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Big Data Engineer · AI Agent & Quant Systems Researcher

My primary profession is big-data development, backed by 9 years of production big-data engineering experience across PB-scale offline data warehouses, large-scale streaming, workflow governance, data quality, and performance optimization.

Outside my primary role, I continuously research AI Agents, quantitative systems, and end-to-end AI products. I explore how models can work with real data and tools, then add durable state, evidence, evaluation, permissions, human approval, and runtime verification so the systems can be iterated responsibly.

Markets I know: perpetual futures and Chinese A-shares. Perpetual futures are my main quantitative research focus, connecting data engineering with market-data processing, strategy validation, Paper trading, and risk monitoring.

Core stack: Flink · Kafka · Spark · Hive · Hadoop · HBase · Airflow · MySQL · PostgreSQL · ClickHouse · Python · Java · Scala

These tracks remain active research and ongoing iteration. I distinguish local validation, research experiments, Paper sessions, competition results, and production outcomes instead of presenting process states as verified achievements.

A governed quantitative-research Agent runtime that turns open-ended research goals into durable, reviewable missions.

• Persists goals, plans, steps, evidence, budgets, and completion conditions
• Combines MCTS and MAP-Elites for diverse strategy-code exploration
• Uses red-team stress tests, regime attribution, and structured negative constraints
• Keeps research, backtesting, Paper trading, and real effects behind explicit control boundaries

Competitions: ARC-AGI-2 · ARC-AGI-3

An ARC-AGI research system spanning ARC-AGI-1/2 program synthesis and ARC-AGI-3 interactive-Agent experiments.

• Grid-transformation DSLs, candidate generation, exact-match validation, and restricted code execution
• Visual-state abstraction, action history, skill routing, and trajectory-based evaluation
• Separates local diagnostics from official benchmark results and retains reproducible evidence

Agent engineering capabilities: Planning · Tool Calling · MCP · JSON Schema · State Machines · Memory · Evaluation · Human Approval · Recovery · Audit

Public leaderboard snapshot as of 15 September 2026, 17:48 (UTC+8). All four competitions are ongoing. Rankings use “current rank / total leaderboard teams” and may change.

• RSNA Knee Abnormality Detection — An ongoing medical-imaging competition focused on multimodal knee abnormality detection using MRI scans and radiology reports.
• Kaggriculture — An ongoing farming strategy competition focused on resource allocation, market decisions, and agent strategy evaluation.

Perpetual futures are my main quantitative research focus; Chinese A-shares are another familiar market I continue to research.

• BitPro · Private Product — Digital-asset research platform focused on perpetual futures research and Paper trading, covering exchange data, long/short strategies, strategy versions, asynchronous backtests, Paper validation, controlled execution, and monitoring

  Strategy running live: Top20 microstructure long/short breakout with pyramiding. The chart below shows Paper validation performance. Verified live results for 15 September 2026, as of 17:41 (UTC+8): +2.12% / +$2.37, including the change in attributed unrealized PnL.

  
  _{Paper performance snapshot · scheduled every 10 minutes (scheduling and image caching may delay updates) · click for the dynamic dashboard}

I treat market-data quality, reproducible computation, costs, fills, and audit trails as prerequisites. Strategy research is presented as research evidence—not as unverified return claims.

Chinese A-share research and engineering:

• Alpha — Open-source A-share research system with trusted market-data ingestion, reproducible workflows, CI, and a public release
• StockPro — Real-time A-share research and monitoring platform with data quality, strategy lifecycle, backtesting, Paper trading, and operational checks
• QuantBase — Research workbench for real market data, Backtrader validation, Paper trading, signal audit, and risk-first development

• Zora · Private Product — AI animation workspace connecting story, characters, storyboards, video generation, voice, composition, quality review, and publishing
• FrameLab · Private Product — AI video platform integrating model APIs, ComfyUI/GPU workers, asynchronous jobs, storage, credits, moderation, testing, and deployment

I use Codex, Cursor, GLM, Grok, and other models according to their capabilities and limits. I remain responsible for business judgment, requirement decomposition, constraints, acceptance criteria, and verification through real data, automated tests, runtime logs, and user-visible outcomes.

A food-ingredient analysis and health-literacy mini program that I continuously operate and improve, covering data organization, product iteration, and promotion through WeChat Search.

A fishing-focused tide and weather mini program integrating time-series tide and weather data, backend services, and a mobile-facing product experience.

Both products are continuously operated and iterated—not one-off demos.

Employer source code, business data, and internal implementation details remain confidential. Private projects are described by capability and verified outcomes without exposing their repositories.

• Reliable batch, streaming, and data-governance systems, including data foundations for model and Agent workloads
• Governed Agent runtimes with evidence, evaluation, memory, and safe tool use
• Perpetual futures long/short strategies, costs, and risk, alongside A-share strategy and data research using real data and Paper validation
• AI video and content-production systems with human review and observable delivery