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Repository: shiyu-coder/Kronos
Branch: master
Commit: d5ffd46ab061
Files: 68
Total size: 7.6 MB
Directory structure:
gitextract_8jfd95yy/
├── .gitignore
├── LICENSE
├── README.md
├── examples/
│ ├── data/
│ │ └── XSHG_5min_600977.csv
│ ├── prediction_batch_example.py
│ ├── prediction_cn_markets_day.py
│ ├── prediction_example.py
│ └── prediction_wo_vol_example.py
├── finetune/
│ ├── config.py
│ ├── dataset.py
│ ├── qlib_data_preprocess.py
│ ├── qlib_test.py
│ ├── train_predictor.py
│ ├── train_tokenizer.py
│ └── utils/
│ ├── __init__.py
│ └── training_utils.py
├── finetune_csv/
│ ├── README.md
│ ├── README_CN.md
│ ├── config_loader.py
│ ├── configs/
│ │ └── config_ali09988_candle-5min.yaml
│ ├── data/
│ │ └── HK_ali_09988_kline_5min_all.csv
│ ├── finetune_base_model.py
│ ├── finetune_tokenizer.py
│ └── train_sequential.py
├── model/
│ ├── __init__.py
│ ├── kronos.py
│ └── module.py
├── requirements.txt
├── tests/
│ ├── data/
│ │ ├── generate_regression_output.py
│ │ ├── regression_input.csv
│ │ ├── regression_output_256.csv
│ │ └── regression_output_512.csv
│ └── test_kronos_regression.py
└── webui/
├── README.md
├── app.py
├── prediction_results/
│ ├── prediction_20250826_163800.json
│ ├── prediction_20250826_164030.json
│ ├── prediction_20250826_164422.json
│ ├── prediction_20250826_170831.json
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│ ├── prediction_20250826_171913.json
│ ├── prediction_20250826_172031.json
│ ├── prediction_20250826_172153.json
│ ├── prediction_20250826_172740.json
│ ├── prediction_20250826_173322.json
│ ├── prediction_20250826_173455.json
│ ├── prediction_20250826_174410.json
│ ├── prediction_20250826_174809.json
│ ├── prediction_20250826_175057.json
│ ├── prediction_20250826_175135.json
│ ├── prediction_20250826_175909.json
│ ├── prediction_20250826_180308.json
│ ├── prediction_20250826_180632.json
│ ├── prediction_20250826_180745.json
│ ├── prediction_20250826_180806.json
│ ├── prediction_20250826_181012.json
│ ├── prediction_20250826_181139.json
│ ├── prediction_20250826_181240.json
│ ├── prediction_20250826_181434.json
│ ├── prediction_20250826_181513.json
│ ├── prediction_20250826_181612.json
│ ├── prediction_20250826_181648.json
│ ├── prediction_20250826_181800.json
│ └── prediction_20250826_181932.json
├── requirements.txt
├── run.py
├── start.sh
└── templates/
└── index.html
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FILE CONTENTS
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FILE: .gitignore
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# Jupyter Notebook
.ipynb_checkpoints
# PyCharm
.idea/
# VS Code
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# macOS
.DS_Store
.AppleDouble
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# Windows
Thumbs.db
ehthumbs.db
Desktop.ini
# Linux
*~
# Data files (large files)
*.feather
*.parquet
*.h5
*.hdf5
# Model files (large files)
*.pth
*.pt
*.ckpt
*.bin
# Logs
*.log
logs/
# Environment
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Temporary files
*.tmp
*.temp
temp/
tmp/
.python-version
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2025 ShiYu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
<div align="center">
<h2><b>Kronos: A Foundation Model for the Language of Financial Markets </b></h2>
</div>
<div align="center">
</a>
<a href="https://huggingface.co/NeoQuasar">
<img src="https://img.shields.io/badge/🤗-Hugging_Face-yellow" alt="Hugging Face">
</a>
<a href="https://shiyu-coder.github.io/Kronos-demo/"> <img src="https://img.shields.io/badge/🚀-Live_Demo-brightgreen" alt="Live Demo"> </a>
<a href="https://github.com/shiyu-coder/Kronos/graphs/commit-activity">
<img src="https://img.shields.io/github/last-commit/shiyu-coder/Kronos?color=blue" alt="Last Commit">
</a>
<a href="https://github.com/shiyu-coder/Kronos/stargazers">
<img src="https://img.shields.io/github/stars/shiyu-coder/Kronos?color=lightblue" alt="GitHub Stars">
</a>
<a href="https://github.com/shiyu-coder/Kronos/network/members">
<img src="https://img.shields.io/github/forks/shiyu-coder/Kronos?color=yellow" alt="GitHub Forks">
</a>
<a href="./LICENSE">
<img src="https://img.shields.io/github/license/shiyu-coder/Kronos?color=green" alt="License">
</a>
</div>
<div align="center">
<!-- Keep these links. Translations will automatically update with the README. -->
<a href="https://zdoc.app/de/shiyu-coder/Kronos">Deutsch</a> |
<a href="https://zdoc.app/es/shiyu-coder/Kronos">Español</a> |
<a href="https://zdoc.app/fr/shiyu-coder/Kronos">Français</a> |
<a href="https://zdoc.app/ja/shiyu-coder/Kronos">日本語</a> |
<a href="https://zdoc.app/ko/shiyu-coder/Kronos">한국어</a> |
<a href="https://zdoc.app/pt/shiyu-coder/Kronos">Português</a> |
<a href="https://zdoc.app/ru/shiyu-coder/Kronos">Русский</a> |
<a href="https://zdoc.app/zh/shiyu-coder/Kronos">中文</a>
</div>
<p align="center">
<img src="./figures/logo.png" width="100">
</p>
> Kronos is the **first open-source foundation model** for financial candlesticks (K-lines),
> trained on data from over **45 global exchanges**.
</div>
## 📰 News
* 🚩 **[2025.11.10]** Kronos has been accpeted by AAAI 2026.
* 🚩 **[2025.08.17]** We have released the scripts for fine-tuning! Check them out to adapt Kronos to your own tasks.
* 🚩 **[2025.08.02]** Our paper is now available on [arXiv](https://arxiv.org/abs/2508.02739)!
<p align="center">
## 📜 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.
<p align="center">
<img src="figures/overview.png" alt="" align="center" width="700px" />
</p>
## ✨ 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 | Params | Open-source |
|--------------|---------------------------------------------------------------------------------| -------------- | ------ |---------------------------------------------------------------------------|
| 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:
```shell
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.
```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, 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
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.
For efficient processing of multiple time series, Kronos provides a `predict_batch` method that enables parallel prediction on multiple datasets simultaneously. This is particularly useful when you need to forecast multiple assets or time periods at once.
```python
# Prepare multiple datasets for batch prediction
df_list = [df1, df2, df3] # List of DataFrames
x_timestamp_list = [x_ts1, x_ts2, x_ts3] # List of historical timestamps
y_timestamp_list = [y_ts1, y_ts2, y_ts3] # List of future timestamps
# Generate batch predictions
pred_df_list = predictor.predict_batch(
df_list=df_list,
x_timestamp_list=x_timestamp_list,
y_timestamp_list=y_timestamp_list,
pred_len=pred_len,
T=1.0,
top_p=0.9,
sample_count=1,
verbose=True
)
# pred_df_list contains prediction results in the same order as input
for i, pred_df in enumerate(pred_df_list):
print(f"Predictions for series {i}:")
print(pred_df.head())
```
**Important Requirements for Batch Prediction:**
- All series must have the same historical length (lookback window)
- All series must have the same prediction length (`pred_len`)
- Each DataFrame must contain the required columns: `['open', 'high', 'low', 'close']`
- `volume` and `amount` columns are optional and will be filled with zeros if missing
The `predict_batch` method leverages GPU parallelism for efficient processing and automatically handles normalization and denormalization for each series independently.
#### 5. Example and Visualization
For a complete, runnable script that includes data loading, prediction, and plotting, please see [`examples/prediction_example.py`](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:
<p align="center">
<img src="figures/prediction_example.png" alt="Forecast Example" align="center" width="600px" />
</p>
Additionally, we provide a script that makes predictions without Volume and Amount data, which can be found in [`examples/prediction_wo_vol_example.py`](examples/prediction_wo_vol_example.py).
## 🔧 Finetuning on Your Own Data (A-Share Market Example)
We provide a complete pipeline for finetuning Kronos on your own datasets. As an example, we demonstrate how to use [Qlib](https://github.com/microsoft/qlib) to prepare data from the Chinese A-share market and conduct a simple backtest.
> **Disclaimer:** This pipeline is intended as a demonstration to illustrate the finetuning process. It is a simplified example and not a production-ready quantitative trading system. A robust quantitative strategy requires more sophisticated techniques, such as portfolio optimization and risk factor neutralization, to achieve stable alpha.
The finetuning process is divided into four main steps:
1. **Configuration**: Set up paths and hyperparameters.
2. **Data Preparation**: Process and split your data using Qlib.
3. **Model Finetuning**: Finetune the Tokenizer and the Predictor models.
4. **Backtesting**: Evaluate the finetuned model's performance.
### Prerequisites
1. First, ensure you have all dependencies from `requirements.txt` installed.
2. This pipeline relies on `qlib`. Please install it:
```shell
pip install pyqlib
```
3. You will need to prepare your Qlib data. Follow the [official Qlib guide](https://github.com/microsoft/qlib) to download and set up your data locally. The example scripts assume you are using daily frequency data.
### Step 1: Configure Your Experiment
All settings for data, training, and model paths are centralized in `finetune/config.py`. Before running any scripts, please **modify the following paths** according to your environment:
* `qlib_data_path`: Path to your local Qlib data directory.
* `dataset_path`: Directory where the processed train/validation/test pickle files will be saved.
* `save_path`: Base directory for saving model checkpoints.
* `backtest_result_path`: Directory for saving backtesting results.
* `pretrained_tokenizer_path` and `pretrained_predictor_path`: Paths to the pre-trained models you want to start from (can be local paths or Hugging Face model names).
You can also adjust other parameters like `instrument`, `train_time_range`, `epochs`, and `batch_size` to fit your specific task. If you don't use [Comet.ml](https://www.comet.com/), set `use_comet = False`.
### Step 2: Prepare the Dataset
Run the data preprocessing script. This script will load raw market data from your Qlib directory, process it, split it into training, validation, and test sets, and save them as pickle files.
```shell
python finetune/qlib_data_preprocess.py
```
After running, you will find `train_data.pkl`, `val_data.pkl`, and `test_data.pkl` in the directory specified by `dataset_path` in your config.
### Step 3: Run the Finetuning
The finetuning process consists of two stages: finetuning the tokenizer and then the predictor. Both training scripts are designed for multi-GPU training using `torchrun`.
#### 3.1 Finetune the Tokenizer
This step adjusts the tokenizer to the data distribution of your specific domain.
```shell
# Replace NUM_GPUS with the number of GPUs you want to use (e.g., 2)
torchrun --standalone --nproc_per_node=NUM_GPUS finetune/train_tokenizer.py
```
The best tokenizer checkpoint will be saved to the path configured in `config.py` (derived from `save_path` and `tokenizer_save_folder_name`).
#### 3.2 Finetune the Predictor
This step finetunes the main Kronos model for the forecasting task.
```shell
# Replace NUM_GPUS with the number of GPUs you want to use (e.g., 2)
torchrun --standalone --nproc_per_node=NUM_GPUS finetune/train_predictor.py
```
The best predictor checkpoint will be saved to the path configured in `config.py`.
### Step 4: Evaluate with Backtesting
Finally, run the backtesting script to evaluate your finetuned model. This script loads the models, performs inference on the test set, generates prediction signals (e.g., forecasted price change), and runs a simple top-K strategy backtest.
```shell
# Specify the GPU for inference
python finetune/qlib_test.py --device cuda:0
```
The script will output a detailed performance analysis in your console and generate a plot showing the cumulative return curves of your strategy against the benchmark, similar to the one below:
<p align="center">
<img src="figures/backtest_result_example.png" alt="Backtest Example" align="center" width="700px" />
</p>
### 💡 From Demo to Production: Important Considerations
* **Raw Signals vs. Pure Alpha**: The signals generated by the model in this demo are raw predictions. In a real-world quantitative workflow, these signals would typically be fed into a portfolio optimization model. This model would apply constraints to neutralize exposure to common risk factors (e.g., market beta, style factors like size and value), thereby isolating the **"pure alpha"** and improving the strategy's robustness.
* **Data Handling**: The provided `QlibDataset` is an example. For different data sources or formats, you will need to adapt the data loading and preprocessing logic.
* **Strategy and Backtesting Complexity**: The simple top-K strategy used here is a basic starting point. Production-level strategies often incorporate more complex logic for portfolio construction, dynamic position sizing, and risk management (e.g., stop-loss/take-profit rules). Furthermore, a high-fidelity backtest should meticulously model transaction costs, slippage, and market impact to provide a more accurate estimate of real-world performance.
> **📝 AI-Generated Comments**: Please note that many of the code comments within the `finetune/` directory were generated by an AI assistant (Gemini 2.5 Pro) for explanatory purposes. While they aim to be helpful, they may contain inaccuracies. We recommend treating the code itself as the definitive source of logic.
## 📖 Citation
If you use Kronos in your research, we would appreciate a citation to our [paper](https://arxiv.org/abs/2508.02739):
```
@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](./LICENSE).
================================================
FILE: examples/data/XSHG_5min_600977.csv
================================================
timestamps,open,high,low,close,volume,amount
2024-06-18 11:15:00,11.27,11.28,11.26,11.27,379.0,427161.0
2024-06-18 11:20:00,11.27,11.28,11.27,11.27,277.0,312192.0
2024-06-18 11:25:00,11.27,11.27,11.26,11.27,380.0,427954.0
2024-06-18 11:30:00,11.26,11.27,11.26,11.27,761.0,856971.0
2024-06-18 13:05:00,11.27,11.27,11.25,11.26,1439.0,1620733.0
2024-06-18 13:10:00,11.26,11.26,11.25,11.25,529.0,595311.0
2024-06-18 13:15:00,11.26,11.27,11.25,11.26,389.0,438017.0
2024-06-18 13:20:00,11.26,11.32,11.26,11.32,4459.0,5037553.0
2024-06-18 13:25:00,11.32,11.32,11.3,11.31,2551.0,2886053.0
2024-06-18 13:30:00,11.31,11.33,11.3,11.32,1185.0,1340985.0
2024-06-18 13:35:00,11.32,11.33,11.31,11.32,1296.0,1467314.0
2024-06-18 13:40:00,11.33,11.33,11.3,11.3,1229.0,1390698.0
2024-06-18 13:45:00,11.31,11.31,11.3,11.3,568.0,641846.0
2024-06-18 13:50:00,11.3,11.32,11.3,11.31,1168.0,1321141.0
2024-06-18 13:55:00,11.31,11.31,11.29,11.29,867.0,979738.0
2024-06-18 14:00:00,11.3,11.3,11.28,11.29,1026.0,1158037.0
2024-06-18 14:05:00,11.28,11.3,11.28,11.29,752.0,849034.0
2024-06-18 14:10:00,11.29,11.3,11.28,11.29,284.0,320630.0
2024-06-18 14:15:00,11.29,11.3,11.29,11.29,485.0,547861.0
2024-06-18 14:20:00,11.29,11.3,11.29,11.29,246.0,277867.0
2024-06-18 14:25:00,11.3,11.3,11.29,11.3,188.0,212415.0
2024-06-18 14:30:00,11.29,11.31,11.29,11.3,469.0,529984.0
2024-06-18 14:35:00,11.3,11.31,11.29,11.3,935.0,1057044.0
2024-06-18 14:40:00,11.3,11.32,11.3,11.31,978.0,1106460.0
2024-06-18 14:45:00,11.32,11.32,11.31,11.31,524.0,592817.0
2024-06-18 14:50:00,11.32,11.33,11.31,11.31,1758.0,1990407.0
2024-06-18 14:55:00,11.31,11.33,11.31,11.31,1173.0,1327496.0
2024-06-18 15:00:00,11.31,11.32,11.31,11.32,2019.0,2284686.0
2024-06-19 09:35:00,11.36,11.37,11.32,11.35,5606.0,6361534.0
2024-06-19 09:40:00,11.35,11.35,11.3,11.3,1773.0,2006819.0
2024-06-19 09:45:00,11.31,11.34,11.31,11.33,1108.0,1254369.0
2024-06-19 09:50:00,11.34,11.35,11.31,11.33,1162.0,1316325.0
2024-06-19 09:55:00,11.33,11.34,11.31,11.31,940.0,1064577.0
2024-06-19 10:00:00,11.31,11.32,11.3,11.31,902.0,1019926.0
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================================================
FILE: examples/prediction_batch_example.py
================================================
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
from model import Kronos, KronosTokenizer, KronosPredictor
def plot_prediction(kline_df, pred_df):
pred_df.index = kline_df.index[-pred_df.shape[0]:]
sr_close = kline_df['close']
sr_pred_close = pred_df['close']
sr_close.name = 'Ground Truth'
sr_pred_close.name = "Prediction"
sr_volume = kline_df['volume']
sr_pred_volume = pred_df['volume']
sr_volume.name = 'Ground Truth'
sr_pred_volume.name = "Prediction"
close_df = pd.concat([sr_close, sr_pred_close], axis=1)
volume_df = pd.concat([sr_volume, sr_pred_volume], axis=1)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
ax1.plot(close_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax1.plot(close_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax1.set_ylabel('Close Price', fontsize=14)
ax1.legend(loc='lower left', fontsize=12)
ax1.grid(True)
ax2.plot(volume_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax2.plot(volume_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax2.set_ylabel('Volume', fontsize=14)
ax2.legend(loc='upper left', fontsize=12)
ax2.grid(True)
plt.tight_layout()
plt.show()
# 1. Load Model and Tokenizer
tokenizer = KronosTokenizer.from_pretrained('/home/csc/huggingface/Kronos-Tokenizer-base/')
model = Kronos.from_pretrained("/home/csc/huggingface/Kronos-base/")
# 2. Instantiate Predictor
predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)
# 3. Prepare Data
df = pd.read_csv("./data/XSHG_5min_600977.csv")
df['timestamps'] = pd.to_datetime(df['timestamps'])
lookback = 400
pred_len = 120
dfs = []
xtsp = []
ytsp = []
for i in range(5):
idf = df.loc[(i*400):(i*400+lookback-1), ['open', 'high', 'low', 'close', 'volume', 'amount']]
i_x_timestamp = df.loc[(i*400):(i*400+lookback-1), 'timestamps']
i_y_timestamp = df.loc[(i*400+lookback):(i*400+lookback+pred_len-1), 'timestamps']
dfs.append(idf)
xtsp.append(i_x_timestamp)
ytsp.append(i_y_timestamp)
pred_df = predictor.predict_batch(
df_list=dfs,
x_timestamp_list=xtsp,
y_timestamp_list=ytsp,
pred_len=pred_len,
)
================================================
FILE: examples/prediction_cn_markets_day.py
================================================
# -*- coding: utf-8 -*-
"""
prediction_cn_markets_day.py
Description:
Predicts future daily K-line (1D) data for A-share markets using Kronos model and akshare.
The script automatically downloads the latest historical data, cleans it, and runs model inference.
Usage:
python prediction_cn_markets_day.py --symbol 000001
Arguments:
--symbol Stock code (e.g. 002594 for BYD, 000001 for SSE Index)
Output:
- Saves the prediction results to ./outputs/pred_<symbol>_data.csv and ./outputs/pred_<symbol>_chart.png
- Logs and progress are printed to console
Example:
bash> python prediction_cn_markets_day.py --symbol 000001
python3 prediction_cn_markets_day.py --symbol 002594
"""
import os
import argparse
import time
import pandas as pd
import akshare as ak
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
from model import Kronos, KronosTokenizer, KronosPredictor
save_dir = "./outputs"
os.makedirs(save_dir, exist_ok=True)
# Setting
TOKENIZER_PRETRAINED = "NeoQuasar/Kronos-Tokenizer-base"
MODEL_PRETRAINED = "NeoQuasar/Kronos-base"
DEVICE = "cpu" # "cuda:0"
MAX_CONTEXT = 512
LOOKBACK = 400
PRED_LEN = 120
T = 1.0
TOP_P = 0.9
SAMPLE_COUNT = 1
def load_data(symbol: str) -> pd.DataFrame:
print(f"📥 Fetching {symbol} daily data from akshare ...")
max_retries = 3
df = None
# Retry mechanism
for attempt in range(1, max_retries + 1):
try:
df = ak.stock_zh_a_hist(symbol=symbol, period="daily", adjust="")
if df is not None and not df.empty:
break
except Exception as e:
print(f"⚠️ Attempt {attempt}/{max_retries} failed: {e}")
time.sleep(1.5)
# If still empty after retries
if df is None or df.empty:
print(f"❌ Failed to fetch data for {symbol} after {max_retries} attempts. Exiting.")
sys.exit(1)
df.rename(columns={
"日期": "date",
"开盘": "open",
"收盘": "close",
"最高": "high",
"最低": "low",
"成交量": "volume",
"成交额": "amount"
}, inplace=True)
df["date"] = pd.to_datetime(df["date"])
df = df.sort_values("date").reset_index(drop=True)
# Convert numeric columns
numeric_cols = ["open", "high", "low", "close", "volume", "amount"]
for col in numeric_cols:
df[col] = (
df[col]
.astype(str)
.str.replace(",", "", regex=False)
.replace({"--": None, "": None})
)
df[col] = pd.to_numeric(df[col], errors="coerce")
# Fix invalid open values
open_bad = (df["open"] == 0) | (df["open"].isna())
if open_bad.any():
print(f"⚠️ Fixed {open_bad.sum()} invalid open values.")
df.loc[open_bad, "open"] = df["close"].shift(1)
df["open"].fillna(df["close"], inplace=True)
# Fix missing amount
if df["amount"].isna().all() or (df["amount"] == 0).all():
df["amount"] = df["close"] * df["volume"]
print(f"✅ Data loaded: {len(df)} rows, range: {df['date'].min()} ~ {df['date'].max()}")
print("Data Head:")
print(df.head())
return df
def prepare_inputs(df):
x_df = df.iloc[-LOOKBACK:][["open","high","low","close","volume","amount"]]
x_timestamp = df.iloc[-LOOKBACK:]["date"]
y_timestamp = pd.bdate_range(start=df["date"].iloc[-1] + pd.Timedelta(days=1), periods=PRED_LEN)
return x_df, pd.Series(x_timestamp), pd.Series(y_timestamp)
def apply_price_limits(pred_df, last_close, limit_rate=0.1):
print(f"🔒 Applying ±{limit_rate*100:.0f}% price limit ...")
# Ensure integer index
pred_df = pred_df.reset_index(drop=True)
# Ensure float64 dtype for safe assignment
cols = ["open", "high", "low", "close"]
pred_df[cols] = pred_df[cols].astype("float64")
for i in range(len(pred_df)):
limit_up = last_close * (1 + limit_rate)
limit_down = last_close * (1 - limit_rate)
for col in cols:
value = pred_df.at[i, col]
if pd.notna(value):
clipped = max(min(value, limit_up), limit_down)
pred_df.at[i, col] = float(clipped)
last_close = float(pred_df.at[i, "close"]) # ensure float type
return pred_df
def plot_result(df_hist, df_pred, symbol):
plt.figure(figsize=(12, 6))
plt.plot(df_hist["date"], df_hist["close"], label="Historical", color="blue")
plt.plot(df_pred["date"], df_pred["close"], label="Predicted", color="red", linestyle="--")
plt.title(f"Kronos Prediction for {symbol}")
plt.xlabel("Date")
plt.ylabel("Close Price")
plt.legend()
plt.grid(True)
plt.tight_layout()
plot_path = os.path.join(save_dir, f"pred_{symbol.replace('.', '_')}_chart.png")
plt.savefig(plot_path)
plt.close()
print(f"📊 Chart saved: {plot_path}")
def predict_future(symbol):
print(f"🚀 Loading Kronos tokenizer:{TOKENIZER_PRETRAINED} model:{MODEL_PRETRAINED} ...")
tokenizer = KronosTokenizer.from_pretrained(TOKENIZER_PRETRAINED)
model = Kronos.from_pretrained(MODEL_PRETRAINED)
predictor = KronosPredictor(model, tokenizer, device=DEVICE, max_context=MAX_CONTEXT)
df = load_data(symbol)
x_df, x_timestamp, y_timestamp = prepare_inputs(df)
print("🔮 Generating predictions ...")
pred_df = predictor.predict(
df=x_df,
x_timestamp=x_timestamp,
y_timestamp=y_timestamp,
pred_len=PRED_LEN,
T=T,
top_p=TOP_P,
sample_count=SAMPLE_COUNT,
)
pred_df["date"] = y_timestamp.values
# Apply ±10% price limit
last_close = df["close"].iloc[-1]
pred_df = apply_price_limits(pred_df, last_close, limit_rate=0.1)
# Merge historical and predicted data
df_out = pd.concat([
df[["date", "open", "high", "low", "close", "volume", "amount"]],
pred_df[["date", "open", "high", "low", "close", "volume", "amount"]]
]).reset_index(drop=True)
# Save CSV
out_file = os.path.join(save_dir, f"pred_{symbol.replace('.', '_')}_data.csv")
df_out.to_csv(out_file, index=False)
print(f"✅ Prediction completed and saved: {out_file}")
# Plot
plot_result(df, pred_df, symbol)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Kronos stock prediction script")
parser.add_argument("--symbol", type=str, default="000001", help="Stock code")
args = parser.parse_args()
predict_future(
symbol=args.symbol,
)
================================================
FILE: examples/prediction_example.py
================================================
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
from model import Kronos, KronosTokenizer, KronosPredictor
def plot_prediction(kline_df, pred_df):
pred_df.index = kline_df.index[-pred_df.shape[0]:]
sr_close = kline_df['close']
sr_pred_close = pred_df['close']
sr_close.name = 'Ground Truth'
sr_pred_close.name = "Prediction"
sr_volume = kline_df['volume']
sr_pred_volume = pred_df['volume']
sr_volume.name = 'Ground Truth'
sr_pred_volume.name = "Prediction"
close_df = pd.concat([sr_close, sr_pred_close], axis=1)
volume_df = pd.concat([sr_volume, sr_pred_volume], axis=1)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
ax1.plot(close_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax1.plot(close_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax1.set_ylabel('Close Price', fontsize=14)
ax1.legend(loc='lower left', fontsize=12)
ax1.grid(True)
ax2.plot(volume_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax2.plot(volume_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax2.set_ylabel('Volume', fontsize=14)
ax2.legend(loc='upper left', fontsize=12)
ax2.grid(True)
plt.tight_layout()
plt.show()
# 1. Load Model and Tokenizer
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
# 2. Instantiate Predictor
predictor = KronosPredictor(model, tokenizer, max_context=512)
# 3. Prepare Data
df = pd.read_csv("./data/XSHG_5min_600977.csv")
df['timestamps'] = pd.to_datetime(df['timestamps'])
lookback = 400
pred_len = 120
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. Make Prediction
pred_df = predictor.predict(
df=x_df,
x_timestamp=x_timestamp,
y_timestamp=y_timestamp,
pred_len=pred_len,
T=1.0,
top_p=0.9,
sample_count=1,
verbose=True
)
# 5. Visualize Results
print("Forecasted Data Head:")
print(pred_df.head())
# Combine historical and forecasted data for plotting
kline_df = df.loc[:lookback+pred_len-1]
# visualize
plot_prediction(kline_df, pred_df)
================================================
FILE: examples/prediction_wo_vol_example.py
================================================
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
from model import Kronos, KronosTokenizer, KronosPredictor
def plot_prediction(kline_df, pred_df):
pred_df.index = kline_df.index[-pred_df.shape[0]:]
sr_close = kline_df['close']
sr_pred_close = pred_df['close']
sr_close.name = 'Ground Truth'
sr_pred_close.name = "Prediction"
close_df = pd.concat([sr_close, sr_pred_close], axis=1)
fig, ax = plt.subplots(1, 1, figsize=(8, 4))
ax.plot(close_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)
ax.plot(close_df['Prediction'], label='Prediction', color='red', linewidth=1.5)
ax.set_ylabel('Close Price', fontsize=14)
ax.legend(loc='lower left', fontsize=12)
ax.grid(True)
plt.tight_layout()
plt.show()
# 1. Load Model and Tokenizer
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
# 2. Instantiate Predictor
predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)
# 3. Prepare Data
df = pd.read_csv("./data/XSHG_5min_600977.csv")
df['timestamps'] = pd.to_datetime(df['timestamps'])
lookback = 400
pred_len = 120
x_df = df.loc[:lookback-1, ['open', 'high', 'low', 'close']]
x_timestamp = df.loc[:lookback-1, 'timestamps']
y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']
# 4. Make Prediction
pred_df = predictor.predict(
df=x_df,
x_timestamp=x_timestamp,
y_timestamp=y_timestamp,
pred_len=pred_len,
T=1.0,
top_p=0.9,
sample_count=1,
verbose=True
)
# 5. Visualize Results
print("Forecasted Data Head:")
print(pred_df.head())
# Combine historical and forecasted data for plotting
kline_df = df.loc[:lookback+pred_len-1]
# visualize
plot_prediction(kline_df, pred_df)
================================================
FILE: finetune/config.py
================================================
import os
class Config:
"""
Configuration class for the entire project.
"""
def __init__(self):
# =================================================================
# Data & Feature Parameters
# =================================================================
# TODO: Update this path to your Qlib data directory.
self.qlib_data_path = "~/.qlib/qlib_data/cn_data"
self.instrument = 'csi300'
# Overall time range for data loading from Qlib.
self.dataset_begin_time = "2011-01-01"
self.dataset_end_time = '2025-06-05'
# Sliding window parameters for creating samples.
self.lookback_window = 90 # Number of past time steps for input.
self.predict_window = 10 # Number of future time steps for prediction.
self.max_context = 512 # Maximum context length for the model.
# Features to be used from the raw data.
self.feature_list = ['open', 'high', 'low', 'close', 'vol', 'amt']
# Time-based features to be generated.
self.time_feature_list = ['minute', 'hour', 'weekday', 'day', 'month']
# =================================================================
# Dataset Splitting & Paths
# =================================================================
# Note: The validation/test set starts earlier than the training/validation set ends
# to account for the `lookback_window`.
self.train_time_range = ["2011-01-01", "2022-12-31"]
self.val_time_range = ["2022-09-01", "2024-06-30"]
self.test_time_range = ["2024-04-01", "2025-06-05"]
self.backtest_time_range = ["2024-07-01", "2025-06-05"]
# TODO: Directory to save the processed, pickled datasets.
self.dataset_path = "./data/processed_datasets"
# =================================================================
# Training Hyperparameters
# =================================================================
self.clip = 5.0 # Clipping value for normalized data to prevent outliers.
self.epochs = 30
self.log_interval = 100 # Log training status every N batches.
self.batch_size = 50 # Batch size per GPU.
# Number of samples to draw for one "epoch" of training/validation.
# This is useful for large datasets where a true epoch is too long.
self.n_train_iter = 2000 * self.batch_size
self.n_val_iter = 400 * self.batch_size
# Learning rates for different model components.
self.tokenizer_learning_rate = 2e-4
self.predictor_learning_rate = 4e-5
# Gradient accumulation to simulate a larger batch size.
self.accumulation_steps = 1
# AdamW optimizer parameters.
self.adam_beta1 = 0.9
self.adam_beta2 = 0.95
self.adam_weight_decay = 0.1
# Miscellaneous
self.seed = 100 # Global random seed for reproducibility.
# =================================================================
# Experiment Logging & Saving
# =================================================================
self.use_comet = True # Set to False if you don't want to use Comet ML
self.comet_config = {
# It is highly recommended to load secrets from environment variables
# for security purposes. Example: os.getenv("COMET_API_KEY")
"api_key": "YOUR_COMET_API_KEY",
"project_name": "Kronos-Finetune-Demo",
"workspace": "your_comet_workspace" # TODO: Change to your Comet ML workspace name
}
self.comet_tag = 'finetune_demo'
self.comet_name = 'finetune_demo'
# Base directory for saving model checkpoints and results.
# Using a general 'outputs' directory is a common practice.
self.save_path = "./outputs/models"
self.tokenizer_save_folder_name = 'finetune_tokenizer_demo'
self.predictor_save_folder_name = 'finetune_predictor_demo'
self.backtest_save_folder_name = 'finetune_backtest_demo'
# Path for backtesting results.
self.backtest_result_path = "./outputs/backtest_results"
# =================================================================
# Model & Checkpoint Paths
# =================================================================
# TODO: Update these paths to your pretrained model locations.
# These can be local paths or Hugging Face Hub model identifiers.
self.pretrained_tokenizer_path = "path/to/your/Kronos-Tokenizer-base"
self.pretrained_predictor_path = "path/to/your/Kronos-small"
# Paths to the fine-tuned models, derived from the save_path.
# These will be generated automatically during training.
self.finetuned_tokenizer_path = f"{self.save_path}/{self.tokenizer_save_folder_name}/checkpoints/best_model"
self.finetuned_predictor_path = f"{self.save_path}/{self.predictor_save_folder_name}/checkpoints/best_model"
# =================================================================
# Backtesting Parameters
# =================================================================
self.backtest_n_symbol_hold = 50 # Number of symbols to hold in the portfolio.
self.backtest_n_symbol_drop = 5 # Number of symbols to drop from the pool.
self.backtest_hold_thresh = 5 # Minimum holding period for a stock.
self.inference_T = 0.6
self.inference_top_p = 0.9
self.inference_top_k = 0
self.inference_sample_count = 5
self.backtest_batch_size = 1000
self.backtest_benchmark = self._set_benchmark(self.instrument)
def _set_benchmark(self, instrument):
dt_benchmark = {
'csi800': "SH000906",
'csi1000': "SH000852",
'csi300': "SH000300",
}
if instrument in dt_benchmark:
return dt_benchmark[instrument]
else:
raise ValueError(f"Benchmark not defined for instrument: {instrument}")
================================================
FILE: finetune/dataset.py
================================================
import pickle
import random
import numpy as np
import torch
from torch.utils.data import Dataset
from config import Config
class QlibDataset(Dataset):
"""
A PyTorch Dataset for handling Qlib financial time series data.
This dataset pre-computes all possible start indices for sliding windows
and then randomly samples from them during training/validation.
Args:
data_type (str): The type of dataset to load, either 'train' or 'val'.
Raises:
ValueError: If `data_type` is not 'train' or 'val'.
"""
def __init__(self, data_type: str = 'train'):
self.config = Config()
if data_type not in ['train', 'val']:
raise ValueError("data_type must be 'train' or 'val'")
self.data_type = data_type
# Use a dedicated random number generator for sampling to avoid
# interfering with other random processes (e.g., in model initialization).
self.py_rng = random.Random(self.config.seed)
# Set paths and number of samples based on the data type.
if data_type == 'train':
self.data_path = f"{self.config.dataset_path}/train_data.pkl"
self.n_samples = self.config.n_train_iter
else:
self.data_path = f"{self.config.dataset_path}/val_data.pkl"
self.n_samples = self.config.n_val_iter
with open(self.data_path, 'rb') as f:
self.data = pickle.load(f)
self.window = self.config.lookback_window + self.config.predict_window + 1
self.symbols = list(self.data.keys())
self.feature_list = self.config.feature_list
self.time_feature_list = self.config.time_feature_list
# Pre-compute all possible (symbol, start_index) pairs.
self.indices = []
print(f"[{data_type.upper()}] Pre-computing sample indices...")
for symbol in self.symbols:
df = self.data[symbol].reset_index()
series_len = len(df)
num_samples = series_len - self.window + 1
if num_samples > 0:
# Generate time features and store them directly in the dataframe.
df['minute'] = df['datetime'].dt.minute
df['hour'] = df['datetime'].dt.hour
df['weekday'] = df['datetime'].dt.weekday
df['day'] = df['datetime'].dt.day
df['month'] = df['datetime'].dt.month
# Keep only necessary columns to save memory.
self.data[symbol] = df[self.feature_list + self.time_feature_list]
# Add all valid starting indices for this symbol to the global list.
for i in range(num_samples):
self.indices.append((symbol, i))
# The effective dataset size is the minimum of the configured iterations
# and the total number of available samples.
self.n_samples = min(self.n_samples, len(self.indices))
print(f"[{data_type.upper()}] Found {len(self.indices)} possible samples. Using {self.n_samples} per epoch.")
def set_epoch_seed(self, epoch: int):
"""
Sets a new seed for the random sampler for each epoch. This is crucial
for reproducibility in distributed training.
Args:
epoch (int): The current epoch number.
"""
epoch_seed = self.config.seed + epoch
self.py_rng.seed(epoch_seed)
def __len__(self) -> int:
"""Returns the number of samples per epoch."""
return self.n_samples
def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
"""
Retrieves a random sample from the dataset.
Note: The `idx` argument is ignored. Instead, a random index is drawn
from the pre-computed `self.indices` list using `self.py_rng`. This
ensures random sampling over the entire dataset for each call.
Args:
idx (int): Ignored.
Returns:
tuple[torch.Tensor, torch.Tensor]: A tuple containing:
- x_tensor (torch.Tensor): The normalized feature tensor.
- x_stamp_tensor (torch.Tensor): The time feature tensor.
"""
# Select a random sample from the entire pool of indices.
random_idx = self.py_rng.randint(0, len(self.indices) - 1)
symbol, start_idx = self.indices[random_idx]
# Extract the sliding window from the dataframe.
df = self.data[symbol]
end_idx = start_idx + self.window
win_df = df.iloc[start_idx:end_idx]
# Separate main features and time features.
x = win_df[self.feature_list].values.astype(np.float32)
x_stamp = win_df[self.time_feature_list].values.astype(np.float32)
# Perform instance-level normalization.
x_mean, x_std = np.mean(x, axis=0), np.std(x, axis=0)
x = (x - x_mean) / (x_std + 1e-5)
x = np.clip(x, -self.config.clip, self.config.clip)
# Convert to PyTorch tensors.
x_tensor = torch.from_numpy(x)
x_stamp_tensor = torch.from_numpy(x_stamp)
return x_tensor, x_stamp_tensor
if __name__ == '__main__':
# Example usage and verification.
print("Creating training dataset instance...")
train_dataset = QlibDataset(data_type='train')
print(f"Dataset length: {len(train_dataset)}")
if len(train_dataset) > 0:
try_x, try_x_stamp = train_dataset[100] # Index 100 is ignored.
print(f"Sample feature shape: {try_x.shape}")
print(f"Sample time feature shape: {try_x_stamp.shape}")
else:
print("Dataset is empty.")
================================================
FILE: finetune/qlib_data_preprocess.py
================================================
import os
import pickle
import numpy as np
import pandas as pd
import qlib
from qlib.config import REG_CN
from qlib.data import D
from qlib.data.dataset.loader import QlibDataLoader
from tqdm import trange
from config import Config
class QlibDataPreprocessor:
"""
A class to handle the loading, processing, and splitting of Qlib financial data.
"""
def __init__(self):
"""Initializes the preprocessor with configuration and data fields."""
self.config = Config()
self.data_fields = ['open', 'close', 'high', 'low', 'volume', 'vwap']
self.data = {} # A dictionary to store processed data for each symbol.
def initialize_qlib(self):
"""Initializes the Qlib environment."""
print("Initializing Qlib...")
qlib.init(provider_uri=self.config.qlib_data_path, region=REG_CN)
def load_qlib_data(self):
"""
Loads raw data from Qlib, processes it symbol by symbol, and stores
it in the `self.data` attribute.
"""
print("Loading and processing data from Qlib...")
data_fields_qlib = ['$' + f for f in self.data_fields]
cal: np.ndarray = D.calendar()
# Determine the actual start and end times to load, including buffer for lookback and predict windows.
start_index = cal.searchsorted(pd.Timestamp(self.config.dataset_begin_time))
end_index = cal.searchsorted(pd.Timestamp(self.config.dataset_end_time))
# Check if start_index lookbackw_window will cause negative index
adjusted_start_index = max(start_index - self.config.lookback_window, 0)
real_start_time = cal[adjusted_start_index]
# Check if end_index exceeds the range of the array
if end_index >= len(cal):
end_index = len(cal) - 1
elif cal[end_index] != pd.Timestamp(self.config.dataset_end_time):
end_index -= 1
# Check if end_index+predictw_window will exceed the range of the array
adjusted_end_index = min(end_index + self.config.predict_window, len(cal) - 1)
real_end_time = cal[adjusted_end_index]
# Load data using Qlib's data loader.
data_df = QlibDataLoader(config=data_fields_qlib).load(
self.config.instrument, real_start_time, real_end_time
)
data_df = data_df.stack().unstack(level=1) # Reshape for easier access.
symbol_list = list(data_df.columns)
for i in trange(len(symbol_list), desc="Processing Symbols"):
symbol = symbol_list[i]
symbol_df = data_df[symbol]
# Pivot the table to have features as columns and datetime as index.
symbol_df = symbol_df.reset_index().rename(columns={'level_1': 'field'})
symbol_df = pd.pivot(symbol_df, index='datetime', columns='field', values=symbol)
symbol_df = symbol_df.rename(columns={f'${field}': field for field in self.data_fields})
# Calculate amount and select final features.
symbol_df['vol'] = symbol_df['volume']
symbol_df['amt'] = (symbol_df['open'] + symbol_df['high'] + symbol_df['low'] + symbol_df['close']) / 4 * symbol_df['vol']
symbol_df = symbol_df[self.config.feature_list]
# Filter out symbols with insufficient data.
symbol_df = symbol_df.dropna()
if len(symbol_df) < self.config.lookback_window + self.config.predict_window + 1:
continue
self.data[symbol] = symbol_df
def prepare_dataset(self):
"""
Splits the loaded data into train, validation, and test sets and saves them to disk.
"""
print("Splitting data into train, validation, and test sets...")
train_data, val_data, test_data = {}, {}, {}
symbol_list = list(self.data.keys())
for i in trange(len(symbol_list), desc="Preparing Datasets"):
symbol = symbol_list[i]
symbol_df = self.data[symbol]
# Define time ranges from config.
train_start, train_end = self.config.train_time_range
val_start, val_end = self.config.val_time_range
test_start, test_end = self.config.test_time_range
# Create boolean masks for each dataset split.
train_mask = (symbol_df.index >= train_start) & (symbol_df.index <= train_end)
val_mask = (symbol_df.index >= val_start) & (symbol_df.index <= val_end)
test_mask = (symbol_df.index >= test_start) & (symbol_df.index <= test_end)
# Apply masks to create the final datasets.
train_data[symbol] = symbol_df[train_mask]
val_data[symbol] = symbol_df[val_mask]
test_data[symbol] = symbol_df[test_mask]
# Save the datasets using pickle.
os.makedirs(self.config.dataset_path, exist_ok=True)
with open(f"{self.config.dataset_path}/train_data.pkl", 'wb') as f:
pickle.dump(train_data, f)
with open(f"{self.config.dataset_path}/val_data.pkl", 'wb') as f:
pickle.dump(val_data, f)
with open(f"{self.config.dataset_path}/test_data.pkl", 'wb') as f:
pickle.dump(test_data, f)
print("Datasets prepared and saved successfully.")
if __name__ == '__main__':
# This block allows the script to be run directly to perform data preprocessing.
preprocessor = QlibDataPreprocessor()
preprocessor.initialize_qlib()
preprocessor.load_qlib_data()
preprocessor.prepare_dataset()
================================================
FILE: finetune/qlib_test.py
================================================
import os
import sys
import argparse
import pickle
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm import trange, tqdm
from matplotlib import pyplot as plt
import qlib
from qlib.config import REG_CN
from qlib.backtest import backtest, executor, CommonInfrastructure
from qlib.contrib.evaluate import risk_analysis
from qlib.contrib.strategy import TopkDropoutStrategy
from qlib.utils import flatten_dict
from qlib.utils.time import Freq
# Ensure project root is in the Python path
sys.path.append("../")
from config import Config
from model.kronos import Kronos, KronosTokenizer, auto_regressive_inference
# =================================================================================
# 1. Data Loading and Processing for Inference
# =================================================================================
class QlibTestDataset(Dataset):
"""
PyTorch Dataset for handling Qlib test data, specifically for inference.
This dataset iterates through all possible sliding windows sequentially. It also
yields metadata like symbol and timestamp, which are crucial for mapping
predictions back to the original time series.
"""
def __init__(self, data: dict, config: Config):
self.data = data
self.config = config
self.window_size = config.lookback_window + config.predict_window
self.symbols = list(self.data.keys())
sel
gitextract_8jfd95yy/
├── .gitignore
├── LICENSE
├── README.md
├── examples/
│ ├── data/
│ │ └── XSHG_5min_600977.csv
│ ├── prediction_batch_example.py
│ ├── prediction_cn_markets_day.py
│ ├── prediction_example.py
│ └── prediction_wo_vol_example.py
├── finetune/
│ ├── config.py
│ ├── dataset.py
│ ├── qlib_data_preprocess.py
│ ├── qlib_test.py
│ ├── train_predictor.py
│ ├── train_tokenizer.py
│ └── utils/
│ ├── __init__.py
│ └── training_utils.py
├── finetune_csv/
│ ├── README.md
│ ├── README_CN.md
│ ├── config_loader.py
│ ├── configs/
│ │ └── config_ali09988_candle-5min.yaml
│ ├── data/
│ │ └── HK_ali_09988_kline_5min_all.csv
│ ├── finetune_base_model.py
│ ├── finetune_tokenizer.py
│ └── train_sequential.py
├── model/
│ ├── __init__.py
│ ├── kronos.py
│ └── module.py
├── requirements.txt
├── tests/
│ ├── data/
│ │ ├── generate_regression_output.py
│ │ ├── regression_input.csv
│ │ ├── regression_output_256.csv
│ │ └── regression_output_512.csv
│ └── test_kronos_regression.py
└── webui/
├── README.md
├── app.py
├── prediction_results/
│ ├── prediction_20250826_163800.json
│ ├── prediction_20250826_164030.json
│ ├── prediction_20250826_164422.json
│ ├── prediction_20250826_170831.json
│ ├── prediction_20250826_171720.json
│ ├── prediction_20250826_171913.json
│ ├── prediction_20250826_172031.json
│ ├── prediction_20250826_172153.json
│ ├── prediction_20250826_172740.json
│ ├── prediction_20250826_173322.json
│ ├── prediction_20250826_173455.json
│ ├── prediction_20250826_174410.json
│ ├── prediction_20250826_174809.json
│ ├── prediction_20250826_175057.json
│ ├── prediction_20250826_175135.json
│ ├── prediction_20250826_175909.json
│ ├── prediction_20250826_180308.json
│ ├── prediction_20250826_180632.json
│ ├── prediction_20250826_180745.json
│ ├── prediction_20250826_180806.json
│ ├── prediction_20250826_181012.json
│ ├── prediction_20250826_181139.json
│ ├── prediction_20250826_181240.json
│ ├── prediction_20250826_181434.json
│ ├── prediction_20250826_181513.json
│ ├── prediction_20250826_181612.json
│ ├── prediction_20250826_181648.json
│ ├── prediction_20250826_181800.json
│ └── prediction_20250826_181932.json
├── requirements.txt
├── run.py
├── start.sh
└── templates/
└── index.html
SYMBOL INDEX (196 symbols across 22 files)
FILE: examples/prediction_batch_example.py
function plot_prediction (line 8) | def plot_prediction(kline_df, pred_df):
FILE: examples/prediction_cn_markets_day.py
function load_data (line 48) | def load_data(symbol: str) -> pd.DataFrame:
function prepare_inputs (line 112) | def prepare_inputs(df):
function apply_price_limits (line 118) | def apply_price_limits(pred_df, last_close, limit_rate=0.1):
function plot_result (line 143) | def plot_result(df_hist, df_pred, symbol):
function predict_future (line 159) | def predict_future(symbol):
FILE: examples/prediction_example.py
function plot_prediction (line 8) | def plot_prediction(kline_df, pred_df):
FILE: examples/prediction_wo_vol_example.py
function plot_prediction (line 8) | def plot_prediction(kline_df, pred_df):
FILE: finetune/config.py
class Config (line 3) | class Config:
method __init__ (line 8) | def __init__(self):
method _set_benchmark (line 122) | def _set_benchmark(self, instrument):
FILE: finetune/dataset.py
class QlibDataset (line 9) | class QlibDataset(Dataset):
method __init__ (line 23) | def __init__(self, data_type: str = 'train'):
method set_epoch_seed (line 77) | def set_epoch_seed(self, epoch: int):
method __len__ (line 88) | def __len__(self) -> int:
method __getitem__ (line 92) | def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
FILE: finetune/qlib_data_preprocess.py
class QlibDataPreprocessor (line 14) | class QlibDataPreprocessor:
method __init__ (line 19) | def __init__(self):
method initialize_qlib (line 25) | def initialize_qlib(self):
method load_qlib_data (line 30) | def load_qlib_data(self):
method prepare_dataset (line 85) | def prepare_dataset(self):
FILE: finetune/qlib_test.py
class QlibTestDataset (line 32) | class QlibTestDataset(Dataset):
method __init__ (line 41) | def __init__(self, data: dict, config: Config):
method __len__ (line 67) | def __len__(self) -> int:
method __getitem__ (line 70) | def __getitem__(self, idx: int):
class QlibBacktest (line 96) | class QlibBacktest:
method __init__ (line 101) | def __init__(self, config: Config):
method initialize_qlib (line 105) | def initialize_qlib(self):
method run_single_backtest (line 110) | def run_single_backtest(self, signal_series: pd.Series) -> pd.DataFrame:
method run_and_plot_results (line 164) | def run_and_plot_results(self, signals: dict[str, pd.DataFrame]):
function load_models (line 207) | def load_models(config: dict) -> tuple[KronosTokenizer, Kronos]:
function collate_fn_for_inference (line 216) | def collate_fn_for_inference(batch):
function generate_predictions (line 239) | def generate_predictions(config: dict, test_data: dict) -> dict[str, pd....
function main (line 302) | def main():
FILE: finetune/train_predictor.py
function create_dataloaders (line 29) | def create_dataloaders(config: dict, rank: int, world_size: int):
function train_model (line 60) | def train_model(model, tokenizer, device, config, save_dir, logger, rank...
function main (line 182) | def main(config: dict):
FILE: finetune/train_tokenizer.py
function create_dataloaders (line 32) | def create_dataloaders(config: dict, rank: int, world_size: int):
function train_model (line 74) | def train_model(model, device, config, save_dir, logger, rank, world_size):
function main (line 218) | def main(config: dict):
FILE: finetune/utils/training_utils.py
function setup_ddp (line 9) | def setup_ddp():
function cleanup_ddp (line 35) | def cleanup_ddp():
function set_seed (line 41) | def set_seed(seed: int, rank: int = 0):
function get_model_size (line 62) | def get_model_size(model: torch.nn.Module) -> str:
function reduce_tensor (line 83) | def reduce_tensor(tensor: torch.Tensor, world_size: int, op=dist.ReduceO...
function format_time (line 105) | def format_time(seconds: float) -> str:
FILE: finetune_csv/config_loader.py
class ConfigLoader (line 6) | class ConfigLoader:
method __init__ (line 8) | def __init__(self, config_path: str):
method _load_config (line 13) | def _load_config(self) -> Dict[str, Any]:
method _resolve_dynamic_paths (line 25) | def _resolve_dynamic_paths(self, config: Dict[str, Any]) -> Dict[str, ...
method get (line 51) | def get(self, key: str, default=None):
method get_data_config (line 63) | def get_data_config(self) -> Dict[str, Any]:
method get_training_config (line 66) | def get_training_config(self) -> Dict[str, Any]:
method get_model_paths (line 69) | def get_model_paths(self) -> Dict[str, str]:
method get_experiment_config (line 72) | def get_experiment_config(self) -> Dict[str, Any]:
method get_device_config (line 75) | def get_device_config(self) -> Dict[str, Any]:
method get_distributed_config (line 78) | def get_distributed_config(self) -> Dict[str, Any]:
method update_config (line 81) | def update_config(self, updates: Dict[str, Any]):
method save_config (line 93) | def save_config(self, save_path: str = None):
method print_config (line 101) | def print_config(self):
class CustomFinetuneConfig (line 109) | class CustomFinetuneConfig:
method __init__ (line 111) | def __init__(self, config_path: str = None):
method _load_all_configs (line 119) | def _load_all_configs(self):
method _compute_full_paths (line 184) | def _compute_full_paths(self):
method get_tokenizer_config (line 192) | def get_tokenizer_config(self):
method get_basemodel_config (line 218) | def get_basemodel_config(self):
method print_config_summary (line 245) | def print_config_summary(self):
FILE: finetune_csv/finetune_base_model.py
class CustomKlineDataset (line 25) | class CustomKlineDataset(Dataset):
method __init__ (line 27) | def __init__(self, data_path, data_type='train', lookback_window=90, p...
method _load_and_preprocess_data (line 52) | def _load_and_preprocess_data(self):
method _split_data_by_time (line 75) | def _split_data_by_time(self):
method set_epoch_seed (line 99) | def set_epoch_seed(self, epoch):
method __len__ (line 104) | def __len__(self):
method __getitem__ (line 107) | def __getitem__(self, idx):
function setup_logging (line 137) | def setup_logging(exp_name: str, log_dir: str, rank: int = 0) -> logging...
function create_dataloaders (line 181) | def create_dataloaders(config):
function train_model (line 239) | def train_model(model, tokenizer, device, config, save_dir, logger):
function main (line 367) | def main():
FILE: finetune_csv/finetune_tokenizer.py
function set_seed (line 24) | def set_seed(seed: int, rank: int = 0):
function get_model_size (line 35) | def get_model_size(model: torch.nn.Module) -> str:
function format_time (line 45) | def format_time(seconds: float) -> str:
function setup_logging (line 49) | def setup_logging(exp_name: str, log_dir: str, rank: int = 0) -> logging...
function create_dataloaders (line 93) | def create_dataloaders(config):
function train_tokenizer (line 151) | def train_tokenizer(model, device, config, save_dir, logger):
function main (line 281) | def main():
FILE: finetune_csv/train_sequential.py
class SequentialTrainer (line 18) | class SequentialTrainer:
method __init__ (line 20) | def __init__(self, config_path: str = None):
method _setup_device (line 29) | def _setup_device(self):
method _setup_distributed (line 40) | def _setup_distributed(self):
method _check_existing_models (line 51) | def _check_existing_models(self):
method _create_directories (line 60) | def _create_directories(self):
method train_tokenizer_phase (line 66) | def train_tokenizer_phase(self):
method train_basemodel_phase (line 148) | def train_basemodel_phase(self):
method run_training (line 264) | def run_training(self):
function main (line 319) | def main():
FILE: model/__init__.py
function get_model_class (line 10) | def get_model_class(model_name):
FILE: model/kronos.py
class KronosTokenizer (line 13) | class KronosTokenizer(nn.Module, PyTorchModelHubMixin):
method __init__ (line 40) | def __init__(self, d_in, d_model, n_heads, ff_dim, n_enc_layers, n_dec...
method forward (line 74) | def forward(self, x):
method indices_to_bits (line 115) | def indices_to_bits(self, x, half=False):
method encode (line 142) | def encode(self, x, half=False):
method decode (line 161) | def decode(self, x, half=False):
class Kronos (line 180) | class Kronos(nn.Module, PyTorchModelHubMixin):
method __init__ (line 198) | def __init__(self, s1_bits, s2_bits, n_layers, d_model, n_heads, ff_di...
method _init_weights (line 225) | def _init_weights(self, module):
method forward (line 239) | def forward(self, s1_ids, s2_ids, stamp=None, padding_mask=None, use_t...
method decode_s1 (line 278) | def decode_s1(self, s1_ids, s2_ids, stamp=None, padding_mask=None):
method decode_s2 (line 310) | def decode_s2(self, context, s1_ids, padding_mask=None):
function top_k_top_p_filtering (line 331) | def top_k_top_p_filtering(
function sample_from_logits (line 373) | def sample_from_logits(logits, temperature=1.0, top_k=None, top_p=None, ...
function auto_regressive_inference (line 389) | def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max...
function calc_time_stamps (line 472) | def calc_time_stamps(x_timestamp):
class KronosPredictor (line 482) | class KronosPredictor:
method __init__ (line 484) | def __init__(self, model, tokenizer, device=None, max_context=512, cli...
method generate (line 508) | def generate(self, x, x_stamp, y_stamp, pred_len, T, top_k, top_p, sam...
method predict (line 519) | def predict(self, df, x_timestamp, y_timestamp, pred_len, T=1.0, top_k...
method predict_batch (line 562) | def predict_batch(self, df_list, x_timestamp_list, y_timestamp_list, p...
FILE: model/module.py
class DifferentiableEntropyFunction (line 10) | class DifferentiableEntropyFunction(Function):
method forward (line 12) | def forward(ctx, zq, basis, K, eps):
method backward (line 27) | def backward(ctx, grad_output):
function codebook_entropy (line 35) | def codebook_entropy(zq, basis, K, eps=1e-4):
class BinarySphericalQuantizer (line 39) | class BinarySphericalQuantizer(nn.Module):
method __init__ (line 40) | def __init__(self, embed_dim, beta, gamma0, gamma, zeta,
method quantize (line 82) | def quantize(self, z):
method forward (line 90) | def forward(self, z, collect_metrics=True):
method soft_entropy_loss (line 131) | def soft_entropy_loss(self, z):
method get_hard_per_sample_entropy (line 157) | def get_hard_per_sample_entropy(self, zb_by_sample):
method codes_to_indexes (line 163) | def codes_to_indexes(self, zhat):
method codes_to_group_indexes (line 171) | def codes_to_group_indexes(self, zhat):
method indexes_to_codes (line 179) | def indexes_to_codes(self, indices):
method group_indexes_to_codes (line 187) | def group_indexes_to_codes(self, group_indices):
method get_entropy (line 196) | def get_entropy(self, count, dim=-1, eps=1e-4, normalize=True):
method get_group_codebook_entry (line 204) | def get_group_codebook_entry(self, group_indices):
method get_codebook_entry (line 214) | def get_codebook_entry(self, indices):
class BSQuantizer (line 225) | class BSQuantizer(nn.Module):
method __init__ (line 227) | def __init__(self, s1_bits, s2_bits, beta, gamma0, gamma, zeta, group_...
method bits_to_indices (line 234) | def bits_to_indices(self, bits):
method forward (line 245) | def forward(self, z, half=False, collect_metrics=True):
class RMSNorm (line 257) | class RMSNorm(torch.nn.Module):
method __init__ (line 258) | def __init__(self, dim: int, eps: float = 1e-5):
method _norm (line 263) | def _norm(self, x):
method forward (line 266) | def forward(self, x):
class FeedForward (line 271) | class FeedForward(nn.Module):
method __init__ (line 272) | def __init__(self, d_model, ff_dim, ffn_dropout_p=0.0):
method forward (line 280) | def forward(self, x):
class RotaryPositionalEmbedding (line 284) | class RotaryPositionalEmbedding(nn.Module):
method __init__ (line 285) | def __init__(self, dim):
method _update_cos_sin_cache (line 293) | def _update_cos_sin_cache(self, x, seq_len):
method forward (line 303) | def forward(self, q, k):
method _rotate_half (line 310) | def _rotate_half(self, x):
class MultiHeadAttentionWithRoPE (line 315) | class MultiHeadAttentionWithRoPE(nn.Module):
method __init__ (line 316) | def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout...
method forward (line 330) | def forward(self, x, key_padding_mask=None):
class MultiHeadCrossAttentionWithRoPE (line 356) | class MultiHeadCrossAttentionWithRoPE(nn.Module):
method __init__ (line 357) | def __init__(self, d_model, n_heads, attn_dropout_p=0.0, resid_dropout...
method forward (line 371) | def forward(self, query, key, value, key_padding_mask=None):
class HierarchicalEmbedding (line 400) | class HierarchicalEmbedding(nn.Module):
method __init__ (line 401) | def __init__(self, s1_bits, s2_bits, d_model=256):
method split_token (line 417) | def split_token(self, token_ids: torch.Tensor, s2_bits: int):
method forward (line 430) | def forward(self, token_ids):
class DependencyAwareLayer (line 446) | class DependencyAwareLayer(nn.Module):
method __init__ (line 447) | def __init__(self, d_model, n_heads=4, attn_dropout_p=0.0, resid_dropo...
method forward (line 452) | def forward(self, hidden_states, sibling_embed, key_padding_mask=None):
class TransformerBlock (line 465) | class TransformerBlock(nn.Module):
method __init__ (line 466) | def __init__(self, d_model, n_heads, ff_dim=1024, ffn_dropout_p=0.0, a...
method forward (line 473) | def forward(self, x, key_padding_mask=None):
class DualHead (line 486) | class DualHead(nn.Module):
method __init__ (line 487) | def __init__(self, s1_bits, s2_bits, d_model):
method compute_loss (line 494) | def compute_loss(self, s1_logits, s2_logits, s1_targets, s2_targets, p...
method forward (line 509) | def forward(self, x):
method cond_forward (line 512) | def cond_forward(self, x2):
class FixedEmbedding (line 516) | class FixedEmbedding(nn.Module):
method __init__ (line 517) | def __init__(self, c_in, d_model):
method forward (line 532) | def forward(self, x):
class TemporalEmbedding (line 536) | class TemporalEmbedding(nn.Module):
method __init__ (line 537) | def __init__(self, d_model, learn_pe):
method forward (line 553) | def forward(self, x):
FILE: tests/data/generate_regression_output.py
function set_seed (line 26) | def set_seed(seed: int) -> None:
function generate_output (line 35) | def generate_output(ctx_len: int) -> None:
FILE: tests/test_kronos_regression.py
function set_seed (line 36) | def set_seed(seed: int) -> None:
function test_kronos_predictor_regression (line 46) | def test_kronos_predictor_regression(context_len):
function test_kronos_predictor_mse (line 91) | def test_kronos_predictor_mse(context_len, expected_mse):
FILE: webui/app.py
function load_data_files (line 60) | def load_data_files():
function load_data_file (line 78) | def load_data_file(file_path):
function save_prediction_results (line 125) | def save_prediction_results(file_path, prediction_type, prediction_resul...
function create_prediction_chart (line 209) | def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=N...
function index (line 331) | def index():
function get_data_files (line 336) | def get_data_files():
function load_data (line 342) | def load_data():
function predict (line 405) | def predict():
function load_model (line 627) | def load_model():
function get_available_models (line 666) | def get_available_models():
function get_model_status (line 674) | def get_model_status():
FILE: webui/run.py
function check_dependencies (line 12) | def check_dependencies():
function install_dependencies (line 27) | def install_dependencies():
function main (line 38) | def main():
Condensed preview — 68 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (8,315K chars).
[
{
"path": ".gitignore",
"chars": 619,
"preview": "# Python\n__pycache__/\n*.py[cod]\n*$py.class\n*.so\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\np"
},
{
"path": "LICENSE",
"chars": 1062,
"preview": "MIT License\n\nCopyright (c) 2025 ShiYu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof t"
},
{
"path": "README.md",
"chars": 16571,
"preview": "<div align=\"center\">\r\n <h2><b>Kronos: A Foundation Model for the Language of Financial Markets </b></h2>\r\n</div>\r\n\r\n\r\n<"
},
{
"path": "examples/data/XSHG_5min_600977.csv",
"chars": 147854,
"preview": "timestamps,open,high,low,close,volume,amount\n2024-06-18 11:15:00,11.27,11.28,11.26,11.27,379.0,427161.0\n2024-06-18 11:20"
},
{
"path": "examples/prediction_batch_example.py",
"chars": 2312,
"preview": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.append(\"../\")\nfrom model import Kronos, KronosTo"
},
{
"path": "examples/prediction_cn_markets_day.py",
"chars": 6451,
"preview": "# -*- coding: utf-8 -*-\n\"\"\"\nprediction_cn_markets_day.py\n\nDescription:\n Predicts future daily K-line (1D) data for A-"
},
{
"path": "examples/prediction_example.py",
"chars": 2457,
"preview": "import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport sys\r\nsys.path.append(\"../\")\r\nfrom model import Kronos, Kron"
},
{
"path": "examples/prediction_wo_vol_example.py",
"chars": 1918,
"preview": "import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport sys\r\nsys.path.append(\"../\")\r\nfrom model import Kronos, Kron"
},
{
"path": "finetune/config.py",
"chars": 6247,
"preview": "import os\r\n\r\nclass Config:\r\n \"\"\"\r\n Configuration class for the entire project.\r\n \"\"\"\r\n\r\n def __init__(self):"
},
{
"path": "finetune/dataset.py",
"chars": 5745,
"preview": "import pickle\r\nimport random\r\nimport numpy as np\r\nimport torch\r\nfrom torch.utils.data import Dataset\r\nfrom config import"
},
{
"path": "finetune/qlib_data_preprocess.py",
"chars": 5645,
"preview": "import os\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nimport qlib\r\nfrom qlib.config import REG_CN\r\nfrom qli"
},
{
"path": "finetune/qlib_test.py",
"chars": 15140,
"preview": "import os\r\nimport sys\r\nimport argparse\r\nimport pickle\r\nfrom collections import defaultdict\r\n\r\nimport numpy as np\r\nimport"
},
{
"path": "finetune/train_predictor.py",
"chars": 10077,
"preview": "import os\r\nimport sys\r\nimport json\r\nimport time\r\nfrom time import gmtime, strftime\r\nimport torch.distributed as dist\r\nim"
},
{
"path": "finetune/train_tokenizer.py",
"chars": 11482,
"preview": "import os\r\nimport sys\r\nimport json\r\nimport time\r\nfrom time import gmtime, strftime\r\nimport argparse\r\nimport datetime\r\nim"
},
{
"path": "finetune/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "finetune/utils/training_utils.py",
"chars": 3830,
"preview": "import os\r\nimport random\r\nimport datetime\r\nimport numpy as np\r\nimport torch\r\nimport torch.distributed as dist\r\n\r\n\r\ndef s"
},
{
"path": "finetune_csv/README.md",
"chars": 3959,
"preview": "# Kronos Fine-tuning on Custom CSV Datasets\n\nThis module provides a comprehensive pipeline for fine-tuning Kronos models"
},
{
"path": "finetune_csv/README_CN.md",
"chars": 2760,
"preview": "# Kronos微调-支持自定义CSV数据集\n\n这是一个在自定义的CSV格式数据上微调Kronos模型的完整流程。包含顺序训练（先训练tokenizer再训练predictor）和单独模块训练，同时支持分布式训练。\n\n\n## 1. 准备数据"
},
{
"path": "finetune_csv/config_loader.py",
"chars": 11688,
"preview": "import os\nimport yaml\nfrom typing import Dict, Any\n\n\nclass ConfigLoader:\n \n def __init__(self, config_path: str):\n"
},
{
"path": "finetune_csv/configs/config_ali09988_candle-5min.yaml",
"chars": 2098,
"preview": "#This is a template config for custom finetuning kronos on csv data\n#这是一份模板config，用于kronos的csv自定义数据微调\n\ndata:\n data_path"
},
{
"path": "finetune_csv/data/HK_ali_09988_kline_5min_all.csv",
"chars": 5852035,
"preview": "timestamps,open,close,high,low,volume,amount\n2019/11/26 9:35,182.45215,184.45215,184.95215,182.45215,15136000,0\n2019/11"
},
{
"path": "finetune_csv/finetune_base_model.py",
"chars": 19127,
"preview": "import os\nimport sys\nimport json\nimport time\nimport pickle\nimport random\nimport pandas as pd\nimport numpy as np\nimport t"
},
{
"path": "finetune_csv/finetune_tokenizer.py",
"chars": 13753,
"preview": "import os\nimport sys\nimport json\nimport time\nimport random\nimport numpy as np\nimport torch\nimport torch.nn.functional as"
},
{
"path": "finetune_csv/train_sequential.py",
"chars": 15358,
"preview": "import os\nimport sys\nimport time\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoa"
},
{
"path": "model/__init__.py",
"chars": 412,
"preview": "from .kronos import KronosTokenizer, Kronos, KronosPredictor\r\n\r\nmodel_dict = {\r\n 'kronos_tokenizer': KronosTokenizer,"
},
{
"path": "model/kronos.py",
"chars": 30796,
"preview": "import numpy as np\r\nimport pandas as pd\r\nimport torch\r\nfrom huggingface_hub import PyTorchModelHubMixin\r\nimport sys\r\n\r\nf"
},
{
"path": "model/module.py",
"chars": 23426,
"preview": "import math\r\n\r\nfrom einops import rearrange, reduce\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch.autograd import Fun"
},
{
"path": "requirements.txt",
"chars": 129,
"preview": "numpy\npandas\ntorch>=2.0.0\n\neinops==0.8.1\nhuggingface_hub==0.33.1\nmatplotlib==3.9.3\npandas==2.2.2\ntqdm==4.67.1\nsafetensor"
},
{
"path": "tests/data/generate_regression_output.py",
"chars": 2814,
"preview": "import random\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom model import Kronos, K"
},
{
"path": "tests/data/regression_input.csv",
"chars": 147854,
"preview": "timestamps,open,high,low,close,volume,amount\n2024-06-18 11:15:00,11.27,11.28,11.26,11.27,379.0,427161.0\n2024-06-18 11:20"
},
{
"path": "tests/data/regression_output_256.csv",
"chars": 674,
"preview": "timestamps,open,high,low,close,volume,amount\n2024-06-25 14:05:00,10.766402,10.778437,10.755835,10.769899,463.83264,47925"
},
{
"path": "tests/data/regression_output_512.csv",
"chars": 673,
"preview": "timestamps,open,high,low,close,volume,amount\n2024-07-03 09:55:00,10.897451,10.931036,10.800024,10.917972,1545.1383,16659"
},
{
"path": "tests/test_kronos_regression.py",
"chars": 5326,
"preview": "import random\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport torch\nfrom tqdm impo"
},
{
"path": "webui/README.md",
"chars": 4074,
"preview": "# Kronos Web UI\n\nWeb user interface for Kronos financial prediction model, providing intuitive graphical operation inter"
},
{
"path": "webui/app.py",
"chars": 30464,
"preview": "import os\nimport pandas as pd\nimport numpy as np\nimport json\nimport plotly.graph_objects as go\nimport plotly.utils\nfrom "
},
{
"path": "webui/prediction_results/prediction_20250826_163800.json",
"chars": 53618,
"preview": "{\n \"timestamp\": \"2025-08-26T16:38:00.302387\",\n \"file_path\": \"/Users/charles/Kronos/data/BTC_USDT_USDT-5m-futures.feath"
},
{
"path": "webui/prediction_results/prediction_20250826_164030.json",
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"preview": "{\n \"timestamp\": \"2025-08-26T16:40:30.376779\",\n \"file_path\": \"/Users/charles/Kronos/data/BTC_USDT_USDT-5m-futures.feath"
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"path": "webui/prediction_results/prediction_20250826_164422.json",
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"preview": "{\n \"timestamp\": \"2025-08-26T16:44:22.447117\",\n \"file_path\": \"/Users/charles/Kronos/data/BCH_USDT_USDT-5m-futures.feath"
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"preview": "{\n \"timestamp\": \"2025-08-26T17:08:31.462977\",\n \"file_path\": \"/Users/charles/Kronos/data/BCH_USDT_USDT-5m-futures.feath"
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},
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"path": "webui/prediction_results/prediction_20250826_171913.json",
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},
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},
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},
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},
{
"path": "webui/requirements.txt",
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"preview": "flask==2.3.3\nflask-cors==4.0.0\npandas==2.2.2\nnumpy==1.24.3\nplotly==5.17.0\ntorch>=2.1.0\nhuggingface_hub==0.33.1\n"
},
{
"path": "webui/run.py",
"chars": 2578,
"preview": "#!/usr/bin/env python3\n\"\"\"\nKronos Web UI startup script\n\"\"\"\n\nimport os\nimport sys\nimport subprocess\nimport webbrowser\nim"
},
{
"path": "webui/start.sh",
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"preview": "#!/bin/bash\n\n# Kronos Web UI startup script\n\necho \"🚀 Starting Kronos Web UI...\"\necho \"================================\"\n"
},
{
"path": "webui/templates/index.html",
"chars": 45939,
"preview": "<!DOCTYPE html>\n<html lang=\"zh-CN\">\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-wi"
}
]
About this extraction
This page contains the full source code of the shiyu-coder/Kronos GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 68 files (7.6 MB), approximately 2.0M tokens, and a symbol index with 196 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.