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We spent the last 18 months building VARRD, a natural-language-to-backtesting engine.

You type an idea in plain English – “What if I buy BTC after CPI beats expectations and hold 10 days?” – and Varrd runs a full quant backtest behind the scenes.

We come out of quantitative futures trading, so under the hood it follows the same workflow a real quant desk uses:

- clean, standardized market data
- clear entry/exit rules and risk controls
- train/test splits and out-of-sample checks
- guardrails against overfitting, p-hacking, and hindsight curve-fitting
 

No “LLM guessing.” Every answer is tied to a traceable test.

• Brokerages, exchanges, and neobanks that want more engagement and order-flow without turning their app into a casino 

• Creator-led and retail-first platforms that care about protecting newer, lower-income users while still giving them access to serious tools 

• Hedge funds who want an AI front-end that respects their process instead of bypassing it
   

We don’t just sound smart – we’re tested where it matters.
 

• Beta results we can feel – Early testers saw fewer impulse “gut” trades and more trades that had actually been stress-tested. Time on platform went up because users kept iterating until they found a real edge.
   
• Backed by research, not vibes – Peer-reviewed studies show that when traders gain confidence in their edge, trading volume rises 20–45% and position sizes increase (e.g. Barber & Odean, 2001; Glaser & Weber, 2007; Yeoh & Wood, 2011). VARRD channels that confidence into statistics instead of hype.
   
• Every answer is traceable – Behind each “yes, this tests out” is a full report: sample size, win rate, drawdown, and out-of-sample performance. No black-box calls, no “just trust the AI.”
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