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

Natural Language Processing on Stocks' Earnings Call Transcripts: An Investment Strategy Backtest Based on S&P Global Papers.

A free Discord bot that pulls and organizes data from two sources, a list of the S&P 500 index from Wikipedia (as it's a commonly updated sources), and a configurable API (currently Finnhub) tracking SEC Form 4 reporting. SEC Form 4 pertains to insider trading, which can be used as a research source prior to making market decisions.

Applied Basic Machine Learning on List of S&P 500 Companies using Yahoo Finance

Using LSTM to predict stock price movement for S&P500

The app to know next day's yield prediction

📈 Fama French and ML models on S&P 500 dataset

Python Repository to ingest, feature engineer, train, backtest, and run a random forest model to predict the direction of the S&P500 at the start of the next day's trading session.

This application compares the performance of Unsupervised machine learning models and Supervised models. It downloads 3 yrs of market daily close data from all SP500 companies and divides them into Sectors to be used as features for learning and training the data, in order to predict wether the index will be a Buy or Sell the next day. The resul…

SP500 stock screener correlating to percent change during time periods.

This system is designed to provide valuable insights into future market movements, enabling users to make informed decisions regarding their investments without directly executing trades. It leverages the VIX (CBOE Volatility Index) as a key indicator for predicting trends, in the SPY (S&P 500 ETF) market.

This project showcases a web application that is designed to perform CAPM calculations for different stocks. The application uses Python programming language and its libraries such as Pandas, NumPy, Streamlit and Plotly, to gather stock data from Yahoo Finance and perform calculations to determine expected returns.

Financial platform combining an interactive dashboard and REST API for investment portfolio analysis, providing real-time performance tracking across multiple stock exchanges.

Determine the preferred portfolio composition from constituents within the S&P 500 index.

A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.

This repository contains a small project where I study feasibility of using knockoff filters in portfolio management. More details are included in the Wiki page

Web Application to sort, analyze, & render data for all SP500 companies.

Historical options data for three major U.S. equity ETFs: SPY (S&P 500), IWM (Russell 2000), and QQQ (Nasdaq-100). The dataset spans January 2008 to December 2025 and includes over 53 million option contracts with Greeks, implied volatilities, and market microstructure variables.

In this project, dive into an interactive app that brings stock data to life! Explore dynamic candlestick charts, track returns, and analyze rolling alpha and beta with ease. Compare your chosen stock to the S&P 500 Index and uncover trends with eye-catching visualizations. Perfect for data enthusiasts and investors alike!

My version of SP 500 data analyzer