Qlib, an open-source AI-oriented quantitative investment platform by Microsoft, continues to evolve with active user engagement and development efforts. However, recent activities highlight ongoing challenges in data handling and model integration, as evidenced by numerous user-reported issues.
Recent issues and pull requests (PRs) indicate a focus on resolving data normalization errors and improving model integration. Users have reported complications with downloading datasets from Yahoo Finance and performance issues with high-frequency trading data. The development team has been addressing these concerns through various bug fixes and enhancements.
you-n-g
Linlang (SunsetWolf)
Young (afe.young@gmail.com)
shenguanjiejie
The development team is actively collaborating on documentation improvements and feature enhancements, particularly focusing on user experience and onboarding.
Summary
Quantified Reports
Detailed Reportsurllib3.| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 2 | 0 | 2 | 0 | 1 |
| 30 Days | 5 | 2 | 2 | 1 | 1 |
| 90 Days | 26 | 7 | 20 | 2 | 1 |
| 1 Year | 144 | 94 | 176 | 8 | 1 |
| All Time | 904 | 690 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Linlang |  |
2 | 1/1/0 | 2 | 2 | 36 |
| Another |  |
1 | 1/1/0 | 1 | 1 | 6 |
| you-n-g |  |
1 | 1/1/0 | 2 | 1 | 3 |
| None (Finorita) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Juanxi Tian (tianshijing) | 0 | 0/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
The Qlib project has seen a significant amount of recent activity, with 214 open issues currently logged. This includes a mix of bugs, questions, and enhancement requests, indicating ongoing user engagement and development efforts. Notably, there are recurring themes around data handling, model training issues, and feature requests for improved functionality.
Several issues exhibit anomalies or complications; for instance, there are multiple reports of errors related to data normalization and downloading datasets, particularly from Yahoo Finance. Additionally, users have raised concerns about the performance of various models and the handling of high-frequency trading data. The presence of numerous questions regarding model integration and custom data suggests that users are actively trying to adapt Qlib to their specific needs but are encountering challenges.
Below are some of the most recently created and updated issues:
Issue #1845: Position和BasePosition代码的一点小瑕疵，不是bug！
Issue #1844: ModuleNotFoundError: No module named 'numpy._core'
Issue #1843: AttributeError: 'LocalDatasetProvider' object has no attribute '_dataset_uri'
Issue #1828: ModuleNotFoundError: No module named raise when make a change in code
Issue #1826: kernels get killed OOM when running 1min data REG_CN
Issue #1818: dump_bin DumpDataUpdate mode append data error
Issue #1815: Deprecated class Text of module typing
Issue #1525: Apple M1 not supported
This analysis highlights the active engagement within the Qlib community, as well as the ongoing challenges faced by users in adapting the platform to their specific use cases.
The analysis of the pull requests (PRs) for the Qlib project reveals a total of 25 open PRs, with a focus on bug fixes, enhancements, and documentation updates. The PRs cover a range of topics including security updates, new features, and improvements to existing functionalities.
PR #1829: Update urllib3 to fix security issue
urllib3 dependency to address a security vulnerability.PR #1817: add dockerfile
PR #1790: fixing issue 1780
PR #1677: Fix the empty price_s case and self.instruments in SBBStrategyEMA
PR #1673: Improve pit performance
PR #1666: fixamount
PR #1661: fix duplicate log
PR #1617: Bump cryptography from 36.0.1 to 41.0.3
PR #1614: Bump certifi from 2021.10.8 to 2023.7.22
PR #1587: Add algorithm trading example
The open pull requests for Qlib reflect several key themes and areas of focus within the project:
A significant number of PRs are dedicated to updating dependencies such as urllib3, cryptography, and certifi. These updates are crucial for maintaining the security integrity of the software, especially given the increasing scrutiny on open-source projects regarding their vulnerability management practices. For instance, PRs like #1829 and #1617 directly address known vulnerabilities, which is essential for user trust and compliance with best practices in software development.
Several PRs aim to enhance functionality or introduce new features, such as the addition of a Dockerfile (#1817) and algorithm trading examples (#1587). These enhancements not only improve usability but also broaden the scope of what can be achieved with Qlib, making it more appealing to potential users and contributors.
Bug fixes are a recurring theme across many PRs, including those addressing specific issues like duplicate logging (#1661) or empty data handling (#1677). This focus on stability is critical as it ensures that users can rely on Qlib for consistent performance in their quantitative trading strategies.
Documentation-related PRs are also prevalent, indicating an ongoing effort to improve user guidance and support materials. For example, PRs such as #1810 and #1751 focus on correcting typos or enhancing installation instructions, which are vital for user onboarding and reducing barriers to entry.
The presence of comments from community members suggests an active engagement process where contributors are encouraged to discuss changes openly. This collaborative atmosphere is beneficial for fostering innovation and ensuring that multiple perspectives are considered when implementing changes.
Despite the positive trends, there are notable concerns regarding PRs that have remained open for extended periods without merges or activity (e.g., PR #1673). This could indicate potential bottlenecks in the review process or resource allocation issues within the project team. Addressing these concerns promptly is essential to maintain momentum and community interest.
In summary, while Qlib's pull requests demonstrate a healthy level of activity focused on security, feature enhancement, bug fixing, and documentation improvement, attention should be given to expediting reviews and merges to sustain community engagement and project growth.
pytorch_hist.py.Overall, the development team demonstrates a proactive approach to both feature development and maintenance, ensuring that the Qlib platform remains robust and user-friendly.