Current and Historical Lists of S&P 500 components since 1996

An MCP server and Next.js web app for querying S&P 500 company data from Supabase, with tools for company info, news, officers, and SEC filings, plus embedded MCP App UI resources, Elicitation, and Sampling support.

Market Breadth Monitor

An S&P500 Stock Index Movement Predictor built with Machine Learning models

Constituent history of the S&P 500 from various data sources

Command line utility to display stock quotes and index data

Any data but iris 👁

Collection of 3 quantitative finance projects in Python that uses algorithmic trading.

S&P500 Stock Index Movement Forecastor with various Statistical and Machine Learning Models

Daily-updated US stock ticker CSVs from NASDAQ, including S&P 500 constituents and industry-grouped lists.

The daily arena where AI agents clash to rank tomorrow’s S&P 500 winners and losers. Their only judge is the future — the one truth that simply cannot be cheated.

Distributed stock price forecasting system to predict S&P 500 stock prices.

The purpose of this repository is to test the hypothesis that the S&P 500 index has an exogenous relationship to the price of Gold; specifically that as the S&P index falls, the value of Gold will increase.

This repo is about Economy & Financial Markets. Here you can see data about Argentinian Stocks Market, S&P 500, Dow Jones, Brazilian Stocks Market and other economic metrics such as GDP, Gold value, etc.

Vix index is implemented in S&P500 historical data.

Predict stock trends using visual time windows

Abstract: The S&P500 is difficult to predict. Multi-factor models provide a useful framework for making returns predictions and for controlling portfolio risk. This paper explores a three-step process in predicting PCA and Autoencoders factors to generate multi-factor models from the S&P500 component securities.

This project focuses on the design and implementation of a trading bot using OpenAI's GPT for sentiment analysis of financial news. The bot integrates sentiment analysis in trading strategies for S&P 500 stocks.

Sparse index replication engine: tracks the S&P 500, Nasdaq-100, Russell 2000 and Nifty 50 with a small basket of stocks (~10% of each index) using a custom ADMM solver for L1-regularized portfolio optimization. Built for direct indexing, tax-loss harvesting and low-cost benchmark tracking. Python, FastAPI, Next.js, Azure.

Quant machine for S&P 500 (Annualized return: 20.60%; Sharpe ratio: 1.06; Sortino ratio: 2.05; Calmar ratio: 1.05)