finsight-research-guide

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