocurrency markets
Cryptocurrency markets exhibit periods of large, recurrent arbitrage opportunities across exchanges. These price deviations are much larger across than within countries, and smaller between cryptocurrencies, highlighting the importance of capital controls for the movement of arbitrage capital. Price deviations across countries co move and open up in times of large bitcoin appreciation. Countries with higher bitcoin premia over the US bitcoin price see widening arbitrage deviations when bitcoin appreciates. Finally, we decompose signed volume on each exchange into a common and an id iosyncratic component. The common component explains 80% of bitcoin returns. The idiosyncratic components help explain arbitrage spreads between exchanges.
Journal of Financial Economics
Igor Makarov Antoinette Schoar
208175563
Competition of noise and collectivity in global cryptocurrency trading: route to a self contained market
Cross correlations in fluctuations of the daily exchange rates within the basket of the 100 highest capitalization cryptocurrencies over the period October 1, 2015 March 31, 2019 are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of the principal eigenvalue, and thus the degree of collectivity, strongly depends on which cryptocurrency is used as a base. It is largest when the base is the most peripheral cryptocurrency; when more significant ones are taken into consideration, its magnitude systematically decreases, nevertheless preserving a sizable gap with respect to the random bulk, which in turn indicates that the organization of correlations becomes more heterogeneous. This finding provides a criterion for recognizing which currencies or cryptocurrencies play a dominant role in the global cryptomarket. The present study shows that over the period under consideration, the Bitcoin (BTC) predominates, hallmarking exchange rate dynamics at least as influential as the U.S. dollar (USD) Even more, the BTC started dominating around the year 2017, while other cryptocurrencies, such as the Ethereum and even Ripple, assumed similar trends. At the same time, the USD, an original value determinant for the cryptocurrency market, became increasingly disconnected, and its related characteristics eventually started approaching those of a fictitious currency. These results are strong indicators of incipient independence of the global cryptocurrency market, delineating a self contained trade resembling the Forex.
Chaos
Stanislaw Drozdz Ludovico Minati Pawel Oswiecimka Marek Stanuszek Marcin Watorek
214641179
Cryptocurrency Trading: A Comprehensive Survey
Since the inception of cryptocurrencies, an increasing number of financial institutions are getting involved in cryptocurrency trading. It is therefore important to summarise existing research papers and results on cryptocurrency trading. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 118 research papers on various aspects of cryptocurrency trading (e.g.,cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return,crypto assets portfolio construction and crypto assets, technical trading and others) This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with promising opportunities in cryptocurrency trading.
ArXiv
Fan Fang Carmine Ventre Michail Basios Hoiliong Kong Leslie Kanthan Linzhi Li David Martinez-Rego Fan Wu
226074459
Shoreline: Data Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms (Student Abstract)
With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto exchanges is asset safety, and all time amount hacked from crypto exchanges until 2018 is over $1.5 billion even with carefully maintained secure trading systems. The most critical vulnerability of crypto exchanges is from the so called hot wallet, which is used to store a certain portion of the total asset online of an exchange and programmatically sign transactions when a withdraw happens. It is important to develop network security mechanisms. However, the fact is that there is no guarantee that the system can defend all attacks. Thus, accurately controlling the available assets in the hot wallets becomes the key to minimize the risk of running an exchange. In this paper, we propose Shoreline, a deep learning based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks.
AAAI 2020
Xitong Zhang He Zhu Jiayu Zhou
213350430
Shoreline: Data Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms (Student Abstract)
With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto exchanges is asset safety, and all time amount hacked from crypto exchanges until 2018 is over $1.5 billion even with carefully maintained secure trading systems. The most critical vulnerability of crypto exchanges is from the so called hot wallet, which is used to store a certain portion of the total asset of an exchange and programmatically sign transactions when a withdraw happens. Whenever hackers managed to gain control over the computing infrastructure of the exchange, they usually immediately obtain all the assets in the hot wallet. It is important to develop network security mechanisms. However, the fact is that there is no guarantee that the system can defend all attacks. Thus, accurately controlling the available assets in the hot wallets becomes the key to minimize the risk of running an exchange. However, determining such optimal threshold remains a challenging task because of the complicated dynamics inside exchanges. In this paper, we propose Shoreline, a deep learning based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks. We conduct extensive empirical studies on the real trading data from a trading platform and demonstrate the effectiveness of the proposed approach.
AAAI
Xitong Zhang He Zhu Jiayu Zhou
233762204
Cryptocurrency Trading Pair Forecasting, Using Machine Learning and Deep Learning Technique
With the volume of activities associated with trading, it has become a very tedious task. The advent of the algorithmic trading has brought with it some positive change such as reduced latency and increase in liquidity in the Financial Market. The Algorithmic Trading also came with some high demands for the technological know how and the resources to run it. This has put the retail trader in a seemingly disadvantaged position as these algo programs are carefully guided secrets by those that have access to it.<br><br>Crypto currency can no longer be ignored as the concept is forming the bedrock for future transactions. Although highly publicized, the concept of these smart contracts is not really known. <br><br>Looking into the future where the cryptocurrencies dominates over the traditional currencies, it has become imperative to give the individual trader/ retailer an additional tool to demystify the "black Box" of the trading crypto pairs with algorithmic trading strategy. The techniques employed are: Long Short Term Memory (LSTM) Auto regressive integrated moving average (ARIMA) Moving Average (MA) Cumulative Moving Average (CMA) and Artificial Neural Networks (ANN)<br><br>The models performance will be measured via correlation, Mean Percentage Error (MPE) Percentage Error (MAPE) Mean Square Error (RMSE) standard deviation and Sharpe ratio (for the trading models)
Ernest Osifo Ritabrata Bhattacharyya
233753523
Decentralized Exchanges: The 'Wild West' of Cryptocurrency Trading
Cryptocurrencies are traded on two types of exchanges centralized and decentralized. Although trading in the largest cryptocurrencies primarily occurs on centralized exchanges, most newly issued tokens can only be exchanged using decentralized platforms. Volumes in these decentralized exchanges (including automated market makers) has recently increased exponentially. We examine the role of this new and unmonitored market, utilizing an extensive sample of tokens exclusively traded on decentralized platforms. We show significant differences in the listing and trading characteristics of these tokens relative to their centralized equivalents. A small selection of these tokens obtain listing on a centralized exchange during the sample period, which is accompanied by a significant increase in trading activity, consistent with market segmentation. A centralized listing results in a migration of volume away from decentralized platforms, revealing a strong preference by token holders for deeper and more liquid markets over the increased security and anonymity offered by decentralized exchanges.
Angelo Aspris Sean Foley Jiri Svec Leqi Wang
225489209
Cryptocurrency Trading Using Machine Learning
We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy and hold approach, we demonstrate how this model yields enhanced risk adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.
Thomas Edward Koker Dimitrios Koutmos
225913356
Backtesting of Algorithmic Cryptocurrency Trading Strategies
This thesis presents a tool for backtesting algorithmic trading strategies for cryptocurrencies. The tool, called quantbacktest, provides a convenient way to automatically run comparisons of multi dimensional parameter spaces for algorithmic trading strategies. The tool supports any algorithmic strategy to be simulated and any parameter spaces to be tested and optimized with minimal adjustments. Also, arbitrary trading frequencies can be tested, from intraday to long term strategies. Many standard return metrics, risk metrics, and robustness test functionalities come out of the box in CSV format and as diagrams. Users can provide signals and price data via CSV or Excel files. Signal processing does not require a technical (code level) understanding of the backtesting tool on behalf of the user.
Jan Sporer
213290579
Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach
The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors' capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real time prices. We tested our proposal algorithm with three Bitcoin (BTC) Litecoin (LTC) and Ethereum (ETH) crypto coins' historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.
Otabek Sattarov Azamjon Muminov Cheolwon Lee Hyun-Kyu Kang Ryumduck Oh Junho Ahn Hyung Jun Oh Heung Seok Jeon