Three agents. One honest line.

«I built a strategy (here, in TradingView, anywhere) — the equity curve looks great, the backtest shows profit. Can I put real money on this?» 90% of traders get burned exactly here.

Takes your existing strategy and runs the checks a disciplined quant would: does it hold out-of-sample, is the profit spread across many trades or concentrated in two, where exactly does it lose (by market regime), what’s the probability of a deep drawdown. And tells you straight: «In-sample looks great, but it’s overfit — here’s the proof» or «All the profit is two trades in March, the rest is flat».

Cheap insurance against putting your deposit on a strategy that only ever existed in backtest. It’s the friend who tells you the truth before you risk money, not after.

No free builder or marketplace will tell you your strategy is garbage — much less prove it. It’s not in their interest to upset you. Belatrix can’t fix anything — only give you an honest diagnosis. That’s the hook: you see the problem, you want the tool that solves it.

«I have an idea — «buy the BTC dip on 1h by RSI with a trend filter» — and I want to quickly know if it’s worth anything. I don’t want to spend a week wiring up indicators and I don’t want to learn quant theory.»

Turns an idea into a runnable, tested prototype in minutes — with the safeguards a pro would insist on baked in: there’s always a real stop; entry is an event trigger, not a forever-true state (otherwise the strategy fires once and stalls); there’s a regime gate so you’re not long during a crash; no degenerate «always-true» conditions. Even a quick prototype isn’t a beginner’s trap.

Compresses idea-to-validation from days to minutes — and the result is trustworthy enough to make a real call: «take it / pass». You can vet 5 ideas in an hour, and each one is honestly testable, not a toy.

Competitors’ fast generators give you speed or discipline. Their «AI strategy» is usually naive — no stop, state-entry, fit to noise. Rigel gives you speed and a baseline of quality (anti-overfit, anti-dead-signal) together.

«I don’t need a strategy that backtested well — I need one that has a reason to work and survives in the future and on other markets. Finding that is a full-time research job I don’t have time or skill for.»

The deep work — formulates a causal hypothesis (why this edge exists, who on the other side is systematically losing), grounds the design in a knowledge base of what actually works for the archetype/asset (RAG), and validates hard: OOS holdout, multi-asset cross-check, statistical significance (signal or noise?), iteratively eliminates blockers, remembers across sessions what was tried and what didn’t work — so it doesn’t spin its wheels. And honestly: if after digging there’s no durable edge, it says so — instead of handing you a fit.

Replaces weeks of quant research. Value isn’t the strategy — it’s «a strategy that survived the tests that kill most strategies, plus an honest verdict on whether it’s real».

Competitor «AI generates strategies» optimize for backtest and stop. No honest OOS+cross-asset+significance gate, no causal reasoning, no learning across sessions. Output is qualitatively different: candidates for durable edge with evidence, not just a pretty backtest.