Python_Finance | Computational Finance related Python Code

by drquant Python Version: Current License: No License

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kandi X-RAY | Python_Finance Summary

Python_Finance is a Python library typically used in Financial Services, Banks, Payments applications. Python_Finance has no bugs, it has no vulnerabilities and it has low support. However Python_Finance build file is not available. You can download it from GitHub.

This repository was orginally created to store python HW scripts for Georgia Institute of Technology Computational Finance I. However, wide distribution of these scripts violates anybody’s honor code. Therefore, I initially refrained from uploading all of the code that I created for that course beyond HW assignment 1 or variants of that code and HW assignment 2. I have now uploaded all scripts related to the Georgia Tech Computational Finance I course to a private SVN and have deleted all scripts related to the Georgia Tech Computational Finance I course from GitHub. I have added additional python scripts for various other projects related to quantitative finance that don’t compromise the Georgia Tech Computational Finance I course. I will also be adding python scripts for the Michael Halls-Moore’s excellent Quantitative Finance tutorials at quantstart.com. Please note that all of the quantstart tutorial code has been run in Python 2.7.5+. To run the mean_reversion_tutorial.py you must install patsy and statsmodels from the terminal command line (sudo pip install patsy and sudo pip install statsmodels).

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Python_Finance has a low active ecosystem.

It has 24 star(s) with 17 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

Python_Finance has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Python_Finance is current.

Quality

Python_Finance has 0 bugs and 0 code smells.

Security

Python_Finance has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Python_Finance code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Python_Finance does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Python_Finance releases are not available. You will need to build from source code and install.

Python_Finance has no build file. You will be need to create the build yourself to build the component from source.

Python_Finance saves you 269 person hours of effort in developing the same functionality from scratch.

It has 651 lines of code, 22 functions and 15 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Python_Finance and discovered the below as its top functions. This is intended to give you an instant insight into Python_Finance implemented functionality, and help decide if they suit your requirements.

Initialize the model .
Download quote data .
Calculates binomial am_stock_am_loss .
Calculate the Hurst exponent of a time series .
Computes tangent portfolio .
Calculate b2 coefficient
calculate the b1 coefficient
b function for b
Test the co - integration test .
Calculate the gamma function .

Get all kandi verified functions for this library.

Python_Finance Key Features

No Key Features are available at this moment for Python_Finance.

Python_Finance Examples and Code Snippets

No Code Snippets are available at this moment for Python_Finance.

Community Discussions

No Community Discussions are available at this moment for Python_Finance.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Python_Finance

You can download it from GitHub.
You can use Python_Finance 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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