Independent Quantitative Research 独立量化研究
One engineer. One market. Built to institutional standard. 一人、一份研究，机构级标准。
K2J AI Quantitative Lab is a daily, full-market multi-factor research system for US equities — regime, breadth, attribution, and risk — designed and built end-to-end by one engineer who also trades the market he studies. K2J AI量化实验室是一套覆盖美股全市场的每日多因子研究系统——涵盖大盘体制、市场广度、统计归因与组合风控——由一人独立设计与构建，研究者本人也是这个市场的活跃交易者。
AI Engineering Stack: Built and generated end-to-end with LangGraph multi-agent orchestration, Agentic RAG, Pinecone vector search, and Deterministic Guardrails. 底层技术栈：基于 LangGraph 多智能体编排、Agentic RAG、Pinecone 向量检索与确定性护栏（Deterministic Guardrails），全流程端到端自主构建与生成。
How each issue is built报告的构成
Every issue runs the same five-layer process, rebuilt from market data each morning. 每一份报告都遵循相同的五层流程，每天早晨基于市场数据重新构建。
Market regime大盘体制判断
Classifies risk-on, risk-off, or transitional conditions before anything else is read.在解读其他信号之前，先判断大盘所处的体制——风险偏好、风险规避，或过渡阶段。
Market breadth全市场广度
Measures how many stocks are actually participating, not just the index-level headline.衡量真正参与行情的个股广度，而不只是指数层面的表面数字。
5-D attribution五维稳健归因
A five-dimensional, noise-robust statistical attribution explains what is actually driving returns.五维稳健统计归因，剔除噪音，解释真正驱动收益的因素。
Theme clusters上涨/下跌主题簇
Clusters the day's movers into coherent themes, not a loose list of tickers.将当日涨跌个股归纳为连贯的主题簇，而非零散的股票列表。
Portfolio & risk组合与风控
Translates the analysis into a portfolio stance and concrete risk-management guidance.将以上分析转化为组合策略立场与具体的风控建议。
Who's building this关于研究者
I'm Justin — an independent AI systems architect with 20+ years in enterprise technology (Dell, Microsoft, IBM, private funds), now building production-grade agentic AI end to end on my own. K2J is the proof: a LangGraph-based, Agentic RAG research system I designed, built, and run daily, alone. 我是Justin，一名独立AI系统架构师，拥有20年以上企业级技术背景（戴尔、微软、IBM、私募基金），现在专注于独立构建生产级Agentic AI系统。K2J就是证明——一套基于LangGraph与Agentic RAG、由我一人设计、构建并每日运行的研究系统。
I'm also an active options and equities trader — this research isn't academic to me; I use the same regime and attribution framework in my own positions. I work in Mandarin and English, and I'm currently open to collaborating with, consulting for, or joining any individuals, teams, and organizations seeking someone who can both build AI systems and read the market — full-time, part-time, advisory, or project-based. 我同时也是一名活跃的期权与股票交易者——这套研究框架对我而言不是纸上谈兵，我自己的持仓也在使用同样的体制与归因逻辑。我可以用中文和英文工作，目前欢迎与所有寻求"既能搭系统、又能读懂市场"的个人、团队与机构展开广泛交流与合作，形式不限，包括全职、兼职、顾问或项目制。
Published every trading day每个交易日更新
Complete archive of daily US equities quantitative analysis reports. 每个交易日更新一期，每日美股市场量化分析报告完整归档。
Frequently asked questions常见问答与技术解析
Key details on research methodology, agentic AI architecture, and collaboration. 关于量化投研框架、Agentic AI 架构设计及合作模式的核心解答。
What is K2J AI Quantitative Lab and what problem does it solve? 什么是 K2J AI 量化实验室？它解决了什么核心问题？
K2J AI Quantitative Lab is an autonomous, full-market daily quantitative research system for US equities. Built to institutional standards, it solves the fragmentation between statistical macro risk modeling and actionable trade synthesis. Each morning before market open, the system synthesizes macro regimes, cross-sectional breadth, 5-dimensional factor attribution, and unsupervised theme clusters into a coherent market stance and tail-risk budget.
K2J AI量化实验室是一套面向美股全市场的每日自主量化投研系统。它以机构级标准构建，致力于解决宏观统计风险建模与交易实盘落地之间的断层。系统在每个交易日开盘前自动运行，将宏观体制识别、截面市场广度、五维因子统计归因与无监督异动主题簇整合成清晰的组合策略立场与尾部风控预算。
How does the 5-layer quantitative methodology work? K2J 的五层量化多因子方法论是如何运作的？
Every morning's issue follows a strict, orthogonal five-layer analytical pipeline: (1) Market Regime classifies Risk-On/Risk-Off macro states via GBDT ensembles; (2) Market Breadth quantifies internal market participation across volume and moving average thresholds; (3) 5-D Attribution performs robust WLS and PCA to isolate Market, Size, Value, Momentum, and Volatility return drivers; (4) Theme Clusters utilizes HDBSCAN and high-dimensional semantic embeddings to group stock movers into narrative drivers; and (5) Portfolio & Risk computes dynamic volatility targets and CVaR tail-risk constraints under Bayesian risk parity.
每一期报告都严格遵循自底向上的五层正交分析流水线：(1) 大盘体制识别：基于 GBDT 集成模型判别 Risk-On / Risk-Off 宏观状态；(2) 全市场广度：多维度量个股实际参与度与量价背离；(3) 五维稳健归因：使用稳健加权最小二乘（WLS）与 PCA 剥离市场、规模、价值、动量与波动率的纯净因子收益；(4) 主题簇提取：通过 HDBSCAN 与高维语义向量聚类识别异动股票背后的核心叙事；(5) 组合与动态风控：在贝叶斯风险平价框架下，输出 CVaR 约束与仓位配置指导。
How is Agentic AI and LangGraph utilized in production? K2J 是如何利用 LangGraph 与 Agentic RAG 构建生产级 AI 架构的？
K2J runs on an autonomous multi-agent orchestration architecture built with LangGraph. Specialized agents handle automated data ingestion, statistical computation, signal cross-verification, natural language synthesis, and multilingual deployment. To ensure zero financial hallucination, the system enforces Deterministic Guardrails — mathematical proofs and hard numeric constraints that gate all generative outputs before publication. Historical market context is augmented via Agentic RAG over a Pinecone vector index.
K2J 基于 LangGraph 构建了端到端的自主多智能体（Multi-Agent）编排架构。专门的子智能体分工负责数据自动摄取、统计模型计算、信号交叉校验、多语言自然语言生成及 CDN 部署。为确保金融严谨性并彻底杜绝大模型幻觉，系统内置了确定性护栏（Deterministic Guardrails），所有输出均需通过严格的数学约束检验；同时结合 Pinecone 向量数据库的 Agentic RAG 进行历史体制情境关联。
Who is behind K2J, and what collaboration opportunities exist? 研究者 Justin 是什么背景？目前支持哪些形式的交流与合作？
K2J is designed, built, and operated solo by Justin, an independent AI systems architect with 20+ years of enterprise technology leadership (Dell, Microsoft, IBM, private funds) and an active options/equities trader. Justin is open to strategic advisory, architecture consulting, and collaborative partnerships (full-time, part-time, advisory, or project-based) with hedge funds, prop trading firms, asset managers, and AI startups looking for proven end-to-end expertise in both AI systems engineering and quantitative market trading.
K2J 由独立 AI 系统架构师 Justin 一人独立设计、构建并每日维护。Justin 拥有 20 年以上企业级核心技术经验（曾任职于戴尔、微软、IBM 及私募基金），同时也是一名美股与期权市场的活跃交易者。Justin 目前欢迎与量化私募、对冲基金、资产管理公司及 AI 创新团队展开多元化合作（包括全职、兼职、战略顾问或具体项目交付），共同打造生产级智能体与量化系统。
Get in touch欢迎联系
Open to conversations with any individuals, teams, and organizations interested in quantitative research, AI systems, or strategic collaboration — full-time, advisory, or project-based. 欢迎所有对量化研究、AI 系统架构或业务合作感兴趣的个人与组织随时联系——交流、顾问或项目合作均可。