u/AdhesivenessNo1241

Been building this for a while. Wanted to share something that's finally live and producing real numbers.

We're running an AI forecaster called Oracle Lens inside SHIU AI that predicts the next five 1-hour Bitcoin candles before they happen. Not a fancy drawing tool. Not a moving average with extra steps. A genuine 5-candle forward projection, validated on chronological unseen data.

The Core Engine: Kronos

The model is built on Kronos — the first open-source foundation model for financial candlesticks (K-lines) . It was trained on data from over 45 global exchanges and is specifically designed to understand price action patterns across timeframes . The live demo for BTC/USDT is public: https://shiyu-coder.github.io/Kronos-demo/

Kronos takes 48 hours of 1‑hour candles as context and projects the next five hourly candles. Instead of forcing a simple up/down label on every candle, we:

· Label a candle BULL only if (Close - Open) / Open > 0.05%
· Label it BEAR only if (Close - Open) / Open < -0.05%
· Treat moves inside ±0.05% as DOJI / NOISE and exclude them from training

This stops the model from hallucinating direction on flat or sideways candles.

The Architecture: We built five separate classifiers — one for each future candle (C1 through C5):

· C1 = the next hour
· C2 = the hour after that
· …all the way to C5 = five hours out

Each classifier is an XGBoost model trained on Kronos's 832‑dimensional context vectors. We used:

· Walk‑forward chronological validation (no leakage)
· Early stopping on validation data
· Probability calibration on a separate calibration set
· Strict separation between training, validation, calibration, and test data

The full pipeline is leak‑free and evaluated on data the model had never seen.

The Numbers: After validation, we ran the final evaluation on an untouched 493‑sample test period. The directional accuracies came back as:

Candle Accuracy
C1 (next hour) 58.62%
C2 57.61%
C3 58.01%
C4 61.46%
C5 53.96%

For context, the baseline directional accuracy of the raw Kronos model on the same test window was 26.92% (C1). So the classifier layer is a meaningful improvement — and it's now live in shadow mode.

What "Shadow Mode" Means

The classifiers are running live, but they are not changing the candle directions shown in the UI. The live directions still come from Kronos‑base. The classifier layer is currently observing and providing confidence scores.

To move from shadow mode to fully live direction control, we need:

· Consistent live performance over time
· Stable confidence calibration
· No regression in the next validation cycle

Why This Matters

Most crypto "AI" tools are just repackaged technical indicators with a marketing spin. This one is different:

· Based on a verified foundation model trained on 45+ exchanges
· Validated on chronological unseen data (no leakage)
· Uses five separate XGBoost classifiers with early stopping and calibration
· Passed regression tests (2/2) and is deployed at checkpoint 7e5fabe2
· Live service is healthy with no training jobs running

We're not claiming 80%+ accuracy. That's not how directional forecasting works in crypto, most academic papers on Bitcoin price direction using ML land in the 54–58% range . 58–61% on the first few candles is solid and tradeable if the risk management is right.

If you're curious to see it in action, the Oracle Lens is live inside SHIU AI. Happy to answer questions about the architecture, the validation methodology, or the shadow‑mode rollout.