Kronos: A Foundation Model for the Language of Financial Markets

Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges.

📜 Introduction

Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. Unlike general-purpose TSFMs, Kronos is designed to handle the unique, high-noise characteristics of financial data. It leverages a novel two-stage framework:

1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into hierarchical discrete tokens.
2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks.

✨ Live Demo

We have set up a live demo to visualize Kronos's forecasting results. The webpage showcases a forecast for the BTC/USDT trading pair over the next 24 hours.

👉 Access the Live Demo Here

📦 Model Zoo

We release a family of pre-trained models with varying capacities to suit different computational and application needs. All models are readily accessible from the Hugging Face Hub.

Model	Tokenizer	Context length	Param	Open-source
Kronos-mini	Kronos-Tokenizer-2k	2048	4.1M	✅ NeoQuasar/Kronos-mini
Kronos-small	Kronos-Tokenizer-base	512	24.7M	✅ NeoQuasar/Kronos-small
Kronos-base	Kronos-Tokenizer-base	512	102.3M	✅ NeoQuasar/Kronos-base
Kronos-large	Kronos-Tokenizer-base	512	499.2M	❌

🚀 Getting Started

Installation

1. Install Python 3.10+, and then install the dependencies:

pip install -r requirements.txt

📈 Making Forecasts

Forecasting with Kronos is straightforward using the KronosPredictor class. It handles data preprocessing, normalization, prediction, and inverse normalization, allowing you to get from raw data to forecasts in just a few lines of code.

Important Note: The max_context for Kronos-small and Kronos-base is 512. This is the maximum sequence length the model can process. For optimal performance, it is recommended that your input data length (i.e., lookback) does not exceed this limit. The KronosPredictor will automatically handle truncation for longer contexts.

Here is a step-by-step guide to making your first forecast.

1. Load the Tokenizer and Model

First, load a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub.

from model import Kronos, KronosTokenizer, KronosPredictor

# Load from Hugging Face Hub
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")

2. Instantiate the Predictor

Create an instance of KronosPredictor, passing the model, tokenizer, and desired device.

# Initialize the predictor
predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)

3. Prepare Input Data

The predict method requires three main inputs:

• df: A pandas DataFrame containing the historical K-line data. It must include columns ['open', 'high', 'low', 'close']. volume and amount are optional.
• x_timestamp: A pandas Series of timestamps corresponding to the historical data in df.
• y_timestamp: A pandas Series of timestamps for the future periods you want to predict.

import pandas as pd

# Load your data
df = pd.read_csv("./data/XSHG_5min_600977.csv")
df['timestamps'] = pd.to_datetime(df['timestamps'])

# Define context window and prediction length
lookback = 400
pred_len = 120

# Prepare inputs for the predictor
x_df = df.loc[:lookback-1, ['open', 'high', 'low', 'close', 'volume', 'amount']]
x_timestamp = df.loc[:lookback-1, 'timestamps']
y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']

4. Generate Forecasts

Call the predict method to generate forecasts. You can control the sampling process with parameters like T, top_p, and sample_count for probabilistic forecasting.

# Generate predictions
pred_df = predictor.predict(
    df=x_df,
    x_timestamp=x_timestamp,
    y_timestamp=y_timestamp,
    pred_len=pred_len,
    T=1.0,          # Temperature for sampling
    top_p=0.9,      # Nucleus sampling probability
    sample_count=1  # Number of forecast paths to generate and average
)

print("Forecasted Data Head:")
print(pred_df.head())

The predict method returns a pandas DataFrame containing the forecasted values for open, high, low, close, volume, and amount, indexed by the y_timestamp you provided.

5. Example and Visualization

For a complete, runnable script that includes data loading, prediction, and plotting, please see examples/prediction_example.py.

Running this script will generate a plot comparing the ground truth data against the model's forecast, similar to the one shown below:

Additionally, we also provide a script that makes predictions without Volume and Amount data, which can be found in examples/prediction_wo_vol_example.py.

📖 Citation

If you use Kronos in your research, we would appreciate a citation to our work. The research paper is currently in preparation.

Paper coming soon!

📜 License

This project is licensed under the MIT License.