{"repo":"Felix-au/QuantX-Stock-Analysis-and-Forecast","free":true,"listed":false,"github":"https://github.com/Felix-au/QuantX-Stock-Analysis-and-Forecast","clone":"git clone https://github.com/Felix-au/QuantX-Stock-Analysis-and-Forecast.git","description":"An advanced stock analysis and forecasting platform. QuantX provides real-time financial metrics, key technical indicators (SMA, EMA, MACD, RSI), and sentiment-analyzed market news. It features a prediction engine that runs and contrasts 8 models, ranging from classical time-series to machine learning.","language":"Python","stars":18,"topics":["finance","machine-learning","plotly","prophet","python","quant-finance","stock-analysis","stock-forecasting","streamlit","xgboost"],"license":"MIT","category":"machine-learning","readme_excerpt":"QuantX: Stock Analysis and Forecast An elegant, high-fidelity AI-powered dashboard to analyze stock trends, compute technical indicators, and forecast stock prices using optimized Machine Learning. Real-time market analytics, automated sentiment scoring, and multi-model financial forecasting driven by Python and Streamlit. --- Table of Contents - Overview - Competitive Comparison - System Architecture - Visual UI Guide - Machine Learning Forecasting Engine - Technical Indicators and Metrics - Access and Launch Instructions - Open Source Source Run - Web Access - Custom Domain Deployment - Directory Structure - Troubleshooting and Failsafes - Author --- Overview QuantX is an open-source financial analytics and machine learning forecasting tool. Built with modern modular Python and managed via the Rust-based uv package manager, QuantX provides real-time market overviews for global indices and individual stocks (including Indian NSE stocks with .NS suffixes). Core Highlights Modular Codebase : Fully refactored into a modular package ( src/ ) separating UI components, mathematical indicators, predictions, and helpers. 8 Forecasting Algorithms : Includes Linear Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, ARIMA, Exponential Smoothing, and Facebook Prophet. Benchmarking Suite : A single-click \"Run All and Compare\" mode that benchmarks all 8 algorithms simultaneously and highlights top performers based on RMSE, MAE, and MAPE. Automated News Sentiment :","default_branch":null,"files":null,"tree":[],"storefront":"/r/Felix-au","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Felix-au/QuantX-Stock-Analysis-and-Forecast/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}