README.md

---
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