论文-研报工作流 · FinAI Research Workflow

> 研究主题一句话 → 收到可核验的 LaTeX 草稿。

> Describe your research topic → receive a verifiable LaTeX draft.

Quick Start (30 秒上手)

# ── 推荐方式：PyPI wheel 安装（首次约 60s）────────────────────────────
# Debian/Ubuntu：先创建虚拟环境，避免与系统 Python 冲突
python3 -m venv .venv && source .venv/bin/activate
pip install "finai-research-workflow[extras]"

# 配置 LLM（DeepSeek 直连，免费）
export DEEPSEEK_API_KEY=sk-xxxx

# 启动流水线（wheel 安装后的写作入口；加 --use-hitl 启用阶段门控）
finai-pipeline --topic "Carbon trading and green innovation" --use-hitl
# 或
python -m finai.pipeline --topic "碳排放权交易与企业绿色创新" --use-hitl

# ── 源码安装（推荐贡献者 / 想改代码的用户）────────────────────────────
git clone https://github.com/csmar432/finai-research.git && cd finai-research
pip install -e ".[extras]"
cp .env.example .env.local   # 编辑 .env.local：DEEPSEEK_API_KEY=sk-...
# 新用户：先澄清（不自动开跑）→ 再写作；或澄清时加 --continue
python scripts/start_research.py --topic "Carbon trading and green innovation"
python scripts/agent_pipeline.py --topic "Carbon trading and green innovation" --use-hitl

# ── Debian/Ubuntu apt 系统 Python ─────────────────────────────────────
# apt 的 Python 被系统管理，直接 pip install 会触发 PEP 668 冲突。
# 解法：使用虚拟环境（见上方），或加 --break-system-packages
pip install --break-system-packages "finai-research-workflow[extras]"

> 重要提示：缺少 `DEEPSEEK_API_KEY` 时，`finai-pipeline` 默认以退出码 4

> 退出（严格模式），并打印明确指引。可以用 `finai-doctor` 诊断配置来源。

> PyPI: finai-research-workflow · 0.2.0a1 · MIT

> · 默认安装 `pip install finai-research-workflow` 不含 fastapi/streamlit（避免 PyJWT/apt 冲突）

> · Web 套件：`pip install 'finai-research-workflow[web]'`

> DOI: 10.5281/zenodo.21262689

Quick Demo

This guided interface walkthrough shows the supported agent hosts, research-brief

checkpoint, separate writing and empirical tracks, fail-closed data routing, and

verifiable delivery package. It is intentionally not presented as a live

research run and contains no mock coefficients, citations, or statistical claims.

Regenerate it deterministically with `python scripts/demo/gen_quick_demo.py`; see

`.github/demo/README.md` for the visual contract.

一次输入 → 8 阶段流水线：想法生成 → 文献综述 → 新颖性验证 → 实证设计 → 数据获取 → 分析 → 论文写作 → 对抗性 Review。每阶段需研究者确认。

3 个核心能力

> ⚠️ AI 生成的因果识别策略、统计结果和引用必须由研究者独立核实后方可投稿。

> ⚠️ Mock / synthetic data 默认禁用，只有用户明确授权后才能启用，且输出必须带有 ⚠️ MOCK DATA 标识。

完整文档: 使用指南.md · CLAUDE.md · 运行 `python scripts/setup_wizard.py --guided`

Why FinAI Research Workflow?

• Built for economists, not generic AI demos — every default is calibrated for the *Journal of Finance* / *经济研究* standard (DID with heterogeneous treatment effects, cluster-robust SEs at the firm level, 19 robustness checks, parallel-trend plots).
• 43 MCP server directories — covers A-share financials, US equities, global macro (FRED/World Bank/IMF/OECD/BEA), and 400M+ academic papers (OpenAlex). The registry contains 28 no-key, 12 API-key, 0 stub, and 3 opt-in legal-risk directories; classification is maintained by `scripts/count_assets.py`.
• 58 econometric method modules, not just OLS — standard DID, event study, Bacon decomposition, heterogeneous-treatment diagnostics, synthetic control, instrumental variables (optional `linearmodels`), panel GMM, RDD, mediation, and more. Methods that require an external backend fail visibly when it is unavailable; see CLAUDE.md for dependency notes.
• 30 journal templates, English/Chinese/Japanese/German — JF, JFE, RFS, JAE, Econometrica, 经济研究, 金融研究, 管理世界, 会计研究, 中国工业经济.
• 18 specialised AI skills (Claude Code / Cursor / GitHub Copilot) — idea discovery, literature review, novelty check, experiment design, data acquisition, paper drafting, figure generation, LaTeX compilation, review loops.
• Human-in-the-loop, never autonomous fabrication — every stage requires explicit checkpoint approval; data sources are verified before use; no synthetic data without user consent.

Why Not Just Use ChatGPT?

FinAI is purpose-built for economic & financial research. Here is what it does that general LLMs cannot:

> [!TIP]

> Start now with zero setup: **Open in GitHub Codespaces**. No local install required.

> For Chinese users: The most comprehensive guide is **使用指南.md** — a complete 13-chapter manual covering installation, workflows, data sources, econometric methods, paper writing, and FAQ.

Who Is This For?

> Not sure? If you've ever spent days downloading data, running regressions, formatting LaTeX tables, or searching for related work — this tool is for you.

MCP Server Profile: Pick What Fits You

`register_mcp_servers.py` supports 4 user-type profiles — pick the one matching your hardware and use case:

# 1) Dry-run first (推荐先看)
python scripts/register_mcp_servers.py --profile academic --prune --dry-run

# 2) Actually apply
python scripts/register_mcp_servers.py --profile academic --prune

# 3) List current registration
python scripts/register_mcp_servers.py --list

See config/mcp_profiles.json for full server lists and the 使用指南.md chapter on installation for step-by-step.

> Default behavior: without `--profile`, all 43 MCP servers are registered (matches `full` profile). Use `--prune` to remove out-of-profile servers.

Cross-Platform Installation

The project supports macOS, Linux, and Windows with platform-specific entry points:

Choose Your Path

This project supports two entry points — pick the one that matches your workflow:

Path A: AI Agent (Recommended)

The AI agent handles the full pipeline end-to-end. No need to remember commands.

# 1) Install once
./run.sh                    # macOS / Linux
run.bat                     # Windows

# 2) Health check
python scripts/health_check.py

# 3) Start an AI Agent (Claude Code / Cursor / Codex) and describe your research:
# "帮我研究关税政策对A股出口型企业创新的影响，设计一篇发表在经济研究的实证论文"

The AI agent automatically calls all 8 pipeline stages, MCP data sources, and LaTeX generators. Each stage requires your checkpoint approval before proceeding.

Path B: CLI (Script-Level Control)

Run individual scripts directly for fine-grained control:

# Writing track
python scripts/agent_pipeline.py --topic "Carbon trading and green innovation" --use-hitl

# Empirical track (production modern DID)
python -m scripts.research_framework.enhanced_pipeline --topic "Carbon trading and green innovation"

# Empirical demo TWFE smoke only
python scripts/research_framework/pipeline.py --mode full --topic "Carbon trading and green innovation"

# Demo: institutional-grade financial report
python scripts/demo_research_report.py --stock 000001.SZ

# MCP tool discovery
python scripts/core/mcp_tool_market.py --search "gdp" --report

# Journal template generation
python scripts/journal_template.py --list
python scripts/journal_template.py --generate JFE output/paper.tex

Platform-Specific Notes

• macOS: Keychain is native; keyring uses `KeychainBackend` automatically
• Linux: Keyring uses SecretService (gnome-keyring). For Chinese fonts, install `fonts-noto-cjk`:

sudo apt install fonts-noto-cjk fonts-wqy-zenhei

• Windows: Keyring uses Credential Manager. Chinese fonts (`SimHei`, `Microsoft YaHei`) come pre-installed

What Works Cross-Platform

• ✅ All `scripts/*.py` entry points
• ✅ 43 MCP servers (pure Python stdlib)
• ✅ Checkpoint (`fcntl.flock` falls back to no-op on Windows)
• ✅ Unit tests (pytest --collect-only; CI matrix: Ubuntu + macOS + Windows; daemon mode uses polling loop on Windows)

Known Cross-Platform Limitations

• ⚠️ `event_monitor.py` uses `signal.pause()` which is Unix-only; on Windows it falls back to a polling loop
• ⚠️ `keychain_setup.py` is macOS-specific; for Windows/Linux, use the cross-platform keyring via `scripts/keychain_manager.py`
• ⚠️ `core/sandbox.py` uses `os.fork` (Unix-only); falls back to `subprocess` on Windows
• ⚠️ `event_monitor.py --daemon` uses `os.fork`; on Windows the daemon exits with a friendly "use --interval 300 instead" message at startup (T2 audit 2026-07-12)
• ✅ Skills sync: `knowledge/skills/`, `.claude/skills/`, and `.github/skills/` are kept in sync via `python scripts/sync_skills.py` (no symlinks, Windows-safe). Run after editing any skill doc.
• ✅ Codex support: `AGENTS.md` at root + `.agents/skills//SKILL.md` per skill (frontmatter with `name` and `description`). Both synced by `sync_skills.py`.

Show Me What It Does

Describe your research in plain Chinese — the agent handles the rest:

帮我研究关税政策对A股出口型企业创新的影响，设计一篇发表在经济研究的实证论文

What the agent produces automatically:

> Footnote on numbers: The table above describes the core pipeline output stages. Idea generation, novelty verification, and literature review are separate stages that run before or in parallel. MCP server counts include 43 registered servers; some require institutional/paid accounts (Tushare Pro, Wind, CSMAR, CEIC) while others work without API keys (yfinance, akshare, World Bank, IMF, OECD, FRED, ArXiv, NBER, OpenAlex). See dependency notes in CLAUDE.md.

Architecture overview:

*Governed dual-track architecture: one research brief feeds a writing track and a separate empirical track; human checkpoints, provenance, and explicit gap files connect both tracks to the final research package.*

> Note: Demo assets are in `.github/demo/` and `docs/assets/`. The project is actively maintained.

Contributor Setup

Source checkout

# 1. Clone the repository
git clone https://github.com/csmar432/finai-research.git
cd finai-research

# 2. Install the package with all common optional integrations
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[extras]"
# extras includes Tushare, akshare, yfinance, MCP, dashboard, and document-processing packages.

# Optional: install econometrics packages (linearmodels, pandas-datareader, pandasql, honestdid)
pip install -e ".[econometrics]"

# 3. Configure API key (at least one required)
cp .env.example .env
# Edit .env and add: DEEPSEEK_API_KEY=sk-your-key
# Other supported: ANTHROPIC_API_KEY, OPENAI_API_KEY

# 4. Clarify the topic first (recommended)
python scripts/start_research.py --topic "碳排放权交易对企业绿色创新的影响"

# Then run the governed writing track
python scripts/agent_pipeline.py --topic "碳排放权交易对企业绿色创新的影响" --use-hitl

# Run real empirics as a separate hand-off
python -m scripts.research_framework.enhanced_pipeline --topic "碳排放权交易对企业绿色创新的影响" --explore

Key numbers (auto-generated by `scripts/count_assets.py`):

> Run `python scripts/count_assets.py` to regenerate these numbers. They are checked into README as a snapshot of the latest count; CI is the source of truth.

MCP Tools Overview

> 43 servers total: 28 work without API keys, 12 require API keys, 3 are opt-in legal-risk. See MCP Tool Marketplace for the complete catalog.

>

> A-share users without institutional accounts: `user-yfinance` (US/ADR) and `user-financial` (akshare free tier) cover basic equity/macro needs. Paid A-share data (CSMAR/Wind/Tushare Pro) requires institutional accounts.

See MCP Tool Marketplace Tutorial for the complete catalog.

Available Skills (18)

Each skill is documented in `.claude/skills/` (Claude Code) and `.github/skills/` (GitHub Copilot). In Cursor, use the `Skill:` command directly.

Tutorials

Documentation

Common Commands

# Paper pipeline
python scripts/research_framework/pipeline.py --topic "碳排放权交易对企业绿色创新的影响"

# Financial report
python scripts/demo_research_report.py --stock 000001.SZ

# MCP tool marketplace
python scripts/core/mcp_tool_market.py --search "gdp" --report

# Event monitor
python scripts/event_monitor.py --interval 300 --test

# Literature review
python scripts/literature_download.py "carbon trading innovation" --source arxiv,semantic,openalex --limit 20

# Or use an AI Agent directly
# "帮我做碳交易创新领域的文献综述"

# Journal template
python scripts/journal_template.py --list
python scripts/journal_template.py --generate JFE output/paper.tex

# Dashboard
streamlit run scripts/dashboard.py --server.port 8050

Data Coverage

Extending the System

Adding a New MCP Server

1. Create directory: `mcp_servers/user_your_server/`

2. Add `SERVER_METADATA.json`

3. Add tool definitions in `tools/*.json`

4. Register in Cursor MCP settings

5. Rebuild registry: `python scripts/core/mcp_tool_market.py --dir mcp_servers`

See MCP Marketplace Tutorial for full guide.

Adding a New Research Direction

1. Create file: `scripts/research_directions/carbon_economics.py` (copy from an existing direction like `green_finance.py` as template)

2. Define `ResearchDirection` class with:

3. Add to `scripts/research_directions/__init__.py`

Contributing

Contributions welcome! Please:

1. Fork the repository

2. Create a feature branch (`git checkout -b feature/amazing-feature`)

3. Commit changes (`git commit -m 'Add amazing feature'`)

4. Push to branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

See CONTRIBUTING.md for full guidelines.

License

This project is licensed under the MIT License. See LICENSE for details.

Acknowledgments

• 5 轮交互式澄清模式参考 Night Owl Research Agent 设计（2026-06-27 命名已重命名）
• Inspired by PaperOrchestra multi-agent architecture
• Data powered by akshare, yfinance, World Bank API, and Tushare Pro

Star History

Built With

Architecture Diagrams

Writing Track (5 Artifacts + Human Checkpoints)

> Dual-track note: Writing (`agent_pipeline.py`) has 5 stages (outline → literature → plotting → writing → refinement) with optional HITL. Empirics are a separate hand-off: `enhanced_pipeline` / `modern_did` for real estimators; `research_framework/pipeline.py` is demo TWFE + design scaffold only. See `docs/ARCHITECTURE.md` §0.

Data Routing (43 directories: 28 no-key + 12 API-key + 3 opt-in legal)

Local empirical panels are checked first. A missing exact variable stops visibly;

it does not trigger an unannounced synthetic or proxy-data substitution.

Modern DID Estimator Selection

The estimator follows treatment timing, comparison-group availability, and the

target estimand. TWFE remains a diagnostic baseline rather than automatic proof

of identification.

How FinAI Fits in the Ecosystem

FinAI focuses on the end-to-end workflow of empirical economic and finance research:

research idea → literature review → empirical design → data acquisition → analysis → paper draft → submission.

General causal-inference libraries (e.g. `dowhy`,

`StatsPAI`,

`diff-diff`) focus on the CI *algorithm* layer.

FinAI focuses on the *research workflow* layer that wraps data, econometrics, journal

templates, and human-in-the-loop gates into one pipeline.

This focus brings complementary features for economists:

• 43 MCP data sources for A-share financials (Tushare/CSMAR/Wind), US equities (yfinance),

global macro (FRED/World Bank/IMF/OECD/BEA), and 400M+ academic papers (OpenAlex/ArXiv).

• 58 econometric method modules including modern staggered DID

(Callaway-Sant'Anna, Sun-Abraham, Borusyak), synthetic control/DiD, IV/2SLS,

panel GMM, RDD, triple-diff, panel quantile, spatial regression, etc.

• 30 journal templates (EN+ZH+JP+DE) covering *JF / JFE / RFS / JPE / Econometrica /

经济研究 / 金融研究 / 管理世界 / 会计研究 / ZWiSt / JNS* and more.

• Human-in-the-loop gates at every pipeline stage to prevent LLM hallucinations.

See Related Projects below for tools that work alongside FinAI.

Maintainer

This project is maintained by **@csmar432**.

• 🐛 Bug reports & feature requests: GitHub Issues
• 💬 Questions & ideas: GitHub Discussions
• 🔒 Security disclosures: GitHub Security Advisories
• 💖 Sponsor / support: GitHub Sponsors · 爱发电

> Contributions of all sizes are welcome — see CONTRIBUTING.md for the workflow.

Cite This Work

If this project helps your research, give it a ⭐ — it tells other economists the project is worth their time.

If you use FinAI Research Workflow in published research, please cite it as:

@software{finai2026,
  title  = {FinAI Research Workflow: An End-to-End AI Agent Pipeline for Economic and Financial Research},
  author = {csmar432},
  year   = {2026},
  month  = jun,
  url    = {https://github.com/csmar432/finai-research},
  note   = {GitHub repository. For a permanent DOI, publish on Zenodo and update this field.}
}

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MIT License — see LICENSE for the full text.