AutoGenAdvanced⏱ ~40 minutes

Financial Analysis Agent

Analyze earnings, build financial models, and generate investment reports 10x faster

★ 4.8(15 reviews)•Research & Analysis

Preview Code ↓

$169$338

✓ Full source code & documentation
✓ Commercial license included
✓ 30-day email support
✓ Free updates for 1 year

What You Get

Everything included in this template package

💻

Working Agent Code

4 AutoGen agents for data collection, analysis, modeling, and reporting

⚙️

Configuration File

Watchlists, analysis templates, and report format settings

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Prompt Templates

12 prompts for different financial analysis types

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Setup Guide

SEC EDGAR and market data API setup guide

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Example I/O

Complete analysis examples for 3 public companies

📐

Architecture Diagram

Financial analysis pipeline diagram

😤

The Problem

Analyzing a single company takes 4-6 hours of pulling data from SEC filings, calculating ratios, building comparisons, and writing up findings. During earnings season, analysts are drowning — processing 10+ companies per week with the same manual workflow. Critical signals get missed.

✨

The Solution

This agent system pulls SEC filings and market data automatically, calculates key financial ratios, builds comparative analyses, identifies trends and anomalies, and generates structured reports with actionable insights — in minutes per company.

How It Works

Your AI crew handles the entire workflow

Input

Your task description, data, or trigger event

→

↓

AI Agents

Data CollectorPulls financial statements, SEC filings, and market data

Ratio AnalystCalculates profitability, liquidity, and efficiency metrics

Model BuilderCreates DCF models and peer comparison matrices

Report GeneratorSynthesizes findings into investment-grade reports

→

↓

Output

Structured results, reports, and actionable insights

Code Preview

Sample of the AutoGen agent setup

Preview only

crew.py

import autogen
from tools import SECEdgarTool, YahooFinanceTool

collector = autogen.AssistantAgent(
    name="DataCollector",
    system_message="You pull financial data from SEC"
        " EDGAR and Yahoo Finance. Extract income"
        " statements, balance sheets, and cash flow"
        " statements for the last 5 years.",
    llm_config=llm_config
)

analyst = autogen.AssistantAgent(
    name="RatioAnalyst",
    system_message="You calculate financial ratios:"
        " P/E, EV/EBITDA, gross margin, FCF yield,"
        " debt/equity. Compare against industry"
        " medians and historical trends.",
    llm_config=llm_config
)

groupchat = autogen.GroupChat(
    agents=[collector, analyst, modeler, reporter],
    messages=[], max_round=8
)

Example Input & Output

See what goes in and what comes out

Input

Company: Cloudflare (NET)
Analysis type: Full financial review
Period: Last 8 quarters
Compare against: Fastly (FSLY), Akamai (AKAM), Zscaler (ZS)

Output

📊 Financial Analysis: Cloudflare, Inc. (NET)

💰 Key Metrics (TTM):
• Revenue: $1.67B (+32% YoY)
• Gross Margin: 78.2% (industry avg: 65%)
• Net Retention Rate: 118%
• FCF Margin: 12.4% (turned positive Q2 2024)
• Rule of 40: 44.4 ✅

📈 Peer Comparison:
| Metric | NET | FSLY | AKAM | ZS |
|--------|-----|------|------|----|
| Rev Growth | 32% | 8% | 6% | 35% |
| Gross Margin | 78% | 54% | 62% | 78% |
| FCF Margin | 12% | -8% | 22% | 18% |

💡 Key Findings:
1. NET's growth + profitability combination is best-in-class
2. FCF inflection suggests sustainable operating leverage
3. Enterprise customer growth (47% of revenue) de-risks concentration

⚠️ Risks: Stock-based comp at 22% of revenue, China exposure