Zibin Zheng, Neng Zhang, Jianzhong Su, Zhijie Zhong · 6 authors
Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().
Yakın geçmişte hayatımıza giren ve kısa zamanda finansal piyasalarda kendisine yer bulan kripto paralar, hem bir değişim aracı hem de bir yatırım aracı olarak kullanılmaktadır. Kripto paraların merkezi bir otoritenin kontrolünde olmaması bu araçların fiyatlarında dalgalanmaları beraberinde getirmiştir. Bu nedenle, akıllı bir tahmin modelinin geliştirilmesi, yatırım yapılacak finansal varlıkların seçimi ve yatırım kararlarının hayata geçirilmesi açısından oldukça önemlidir. Derin öğrenme ve yapay zeka, yatırım yapılacak olan kripto para birimi ve diğer yatırım araçlarının seçiminde kullanılmaktadır. Tekrarlayan Sinir Ağı (RNN), Uzun-Kısa Süreli Bellek (LSTM) ve Geçitli Yinelenen Birim (GRU) modeli gibi derin öğrenme modellerinin, kripto para birimi fiyat tahmininde geleneksel zaman serisi modellerinden daha iyi performans gösterdiği araştırmacılar tarafından kanıtlanmıştır. Bundan dolayı bu çalışmada, özel bir RNN yöntemi olan LSTM ve GRU’dan yararlanılarak, günümüzde piyasa değeri ve işlem hacmi en yüksek olan kripto paralardan Bitcoin, Ethereum ve Ripple’ın 30 günlük fiyat tahmininde bulunulmuştur. Araştırmanın sonucunda her iki modelde de en iyi tahmin sonucunu Bitcoin vermiştir. İkinci en iyi tahmin sonucu Ripple, sonrasında ise Ethereum için bulunmuştur. Kullanılan yöntemler karşılaştırıldığında ise MAPE performans ölçütüne göre en iyi tahmin sonucuna Bitcoin ve Ripple için GRU, Ethereum için ise LSTM modeli ile ulaşılmıştır.
Non-Fungible Tokens (NFTs) are a type of digital asset that represents a proof of ownership over a particular digital item such as art, music, or real estate. Due to the non-fungible nature of NFTs, duplicate tokens should not possess the same value. However, with the surge of new blockchains and a massive influx of NFTs being created, a wealth of NFT data is being generated without a method of tracking similarity. This enables people to create almost identical NFTs by changing one pixel or one byte of data. Despite the similarity among NFTs, each NFT is assigned a completely different token ID. To address the NFT duplication issue, we developed a modular, easily-extendable, hardware-agnostic, cloud-centered NFT processing system that represents NFTs as vectors. We established a database containing a vector representation of the NFTs in accordance with the Ethereum Request for Comment 721 (ERC-721) token standards to initiate the process of aggregating NFT data from various blockchains. Finally, we developed an NFT visualization dashboard application with a user-friendly graphical user interface (GUI) to provide non-technical users access to the aggregated NFT data. The Universal NFT Vector Database is an off-chain framework for NFT data aggregation based on similarity, which provides an organized way to query and analyze NFT data that was previously unavailable through on-chain solutions.
Efficient and convenient vulnerability detection for smart contracts is a key issue in the field of smart contracts. The earlier vulnerability detection for smart contracts mainly relies on static symbol analysis, which has high accuracy but low efficiency and is prone to path explosion. In this paper, the authors propose a static method for vulnerability detection based on deep learning. It first disassembles Ethereum smart contracts into opcode sequences and then converts the vulnerability detection problem into a natural language text classification problem. The word vector method is employed to map each opcode to a uniform vector space, and the opcode sequence matrix is trained by the TextCNN method to detect vulnerabilities. Furthermore, a code obfuscation method is given to enhance and balance the dataset, while three different opcode sequence generation methods are proposed to construct features. The experimental results verify that the average prediction accuracy of each smart contract exceeds 96%, and the average detection time is less than 0.1 s.
Cesar Munoz-Ausecha, Jorge Gómez Gómez, Juan Ruiz-Rosero, Gustavo Ramírez-González
In the present, many organizations grow on a daily basis, using many assets to perform their activities and generate profit. In large organizations, all of these assets must be managed, occasionally leading to challenges depending on the organization’s size. For this reason, the role of asset custodian is needed. This role entails assigning the fixed assets to one person for their care, maintenance, and safekeeping. In this process, it is necessary to update information in the central system, leading to further administrative processes, which, in the majority of cases, are carried out through traditional methods. This involves time to obtain wet signatures, a great deal of paperwork, and time for the person or people in charge to update the information. Due to these reasons, the process can be updated partially or entirely to use digital means in order to solve the mentioned inconveniences. This paper presents a proof-of-concept system to offer a modernized and practical solution to this problem using the advantages of blockchain technology, and speeding up the process by using assets identified with UHF RFID technology to permit the reading of many tags that can be embedded and hidden with no need for line-of-sight, allowing fast ownership transfer, using smart contracts in the Ethereum private blockchain.
Blockchain technology has gained momentum due to its immutability and transparency. Several blockchain platforms, each with different consensus protocols, have been proposed. However, choosing and configuring such a platform is a non-trivial task. Numerous benchmarking tools have been introduced to test the performance of blockchain solutions. Yet, these tools are often limited to specific blockchain platforms or require complex configurations. Moreover, they tend to focus on one-off batch evaluation models, which may not be ideal for longer-running instances under continuous workloads. In this work, we presentBlockCompass, an all-inclusive blockchain benchmarking tool that can be easily configured and extended. We demonstrate howBlockCompasscan evaluate the performance of various blockchain platforms and configurations, including Ethereum Proof-of-Authority, Ethereum Proof-of-Work, Hyperledger Fabric Raft, Hyperledger Sawtooth with Proof-of-Elapsed-Time, Practical Byzantine Fault Tolerance, and Raft consensus algorithms, against workloads that continuously fluctuate over time. We show how continuous transactional workloads may be more appropriate than batch workloads in capturing certain stressful events for the system. Finally, we present the results of a usability study about the convenience and effectiveness offered byBlockCompassin blockchain benchmarking.
Shaima AL Amri, Leonardo Aniello, Vladimiro Sassone
The Ethereum blockchain is one of the main public platforms to run smart contracts and enable decentralised applications. Since data stored in a blockchain is considered immutable, smart contracts deployed in Ethereum are regarded as tamper-proof and therefore offer strong protection against attacks aiming at tinkering with the execution flow of an application. Yet, like any other software, a smart contract needs to be maintained over time to fix bugs or add new features. Deploying every updated version as a brand-new smart contract in Ethereum leads to problems such as migrating the contract state from the old version and enabling clients to point to the new version in a timely fashion. The OpenZeppelin framework addresses this limitation by providing libraries that enable the deployment of upgradeable smart contracts. This is achieved by relying on proxies that act as intermediaries between clients and smart contracts, allowing the latter to be updated transparently. In this paper, we present the upgradeable smart contract patterns supported by OpenZeppelin and compare them in terms of security, cost, and performance. To show this paradigm’s prevalence in Ethereum, we also analyse the usage of OpenZeppelin Upgradeable smart contracts over the last four years.
Bubbles in asset prices have attracted the attention of economists for centuries. Extreme increases in asset prices, followed by their sudden decline, create a turbulent effect on the economy and even invite crises in time. For this reason, some measurement techniques have been employed to investigate the price bubbles that may occur. This study explores the possible speculative price bubbles of Bitcoin, Ethereum, and Binance Coin cryptocurrencies, compares them with the pre-and post-COVID-19 period, and examines asymmetric causality relationships between variables. Therefore, we analyzed the price bubbles of these cryptocurrencies using the closing price for daily data between 16.01.2018 and 31.12.2021 by the Supremum Augmented Dickey-Fuller (SADF) and the Hatemi-J (2012) asymmetric causality test. In this context, 1446 observations, 723 of which were before COVID-19 and 723 after COVID-19, were employed in the study. Looking at the SADF analysis results, we detected 103 price bubbles before COVID-19 for the three cryptocurrencies, while we determined 599 price bubbles after COVID-19. The common finding in the asymmetric causality test results is that there is a causality relationship between the negative shocks faced by one cryptocurrency and the positive shocks faced by the other cryptocurrencies.
Monika di Angelo, Thomas Durieux, João F. Ferreira, Gernot Salzer
Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.
Decentralized applications rely on non-centralized technical infrastructures and coordination principles. Without trusted third parties, their execution is not controlled by entities exercising centralized coordination but is instead realized through technologies supporting distribution such as blockchains and serverless computing. Executing decentralized applications with these technologies, however, is challenging due to the limited transparency and insight in the execution, especially when involving centralized cloud platforms. This paper extends an approach for execution and instance tracking on blockchains and cloud platforms permitting distributed parties to observe the instances and states of executable models. The approach is extended with (1.) a metamodel describing the concepts for instance tracking on cloud platforms independent of concrete models or implementation, (2.) a multidimensional data model realizing the concepts accordingly, permitting the verifiable storage, tracking, and analysis of execution states for distributed parties, and (3.) an implementation on the Ethereum blockchain and Amazon Web Services (AWS) using state machine models. Towards supporting decentralized applications with high scalability and distribution requirements, the approach establishes a consistent view on instances for distributed parties to track and analyze the execution along multiple dimensions such as specific clients and execution engines.
Asif Ali Wagan, Abdullah Ayub Khan, Yen‐Lin Chen, Lip Yee Por · 6 authors
Game-based learning in schools and colleges, with the help of AI-enabled augmented intelligence techniques, is reported to improve children’s neurodevelopment, intellectual sensing, and specific learning abilities, according to US officials. There is currently a huge transformation from traditional assisted learning to augmented reality-enabled computer-based learning. Globally, there has been a dramatic increase in the use of game-based augmented learning in most schools and colleges. A few problems arise that create concern, such as the emerging effects of gaming on institutional premises, the disordering of children’s involvement after game-learning, the rate of learning and attendance, adaptation, and teachers’ experience. To address these individual aspects, we proposed a blockchain Ethereum-enabled, secure AI-based augmented game learning environment, called B-AIQoE, in which protected on-chain and off-chain channels are designed to handle all the transactions and exchanges among students before analysis in terms of color transition, redundancy, unethical transmission, and related vulnerabilities. On the other hand, the proposed system examines and analyzes the Quality of experience (QoE) and improves accessibility as it receives feedback from the students and teachers. For the purpose of automating game-based transactions, three different aspects are discussed, such as verifying and validating student-teacher registration, creating content for game-based learning and privacy, and updating students’ records and exchanges. Finally, this paper separates, analyzes, and discusses a list of emerging challenges and limitations and their possible solutions involved in creating the proposed system.
Mikel Cortes-Goicoechea, Tarun Mohandas-Daryanani, José L. Muñoz, Leonardo Bautista-Gomez
Like most modern blockchain networks, Ethereum has relied on economic incentives to promote honest participation in the chain's consensus. The distributed character of the platform, together with the “randomness” or “luck” factor that both proof of work (PoW) and proof of stake (PoS) provide when electing the next block proposer, pushed the industry to model and improve the reward system of the system. With several improvements to predict PoW block proposal rewards and to maximize the extractable rewards of the same ones, the ultimate Ethereum's transition to PoS applied in the Paris Hard-Fork, more generally known as “The Merge”, has meant a significant modification on the reward system in the platform. In this paper, we aim to break down both theoretically and empirically the new reward system in this post-merge era. We present a highly detailed description of the different rewards and their share among validators' rewards. Ultimately, we offer a study that uses the presented reward model to analyze the performance of the network during this transition.
Blockchain technology is becoming widely popular nowadays along with their decentralized peer- to-peer network and its privacy. Bitcoin is also widely storming in the world. Blockchain technology changes the lifestyle of people and business views on many fields through its privacy and security. Many researches were done on this technology because of its security and requirements in various fields of life. In the current era major issues are security on online translation, cloud computing, large data and Blockchain more focus on designing secure service. The objective of writing this review paper is to summarize what Blockchain is and spread awareness about its usage, security and how it works. Key Words: Blockchain, Ethereum, Cryptocurrency, Bitcoin, Consensus Methodor Algorithm, consensus rules, Hash,Genesis Block, Applications of Blockchain, Booming Domain of Blockchain
Bitcoin was created in 2008 as the first decentralized cryptocurrency, providing an innovative data management technology, which was later named blockchain. It ensured data validation without intervention from intermediaries. During its early stages, it was conceived as a financial technology by most researchers. It was not until 2015, when the Ethereum cryptocurrency was officially launched worldwide, along with its revolutionary technology called smart contracts, that researchers began to change their perception of the technology and look for uses outside the financial world. This paper analyzes the literature since 2016, one year after Ethereum, analyzing the evolution of interest in the technology to date. For this purpose, a total of 56,864 documents created between 2016 and 2022 from four major publishers were analyzed, providing answers to the following questions. Q1: How has interest in blockchain technology increased? Q2: What have been the major blockchain research interests? Q3: What have been the most outstanding works of the scientific community? The paper clearly exposes the evolution of blockchain technology, making it clear that, as the years go by, it is becoming a complementary technology instead of the main focus of studies. Finally, we highlight the most popular and recurrent topics discussed in the literature over the analyzed period of time.
A Blockchain is a decentralized, distributed ledger that keeps track of transactions on numerous computers to prevent any changes from being made retrospectively without affecting all blocks after it and obtaining network consensus.\n\n\nA Blockchain eliminates the requirement for a reliable intermediary to mediate digital relationships. It enables safe and open record-keeping, and by streamlining procedures and lowering costs, it has the potential to upend a variety of businesses. A Blockchain has many important features, such as Decentralization: The network is not managed by a centralized body. A network of computers that collaborate to validate and record transactions instead maintains it. Once a transaction has been broadcast to the Ethereum network, it is typically impossible to undo it.\n\n\n 
Blockchain is a peer-to-peer distributed ledger technology, nowadays widely used and a hot topic in the industries. A Smart Contract is a virtual contract based on Blockchain technology which executes and operates between two parties (especially a buyer and a dealer) when a certain condition of the contract is met. The objective of this research is to present a decentralized transaction system based on Blockchain technology implemented using Smart Contract. Blockchain based cryptocurrencies are an evidence for a secure implementation of any currency-based centralized systems put forth in use in a decentralized way. Current transaction systems and payment gateways are centralized systems that need monitoring and verification from a central agency like a bank or a third party organization which are not completely automated as some of the processes are human controlled. Also deposition and withdrawal of money sometimes take a long procedural time. Still these systems are neither completely error free nor secure and time to time certain issues may arise. Even nowadays some payment methods charges transaction fee which is like paying for the money you are paying for. In this research paper we are proposing an automated Ethereum blockchain technology based transactionsystem which will eliminate the bank and third party verification.Each block of the Ethereum blockchain in the system will store the transaction details also linked with all the previous transaction details in the form of a chain. A Smart Contract is used to define the terms and conditions of the transaction which include account creation, ether deposition and withdrawal. This system enables users to carry out transaction without any fraud and losses from anywhere around the globe within split seconds. Keywords: Transaction system ,Blockchain, Smart Contract, decentralized,crypto-currency
Blockchain is a recently developed advanced technology. It has been assisted by a lot of interest in a decentralized and distributed public ledger system integrated as a peer-to-peer network. A tamper-proof digital framework is created for sharing and storing data, where the linked block structure is utilized to verify and store the data. A trusted consensus method has been adopted to synchronize the changes in the original data. However, it is challenging for Ethereum to maintain security at all blockchain levels. As such, “public–private key cryptography” can be utilized to provide privacy over Ethereum networks. Several privacy issues make it difficult to use blockchain approaches over various applications. Another issue is that the existing blockchain systems operate poorly over large-scale data. Owing to these issues, a novel blockchain framework in the Ethereum network with soft computing is proposed. The major intent of the proposed technology is to preserve the data for transmission purposes. This new model is enhanced with the help of a new hybrid algorithm: Adaptive Border Collie Rain Optimization Algorithm (ABC-ROA). This hybrid algorithm generates the optimal key for data restoration and sanitization. Optimal key generation is followed by deriving the multi objective constraints. Here, some of the noteworthy objectives, such as information preservation (IP) rate, degree of modification (DM), false rule (FR) generation, and hiding failure (HF) rate are considered. Finally, the proposed method is successfully implemented, and its results are validated through various measures. The recommended module ensures a higher security level for data sharing.
Open access
2 source records
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow for investing and rewarding with critical opcode instructions. Then, an intuitive directed-graph based visualization is proposed to display the investing and rewarding flows and the crucial execution paths, enabling easy identification of Ponzi schemes on Ethereum. Two usage scenarios involving both Ponzi and non-Ponzi schemes demonstrate the effectiveness of PonziLens.
Francesco Bruschi, Donatella Sciuto, Tommaso Paulon, Andrea Marchesi
Blockchain technology allows players to own in-game assets and to be rewarded with NFTs or tokens for their game achievements, thus can be a game changer for all the gaming industry. One central issue is how to check that conditions for achievements are met (e.g., that the player completed level 10). Current approaches open cheating backdoors (e.g. if the client checks the conditions) or introduce centralization points (if a backend checks the condition). Ideally, we would like to “run” games on chain, but so far that has not been possible due to the high computational cost, especially on Ethereum; however, the development of technologies like proofs of computation can solve this problem. Being able to run games on the blockchain, new decentralized rewarding systems can be built to ensure the fair and transparent rewarding of game achievements.
Abstract To address the problems of unclear data ownership and security in data transaction scenarios, a decentralized data transaction method and platform based on Ethereum + IPFS are proposed in this paper. The proposed method first performs similar calculations on text using natural language processing technology to realize data rights, then build a transaction body-centered data encryption; finally execute a decryption process through smart contracts to avoid malicious transaction behavior. The designed data-trading platform consists of three parts: customer front-end, back-end, and database. The database adopts the Ethereum and interplanetary file system. Experiments show that the designed platform can solve the problems of unclear data ownership and security in the process of data transactions, reduce data transaction costs, and provide safe and reliable data transaction services for buyers and sellers.
Evripidis P. Kechagias, Sotiris P. Gayialis, Georgios A. Papadopoulos, Georgios Papoutsis
In today's era, humanity has been overwhelmed by technological revolutions that have changed and will continue to change how business operations are performed, directly or indirectly. At the same time, the processes within the supply chain are quite complex, and as technology and processes evolve, they become more and more challenging. Traceability has become a critical issue in the food industry to ensure safety, quality, and compliance with regulations. The adoption of blockchain technology in the food supply chain has gained significant attention as a potential solution to improve traceability. This paper presents the development of a distributed application for table olives' traceability on the Ethereum network. The paper also presents a methodological framework, which can help anyone aiming to implement an Ethereum decentralized application and demonstrates the practical use of the developed application by a Greek table olives producer. The application significantly improved the producer's product traceability by providing a secure, transparent, and efficient solution for tracking and tracing the products in the supply chain. The app reduced the time, increased the accuracy and reliability of data, improved supply chain efficiency, and helped the producer comply with international regulations and standards.
Web 3.0 atau dikenal juga dengan Web3 atau Web 3 merupakan salah satu terobosan dalam dunia internet. Kehadiran Web 3.0 tidak hanya dapat menafsirkan secara akurat apa yang diketikkan ke dalam mesin pencarian, tetapi juga benar-benar memahami semua yang dikirimkan, baik melalui teks, audio, atau media lainnya. Trilema blockchain yaitu terdesentralisasi, dapat diskalakan, dan aman adalah masalah yang dipelajari dengan baik oleh para peneliti dan pelaku pasar. Mekanisme konsensus Satoshi Plus pada blockchain CORE yang memanfaatkan hashrate penambangan Bitcoin dan Ethereum Virtual Machine (EVM). Solusi untuk trilema tersebut adalah konsensus Satoshi Plus yang menggabungkan Proof of Work (PoW) dan Delegated Proof of Stake (DPoS) untuk memanfaatkan kekuatan masing-masing sekaligus memperbaiki kekurangannya masing-masing.
This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with asymmetric-threshold GARCH models for marginal distributions, detects the directional dependence better than the copula directional dependence method by an asymmetric GARCH model.
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s— t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.