How to resolve these WARNINGs?
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Description
❓ Questions and Help
We sincerely suggest you to carefully read the documentation of our library as well as the official paper. After that, if you still feel puzzled, please describe the question clearly under this issue.
Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.
Please upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.
Users of this version of Gym should be able to simply replace 'import gym' with 'import gymnasium as gym' in the vast majority of cases.
See the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.
WARNING - qlib.data - [data.py:665] - load calendar error: freq=day, future=True; return current calendar!
[113940:MainThread](2026-03-16 16:58:20,900) WARNING - qlib.data - [data.py:668] - You can get future calendar by referring to the following document: https://github.com/microsoft/qlib/blob/main/scripts/data_collector/contrib/README.md
WARNING - qlib.BaseExecutor - [executor.py:121] - common_infra is not set for <qlib.backtest.executor.SimulatorExecutor object at 0x76a2cd7633b0>
WARNING - qlib.online operator - [exchange.py:219] - $close field data contains nan.
index_data.py:492: RuntimeWarning: Mean of empty slice
return np.nanmean(self.data)
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the reported warnings and read the referenced locations in data.py:665-668, executor.py:121, exchange.py:219, and index_data.py:492. Check the linked Qlib data-collection and Gymnasium migration documentation. Done means each warning has a confirmed cause and a documented resolution or an identified expected condition.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data, fintech-quant, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100