Unlock Precise K-Line Forecasts with Kronos: Open-Source AI for Stocks, Crypto & Futures

The article introduces Kronos, an open-source AI model specifically designed for financial K-line (OHLCV) data, addressing the shortcomings of traditional time‑series tools such as LSTM and ARIMA.

Key Innovations

K-line Semantic Tokenizer : Converts open, high, low, close, and volume values into financial‑language tokens (e.g., “short‑term pressure”, “strong upward”), enabling the model to truly understand candlestick patterns.

Auto‑regressive Transformer architecture : Captures both long‑term daily trends and short‑term minute‑level turning points, improving prediction accuracy in extreme market conditions by more than 30% compared with LSTM.

Cross‑market pre‑training : Trained on data from 45 global exchanges covering stocks, cryptocurrencies and futures, allowing out‑of‑the‑box forecasts for assets such as BTC/USDT, Chinese A‑shares, and oil futures.

Performance Highlights

Benchmarks show a 22% lower error than LSTM when forecasting the 24‑hour closing price of BTC/USDT, and a 100% hit rate on five key rise/fall signals for a Chinese consumer stock, demonstrating the model’s practical reliability.

Core Features

Financial‑aware predictions : Detects price‑volume relationships, supports multiple time‑frames (1 min to weekly), handles extreme‑event scenarios, and outputs multi‑dimensional results (price, volume, probability).

Zero‑code Web UI : Users upload a CSV file, select a model, set prediction length, and obtain results within seconds. Example command to start the service: cd Kronos/webui && python app.py Open‑source and extensible : Three model sizes (mini 4.1 M, small 24.7 M, base 110 M) with CPU/GPU options; one‑line code to load a pre‑trained model:

# Import utilities
from model import Kronos, KronosTokenizer, KronosPredictor

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-2k")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
predictor = KronosPredictor(model, tokenizer, device="cuda:0")

pred_df = predictor.predict(df=btc_kline_data, pred_len=120, verbose=True)
print(pred_df.head())

Fine‑tuning on private data : Users can adapt the model to specific instruments with provided scripts, improving accuracy for niche markets.

Supported Markets and Data Formats

Cryptocurrencies: BTC/USDT, ETH/USDT, etc., from Binance, Coinbase.

Stocks: Chinese A‑shares (e.g., Kweichow Moutai), US stocks (Apple, Tesla), Hong‑Kong stocks (Tencent).

Futures: Gold, crude oil, rebar, index futures.

Data formats: CSV, Feather, Parquet – direct upload without field conversion.

Getting Started

Clone the repository and install dependencies:

git clone https://github.com/shiyu-coder/Kronos.git
cd Kronos
pip install -r requirements.txt

Obtain K-line data (example file data/btc_usdt_1min.csv or download from Binance).

Run the Web UI, upload the CSV, choose a model (e.g., Kronos-small), set prediction length, and start prediction.

Interpret the generated K-line comparison chart and volume bar chart; use probability scores for short‑term decision making.

Export results to Excel for back‑testing or integration into trading strategies.

Community and Roadmap

The project has over 12 k stars on GitHub, an active discussion forum with 600+ contributors, and a roadmap that includes options forecasting and multi‑factor fusion.

Project repository: https://github.com/shiyu-coder/Kronos