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MarketMind AI

A market analyst agent: live prices, news mood and signals, answered in plain English

2.6 saverage answer, up to 4 tool calls

See the code on GitHub (opens in a new tab)

Proof you can check:Code on GitHub

AI agents

15 stocks, 131 news articles, one agent

  • TechnologyAAPLMSFTNVDAGOOGLAMZN
  • FinanceJPMGSBAC
  • EnergyXOMCVX
  • HealthcarePFEJNJ
  • ConsumerTSLAWMTKO
  • 131news articles scored
  • 6agent tools
  • 2.6 saverage answer

Lubo Bali · lubobali.com

No. 09Databricks · Delta Live Tables

The problem

A retail investor can't follow prices, news and signals at once, and by the time they do, the moment has passed.

What I built

A streaming pipeline (Bronze → Silver → Gold) for prices and news, news scored for mood (sentiment analysis), buy/sell/hold signals from RSI, MACD and Bollinger Bands, and an agent that answers questions about all of it.

Tech & why

Spark Structured Streaming and Delta Live Tables on Databricks for clean data with checks, and Llama 3.3 70B with tool calling so answers come from the tables, not memory.

  • Databricks
  • Apache Spark
  • Delta Live Tables
  • Llama 3.3
  • MLflow

The result

15 stocks in 5 sectors, 131 articles scored, 15 Delta tables, and an agent with 6 tools that answers in 2.6 seconds on average. 308 tests.

How this agent is built

Every AI agent has the same five parts. Here is what each one is in MarketMind AI, so you can judge any agent the same way.

  1. ModelMeta Llama 3.3 70B with tool calling (Databricks Foundation Models).
  2. HarnessAn agent loop with up to 4 tool calls per question, tracked in MLflow.
  3. GuardrailsAnswers come only from the Delta tables through 6 tools; data quality checks quarantine bad rows before they reach Gold.
  4. KnowledgePrices for 15 stocks and 131 news articles, through Bronze, Silver and Gold tables.
  5. ToolsPrice, sector performance, market mood, technical signals, compare two stocks, market summary.

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