Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 364, in tsfile.tsfile_reader.TsFileReaderPy.__init__
                File "tsfile/tsfile_reader.pyx", line 367, in tsfile.tsfile_reader.TsFileReaderPy.init_reader
                File "tsfile/tsfile_py_cpp.pyx", line 789, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
                File "tsfile/tsfile_py_cpp.pyx", line 55, in tsfile.tsfile_py_cpp.check_error
              tsfile.exceptions.FileOpenError: 28: Failed to open file
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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.

OHLCV Brazilian Stocks with Technical Indicators (TsFile)

Apache TsFile version of Wendelia/ohlcv-br-ativos.

Overview

Daily OHLCV series for five Brazilian stocks (PETR4, VALE3, ITUB4, WEGE3, MGLU3) together with the technical indicators the author computed for fine-tuning TimesFM 2.5 with LoRA: moving averages, RSI14, MACD, Bollinger bands, returns and volume ratios. Prices come from OpenBB (yfinance provider) and cover May 2022 – May 2026.

  • Tables: itub4_daily, itub4_tecnicos, mglu3_daily, mglu3_tecnicos, petr4_daily, petr4_tecnicos, vale3_daily, vale3_tecnicos, wege3_daily, wege3_tecnicos
  • Rows: 9,805 across 10 TsFile table(s).
    • itub4_daily: 1,080 rows, 2022-01-03 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • itub4_tecnicos: 881 rows, 2022-10-18 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • mglu3_daily: 1,080 rows, 2022-01-03 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • mglu3_tecnicos: 881 rows, 2022-10-18 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • petr4_daily: 1,080 rows, 2022-01-03 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • petr4_tecnicos: 881 rows, 2022-10-18 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • vale3_daily: 1,080 rows, 2022-01-03 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • vale3_tecnicos: 881 rows, 2022-10-18 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • wege3_daily: 1,080 rows, 2022-01-03 00:00:00 → 2026-04-30 00:00:00 (UTC)
    • wege3_tecnicos: 881 rows, 2022-10-18 00:00:00 → 2026-04-30 00:00:00 (UTC)

Schema (TsFile structure)

itub4_daily.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (7): open (double), high (double), low (double), close (double), volume (int64), split_ratio (double), dividend (double)

itub4_tecnicos.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (23): open (double), high (double), low (double), close (double), volume (int64), split_ratio (double), dividend (double), sma20 (double), sma50 (double), sma200 (double), rsi14 (double), macd (double), macd_sinal (double), macd_hist (double), bb_medio (double), bb_superior (double), bb_inferior (double), retorno (double), volatilidade_20d (double), volume_medio_20d (double), volume_rel (double), log_ret (double), range_pct (double)

mglu3_daily.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (7): open (double), high (double), low (double), close (double), volume (int64), split_ratio (double), dividend (double)

mglu3_tecnicos.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (23): open (double), high (double), low (double), close (double), volume (int64), split_ratio (double), dividend (double), sma20 (double), sma50 (double), sma200 (double), rsi14 (double), macd (double), macd_sinal (double), macd_hist (double), bb_medio (double), bb_superior (double), bb_inferior (double), retorno (double), volatilidade_20d (double), volume_medio_20d (double), volume_rel (double), log_ret (double), range_pct (double)

petr4_daily.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (6): open (double), high (double), low (double), close (double), volume (int64), dividend (double)

petr4_tecnicos.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (22): open (double), high (double), low (double), close (double), volume (int64), dividend (double), sma20 (double), sma50 (double), sma200 (double), rsi14 (double), macd (double), macd_sinal (double), macd_hist (double), bb_medio (double), bb_superior (double), bb_inferior (double), retorno (double), volatilidade_20d (double), volume_medio_20d (double), volume_rel (double), log_ret (double), range_pct (double)

vale3_daily.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (6): open (double), high (double), low (double), close (double), volume (int64), dividend (double)

vale3_tecnicos.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (22): open (double), high (double), low (double), close (double), volume (int64), dividend (double), sma20 (double), sma50 (double), sma200 (double), rsi14 (double), macd (double), macd_sinal (double), macd_hist (double), bb_medio (double), bb_superior (double), bb_inferior (double), retorno (double), volatilidade_20d (double), volume_medio_20d (double), volume_rel (double), log_ret (double), range_pct (double)

wege3_daily.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (6): open (double), high (double), low (double), close (double), volume (int64), dividend (double)

wege3_tecnicos.tsfile

  • Time (INT64, milliseconds) — the source date timestamp as INT64 milliseconds.
  • TAG: none — the table is a single device / single series.
  • FIELD (22): open (double), high (double), low (double), close (double), volume (int64), dividend (double), sma20 (double), sma50 (double), sma200 (double), rsi14 (double), macd (double), macd_sinal (double), macd_hist (double), bb_medio (double), bb_superior (double), bb_inferior (double), retorno (double), volatilidade_20d (double), volume_medio_20d (double), volume_rel (double), log_ret (double), range_pct (double)

Conversion notes

  • Each source CSV becomes its own TsFile table: <ticker>_daily (raw OHLCV) and <ticker>_tecnicos (OHLCV + technical indicators) for the five tickers, 10 tables in total. The per-ticker tables are the device dimension, so no TAG column is needed.
  • Time is the date column (trading day) as INT64 milliseconds at 00:00 UTC.
  • The *_daily tables cover a longer history than the *_tecnicos tables, whose rows start after the indicator warm-up period; both are kept, and no rows or columns are dropped.
  • Not converted: dataset_lora_timesfm.parquet — consolidated copy of the five *_tecnicos.csv tables rounded to four decimals and missing 6 of their columns (sma200, bb_medio, retorno, volume_medio_20d, split_ratio, dividend); the CSVs are converted instead so no precision or column is lost.
  • Not converted: resultados_backtest.csv, timesfm_lora_output/resultados_lora_teste.csv — back-test result tables (one row per ticker/horizon), not time series.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("itub4_daily.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

  • Original dataset: Wendelia/ohlcv-br-ativos
  • Source card title / publisher: OHLCV Brazilian Stocks with Technical Indicators
  • License: mit (as declared by the original dataset).
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