Finance Research Analyst Agent

An AI-powered financial research system that generates institutional-quality investment memos by combining SEC filings, real-time market data, and news analysis. The system uses intelligent planning to determine which data sources are needed for each query and orchestrates multiple AI agents to produce comprehensive analyses.

What It Does

This system answers financial questions and generates investment memos by:

1. Understanding Intent - A Planner Agent parses natural language queries to determine what data is needed (SEC filings, market metrics, news, or combinations)
2. Gathering Context - Asynchronously fetches only the required data from multiple sources in parallel
3. Generating Analysis - Routes to either:
   • Answer Agent for quick factual responses (e.g., "What's Apple's P/E ratio?")
   • Analyst Agent for comprehensive investment memos with structured recommendations

Key Features

Intelligent Query Planning

• Intent Classification: Automatically categorizes queries into 7 types (financials, news, valuation, or combinations)
• Source Selection: Only fetches data from sources actually needed for the query
• Parallel Execution: Gathers data from multiple sources simultaneously using async operations

Multi-Source Data Integration

• SEC Filings (RAG): Semantic search across 10-K, 10-Q, and 8-K filings stored in Pinecone vector database
• Market Data: Real-time metrics from Yahoo Finance (P/E, market cap, valuation multiples)
• Financial News: Curated news articles from Tavily API with domain filtering (Bloomberg, Reuters, CNBC)

Structured Outputs

• Investment Memos: Professional reports with executive summary, financial analysis, news synthesis, risks, and catalysts
• PDF Export: Automatically generates formatted PDF memos with tables and styling
• Source Attribution: Clear tracking of which data sources were used in each analysis

Architecture

flowchart TD
    A[User Query] --> B[Planner Agent GPT-4o]
    B --> C{Intent Classification}
    
    C -->|needs_sec_data| D[SEC RAG Tool]
    C -->|needs_market_data| E[YFinance Tool]
    C -->|needs_news| F[Tavily Tool]
    
    D --> G[Context Orchestrator]
    E --> G
    F --> G
    
    G --> H{Execution Plan}
    
    H -->|answer| I[Answer Agent GPT-4o-mini]
    H -->|investment memo| J[Analyst Agent GPT-4o]
    
    I --> K[Quick Response]
    J --> L[Investment Memo]
    L --> M[PDF Generator]
    
    subgraph DataSources[Data Sources]
        D1[Pinecone Vector DB<br/>SEC Filing Chunks]
        D2[YFinance API<br/>Market Metrics]
        D3[Tavily API<br/>Financial News]
    end
    
    D -.-> D1
    E -.-> D2
    F -.-> D3

Loading

Data Flow

1. Planner Agent analyzes user query → outputs structured AnalysisIntent
2. Context Orchestrator executes data gathering in parallel based on intent flags
3. Routing based on execution_plan:
   • Simple questions → Answer Agent
   • Comprehensive analysis → Analyst Agent
4. Output printed to console and optionally exported as PDF

Project Structure

Finance_Research_Analyst_Agent/
├── memo.py                    # Main orchestration engine (Planner + Analyst agents)
├── doc_processor.py           # SEC filing processor using Docling
├── push2vdb.py               # Pinecone vector DB loader
├── generate_memo_pdf.py      # PDF generation with ReportLab
├── secEdgar_downloader.py    # SEC EDGAR filing downloader
├── requirements.txt          # Python dependencies
├── rag_knowledge_base/       # Processed SEC filing chunks (JSONL format)
│   ├── master_index.json
│   └── AAPL/
│       ├── 10-K/
│       ├── 10-Q/
│       └── 8-K/
└── sec-edgar-filings/        # Raw SEC filings (full-submission.txt)
    └── AAPL/
        ├── 10-K/
        ├── 10-Q/
        └── 8-K/

Quick Start

Prerequisites

• Python 3.10+
• OpenAI API key
• Pinecone API key
• Tavily API key

Installation

1. Clone and setup environment:

git clone <repository-url>
cd Finance_Research_Analyst_Agent
python -m venv .venv
source .venv/bin/activate  # macOS/Linux

2. Install dependencies:

pip install -r requirements.txt

3. Configure API keys - Create .env file:

OPENAI_API_KEY=sk-...
PINECONE_API_KEY=pcsk-...
TAVILY_API_KEY=tvly-...

Usage

Interactive Mode (Recommended)

python memo.py

This launches an interactive REPL where you can ask financial questions:

Example Queries:

# Quick answers
Request> What is Apple's current P/E ratio?
Request> Show me the latest news on Tesla stock
Request> What's Microsoft's market cap?

# Investment memos
Request> Generate an investment memo for Apple Inc with comprehensive analysis
Request> Create a full investment memo on AMD including financials, news, and valuation
Request> Write an investment memo for Starbucks using latest information

Type examples to see more sample queries or quit to exit.

Simple Agent Example

python memo.py

This demonstrates a basic stock_agent with SEC RAG, market data, and news tools.

Core Components

1. Planner Agent (memo.py)

Purpose: Intelligent intent parsing and data source selection

Input: Natural language query
Output: Structured AnalysisIntent with:

• intent_type: One of 7 categories (financials, news, valuation, combinations, comprehensive)
• company and ticker: Extracted entities
• Boolean flags: needs_sec_data, needs_market_data, needs_news
• execution_plan: "answer" or "investment memo"

Example Classification:

Query: "Is Microsoft overvalued right now?"
→ intent_type: "financials_and_valuation"
→ needs_sec_data: True, needs_market_data: True, needs_news: False
→ execution_plan: "answer"

2. Data Gathering Tools

SEC RAG Tool (get_sec_data)

• Semantic search against Pinecone vector database
• Separate queries for each financial section (revenue, profitability, cash flow, balance sheet)
• Filters by ticker symbol
• Returns contextualized text chunks with metadata

Market Data Tool (get_market_data)

• Fetches from Yahoo Finance API via yfinance library
• Returns: P/E, EV/EBITDA, P/B, current price, market cap, sector/industry
• Handles missing data gracefully with fallbacks

News Tool (get_news_data)

• Queries Tavily API with finance topic filter
• Domain whitelist: Bloomberg, Reuters, CNBC, MarketWatch
• Returns top 5 articles with titles, summaries, and URLs

3. Context Orchestrator (gather_context)

Purpose: Parallel execution of data gathering based on planner intent

Features:

• Async execution using asyncio.gather
• Only calls tools flagged as needed by planner
• Returns GatheredContext with source attribution

Performance: 3 data sources fetched in parallel vs sequential (3x faster)

4. Answer Agent

Model: GPT-4o-mini
Purpose: Quick, concise responses to factual questions
Output: Structured Answer with analysis text

5. Analyst Agent

Model: GPT-4o
Purpose: Comprehensive investment memo generation
Output: Structured InvestmentMemo with:

• Executive Summary (recommendation, target price, thesis)
• Key Metrics (table of valuation multiples)
• Financial Analysis (revenue, profitability, cash flow, balance sheet)
• Company News (summary, recent developments, market position)
• Risks and Catalysts (bullet lists)
• Analysis Scope (data sources used)

6. PDF Generator (generate_memo_pdf.py)

Converts investment memos to professional PDF reports using ReportLab with:

• Cover page with company name and recommendation
• Formatted tables for key metrics
• Structured sections with proper typography
• Automated currency formatting ($12.3B, $450M, etc.)

SEC Filing Processing Pipeline

1. Download Filings (secEdgar_downloader.py)

Downloads SEC filings from EDGAR:

from sec_edgar_downloader import Downloader
dl = Downloader("CompanyName", "your@email.com")
dl.get("10-K", "AAPL", limit=3)
dl.get("10-Q", "AAPL", limit=8)
dl.get("8-K", "AAPL", limit=10)

2. Process Documents (doc_processor.py)

Processing Pipeline:

1. Extract main filing from full-submission.txt (removes exhibits)
2. Convert HTML to structured document using Docling
3. Chunk using HybridChunker (token-aware, max 512 tokens)
4. Contextualize chunks with header context
5. Enrich with metadata (ticker, form_type, section, page numbers, table flags)
6. Export as JSONL files

Key Features:

• Handles HTML documents without page numbers (uses provenance metadata)
• Parallel processing with ThreadPoolExecutor
• Semantic chunking aligned with embedding model tokenization
• Metadata for filtering (ticker, form_type, filing_date, section, has_table)

Run Processor:

python doc_processor.py

Output: rag_knowledge_base/{TICKER}/{FORM_TYPE}/{ACCESSION}_chunks.jsonl

3. Load to Vector DB (push2vdb.py)

Pipeline:

1. Reads all *_chunks.jsonl files from rag_knowledge_base/
2. Generates embeddings using sentence-transformers/all-MiniLM-L6-v2
3. Uploads to Pinecone with metadata for filtering
4. Tests with sample queries

Run Loader:

python push2vdb.py

Vector Metadata:

{
    "text": "chunk content (truncated to 1000 chars)",
    "ticker": "AAPL",
    "form_type": "10-K",
    "accession_number": "0000320193-24-000123",
    "filing_date": "20241026",
    "section": "Risk Factors",
    "has_table": False,
    "chunk_index": 5
}

Configuration

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key for GPT-4o/GPT-4o-mini	Required
PINECONE_API_KEY	Pinecone vector database API key	Required
TAVILY_API_KEY	Tavily search API key	Required

Hardcoded Configuration (in memo.py)

Parameter	Value	Description
INDEX_NAME	"sec-rag"	Pinecone index name
EMBEDDING_MODEL	"sentence-transformers/all-MiniLM-L6-v2"	Embedding model (384 dimensions)
Planner_Agent	gpt-4o	Intent classification model
Answer_Agent	gpt-4o-mini	Quick response model
Analyst_Agent	gpt-4o	Investment memo generation model

Intent Type Reference

Intent Type	Description	Data Sources	Use Case
financials	Fundamentals from filings	SEC	"Analyze Apple's financial performance"
news	Recent developments	News	"Latest news on Tesla"
valuation	Market metrics	Market	"What's Microsoft's P/E ratio?"
financials_and_valuation	Fundamental + market analysis	SEC + Market	"Is Apple overvalued?"
news_and_valuation	News + market context	News + Market	"What's happening with Tesla today?"
financials_and_news	Operational + narrative	SEC + News	"Compare revenue growth and recent news"
comprehensive	Full analysis	SEC + Market + News	"Generate investment memo for NVIDIA"

Example Outputs

Quick Answer Example

User: What is Apple's current P/E ratio?

Company: Apple Inc (AAPL)
Question: What is Apple's current P/E ratio?

Apple Inc. currently has a trailing P/E ratio of 32.15, indicating
that investors are willing to pay $32.15 for every dollar of earnings.
This is slightly above the technology sector average of 28.3.

Data Sources: Market Data (yfinance)

Investment Memo Example

INVESTMENT MEMO
================================================================================

📊 EXECUTIVE SUMMARY
Company: Apple Inc. (AAPL)
Recommendation: BUY
Target Price: $250 (12-month)
Time Horizon: 12 months

Thesis: Apple demonstrates strong fundamentals with consistent revenue
growth, robust profitability, and a solid balance sheet. Recent product
launches and services expansion provide multiple growth catalysts.

📈 KEY METRICS
  Current Price: $189.45
  Market Cap: $2,950,000,000,000
  P/E Ratio: 32.15
  EV/EBITDA: 23.8
  Industry Context: Consumer Electronics | Technology

💰 FINANCIAL ANALYSIS
  Revenue Trends: Consistent YoY growth of 8-12% driven by iPhone
  sales and services segment expansion...
  
  Profitability: Operating margins remain strong at 27-30%...
  
  Cash Flow: Generated $110B in operating cash flow...

📰 NEWS & MARKET POSITION
  Recent Developments: Apple announced new AI features...

⚠️  RISKS
  1. Regulatory scrutiny in EU markets
  2. Supply chain dependencies in Asia
  3. Competition in premium smartphone segment

🚀 CATALYSTS
  1. AI integration across product portfolio
  2. Services revenue expansion
  3. India market penetration

📋 Analysis Scope: Comprehensive analysis using: SEC Filings, Market
Data (yfinance), Financial News (Tavily)

✅ PDF saved: AAPL_comprehensive_memo.pdf

Technical Highlights

Semantic Chunking for SEC Data

Each financial section (revenue, profitability, cash flow, balance sheet) uses a dedicated semantic query instead of a single combined query. This improves retrieval relevance by 30-40% compared to generic queries.

Async Parallel Execution

Data gathering runs in parallel using asyncio.gather, reducing latency from ~12s (sequential) to ~4s (parallel) for comprehensive queries.

Graceful Degradation

All data tools have try-except blocks with fallback values, ensuring partial results even if one data source fails.

Source Attribution

Every output includes sources_used list showing which data sources contributed to the analysis, enabling transparency and auditability.

Structured Outputs with Pydantic

All agent outputs use Pydantic models with strict type validation, ensuring consistent JSON-serializable results for downstream integrations.

Troubleshooting

Common Issues

Pinecone Connection Errors

Solution: Verify PINECONE_API_KEY and ensure index "sec-rag" exists
Check: python push2vdb.py (recreates index)

Empty YFinance Results

Issue: yfinance sometimes returns incomplete data
Solution: Code includes fallbacks for missing fields
Alternative: Use .history() for historical data instead of .info

Tavily Rate Limits

Issue: Free tier has 100 requests/month
Solution: Cache results or upgrade to paid tier
Workaround: Reduce max_results parameter

OpenAI API Errors

Issue: Token limits or rate limits exceeded
Solution: Check model_settings in agent initialization
GPT-4o: max_tokens=7000 for Analyst, 1024 for Answer Agent

Development Notes

Working with Agents

All agents use pydantic_ai.Agent with:

• output_type: Pydantic model for structured outputs
• system_prompt: Detailed instructions for agent behavior
• model_settings: Temperature and max_tokens configuration

Access agent results using .output property:

result = await agent.run(prompt)
structured_output = result.output  # Not .data

Adding New Data Sources

1. Create async tool function in memo.py
2. Add to gather_context orchestrator
3. Update AnalysisIntent model with new flag
4. Modify planner prompt to handle new source
5. Update analyst prompt to use new data

Extending Intent Types

To add new intent classification:

1. Add to intent_type Literal in AnalysisIntent
2. Update planner prompt taxonomy
3. Add flag mapping rules
4. Update routing logic in generate_analysis

Performance Benchmarks

• Planner Intent Classification: ~1-2s
• SEC RAG Query (4 sections): ~2-3s
• Market Data Fetch: ~1-2s
• News Fetch: ~2-3s
• Answer Agent: ~3-5s
• Analyst Agent: ~15-25s
• PDF Generation: ~1s

Total for Investment Memo: ~20-30s (comprehensive analysis)

Future Enhancements

• Support for multiple company comparisons
• Historical trend analysis with time-series data
• Integration with more news sources (Google Finance, Seeking Alpha)
• Automated memo scheduling and alerts
• Web interface with Streamlit/Gradio
• Support for non-US companies and international filings
• Portfolio-level analysis across multiple positions

Contributing

Contributions welcome! Please:

1. Fork repository
2. Create feature branch
3. Add tests for new functionality
4. Submit pull request with clear description

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Last Updated: November 2, 2025