Mohammad Khalid
(09)Case study← All work

A multi-agent stock analysis system that synthesizes opposing investment perspectives into balanced recommendations using Munger-Buffett analyst swarm.

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09 / 09
Stack
Python · Gemini AI · Streamlit
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github.com/iamkhalid2/kwealth ↗
Fig. 09 · SpectrumOne emission line per tool, each at a fixed wavelength, so projects built with the same tool light the same line.Same lines as Pilot, MicroGPT Course

Overview

KWealth is an AI-powered stock analysis platform that uses a multi-agent system to generate balanced investment recommendations by simulating opposing analytical perspectives.

Key Features

  • Multi-Agent Architecture: Scout agent fetches data, Bear analyst (Munger-style) analyzes risks, Bull analyst (Buffett-style) finds value
  • Real-Time Analysis: Fetches live financial metrics and news via yfinance
  • Balanced Recommendations: Synthesizes opposing perspectives with confidence scoring
  • Structured Reports: Provides STRONG_BUY to STRONG_SELL ratings with detailed caveats

How It Works

  1. Scout agent gathers financial data and news
  2. Bear analyst generates skeptical risk-focused memo
  3. Bull analyst creates value-focused analysis
  4. Synthesizer weighs both theses for final recommendation

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