Stock Trade Prediction Using Machine Learning

Abstract

The importance of stock market predictions cannot be underestimated because correct predictions ensure good profit and minimize risks for investors. With the expansion of historical data and technological developments in machine learning, the performance of such automatic prediction systems improves significantly. In this paper, the authors consider different prediction techniques with a focus on XGBoostClassifier-based stock market prediction models using technical indicators such as Moving Average, Relative Strength Index, MACD and Volume. Such a model aims to create Buy/Sell signals and calculate the probability of gain to provide investors with additional confidence regarding their investment decisions. There are multiple models and approaches discussed in literature, and the review shows how powerful the classification models are in financial technologies. According to experimental results, the created system can provide trading signals with up to 78% confidence in bulls\' probability. Thus, there are high chances for predicting bullish trends with this system. Consequently, the effectiveness of combining XGBoostClassifier with technical indicators is proved by the research.

Introduction

Stock market indices such as NIFTY 50 and S&P 500 represent overall market performance and are widely used for investment decisions and economic analysis. However, predicting stock price movements is difficult due to complex influences like economic conditions, investor sentiment, and global events. Traditional methods like fundamental and technical analysis depend heavily on human judgment and struggle with large-scale, fast-changing market data.

To address these limitations, the study introduces an AI-based trading system called TradePredict, which uses machine learning—especially the XGBoostClassifier—along with technical indicators like Moving Average, RSI, MACD, and Volume. These features are extracted from historical OHLCV data (sourced from platforms like Yahoo Finance) and used to generate Buy/Sell signals and bullish probability scores, helping traders make data-driven decisions.

The literature review highlights that machine learning and deep learning models generally outperform traditional statistical methods in stock prediction, especially when handling nonlinear and volatile market behavior. Hybrid and ensemble approaches further improve accuracy.

Methodologically, the system involves data collection, preprocessing, feature engineering using technical indicators, and training the XGBoost model. The model learns patterns in historical price movements to forecast future trends.

Results show that the system can effectively identify Buy and Sell signals aligned with real market trends. It also calculates bullish probability (around 78% in experiments), indicating strong confidence in upward movements. Overall, XGBoost performs well in capturing complex financial patterns and provides stable, reliable predictions.

Conclusion

In this research, we have presented a stock market prediction system which is basically based on the XGBoostClassifier algorithm and is combined with technical indicators like Moving Average, RSI, MACD, and Volume.The results we got show that the system is able to generate accurate Buy/Sell trading signals and also calculate bullish probability which helps in making better investment decisions. The 78% bullish probability confidence that we achieved indicates that the system performs quite well in identifying upward market trends. Basically combining XGBoostClassifier with technical analysis through ensemble learning gives a strong framework for financial forecasting. This also helps in dealing with the limitations of traditional analysis methods as it can handle nonlinear market relationships and can also adapt to changing market conditions quite well. This system is quite useful for traders and investors as it gives data driven insights and also a way to measure confidence for their trading decisions.

References

[1] Z. Fathali, Z. Kodia, and L. Ben Said, Stock Market Prediction of NIFTY 50 Index Applying Machine Learning Techniques, 2020. [2] T. H. H. Aldhyani and A. Alzahrani, Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms, 2020. [3] A. B. Omar et al., Stock Market Forecasting Using Random Forest and Deep Neural Network Models, 2021. [4] D. Xiao and J. Su, Research on Stock Price Time Series Prediction Based on Deep Learning and ARIMA, 2020. [5] J. Shen and M. O. Shafiq, Short-Term Stock Market Price Trend Prediction Using a Comprehensive Deep Learning System, 2020. [6] M. Vijh et al., Stock Closing Price Prediction Using Machine Learning Techniques, 2020. [7] Y. Guo, Stock Price Prediction Using Machine Learning, Master’s Thesis, Stanford University, 2019.

Copyright

Copyright © 2026 Asst. Prof. Supriya Jawale, Rudrani Dhomne, Amey Samarth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.