QuantMind: Quantitative Finance Knowledge Engine
Transform raw financial information into structured, traceable, queryable knowledge.
- Source repo
- LLMQuant/quant-mind
- Stars
- ★ 3.1k
- Last updated
- 2mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 35/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A quantitative researcher extracts structured knowledge from arXiv papers for strategy development.
- Not a fit if
- Users who want a ready-made tool, not an agent editing the checkout
- Teams needing SEC filings or prediction-market sources not yet shipped
- Source review
- 35/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
QuantMind is an information processing framework for quantitative finance, converting raw financial data (papers, news, filings) into typed knowledge with citations and timestamps. It features deterministic preprocessing (fetch, parse, format, clean), config-driven operations (PaperFlow, collect_news), batch processing (batch_run), and multiple retrieval modes (RAG, library, mind). The repository also emphasizes 'harness engineering,' using AGENTS.md contracts, progressive-disclosure contexts, portable skills, and deterministic verification to turn general coding agents into domain specialists. QuantMind is available as an open-source Python library and powers the LLMQuant Data production platform. The project's paper was accepted at the NeurIPS 2025 GenAI in Finance Workshop.
QuantMind performs end-to-end knowledge extraction and retrieval: it first fetches and cleans data via deterministic preprocessing steps (fetch/parse/format/clean), then uses config-driven operations like PaperFlow to transform inputs (e.g., arXiv identifiers) into typed knowledge structures (e.g., PaperStructureTree or PaperSemanticResult). It also offers news collection (collect_news), batch processing (batch_run), and intent resolution (magic). For retrieval, it provides RAG (chunking + BM25/similarity), a local persistence library (library/), and agentic reasoning-based retrieval (mind/). These operations are accessible via Python APIs and can be driven by coding agents within the repository.
- A quantitative researcher extracts structured knowledge from arXiv papers for strategy development.
- A financial analyst automatically collects and organizes news from a specific time window for event-driven analysis.
- A data scientist unifies multiple financial data sources into a timestamped, traceable knowledge base for downstream RAG applications.
- A developer wants to build an agent-based financial data pipeline with pre-built retrieval and verification tools.
- A platform like LLMQuant Data integrates QuantMind as its knowledge extraction engine.
How do you install or deploy this agent?
QuantMind requires Python 3.8+ and the uv package manager. Clone the repository and install with uv: git clone https://github.com/LLMQuant/quant-mind.git, then cd quant-mind && uv venv && source .venv/bin/activate && uv pip install -e .
How do you use this agent?
The recommended usage is to let a coding agent (e.g., Claude Code or Codex) work inside the repository: clone it, describe the desired pipeline in the agent session, and the agent reads contracts, loads contexts, writes code, and runs verification. Alternatively, use it as a Python library: import quantmind, configure a config class (e.g., PaperStructureCfg), instantiate a flow (e.g., PaperFlow), and call build with an input (e.g., ArxivIdentifier). Examples live in the examples/ directory.
What are this agent's strengths and limitations?
- Deterministic preprocessing ensures exact source provenance and replayability, reducing model hallucination risk.
- Typed knowledge artifacts carry timestamps and citations, enabling point-in-time querying and standalone persistence.
- Agent-native design with contracts, contexts, skills, and verification boosts coding agent output quality.
- Multiple retrieval approaches (RAG, library, mind) cater to different use cases.
- Evaluation is still in the design phase; no benchmark results are published to quantify performance.
- Currently supports only papers and news; other sources like SEC filings are not yet implemented.
- Requires configuration of an LLM model (e.g., gpt-5.6-luna), creating dependency on a model provider.
- Documentation does not list all dependencies explicitly; installation may require additional system configuration.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| QuantMind: Quantitative Finance Knowledge Engine This agent | 35 · Major gaps | Agent plugin / skillFree + model costs | ★ 3.1k | 2mo ago | Python | Codex · Claude Code |
| OpenBB Open Data Platform | 37 · Major gaps | Library / SDKFreemium | ★ 74k | today | Python | — |
| ALTA Research Trading Workbench | 83 · Good | Web app | ★ 1.1k | 1mo ago | Python | — |
| EDSL (Expected Parrot Domain-Specific Language) | 63 · Some gaps | Library / SDKFree + model costs | ★ 507 | 1d ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows no mechanisms for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. No malicious behavior found, but no security controls either. Hence all trust criteria scored 0.
Self-consistency: README, pyproject.toml, and CI workflows are consistent in architecture and verification, but no execution verification. Dependency availability: dependency list is complete but availability not verified. Failure messages: documentation mentions error handling but no specific messages.
Audience and scenarios: clearly targets quantitative finance and coding agents. Capability boundaries: defined via architecture contracts and verification scripts. Trigger precision: magic feature exists but not detailed. Environment fit: supports Python 3.10+, has CI config.
Information architecture: clear directory structure and documentation. Install notes: provides uv installation steps. Naming stability: version 0.2.0 but no changelog. Examples and FAQ: example code present, no FAQ. Known limitations: evaluation in design phase mentioned. License: MIT. Versioning: version number but no changelog. Maintenance responsibility: not explicit.
Output usability: provides structured knowledge outputs. Marginal value: offers unique agent-native framework. Cost-benefit: no performance or cost data provided.
Claim traceability: some claims have references but not verified. Cross-source corroboration: not provided. Fact-inference separation: not clearly distinguished.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- No security controls found, such as least privilege, user confirmation, data flow transparency.
- Dependency security not assessed, potential vulnerability risk.
- Evaluation results not published, performance claims unverified.