What is quantitative stock analysis?

Quantitative stock analysis uses mathematical models, statistical methods, and machine learning algorithms to evaluate securities. StoQuant combines 93 features including technical indicators, fundamental ratios, sentiment signals, and macro data to generate alpha scores for 822 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 93 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.

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 93-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.

Editorial vs Data-Driven

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 93 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?

822 stocks (S&P 500 + Russell 2000), refreshed daily. We exclude micro-caps below $250M to maintain data quality.