Three agents. One honest line.

«Questions come up the whole way: what does this metric mean, why did the backtest sag here, where do I find this in the platform — and the big one: the equity curve looks great, but will it hold up outside the backtest?» That last question is exactly where most strategies fall apart.

Bellatrix is the platform’s global AI assistant — it lives on every page, not just the agents workspace. It answers any question about the product, your strategies, trading and your stats; reviews the page you’re on — a strategy, a backtest report, your journal; explains any text you select, right in place. And when you ask for the truth about a strategy, it runs the checks a disciplined quant would — out-of-sample hold, profit concentration, losses by market regime, drawdown probability — and says it straight: «In-sample looks great, but it’s overfit — here’s the proof».

The answer lives where the question appears — no docs digging, no tab switching. And it’s still cheap insurance against putting your deposit on a strategy that only ever existed in backtest: 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. Bellatrix will — right on the page where the problem lives. It doesn’t build or fix strategies, though — that’s Rigel’s and Betelgeuse’s job.

«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 the knowledge base our research agent fills around the clock — what worked and what died — so it doesn’t repeat known dead ends, builds the strategy in a dialogue with you — it asks rather than assumes — and validates hard: the full metrics set, trade clustering by regime, out-of-sample and a multi-asset cross-check. Then it makes the call itself, edge or noise, against that knowledge base, and explains why. It writes nothing back to the base, and your strategies never enter it. It’s not an agent that grinds on to burn tokens: if the strategy is dead, it says there’s no point going further. 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».

Competitors’ «AI generates strategies» tools optimize for the backtest and stop. No honest OOS or cross-asset check, no read of the metrics and trade clusters behind the call, no causal reasoning, no research-grade knowledge base behind the design. Output is qualitatively different: candidates for durable edge with evidence, not just a pretty backtest.