Extreme Gradient Boosting for Predicting Stock Price Direction in Context of Indian Equity Markets Sachin Jadhav , Vrushal Chaudhari , Pratik Barhate , Kunal Deshmukh , and Tarun Agrawal Abstract Algorithmic Trading is becoming ever increasing field. We implemented various machine learning methods to predict stock price direction in the following paper. Technical Indicators are taken as features and models are trained upon them. Stock price direction forecasting helps us to choose from daily trading techniques and increases the probability of having massive profits while maintaining low-risk profile. In our research extreme gradient boosting (XGBoost) technique has given the highest accuracy, i.e. 73.1% and have outperformed other machine learning techniques used. Our Proposed model outperforms existing models in literature and adds forecasting on day-by-day basis. Keywords Trading · XGBoost · Equity markets · Algorithmic model · Machine learning for finance · NSE · NIFTY50 1 Introduction Predicting the stock price direction is an ongoing topic of discussion among all financial forums. The main motive behind this trend to get profits from stock market while minimizing the risks associated. It can be achieved through algorithmic trading, using machines instead of humans to do the actual trades. Stock price direction prediction can be done in two ways, i.e. long-term stock price prediction and short- term stock price prediction. While long-term stock price prediction is usually done through fundamental analysis of stock, Short-term stock price prediction is done using technical indicators. Various new techniques like Data mining, Sentimental analysis and Arbitrage analysis are also being used by professionals to achieve desired results. In this paper, we will be focussing on short-term prediction of stock price direc- tion. Techniques such as sma-rsa overlap, morning breakout, start gap analysis, momentum-price and market modules are used in Technical Analysis. But the main S. Jadhav (B ) · V. Chaudhari · P. Barhate · K. Deshmukh · T. Agrawal Department of Information Technology, PCCoE, Pune, Maharashtra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A. K. Nagar et al. (eds.), Intelligent Sustainable Systems, Lecture Notes in Networks and Systems 334, https://doi.org/10.1007/978-981-16-6369-7_28 321