
Finsight Research Guide
- 1 installs
- 269 repo stars
- Updated June 19, 2026
- wentorai/research-plugins
Run deep financial research with the FinSight multi-agent system to produce market, company, and sector analysis reports.
About
Documents FinSight, a multi-agent deep-research system for financial analysis that retrieves data, reasons over fundamentals, and generates cited research reports. A developer uses it to automate market, company, or sector financial research.
- Describes specialized retrieval, data, analysis, reasoning, and report agents
- Includes install and research-query-to-report Python examples
Finsight Research Guide by the numbers
- 1 all-time installs (skills.sh)
- Ranked #909 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
- Data as of Aug 1, 2026 (Skillselion catalog sync)
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| Installs | 1 |
|---|---|
| repo stars | ★ 269 |
| Last updated | June 19, 2026 |
| Repository | wentorai/research-plugins ↗ |
What it does
Run deep financial research with the FinSight multi-agent system to produce market, company, and sector analysis reports.
Files
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