Technical Analysis-Based Data Mining Strategies for Stock Market Trend Observation

Abstract

This study introduces a comprehensive approach that utilizes technical analysis-based data mining strategies to observe and predict stock market trends, by leveraging historical trading data, technical indicators such as moving averages, RSI, and MACD, to systematically analyze and interpret market behavior, thereby providing investors and traders with actionable insights for making informed decisions in the volatile environment of stock trading. By integrating quantitative analysis with predictive modeling, the methodology aims to enhance the accuracy of trend forecasts and identify profitable trading opportunities. Through the application of cross-validation and backtesting techniques, the effectiveness of these strategies is rigorously evaluated against actual market movements, offering a robust framework for risk management and portfolio optimization. This interdisciplinary approach not only demystifies the complexities of the stock market but also opens new avenues for research and development in financial technology, promising a significant contribution to the field of economic forecasting and investment strategy.