Huned Materwala, Shraddha M. Naik, Ali S. Taha, Tala Abdulrahman Abed · 5 authors
Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.
This research gives a detailed analysis of the application of blockchain technology to the airline reservation systems in order to bolster trust, transparency, and operational efficiency by overcoming several challenges including customer control and data integrity issues. The study investigates the major components of blockchain technology such as decentralised databases, permanent records of transactions and transactional clauses executed via codes of programs and their impacts on automated systems and real-time tracking of audits. The results show a 30% decrease in booking variations together with greater data synchronization as a result of consensus processes and resistant data formations. The approach to the implementation of a blockchain technology for the purpose of this paper includes many APIs for the automatic multi-faceted record-keeping system including the smart contract execution and controllable end-users approach. Smart contracts organized the processes improving the cycle times by 40% on the average while guaranteeing no breach of agreements. In addition to this, the architecture of the system has no single point failure with over 98% reliability while measures taken to improve security have led to 85% of the customers expressing trust in the services provided. In summation, the results suggest that reservations in the airline sector stand a chance of being redefined with blockchain through savoring the benefits of a single source of truth while attempting to resolve this intrinsic problem of overcomplexity. Although the system improves the experience of customers and the level of operational transparency, issues concerning scalability and regulatory adherence. This research is also a stepping stone for further studies that are intended to address these challenges and more applicable to the airline industry.
Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its price fluctuations in the market. However, papers that consider the sentiment of the market associated with financial Technical Analysis indicators in order to predict Bitcoin's price are still scarce. In this paper, we present a novel approach for predicting Bitcoin price movements by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms. This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. Our initial experiments demonstrate promising results considering investment returns, surpassing the Buy & Hold baseline, and offering valuable insights about the combination of indicators of sentiment and market in a cryptocurrency prediction model.
The Lightning Network is a peer-to-peer network designed to address Bitcoin's scalability challenges, facilitating rapid, cost-effective, and instantaneous transactions through bidirectional, blockchain-backed payment channels among network peers. Due to a source-based routing of payments, different pathfinding strategies are used in practice, trading off different objectives for each other such as payment reliability and routing fees. This paper explores differences within pathfinding strategies used by prominent Lightning Network node implementations, which include different underlying cost functions and different constraints, as well as different greedy algorithms of shortest path-type. Surprisingly, we observe that the pathfinding problems that most LN node implementations attempt to solve are NP-complete, and cannot be guaranteed to be optimally solved by the variants of Dijkstra's algorithm currently deployed in production. Through comparative analysis and simulations, we evaluate efficacy of different pathfinding strategies across metrics such as success rate, fees, path length, and timelock. Our experiments indicate that the strategies used by Eclair are advantageous in terms of payment reliability and result in paths with low fees. LND exhibits moderate success rates, while LDK results in paths with higher fee levels for smaller payment amounts; furthermore, CLN stands out for its minimal timelock paths. Additionally, we investigate the impact of Lightning node connectivity levels on routing efficiency. The findings of our analysis provide insights towards future improvements of pathfinding strategies and algorithms used within the Lightning Network.
Decentralized Finance (DeFi) has revolutionized lending by replacing intermediaries with algorithm-driven liquidity pools. However, existing platforms like Aave and Compound rely on static interest rate curves and collateral requirements that struggle to adapt to rapid market changes, leading to inefficiencies in utilization and increased risks of liquidations. In this work, we propose a dynamic model of the lending market based on evolving demand and supply curves, alongside an adaptive interest rate controller that responds in real-time to shifting market conditions. Using a Recursive Least Squares algorithm, our controller tracks the external market and achieves stable utilization, while also controlling default and liquidation risk. We provide theoretical guarantees on the interest rate convergence and utilization stability of our algorithm. We establish bounds on the system's vulnerability to adversarial manipulation compared to static curves, while quantifying the trade-off between adaptivity and adversarial robustness. We propose two complementary approaches to mitigating adversarial manipulation: an algorithmic method that detects extreme demand and supply fluctuations and a market-based strategy that enhances elasticity, potentially via interest rate derivative markets. Our dynamic curve demand/supply model demonstrates a low best-fit error on Aave data, while our interest rate controller significantly outperforms static curve protocols in maintaining optimal utilization and minimizing liquidations.
Tanusree Sharma, Yujin Potter, Kornrapat Pongmala, Henry Wang · 7 authors
Decentralized Autonomous Organizations (DAOs) resemble early online communities, particularly those centered around open-source projects, and present a potential empirical framework for complex social-computing systems by encoding governance rules within “smart contracts” on the blockchain. A key function of a DAO is collective decision-making, typically carried out through a series of proposals where members vote on organizational events using governance tokens, signifying relative influence within the DAO. In just a few years, the deployment of DAOs surged with a total treasury of $24.5 billion and 11.1M governance token holders collectively managing decisions across over 13,000 DAOs as of 2024 . In this study, we examine the operational dynamics of 100 DAOs, like pleasrdao, lexdao, lootdao, optimism collective, uniswap, etc. With large-scale empirical analysis of a diverse set of DAO categories and smart contracts and by leveraging on-chain (e.g., voting results) and off-chain data, we examine factors such as voting power, participation, and DAO characteristics dictating the level of decentralization, thus, the efficiency of management structures. As such, our study highlights that increased grassroots participation correlates with higher decentralization in a DAO, and lower variance in voting power within a DAO correlates with a higher level of decentralization, as consistently measured by Gini metrics. These insights closely align with key topics in political science, such as the allocation of power in decision-making and the effects of various governance models. We conclude by discussing the implications for researchers, and practitioners, emphasizing how these factors can inform the design of democratic governance systems in emerging applications that require active engagement from stakeholders in decision-making.
Ethereum, as a representative of Web3, adopts a novel framework called Proposer Builder Separation (PBS) to prevent the centralization of block profits in the hands of institutional Ethereum stakers. Introducing builders to generate blocks based on public transactions, PBS aims to ensure that block profits are distributed among all stakers. Through the auction among builders, only one will win the block in each slot. Ideally, the equilibrium strategy of builders under public information would lead them to bid all block profits. However, builders are now capable of extracting profits from private order flows. In this paper, we explore the effect of PBS with private order flows. Specifically, we propose the asymmetry auction model of MEV-Boost auction. Moreover, we conduct empirical study on Ethereum blocks from January 2023 to May 2024. Our analysis indicates that private order flows contribute to 54.59% of the block value, indicating that different builders will build blocks with different valuations. Interestingly, we find that builders with more private order flows (i.e., higher block valuations) are more likely to win the block, while retain larger proportion of profits. In return, such builders will further attract more private order flows, resulting in a monopolistic market gradually. Our findings reveal that PBS in current stage is unable to balance the profit distribution, which just transits the centralization of block profits from institutional stakers to the monopolistic builder.
BRC20 tokens are a type of non-fungible asset on the Bitcoin network. They allow users to embed customised content within Bitcoin's satoshis. The token frenzy reached a market size of US\$2.811\,b (2023Q3--2025Q1). However, this intuitive design has not undergone serious security scrutiny. We present the first analysis of BRC20's \emph{transfer} mechanism and identify a new attack vector. A typical BRC20 transfer involves two "bundled" on-chain transactions with different fee levels: the first (i.e., \textbf{Tx1}) with a lower fee inscribes the \textsf{transfer} request, while the second (i.e., \textbf{Tx2}) with a higher fee finalizes the actual transfer. An adversary can send a manipulated fee transaction (falling between the two fee levels), which causes \textbf{Tx1} to be processed while \textbf{Tx2} is pinned in the mempool. This locks BRC20 liquidity and disrupts normal withdrawal requests from users. We term this the \emph{BRC20 pinning attack}. We validated the attack in real-world settings in collaboration with Binance researchers. With their knowledge and permission, we conducted a controlled test against Binance's ORDI hot wallet, resulting in a temporary suspension of ORDI withdrawals for 3.5 hours. Recovery was performed shortly after. Further analysis confirms that the attack can be applied to over \textbf{90\%} of inscription-based tokens within the Bitcoin ecosystem.
Cryptocurrency lending pools are services that allow lenders to pool together assets in one cryptocurrency and loan it out to borrowers who provide collateral worth more (than the loan) in a separate cryptocurrency. Borrowers can repay their loans to reclaim their collateral unless their loan was liquidated, which happens when the value of the collateral dips significantly. Interest rates for these pools are currently set via supply and demand heuristics, which have several downsides, including inefficiency, inflexibility, and being vulnerable to manipulation. Here, we reduce lending pools to options, and then use ideas from options pricing to search for fair interest rates for lending pools. In a simplified model where the loans have a fixed duration and can only be repaid at the end of the term, we obtain analytical pricing results. We then consider a more realistic model, where loans can be repaid dynamically and without expiry. Our main theoretical contribution is to show that fair interest rates do not exist in this setting. We then show that impossibility results generalize even to models of lending pools which have no obvious reduction to options. To address these negative results, we introduce a model of lending pools with fixed fees, and model the ability of borrowers to top-up their loans to reduce the risk of liquidation. As a proof of concept, we use simulations to show how our model's predicted interest rates compare to interest rates in practice.
Krzysztof Gogol, Manvir Schneider, Tessone, Claudio, Livshits, Benjamin
Layer-2 (L2) blockchains inherit Ethereums security guarantees while reducing gas fees. As a result, they are gaining traction among traders at Automated Market Makers (AMMs), sparking debate over whether they contribute to liquidity fragmentation of Ethereum. Our research suggests that such fragmentation is not currently occurring. However, it could emerge in the future, particularly if Liquidity Providers (LPs) recognize the higher returns available on L2s. Using Lagrangian optimization, we develop a model for optimal liquidity allocation across AMMs on Ethereum and its L2s, using staking as a benchmark. We show that, in equilibrium, AMM liquidity provision returns converge to this reference rate. Additionally, we measure the elasticity of trading volume with respect to Total Value Locked (TVL) in AMMs and find that, on well-established blockchains, an increase in TVL does not necessarily lead to higher trading volume. Finally, our empirical findings reveal that Ethereums liquidity pools are oversubscribed compared to those on L2s and often yield lower returns than staking Ether. LPs could maximize their rewards by reallocating more than two-thirds of their liquidity to L2s and staking.
Decentralized Exchanges (DEXs) are pivotal applications in the Decentralized Finance (DeFi) landscape, aiming to facilitate trustless cryptocurrency trading by relying on smart contracts and blockchain networks. The developments in the DEXs sector began with the implementation of an Automated Market Maker (AMM) system using a simple math formula by Uniswap in 2018. Absorbing significant funding and the attention of web3 enthusiasts, DEXs have seen numerous advancements in their evolution. A notable recent advancement is the introduction of hooks in Uniswap v4, which allows users to take advantage of a wide range of plugin-like features with liquidity pools. This paper provides a comprehensive classification and comparative analyses of prominent DEX protocols, namely Uniswap, Curve, and Balancer, in addition to investigating other protocols' noteworthy aspects. The evaluation framework encompasses mechanisms, components, mathematical formulations, and the performance of liquidity pools. The goals are to elucidate the strengths and limitations of different AMM models, highlight emerging concepts in DEX development, outline current challenges, and differentiate optimal models for specific applications. The results and comparative insights can be a reference for web3 developers, blockchain researchers, traders, and regulatory parties.
We analyze the token transfer network on Ethereum, focusing on accounts associated with Alameda Research, a cryptocurrency trading firm implicated in the misuse of FTX customer funds. Using a multi-token network representation, we examine node centralities and the network backbone to identify critical accounts, tokens, and activity groups. The temporal evolution of Alameda accounts reveals shifts in token accumulation and distribution patterns leading up to its bankruptcy in November 2022. Through network analysis, our work offers insights into the activities and dynamics that shape the DeFi ecosystem.
Regulatory authorities aim to tackle illegal activities by targeting the economic incentives that drive such behaviour. This is typically achieved through the implementation of financial sanctions against the entities involved in the crimes. However, the rise of cryptocurrencies has presented new challenges, allowing entities to evade these sanctions and continue criminal operations. Consequently, enforcement measures have been expanded to include crypto assets information of sanctioned entities. Yet, due to the nature of the crypto ecosystem, blocking or freezing these digital assets is harder and, in some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely as deterrents. For this reason, in this study, we aim to assess the impact of these sanctions on entities' crypto activities, particularly those related to the Bitcoin ecosystem. Our objective is to shed light on the validity and effectiveness (or lack thereof) of such countermeasures. Specifically, we analyse the transactions and the amount of USD moved by punished entities that possess crypto addresses after being sanctioned by the authority agency. Results indicate that while sanctions have been effective for half of the examined entities, the others continue to move funds through sanctioned addresses. Furthermore, punished entities demonstrate a preference for utilising rapid exchange services to convert their funds, rather than employing dedicated money laundering services. To the best of our knowledge, this study offers valuable insights into how entities use crypto assets to circumvent sanctions.
This study performs analysis of Predictive statements, Hope speech, and Regret Detection behaviors within cryptocurrency-related discussions, leveraging advanced natural language processing techniques. We introduce a novel classification scheme named "Prediction statements," categorizing comments into Predictive Incremental, Predictive Decremental, Predictive Neutral, or Non-Predictive categories. Employing GPT-4o, a cutting-edge large language model, we explore sentiment dynamics across five prominent cryptocurrencies: Cardano, Binance, Matic, Fantom, and Ripple. Our analysis reveals distinct patterns in predictive sentiments, with Matic demonstrating a notably higher propensity for optimistic predictions. Additionally, we investigate hope and regret sentiments, uncovering nuanced interplay between these emotions and predictive behaviors. Despite encountering limitations related to data volume and resource availability, our study reports valuable discoveries concerning investor behavior and sentiment trends within the cryptocurrency market, informing strategic decision-making and future research endeavors.
Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally.
The remarkable growth of digital assets, starting from the inception of Bitcoin in 2009 into a 1 trillion market in 2024, underscores the momentum behind disruptive technologies and the global appetite for digital assets. This paper develops a framework to enhance actuaries' understanding of the cyber risks associated with the developing digital asset ecosystem, as well as their measurement methods in the context of digital asset insurance. By integrating actuarial perspectives, we aim to enhance understanding and modeling of cyber risks at both the micro and systemic levels. The qualitative examination sheds light on blockchain technology and its associated risks, while our quantitative framework offers a rigorous approach to modeling cyber risks in digital asset insurance portfolios. This multifaceted approach serves three primary objectives: i) offer a clear and accessible education on the evolving digital asset ecosystem and the diverse spectrum of cyber risks it entails; ii) develop a scientifically rigorous framework for quantifying cyber risks in the digital asset ecosystem; iii) provide practical applications, including pricing strategies and tail risk management. Particularly, we develop frequency-severity models based on real loss data for pricing cyber risks in digit assets and utilize Monte Carlo simulation to estimate the tail risks, offering practical insights for risk management strategies. As digital assets continue to reshape finance, our work serves as a foundational step towards safeguarding the integrity and stability of this rapidly evolving landscape.
The supply chain is a critical segment of the product manufacturing cycle, continuously influenced by risky, uncertain, and undesirable events. Optimizing flexibility in the supply chain presents a complex, multi-objective, and nonlinear programming challenge. In the poultry supply chain, the development of mass customization capabilities has led manufacturing companies to increasingly focus on offering tailored and customized services for individual products. To safeguard against data tampering and ensure the integrity of setup costs and overall profitability, a multi-signature decentralized finance (DeFi) protocol, integrated with the IoT on a blockchain platform, is proposed. Managing the poultry supply chain involves uncertainties that may not account for parameters such as delivery time to retailers, reorder time, and the number of requested products. To address these challenges, this study employs an adaptive neuro-fuzzy inference system (ANFIS), combining neural networks with fuzzy logic to compensate for the lack of data training in parameter identification. Through MATLAB simulations, the study investigates the average shop delivery duration, the reorder time, and the number of products per order. By implementing the proposed technique, the average delivery time decreases from 40 to 37 minutes, the reorder time decreases from five to four days, and the quantity of items requested per order grows from six to eleven. Additionally, the ANFIS model enhances overall supply chain performance by reducing transaction times by 15\% compared to conventional systems, thereby improving real-time responsiveness and boosting transparency in supply chain operations, effectively resolving operational issues.
Decentralized Finance (DeFi) is increasingly studied and adopted for its potential to provide accessible and transparent financial services. Analyzing how investors use DeFi is important for reaching a better understanding of their usage and for regulation purposes. However, analyzing DeFi transactions is challenging due to often incomplete or inaccurate labeled data. This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs while providing insights into account activities.
Maruf Ahmed Mridul, Kaiyang Chang, Aparna Gupta, Oshani Seneviratne
The global financial landscape is experiencing significant transformation driven by technological advancements and evolving market dynamics. Moreover, blockchain technology has become a pivotal platform with widespread applications, especially in finance. Cross-border payments have emerged as a key area of interest, with blockchain offering inherent benefits such as enhanced security, transparency, and efficiency compared to traditional banking systems. This paper presents a novel framework leveraging blockchain technology and smart contracts to emulate cross-border payments, ensuring interoperability and compliance with international standards such as ISO20022. Key contributions of this paper include a novel prototype framework for implementing smart contracts and web clients for streamlined transactions and a mechanism to translate ISO20022 standard messages. Our framework can provide a practical solution for secure, efficient, and transparent cross-border transactions, contributing to the ongoing evolution of global finance and the emerging landscape of decentralized finance.
Blockchain technology is essential for the digital economy and metaverse, supporting applications from decentralized finance to virtual assets. However, its potential is constrained by the "Blockchain Trilemma," which necessitates balancing decentralization, security, and scalability. This study evaluates and compares two leading proof-of-stake (PoS) systems, Algorand and Ethereum 2.0, against these critical metrics. Our research interprets existing indices to measure decentralization, evaluates scalability through transactional data, and assesses security by identifying potential vulnerabilities. Utilizing real-world data, we analyze each platform’s strategies in a structured manner to understand their effectiveness in addressing trilemma challenges. The findings highlight each platform’s strengths and propose general methodologies for evaluating key blockchain characteristics applicable to other systems. This research advances the understanding of blockchain technologies and their implications for the future digital economy. Data and code are available on GitHub as open source.
The rapid advancements in artificial intelligence, big data analytics, and cloud computing have precipitated an unprecedented demand for computational resources. However, the current landscape of computational resource allocation is characterized by significant inefficiencies, including underutilization and price volatility. This paper addresses these challenges by introducing a novel global platform for the commodification of compute hours, termed the Global Compute Exchange (GCX) (Patent Pending). The GCX leverages blockchain technology and smart contracts to create a secure, transparent, and efficient marketplace for buying and selling computational power. The GCX is built in a layered fashion, comprising Market, App, Clearing, Risk Management, Exchange (Offchain), and Blockchain (Onchain) layers, each ensuring a robust and efficient operation. This platform aims to revolutionize the computational resource market by fostering a decentralized, efficient, and transparent ecosystem that ensures equitable access to computing power, stimulates innovation, and supports diverse user needs on a global scale. By transforming compute hours into a tradable commodity, the GCX seeks to optimize resource utilization, stabilize pricing, and democratize access to computational resources. This paper explores the technological infrastructure, market potential, and societal impact of the GCX, positioning it as a pioneering solution poised to drive the next wave of innovation in commodities and compute.
We engineer blockchain based risk managed portfolios by creating three funds with distinct risk and return profiles: 1) Alpha - high risk portfolio; 2) Beta - mimics the wider market; and 3) Gamma - represents the risk free rate adjusted to beat inflation. Each of the sub-funds (Alpha, Beta and Gamma) provides risk parity because the weight of each asset in the corresponding portfolio is set to be inversely proportional to the risk derived from investing in that asset. This can be equivalently stated as equal risk contributions from each asset towards the overall portfolio risk. We provide detailed mechanics of combining assets - including mathematical formulations - to obtain better risk managed portfolios. The descriptions are intended to show how a risk parity based efficient frontier portfolio management engine - that caters to different risk appetites of investors by letting each individual investor select their preferred risk-return combination - can be created seamlessly on blockchain. Any Investor - using decentralized ledger technology - can select their desired level of risk, or return, and allocate their wealth accordingly among the sub funds, which balance one another under different market conditions. This evolution of the risk parity principle - resulting in a mechanism that is geared to do well under all market cycles - brings more robust performance and can be termed as conceptual parity. We have given several numerical examples that illustrate the various scenarios that arise when combining Alpha, Beta and Gamma to obtain Parity. The final investment frontier is now possible - a modification to the efficient frontier, thus becoming more than a mere theoretical construct - on blockchain since anyone from anywhere can participate at anytime to obtain wealth appreciation based on their financial goals.
The latest advancements in Distributed Ledger Technology (DLT), and payment architectures such as the UK's New Payments Architecture, present opportunities for leveraging the hidden informational value and intelligence within payments. In this paper, we present Smart Money, an infrastructure capability for a Central Bank Digital Currency (CBDC) which enables real-time Value Added Tax split payments, oversight, controlled access and smart policy implementation. This capability is implemented as a prototype, called Making Tax Smart (MTS), which is based on the open source R3 Corda framework. The results presented herein confirm that it is feasible to build a MTS capability which is scalable and co-exists with the current payment systems. Smart Money CBDC has the potential to mobilise payments data in order to transform the role of money from a blunt instrument to a government policy sensor and actuator without disrupting the existing money system. DLT, smart contracts and programmable money have a crucial role to play with benefits for government departments, the economy and society as a whole.
Predicting the trend of Bitcoin, a highly volatile cryptocurrency, remains a challenging task. Accurate forecasting holds immense potential for investors and market participants dealing with High Frequency Trading systems. The purpose of this study is to demonstrate the significance of using a systematic approach toward selecting informative observations for enhancing Bitcoin minute trend prediction. While a multitude of data collection methods exist, a crucial barrier remains: efficiently selecting the most informative data for building powerful prediction models. This study tackles this challenge head-on by introducing the Separation Index, a groundbreaking tool for fast and effective data (feature) subset selection. The Separation Index operates by measuring the improvement in class separability (i.e. upward vs. downward trends) with each added feature set. This innovative metric guides the creation of a highly informative dataset, maximizing the model's ability to differentiate between price movements. Our research demonstrates the effectiveness of this approach, achieving unprecedented accuracy in minute-scale Bitcoin trend prediction, surpassing the performance of previous studies. This significant advancement paves the way for a new era of data-driven decision-making in the dynamic world of cryptocurrency markets.