From an idea to a sized allocation — without a thousand browser tabs.
Strata’s workflow is one loop: idea → build → backtest → analyze → allocate. Scroll through each stage.
Start from the question, not the code.
Most strategies die because the idea was wrong, not because the code was wrong. Strata starts you with the question a good portfolio manager would ask: what edge are you trying to capture — what session, what timeframe, what's your hypothesis for why this works? An AI assistant that pressure-tests the idea with you is coming soon.
Indicators or Python — your call.
Drag in standard signals (EMA cross, ATR breakout, vol regime filter). Or open the Python editor and write it yourself with full libraries. Describing it in English and getting an AI-drafted first version is on the roadmap. The output is the same: a strategy object you can backtest.
Intraday realism, no curve-fitting safety net.
Run against 1-minute CME data from Databento with realistic slippage, commission, and session-aware fills. Walk-forward windows are on by default — you can turn them off, but Strata won't pretend an in-sample 4.0 Sharpe means anything. Monte Carlo on the trade-list when you want to stress the equity curve.
How does this play with your other strategies?
This is the step every other tool skips, and it's coming soon to Strata: a correlation matrix that maps the new strategy against everything else in your book — correlation, return concentration, drawdown overlap. Sometimes the strategy that looks best in isolation is the one you shouldn't add.
Size it for the account that's actually trading it.
Risk-based sizing across your book. If you're trading a funded account, Strata simulates the daily-loss and trailing-drawdown rules so you know whether the strategy survives them. Broker bridges for routing the sized allocation live are on the roadmap — today you run the exported Pine on your platform.
Want to run the loop?
The platform is live. Spin up an account in under a minute — your first three backtests run on us.