PyPortfolioOpt VS okama

Compare PyPortfolioOpt vs okama and see what are their differences.

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PyPortfolioOpt okama
155 5
4,123 185
- 5.4%
5.7 9.0
about 1 month ago about 2 months ago
Jupyter Notebook Python
MIT License MIT License
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.

PyPortfolioOpt

Posts with mentions or reviews of PyPortfolioOpt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-16.

okama

Posts with mentions or reviews of okama. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-13.

What are some alternatives?

When comparing PyPortfolioOpt and okama you can also consider the following projects:

Riskfolio-Lib - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

bt - bt - flexible backtesting for Python

plutus_backtest - plutus_backtest is a python package for backtesting investment decisions using Python 3.6 and above.

mlfinlab - MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

universal-portfolios - Collection of algorithms for online portfolio selection

fwoba - Frequency Weighted OrderBook Analysis

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.

lakshmi - Investing library and command-line interface inspired by the Bogleheads philosophy

simfin - Simple financial data for Python