Kronos: A Foundation Model That Teaches Candlestick Charts to Speak Transformer Language
Review of Kronos - a foundation model for OHLCV candle forecasting. BSQ tokenizer, hierarchical decoder, two-stage sampling, Qlib training pipeline. How a model learns the 'language' of the exchange.
If the market is noise, then any attempt to predict the next candle is like decoding radio static. The authors of Kronos propose a radical approach: convert exchange candles into discrete tokens (like words in a language) and train a transformer to predict the "next word" — that is, the next candle.
This isn't a universal time-series transformer "for all occasions" but a specialized model for multidimensional K-lines (OHLCV) with calendar features. The repository shiyu-coder/Kronos is open under MIT, weights on Hugging Face, paper accepted at AAAI 2026.
Core Idea: Candles = Language
Just as ChatGPT learns to predict the next word in text, Kronos learns to predict the next candle in a price series. But first, you need to solve a key problem: a candle is a continuous vector (open, high, low, close, volume), while transformers work with discrete tokens.
The solution — a two-level architecture:
- Tokenizer (BSQ) — compresses the continuous candle into a discrete code
- Decoder (transformer) — predicts the next code autoregressively
BSQ Tokenizer: How to Compress a Candle into Two Numbers
The key module — Binary Spherical Quantizer (BSQ):
- The continuous candle vector is projected onto a sphere (
F.normalize) - Then quantized into a binary code
- The code is split into two levels: coarse (S1) and detailed (S2)
This is vocabulary factorization: instead of one massive dictionary of size S1 × S2, the model uses two small embedding tables — much more efficient.
Hierarchical Decoder: First the Scenario, Then Details
The dual head (DualHead) works in sequence:
- First the model predicts S1 — the "rough scenario" (going up? down? approximately how much?)
- Then with S1 fixed, refines S2 — details (exact prices, volume)
Links
- 💻 GitHub: shiyu-coder/Kronos
- 📄 Paper: arXiv:2508.02739
- 📄 License: MIT
Conclusion
Kronos isn't "yet another transformer for time series" but a coherent pipeline: BSQ tokenizer with codebook control, hierarchical language model with two discretization levels, autoregression with two-stage sampling, local window normalization. It's an open implementation of the "candles = language" idea.
