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.
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.
For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.
Financial Technology (FinTech) has transformed Capital Markets, Payments, Lending and Risk Management through faster processing of financial decisions in real-time and expanding access to financial services digitally. Beneath these user-friendly applications, FinTech platforms rely on foundational Data Structures and Algorithms to provide consistent Latency, Scalable Throughput, Auditing capabilities, and Resilience under adversarial conditions. The purpose of this paper is to review which core Data Structures (Arrays, Linked Lists, Hash Tables, Balanced Trees, Heaps and Graphs) are used to support key FinTech Workload applications (Algorithmic Trading, Fraud Detection, Credit Risk Assessment and Blockchain-based Recordkeeping). In addition, this paper will review the Algorithmic Foundations used to enable common tasks across all these workload applications including Sorting/Searching, Optimization, Statistical Learning and Cryptography, and how Asymptotic Complexity must be evaluated with respect to practical system constraints including Caching Behavior, Concurrency and Failure Modes. There is evidence from the academic literature that Algorithmic Trading can increase liquidity in certain Market Structures while also introducing Systemic Fragility during Stress [1],[2],[3]. And, similarly, there is evidence that Fraud Detection is an inherently adversarial domain where Models and Features must evolve as Attacker Behavior evolves [4],[5]. Lastly, the Paper will discuss several open challenges associated with Scale, Security, Model Governance and Privacy; and evaluate Future-Facing Directions such as Privacy-Preserving Analytics (Federated Learning and Zero-Knowledge Proofs) and Cryptographic Agility to prepare for post-Quantum risk.
Bitcoin en popüler ve yaygın olarak kullanılan dijital para birimidir. Bu nedenle, Bitcoin fiyat hareketinin tahmini finansal piyasalar için büyük önem taşımaktadır. Bitcoin fiyat tahmininde ekonometrik modellerin yanında veri madenciliği yöntemlerinden de faydalanılmaktadır. Veri madenciliğinde kullanılan araç ve yöntemler yardımıyla veriler modellenerek yararlanılacak bilgilere dönüştürülürler. K-Star algoritması veri madenciliği, obje tanımlama ve kontrol sistemleri gibi birçok alanda kullanılmakta olan örnek tabanlı bir yaklaşımdır. Bu çalışmada Makroekonomik değişkenlerin Bitcoin fiyatlarını etkileme seviyeleri, Makine Öğrenme yöntemlerinden Lazy Learning Öğrenmeye Dayalı K-Star Algoritması kullanılarak analiz edilmiştir. Çalışmanın veri seti, bağımlı ve bağımsız değişkenlerin 3 Ocak 2017 - 30 Ocak 2019 yılları arasındaki iş günü bazında 510 adet gözlem değerini içermektedir. Bu gözlemlerin 474 adedi (%93’ü) algoritmanın modellenmesi (eğitim) için, 36 adedi (%7’si) ise sınıflandırma (test) için kullanılmıştır. Modelin Bitcoin fiyatlarını gelecek dönem “yükseliş” mi yoksa “düşüş” mü göstereceğine ilişkin sınıflandırma başarısının %61,1 oranında olduğu, Bitcoin fiyatlarının “yükseliş” göstereceğine ilişkin doğru sınıflandırma başarısının %71,42, “düşüş” göstereceğine ilişkin doğru sınıflandırma başarısının ise %46,66 olduğu tespit edilmiştir. Sonuç olarak Makine Öğrenme Tekniğinin belli bir performans gösterdiği ancak Bitcoin fiyatlarının öngörülebilirliğinin henüz beklentinin altında olduğu ortaya çıkmıştır.
M. Akhil Sai, K. Sarath Chandra Sai, M. Manu Koushik, K. Gowri Raghavendra Narayan
ML and AI-helped exchanging have pulled in developing enthusiasm for as far back as not many years.We examine day-by-day information for different digital currencies over some stretch of time. We show that straightforward exchanging methodologies helped by innovative AI calculations outflank standard benchmarks. We have picked two Machine Learning Algorithms to play out a Comparative Study to foresee cost of a Bitcoin; we have utilized Decision tree regressor and LSTM Algorithms and watched execution of every calculation as far as anticipating the cost of Bitcoin. We saw that Decision tree regressor gives progressively effective and precise outcomes when contrasted with others.
Son yıllarda, bloglar, tweet’ler, forumlar, e-postalar gibi Web 2.0 hizmetleri iletişim kanalı olarak yaygın bir şekilde kullanılmaktadır. Ayrıca sosyal medya; gerek bilgi paylaşımı gerekse istek, şikayet ve dilekler gibi görüşleri belirtmenin en kolay ve en güncel yolu olarak düşünülmektedir. Sosyal medyanın, birçok alana olduğu gibi Bitcoin fiyatlarına olan etkisi de son yıllarda tartışılmaktadır. Bitcoin yıllardır üzerinde durulan ve popülerliği her geçen gün artan bir yatırım aracıdır. Merkezi olmayan bir elektronik para birimi sistemi olan Bitcoin, çok sayıda kullanıcının ilgisini çeken, finansal sistemlerdeki köklü bir değişikliği ifade etmektedir. Bu çalışmada sosyal medyanın, özellikle Twitter kanalından elde edilen tweet’ler bazında, Bitcoin fiyatı ile etkileşimi ortaya konulmuştur. Bunun için 06.10.2018-19.05.2019 tarihleri arasında Twitter kullanıcıları tarafından atılan toplam 2.819.784 tweet üzerinden makine öğrenmesi yöntemlerinden sınıflandırma algoritmaları kullanılarak çeşitli analizler gerçekleştirilmiştir. Bulgular değerlendirildiğinde metin sınıflandırmada %90 ile en yüksek doğruluk oranına sahip olan Yapay Sinir Ağları kullanılmıştır. Ayrıca Bitcoin fiyatları ve sınıflandırılmış olumlu/olumsuz tweet oranları ile ikili korelasyon yapılmıştır. Elde edilen 0,681 korelasyon katsayısı ile pozitif yönde orta üstü kuvvetli ilişki tespit edilmiştir.
2008 yılında temelleri atılmış olan Kiripto para kavramı, 2017 yılı Aralık ayı itibari ile 19.060 ABD dolarına ulaşmış ve tanınırlığını arttırmıştır. Bitcoin ve sayıları 2700’ü bulan diğer kripto paralar hızlı kazanç elde etmek isteyen yatırımcıların dikkatini çekmeyi başarmıştır. Bu kapsamda kripto paraların fiyatının nasıl ve ne yönde değişeceği birçok kesim tarafından araştırma konusu olmuştur. Bu çalışmanın amacı, Bitcoin, Ethereum, IOTA ve Ripple gibi farklı altyapısal özellikleri olan kripto paraların gelecek fiyatını geçmişte gerçekleşen fiyatlardan hareketle tahmin etmektir. Çalışmada Deng Ju-Long tarafından 1980’li yıllarda ortaya atılan gri sistem teorisi ile fiyat tahminlemesi yapılmıştır. Çalışmada kullanılan geçmiş fiyatlar 11 günlük süreci kapsamaktadır. Literatüre göre kısa sayılabilecek bu süre modelin diğer modellere görece üstünlüğünü göstermektedir. Elde edilen sonuçlara göre GM(1,1) model ve Rolling-GM(1,1) model sonuçlarının birbirine çok yakın hata oranlarıyla tahmin yaptıkları ve yapılan tahminlere ait hata oranlarının çok düşük olduğu görülmüştür.
Tri Wijayanti Septiarini, Muhammad Rifki Taufik, Mufti Afif, Atika Rukminastiti Masyrifah
Abstract The objective of this study were (i) to construct the classical statistic and artificial intelligent model for predicting bitcoin cryptocurrency, and (ii) to compare the predicting performance by using root mean square error (RMSE) and mean square error (MSE) as forecasting evaluation tool. The observation data used in this study were collected during January, 5 2017 to October, 1 2019 (in total 1,000 daily observation data). The statistical method used in this study were ARIMA (Autoregressive Moving Average) and Exponential Smoothing. The artificial intelligent model were used in this study were fuzzy time series and ANFIS (Adaptive Neuro Fuzzy Inference System). The partitions data set were of 75%-25% of training and testing, respectively. The cryptocurrency investigated was bitcoin (BTC) which is the top three of most widely traded cryptocurrency. The forecasting results show that the classical method has the smallest value of RMSE and MSE which is exponential smoothing with 9749.81 for MSE and 98.74 for RMSE. However, the performance of forecasting method cannot be guaranteed from either classical or modern forecasting method. Analyzing with different method can be considered for future study, for example machine learning, neural network, modified fuzzy time series, etc.
This article analyzes the relationship between Bitcoin and the stock market by using a vector autoregressive model. To enhance the impulse response signal, the Sliding Window technique is applied. Study results show the relationship between Bitcoin and the stock market. First, the S&P 500 has a relatively significant effect on Bitcoin, while the influence caused by the S&P 500 is weak. In addition, after involving the Sliding Window technique, the effects caused by the standard deviation of the S&P 500 and the mean of the Dow Jones are remarkably strong on the mean of Bitcoin and the standard deviation of the S&P 500 has a comparatively significant effect on the standard deviation of Bitcoin as well. Generally, the S&P 500 and the Dow Jones indexes have an advantageous effect on Bitcoin. Financial investment can be made based on this model and conclusion.
Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.
Otabek Sattarov, Azamjon Muminov, Cheol Won Lee, Hyun Kyu Kang · 8 authors
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.
Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having\nbeen widely used as an exchange medium in areas such as financial transaction\nand asset transfer verification. However, there has been a lack of solutions\nthat can support real-time price prediction to cope with high currency\nvolatility, handle massive heterogeneous data volumes, including social media\nsentiments, while supporting fault tolerance and persistence in real time, and\nprovide real-time adaptation of learning algorithms to cope with new price and\nsentiment data. In this paper we introduce KryptoOracle, a novel real-time and\nadaptive cryptocurrency price prediction platform based on Twitter sentiments.\nThe integrative and modular platform is based on (i) a Spark-based architecture\nwhich handles the large volume of incoming data in a persistent and fault\ntolerant way; (ii) an approach that supports sentiment analysis which can\nrespond to large amounts of natural language processing queries in real time;\nand (iii) a predictive method grounded on online learning in which a model\nadapts its weights to cope with new prices and sentiments. Besides providing an\narchitectural design, the paper also describes the KryptoOracle platform\nimplementation and experimental evaluation. Overall, the proposed platform can\nhelp accelerate decision-making, uncover new opportunities and provide more\ntimely insights based on the available and ever-larger financial data volume\nand variety.\n
Yunan Ye, Hengzhi Pei, Boxin Wang, Pin‐Yu Chen · 7 authors
Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
High frequency Bitcoin price series are often non-linear and non-stationary and hence forecasting the price of Bitcoin directly or by transformation using statistical models is subject to large errors. This paper presents an ensemble model using variational mode decomposition (VMD) and Generalized additive model (GAM) to forecast intraday Bitcoin price. To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM. The results showed that VMD-GAM model performed better than the EMD-GAM ensemble model in terms of three evaluation metrics (root mean square error, mean absolute percentage error, and bias) used.
We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people's sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.
Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.
This paper studies how to forecast daily closing price series of Bitcoin,\nusing data on prices and volumes of prior days. Bitcoin price behaviour is\nstill largely unexplored, presenting new opportunities. We compared our results\nwith two modern works on Bitcoin prices forecasting and with a well-known\nrecent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ\nclosing prices spanning a 3-year interval. We followed different approaches in\nparallel, implementing both statistical techniques and machine learning\nalgorithms. The SLR model for univariate series forecast uses only closing\nprices, whereas the MLR model for multivariate series uses both price and\nvolume data. We applied the ADF -Test to these series, which resulted to be\nindistinguishable from a random walk. We also used two artificial neural\nnetworks: MLP and LSTM. We then partitioned the dataset into shorter sequences,\nrepresenting different price regimes, obtaining best result using more than one\nprevious price, thus confirming our regime hypothesis. All the models were\nevaluated in terms of MAPE and relativeRMSE. They performed well, and were\noverall better than those obtained in the benchmarks. Based on the results, it\nwas possible to demonstrate the efficacy of the proposed methodology and its\ncontribution to the state-of-the-art.\n