---
language:
- en
- zh
license: mit
tags:
- tokenizer
- time-series
- bitcoin
- btc
- cryptocurrency
- numeric-encoding
---

# BTCUSDT 1-Hour Tokenizer

## Tokenizer Description

This is a specialized tokenizer designed for **time-series cryptocurrency data encoding**, specifically fine-tuned for BTCUSDT (Bitcoin/USDT) 1-hour candlestick data. It converts numerical trading data (OHLCV - Open, High, Low, Close, Volume) into token representations suitable for transformer-based models.

### Tokenizer Details

- **Type**: Numeric Time-Series Tokenizer
- **Vocabulary Size**: Model-specific
- **Input Format**: BTCUSDT candlestick data (OHLCV)
- **Output**: Token sequences for model inference
- **Framework**: Hugging Face Transformers compatible

## Purpose

This tokenizer is used to preprocess historical BTCUSDT 1-hour trading data before feeding it into the fine-tuned prediction model. It handles:

- **Price normalization**: Converts raw price values to a standardized token space
- **Volume encoding**: Encodes trading volume information
- **Temporal sequences**: Preserves time-series relationships in data
- **Model compatibility**: Ensures proper input format for the BTCUSDT 1h fine-tuned model

## How to Use

### Installation

```bash
pip install transformers torch
```

### Loading the Tokenizer

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-huggingface-username/BTCUSDT-1h-tokenizer")
```

### Tokenizing BTCUSDT Data

```python
# Example: Tokenize BTCUSDT candlestick data
candlestick_data = "BTCUSDT 1h: Open=45230.5, High=45600.2, Low=45100.3, Close=45450.8, Volume=2345.67"

tokens = tokenizer.encode(candlestick_data, return_tensors="pt")
print(tokens)

# Decode tokens back to readable format
decoded = tokenizer.decode(tokens[0])
print(decoded)
```

### Integration with Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-huggingface-username/BTCUSDT-1h-tokenizer")
model = AutoModelForCausalLM.from_pretrained("your-huggingface-username/BTCUSDT-1h-finetuned")

# Prepare data
historical_data = "OHLCV data here..."
tokens = tokenizer.encode(historical_data, return_tensors="pt")

# Get predictions
outputs = model.generate(tokens, max_length=50)
predictions = tokenizer.decode(outputs[0])
```

## Technical Specifications

- **Compatible with**: BTCUSDT 1-Hour Fine-tuned Model
- **Data Format**: Open, High, Low, Close, Volume (OHLCV)
- **Time Granularity**: 1-hour candlesticks
- **Supported Operations**: Encoding, decoding, tokenization
- **Framework**: PyTorch / TensorFlow compatible

## Training Data

- **Dataset**: BTCUSDT 1-hour historical candles
- **Source**: Cryptocurrency exchange data
- **Time Coverage**: Historical trading data up to October 2025
- **Data Points**: Thousands of 1-hour candles

## Limitations

- **Specialized for BTCUSDT**: Not recommended for other cryptocurrency pairs or timeframes
- **1-Hour Granularity**: Designed specifically for 1-hour candlestick data
- **Numeric Focus**: Optimized for OHLCV data format
- **Normalization**: Assumes price ranges similar to historical BTCUSDT data

## Usage Notes

⚠️ **Important**:
- This tokenizer should be used **exclusively with the BTCUSDT 1h fine-tuned model**
- Do not use this tokenizer with other models or datasets
- Ensure your input data follows the OHLCV format
- Maintain consistent data normalization across datasets

## Related Models

- **Fine-tuned Model**: [BTCUSDT 1h Fine-tuned Model](https://huggingface.co/your-huggingface-username/BTCUSDT-1h-finetuned)
- **Base Model**: [Kronos](https://huggingface.co/antonop/Kronos-1B-MSN)

## License

This tokenizer is released under the **MIT License**.

## Citation

If you use this tokenizer, please cite:

```bibtex
@misc{btcusdt_tokenizer_2025,
  title={BTCUSDT 1-Hour Tokenizer},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/BTCUSDT-1h-tokenizer}}
}
```

## Acknowledgments

- Base framework: [Hugging Face Transformers](https://huggingface.co/transformers/)
- Compatible with: [BTCUSDT 1h Fine-tuned Model](https://huggingface.co/your-huggingface-username/BTCUSDT-1h-finetuned)

## Contact & Support

For questions:
- GitHub: [https://github.com/Liucong-JunZi/Kronos-Btc-finetune](https://github.com/Liucong-JunZi/Kronos-Btc-finetune)


---

**Last Updated**: October 20, 2025