Show IH: I have zero pro dev experience and built a 220k LOC automated trading platform with AI. It analyzes the market better than a human.
Hey everyone,
For the last 6 months, I've been building DepthSight.
It's a full-fledged algorithmic trading platform with a visual strategy builder, backtesting, and a natively integrated AI assistant.
I built it alone, with zero professional development experience, mostly using AI.
The core idea: While a human trader gets tired, emotional, and biased, the AI can analyze thousands of data points with cold, relentless logic. It finds patterns a human brain simply cannot process.
The platform is in open beta. Live trading is disabled. You can use it to build and backtest your own strategies on historical data.
I'm not selling a "magic money button." I'm offering a professional tool for traders who want to augment their intuition with machine logic.
Platform: depthsight.pro
The full technical breakdown is here: Medium.
I believe this is a precedent for what a single indie hacker can now achieve, but maybe I'm wrong.
So, my question to you is:
Is this approach — a solo founder building a "holy grail" project in symbiosis with an AI — the future of software development? Or is it just a dangerous anomaly?
Let me know what you think.
That’s an impressive engineering effort — shipping ~220k LOC platforms is no joke, especially with automated trading logic in play. What often separates interesting prototypes from robust production systems in algorithmic trading is how you handle unexpected edge conditions, data quality drift, and system latency under load.
In real trading systems, the first signal that a model or automation is actually useful is not just profitability in backtests but stability of decisions under distribution shifts (e.g., regime changes, microstructural noise).
Curious — in your deployment, what metric or validation behavior do you treat as the most reliable indicator that your AI engine is actually generalizing rather than just fitting to historical patterns (e.g., live latency, error rates under regime change events, real-time decode confidence, or something else)? That usually tells engineers whether something is truly production-worthy or overfit to past data.