jqfactor_analyzer | 聚宽单因子分析工具
kandi X-RAY | jqfactor_analyzer Summary
kandi X-RAY | jqfactor_analyzer Summary
jqfactor_analyzer is a Python library. jqfactor_analyzer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
聚宽单因子分析工具
聚宽单因子分析工具
Support
Quality
Security
License
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Support
jqfactor_analyzer has a low active ecosystem.
It has 352 star(s) with 147 fork(s). There are 24 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 4 have been closed. On average issues are closed in 107 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of jqfactor_analyzer is current.
Quality
jqfactor_analyzer has 0 bugs and 0 code smells.
Security
jqfactor_analyzer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
jqfactor_analyzer code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
jqfactor_analyzer is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
jqfactor_analyzer releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
It has 2941 lines of code, 163 functions and 17 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed jqfactor_analyzer and discovered the below as its top functions. This is intended to give you an instant insight into jqfactor_analyzer implemented functionality, and help decide if they suit your requirements.
- Creates a full tear sheet
- Calculate factor returns
- Compute factor returns
- Plot cumulative returns
- Decorator for non unique bin edges
- Re - raise an exception
- Return a pandas DataFrame containing the Circulating Market
- Get trade days between start_date and end_date
- Calculate the factorization statistics for each day
- Calculate the IC Mean Information Coefficient
- Plot the event distribution
- Create a tear sheet for event returns
- Calculate mean return by group
- Compute mean return by quantile
- Calculate mean return standard deviation by quantile
- Calculate the autocorrelation factor for n days
- Get prices for a given market
- Decorator to customize plotting context
- Returns the quantile of each period of the portfolio
- Calculate the quantile factor over n days
- Get weights for a given market
- Create information tear sheet
- Plots the distribution of missing events
- Create a tear sheet
- Get market cap
- Create a summary sheet for the summary plot
Get all kandi verified functions for this library.
jqfactor_analyzer Key Features
No Key Features are available at this moment for jqfactor_analyzer.
jqfactor_analyzer Examples and Code Snippets
No Code Snippets are available at this moment for jqfactor_analyzer.
Community Discussions
No Community Discussions are available at this moment for jqfactor_analyzer.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install jqfactor_analyzer
You can download it from GitHub.
You can use jqfactor_analyzer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use jqfactor_analyzer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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