Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach

This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance. The neural network model demonstrated the highest accuracy, achieving a 75% success rate, significantly outperforming traditional models. Additionally, trading strategies derived from these ML models yielded a return on investment (ROI) of up to 12%, compared to an 8% benchmark index ROI. These findings underscore the transformative potential of ML in refining trading strategies, providing critical insights for financial analysts, investors, and developers. The study draws on insights from 15 peer-reviewed articles, financial datasets, and industry reports, establishing a robust foundation for future exploration of ML-driven financial forecasting. Tools and Technologies Used †PineScript PineScript, a scripting language integrated within the TradingView platform, was the primary tool used to develop and implement the machine learning models. Its robust features allowed for custom indicator creation, strategy backtesting, and real-time market data analysis. †Python Python was utilized for data preprocessing, model training, and performance evaluation. Key libraries included: Pandas

本研究探讨机器学习（Machine Learning, ML）模型在股市预测中的应用，重点关注其与面向算法交易的领域特定语言PineScript的集成。本研究依托包含历史股价与市场情绪数据在内的多源数据集，开发并测试了神经网络、决策树、线性回归等多种机器学习模型。通过在多时间框架与多样市场环境下开展严格的回测分析，我们得以评估模型的预测准确率与财务表现。其中神经网络模型的准确率最为出众，预测成功率达75%，显著优于传统模型。此外，基于这些机器学习模型构建的交易策略可实现最高达12%的投资回报率（Return on Investment, ROI），相较之下基准指数的投资回报率仅为8%。本研究结果凸显了机器学习在优化交易策略中的变革性潜力，可为金融分析师、投资者及开发者提供关键决策参考。本研究参考了15篇同行评议论文、金融数据集与行业报告，为后续机器学习驱动的金融预测研究奠定了坚实基础。所用工具与技术：
† PineScript：PineScript是集成于TradingView平台的脚本语言，为开发与部署机器学习模型的核心工具。其强大功能支持自定义指标构建、策略回测与实时市场数据分析。
† Python：本研究使用Python开展数据预处理、模型训练与性能评估工作，所用核心库包括：Pandas