Update README.md

README.md

@@ -5,51 +5,67 @@ tags:
 - Finance
 - Candlestick
 - K-line
-library_name: Kronos
 ---
 
 # 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**. It was presented in the paper [Kronos: A Foundation Model for the Language of Financial Markets](https://huggingface.co/papers/2508.02739).
+[![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)
 
-For full details, including the complete codebase and additional examples, please visit our [GitHub page](https://github.com/shiyu-coder/Kronos).
+<p align="center">
+  <img src="https://github.com/shiyu-coder/Kronos/blob/master/figures/logo.jpeg?raw=true" alt="Kronos Logo" width="100">
+</p>
+
+**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
 
-Try out our live demo showcasing Kronos's forecasting results for the BTC/USDT trading pair: [Access the Live Demo Here](https://shiyu-coder.github.io/Kronos-demo/)
+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.
 
 <p align="center">
-    <img src="https://github.com/shiyu-coder/Kronos/raw/main/figures/overview.png" alt="Kronos Overview" width="700px" />
+    <img src="https://github.com/shiyu-coder/Kronos/blob/master/figures/overview.png?raw=true" alt="Kronos Overview" align="center" width="700px" />
 </p>
 
-## Abstract
-The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces 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. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis. Our pre-trained model is publicly available at this https URL.
+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  | Open-source                                                               |
-|--------------|---------------------------------------------------------------------------------| -------------- | ------ |---------------------------------------------------------------------------|
+| 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 | ❌                                                                         |
-
-## Getting Started
-
-### Installation
-1. Install Python 3.10+, and then install the dependencies:
+| Kronos-large | [Kronos-Tokenizer-base](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) | 512            | 499.2M | ❌ Not yet publicly available                                               |
 
-```shell
-pip install -r requirements.txt
-```
+## Getting Started: Making Forecasts
 
-### 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
+### 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
@@ -60,7 +76,8 @@ tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
 model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
 ```
 
-#### 2. Instantiate the Predictor
+### 2. Instantiate the Predictor
+
 Create an instance of `KronosPredictor`, passing the model, tokenizer, and desired device.
 
 ```python
@@ -68,7 +85,8 @@ Create an instance of `KronosPredictor`, passing the model, tokenizer, and desir
 predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)
 ```
 
-#### 3. Prepare Input Data
+### 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`.
@@ -77,7 +95,7 @@ The `predict` method requires three main inputs:
 ```python
 import pandas as pd
 
-# Load your data
+# 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'])
 
@@ -91,7 +109,8 @@ x_timestamp = df.loc[:lookback-1, 'timestamps']
 y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']
 ```
 
-#### 4. Generate Forecasts
+### 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
@@ -112,28 +131,34 @@ 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).
+### 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:
 
 <p align="center">
-    <img src="https://github.com/shiyu-coder/Kronos/raw/main/figures/prediction_example.png" alt="Forecast Example" width="600px" />
+    <img src="https://github.com/shiyu-coder/Kronos/blob/master/figures/prediction_example.png?raw=true" alt="Forecast Example" align="center" width="600px" />
 </p>
 
-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`](https://github.com/shiyu-coder/Kronos/blob/main/examples/prediction_wo_vol_example.py).
+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).
 
 ## 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},
+      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},
+      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).