The app to know next day's yield prediction
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Updated
Jan 6, 2024 - Python
The app to know next day's yield prediction
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.
Natural Language Processing on Stocks' Earnings Call Transcripts: An Investment Strategy Backtest Based on S&P Global Papers.
Simple script to compare the correlation on assets beetween S&P500 and FED Asset Balance.
SP500 stock screener correlating to percent change during time periods.
A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.
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.
Applied Basic Machine Learning on List of S&P 500 Companies using Yahoo Finance
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.
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.
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