brycewang-stanford FinSight Research Guide
Overview
FinSight is a deep research agent designed specifically for financial analysis. Developed by RUC-NLPIR, it combines multi-source data retrieval, financial reasoning, and report generation to produce publication-ready financial research. It handles market analysis, company fundamentals, sector comparisons, and macroeconomic assessment through specialized agents.
Installation
git clone https://github.com/RUC-NLPIR/FinSight.git
cd FinSight && pip install -e .
Core Capabilities
Research Query to Report
from finsight import FinSightAgent
agent = FinSightAgent(llm_provider="anthropic")
# Generate comprehensive financial analysis
report = agent.research(
"Analyze the competitive landscape of the global EV battery "
"market. Compare CATL, LG Energy, and Panasonic on market "
"share, technology, margins, and growth outlook."
)
print(report.summary)
report.save("ev_battery_analysis.pdf")
Agent Architecture
| Agent | Role |
|---|---|
| Retrieval Agent | Fetches data from SEC filings, financial APIs, news |
| Data Agent | Processes financial statements, ratios, time series |
| Analysis Agent | Performs fundamental, technical, and comparative analysis |
| Reasoning Agent | Synthesizes findings, identifies trends and risks |
| Report Agent | Generates structured research reports with citations |
Financial Data Sources
# FinSight integrates with multiple data sources
config = {
"sec_edgar": True, # SEC filings (free)
"fred": True, # Federal Reserve economic data
"yahoo_finance": True, # Market data (free)
"news_api": True, # Financial news
"world_bank": True, # Macro indicators
}
Analysis Types
# Company fundamental analysis
report = agent.research(
"Provide a fundamental analysis of NVIDIA including "
"revenue trends, margin analysis, valuation multiples, "
"and competitive moat assessment."
)
# Sector analysis
report = agent.research(
"Compare the top 5 cloud computing companies by revenue "
"growth, operating margins, and R&D investment intensity."
)
# Macro analysis
report = agent.research(
"Analyze the impact of rising interest rates on US "
"commercial real estate valuations since 2022."
)
Report Structure
Generated reports typically include:
- Executive Summary — Key findings in 3-5 bullets
- Market Overview — Industry size, growth, trends
- Company Analysis — Financials, competitive position
- Risk Assessment — Key risks and mitigation
- Outlook — Forward-looking analysis with scenarios
- Sources — Cited data sources and references
Use Cases
- Investment research: Company and sector deep dives
- Due diligence: Comprehensive target company analysis
- Academic research: Financial economics research support
- Market intelligence: Competitive landscape mapping