lppls
PyPortfolioOpt
lppls | PyPortfolioOpt | |
---|---|---|
1 | 155 | |
331 | 4,210 | |
3.3% | - | |
6.0 | 4.4 | |
4 months ago | 18 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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.
lppls
PyPortfolioOpt
- PyPortfolioOpt: Financial portfolio optimisation, including classical efficient frontier and advanced methods. Portfolio Selection and Optimisation - star count:3788.0
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dcapal alternatives - PyPortfolioOpt and Riskfolio-Lib
3 projects | 16 Sep 2023
- PyPortfolioOpt: Financial portfolio optimisation, including classical efficient frontier and advanced methods. Portfolio Selection and Optimisation - star count:3706.0
What are some alternatives?
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Riskfolio-Lib - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
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qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
FinanceExamplesPy - Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir). [Moved to: https://github.com/mrtkp9993/QuantitaveFinanceExamplesPy]
Statmetrics-Android - Mobile App Solution for Portfolio Analytics and Investment Management
cvxportfolio - Portfolio optimization and back-testing.
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