TradeSage AI

TradeSage AI is an intelligent multi-agent financial analysis platform built using ADK, Agent Engine, Cloud Run and Vertex AI, that revolutionizes trading hypothesis evaluation.

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Inspiration

We were inspired by the recent advances in AI agents and the realization that different aspects of investment analysis - hypothesis formation, risk assessment, market research, and alert generation - could be handled by specialized AI agents working in concert.

What it does

TradeSage AI is a sophisticated multi-agent system that transforms raw trading ideas into comprehensive investment analyses. Users input a trading hypothesis, and the system orchestrates six specialized AI agents to:

  • Hypothesis Agent: Structures and refines the trading idea into a clear, measurable hypothesis
  • Context Agent: Extracts key information about the asset, including sector, competitors, and business model
  • Research Agent: Gathers real-time market data, news, and financial metrics using integrated APIs
  • Contradiction Agent: Identifies risks, challenges, and factors that could invalidate the hypothesis
  • Synthesis Agent: Balances contradictions against confirmations to generate a confidence score and comprehensive analysis
  • Alert Agent: Creates actionable alerts with specific entry points, risk management levels, and monitoring triggers

The system provides a beautiful dashboard showing all active hypotheses with their confidence scores, supporting/opposing factors, and real-time price tracking.

How we built it

We used:

  • Google's Agent Development Kit (ADK) for orchestrating AI agents
  • Vertex AI/Gemini models for powering each specialized agent
  • FastAPI for the REST API layer
  • Cloud SQL PostgreSQL with pgvector for storing analyses and enabling RAG capabilities
  • Real-time market data integration via Alpha Vantage, Yahoo Finance, and Financial Modeling Prep APIs
  • React with TypeScript for a responsive, modern UI
  • Google Cloud Run for serverless deployment

Challenges we ran into

  • collecting a large corpus of financial documents to serve as RAG
  • deploying to Agent Engine, finally we put the backend on Cloud Run as an intermediate step
  • ensuring agents produced consistent, high-quality outputs required extensive prompt engineering and response parsing logic
  • moving from LangGraph to Google ADK required rewriting our agent orchestration
  • not finding much free tier financial market APIs with generous rate limiting

Accomplishments that we're proud of

  • functional solution/tool with a nice dashboard
  • learned to create a video with a voiceover audio overlay and embedding some slides
  • seamlessly blending AI insights with live market data
  • building on Google Cloud with proper separation of concerns and microservices architecture

What we learned

  • the quality of agent outputs depends heavily on precise, example-driven prompts
  • raw LLM outputs need sophisticated parsing and validation to ensure consistency
  • when dealing with external APIs and AI models, robust fallbacks prevent system failures

What's next for TradeSage AI

Features to be worked upon:

  • real-time price tracking
  • large corpus of documents for RAG, expanded data sources
  • agent engine deployment
  • integration with technical analysis indicators
  • multi-user workspaces
  • compliance and audit trails

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