Current and Historical Lists of S&P 500 components since 1996
-
Updated
Dec 10, 2024 - Jupyter Notebook
#
sp500
Here are 77 public repositories matching this topic...
Command line utility to display stock quotes and index data
windows linux finance snap command-line stock-market trending trends scraped-data command-line-tool financial-data nasdaq trend historical-data stock-quote securities fatjar quoter dow sp500
-
Updated
Mar 20, 2024 - Java
Any data but iris 👁
data r google-apps starwars kiva starwars-api gapminder movielens-dataset movielens iris-dataset pisa-data world-happiness-report mtcars fashion-mnist sp500 big-five-model water-risk
-
Updated
Sep 8, 2019 - R
Distributed stock price forecasting system to predict S&P 500 stock prices.
finance distributed-systems machine-learning deep-learning trading pytorch pyspark lstm stock-market stock-price-prediction technical-analysis fundamental-analysis sp500 pytorch-lightning
-
Updated
Nov 12, 2021 - Jupyter Notebook
S&P500 Stock Index Movement Forecastor with various Statistical and Machine Learning Models
-
Updated
Mar 25, 2023 - Jupyter Notebook
Collection of 3 quantitative finance projects in Python that uses algorithmic trading.
-
Updated
Jan 20, 2021 - Jupyter Notebook
Constituent history of the S&P 500 from various data sources
-
Updated
Dec 25, 2024 - R
Predict stock trends using visual time windows
machine-learning iex stock-price-prediction image-classification convolutional-neural-networks pandas-datareader sp500
-
Updated
Oct 29, 2018 - Python
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.
-
Updated
Nov 27, 2023 - Jupyter Notebook
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.
finance linear-regression pca-analysis autoencoders autoregressive sp500 autoregressive-moving-average
-
Updated
Jan 18, 2020 - HTML
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.
-
Updated
Mar 4, 2021 - Jupyter Notebook
Developed a predictive model using LSTM networks to forecast strong daily uptrends in the SPY ETF. This project includes feature engineering, model building and hyper parameter tuning and a backtested trading strategy that outperforms the market.
deep-neural-networks deep-learning neural-networks lstm-model trading-strategies trading-algorithms spy lstm-neural-networks sp500 trendprediction
-
Updated
Aug 29, 2024 - Jupyter Notebook
Applied Basic Machine Learning on List of S&P 500 Companies using Yahoo Finance
-
Updated
Nov 8, 2020 - Python
Vix index is implemented in S&P500 historical data.
-
Updated
Jun 14, 2021 - Python
Various Crypto/US Stock Alerts
-
Updated
Dec 24, 2024 - Python
Quant machine for S&P 500 (Annualized return: 20.60%; Sharpe ratio: 1.06; Sortino ratio: 2.05; Calmar ratio: 1.05)
-
Updated
Nov 1, 2023 - HTML
Map showing the location of the headquarters of the top US companies over many years. (visualization part)
-
Updated
Oct 8, 2024 - JavaScript
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.
ml polygon quant quantitative-finance black-scholes options-trading sp500 options-pricing sp500-data-analysis
-
Updated
Nov 3, 2023 - Python
The app to know next day's yield prediction
-
Updated
Dec 23, 2024 - Python
Improve this page
Add a description, image, and links to the sp500 topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the sp500 topic, visit your repo's landing page and select "manage topics."