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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'episodes_reaching_it'}) and 1 missing columns ({'sessions_below_that_peak'}).

This happened while the csv dataset builder was generating data using

hf://datasets/tekkick/xauusd-trailing-drawdown/episodes_reaching_threshold.csv (at revision a2bf2ef689cae89c8aea3a027dcd51ada183fe34), ['hf://datasets/tekkick/xauusd-trailing-drawdown@a2bf2ef689cae89c8aea3a027dcd51ada183fe34/days_below_peak_by_threshold.csv', 'hf://datasets/tekkick/xauusd-trailing-drawdown@a2bf2ef689cae89c8aea3a027dcd51ada183fe34/episodes_reaching_threshold.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              drawdown_threshold_pct: int64
              episodes_reaching_it: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 555
              to
              {'drawdown_threshold_pct': Value('int64'), 'sessions_below_that_peak': Value('int64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'episodes_reaching_it'}) and 1 missing columns ({'sessions_below_that_peak'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/tekkick/xauusd-trailing-drawdown/episodes_reaching_threshold.csv (at revision a2bf2ef689cae89c8aea3a027dcd51ada183fe34), ['hf://datasets/tekkick/xauusd-trailing-drawdown@a2bf2ef689cae89c8aea3a027dcd51ada183fe34/days_below_peak_by_threshold.csv', 'hf://datasets/tekkick/xauusd-trailing-drawdown@a2bf2ef689cae89c8aea3a027dcd51ada183fe34/episodes_reaching_threshold.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

drawdown_threshold_pct
int64
sessions_below_that_peak
int64
2
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161
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155
8
135
10
113
2
null
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null

XAUUSD Retracement From Running High (2025-2026)

How much of its time gold spends below its own running peak, and how deep those retracements go. The shape a trailing drawdown rule is actually set against.

What this measures

Peak-to-trough retracement on XAUUSD measured from the running high of the price series, plus how many sessions sat below a given retracement threshold and how many distinct episodes reached each depth.

Headline finding

Gold closed below its own running high on 84.5% of sessions. The median retracement was 9.47% and the deepest was 27.7%. A 5% trailing limit is therefore set against an instrument whose typical retracement is roughly twice that.

Method

70,952 five-minute bars aggregated to server days, 2025-07-31 to 2026-08-04. 258 sessions used, 56 partial sessions dropped. Retracement is measured on the price series itself, not on an account equity curve — an account trading it inherits at least this shape, and usually a worse one.

Source data is a single archive of five-minute XAUUSD bars recorded from a live MetaTrader 5 feed on a raw-spread account. Nothing here is simulated. Sessions that were partially recorded were dropped rather than padded, and the count of dropped sessions is stated above.

Files

  • days_below_peak_by_threshold.csv — sessions spent below each retracement depth
  • episodes_reaching_threshold.csv — how many distinct episodes reached each depth

Full write-up

The complete analysis, including the charts and the caveats, is published at https://techkick.me/blog/trailing-drawdown.

Citation

The underlying bar archive is deposited on Zenodo with a permanent identifier.

@dataset{techkick_xauusd,
  author       = {Tech Kick},
  title        = {XAUUSD hourly bar archive},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21973215},
  url          = {https://doi.org/10.5281/zenodo.21973215}
}

Licensed CC BY 4.0. Reuse commercially, attribute, no permission needed.

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