HOW IT WORKS

Test your trading edge before you risk real money on it

Every trader has ideas they believe in but have never actually tested. quantstr.at turns "I think this would work" into a real, data-backed answer — in minutes, without learning to code.

THE PROBLEM

Testing a strategy usually means one of two bad options

Learn to code it yourself
Weeks of learning R or Python, wiring up indicators by hand, and debugging silent logic errors — before you've tested a single idea.
Trust a black-box tool
Plenty of tools will run your idea and hand back a number. Almost none show their work — so a bug that inflates your Sharpe ratio looks identical to a real edge.

Both leave you guessing. quantstr.at was built to remove the coding barrier and the trust problem at the same time.

THE PROCESS

From an idea in your head to a number you can trust

1
Describe your strategy, no jargon required
Write it the way you'd explain it to a friend — "buy when the 20-day average crosses above the 50-day, sell on the reverse." No JSON, no indicator syntax, no setup screen to fight with.
2
AI turns your words into a real strategy
Behind the scenes, a chain of specialist AI agents builds the actual indicators, entry/exit signals, and risk rules your idea needs — drawing from a 63-indicator library used by real quant workflows, not a toy simulator.
3
A second, independent AI checks the work — before you see anything
This is the part almost nobody else does. A separate AI compares what got built against what you actually asked for, catching mismatches a single-pass tool would silently ship as "results." If it doesn't match, it's automatically revised and re-checked — up to 5 rounds — until it passes. You never see an unverified number.
4
Get the full picture, not just a P&L
Total and annualized return, Sharpe ratio, max drawdown, win rate, profit factor, an equity curve, and every trade in the log — the same risk-adjusted view a professional would insist on before trusting a strategy with real capital.
WHY THIS MATTERS

A great-looking backtest is worthless if you can't trust the logic behind it

The most common failure mode in strategy testing isn't a bad idea — it's a correct-looking result built on a subtle bug: a signal firing a day early, a stop that never actually triggers, a rule applied to the wrong side of a trade. Those mistakes make backtests look better than the strategy actually is, and they're invisible unless something independently checks the implementation. That's the entire reason quantstr.at runs a second AI audit on every single backtest, automatically, at no extra cost to you.

Stop wondering if your strategy would have worked

Find out in minutes, not weeks. No credit card required to start.