Welcome to the Data-Science portion of Investment Risk Analysis which is being developed as part of Lambda School Labs. This README provides an outline on the project, as well as links to further documentation in each sub-section.
| Alexander Witt | Damerei Jha | Hira Khan | Joe Bender | Jor Ming Poon |
|---|---|---|---|---|
 |
|
|
|
The Investment Risk Ratings Project has one overarching goal: to make equities investing simpler and safer by accurately assessing what the market factors that contribute to the risk of investing in a given company are. All investors, from the retail investor to the professional hedge fund manager, are faced with the daunting task of assimilating a forbiddingly vast amount of information that is changing on a daily basis, a cognitive demand that no one can master.
By systematically breaking down the movement of a company’s stock price into its constituent factors - whether macroeconomic, technical, or fundamental - we can help diminish the overwhelming complexity of the investment process, and in turn make investing both a safer and more rational process.
This is a Python 3 product. Data is acquired via Quandl, Intrinio and Alpha Vantage and manipulated using Pandas. Machine Learning frameworks include Sci-kit learn, Tensorflow, and Keras.
Deep Learning with Keras and TensorFlow
Coming Soon
- Equities Pricing
- Index Pricing
- Macroeconomic Indicators
- Technical Indicators
- Company Fundamentals
- [Alpha Vantage API] (https://www.alphavantage.co/documentation/)
- [Intrinio API] (https://docs.intrinio.com/documentation/python)
- [Quandl API] (https://www.quandl.com/)
There currently is no web API.
The API is not yet deployed. This field will be updated when this changes
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.
Please note we have a code of conduct. Please follow it in all your interactions with the project.
If you are having an issue with the existing project code, please submit a bug report under the following guidelines:
- Check first to see if your issue has already been reported.
- Check to see if the issue has recently been fixed by attempting to reproduce the issue using the latest master branch in the repository.
- Create a live example of the problem.
- Submit a detailed bug report including your environment & browser, steps to reproduce the issue, actual and expected outcomes, where you believe the issue is originating from, and any potential solutions you have considered.
We would love to hear from you about new features which would improve this app and further the aims of our project. Please provide as much detail and information as possible to show us why you think your new feature should be implemented.
If you have developed a patch, bug fix, or new feature that would improve this app, please submit a pull request. It is best to communicate your ideas with the developers first before investing a great deal of time into a pull request to ensure that it will mesh smoothly with the project.
Remember that this project is licensed under the MIT license, and by submitting a pull request, you agree that your work will be, too.
- Ensure any install or build dependencies are removed before the end of the layer when doing a build.
- Update the README.md with details of changes to the interface, including new plist variables, exposed ports, useful file locations and container parameters.
- Ensure that your code conforms to our existing code conventions and test coverage.
- Include the relevant issue number, if applicable.
- You may merge the Pull Request in once you have the sign-off of two other developers, or if you do not have permission to do that, you may request the second reviewer to merge it for you.
These contribution guidelines have been adapted from this good-Contributing.md-template.
See the README in the data directory for details on the modules produces during research and development.