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 Koyfin: predictive ML research vs fundamental data terminal

Koyfin is a Bloomberg-like data terminal with 5,900+ filters and 10-year financials. StoQuant is a predictive quant platform that validates its models out-of-sample and optimizes portfolios with Black-Litterman.

Depth of data vs predictive edge

Koyfin's strength is breadth: 5,900+ screening criteria, 10+ years of financials, global coverage, and customizable dashboards. It is the best retail tool for investors who want to analyze financial statements deeply. StoQuant's strength is prediction: we use 149 engineered features, a 3-model stacking ensemble, and Venn-ABERS calibration to produce a Q-Score that maps to a true probability of outperformance. Every day we publish the out-of-sample track record on /proof.

Feature Comparison

When to choose each platform

Choose Koyfin if you need global coverage, long financial histories, and a data terminal for deep statement analysis. Choose StoQuant if you want predictive scoring, a published track record, value screening, portfolio optimization, and AI-agent access. The two tools solve different parts of the research stack.

Related on StoQuant

Compare StoQuant vs Finviz (stoquant.com/compare/finviz) or explore the Q-Score Methodology (stoquant.com/learn/q-score-methodology) and Walk-Forward Validation (stoquant.com/learn/walk-forward-validation).

FAQ

Can StoQuant replace Koyfin?

For predictive stock picking and portfolio optimization, yes. For deep 10-year financial statement analysis and global coverage, Koyfin remains stronger. Many investors use Koyfin to drill into fundamentals after StoQuant surfaces candidates.

Does StoQuant have 5,900 filters?

No. StoQuant deliberately uses a focused 149-feature predictive pipeline rather than maximizing filter count. The goal is signal quality and interpretability, not filter breadth.

Which platform is better for portfolio construction?

StoQuant. We integrate ML views with the Black-Litterman model to produce regime-aware allocations. Koyfin does not offer portfolio optimization.

Does StoQuant cover international stocks?

StoQuant currently covers US-listed stocks. Koyfin is the better choice for international equity screening.

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.