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Building an MCP Agentic Stock Trading System - Part 3: The Agentic Loop
Learning Bart Gottschalk 11/26/25 Learning Bart Gottschalk 11/26/25

Building an MCP Agentic Stock Trading System - Part 3: The Agentic Loop

The agentic loop is where LLMs become active problem-solvers instead of passive responders. The LLM doesn't just answer once—it iteratively calls tools, analyzes results, and decides what to check next. My trading agent uses this to analyze stocks: fetch data, calculate indicators, check trends, then make a decision.

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Building an MCP Agentic Stock Trading System - Part 2: The MCP Servers and Tools
Learning Bart Gottschalk 11/25/25 Learning Bart Gottschalk 11/25/25

Building an MCP Agentic Stock Trading System - Part 2: The MCP Servers and Tools

MCP servers are like USB hubs for AI—they provide standardized tools that any agent can plug into. My trading system has two: one fetches market data, the other calculates technical indicators. Write them once, use them with Claude, local LLMs, or even traditional code.

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Building an MCP Agentic Stock Trading System - Part 1: The Architecture
Learning Bart Gottschalk 11/24/25 Learning Bart Gottschalk 11/24/25

Building an MCP Agentic Stock Trading System - Part 1: The Architecture

I wanted to experiment with Model Context Protocol (MCP) and compare local LLMs against API-based ones. So I built a stock paper-trading system with three brains: a rules-based trader, Claude API, and a local Llama model in LM Studio. Same market data, different decision-making approaches.

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Adding nano-banana 3 Support to My CLI Wrapper
Building Bart Gottschalk 11/23/25 Building Bart Gottschalk 11/23/25

Adding nano-banana 3 Support to My CLI Wrapper

Twenty-four hours after Google dropped nano-banana 3, I shipped support for it. New model, new resolutions (4K!), new features. This is what building with AI is like at this point in November 2025.

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