Developing Trading Strategies in Decentralized Market Prediction by Using AI, ML, and Blockchain Technology

Abstract

This research aims to explore the potential application of blockchain, artificial intelligence (AI), and machine learning (ML) technologies in the finance industry, specifically for developing trading strategies in decentralized markets. The emergence of these markets, enabled by blockchain technology, has revolutionized the trading landscape, offering new opportunities for investors and traders. However, challenges such as complexity, trading volume, liquidity, GDP, volatility, and lack of transparency make it difficult to create effective trading strategies. This study employs quantitative data analysis and ML algorithms to analyse trading indicators and social media sentiment data, improving the accuracy of trading signals and generating more profits in decentralized markets. The hypothesis is that the combined implementation of blockchain, AI, and ML in decentralized trading markets can result in more accurate market predictions and generate profitable trading signals. Linear regression, decision tree, random forest, and XGBoost ML models have been built and compared for a recommendation, with the XGBoost model performing the best at 95% by R square and RMSE values comparison. Additionally, AI models, such as logistic regression for sentiment analysis on tweet stock datasets, were utilized to analyse sentiment scores for better trading decisions, with model performance evaluated using a confusion matrix and the overall accuracy result was 95% and model evaluation performance metrics like precision 95%, recall 95%, and f-1 score 95% as weighted avg. This research further explores the integration of ML and AI models into a blockchain framework, enhancing transparency, security, and efficiency in decentralized trading markets. The potential of different AI and ML techniques for predicting market trends and generating profitable trading signals is investigated, along with addressing the likelihood of prejudice, discrimination, and mining strategies in Bitcoin to mitigate these risks in the future.