fix(calendar): preserve weekly and monthly periods in sparse calendars

[Oleg Zholobov (parlorsky)]

Description

Fix weekly and monthly calendar resampling when the source calendar has gaps or is already sampled at those frequencies. Determine period boundaries from complete calendar weeks/months, then keep the existing first-available-date and frequency-count behavior.

Motivation and Context

The current implementation detects a new week/month only when the weekday/day-of-month decreases. This silently drops periods when there are gaps: resampling 2024-01-01, 2024-01-02, 2024-01-09, 2024-01-10, 2024-01-17 to weeks returns only January 1 instead of January 1, 9, and 17. Weekly inputs that always fall on Monday and monthly inputs that always fall on the first have the same problem when resampling to 2week/2month.

The regression cases cover sparse daily calendars, weekly/monthly inputs, year boundaries, intraday normalization, frequency counts, and empty calendars. Four cases fail on the original implementation.

How Has This Been Tested?

• The upstream tests/test_all_pipeline.py suite: 3 passed (training, backtesting, experiment manager). Used a local runner to point TestAutoData at an isolated data directory; the test implementation was unchanged.
• pytest tests/misc/test_resam.py tests/misc/test_utils.py -q: 11 passed.
• Black and flake8 on the changed files using the project's settings; git diff --check.

Environment: Python 3.12, macOS arm64, pandas 2.3.3, MLflow 3.12.0. The test runs emit existing deprecation/runtime warnings.

Types of changes

• Fix bugs
• Add new feature
• Update documentation

[Oleg Zholobov (parlorsky)]

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