3. System Architecture

ZKSeer follows a modular, multi-stage pipeline:

Data Ingestion Layer#

Accepts token address or ticker. Retrieves OHLCV candles, price, volume, liquidity, circulating supply, and token age from public market data sources.

Context Reconstruction Module#

Replays recent price action to establish the current market regime, volatility state, and structural context.

Forecasting Engine#

Custom-trained models generate forward projections at the native candle interval. Horizon length is dynamically adjusted based on token age and current volatility (typically ranging from 5 minutes to 1 hour).

Risk Parameterization Layer#

Calculates target level, expected high/low range, and invalidation threshold.

Explanation Generator#

A dedicated reasoning component produces a concise, human-readable justification for the predicted direction and levels.

Output Interface#

Results are delivered as prediction candles overlaid on the chart, accompanied by structured prediction metrics and the AI explanation.