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How to fine-tune Kronos on your own market dataset with Kronos

Adapt the Kronos tokenizer and predictor to your own candlestick data using the Qlib-based finetuning pipeline.

Who it is forA quant researcher with local Qlib market data and multiple GPUs who wants Kronos tuned to a specific market or universe.

The problemA general pre-trained model may not match the distribution of a specific market, so you need a repeatable way to adapt it.

The outcomeTrain, validation and test pickle files plus fine-tuned tokenizer and predictor checkpoints saved to the paths in your config.

What you need first

  • Python 3.10 or newer
  • pip, and access to install requirements.txt
  • Internet access to load models from the Hugging Face Hub
  • Historical candlestick data in a pandas DataFrame with open, high, low, close columns
  • For fine-tuning only: a local Qlib data directory, a GPU setup that works with torchrun, and optionally a Comet.ml account

Steps

  1. Run pip install pyqlib and set up your Qlib daily data by following the official Qlib guide.
  2. Edit finetune/config.py to set qlib_data_path, dataset_path, save_path, backtest_result_path, pretrained_tokenizer_path, and pretrained_predictor_path, and set use_comet = False if you do not use Comet.ml.
  3. Run python finetune/qlib_data_preprocess.py to create train_data.pkl, val_data.pkl and test_data.pkl.
  4. Run torchrun --standalone --nproc_per_node=NUM_GPUS finetune/train_tokenizer.py, replacing NUM_GPUS with your GPU count.
  5. Run torchrun --standalone --nproc_per_node=NUM_GPUS finetune/train_predictor.py, then evaluate the saved checkpoint with the backtesting step.

Prompt for your agent

In the Kronos repo, edit finetune/config.py for my local Qlib data at <qlib data path>, run the preprocessing script, then run both torchrun training scripts with <number of GPUs> GPUs. Verify that train_data.pkl, val_data.pkl and test_data.pkl exist in dataset_path before starting training.

Caveat. The README describes this pipeline as a simplified demonstration, not a production-ready quantitative trading system, and says a robust strategy needs more techniques such as portfolio optimization and risk factor neutralization.

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