Abstract
With the development of machine learning and big data, traditional equity trading system methods can no longer meet the current trading needs, and there are still problems such as low operating efficiency and serious homogeneity. Blockchain technology has the characteristics of decentralization and can also complete transactions through smart contracts, innovating the way of equity system transactions. The purpose of this paper is to build an equity trading system in combination with blockchain in the context of machine learning and big data and provide innovative trading methods, so as to provide reference and reference significance for the construction of my country’s equity market. This article uses literature data method, comparative analysis method, factor analysis method, and other methods to carry out research, in‐depth study of machine learning and big data, blockchain‐related concepts, system composition, application situation, etc., and discusses the allocation of equity trading market The functions of resources, risk diversification, risk transfer, price determination, etc., have built a blockchain equity trading system, designed a consensus mechanism, block generation protocol, block verification, decentralization, and smart contract platform, and finally conducted a national equity transaction the background of the market is analyzed, and the experimental results, simulation indicators, transaction time, transmission consumption, and other content of the system constructed in this article are analyzed. In the single‐node test, the CPU usage of the PoW consensus mechanism algorithm reached 100%, but the improved PBFT consensus mechanism was only 16%, which saved a lot of computing power and improved computing performance.
Highlights
After the emergence of Bitcoin in 2009, blockchain technology has slowly entered people’s field of vision
Blockchain technology has the characteristics of decentralization, transparency, reliability, etc., so it has attracted the attention of experts and researchers
This article mainly researches the blockchain equity system trading methods and systems based on machine learning and big data algorithms
Summary
After the emergence of Bitcoin in 2009, blockchain technology has slowly entered people’s field of vision. They solved these challenges through declarative ML: (1) increased the productivity of data scientists because they were able to express custom algorithms in familiar domain-specific languages, including linear algebra primitives and statistical functions; (2) in distributed computers to run these ML algorithms transparently, the data parallel framework applies cost-based compilation techniques to generate efficient, low-level execution plans, which include single-node and large-scale distributed operations in memory He described the end-to-end system ML on apache spark, including an in-depth understanding of various optimizers and runtime technologies and performance characteristics. The main innovations of this paper are (1) using literature analysis, comparative analysis, and other methods to describe and analyze the equity trading system, through the comparison of different system models, highlight the feasibility and effectiveness of the framework of this paper; (2) the combination of theoretical analysis and empirical research analyzes the shortcomings of the traditional equity trading system and explains possible countermeasures and collects data and analyzes through data comparison
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