Submission: stock_analyzer (Python)

Submitting Author: Kangyu (Mark) Wang (@ZIBOWANGKANGYU), Sicheng (Marc) Sun (@MarcSunUBC), Tingyu Zhang (@Tammy1128), William Xu (@williamxu7) Package Name: stock_analyzer One-Line Description of Package: A python package that analyzes stock data and provides data visualizations. Repository Link: stock_analyzer Version submitted: 0.1.13 Editor: Tiffany Timbers (@ttimbers) Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

Description

Include a brief paragraph describing what your package does:

This is a Python package that provides basic time series modelling functionalities to analyze historical stock prices. Investment in the stock market requires not only knowledge about the listed companies, but also basic summary statistics and modellings of individual stock prices. Given time-series stock price data, this package provides key summary statistics, applies moving average and exponential smoothing models to the data, and visualizes in-sample moving average as well as exponential smoothing fits. A convenient use case for this package is to combine it with the pandas_datareader package, which can provide well-formated stock price data from Yahoo Finance dataset with customized date range setting.

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [ ] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [ ] Geospatial
- [ ] Education
- [x] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.

Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

The stock_analyzer package analyzes the data and provides visualizations for stock data, matching with the data visualization category.

Who is the target audience and what are scientific applications of this package?

The target audiences are people interested in the stock market and want to use this package to analyze and visualize stock data patterns.

Are there other Python packages that accomplish the same thing? If so, how does yours differ?

A similar python package called PyNance can retrieve, analyze, and visualize data. The difference between this package and our package is that our package involves statistical analysis of the stock data and is much easier for users to implement.

If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has an OSI approved license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

[ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

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[x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

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Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
- Usage descriptions are present for all functions in readme together with screenshot. Well done!
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.
- In some places, not ALL authors are listed.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[ ] Any additional setup required (authentication tokens, etc)
- N/A
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[ ] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider:

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[ ] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
- All the tests pass smoothly, however, there is ModuleNotFoundError: No module named '_cffi_backend' error, probably the dependencies is not included. I manually install it to run the test.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements. N/A

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 2 hours

Review Comments

Hi Mark, Marc, Tingyu, and William,

Thanks for putting this package together. I had fun playing around with some other stock symbols using your function! The functions work very smoothly and definitely a time saver for people who want to see basic summary statistics and modelings of individual stock prices.

I have a few comments below.

Documentation: not ALL authors are listed, probably you guys could include the GitHub ID and email address of team members as well.
Test: During the test, there is a ModuleNotFoundError: No module named '_cffi_backend' error, probably the dependency is not included. I manually install it to run the test.
Package name: You use a similar package name for R and Python packages. It can be a bit confusing especially with the minor difference. (This is just my personal opinion)

Great job in putting together this package. I find this package really useful especially when I am also an active stock trader. I really like the visualization. Overall, everything is good with all the installation, test cases, documents, and CI running. Although I included a few remarks, feel free to take what's useful. Let me know if you have any questions about this feedback.

Joshua

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[ ] Citation information

Usability

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1.5

Review Comments

Hi guys, here is some additional feedback for the stock_analyzer package - hope you find it helpful.

Module organization: I noticed that you decided to organize this package in a single submodule (stock_analyzer.stock_analyzer submodule contains all functions) and while there is nothing wrong with that, I think the organization of the package could benefit from splitting out the related functions into their own submodules ex. there could be a module for the functions related to profiling stocks according to moving average, a module for doing it according to exponential smoothing and a module for the summary statistics. As it stands right now there is a single ~400 line .py file with all the modules.
Documentation: All the functions are well documented and I was able to easily use them as described in the documentation, however I think there is a small issue with how it is being displayed on readthedocs. As you can hopefully see in the image below there seems to be a problem with the formatting of the function parameters for several functions (parameters are not on separate lines and have inconsistent formatting).
User workflow: I think it would be nice from a users perspective to be able to fit one of the stock profiling models you have provided and to visualize the result all as part of the same function instead of as separate functions for profiling and visualization. One way I could see this being implemented is to add an extra argument to the exponentialSmoothing and movingAverage functions for whether the function should return a plot or not ex. movingAverage(data, window, newColumnNames, visualization=True)
Function parameters: I think it would be beneficial to users for there to be default arguments for some function parameters such as window, newColumnNames and alpha (alpha had a default argument for the profiling functions but not in visualization ones...)
Visualizations: I think enabling interactivity by default for the altair objects which are returned by the visualization functions would benefit users as they would then be able to zoom in/out and otherwise manipulate the plots.

Overall I was very impressed by the functionality that was included in this package as well as the thorough documentation included with each function. Nice work team!

Mike Lynch

Thank you @mikelynch416 and @jiajie0225 for your detailed reviews. Thanks for putting so much efforts on it! Good luck on the final block!

UBC-MDS / software-review-2021