NeoQuasar

--- license: mit pipeline_tag: time-series-forecasting tags: - Finance - Candlestick - K-line library_name: pytorch ---

--- license: mit pipeline_tag: time-series-forecasting tags: - Finance - Candlestick - K-line - time-series ---

Kronos: A Foundation Model for the Language of Financial Markets [](https://arxiv.org/abs/2508.02739) [](https://shiyu-coder.github.io/Kronos-demo/) [](https://github.com/shiyu-coder/Kronos) Kronos...

Kronos: A Foundation Model for the Language of Financial Markets [](https://arxiv.org/abs/2508.02739) [](https://shiyu-coder.github.io/Kronos-demo/) [](https://github.com/shiyu-coder/Kronos) 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. 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. 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. 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. 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 | 2048 | 4.1M | ✅ NeoQuasar/Kronos-mini | | Kronos-small | Kronos-Tokenizer-base | 512 | 24.7M | ✅ NeoQuasar/Kronos-small | | Kronos-base | Kronos-Tokenizer-base | 512 | 102.3M | ✅ NeoQuasar/Kronos-base | | Kronos-large | Kronos-Tokenizer-base | 512 | 499.2M | ❌ Not yet publicly available | 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 `maxcontext` 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. Install Python 3.10+, and then install the dependencies from the GitHub repository's `requirements.txt`: First, load a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub. Create an instance of `KronosPredictor`, passing the model, tokenizer, and desired device. 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. - `xtimestamp`: A pandas Series of timestamps corresponding to the historical data in `df`. - `ytimestamp`: A pandas Series of timestamps for the future periods you want to predict. Call the `predict` method to generate forecasts. You can control the sampling process with parameters like `T`, `topp`, and `samplecount` for probabilistic forecasting. The `predict` method returns a pandas DataFrame containing the forecasted values for `open`, `high`, `low`, `close`, `volume`, and `amount`, indexed by the `ytimestamp` you provided. For a complete, runnable script that includes data loading, prediction, and plotting, please see `examples/predictionexample.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: Additionally, a script that makes predictions without Volume and Amount data can be found in `examples/predictionwovolexample.py`. 🔧 Finetuning on Your Own Data (A-Share Market Example) If you use Kronos in your research, we would appreciate a citation to our paper:

Regarding how to use this model, please refer to this GitHub repository