Improve PIT performance

🌟 Feature Description

Current PIT implementation has a lot of performance trap and should be fixed.

Motivation

PIT feature is about 100 times slower than normal feature which is ridiculous. Financial PIT data usually have four points per year so it should be 50 faster than normal feature.

During review PIT code, I found following problems:

1. PIT part

https://github.com/microsoft/qlib/blob/ecbeeafdc141ed89d5daf37ddfa20717190dfdb1/qlib/data/pit.py#L23-L48

In line 28, we loop every step in time series, and in each _load_feature function in line 39, we read the whole data file and index file.

Which makes about 1000 times slower for 1000 trade day. data file and index file should be read only once for one feature.

2. LocalPITProvider part

_load_feature is actually implemented here.

https://github.com/microsoft/qlib/blob/ecbeeafdc141ed89d5daf37ddfa20717190dfdb1/qlib/data/data.py#L787-L794

Here, we read the whole data file but we pass data_path to nested function instead of data object!

https://github.com/microsoft/qlib/blob/ecbeeafdc141ed89d5daf37ddfa20717190dfdb1/qlib/data/data.py#L813-L817

This will cause another 2 times slower.

3. read_period_data part

Line 150, read file in python loop:

https://github.com/microsoft/qlib/blob/ecbeeafdc141ed89d5daf37ddfa20717190dfdb1/qlib/utils/__init__.py#L147-L156

OK, this may be acceptable in C but not in python. Python loop is very slow and even in C, deal with file content without stream buffer is not recommend.

Alternatives

Use current slow implementation.

Additional Notes

I'll try to re-implement PIT workflow.

Start by reading the referenced PIT implementation in qlib/data/pit.py, then inspect LocalPITProvider in qlib/data/data.py and read_period_data in qlib/utils/init.py. Benchmark the current PIT workflow against normal features, then verify that repeated file reads and the Python file-processing loop are removed while PIT results remain correct and performance improves substantially.