How To Build Your Own Claude AI Trading Assistant (Easy For Beginners)

Key Concepts

• Operational Edge: Using AI to improve workflow efficiency (prep, research, execution, review) rather than relying on AI for stock price prediction.
• Agentic AI: The shift from simple chatbots to "agents" (like Claude Code/Co-work) that can actively manipulate files, execute code, and perform tasks on a computer.
• Vibe Coding: A colloquial term for building software by describing the desired outcome to an AI agent and iterating through prompts, rather than writing code manually.
• Plan Mode vs. Build Mode: A methodology in AI development where one first brainstorms and defines a project brief (Plan) before allowing the AI to execute the technical implementation (Build).
• Data-Driven Conviction: Using AI to backtest specific setups or analyze historical data to gain the confidence needed to size positions appropriately.

1. The Shift in AI Utility for Traders

The speakers argue that most traders misuse AI by seeking "cheat codes" for price prediction. Instead, the true competitive advantage lies in operational infrastructure. By using AI to automate repetitive tasks—such as parsing research emails, organizing playbooks, and building custom scanners—traders can save hours of manual labor, allowing them to focus on high-level decision-making and personal well-being.

2. Practical Application: The Pre-Market Research Report

Justin Spiro, a trader at SMB, developed an AI-driven system that automates his morning routine:

• Process: The AI parses multiple financial newsletters (e.g., Vital Knowledge, Hammerstone, Bloomberg) and extracts key market catalysts.
• Output: A structured report containing a macro rundown, an economic calendar, earnings highlights, and the AI’s assessment of which stocks are most likely to move based on specific catalysts.
• Efficiency: What previously took hours of manual reading is now distilled into a concise report, allowing the trader to focus on chart analysis and trade preparation.

3. Methodology: Building Without Coding Skills

The podcast outlines a framework for non-technical traders to build their own tools:

1. Identify the Manual Bottleneck: Start by documenting a daily process that is currently done manually (e.g., trade tracking, scanning, or data synthesis).
2. Define the Goal: Use a standard LLM (like ChatGPT) to help articulate the requirements clearly.
3. Leverage Agentic Tools: Use tools like Claude Code or Claude Co-work to act as the "agent" that builds the tool.
4. Iterative Debugging: When the AI hits a "speed bump," use the LLM to debug the issue. If the AI provides incorrect data (e.g., stale VIX levels), refine the prompt to exclude or verify that specific data point.
5. Personalization: Feed the AI your specific "playbooks," rules, and "checks in favor" so the output is tailored to your unique trading style rather than generic market advice.

4. Advanced Applications: Backtesting and Scanners

The team discusses moving beyond simple summaries into quantitative analysis:

• Custom Scanners: Building scanners that filter for specific criteria (e.g., low float, dollar volume, gap percentage) in minutes rather than weeks.
• Statistical Conviction: Using AI to analyze historical data (e.g., "How does this stock behave when it gaps up half an ATR?") to determine if a trade setup has a positive expectancy.
• API Integration: Future iterations involve connecting to real-time data providers (like Polygon) to allow the AI to monitor live market conditions against the trader's specific strategy.

5. Notable Quotes

• "The real edge is operational. How quickly you prepare, how clean your process is, and how effectively you can execute on your ideas." — Garrett (Host)
• "I'm the boss. You have to remind it [the AI]... it just starts going on a tangent sometimes." — Justin Spiro
• "It's not always that this has so much edge and this is helping me crush markets... but if I can just funnel my workflow... that can be great because then the rest of the time I'm just looking at charts." — Garrett (Host)

6. Synthesis and Conclusion

The core takeaway is that AI has democratized the ability to build sophisticated trading infrastructure. Traders no longer need to be expert Python developers to create custom dashboards, scanners, or backtesting models. By treating the AI as an assistant that requires clear instructions, constant supervision, and "training" on one's personal trading philosophy, any trader can significantly reduce their administrative burden and increase their statistical conviction. The goal is not to let the AI trade for you, but to use it to clear the "noise" so you can trade more effectively.