Blockchain-Based Algorithmic Trading: Efficiency and Security Analysis using Cryptographic Protocols and Smart Contracts

Abstract

The integration of distributed ledger technology with financial markets has precipitated a paradigm shift in how algorithmic trading strategies are conceived, executed, and settled. This paper presents a comprehensive analysis of blockchain-based algorithmic trading systems, focusing specifically on the dual challenges of execution efficiency and cryptographic security. While traditional high-frequency trading relies on centralized exchanges and proprietary networks to minimize latency, decentralized trading protocols introduce novel constraints related to block generation intervals, consensus mechanisms, and network propagation delays. We examine the implementation of algorithmic strategies via smart contracts, evaluating the trade offs between on-chain transparency and the privacy requirements of institutional investors. Furthermore, the study investigates critical vulnerabilities inherent to decentralized exchanges, such as Miner Extractable Value and front-running attacks, and proposes mitigation strategies utilizing commit-reveal schemes and zero-knowledge proofs. By analyzing the performance metrics of automated market makers against order book models, we provide empirical evidence regarding the current limitations and potential scalability of blockchain-based trading environments. The findings suggest that while blockchain architectures offer superior settlement finality and auditability, significant advancements in layer-two scaling solutions and privacy preserving cryptographic protocols are requisite for these systems to compete with traditional financial infrastructure in terms of throughput and latency.