OlaXBT releases a comprehensive upgrade of the Data Layer white paper: reconstructing the crypto signal production line with "Data Layer × deAI ecosystem"

"We use a patented data processing and evaluation framework to turn 'available data' into 'actionable signals', and then use AgentFi to safely and transparently push strategies to the live market. This is not a single function, but an auditable engineering project." ------ Jason, OlaXBT CEO

On September 22, 2025, at Korea Blockchain Week (KBW), OlaXBT CEO Jason officially released the new version of the "OlaXBT Data Layer Whitepaper" during his keynote speech titled "Advancing Crypto Signal Generation: A Patented Framework for Blockchain Data Processing and Evaluation." Centered around a data layer that is "pre-processed, standardized, and verifiable," it connects the MCP market (Model-Context-Protocol), deAI autonomous agents, and compliance audit anchoring, providing trading teams and institutions with shorter Speed-to-Signal, research-to-live Quant capabilities, and end-to-end trust and auditability. With a patented on-chain data processing and evaluation framework, the path from "data to execution" is compressed into a traceable, verifiable, and regulatory-compliant high-speed pipeline.

1. Why Now? --- From "Data Flood" to "Signal Sovereignty"

The crypto market is highly volatile, and narratives change rapidly. The fragmentation of on-chain/off-chain signals and high cleaning costs often lead teams to spend 70% of their energy on collection and cleaning (rather than research and execution). OlaXBT's Data Layer provides users with "a priori clean" data products that handle the heavy lifting: converging multi-source data, standardizing fields, and performing consistency and traceability checks, directly outputting "atomic production-ready" datasets that allow research to be shortened from days to minutes.

2. Four Design Principles of the Data Layer (USPs)

Atomic --- Production-Ready

Data pre-processing/standardization/verifiability achieves atomic-level granularity that can directly enter production, eliminating cumbersome cleaning and verification processes.

Velocity --- Speed-to-Signal

Designed for low-latency retrieval, it shortens the time from "question to insight"; internal evaluations show faster retrieval and cleaner signals.

Quant --- Built for Research & Execution

Provides a consistent environment for backtesting, strategy simulation, and on-chain deployment; SDKs (Python/Rust/Solidity) and declarative Queries allow research to directly interface with execution.

Trust --- Security & Auditability

ZK proofs ensure the aggregation process is verifiable, FHE supports encrypted computation, and audit anchoring maintains traceability and compliance metrics.

3. Patented Methodology: From "Indicator Sea" to "Model-Ready"

The whitepaper reveals that OlaXBT's patented technology filters indicators for on-chain data and trading signals, classifies true and false positives, and calibrates gradients and buffers, refining multi-modal inputs (technical indicators, on-chain behavior, macro/narrative, token fundamentals, portfolio exposure, quantitative construction signals) into trainable/testable model-level features.

Process Coverage: Multi-source collection → Normalization → Consistency verification → Simulation evaluation/ranking → Meta-data and noise estimation → Declarative retrieval.

Factor Families

1) Macro and market, 2) On-chain indicators, 3) Technical and token information, 4) Sentiment and narrative, 5) Portfolio exposure, 6) Quantitative strategy signals ------ all output extractable and reproducible signals through a consistent pipeline.

4. deAI Ecosystem: MCP × AgentFi, Turning "Signals" into "Execution"

Data Layer → MCP (Protocol Orchestration) → deAI Agents (Execution) form a self-reinforcing flywheel:

Data is further fed into configurable Agent-as-a-Service (driven by reinforcement learning), automating execution in scenarios such as market making, risk control, rebalancing, and narrative trading; execution results feed back into the data layer, forming a closed-loop learning system. Privacy and compliance are covered across the entire chain with ZK/FHE and audit anchoring.

5. "Regulatory Grounding" for Institutions: From Market Making to Audit Anchoring

Market making and risk control iteration: A unified data layer supports multi-market making, factor exposure, and term structure management, aligning research hypotheses with intraday behavior.

Audit anchoring and transparent indicators: Data/process hashing and on-chain anchoring, freshness indicators are public, supporting external reviews and audit sampling.

Privacy computing: FHE allows for portfolio analysis and risk aggregation without decryption, meeting both transparency and confidentiality requirements.

6. Productization and Business Model: Layered Access, Token Economics, and Ecosystem Revenue Sharing

Access Levels: Open (batch/slight delay), Professional (staking acceleration), Institutional (custom collections/capacity quotas).

Pull Billing: Pricing based on request volume and immediacy;

Ecosystem Revenue Sharing: Revenue generated from protocol expansion and joint optimization is distributed according to mechanisms, linked to token utility.

About OlaXBT Data Layer

OlaXBT Data Layer is the core infrastructure of the deAI ecosystem: transforming multi-source on-chain/off-chain data into model-ready signals through "pre-processing, standardization, and verifiability," and natively integrating MCP Marketplace and deAI Agents, forming a closed loop of "data → protocol orchestration → strategy execution → performance feedback." The four pillars cover: Atomic (Production-Ready), Velocity (Speed-to-Signal), Quant (consistent environment from research to execution), and Trust (ZK/FHE, audit anchoring). The data layer reduces retrieval time by approximately 25% and decreases the workload for data preparation by 70-80%, allowing teams to shift their focus from cleaning to strategy. Through declarative Queries and SDKs (Python/Rust/Solidity), research can plug and play into tasks such as market making, risk control, and rebalancing, continuously optimizing strategy quality in a compliant and traceable manner.