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.

StoQuant vs Finviz: ML ensemble + intrinsic value + proof vs filter builder

Finviz excels at filtering stocks by fundamentals and technicals. StoQuant adds a 93-feature ML ensemble, Benjamin Graham intrinsic-value scoring, and published walk-forward validation. Get intelligent predictions, not just filtered lists.

Filters are not predictions

Finviz's filter UI is powerful for hypothesis testing: "Show me high-dividend, low-PE stocks" is quick and visual. But filters don't predict forward returns. StoQuant builds on top of fundamental filters with a 93-feature ML ensemble (LightGBM + CatBoost + XGBoost with Venn-ABERS calibration), Benjamin Graham intrinsic-value analysis, and Hidden Markov Model market-regime detection. Then it publishes the walk-forward validation: hit rates, quintile spreads, out-of-sample AUC. For traders who want actionable signals, not just filtered lists, ML + proof wins.

Filter UI vs AI-Driven Picks

Go beyond filters

Finviz freed retail traders from expensive screeners by offering a free, fast filter UI. StoQuant respects that legacy but pushes further: ML predictions + intrinsic value + proven results on real data. Every day, the Q-Score leaderboard ranks 822 stocks. Every screen publishes its validation metrics. You're not guessing which filter combination works; you're buying signals backed by walk-forward proof.

Related on StoQuant

Start here: Best Stock Screener (stoquant.com/best-stock-screener).

FAQ

Why is StoQuant better than Finviz?

Finviz is a powerful filter builder for hypothesis testing. StoQuant goes further: a 93-feature ML ensemble predicts which undervalued stocks will rise next, Benjamin Graham intrinsic-value scoring identifies margin-of-safety candidates, and walk-forward validation proves the results on out-of-sample data. For actionable predictions, not just filtered lists, StoQuant delivers measurable edge.

Is StoQuant free?

Yes. Hidden gems, Q-Score leaderboard, insider tracking, and 822-stock coverage are free. Power tier adds unlimited screens, full API, and MCP server.

What's the Benjamin Graham formula?

Graham intrinsic value = EPS × (8.5 + 2 × growth rate). StoQuant uses this to screen for margin of safety: stocks trading at least 30% below intrinsic value. It's a time-tested tool for value investors.

How does StoQuant's ML ensemble differ from Finviz filters?

Finviz lets you manually combine filters (e.g., "P/E < 15 AND Debt < 0.5"). StoQuant's ML ensemble captures non-linear relationships across 93 features (momentum, sentiment, insider activity, technical breakouts, sector relative strength, etc.) and learns which patterns predict forward returns. The ensemble is validated on out-of-sample data daily.

How many stocks does StoQuant cover?

822 stocks (S&P 500 + Russell 2000), updated daily. Finviz covers most US stocks but lacks daily ML predictions and walk-forward validation.

Can I integrate StoQuant with Claude?

Yes. StoQuant's MCP server lets Claude query picks, run portfolio optimization, and fetch insider trades. Finviz does not offer MCP integration.