--- title: Kronos-base canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Kronos-base" md_url: "https://www.modelscope.cn/models/AI-ModelScope/Kronos-base.md" repository: AI-ModelScope/Kronos-base last_updated: 2025-09-09 license: mit parameters: 102.3M tensor_type: - F32 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 2100 stars: 7 tags: - Finance - Candlestick - K-line --- # Kronos-base > Kronos-base - AI-ModelScope 在 ModelScope 开源的模型。Kronos: A Foundation Model for the Language of Financial Markets AI-ModelScope/Kronos-base 是 ModelScope 魔搭社区上的 102.3M 参数机器学习模型，采用 mit 许可。 - **Repository**: AI-ModelScope/Kronos-base - **License**: mit - **Parameters**: 102.3M - **Tags**: Finance, Candlestick, K-line - **Downloads**: 2100 - **Stars**: 7 - **Last updated**: 2025-09-09 Source: https://www.modelscope.cn/models/AI-ModelScope/Kronos-base --- # Kronos: A Foundation Model for the Language of Financial Markets [![Paper](https://img.shields.io/badge/Paper-2508.02739-b31b1b.svg)](https://arxiv.org/abs/2508.02739) [![Live Demo](https://img.shields.io/badge/%F0%9F%9A%80-Live_Demo-brightgreen)](https://shiyu-coder.github.io/Kronos-demo/) [![GitHub](https://img.shields.io/badge/%F0%9F%92%BB-GitHub-blue?logo=github)](https://github.com/shiyu-coder/Kronos)

Kronos Logo

**Kronos** is the **first open-source foundation model** for financial candlesticks (K-lines), trained on data from over **45 global exchanges**. It is designed to handle the unique, high-noise characteristics of financial data. ## Introduction Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. 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.

Kronos Overview

The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). Kronos addresses existing limitations by introducing a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks, including price series forecasting, volatility forecasting, and synthetic data generation. ## 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](https://shiyu-coder.github.io/Kronos-demo/) ## 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 | Hugging Face Model Card | |--------------|---------------------------------------------------------------------------------| -------------- | ------ |--------------------------------------------------------------------------| | Kronos-mini | [Kronos-Tokenizer-2k](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-2k) | 2048 | 4.1M | ✅ [NeoQuasar/Kronos-mini](https://huggingface.co/NeoQuasar/Kronos-mini) | | Kronos-small | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512 | 24.7M | ✅ [NeoQuasar/Kronos-small](https://huggingface.co/NeoQuasar/Kronos-small) | | Kronos-base | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512 | 102.3M | ✅ [NeoQuasar/Kronos-base](https://huggingface.co/NeoQuasar/Kronos-base) | | Kronos-large | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512 | 499.2M | ❌ Not yet publicly available | ## Getting Started: 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. ### Installation 1. Install Python 3.10+, and then install the dependencies from the [GitHub repository's `requirements.txt`](https://github.com/shiyu-coder/Kronos/blob/main/requirements.txt): ```shell pip install -r requirements.txt ``` ### 1. Load the Tokenizer and Model First, load a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub. ```python 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. ```python # 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. ```python import pandas as pd # Load your data (example data can be found in the GitHub repo) 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. ```python # 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`](https://github.com/shiyu-coder/Kronos/blob/main/examples/prediction_example.py) in the GitHub repository. Running this script will generate a plot comparing the ground truth data against the model's forecast, similar to the one shown below:

Forecast Example

Additionally, a script that makes predictions without Volume and Amount data can be found in [`examples/prediction_wo_vol_example.py`](https://github.com/shiyu-coder/Kronos/blob/main/examples/prediction_wo_vol_example.py). ## 🔧 Finetuning on Your Own Data (A-Share Market Example) Refer to the [README](https://github.com/shiyu-coder/Kronos) of GitHub repository. ## Citation If you use Kronos in your research, we would appreciate a citation to our [paper](https://huggingface.co/papers/2508.02739): ```bibtex @misc{shi2025kronos, title={Kronos: A Foundation Model for the Language of Financial Markets}, author={Yu Shi and Zongliang Fu and Shuo Chen and Bohan Zhao and Wei Xu and Changshui Zhang and Jian Li}, year={2025}, eprint={2508.02739}, archivePrefix={arXiv}, primaryClass={q-fin.ST}, url={https://arxiv.org/abs/2508.02739}, } ``` ## License This project is licensed under the [MIT License](https://github.com/shiyu-coder/Kronos/blob/main/LICENSE).