Backtest 10 strategies before lunch.

Write it. Test it. Done.

Other traders spend 3-5 days coding a single strategy in Python — debugging syntax errors, fixing data pipelines, wiring up performance metrics.
You'll describe your rules in plain English and get a complete backtest in minutes. Iterate on 20 variations in the time it used to take to build one.
"Buy SPY when RSI(14) crosses above 30 and hold for 10 days" → fully backtested system. That's it.

Backtests you can actually trust

Most retail backtesting tools lie to you. They use adjusted prices that leak future information. They quietly exclude delisted stocks (the ones that failed). They fabricate returns that don't survive contact with real markets.
We don't.
Quant Composer integrates with institutional-grade data — point-in-time accurate, survivorship-bias-free, with 20+ years of history across US equities, ETFs, and futures. The same data hedge funds pay six figures for.

Portfolio analytics that actually matter

Test a single strategy or a full portfolio of 50. Either way, you get the metrics professional quants rely on:
Sharpe, Sortino, Calmar, max drawdown, rolling correlations, regime analysis, and Monte Carlo stress tests — all benchmarked against SPY, sector ETFs, or a custom index of your choice.
No exporting to Excel. No building reports. Just insights.

Built for traders, not PhDs

If you're new to systematic trading: skip the 6-month Python learning curve. Start testing real strategies today.
If you're an experienced quant: stop rewriting the same boilerplate. Use Quant Composer for rapid prototyping, then export clean Python code when you're ready to deploy.
Either way — your edge comes from ideas, not infrastructure.