Screen a stock universe with quantitative filters. Returns ranked candidates with scores and metrics.

Use this tool when you need to find stocks matching specific criteria — value stocks, momentum leaders, quality companies, or multi-factor ranked candidates. Supports 7 screen types across 3 universes (S&P 500, Russell 2000, Nasdaq 100).

After screening, use backtest_strategy to test the screen as a trading strategy, or factor_analysis to understand the factor exposures of the selected stocks.

Run a historical backtest on a stock screening strategy. Uses a rebalance-loop engine that re-runs the screening pipeline on each rebalance date, sizes positions, enforces risk limits, and tracks daily P&L.

Returns equity curve, trade log, and performance metrics including CAGR, Sharpe ratio, maximum drawdown, Calmar ratio, win rate, and turnover.

The backtest is fully deterministic — same inputs always produce identical results.

After backtesting, use factor_analysis on the equity_curve to decompose returns into Fama-French factors (market, size, value, momentum) and estimate true alpha.

Decompose strategy or portfolio returns into Fama-French factors using OLS regression.

Breaks down returns into exposures to four systematic factors:

• Mkt-RF (market risk premium): how much return comes from overall market movement

• SMB (small minus big): size factor exposure

• HML (high minus low): value factor exposure

• Mom (momentum): momentum factor exposure

Also estimates alpha (excess return not explained by factors) with t-statistic for statistical significance. A |t-stat| > 2 suggests statistically significant alpha.

Returns alpha (daily and annualized), factor loadings with t-statistics, R-squared (how much of return variance is explained by factors), and residual volatility.

Use this after backtest_strategy to understand WHERE your returns come from — is it genuine alpha or just factor exposure?