Datasets:
File size: 2,485 Bytes
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license: mit
task_categories:
- time-series-forecasting
tags:
- tsfile
- timeseries
- time-series
- finance
- trading
- bitcoin
- cryptocurrency
- technical-analysis
- format:tsfile
pretty_name: Multi-Model Trading Data
configs:
- config_name: default
data_files:
- split: historical
path: multi_model_trading_data_historical.tsfile
- split: features
path: multi_model_trading_data_features.tsfile
---
# Multi-Model Trading Data (TsFile)
Apache TsFile version of
[`AdityaaXD/Multi-Model-Trading-Data`](https://huggingface.co/datasets/AdityaaXD/Multi-Model-Trading-Data).
## Overview
Bitcoin (BTC-USD) historical daily prices with technical indicators for
ML/DL trading models, 2015-2024. The repo ships two CSVs with different
column sets; both are converted to their own TsFile:
- `btc_usd_historical.csv` (3,653 daily rows, 2015-01-01..2024-12-31): raw
OHLCV (open, high, low, close, volume).
- `btc_usd_features.csv` (3,603 daily rows, 2015-02-19..2024-12-30 — the
indicator warm-up period is absent): raw OHLCV plus rsi, macd,
macd_signal, bb_width, atr, dist_sma50, obv_pct, adx, stoch_rsi_k,
stoch_rsi_d, `return` and the binary `target` label (next-day direction).
## Schema (TsFile structure)
Both files: **Time** (INT64, ms, daily date naive), no TAG (dates unique),
all remaining columns FIELD with their source types (`volume`, indicator
columns DOUBLE, `target` INT64 in the features file).
## Usage
Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
```python
from pathlib import Path
from tsfile import TsFileReader
path = Path("multi_model_trading_data_features.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: <https://huggingface.co/datasets/AdityaaXD/Multi-Model-Trading-Data>
- License: MIT
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