Content
# 论文-研报工作流 · FinAI Research Workflow
> **Describe your research topic → receive a submission-ready LaTeX draft.**
>
> An AI-assisted research workflow for economic and financial research — from idea to LaTeX manuscript draft. Integrates **43 MCP server directories (41 real implementations + 2 mock-only for institutional data + 3 opt-in legal-risk)**, modern causal inference (DID/IV/RDD/PSM/GMM, see dependency notes), LaTeX formatting for 30 journal templates (English/Chinese/Japanese/German), and AI-assisted review loops.
>
> ⚠️ **Important**: This tool generates manuscript drafts that require human review before submission. All causal identification strategies, statistical results, and citations must be verified by a researcher.
>
> ⚠️ **Legal Risk Servers (CNKI / Wanfang / Chinese Literature)**: These 3 MCP servers scrape websites that prohibit automated access. **They are disabled by default for ALL users** — including the `full` profile. To use them, you must: (1) read [LEGAL_CONSENT.md](LEGAL_CONSENT.md) carefully, (2) set `CLI_ACCEPT_RISK=cnki,wanfang,chinese-literature` in your environment. Without this variable, these servers are never loaded. Users take full legal responsibility.
> 🔴 **科研诚信 — Mock 数据默认关闭 (2026-06-28)**: 以下 5 个 MCP 服务器返回**模拟/硬编码/公开数据快照**，**默认禁用**，防止用户基于伪造数据发表错误结论：
>
> - `user_nber_wp` — 返回 3 个编造的 paper_id（w32456/w32098/w31567）+ 硬编码引用计数
> - `user_bea_data` — 无论 `year` 参数如何，都返回硬编码 GDP（$27.36T）
> - `user_csmar` — 文件头自承认"提供模拟数据用于演示"
> - `user_wuhan_stats` / `user_macro_datas` — 公开数据快照（统计局发布，非实时 API）
>
> **临时启用**（如演示场景）：`export MCP_MOCK_MODE=allow`。**禁止**用于任何拟发表的研究输出。详见 [docs/MOCK_DATA_POLICY.md](docs/MOCK_DATA_POLICY.md)。
[](https://github.com/csmar432/finai-research)
[](https://opensource.org/licenses/MIT)
[](https://github.com/csmar432/finai-research#installation)
[](https://github.com/csmar432/finai-research/releases)
[](https://github.com/csmar432/finai-research/stargazers)
[](https://codespaces.new/csmar432/finai-research)
[](https://arxiv.org/)
[](https://github.com/csmar432/finai-research/actions)
[](https://github.com/csmar432/finai-research/actions)
[](https://codecov.io/gh/csmar432/finai-research)
[](https://github.com/PyCQA/bandit)
[](https://github.com/pre-commit/pre-commit)
[](https://github.com/astral-sh/ruff)
[](https://github.com/csmar432/finai-research/stargazers)
[](docs/audit/audit-2026-07-04.md)
[](https://codecov.io/gh/csmar432/finai-research)
[](https://github.com/csmar432/finai-research/discussions)
[](https://doi.org/10.5281/zenodo.21262689)
---
<!--
[🇨🇳 **中文文档**](README.md) · [🇬🇧 **English Documentation**](README_EN.md)
-->
**Languages**: 🇨🇳 [简体中文](README.md) (默认) · 🇬🇧 [English](README_EN.md)
---
## Quick Navigation
| I'm looking for... | Go here |
|---|---|
| **🧭 交互式配置向导** | `python scripts/setup_wizard.py --guided` · 首次安装推荐 |
| **🩺 系统自检** | `python scripts/health_check.py --json` · 验证环境就绪 |
| **Complete Chinese guide** | [使用指南.md](使用指南.md) · 完整的 13 章中文手册 |
| **~20 econometric method implementations** | [使用指南.md - 实证分析方法](使用指南.md#8-实证分析方法) |
| **43 MCP server directories** | [使用指南.md - MCP 数据源](使用指南.md#6-mcp-数据源)；**41 真实实现（含 stdlib HTTP/数据库）+ 2 mock-only (CSMAR/Wind 需机构账号) + 3 opt-in 法律风险（CNKI/Wanfang/中文文献）** |
| **17 AI Skills** | [knowledge/skills/](knowledge/skills/) |
| **Benchmark results** | [BENCHMARK.md](BENCHMARK.md) · 5 计量方法精度验证 |
| **API reference** | [scripts/](scripts/) 目录下的每个模块都含 docstring 和类型注解 |
| **Troubleshooting** | [使用指南.md - 常见问题](使用指南.md#13-常见问题) |
## 60-Second Demo
```bash
$ python scripts/agent_pipeline.py --topic "Carbon trading and green innovation"
```
**From a one-line research question to a structured research plan — time and API costs vary by topic complexity, data availability, and LLM model used.** All eight stages (idea generation → literature review → novelty check → empirical design → data acquisition → analysis → writing → adversarial review) include human-in-the-loop checkpoints.
## 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 200M+ academic papers. **41 directories have full Python implementations; 2 are mock-only (user-csmar, user-wind require institutional accounts); 3 are opt-in legal-risk (CNKI, Wanfang, Chinese Literature)**. Free alternatives exist via `user-financial` (akshare) and `user-yfinance`.
- **~20 econometric method implementations, not just OLS** — standard DID, event study, Bacon decomposition, staggered DID (Callaway-Sant'Anna/Sun-Abraham/Borusyak/Goodman-Bacon, requires `pip install diff-in-diff2`), synthetic control, instrumental variables (requires `linearmodels`), panel GMM, RDD, event studies, mediation, and more. See CLAUDE.md for the full list with 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:
| Capability | ChatGPT / Claude (General) | FinAI (Specialized) |
|---|---|---|
| **A-share financial data** | Manual download, error-prone | ✅ 43 MCP servers auto-fetch |
| **DID with 19 robustness checks** | Generic response | ✅ Cluster-robust SEs, Bacon decomposition, event studies |
| **JF / 经济研究 LaTeX templates** | Manual formatting | ✅ 30 journal templates, one command |
| **Causal identification strategy** | Generic suggestions | ✅ Econometrics expert knowledge embedded |
| **Literature review with provenance** | Copy-paste citations | ✅ Source tracking, citation verification |
| **Multi-stage pipeline with checkpoints** | One-off answers | ✅ 8-stage pipeline with human approval |
> [!TIP]
> Start now with zero setup: **[Open in GitHub Codespaces](https://codespaces.new/csmar432/finai-research)** (free, 120 hours/month). No install required.
> **For Chinese users:** The most comprehensive guide is **[使用指南.md](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?
| Audience | Use Case |
|----------|----------|
| **PhD students / researchers** | Design empirical studies, run econometric analysis, generate LaTeX manuscripts for JF/JFE/RFS/经济研究/金融研究 |
| **Finance professors** | Automate literature reviews, track policy experiments, benchmark against published papers |
| **Graduate students** | Learn econometric methods (DID/IV/RDD) with automated validation and robustness checks |
| **Quantitative analysts** | Access A-share data, run factor analysis, generate institutional-grade research reports |
| **AI/ML researchers** | Explore LLM applications in financial research automation, provenance tracking, HITL design |
> **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:
| Profile | Servers | Startup | Memory | Best For |
|---------|---------|---------|--------|----------|
| `minimal` | 5 | ~1s | ~30 MB | 演示/教学 (Demo / Teaching) — low-end laptops |
| `academic` | 18 | ~4s | ~100 MB | 学生/个人研究者 (Student / Individual) — no institution account |
| `quant` | 30 | ~8s | ~180 MB | 机构/量化 (Quant / Institution) — has Tushare/Wind/CSMAR |
| `full` | 43 | ~12s | ~220 MB | 重度用户 (Power User) — all data sources, RAM ≥ 16 GB |
```bash
# 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](config/mcp_profiles.json) for full server lists and the [使用指南.md](使用指南.md#2-安装配置) 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:
| OS | Entry Script | Prerequisites |
|----|-------------|---------------|
| **macOS** (12+) | `./run.sh` | Python 3.10+ (Homebrew recommended) |
| **Linux** (Ubuntu 20.04+, Debian 11+, Fedora 35+) | `./run.sh` | `sudo apt install python3.10 python3-venv` (or distro equivalent) |
| **Windows** (10/11) | `run.bat` | Python 3.10+ ([python.org](https://www.python.org/downloads/)) — **check "Add to PATH"** in installer |
### 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.
```bash
# 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:
```bash
# Full research pipeline
python scripts/agent_pipeline.py --topic "Carbon trading and green innovation"
# Research execution layer (DID/IV/RDD + writing)
python scripts/research_framework/pipeline.py --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`:
```bash
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)
- ✅ 2,234 unit tests (pytest --collect-only; CI matrix: Ubuntu + macOS; no 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
---
## 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:**
| Stage | Output |
|-------|--------|
| Research Design | DID/IV/RDD identification strategy + data sourcing plan |
| Empirical Analysis | ~30 econometric methods, automated robustness tests (18 types) |
| Paper Draft | LaTeX manuscript in journal format (JF/JFE/RFS/经济研究/金融研究/管理世界) |
| Review Loop | AI-assisted adversarial review with researcher verification required |
> **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:**
*Multi-agent pipeline: User Input → AI Agent → 5-Stage Research Pipeline (outline → literature → plotting → writing → refinement, with optional HITL gates at each stage) → 43 MCP Servers → ~30 Econometric Methods → 20 Chart Types → LaTeX Paper*
> **Note:** Demo assets are in `.github/demo/` and `docs/assets/`. The project is actively maintained.
---
## Key Features
| Feature | Description |
|---------|-------------|
| **Multi-Agent Pipeline** | Orchestrates 5 pipeline agents (outline → literature → plotting → writing → refinement) with optional HITL gates |
| **43 MCP Data Servers** | 43 registered MCP server directories; **41 are fully implemented in Python (stdlib HTTP + databases)**; 2 are mock-only (user-csmar, user-wind require institutional accounts); 3 are opt-in legal-risk (user-cnki, user-wanfang, user-chinese-literature). Of the 41 real servers, ~28 work without API keys (yfinance, akshare, World Bank, IMF, OECD, FRED, ArXiv, NBER, OpenAlex, SEC EDGAR, eastmoney, etc.); 11 require API keys (Tushare Pro, CEIC, EODHD, etc.). Run `python scripts/count_assets.py` for the latest breakdown. |
| **~30 Econometric Methods** | DID (5 variants), RDD, synthetic control, panel GMM, spatial regression, IV/2SLS, causal ML, GARCH, survival analysis, panel cointegration — JF/JFE/RFS standard. Modern staggered DID (Callaway-Sant'Anna, Borusyak, Sun-Abraham) requires `pip install diff-in-diff2` |
| **Provenance Tracking** | Full data lineage from raw API to final chart/table |
| **HITL Gates** | Human-in-the-loop approval at critical pipeline stages |
| **Analyst Agents** | Financial analysis agents for fundamental, valuation, risk, earnings, competitive, and macro research |
| **Self-Evolution** | Continuous improvement based on task outcomes |
| **45 Journal Templates** | 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 etc. |
---
## Quick Start
### 5-Minute Setup
```bash
# 1. Clone the repository
git clone https://github.com/csmar432/finai-research.git
cd finai-research-workflow
# 2. Install dependencies
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
# Optional: install econometrics extras (includes diff-in-diff2 for CS/BJS/Gardner DiD)
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. Run your first research pipeline
python scripts/research_framework/pipeline.py --topic "碳排放权交易对企业绿色创新的影响"
# Or use an AI Agent (recommended) for the full interactive workflow
```
### Via Cursor (Recommended)
Simply describe your research goal in natural language:
```
帮我分析碳排放权交易对企业绿色创新的影响，设计一篇实证论文，发表在经济研究
```
AI Agent will automatically call all necessary modules.
---
## Architecture
The system uses a **layered agent architecture** with an AI Agent (Claude Code / Cursor / Codex) as the orchestrator:
**Key numbers** (auto-generated by `scripts/count_assets.py`):
| Metric | Count |
|--------|------:|
| MCP server directories | 43 (28 free, 12 API-key, 0 stub, 3 opt-in) |
| Econometric method modules | 47 |
| Journal templates | 30 |
| AI Skills | 17 |
| Research directions | 12 |
| Test files / test functions | 98 / 296 |
| research_framework modules with tests | 21/47 |
> 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](docs/tutorials/04-mcp-marketplace.md) for the complete catalog.
>
> | Badge | Meaning |
> |-------|---------|
> | 💰 Paid | Requires institutional/paid account (Tushare Pro / Wind / CSMAR / CEIC) |
> | ⚠️ Limited | Free tier available but rate-limited or requires registration |
> | ✅ Free | No account required — works out of the box |
| MCP Server | Function | Cost | Free Tier |
|-----------|----------|------|---------|
| **user-tushare** | A-share data (quotes, financials, margin) | 💰 Paid | akshare alternative |
| **user-yfinance** | US stock, ETF, options, financials | ✅ Free | Full |
| **user-sec-edgar** | SEC 10-K/10-Q/8-K filings | ✅ Free | Full |
| **user-financial** | China macro (GDP/CPI/M2) | ✅ Free | Full |
| **user-eodhd** | US yield curve, economic calendar | ⚠️ Limited | Registration required |
| **user-fed-data** | Federal Reserve, FOMC, Beige Book | ✅ Free | Full |
| **user-wb-data** | World Bank Data API | ✅ Free | Full |
| **user-imf-data** | IMF World Economic Outlook | ✅ Free | Full |
| **user-oecd-data** | OECD Economic Data | ✅ Free | Full |
| **user-bea-data** | Bureau of Economic Analysis (US GDP) | ✅ Free | Full |
| **user-eastmoney-reports** | Research reports, news, analyst rankings | ✅ Free | Full |
| **user-enhanced-finance** | Forex, shipping indices, commodities | ✅ Free | Full |
| **user-openalex** | 250M+ academic papers + citation graph | ✅ Free | Full |
| **user-arxiv** | Academic paper search and download | ✅ Free | Full |
| **user-context7** | Full-text retrieval for papers (ArXiv/DOI) | ✅ Free | Full |
| **user-semantic-scholar** | AI-enhanced paper search | ⚠️ Limited | Optional API key |
| **user-nber-wp** | NBER Working Papers | ✅ Free | Full |
| **user-brave-search** | Web search (Chinese/English) | ⚠️ Limited | Registration required |
| **user-chinese-literature** | CSSCI, CNKI-style search | ⚠️ Limited | See legal notice in SECURITY.md |
> **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](docs/tutorials/04-mcp-marketplace.md) for the complete catalog.
---
## Available Skills (17)
Each skill is documented in `.claude/skills/` (Claude Code) and `.github/skills/` (GitHub Copilot). In Cursor, use the `Skill:` command directly.
| Skill | Description | Key Modules |
|-------|-------------|------------|
| `fin-full-pipeline` | End-to-end: topic → paper PDF | `scripts/agent_pipeline.py` |
| `fin-idea-discovery` | Idea generation + data validation | `scripts/research_framework/pipeline.py` |
| `fin-lit-review` | Systematic literature review | `scripts/citation_graph.py`, MCP multi-source |
| `fin-generate-idea` | 8-12 ranked ideas with实证验证 | MCP data validation |
| `fin-novelty-check` | Novelty validation against JF/JFE/RFS | NBER, Chinese journals search |
| `fin-experiment-design` | Complete empirical design | `modern_did.py`, `regression_engine.py` |
| `fin-paper-writing` | Writing orchestration | `report_generator.py` |
| `fin-paper-draft` | Body text generation (LaTeX) | `journal_template.py` |
| `fin-paper-plan` | Outline generation | 30 journal templates |
| `fin-paper-figure` | Chart generation (≥300 DPI) | `fin_charts.py`, `chart_factory.py` |
| `fin-paper-convert` | LaTeX compilation | `xelatex`/`pdflatex` + journal templates |
| `fin-review-loop` | Multi-round adversarial review | 5-dimension scoring |
| `fin-submit-check` | Pre-submission checklist | Format, DPI, citations audit |
| `fin-data-acquisition` | Data fetch + regression scripts | 43 MCP servers |
| `fin-brief-generator` | Auto-generate `FIN_BRIEF.md` | 5 enhanced tools |
| `fin-ref-paper` | BibTeX reference management | CrossRef DOI API |
| `fin-viz-launch` | Natural language → academic charts | `chart_pipeline.py`, 20+ types |
---
## Tutorials
| Tutorial | Description | Time |
|----------|-------------|------|
| [01 - Quick Start](docs/tutorials/01-quickstart.md) | Setup and run your first pipeline | 5 min |
| [02 - Financial Reports](docs/tutorials/02-financial-report.md) | Generate institutional research reports | 10 min |
| [03 - Research Directions](docs/tutorials/03-research-directions.md) | Design empirical studies with DID/RDD/IV | 15 min |
| [04 - MCP Marketplace](docs/tutorials/04-mcp-marketplace.md) | Discover and add MCP tools | 15 min |
| [05 - Event-Driven Research](docs/tutorials/05-event-driven-research.md) | Automate research via event monitoring | 20 min |
---
## Documentation
| Document | Description |
|----------|-------------|
| [SETUP_GUIDE.md](SETUP_GUIDE.md) | Environment setup, API keys, Docker |
| [USAGE_GUIDE.md](USAGE_GUIDE.md) | Complete usage guide (Chinese) |
| [QUICKSTART.md](QUICKSTART.md) | 5-minute quick start |
| [CLAUDE.md](CLAUDE.md) | Agent configuration and capabilities |
| [CONTRIBUTING.md](CONTRIBUTING.md) | Contribution guidelines |
| [docs/tutorials/](docs/tutorials/) | Step-by-step tutorials |
| [docs/api_reference.md](docs/api_reference.md) | API documentation |
| [docs/MANUAL_TASKS_RUNBOOK.md](docs/MANUAL_TASKS_RUNBOOK.md) | Operations runbook for GitHub-side manual steps |
| [docs/MOCK_DATA_POLICY.md](docs/MOCK_DATA_POLICY.md) | Mock data policy (5 servers disabled by default) |
| [docs/DOCKER_INSTALL.md](docs/DOCKER_INSTALL.md) | Docker installation guide |
| [docs/CITATION_GUIDE.md](docs/CITATION_GUIDE.md) | Citation guidance for derived work |
| [docs/GITHUB_DISCUSSIONS_SETUP.md](docs/GITHUB_DISCUSSIONS_SETUP.md) | GitHub Discussions enablement |
| [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) | System architecture overview |
| [docs/audit/audit-2026-07-04.md](docs/audit/audit-2026-07-04.md) | Latest CI coverage governance audit |
---
## Common Commands
```bash
# 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/research_framework/pipeline.py --mode lit-review --topic "carbon trading innovation"
# 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
| Market | Source | Data Types |
|--------|--------|------------|
| **A-shares** | `user-tushare` (free) | Daily quotes, financials, margin, north flow |
| **US Stocks** | yfinance + Finviz (free) | Quotes, financials, ESG, options, SEC filings |
| **Macro (Global)** | World Bank + IMF + OECD (free) | GDP, CPI, population, trade, debt |
| **Macro (China)** | `user-financial` + NBS (free) | CPI, PPI, PMI, M2, FDI, retail sales |
| **Macro (US)** | FRED + BEA + Fed (free) | NIPA, FOMC, Beige Book, yield curve |
| **Fixed Income** | EODHD (key) / `user-financial` (free) | Treasury yields, bond prices, credit spreads |
| **Forex & Commodities** | `user-enhanced-finance` + `user-financial` (free) | FX rates, shipping indices, precious metals |
| **Research Reports** | 东方财富 (free) | Analyst reports, news, sector analysis |
| **Academic** | arXiv + NBER (free) | Working papers, citations |
---
## 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](docs/tutorials/04-mcp-marketplace.md) 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:
- Research questions
- Data requirements
- Hypothesis derivation
- Empirical strategy
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](CONTRIBUTING.md) for full guidelines.
---
## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
---
## Acknowledgments
- 5 轮交互式澄清模式参考 [Night Owl Research Agent](https://github.com/GRIND-Lab-Core/night_owl_research_agent) 设计（2026-06-27 命名已重命名）
- Inspired by [PaperOrchestra](https://github.com/google-research/paper-orchestra) multi-agent architecture
- Data powered by akshare, yfinance, World Bank API, and Tushare Pro
---
## Star History
[](https://star-history.com/#csmar432/finai-research&Timeline)
---
## Built With
| Layer | Technology |
|-------|------------|
| **AI Orchestration** | Claude Code / Cursor / Codex, Claude API, OpenAI API, Anthropic API |
| **Data (43 servers)** | `user-tushare`, `user-yfinance`, `user-financial`, `user-sec-edgar`, `user-eastmoney-*`, World Bank API, IMF API |
| **Econometrics** | statsmodels, linearmodels, scipy |
| **Visualization** | matplotlib, seaborn, plotly |
| **Pipeline** | Python 3.10+ |
| **Testing** | pytest, ruff |
| **Documentation** | MkDocs Material |
| **Containerization** | Docker, Docker Compose |
---
## Architecture Diagrams
### Pipeline DAG (8 Stages + 4 Human-in-the-Loop Checkpoints)
```mermaid
flowchart TD
Start([User inputs research topic]) --> P1[1. Outline<br/>Research framework + venue template]
P1 -->|HITL gate| P2[2. Literature Review<br/>OpenAlex + ArXiv + Context7 + NBER]
P2 -->|HITL gate| P3[3. Plotting<br/>Parallel chart generation]
P3 --> P4[4. Paper Writing<br/>Full manuscript draft]
P4 -->|HITL gate| P5[5. Refinement<br/>Multi-round adversarial review]
P5 --> Done([LaTeX manuscript draft])
IdeaStage[Idea Generation<br/>Stage 1 of 3] -.->|optional| P1
DataStage[Data Acquisition<br/>Stage 2 of 3] -.->|feeds into| P3
NoveltyStage[Novelty Check<br/>Stage 3 of 3] -.->|feeds into| P1
style P1 fill:#e94560,color:#fff
style P2 fill:#0f3460,color:#fff
style P3 fill:#533483,color:#fff
style P4 fill:#16213e,color:#fff
style P5 fill:#0f3460,color:#fff
style IdeaStage fill:#444,color:#fff,stroke-dasharray:3
style DataStage fill:#444,color:#fff,stroke-dasharray:3
style NoveltyStage fill:#444,color:#fff,stroke-dasharray:3
classDef hitl_gate stroke:#f0ad4e,stroke-width:3px
class P1,P2,P4,P5 hitl_gate
```
> **Pipeline stages note:** The core pipeline has **5 stages** (outline → literature → plotting → writing → refinement) with optional HITL gates. Idea generation, novelty verification, and data acquisition run as parallel/prior stages. The research framework CLI (`scripts/research_framework/pipeline.py`) provides a focused DID/IV/RDD analysis mode.
### MCP Data Source Selection (43 Directories: 41 Real + 2 Mock + 3 Opt-in Legal)
```mermaid
flowchart LR
Req[Data Request<br/>e.g. A-share ROA] --> Router{Smart Router}
Router --> Tier1[Tier 1<br/>CSMAR/Wind<br/>Highest quality]
Router -->|unavailable| Tier2[Tier 2<br/>Tushare<br/>+ patent data]
Router -->|no key| Tier3[Tier 3<br/>akshare<br/>Free, slower]
Router -->|no data| Tier4[Tier 4<br/>yfinance/synthetic<br/>Last resort]
Tier1 --> Cache[(Local Cache<br/>SQLite)]
Tier2 --> Cache
Tier3 --> Cache
Tier4 --> Cache
Cache --> Result[Validated Data +<br/>Provenance Hash]
style Tier1 fill:#28c840,color:#fff
style Tier2 fill:#febc2e,color:#000
style Tier3 fill:#0f3460,color:#fff
style Tier4 fill:#e94560,color:#fff
style Cache fill:#1a1a2e,color:#fff
```
### Modern DID Estimator Selection
```mermaid
flowchart TD
DID[DID with Staggered Treatment] --> Check{Never-treated<br/>available?}
Check -->|Yes| Q1{Heterogeneous<br/>effects suspected?}
Check -->|No| Q2{Continuous<br/>treatment?}
Q1 -->|Yes| CS[Callaway-Sant'Anna<br/>2021 - default]
Q1 -->|No| SA[Sun-Abraham<br/>2021]
Q1 -->|Wants imputation| BJJ[Borusyak-Jaravel-Spiess<br/>2024]
Q2 -->|Yes| ContDID[Continuous DID<br/>Callaway-DiTraglia 2024]
Q2 -->|No| Decompose[Bacon Decomposition<br/>diagnose TWFE bias]
CS --> Synth[Synthetic DiD<br/>Arkhangelsky 2021]
SA --> Synth
BJJ --> Synth
style CS fill:#e94560,color:#fff
style SA fill:#0f3460,color:#fff
style BJJ fill:#533483,color:#fff
style Synth fill:#16213e,color:#fff
style Decompose fill:#16213e,color:#fff
style ContDID fill:#0f3460,color:#fff
```
---
## Comparison with Existing Tools
| Feature | **FinAI Research Workflow** | [dowhy](https://github.com/py-why/dowhy) | [StatsPAI](https://github.com/brycewang-stanford/StatsPAI) |
|---------|--------------------------|------------------------------------------|--------------------------------------|
| **Domain** | Economic & financial research | Industrial causal inference | Causal inference toolkit |
| **Data sources** | 43 MCP servers (A股/US/FRED/OECD; 28 free + 12 API-key + 3 opt-in) | None (import only) | None (import only) |
| **Econometric methods** | ~30 (DID/IV/RDD/GMM focus) | 0 (general framework) | 550+ (general) |
| **Journal templates** | 45 (JF/JFE/RFS + 中文顶刊) | 0 | 0 |
| **Chinese market** | ✅ Tushare/CSMAR/Wind | ❌ | ❌ |
| **Human-in-the-loop** | ✅ HITL gates at pipeline stages | ❌ | ❌ |
| **Adversarial review** | ✅ Multi-round AI review | ❌ | ❌ |
| **Best for** | Economists: JF/JFE/RFS/经济研究 | Production causal ML | Causal inference devs |
---
## Maintainer
This project is maintained by **[@csmar432](https://github.com/csmar432)**.
- 🐛 **Bug reports & feature requests**: [GitHub Issues](https://github.com/csmar432/finai-research/issues)
- 💬 **Questions & ideas**: [GitHub Discussions](https://github.com/csmar432/finai-research/discussions)
- 🔒 **Security disclosures**: [GitHub Security Advisories](https://github.com/csmar432/finai-research/security/advisories/new)
- 💖 **Sponsor / support**: [GitHub Sponsors](https://github.com/sponsors/csmar432) · [爱发电](https://afdian.net/a/finresearch)
> Contributions of all sizes are welcome — see [CONTRIBUTING.md](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:
```bibtex
@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.}
}
```
## Related Projects
- [**dowhy**](https://github.com/py-why/dowhy) — causal inference library (8.1K ⭐)
- [**StatsPAI**](https://github.com/brycewang-stanford/StatsPAI) — agent-native causal inference toolkit (50 ⭐)
- [**moderndid**](https://github.com/jordandeklerk/moderndid) — GPU-accelerated modern DiD
- [**diff-diff**](https://github.com/igerber/diff-diff) — sklearn-like DiD in Python
- [**PaperOrchestra**](https://github.com/google-research/paper-orchestra) — Google's multi-agent paper writing (58 ⭐)
- [**E2ER-project**](https://github.com/bhanneke/E2ER-project) — end-to-end empirical research pipeline
- [**econ-paper-studio**](https://github.com/gaaiyun/econ-paper-studio) — agent-native CLI for empirical economics
## License
MIT License — see [LICENSE](LICENSE) for the full text.
MCP Config
Below is the configuration for this MCP Server. You can copy it directly to Cursor or other MCP clients.
mcp.json
Connection Info
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