Building a Trading Dashboard: The Real Story

📅 Timeline: 7 days
🤖 Tool: Claude AI
💹 Market: ES Futures
📊 21,434 lines of code

The Reality Check

As a CTO who's deep in the AI development trenches daily, I wanted to share how this ES futures dashboard actually came together - the good, the messy, and the genuinely useful parts.

What worked surprisingly well:

Claude handled complex financial calculations I would've spent weeks debugging. The volume profile analysis algorithms? Generated in an afternoon and actually worked correctly with real market data. My proprietary momentum-based support and resistance levels, built and tested with Claude Opus in just one evening, turned out more robust than my previous version.

What was trickier than expected: Getting target calculations right. First attempt had T1 targets 100+ points away from support levels - completely useless for actual trading. Took several iterations to build logic that made sense.

The actual challenge: Not the coding (Claude's great at that) - it was knowing how to put it all together. The content, the full stack architecture, and the pipeline.

The Honest Process

Day 1 reality:

What surprised me: The speed. Not just Claude generating code fast, but how quickly you can test ideas with real market data. Build something in the morning, trade with it by afternoon, improve it that evening.

The Opportunity Here

For developers: You don't need to be a domain expert to build useful tools. Partner with someone who knows the field deeply. They guide what to build, you (and AI) handle how to build it.

For domain experts: If you understand your field but not coding, this approach works. Focus on the problem-solving, let AI handle implementation. The barrier between "knowing what's needed" and "building it" is vanishing quickly.

The combination that works:

Deep domain knowledge + AI implementation = actually useful tools, built fast.

Testing Strategy: Reality vs. Convention

The Testing Approach That Actually Worked

This project used minimal automated testing but extensive real-world validation. Here's why this approach made sense for a trading tool:

What We Tested Instead

Why This Worked Better Than Unit Tests

Key insight: For specialized tools used by domain experts, real-world validation often trumps traditional testing approaches. The combination of AI-generated code + immediate real-data testing + expert validation created a more effective quality assurance process.

Debashis Biswas's Reality Check

My son is to programming what Bruce Willis was to Die Hard. So I make it a point to listen to him. After seeing this project and hearing my thoughts about how AI might change programming education - perhaps schools shouldn't focus on teaching syntax anymore since the new generation can communicate directly in English with AI - he offered this reality check:

"Dad, you have domain knowledge across multiple fields. Of course you can direct AI to build something useful in 7 days. But someone without a computer science background? They wouldn't even know what questions to ask or how to architect the system. You're not proving that anyone can do this - you're proving that experienced engineers can do it faster with AI."

Thinking Through His Point

He might be onto something. When I look back at those 7 days, I realize how much was happening under the hood that I took for granted.

Maybe the real barrier isn't technical implementation anymore, but the experience to know how systems should be built. AI can write the code, but can it teach system design intuition?

I'm honestly not sure if he's right or if I'm being too optimistic. The tools are incredibly powerful, and I've seen non-technical people build impressive things with AI assistance. But building something that works vs. building something that works well and reliably - that might still require more experience than I initially thought.

What This Means Going Forward

The opportunity: Teams with the right mix of domain knowledge and technical experience can build professional tools incredibly fast.

The challenge: The barrier might not be gone - just shifted from "can you code?" to "do you understand how systems work?"

The opportunity: Small teams (or even individuals) can build professional-grade tools quickly when you combine domain expertise with AI development capabilities.

The challenge: Everyone's going to be building tools now. The differentiator isn't technical skill anymore - it's understanding real problems and building solutions that actually help people.

The reality: This dashboard works because it combines trading knowledge with engineering experience, accelerated by AI. Whether that's replicable by anyone or just by experienced developers with domain expertise - I'm still figuring that out.

What I do know: the technology is remarkable, and the possibilities are genuinely exciting.

Project Statistics

21,434 Lines of Code
60+ Major Features
26 Code Files
12 Functional Categories

Complete Feature Inventory

Core Analysis Engine

Market Structure Analysis

Trading Strategy Components

Advanced Analytics

Web Dashboard Interface

// Technical Architecture Lines of Code: 21,434 (committed files) Files: 26 Python + HTML files Development: 7 days Deployment: AWS S3 with automated pipeline Validation: Real market data + domain expertise

The Momentum-Based S/R Breakthrough

Algorithm Rebuild: Working with Claude Opus, I rebuilt my proprietary support and resistance algorithm from the ground up. The new momentum-based calculations proved more robust than years of previous iterations.

Innovation in Clustering: The real breakthrough came with cluster zone detection and level ranking. The system now:

  • Groups S/R levels based on proximity to current price
  • Makes it easier to identify high-probability trades for the next session
  • Assigns each level a proprietary importance score
  • Helps traders prioritize which zones to engage and which to avoid

AI Development Power: This exemplifies where AI development really shines - taking complex domain knowledge and rapidly iterating to better solutions that solve real trading problems.

Results & Lessons

12 Total Days
7 Active Dev Days
21K+ Lines of Code
60+ Features Built

Key Takeaways

Try It Yourself

Check out the live dashboard and see the results of this 5-day AI-powered development journey:

View Live Dashboard →

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