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80 BARSGLOBAL CLOCKNEXT UPDATE: --:--Events: 0
AI VOL CONE + SCENARIO
Please read the following carefully before proceeding
This project uses AI deep learning models to predict K-line (candlestick) price trends. The core pipeline includes:
① GAF (Gramian Angular Field) Transform — Converts time-series OHLC data into polar angular fields, generating 12-channel multi-timeframe GASF feature tensors;
② Transformer Inference — A Kline-detector Transformer model encodes GAF features, outputting DCT frequency coefficients and a 128-dim feature vector to capture the trend skeleton;
③ DDPM Diffusion Model — A conditional UNet performs 1000-step reverse diffusion sampling, synthesizing complete OHLC prediction residuals on top of the Transformer trend skeleton.
All model inference runs locally in the browser or Node.js environment — no remote server data transmission is involved. Predictions are generated entirely by AI models based on historical data patterns and do not represent any real market signals.
⚠ AI Hallucination Risk: Deep learning models may produce "hallucinated" predictions that deviate entirely from actual market trends, especially when data distribution shifts from the training set;
⚠ Market Unpredictability: Financial markets are influenced by macroeconomics, policy changes, and black-swan events — no AI model can guarantee prediction accuracy;
⚠ Data Quality Dependency: Predictions are highly dependent on input data quality. Screenshot recognition may introduce errors; real-time data may have latency or gaps;
⚠ Overfitting Risk: Strong performance on training data does not imply predictive capability for future trends. Historical backtest results do not guarantee future returns;
⚠ Technical Limitations: Kline-detector models use fixed parameters for inference and cannot adapt to structural market changes in real time. They lack self-learning and online update capabilities.
This project is a technical research and learning tool only. All predictions provided are for reference and technical demonstration purposes.
This project does not constitute investment advice, financial guidance, or trading recommendations of any kind. The authors and contributors bear no responsibility for any investment decisions made based on the outputs of this project.
Users assume all risks and consequences of their trading decisions. The authors and contributors shall not be liable for any direct or indirect losses arising from the use of this project, including but not limited to financial losses, opportunity costs, or data loss.
Please consult a licensed professional financial advisor before making any investment decisions, and rely on analysis and advice from legitimate financial institutions.
This project is open-source software hosted on GitHub:
https://github.com/lin7c/kline-de-preYou are welcome to review the source code, submit issues, or contribute. This project is released under an open-source license — users may freely use, modify, and distribute the code while retaining original copyright notices and license terms.
Global prediction synced
Scenarios show volatility range, not direction