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 Seeking Alpha: Data-driven ML vs editorial quant blends

Seeking Alpha blends editorial articles with proprietary quant ratings behind a paywall. StoQuant delivers transparent, data-driven ML predictions with published walk-forward validation and a free tier. No opinion paywalls. Just methodology and receipts.

Methodology and receipts over editorial opinion

Seeking Alpha's editorial coverage is popular, but most premium insights hide behind a paywall. The quant ratings are proprietary and unvalidated. StoQuant takes the opposite approach: publish the methodology, show the walk-forward proof, keep the free tier robust. A 149-feature ML ensemble (LightGBM + CatBoost + XGBoost with Venn-ABERS calibration) predicts forward returns. Benjamin Graham intrinsic value screens for margin of safety. Hidden Markov regimes adapt to bull/range/bear cycles. And every result is benchmarked on out-of-sample data with real transaction costs baked in.

Feature Comparison

Feature Comparison: StoQuant vs Seeking Alpha
Feature	StoQuant	Seeking Alpha
Price	Free core tier; Power plan for full API + MCP access	Free; Premium plan for full access
Free tier limits	Full Q-Score, hidden gems, and public research pages	Limited articles, quant ratings
Stocks covered	3,500+ US stocks scored weekly	US stocks
Core scoring / key differentiators	149-feature ML ensemble with Venn-ABERS calibration	Editorial + Quant ratings
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)	Earnings estimates, news, contributor ratings

Transparency wins

Seeking Alpha's editorial voice is valuable for context and narrative. But when you need actionable signals, opinions don't scale. StoQuant's transparency means you can understand every factor in a 0–100 score, trust the methodology because it's published, and verify the proof because it's on /proof. No proprietary black boxes. No paywalled insights. Just data, open source, and out-of-sample results.

Related on StoQuant

Track insider activity: Insider Buying Tracker (stoquant.com/insider-buying-tracker).

FAQ

Why is StoQuant better than Seeking Alpha?

Seeking Alpha blends editorial articles with proprietary quant, both behind paywalls. StoQuant is data-first: transparent methodology, published walk-forward validation, and a robust free tier. If you prefer open methodology and proven results over editorial opinion, StoQuant delivers measurable edge.

Is StoQuant free?

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

What does "walk-forward validation" mean?

Walk-forward training tests the model on out-of-sample data it has never seen. This is the gold standard for honest quant evaluation. Seeking Alpha does not publish walk-forward metrics; StoQuant's /proof page shows daily results.

How is StoQuant's methodology different?

StoQuant publishes all 149 ML features, the Benjamin Graham intrinsic-value formula, Hidden Markov regime states, and insider-cluster thresholds. Every input is transparent and adjustable. Seeking Alpha's quant ratings are proprietary.

Can I use StoQuant with Claude or AI agents?

Yes. StoQuant's MCP server lets Claude query stock picks, run portfolio analysis, and fetch insider trades. Seeking Alpha does not offer MCP integration.

How many stocks does StoQuant cover?

3,500+ US stocks, each rescored within a week. We exclude micro-caps below $250M to maintain data quality.