The purpose of this project is to develop a machine learning and AI-based solution for predicting the high, low, and close values of stocks or indices in the next trading day. We leverage the power of LSTM (Long Short-Term Memory) models to analyze sequential data and provide accurate predictions. Our application can be applied to various stocks and indices, enabling users to make informed investment decisions.

Stock Market?

The stock market is a complex and dynamic environment where prices of financial assets are determined by various factors such as economic indicators, company performance, market sentiment, and geopolitical events.

Why Predicting stock market prices are challanging?

• Complex Data Analysis

  The stock market generates an enormous amount of data, including historical prices, financial reports, news articles, social media sentiment, and more. AI models can effectively analyze and process this vast volume of data, identifying patterns and correlations that may not be readily apparent to human analysts.

• Automation and Efficiency

  Manual analysis of stock market data can be time-consuming and prone to human biases. AI models automate the process, significantly reducing the time and effort required to analyze data and make predictions. This enables investors and traders to react quickly to market changes and identify potential opportunities or risks..

• Pattern Recognition

  AI models excel at identifying complex patterns and trends in data. By training on historical market data, an AI model can learn from past market behavior and recognize similar patterns in real-time. This allows it to make predictions based on historical precedents, potentially improving the accuracy of forecasts.

• Incorporating Multiple Factors

  Stock prices are influenced by a multitude of factors, including economic indicators, company performance, market sentiment, and geopolitical events. AI models can integrate and weigh these various factors simultaneously, considering a broader range of information compared to traditional models. This holistic approach can provide more comprehensive insights into stock price movements.

There for Predicting should be automated...!

Automated Price Predictors are already available. But..

• Developers have limited understanding about Market dynamics

• Overfitting and Generalization

• Changing Market Regimes