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:

1. Executive Summary — Key findings in 3-5 bullets
2. Market Overview — Industry size, growth, trends
3. Company Analysis — Financials, competitive position
4. Risk Assessment — Key risks and mitigation
5. Outlook — Forward-looking analysis with scenarios
6. Sources — Cited data sources and references

Use Cases

1. Investment research: Company and sector deep dives
2. Due diligence: Comprehensive target company analysis
3. Academic research: Financial economics research support
4. Market intelligence: Competitive landscape mapping

References

• FinSight GitHub
• RUC-NLPIR Lab