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theonegareth
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daily-usd-idr

Tags:
finance
currency
usd-idr
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
Date: string
Open: double
High: double
Low: double
Close: double
Volume: int64
Dividends: double
Stock Splits: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1177
to
{'Date': Value('string'), 'Open': Value('float64'), 'High': Value('float64'), 'Low': Value('float64'), 'Close': Value('float64'), 'Volume': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2406, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2280, in cast_table_to_features
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Date: string
              Open: double
              High: double
              Low: double
              Close: double
              Volume: int64
              Dividends: double
              Stock Splits: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1177
              to
              {'Date': Value('string'), 'Open': Value('float64'), 'High': Value('float64'), 'Low': Value('float64'), 'Close': Value('float64'), 'Volume': Value('int64')}
              because column names don't match

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Daily USD/IDR exchange rates

Daily USD/IDR (US dollar to Indonesian rupiah) exchange rates from Yahoo Finance: open, high, low, close. Daily rows from 2001-06-28 to the present, refreshed daily.

Files

Two CSVs, same 6,348 rows and same date range.

usd_idr_daily_cleaned.csv is the one to use:

Column Type Meaning
Date string Trading date, YYYY-MM-DD HH:MM:SS+01:00
Open float Opening rate, IDR per USD
High float Session high
Low float Session low
Close float Closing rate

usd_idr_daily.csv is the raw Yahoo pull. It adds Volume, Dividends and Stock Splits, all three of which are zero in every row — Yahoo reports no volume for spot FX. The cleaned file drops them.

Empty price cells

The four price columns are nullable. Yahoo's history for IDR=X carries a small number of impossible prints — an Open of 9.0, a Low of 4.0, a High of 197804 — against a rate that has never left the 8,000–19,000 band. These are feed glitches, not market moves, and Yahoo still serves them today.

Any price more than 2x away from its reference (the Close against the previous valid Close, the other legs against their own row's Close) is written as an empty cell. Nothing is interpolated: a hole is honest, an invented price is not. The row itself is kept, so a day whose Low was corrupt still contributes its valid Open, High and Close.

As of 2026-09-10 this affects 40 cells across 20 rows (0.3% of the file), all between 2006 and 2014: Open 15, Low 18, High 5, Close 2. Handle them as you would any missing value:

df = pd.read_csv("usd_idr_daily_cleaned.csv", parse_dates=["Date"])
df["Close"].dropna()          # or .ffill(), depending on what you are doing

There are no splits. Split by date range yourself if you need train/validation/test.

Loading 

import pandas as pd

df = pd.read_csv("usd_idr_daily_cleaned.csv", parse_dates=["Date"])

Limitations

  • Weekends and Indonesian market holidays are absent, so the series has gaps.
  • Coverage depends on Yahoo Finance's records. Yahoo revises history without notice, so a re-download can change old rows.
  • Yahoo's impossible prints are blanked, not repaired — see Empty price cells. If you need those 20 days complete, source them elsewhere.
  • Timestamps carry a +01:00 offset from the source, not Jakarta time. Convert before joining against WIB-stamped data.
  • The 2001-06-28 start is where Yahoo's series begins, not where the currency pair began.

The data is public market data. It contains nothing personal.

Citation 

@dataset{daily_usd_idr,
  title={Daily USD/IDR Dataset},
  author={Gareth Aurelius Harrison},
  year={2024},
  url={https://huggingface.co/datasets/theonegareth/daily-usd-idr}
}

Data from Yahoo Finance.
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