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 | 🗞️ News | 📝 Description | |----------------|-------------------------------------------------------------------------------| | 🎉 Accepted at NeurIPS 2025 Workshop | Our paper **[Quant-Mind](#)** has been accepted to the **[NeurIPS 2025 GenAI in Finance Workshop](https://sites.google.com/view/neurips-25-gen-ai-in-finance/home)** !🚀 | | 📢 First Release on GitHub | **Quant-Mind** is now live on GitHub — please check it out and join us! 🤗 | ### 🧐 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. ```text 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](https://github.com/astral-sh/uv) for fast and reliable Python package management. **Prerequisites:** - Python 3.8+ - Git **Installation:** 1. **Install uv (if not already installed):** ```bash # 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:** ```bash git clone https://github.com/LLMQuant/quant-mind.git cd quant-mind ``` 3. **Create and activate virtual environment:** ```bash # Create a virtual environment uv venv # Activate it # On macOS/Linux: source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 4. **Install dependencies:** ```bash uv pip install -e . ``` ### 📚 Usage Examples #### Run a single paper through `paper_flow` ```python 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` ```python 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` ```python 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](https://github.com/LLMQuant/quant-mind/issues/71)). PR5 lands the > apex layer (`flows/` + `magic.py`); the remaining work is the `mind/` > memory + store layer scheduled for PR6 and PR7. --- ### 🗺️ Roadmap - [x] Better `flow` design for user-friendly usage - [x] 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 > [!IMPORTANT] > **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. > [!NOTE] > **Future Conceptual Example (PR6 brings `FilesystemMemory`):** > > ```python > 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. > [!IMPORTANT] > **For Contributors**: Please read [CONTRIBUTING.md](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**: ```bash 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](https://github.com/LLMQuant/quant-mind/issues) 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.