resistance
FinancePy
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resistance | FinancePy | |
---|---|---|
1 | 97 | |
610 | 1,830 | |
- | - | |
2.1 | 8.8 | |
8 months ago | 17 days ago | |
Jupyter Notebook | Jupyter Notebook | |
Creative Commons Zero v1.0 Universal | GNU General Public License v3.0 only |
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.
resistance
We haven't tracked posts mentioning resistance yet.
Tracking mentions began in Dec 2020.
FinancePy
We haven't tracked posts mentioning FinancePy yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
DCF - Basic Discounted Cash Flow library written in Python. Automatically fetches relevant financial documents for chosen company and calculates DCF based on specified parameters.
PyPortfolioOpt - Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
gs-quant - Python toolkit for quantitative finance
dcf-basic - Basic DCF model to quickly value public companies.
pyfolio - Portfolio and risk analytics in Python
alphalens - Performance analysis of predictive (alpha) stock factors
FinRL-Library - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]
ruby-cff - A Ruby library for manipulating CITATION.cff files.
okama - Investment portfolio and stocks analyzing tools for Python with free historical data
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
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.