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Core Team

QQ Omega is built at the intersection of engineering and market architecture.
It emerges from a precise collaboration between two roles with different responsibilities: qqAlpha, who builds and operates the technical system, and qqSigma, who designs its structure and logic.
Their work follows a clear progression that defines QQ Omega itself: Alpha → Sigma → Omega.

Alpha → Sigma → Omega​

  • Alpha is execution.
    It is the full hardware and software layer that captures reality through data pipelines, integrations, and live signals.
    Alpha answers one question: what is happening.

  • Sigma is architecture.
    It is the conceptual design that organizes what Alpha produces into models, metrics, and scoring systems.
    Sigma answers one question: what does it mean.

  • Omega is outcome.
    It is where execution and architecture converge into decisions, rankings, and alerts.
    Omega answers one question: what do we do now.

QQ Omega represents the final convergence of data, architecture, and decision, expressed as actionable signal.

@qqAlpha | Co-Founder & Lead Developer​

Alpha leads the engineering and delivery of QQ Omega.

He runs an IT company and brings a pragmatic “ship-it” mindset: build fast, build clean, and keep systems stable under real usage. His focus is turning ideas into production-grade software that can scale, integrate, and stay reliable over time.

What he owns:

  • Data pipelines, indexing, APIs, and infrastructure
  • On-chain + third-party integrations (feeds, analytics, connectors)
  • Performance engineering, reliability, and security hardening
  • Production implementation of logic, automation, and monitoring
  • Tooling, deployments, and maintaining dev velocity without breaking things

@qqSigma | Co-Founder & System Architect​

sigma leads the system design and decision logic of QQ Omega.

He brings an asset management background and approaches the product like a portfolio: define the rules, measure what matters, manage uncertainty, and avoid narratives that don’t survive contact with data. The goal is a system that produces signals that are coherent, auditable, and repeatable.

What he owns:

  • Metrics, categories, and scoring frameworks (what gets measured and why)
  • Rules of interpretation (how raw data becomes meaning)
  • Evaluation methodologies (weighting, thresholds, confidence, time decay)
  • Consistency of the model across narratives and market regimes
  • Long-term architecture of the scoring engine and its evolution over time

Core Team