What is quantitative stock analysis?

Quantitative stock analysis uses mathematical models, statistical methods, and machine learning algorithms to evaluate securities. StoQuant combines 93 features including technical indicators, fundamental ratios, sentiment signals, and macro data to generate alpha scores for 822 stocks.

How does the hidden gem screener work?

The hidden gem screener uses the Benjamin Graham intrinsic value formula (V = EPS x (8.5 + 2g)) to identify undervalued small-cap stocks. It filters for P/E under 15, P/B under 1.5, PEG under 1.0, debt-to-equity under 0.5, and flags stocks trading at least 30% below intrinsic value with fewer than 8 analyst coverage.

What machine learning models power the predictions?

StoQuant uses a 3-model stacking ensemble: LightGBM, CatBoost, and XGBoost with a logistic regression meta-learner. Predictions are calibrated with Venn-ABERS conformal prediction. The system uses 93 features spanning technical, fundamental, sentiment, macro, and alternative data signals.

How does portfolio optimization work?

StoQuant integrates ML price forecasts with the Black-Litterman portfolio optimization model. ML predictions serve as investor views (Q vector) with model confidence intervals mapped to uncertainty (omega matrix). This produces optimal portfolio allocations that blend market equilibrium with AI-driven insights.

Is my portfolio data secure?

Yes. StoQuant uses JWT authentication with timing-safe comparisons, rate limiting (300 req/min per IP), secure headers (HSTS, X-Frame-Options, X-Content-Type-Options), and portfolio ownership verification on every request. No sourcemaps are exposed in production.

What data sources does StoQuant use?

StoQuant aggregates data from SEC EDGAR filings, FMP financial data, social media sentiment (Reddit, StockTwits, Twitter), Google Trends, insider trading records, institutional holdings, options flow, and macro economic indicators from FRED. All data is stored as append-only time series.

Does StoQuant work with Claude Desktop or Claude Code?

Yes. StoQuant ships an official Model Context Protocol (MCP) server, stoquant-mcp, available on npm. One command — npx -y stoquant-mcp install — wires Claude Desktop, Claude Code, or any MCP-compatible client into roughly 30 read-only stock-research tools: prices, Q-Score, ML predictions, hidden-gem screener, insider trades, market regime, and macro dashboards. Power-tier subscription required.

The best stock screener with AI, fundamentals, and walk-forward proof

Stop guessing. StoQuant blends a 93-feature ML ensemble with Benjamin Graham intrinsic value and shows you the out-of-sample track record on every screen.

A stock screener that proves itself out of sample

A stock screener should help you make decisions, not bury you in filters. StoQuant combines a 93-feature machine-learning ensemble (LightGBM + CatBoost + XGBoost with Venn-ABERS calibration), the Benjamin Graham intrinsic-value formula, Black-Litterman portfolio construction, and Hidden Markov Model market-regime detection into a single Q-Score from 0 to 100. Every screen we publish is benchmarked against Russell 2000 buy-and-hold with realistic transaction costs and slippage. No opinions. No paywalled commentary. Just data and the receipts.

How it works

Pick a screen — Hidden gems, insider clusters, breakout candidates, undervalued small caps — every screen is preconfigured with sensible filters and a published methodology.
See the Q-Score and the why — Each pick comes with a 0–100 Q-Score, the top contributing factors, and the model’s confidence interval. Click any ticker for the full research report.
Track real forward returns — Q-Score outputs are stored append-only the day they are generated. The Proof page shows quintile spread, hit rate, and AUC against actual forward returns.

Related on StoQuant

Learn the theory behind the screener: Q-Score Methodology (stoquant.com/learn/q-score-methodology) explains the nine dimensions. Walk-Forward Validation (stoquant.com/learn/walk-forward-validation) shows why out-of-sample testing proves real alpha. See today's top picks at Today's Top Q-Score (stoquant.com/today/top-q-score). Compare against competitors: StoQuant vs Finviz (stoquant.com/compare/finviz).

FAQ

Is StoQuant’s stock screener free?

Yes. The hidden-gem screener and the daily Q-Score leaderboard are free. Power-tier adds the MCP server, full API, and unlimited screens.

How is this different from Finviz or TradingView?

Finviz and TradingView are filter UIs over fundamentals. StoQuant adds a 93-feature ML ensemble, intrinsic-value scoring, and walk-forward out-of-sample validation, with the track record published on /proof.

What’s a walk-forward backtest?

Walk-forward validation trains the model on a rolling window of past data, then tests it on the next out-of-sample window — never on data the model has seen. It’s the standard for honest quant evaluation.

How many stocks does the screener cover?

822 stocks across S&P 500 and Russell 2000, refreshed daily. We deliberately exclude micro-caps below $250M to avoid liquidity noise.