Your Trading Ideas,
Tested and Running
From indicator strategies to stochastic calculus. From fundamental screening to ML models. Build, backtest, validate, and paper trade — no coding required. All in your browser.
From indicator strategies to stochastic calculus. From fundamental screening to ML models. Build, backtest, validate, and paper trade — no coding required. All in your browser.
Every strategy follows a structured path from idea to live execution. QuanterLab guides you through each stage.
Scan the S&P 500 for mean reversion setups, momentum breakouts, pairs cointegration, and trend regime shifts. Filter by Hurst exponent, half-life, ADX, and more.
Configure strategies visually with up to 20 confirmation layers. Four specialized builders plus a universal builder with 130+ indicators. Export standalone Python code you own — run fine-mesh grid searches locally, then bring results back to the platform.
Calibrate Ornstein-Uhlenbeck processes, run Kalman filters, and trade pairs via cointegration. Visualize the entire parameter landscape as interactive 3D surfaces — see how Sharpe ratio changes across every entry/exit combination, then export the grid search to run locally at higher resolution.
Go beyond static backtesting. The Regime Optimizer compares static, adaptive (per-regime), regression, and rolling mean thresholds side by side. Walk-forward validation confirms your strategy holds up on unseen data — not just the sample it was fitted on.
A backtest that looks great on historical data means nothing if it was curve-fitted. Walk-forward validation splits data into training and test folds, optimizes on each training window, then scores on unseen data. You get a composite out-of-sample score that tells you if your strategy generalizes — or if it just memorized the past.
Screen the S&P 500 by 23 fundamental metrics with z-score ranking. Backtest factor portfolios with survivorship bias correction using historical index constituents. Then dissect results period by period with the Autopsy module.
Construct portfolios with MVO, HRP, and Inverse Volatility. Then dissect them: the Autopsy module shows how regime-aware signal weights would change your portfolio vs. your discretionary allocation. Compare your weights against signal-driven weights period by period, with factor radar charts and performance attribution.
Train classification and regression models on 100+ auto-generated features. Compare Random Forest, XGBoost, LightGBM, CatBoost, and Ensemble models. Deploy trained models to paper trade in real time.
Deploy strategies in a simulated environment. Three dedicated paper trading engines handle indicator strategies, universal backtester configs, and ML models independently — each with their own capital pool and position management.
Build and monitor paper portfolios. Import optimized allocations from MVO, HRP, or Inverse Volatility, track cumulative performance, and compare multiple portfolios side by side.
From hypothesis to execution. Build, test, and deploy quantitative strategies in one platform. Free to start.