gs-quant VS FinancePy

Compare gs-quant vs FinancePy and see what are their differences.

FinancePy

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives. (by domokane)
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gs-quant FinancePy
96 97
2,456 1,898
4.1% -
7.9 8.8
8 days ago 26 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

gs-quant

Posts with mentions or reviews of gs-quant. We have used some of these posts to build our list of alternatives and similar projects.

FinancePy

Posts with mentions or reviews of FinancePy. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing gs-quant and FinancePy you can also consider the following projects:

fastquant - fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!

DCF - Basic Discounted Cash Flow library written in Python. Automatically fetches relevant financial documents for chosen company and calculates DCF based on specified parameters.

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.

PyPortfolioOpt - Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

resistance - Pre-crisis Risk Management for Personal Finance

dcf-basic - Basic DCF model to quickly value public companies.

notebooks - Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

alphalens - Performance analysis of predictive (alpha) stock factors

Mad-Money-Backtesting - Backtesting recommendations from Mad Money and "The Cramer Effect/Bounce"

ruby-cff - A Ruby library for manipulating CITATION.cff files.

okama - Investment portfolio and stocks analyzing tools for Python with free historical data