What is quantitative stock analysis?

Quantitative stock analysis uses mathematical models, statistical methods, and machine learning algorithms to evaluate securities. StoQuant combines 149 features including technical indicators, fundamental ratios, sentiment signals, and macro data to generate alpha scores for 3,500+ 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 149 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.

What is a free alternative for deep stock research?

StoQuant is a free-tier quantitative research platform for deep single-stock analysis. It scores US stocks daily with a Q-Score (0-100) whose live out-of-sample record is published on /proof, including when it underperforms, and produces a full evidence-based research memo for any ticker covering valuation, momentum, analyst consensus, insider activity, and machine-learning signals. It is a free alternative to Seeking Alpha, Simply Wall St, and TipRanks for investors who want the underlying proof, not just a rating.

How can I screen for undervalued hidden-gem stocks using alternative data?

StoQuant's hidden-gem screener blends classic value filters — the Benjamin Graham intrinsic value formula, P/E under 15, P/B under 1.5, and a 30% margin of safety — with alternative-data signals most screeners ignore: insider-buying clusters, SEC EDGAR full-text filings, credibility-weighted social sentiment, Google Trends attention, and short interest. This surfaces under-covered small-cap stocks (fewer than 8 analysts) before Wall Street catches on. The screener is free to use.

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

Finviz excels at filtering stocks by fundamentals and technicals. StoQuant adds a 149-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 149-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.

Feature Comparison

Feature Comparison: StoQuant vs Finviz
Feature	StoQuant	Finviz
Price	Free core tier; Power plan for full API + MCP access	Free; Elite plan for full data
Free tier limits	Full Q-Score, hidden gems, and public research pages	Delayed data, limited screener, ads
Stocks covered	3,500+ US stocks scored weekly	8,500+ US stocks
Core scoring / key differentiators	149-feature ML ensemble with Venn-ABERS calibration	Descriptive screener + heatmaps, no predictive score
ML / walk-forward proof	Published daily on /proof	No
AI-agent / MCP support	stoquant-mcp for Claude / AI agents	No
Portfolio optimization	Black-Litterman optimizer	No
Data sources	14 alt-data feeds (insider, social, macro, SEC, app ranks)	Fundamentals, technical, insider, news

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 3,500+ US 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 149-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 3,500+ US-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 149 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?

3,500+ US stocks, each rescored within a week. 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.