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 14,000+ 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 AltIndex: predictive quant research vs alt-data AI picks

AltIndex packages alternative data into a daily AI Score. StoQuant layers alt-data on top of a 149-feature ML ensemble, Benjamin Graham value screens, Black-Litterman optimization, and published walk-forward validation.

Alt-data-only vs quant synthesis

AltIndex is one of the best retail alt-data platforms. It synthesizes Reddit sentiment, web traffic, app downloads, job postings, insider filings, and Congress trades into a single AI Score. That is valuable, but alt-data alone can be noisy. StoQuant combines the same alt-data themes with 149 features across valuation, quality, momentum, growth, sentiment, macro, forensic accounting, and cross-sectional ranks — then validates the entire ensemble out-of-sample. The result is a more robust signal and a portfolio optimizer that knows how to size positions in different macro regimes.

Feature Comparison

When to choose each platform

Choose AltIndex if you want a clean alt-data dashboard focused on early inflection signals. Choose StoQuant if you want alt-data combined with fundamental value, predictive ML, portfolio optimization, and a published track record.

Related on StoQuant

Explore the Alt-Data Stock Screener (stoquant.com/alt-data-stock-screener), the Q-Score Methodology (stoquant.com/learn/q-score-methodology), and /proof for the live track record.

FAQ

Which platform has better AI stock picks?

StoQuant emphasizes calibrated probabilities and walk-forward proof. AltIndex emphasizes alternative-data inflections. If you want evidence-based predictive scoring, StoQuant is stronger. If you want pure alt-data novelty, AltIndex is a strong competitor.

Does StoQuant use alternative data?

Yes. StoQuant includes Reddit/StockTwits/Twitter sentiment, Google Trends, insider filings, institutional flow, options skew, and FINRA short volume as part of its 149-feature pipeline.

Can StoQuant replace AltIndex?

For investors who want alt-data + fundamentals + ML + optimization, yes. AltIndex may still be useful if you want a dedicated alt-data UI.

Is StoQuant free?

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

Does StoQuant publish its track record?

Yes. The /proof page shows walk-forward quintile spread, hit rate, AUC, and confidence intervals against Russell 2000 buy-and-hold.