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

pip install transformers torch

Loading the Tokenizer

from transformers import AutoTokenizer

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

Tokenizing BTCUSDT Data

# 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

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
• Base Model: Kronos

License

This tokenizer is released under the MIT License.

Citation

If you use this tokenizer, please cite:

@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
• Compatible with: BTCUSDT 1h Fine-tuned Model

Contact & Support

For questions:

• GitHub: https://github.com/Liucong-JunZi/Kronos-Btc-finetune

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Last Updated: October 20, 2025