Suits quant-curious developers and ML students who know Python, pandas and basic PyTorch, and who understand that forecasts are not trading advice.

What can you build with Kronos?

Forecast the next hours of a crypto pair from its recent candles. Run batch forecasts across many stocks or time periods at once. Fine-tune a Kronos model on your own market's daily data. Backtest a simple top-K strategy using the model's predicted price changes. Compare Kronos forecasts with other time-series models as a research baseline.

shiyu-coder /

Kronos

Pre-trained Transformer models that forecast candlestick (K-line) price data

43.2k stars
3.4k stars since 3 Oct
Python
Updated 6 months ago
Some experience
Models & local AI

Open on GitHub (opens in a new tab)

Financial candlestick charts are noisy, and general time-series models often handle them poorly. Kronos is built for this one kind of data. It treats sequences of K-lines (open, high, low, close, volume) like a language a model can learn to continue.

It works in two stages. A tokenizer first turns the continuous price numbers into hierarchical discrete tokens. Then an autoregressive Transformer, trained on those tokens, predicts what comes next. The README contrasts it with general-purpose time-series foundation models.

Four model sizes are listed, from 4.1M to 499.2M parameters. Mini, small and base are on Hugging Face. Large is not released. The repo also has a fine-tuning pipeline, shown with Chinese A-share data via Qlib, plus a simple backtest. The authors say it is a demo, not a production trading system.

What it does

Simple forecasting API
KronosPredictor takes a pandas DataFrame of K-lines plus timestamps. It handles normalization, prediction and inverse normalization, then returns a DataFrame of forecasts.
Sampling controls
Temperature, top-p and sample_count let you generate several forecast paths and average them.
Batch prediction
predict_batch forecasts many series in parallel on a GPU. All series need the same lookback and prediction length.
Several model sizes
Kronos-mini, small and base can be downloaded from Hugging Face. Context length is 2048 for mini and 512 for the others.
Fine-tuning pipeline
Scripts prepare Qlib data, fine-tune the tokenizer and predictor with torchrun on multiple GPUs, and run a top-K backtest.

What you could build

Forecast the next hours of a crypto pair from its recent candles.
Run batch forecasts across many stocks or time periods at once.
Fine-tune a Kronos model on your own market's daily data.
Backtest a simple top-K strategy using the model's predicted price changes.
Compare Kronos forecasts with other time-series models as a research baseline.

Get started

Install Python 3.10+ and the requirements. Then load a tokenizer and model from Hugging Face, wrap them in KronosPredictor, and call predict on a DataFrame of candles. The examples/prediction_example.py script runs the whole flow and plots the result.

pip install -r requirements.txt

Commands are copied from the project's README. Read it on GitHub for the full setup.