SuperAPI AI Directory

finsight-research-guide

Writing & research

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Updated 10-16 07:17

About this skill

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

```python
# 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