According to the PIT test results image you provided, I did the test and found some error messages and would like to get your help to fix it.

hi, @SunsetWolf, in qlib/data/data.py 1166 line:

try:
    return DatasetD.dataset(
        instruments, fields, start_time, end_time, freq, disk_cache, inst_processors=inst_processors
    )
except TypeError:
    return DatasetD.dataset(instruments, fields, start_time, end_time, freq, inst_processors=inst_processors)

It will catch the TypeError exception, but the exception is not captured in the screenshot you can provide, can you provide more information?

I would be more than happy to provide you with more detailed information about the error I received, and I hope this information is useful to you in helping me resolve this issue. Thanks!

Running this test requires initializing the calendar and instrument data.
Please execute the following command before running the test(you can find them from the comments in test_pit.py):

1. python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn

2. python scripts/data_collector/pit/collector.py download_data --source_dir ~/.qlib/stock_data/source/pit --start 2000-01-01 --end 2020-01-01 --interval quarterly --symbol_regex "^(600519|000725).*"

3. python scripts/data_collector/pit/collector.py normalize_data --interval quarterly --source_dir ~/.qlib/stock_data/source/pit --normalize_dir ~/.qlib/stock_data/source/pit_normalized

4. python scripts/dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly

Everything looks much simpler and cleaner than Qlib's initial version!
It looks great!
Thanks!