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 walk-forward-proven Q-Score (0-100), publishes its measured out-of-sample track record, 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 Prospero.ai: open ML methodology + walk-forward proof vs mobile AI signal app

Prospero.ai is a popular mobile-first AI investing app that delivers signal-driven stock picks from institutional data analysis. StoQuant is an open-methodology quantitative research platform: a 149-feature ML pipeline with published walk-forward validation, Benjamin Graham intrinsic-value screening, and Black-Litterman portfolio construction. Choose transparency and verifiable proof over a black-box mobile signal.

Mobile signals vs verifiable research

Prospero.ai delivers a polished mobile experience: open the app, see AI-powered stock signals, and act on them. It's a legitimate tool for investors who want quick, data-driven picks without deep research. But Prospero's methodology is proprietary — you see the signal, not how it's built, how it's validated, or how it performed out-of-sample. StoQuant takes the opposite approach: every signal is built from 149 published features across a 3-model stacking ensemble (LightGBM + CatBoost + XGBoost) with Venn-ABERS conformal calibration. The model files are open, the walk-forward validation metrics are on /proof, and the Benjamin Graham intrinsic-value calculation is documented on /learn/benjamin-graham-formula. For investors who need to understand and trust their edge before deploying capital, transparency is the only path.

Feature Comparison

Feature Comparison: StoQuant vs Prospero.ai
Feature	StoQuant	Prospero.ai
Price	Free core tier; Power plan for full API + MCP access	Free app with in-app purchases
Free tier limits	Full Q-Score, hidden gems, and public research pages	Signal-based stock picks, limited features
Stocks covered	3,500+ US stocks scored weekly	US stocks (app-based)
Core scoring / key differentiators	149-feature ML ensemble with Venn-ABERS calibration	Proprietary AI signal score, methodology not published
ML / walk-forward proof	Published daily on /proof	Claimed win-rate metrics, no walk-forward published
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)	Institutional data, market movements, sentiment

Desktop-grade research vs mobile-first

Prospero.ai is mobile-first: its strength is delivering signals on the go. StoQuant is desktop-first: research pages, scorecards, portfolio analytics, and MCP server integration for AI agents. For investors who do serious research at a desk — running screens, analyzing insider clusters, optimizing portfolios with Black-Litterman — StoQuant's depth is unmatched. For investors who want quick signals on their phone, Prospero.ai is a viable option. The two tools serve different workflows.

Related on StoQuant

Explore the AI Stock Picks page (stoquant.com/ai-stock-picks), Hidden Gems (stoquant.com/gems), and the live proof dashboard (stoquant.com/proof).

FAQ

Why choose StoQuant over Prospero.ai?

Prospero.ai delivers AI-powered stock signals through a mobile app, but its methodology is proprietary and unverifiable. StoQuant publishes all 149 features, model files, and walk-forward validation metrics. You can verify the predictions, understand the logic, and trust the results because they're proven on out-of-sample data.

Is StoQuant available on mobile?

StoQuant is a responsive web application that works on mobile browsers, but our depth is best experienced on desktop — research pages, scorecards, portfolio analytics, and the MCP server. Prospero.ai is a native mobile app optimized for on-the-go signal delivery.

How does StoQuant validate its predictions?

Walk-forward validation trains on past data, then tests on future out-of-sample periods the model has never seen. Metrics are published daily on /proof. Prospero.ai publishes newsletter win-rate claims but does not disclose walk-forward methodology or feature composition.

Is StoQuant free?

Yes. Q-Score, hidden gems, insider tracking, and public research pages are free. Power tier adds the MCP server, full API, and unlimited screens. Prospero.ai is free to download with in-app purchases for premium signals.

Can I integrate StoQuant with Claude or other AI agents?

Yes. StoQuant's MCP server lets Claude and other agents query picks, run portfolio analysis, and fetch insider trades directly. Prospero.ai does not offer MCP integration.

Which platform has better portfolio construction tools?

StoQuant. We integrate ML views with the Black-Litterman model to produce regime-aware allocations with position sizing — a feature no mobile signal app offers.