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FinSage, Your AI-Powered Investment Portfolio Assistant. A modern, responsive fintech platform that offers real-time stock analytics, personalized AI financial guidance, portfolio tracking, and financial literacy resources, built with React, TypeScript, Vite, Tailwind CSS, and integrated with the Finnhub API.

Streamlit dashboard for stock analysis with yfinance, Plotly charts, and scikit-learn price predictions.

This project provides a powerful tool for stock market analysis and portfolio management, combining multiple forecasting models, technical analysis, and risk management features. Built with modern Python libraries and Streamlit for an intuitive user interface.

AI-powered tool that fetches and analyzes NVIDIA news using semantic search, sentiment analysis, and Gemini-based investment summaries.

Multi-Agent stock analysis system with telegram integration

StockSense is a stock tracking and analysis web app built using Node.js, Express, EJS, and real-time stock market APIs. It provides live stock data, charts, and insights through a clean, dynamic EJS interface.

Config files for my GitHub profile.

Mercado Search Trends & Stock Analysis with Prophet

This project aims to create a user-friendly stock analysis dashboard using Python and the Streamlit library. By leveraging the YFinance library, we fetch historical stock prices, calculate various technical indicators, and visualize trends in an interactive manner.

A Python-based stock market analytics dashboard that processes NSE large-cap trade data through Pandas and visualises it via an interactive Streamlit interface. Features real-time filtering, sector drill-downs, KPI computation, and publication-grade Plotly charts — all from a single CSV file, with no frontend code required.

Insider trading activity with buy/sell volumes for each quarter and Social Sentiment for your selected stock

Production-grade Spring Boot API for stock market analysis using RSI, MACD, SMA, and EMA indicators.

Investment Portfolio Builder is a Python library that provides portfolio optimization methods, including classical mean-variance optimization techniques, Black-Litterman allocation, and more recent developments like shrinkage and Hierarchical Risk Parity.

AI-powered stock intelligence and automated reporting system using Python, NLP, sentiment analysis, and financial analytics.

calculate stock target price using DCF model.