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What 2 Years of Building an AI-Assisted Trading Platform Taught Me

A solo developer built IMALI, an AI-assisted trading platform, over two years while driving Uber to fund development. The project involved running over 75,000 paper trades, testing with personal capital, and multiple dashboard rebuilds to improve user trust. Key lessons included the importance of building for recovery from API changes and WebSocket disconnections, and that risk management is more critical than predicting market moves.

read2 min views1 publishedJun 29, 2026

Two years ago I thought the hard part would be writing trading algorithms.

I was wrong.

The hard part was building software that keeps working when real markets, real APIs, and real users get involved.

I built IMALI as a solo developer while driving Uber to fund development. There were plenty of nights where I'd spend hours chasing bugs after finishing a shift.

Along the way I ran more than 75,000 paper trades, tested with my own capital, and rebuilt large parts of the platform after watching early users struggle with the interface.

Lessons I learned

Exchange APIs change.

WebSockets disconnect.

Rate limits happen at the worst possible time.

You don't build for the happy path—you build for recovery.

A believable paper trading experience isn't just fake orders.

It has to simulate execution, update positions correctly, calculate P&L, and give users confidence before they ever risk real money.

That became one of the biggest parts of the project.

Finding trade opportunities is interesting.

Managing risk is what keeps software usable.

Position sizing, stop-losses, trailing stops, confidence thresholds, and market filters became more important than trying to predict every market move.

I rebuilt the dashboard multiple times.

Not because the trading logic changed—but because users couldn't understand what the bot was doing.

The best algorithm doesn't matter if users don't trust it.

The platform is still evolving.

But after two years it reached a point where I was comfortable putting real users on it instead of endlessly adding features.

Current architecture

Crypto Spot Trading

Crypto Futures

U.S. Stock Trading

Paper Trading

Live Trading

AI-assisted confidence scoring

Market regime detection

White-label platform for custom trading strategies

I'm still learning every week, but this project has made me a far better engineer than I was when I started.

If you're building something ambitious by yourself, keep going. Sometimes the biggest milestone isn't writing a clever algorithm.

It's building software people are willing to trust.

I'd love feedback from other developers who have built trading systems, fintech products, or software that depends heavily on third-party APIs.

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