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Architecture Diagram
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Live Trading Decision Screenshot #2 (Market Regime, Bias, Entry Quality).
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Live Trading Decision Screenshot #11 (Final Decision).
Inspiration
This project began with a personal challenge.
After many years of investing and trading, I realized that my biggest obstacle wasn't understanding markets—it was making consistently disciplined decisions in real time. Like many traders, I had access to charts, indicators, and endless information, but turning all of that into one clear, objective decision was still difficult.
Earlier this year, I began exploring what was possible with ChatGPT. I don't come from a traditional software engineering background, but I do enjoy designing systems and simplifying complex problems. As I learned how to work collaboratively with AI, I discovered that my strengths weren't in writing every line of code—they were in designing workflows, defining decision logic, and building systems that solve real-world problems.
What started as an idea to improve my own trading process gradually evolved into a complete AI-powered decision operating system.
Rather than building another tool that simply tells users to buy or sell, I wanted to build a system that encourages patience, consistency, and capital preservation by separating market direction from trade quality and providing structured reasoning before every decision.
Although this project focuses on trading, the underlying architecture is really about improving decision-making under uncertainty. The same concepts could eventually be adapted to investing, healthcare, business operations, education, or any field where structured, explainable decisions are valuable.
How I Built It
The project combines several technologies into a single decision workflow.
A ChatGPT Custom GPT serves as the user interface and communicates through OpenAI Actions with a FastAPI backend. The backend gathers live market data, evaluates market conditions, and produces structured decision outputs that assess Market Regime, Market Bias, Entry Quality, Risk, and recommended actions.
Interactive Brokers integration provides broker connectivity, while Cloudflare Tunnel securely exposes the local API for use within the Custom GPT.
Throughout Build Week, GPT-5.6 was used extensively to help refine prompts, improve structured reasoning, document the architecture, review implementation decisions, and improve the overall user experience.
Codex was used to assist with repository organization, development planning, code review, documentation improvements, and preparing the project for public release.
Challenges
The biggest technical challenge was not building an AI that could generate opinions—it was designing a system that consistently produces structured, explainable decisions.
Creating clear separation between Market Regime, Market Bias, Entry Quality, and Risk required multiple iterations of prompt engineering, workflow design, and API architecture.
Another challenge was resisting the temptation to continually redesign the system. One of the most valuable lessons from this project was learning the importance of freezing a stable version, documenting it clearly, and treating it as real software rather than an endless prototype.
What I Learned
Perhaps the biggest lesson wasn't technical—it was discovering a new way to build.
Working with ChatGPT changed how I think about software development. Instead of needing to be an expert in every programming language, I learned that strong system design, clear thinking, iterative problem solving, and effective collaboration with AI can produce sophisticated software.
This project gave me the confidence to see AI not simply as a chatbot, but as a collaborative development partner that helps transform ideas into working systems.
AI Trading Decision OS is the first implementation of that philosophy, and I hope it is only the beginning
What's Next
AI Trading Decision OS is the first implementation of a broader idea.
The long-term vision is to build AI-powered decision operating systems that help people make better decisions in many different domains, not only trading.
The same structured workflow used here could eventually support investing, healthcare, education, business operations, coaching, and other fields where complex decisions benefit from clear reasoning, structured workflows, and human oversight.
This project represents the foundation of that larger vision.
I'm excited to continue exploring what becomes possible when human experience, thoughtful system design, and AI work together to solve real-world problems.
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