SP500 stock screener correlating to percent change during time periods.
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Updated
Aug 29, 2020 - Python
SP500 stock screener correlating to percent change during time periods.
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.
A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.
Simple script to compare the correlation on assets beetween S&P500 and FED Asset Balance.
Determine the preferred portfolio composition from constituents within the S&P 500 index.
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.
Applied Basic Machine Learning on List of S&P 500 Companies using Yahoo Finance
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!
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.
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.
My version of SP 500 data analyzer
Fama French models on S&P 500 dataset
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.
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.
Using LSTM to predict stock price movement for S&P500
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…
The app to know next day's yield prediction
Constituent history of the S&P 500 from various data sources
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