Kronos Studio

A clean, interactive UI for testing and exploring Kronos the first open-source foundation model for financial candlestick (K-line) data, pre-trained on data from 45+ global exchanges.

Kronos Studio gives you a no-code interface to run Kronos models, visualize OHLCV forecasts, and manage results. If you want to quickly test what the models can do without writing any Python, this is the easiest way to do it.

Kronos paper: arXiv 2508.02739 · Accepted at AAAI 2026
Kronos repository: https://github.com/shiyu-coder/Kronos

What You Can Do

• Run price predictions on any crypto or stock ticker using Kronos transformer models
• Choose your data source — Binance (crypto), yFinance (stocks), or upload your own CSV
• Single or Batch mode — predict one asset or many at once
• Interactive OHLCV charts — history transitions smoothly into the forecast region with your choice of chart style
• TradingView-powered charts — switch between a standard candlestick view and a TradingView chart for a professional trading terminal feel
• Export results — download charts as SVG/PNG or raw data as CSV
• Save & revisit — store prediction results in a local SQLite database and browse them later

Watch Demo

Screenshots

1. Single Prediction — ETHUSDT (Binance, 1h)

Configure your model, data source, and sampling parameters on the left. The forecast appears instantly on the right as an interactive chart showing historical candles blending into the predicted region.

2. Batch Prediction — Multiple Assets

Add up to 20 symbol/source combinations. Run them all at once and switch between results using the dropdown. A Download All button exports every chart as a ZIP.

3. Saved Results

All saved predictions are stored locally and accessible from the Saved Results page. Each entry shows the market, model, interval, and forecast length — along with a full chart replay.

4. TradingView Chart Mode — SOLUSDT (Binance, 1h)

Switch to TradingView chart mode for a professional trading terminal experience. History candles and the predicted forecast region are rendered using TradingView's familiar charting layout, with full OHLCV detail on hover.

Project Structure

Kronos-Studio/
├── run-project.cmd           # One-click launcher for Windows (double-click to start)
├── server/                   # FastAPI prediction server (Python 3.13)
│   ├── model/                # Kronos model definitions (PyTorch)
│   ├── routers/              # API route handlers
│   ├── schemas/              # Pydantic request/response models
│   ├── services/             # Business logic (prediction, data, results)
│   ├── db/                   # SQLite connection & migrations
│   ├── errors/               # Custom exception classes
│   ├── constants/            # Shared constants
│   ├── tests/                # Pytest test suite
│   └── main.py               # Application entry point
└── frontend/                 # Next.js 16 web UI (TypeScript)
    └── src/
        ├── app/              # Next.js App Router pages
        ├── components/       # Reusable React components
        ├── hooks/            # Custom React hooks
        ├── stores/           # Zustand global state
        ├── schemas/          # Zod validation schemas
        ├── lib/              # API client & utilities
        └── utils/            # Download & export helpers

Prerequisites

Tool	Minimum Version	Purpose
Python	3.13+	Server runtime
uv	latest	Python package manager
Node.js	18+	Frontend runtime
npm	9+	Frontend package manager

Install uv (Python package manager)

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or via pip
pip install uv

Getting Started

Option A — One-Click Launcher (Windows)

The easiest way to get started on Windows. Simply double-click run-project.cmd from the Kronos Studio root folder.

The launcher will:

1. Check that Node.js, npm, and uv are installed and on your PATH
2. Install backend dependencies (uv sync) if not already present
3. Install frontend dependencies (npm install) if not already present
4. Build the frontend (npm run build)
5. Open two separate terminal windows — one for the API server and one for the frontend

Once both windows are running, open http://localhost:3000 in your browser. You can close the launcher window after startup; the server and frontend keep running in their own windows.

Note: On first launch, Kronos models are downloaded automatically from Hugging Face Hub. Make sure you have an internet connection ready.

Option B — Manual Setup

If you prefer to run each step yourself, open two terminals:

Install dependencies

# Backend
cd server && uv sync

# Frontend
cd frontend && npm install

Run

# Terminal 1 — Backend
cd server
uv run .\start_server.py

# Terminal 2 — Frontend
cd frontend
npm run build && npm run start

Then open http://localhost:3000.

How to Use

Single Prediction

1. Open the app and select the Single tab.
2. Pick a Kronos model (mini, small, or base).
3. Choose a data source: Binance, yFinance, or Local CSV.
4. Enter a ticker symbol — e.g. ETHUSDT, AAPL, TSLA.
5. Set your lookback window, prediction length, and sampling parameters.
6. Click Predict — the chart renders history + forecast in seconds.
7. Use the toolbar to Save, export as SVG/PNG, or download raw CSV.

Switching Chart Styles

After a prediction renders, use the Candles / Line toggle at the top of the chart panel to switch between chart types. You can also switch to TradingView chart mode for a full trading terminal layout — history and forecast candles are displayed side by side with TradingView's familiar interface, including OHLCV data on hover and a volume panel below.

Batch Predictions

1. Switch to the Batch tab.
2. Click + Add to add symbol entries (up to 20).
3. Each symbol can have its own source, interval, limit, and sampling params.
4. Click Predict Batch — all predictions run in parallel.
5. Use the dropdown to switch between results; hit Download All to get a ZIP of all charts.

Saved Results

1. After any prediction, click Save to store it with an optional label.
2. Navigate to Saved Results (top-right) to browse stored predictions.
3. Each result shows market info, model used, forecast length, and a full chart.
4. Results are stored locally in server/db/kronos.db.

Local CSV Upload

Your CSV must include OHLCV columns (auto-normalized):

Date, Open, High, Low, Close, Volume

1. Select Local as the data source and upload your file.
2. The server stores it and returns a path this is used automatically for prediction.

Available Kronos Models

Model	Params	Context Length	Best For
kronos-mini	4.1M	2048 tokens	Fast inference, quick prototyping
kronos-small	24.7M	512 tokens	Balanced speed vs. accuracy
kronos-base	102.3M	512 tokens	Best accuracy, more compute

First run: Models download automatically from Hugging Face Hub on first use. An internet connection is required.

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Troubleshooting

Model download fails Kronos models are pulled from Hugging Face on first use. Check your internet connection and ensure huggingface_hub is installed via uv sync.

Frontend can't reach the API Confirm the backend is running on port 8000 and CORS middleware allows * in server/main.py.

CUDA / GPU not detected Set device to "cpu" in the prediction request, or ensure PyTorch with CUDA support is installed and GPU drivers are up to date.

run-project.cmd closes immediately Make sure you are double-clicking the file from the Kronos Studio root folder (the folder that contains both server/ and frontend/). If a required tool is missing, the launcher will print the error and pause before closing — read the message to see what needs to be installed.

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Disclaimer

Kronos Studio is intended for research and educational purposes only. All price forecasts generated by this tool are the output of a machine learning model and do not constitute financial advice. Past model performance does not guarantee future accuracy. Do not make investment decisions based solely on these predictions. Always consult a qualified financial professional before trading or investing.

Contributing

PRs, ideas, and discussions are welcome! Please open an issue if you have suggestions or find bugs.

License

This project is licensed under the MIT License.