fix: migrate from gym to gymnasium for NumPy 2.0+ compatibility

[ishanrajsingh]

• Replace gym dependency with gymnasium (maintained drop-in replacement)
• Update all gym imports to use gymnasium
• Add proper error handling for missing gymnasium dependency
• Add lazy loading of gymnasium in backtest module
• Fix hard crashes when using NumPy 2.0+

Fixes #2018

Description

Motivation and Context

How Has This Been Tested?

• Pass the test by running: pytest qlib/tests/test_all_pipeline.py under upper directory of qlib.
• If you are adding a new feature, test on your own test scripts.

Screenshots of Test Results (if appropriate):

1. Pipeline test:
2. Your own tests:

Types of changes

• Fix bugs
• Add new feature
• Update documentation

[ishanrajsingh]

@microsoft-github-policy-service agree

[Abhijais4896]

class Avg15minLoader(QlibDataLoader):
def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
df = super(Avg15minLoader, self).load(instruments, start_time, end_time)
if self.is_group:
# feature_day(day freq) and feature_15min(1min freq, Average every 15 minutes) renamed feature
df.columns = df.columns.map(lambda x: ("feature", x[1]) if x[0].startswith("feature") else x)
df.columns = df.columns.map(
lambda x: ("feature", x[1]) if x[0].startswith("feature") else x
)
return df

[ishanrajsingh]

refactor: add type safety checks to Avg15minLoader column normalization to prevent AttributeError with unexpected column structures

[Abhijais4896]

for i, _f in enumerate(fields):
_fields = [f"Ref(Mean({_f}, 15), {j * 15})" for j in range(1, 240 // 15)]
_names = [f"{names[i][:-1]}{int(names[i][-1])+j}" for j in range(240 // 15 - 1, 0, -1)]
_names = [
f"{names[i][:-1]}{int(names[i][-1])+j}"
for j in range(240 // 15 - 1, 0, -1)
]
_fields.append(f"Mean({_f}, 15)")
_names.append(f"{names[i][:-1]}{int(names[i][-1])+240 // 15}")
tmp_fields += _fields