LLMQuant 独家资源概览

!!! note "Our Goal" TheQuantHackers aims to leverage the power of AI to lower the threshold for quantitative research and practice, and promote knowledge sharing and strategic innovation.

Quant Question: Quantitative question answering/job search platform

??? example "Quant Question Quantitative job search/question-brushing platform"

**Quant Wiki is responsible for "learning", and Quant Question is responsible for "practicing". **

In order to avoid "talking on paper", we recommend you to use our current main product Quant Question. A professional AI-driven quantitative interview/job search preparation platform that provides 1,300+ quantitative test questions, 1,000+ quantitative positions at top companies, real question sheets, QuantAI real-time personalized coaching and AI-driven job tracking tools to help you successfully cope with quantitative interviews and job searches at top hedge fund companies.

We are well aware of the value of learning resources, so while providing advanced functions, the platform insists on opening up a large number of free selected question sheets**. Whether you are consolidating your knowledge of probability and statistics in the Wiki or studying real questions from major manufacturers, you can find the corresponding free resources here.

???+ success "main features"

• FREE REAL QUESTIONS: Featuring real interview questions from top companies like Jane Street.
• Classic question sheets: Contains classic question sheets such as the "Green Book" in the field of quantitative finance.
• Quant AI: Provides real-time personalized coaching based on top-notch artificial intelligence models.
• Job Tracking: AI-powered job tracking tool to help you successfully handle quantitative interviews and job searches at top hedge fund companies.

Quant Mind: Quantitative Financial Intelligence Agent Framework

QuantMind is a knowledge extraction and retrieval framework for quantitative finance that helps developers quickly build AI Agents in the financial field and transform unstructured content such as papers, research reports, and news into actionable structured knowledge.

🚩Welcome to visit GitHub repository to learn more.

???+ success "main features"

• Agent native: Built-in Agentic workflow and tool integration, supporting the construction of vertical scenario Agents such as paper reading and research report analysis.
• Context Engineering: Emphasizes the structured processing of financial context to provide high-quality domain knowledge input for LLM.
• Two-stage architecture: Decoupled design of knowledge extraction (Source → Parser → Knowledge Base) and intelligent retrieval (Embedding → RAG/DeepResearch).
• Multi-source access: Supports multiple data sources such as arXiv, news API, financial blogs, etc., and unifies knowledge format standards.
• Open source and extensible: MIT protocol, modular design, easy to customize data sources, retrieval strategies and Agent logic.

Quant Paper: AI-driven quantitative paper platform

Quant Paper is an AI-driven quantitative finance academic paper retrieval and reading platform. It contains 100+ selected quantitative finance papers and supports AI intelligent search and multi-dimensional tag filtering.

???+ success "main features"

• AI Intelligent Search: Supports keyword search and AI Deep Research mode to quickly locate relevant papers.
• Multi-dimensional classification tags: Organized by market/asset (23 tags) and research topic (56 tags).
• Structured Summary: Each paper provides a structured extraction of key information for quick and easy reading.
• Continuously updated: Regularly includes the latest quantitative financial research results.

Quant Strategy: Quantitative strategy sharing platform (coming soon)

We are preparing a quantitative strategy sharing platform, Quant Strategy, designed to promote strategy exchange and learning among community members.

???+ info "Stay tuned"

• Main content: Share the core ideas, implementation logic, relevant code snippets or complete code of quantitative strategies.
• Platform value:
• Provide a platform for strategy developers to display and communicate.
• Help beginners and advanced players learn and learn from different strategic ideas.
• Promote the community to jointly build and improve the quantitative strategy library.
• Form: May include strategy articles, code warehouse links, backtest results display, etc.

We believe these tools will bring unique value to the quant community. You are welcome to follow the progress of TheQuantHackers and participate in our open source projects!

欢迎了解 TheQuantHackers 提供的独家量化资源！我们致力于结合大型语言模型（LLM）与量化金融，为社区提供实用、前沿的工具与平台。以下是我们目前及未来计划推出的核心资源：

!!! note "我们的目标" TheQuantHackers 旨在利用 AI 的力量，降低量化研究和实践的门槛，促进知识共享与策略创新。

Quant Question: 量化刷题/求职平台

??? example "Quant Question 量化求职/刷题平台"

欢迎了解 TheQuantHackers 提供的独家量化资源！我们致力于结合大型语言模型（LLM）与量化金融，为社区提供实用、前沿的工具与平台。以下是我们目前及未来计划推出的核心资源：

!!! note "我们的目标" TheQuantHackers 旨在利用 AI 的力量，降低量化研究和实践的门槛，促进知识共享与策略创新。

Quant Question: 量化刷题/求职平台

??? example "Quant Question 量化求职/刷题平台"

Quant Wiki 负责“学”，Quant Question 负责“练”。

为了避免“纸上谈兵”，我们推荐你使用我们目前主力打造的产品 Quant Question。专业的 AI 驱动量化面试/求职准备平台，提供1300+量化刷题题库、1000+顶级公司量化职位、真题题单、QuantAI 实时个性化辅导和 AI 驱动的职位追踪工具，助您成功应对顶尖对冲基金公司的量化面试和求职。

我们深知学习资源的可贵，因此平台在提供高级功能的同时，坚持开放大量免费的精选题单。无论是巩固 Wiki 中的概率统计知识，还是刷大厂真题，你都可以这里找到对应的免费资源。

???+ success "主要特点" * Agent 原生: 内置 Agentic 工作流与工具集成，支持构建论文阅读、研报分析等垂直场景 Agent。 * Context Engineering: 强调金融上下文的结构化处理，为 LLM 提供高质量的领域知识输入。 * 两阶段架构: 知识提取（Source → Parser → Knowledge Base）与智能检索（Embedding → RAG/DeepResearch）解耦设计。 * 多源接入: 支持 arXiv、新闻 API、金融博客等多种数据源，统一知识格式标准。 * 开源可扩展: MIT 协议，模块化设计，易于定制数据源、检索策略与 Agent 逻辑。

Quant Paper: AI 驱动的量化论文平台

Quant Paper 是一个 AI 驱动的量化金融学术论文检索与阅读平台，收录 100+ 篇精选量化金融论文，支持 AI 智能搜索与多维标签筛选。

Quant Wiki 负责“学”，Quant Question 负责“练”。

为了避免“纸上谈兵”，我们推荐你使用我们目前主力打造的产品 Quant Question。专业的 AI 驱动量化面试/求职准备平台，提供1300+量化刷题题库、1000+顶级公司量化职位、真题题单、QuantAI 实时个性化辅导和 AI 驱动的职位追踪工具，助您成功应对顶尖对冲基金公司的量化面试和求职。

我们深知学习资源的可贵，因此平台在提供高级功能的同时，坚持开放大量免费的精选题单。无论是巩固 Wiki 中的概率统计知识，还是刷大厂真题，你都可以这里找到对应的免费资源。