做量化研究的朋友，每天面对海量的金融研报和前沿论文，靠人工筛选有价值内容，无疑像大海捞针。

最近发现一个叫 QuantMind 的开源项目，专门做量化金融的智能知识提取与检索。

能自动抓取论文、新闻和博客等内容，把非结构化的文档转化为可查询的结构化知识库。

结合针对金融领域微调的大模型，帮我们快速理解复杂内容，并自动构建语义知识图谱。

直接用自然语言提问，就能在极短时间内检索到需要的因子策略和市场洞察。

GitHub：http://github.com/LLMQuant/quant-mind…

提供一键运行脚本，支持单篇提取、批量并发运行，甚至能直接用自然语言下达处理指令。

如果平时需要处理大量金融研究报告，或者正在做量化策略研究，这个项目能帮到我们。

LLMQuant/quant-mind

Source: https://github.com/LLMQuant/quant-mind

Transform Financial Knowledge into Actionable Intelligence

Why QuantMind • Architecture • Quick Start • Usage • Roadmap • Vision • Contributing

QuantMind is an intelligent knowledge extraction and retrieval framework for quantitative finance. It transforms unstructured financial content—papers, news, blogs, reports—into a queryable knowledge base, enabling AI-powered research at scale.

📰 News

🧐 Overview

QuantMind is a next-generation AI platform that ingests, processes, and structures every new piece of quantitative-finance research, including papers, news, blogs, and SEC filings into a semantic knowledge graph. Institutional investors, hedge funds, and research teams can now explore the frontier of factor strategies, risk models, and market insights in seconds, unlocking alpha that would otherwise remain buried.

✨ Why QuantMind?

The financial research landscape is overwhelming. Every day, hundreds of papers, articles, and reports are published.

🌐 The Opportunity

• Information Overload: 500 new research papers & reports published daily. Manual review takes weeks—costly, error-prone, and non-scalable
• Massive Market: Financial data & analytics market ≫ expected to grow to US$961.89 billion by 2032, with a compound annual growth rate of 13.5%. Tens of thousands of quant teams & asset managers hungry for speed
• High ROI: 1% improvement in research efficiency can translate to millions saved or earned in trading performance

💡 QuantMind solves this by

• 🔍 Extracting structured knowledge from any source (PDFs, web pages, APIs)
• 🧠 Understanding content with domain-specific LLMs fine-tuned for finance
• 💾 Storing information in a semantic knowledge graph
• 🚀 Retrieving insights through natural language queries

System Architecture

QuantMind is built on a decoupled, two-stage architecture. This design separates the concerns of data ingestion from intelligent retrieval, ensuring both robustness and flexibility.

Stage 1: Knowledge Extraction

This layer is responsible for collecting, parsing, and structuring raw information into standardized knowledge units.

Source APIs (arXiv, News, Blogs) → Intelligent Parser → Workflow/Agent → Structured Knowledge Base

• Source: Connects to various sources (academic APIs, news feeds, financial blogs, perplexity search source) to pull content
• Parser: Extracts text, tables, and figures from PDFs, HTML, and other formats
• Tagger: Automatically categorizes content into research areas and topics
• Workflow/Agent: Orchestrates the extraction pipeline with quality control and deduplication

Stage 2: Intelligent Retrieval

This layer transforms structured knowledge into actionable insights through various retrieval mechanisms.

Knowledge Base → Embeddings → Solution Scenarios (DeepResearch, RAG, Data MCP, ...)

• Embedding Generation: Converts knowledge units into high-dimensional vectors for semantic search

• Solution Scenarios: Multiple retrieval patterns including:

  • DeepResearch: Complex multi-hop reasoning across documents
  • RAG: Retrieval-augmented generation for Q&A
  • Data MCP: Structured data access protocols
  • Custom retrieval patterns based on use case

🚀 Quick Start

We use uv for fast and reliable Python package management.

Prerequisites:

• Python 3.8+
• Git

Installation:

1. Install uv (if not already installed):

   # On macOS and Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # On Windows
   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
   
   # Or using pip
   pip install uv
   

2. Clone the repository:

   git clone https://github.com/LLMQuant/quant-mind.git
   cd quant-mind
   

3. Create and activate virtual environment:

   # Create a virtual environment
   uv venv
   
   # Activate it
   # On macOS/Linux:
   source .venv/bin/activate
   
   # On Windows:
   .venv\Scripts\activate
   

4. Install dependencies:

   uv pip install -e .
   

📚 Usage Examples

Run a single paper through paper_flow

import asyncio

from quantmind.configs import PaperFlowCfg
from quantmind.configs.paper import ArxivIdentifier
from quantmind.flows import paper_flow


async def main() -> None:
    paper = await paper_flow(
        ArxivIdentifier(id="2401.12345"),
        cfg=PaperFlowCfg(model="gpt-4o-mini"),
    )
    print(paper.model_dump_json(indent=2))


asyncio.run(main())

Fan out a batch with batch_run

import asyncio

from quantmind.configs import PaperFlowCfg
from quantmind.configs.paper import ArxivIdentifier
from quantmind.flows import batch_run, paper_flow


async def main() -> None:
    inputs = [ArxivIdentifier(id=aid) for aid in (
        "2401.12345", "2401.12346", "2401.12347",
    )]
    result = await batch_run(
        paper_flow,
        inputs,
        cfg=PaperFlowCfg(model="gpt-4o-mini"),
        concurrency=3,
        on_error="skip",
        on_progress=lambda done, total: print(f"{done}/{total}"),
    )
    print(f"ok={result.success_count} failed={result.failure_count}")


asyncio.run(main())

Resolve free-form intent with magic

import asyncio

from quantmind.flows import paper_flow
from quantmind.magic import resolve_magic_input


async def main() -> None:
    inp, cfg = await resolve_magic_input(
        "Pull arXiv 2401.12345 about cross-sectional momentum; use gpt-4o-mini.",
        target_flow=paper_flow,
    )
    paper = await paper_flow(inp, cfg=cfg)
    print(paper.model_dump_json(indent=2))


asyncio.run(main())

Note: QuantMind is mid-migration to OpenAI Agents SDK (see #71). PR5 lands the apex layer (flows/ + magic.py); the remaining work is the mind/ memory + store layer scheduled for PR6 and PR7.

🗺️ Roadmap

• Better flow design for user-friendly usage
• First production level example (Quant Paper Agent)
• Migrate Agent layer to OpenAI Agents SDK
• Standardize knowledge format with knowledge/ (Pydantic-based)
• Additional content sources (financial news, blogs, reports)
• Cross-step working memory (mind/memory) for batch document processing

The Vision: An Intelligent Research Framework

This section describes our long-term vision, not current capabilities. While QuantMind today provides a solid knowledge extraction framework, the features described below represent our aspirational goals for future development.

QuantMind is designed with a larger vision: to become a comprehensive intelligence layer for all financial knowledge. We’re building toward a system that understands the interconnections between academic research, market news, analyst reports, and social sentiment—creating a unified knowledge base that powers better financial decisions.

The foundation we’re building today—starting with papers—will expand to encompass the entire financial information ecosystem.

Future Conceptual Example (PR6 brings FilesystemMemory):

from quantmind.configs.paper import ArxivIdentifier
from quantmind.flows import paper_flow
from quantmind.knowledge import Paper
from quantmind.mind.memory import FilesystemMemory  # PR6

memory = FilesystemMemory("./mem/factor-research/")
for arxiv_id in arxiv_ids:
    paper: Paper = await paper_flow(ArxivIdentifier(id=arxiv_id), memory=memory)

This future state represents our commitment to moving beyond simple data aggregation and toward genuine machine intelligence in the financial domain.

🤝 Contributing

We welcome contributions of all forms, from bug reports to feature development.

For Contributors: Please read CONTRIBUTING.md for essential development setup including pre-commit hooks, coding standards, and testing requirements.

Quick Start for Contributors:

1. Fork the repository

2. Setup development environment:

   uv venv && source .venv/bin/activate
   uv pip install -e .
   ./scripts/pre-commit-setup.sh
   

3. Create feature branch (git checkout -b feat/my-feature)

4. Follow conventional commits (feat: add new feature)

5. Submit PR with our template

Before Contributing:

• Open an issue to discuss significant changes
• Use our issue templates for bug reports and feature requests
• Ensure all pre-commit hooks pass before submitting PR

License

QuantMind is released under the MIT License—see LICENSE for details.

❤️ Acknowledgements

• arXiv for providing open access to a world of research.
• The open-source community for the tools and libraries that make this project possible.