Datasets:
Tasks:
Time Series Forecasting
Modalities:
Time-series
Languages:
Portuguese
Size:
1K<n<10K
License:
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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datetimestamp 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
datecolumn (trading day) as INT64 milliseconds at 00:00 UTC. - The
*_dailytables cover a longer history than the*_tecnicostables, 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.csvtables 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).
- Downloads last month
- 23