Top AI Product

Quants got their own foundation model. Kronos takes the playbook Google used for TimesFM and ports it to financial OHLCV data — feed it candlesticks, get back multi-period forecasts. AAAI 2026 accepted, 23k GitHub stars, still climbing today.

How it works

Two stages. A tokenizer first quantizes continuous multi-dim K-line data (open, high, low, close, volume) into discrete tokens. Then an autoregressive Transformer predicts the next ones, the same way an LLM predicts the next word. Pre-trained on data from 45 global exchanges with up to 2,048 tokens of context. Sizes range from 4.1M to 499.2M parameters, so the small variant runs on a laptop and the large one is worth fine-tuning for a real strategy.

What you can do with it

Weights are on Hugging Face Hub. The repo ships a full fine-tuning pipeline plus a live BTC/USDT demo that streams 24-hour predictions in real time. Batch inference works out of the box — plug it into a backtester, fine-tune on your asset universe, or wire it into an agent loop.

The bet: if Transformers can model language, why not candlesticks?

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