

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

- Find every time SPY was up 3 days in a row going into a full moon
- What happens when Gold and Silver's correlation diverts
- Search for edges on tech companies with positive momentum and net debt is under 5 billion
- Find me some interesting set up AAPL has this week
- How many times has yen made ATH while nikkei is 2% or more below its 200 day MA
If it can be done in python Varrd can do it

Varrd knows exactly the best route for backtesting a set up or strategy and routes accordingly without the user needing to know anything
- Real Quant guardrails on the entire time making sure k-tracking, p-hacking, OOS tampering, and others are accounted for

Optimize until a statistically profitable set up occurs and learn EXACTLY how to trade with state of the art risk management. Add this set up to your list and get a notification when the set up YOU found is happening when you aren't even watching
We don't tell people you should buy this or sell that, we simply state whether the strategy/ edge they find would have worked in the past and if it is significant to work in the future.