finai-research-workflow 0.2.0a0

经济金融领域 AI 学术研究工作流 — 论文写作（JF/JFE/RFS/经济研究/金融研究）与金融研报生成。集成 MCP 数据获取、因果推断（DID/IV/PSM/GMM）、LaTeX 排版和对抗性 review 循环。

论文-研报工作流 · FinAI Research Workflow

研究主题一句话 → 收到可投稿的 LaTeX 草稿。 Describe your research topic → receive a submission-ready LaTeX draft.

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Quick Start (30 秒上手)

# 1. 安装
git clone https://github.com/csmar432/finai-research.git && cd finai-research
pip install -e ".[extras]"

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

# 3. 开始研究
python scripts/agent_pipeline.py --topic "Carbon trading and green innovation"

Real recording of python3 scripts/cli.py health followed by python3 scripts/count_assets.py. Captured with asciinema + agg (100×30 terminal, ~6 sec, 60 KB). Source cast: .github/demo/demo.cast.

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。

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3 个核心能力

43 个 MCP 数据源	A 股财务 / 美股 / 宏观（FRED/IMF/世界银行） / 学术论文（OpenAlex/ArXiv），28 个无需 API Key
47 种计量方法	标准 DID / 交错 DID（CS/SunAb/Borusyak） / IV / RDD / 合成控制 / 面板 GMM，JF/JFE 级别稳健性检验
30 种期刊模板	JF / JFE / RFS / 经济研究 / 金融研究 / 管理世界，中英日德四国语言

⚠️ 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。 ⚠️ 5 个模拟数据服务器默认禁用，启用时输出带有 ⚠️ MOCK DATA 标识。

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完整文档: 使用指南.md · CLAUDE.md · [交互式配置向导](python scripts/setup_wizard.py --guided)

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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). 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.
• 47 econometric method modules, 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.
• 17 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 (free, 120 hours/month). No 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.

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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.

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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

# 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.

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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) — 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.

# 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:

# 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:

  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

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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	47 econometric methods, automated robustness tests (19 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 → 47 Econometric Methods → 20 Chart Types → LaTeX Paper

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

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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.
47 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.

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Quick Start

5-Minute Setup

# 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.

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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.

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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.

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	400M+ 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 for the complete catalog.

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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

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Tutorials

Tutorial	Description	Time
01 - Quick Start	Setup and run your first pipeline	5 min
02 - Financial Reports	Generate institutional research reports	10 min
03 - Research Directions	Design empirical studies with DID/RDD/IV	15 min
04 - MCP Marketplace	Discover and add MCP tools	15 min
05 - Event-Driven Research	Automate research via event monitoring	20 min

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Documentation

Document	Description
SETUP_GUIDE.md	Environment setup, API keys, Docker
USAGE_GUIDE.md	Complete usage guide (Chinese)
QUICKSTART.md	5-minute quick start
CLAUDE.md	Agent configuration and capabilities
CONTRIBUTING.md	Contribution guidelines
docs/tutorials/	Step-by-step tutorials
docs/api_reference.md	API documentation
docs/MANUAL_TASKS_RUNBOOK.md	Operations runbook for GitHub-side manual steps
docs/MOCK_DATA_POLICY.md	Mock data policy (5 servers disabled by default)
docs/DOCKER_INSTALL.md	Docker installation guide
docs/CITATION_GUIDE.md	Citation guidance for derived work
docs/GITHUB_DISCUSSIONS_SETUP.md	GitHub Discussions enablement
docs/ARCHITECTURE.md	System architecture overview
docs/audit/audit-2026-07-04.md	Latest CI coverage governance audit

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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/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

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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

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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:
   • Research questions
   • Data requirements
   • Hypothesis derivation
   • Empirical strategy
3. Add to scripts/research_directions/__init__.py

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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.

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License

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

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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

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Star History

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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

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Architecture Diagrams

Pipeline DAG (8 Stages + 4 Human-in-the-Loop Checkpoints)

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)

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

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

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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).
• 47 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.

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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.}
}

Related Projects

• dowhy — causal inference library (8.2K ⭐)
• StatsPAI — agent-native causal inference toolkit (274 ⭐)
• moderndid — GPU-accelerated modern DiD (25 ⭐)
• diff-diff — sklearn-like DiD in Python (280 ⭐)
• PaperOrchestra — Google's multi-agent paper writing (82 ⭐)
• E2ER-project — end-to-end empirical research pipeline (1 ⭐)
• econ-paper-studio — agent-native CLI for empirical economics (2 ⭐)

MIT License — see LICENSE for the full text.