The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
messages: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
text: string
to
{'text': Value('string')}
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 478, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
messages: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
text: string
to
{'text': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Stock Analyst Financial Trading Signals Dataset
A specialized dataset for fine-tuning Large Language Models (LLMs) to act as quantitative financial analysts. This dataset contains structured technical indicator data for stocks paired with their resulting directional trading signals (BUY, SELL, HOLD).
It is formatted specifically for Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT), utilizing a hard-negative rejection strategy to force the model to learn fine-grained decision boundaries.
Dataset Structure
The dataset is provided in .jsonl format. Each line is a JSON object containing a text field, which contains the full prompt and target response.
Example Entry
{
"text": "<|im_start|>system\nYou are a financial analyst. Output ONLY ONE WORD: BUY, SELL, or HOLD.<|im_end|>\n<|im_start|>user\nAnalyze AMZN as of 2019-11-12 and predict the stock direction.\nPrice history: $88.37 -> $89.37 -> $89.28 -> $88.11 -> $88.90\nCurrent price: $88.90\nTechnical indicators:\n- RSI(14): 55.1\n- Volume Multiplier: 0.69x\n- Volatility Multiplier: 0.88x<|im_end|>\n<|im_start|>assistant\nBUY<|im_end|>"
}
Features Included
The prompts contain the following technical data injected programmatically:
- Ticker Symbol and Date
- 5-Day Price History
- Current Price
- RSI(14) (Relative Strength Index)
- Volume Multiplier (relative to 20-day moving average)
- Volatility Multiplier (relative to 20-day moving average)
Dataset Creation and Curation
Data Leakage Prevention
To ensure robust evaluation, this dataset underwent a strict ticker-level leakage audit. Stocks appearing in the validation split do not appear in the train split. If overlap exceeded 30% during generation, a deterministic ticker-hash-based splitting strategy was enforced to guarantee absolute separation.
Class Balancing
Financial data is often heavily skewed. This dataset has been class-balanced to ensure an equal distribution of BUY, SELL, and HOLD signals during the training phase, preventing the model from collapsing into a majority-class prediction.
Hard-Negative Generation for DPO
For preference optimization pipelines, negative examples are generated using a hard-confusable strategy rather than trivial opposites:
BUYis paired againstHOLDSELLis paired againstHOLDHOLDis paired againstBUY
This forces the model to learn the subtle boundaries of market momentum rather than obvious extremes.
Usage
You can load this dataset directly using the Hugging Face datasets library:
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("ewinregirgojr/minicpm5-gguf-stock-analyst")
# Access train and validation splits
train_data = dataset["train"]
val_data = dataset["validation"]
Linked Models
This dataset was used to train ewinregirgojr/LFM2.5-Stock-Analyst-Final, an ensemble meta-learner achieving a 2x improvement in macro F1 score over the base LiquidAI/LFM2.5-1.2B-Instruct model.
Disclaimer
This dataset is intended for educational, machine learning, and quantitative research purposes only. It does not constitute financial advice. The technical indicators and signals provided are historical and synthetic for the purpose of language model alignment.
- Downloads last month
- 39