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

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

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

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.

The app to know next day's yield prediction

IME-published article on Long-term Real Dynamic Investment Planning. While we enhance predictability of the real returns of S&P500 Index, we derive optimal non-myopic investment strategy, and we compare its performance with near-optimal Dynamic and Constant Merton investment strategies.

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…

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.

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

SP500 stock screener correlating to percent change during time periods.

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

Using LSTM to predict stock price movement for S&P500

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

This Java program simulates different investment strategies using historical stock market data. It allows users to test various strategies such as buy and hold, moving average, buying when the stock price is lower than the last purchase, and dollar-cost averaging.

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.

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!

Algorithmic Trading means using computers to make investment decisions. We will be using World's most popular S&P 500 Stock market index in order to do Data Analysis and generate predictions. Let us make investments on Stocks, easy for everyone!

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

My version of SP 500 data analyzer