Kronos: A Foundation Model for the Language of Financial Markets

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Summary

Kronos is a new foundation model for financial K-line data that uses a specialized tokenizer and autoregressive pre-training to outperform existing models in forecasting and synthetic data generation.

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 https://github.com/shiyu-coder/Kronos.
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Paper page - Kronos: A Foundation Model for the Language of Financial Markets

Source: https://huggingface.co/papers/2508.02739

Abstract

Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.

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 tofinancial candlestick(K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such asvolatility predictionandsynthetic 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 intotoken sequences, preserving bothprice dynamicsandtrade activity patterns. We pre-train Kronos using anautoregressive objectiveon 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 azero-shot settingacross a diverse set of financial tasks. On benchmark datasets, Kronos boostsprice series forecastingRankICby 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lowerMAEin volatility forecasting and a 22% improvement ingenerative fidelityfor 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 https://github.com/shiyu-coder/Kronos.

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#### NeoQuasar/Kronos-base Time Series Forecasting• UpdatedSep 9, 2025 • 831k • 147 #### NeoQuasar/Kronos-Tokenizer-base Time Series Forecasting• UpdatedSep 9, 2025 • 2.6M • 51 #### NeoQuasar/Kronos-mini Time Series Forecasting• UpdatedSep 9, 2025 • 691k • 19 #### NeoQuasar/Kronos-small Time Series Forecasting• UpdatedSep 9, 2025 • 1.14M • 18 Browse 33 models citing this paper## Datasets citing this paper0

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Kronos is an open-source foundation model for financial K-line sequences, trained on data from over 45 global exchanges. It uses a specialized tokenizer and a decoder-only Transformer, and has been accepted at AAAI 2026.

@Huanusa: This is absolutely mind-blowing! Someone actually built an AI that can directly read candlestick trading, and its performance is through the roof! It's called Kronos — the world's first open-source foundational large model designed specifically for financial markets! Trained from scratch on 12 billion real candlestick data points from 45 exchanges, not a repurposed general AI. It can: price prediction + volatility prediction and more.

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Kronos is the world's first open-source foundational large model for financial markets, trained from scratch on 12 billion real candlestick data points, supporting price prediction and volatility forecasting, far outperforming general models, and completely free and open-source.

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