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 Simply Wall St: ML ensemble + walk-forward proof vs snowflake charts

StoQuant combines a 93-feature ML ensemble (LightGBM + CatBoost + XGBoost), Benjamin Graham intrinsic value, and Hidden Markov regime detection with published walk-forward validation. Simply Wall St visualizes intrinsic value with a snowflake chart. Choose data-driven predictions over visualization alone.

Data-driven picks with out-of-sample proof

Simply Wall St excels at visualizing intrinsic value with its iconic snowflake chart. But visualization alone doesn't tell you when to buy or sell. StoQuant layers a 93-feature machine-learning ensemble on top of fundamentals, adds Hidden Markov Model market-regime detection, and publishes the walk-forward validation receipts on every screen. Every pick is benchmarked against Russell 2000 buy-and-hold with realistic transaction costs. No opinions. Data first.

Feature Comparison

Why StoQuant for predictive accuracy

Simply Wall St's snowflake visualization is intuitive for understanding fair value. But prediction requires more than a static intrinsic-value metric. StoQuant's ML ensemble captures non-linear relationships between 93 features (sentiment, momentum, insider activity, sector relative strength, technical breakouts), adapts to market regimes, and proves itself on out-of-sample data every day. For traders and portfolio managers who need forward returns, not just past fundamentals, StoQuant's data-first approach delivers measurable edge.

Related on StoQuant

Try the platform: Undervalued Small Cap Stocks (stoquant.com/undervalued-small-cap-stocks) and Portfolio Optimizer (stoquant.com/portfolio-optimizer).

FAQ

Why is StoQuant better than Simply Wall St?

Simply Wall St excels at visualizing current fair value. StoQuant goes further by predicting forward returns using a 93-feature ML ensemble, Hidden Markov regime detection, and published walk-forward validation. If you want to know not just whether a stock is undervalued, but when to buy or sell, StoQuant's data-driven approach delivers measurable edge.

Does StoQuant have a snowflake chart like Simply Wall St?

No. StoQuant focuses on predictive scores (Q-Score 0–100) and the underlying factors driving the prediction, rather than visualization. The /proof page shows walk-forward validation metrics, not a visual representation of fair value.

Is StoQuant free?

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

What does "walk-forward" validation mean?

Walk-forward validation trains the model on past data, then tests it on future out-of-sample data the model has never seen. It's the gold standard for honest quant evaluation. Simply Wall St does not publish walk-forward metrics.

Can I integrate StoQuant with AI agents or Claude?

Yes. StoQuant's MCP server lets Claude and other AI agents query stock picks, run portfolio analysis, and fetch insider trades directly. Simply Wall St does not offer MCP integration.

How many stocks does StoQuant cover?

822 stocks across S&P 500 and Russell 2000, refreshed daily. We deliberately exclude micro-caps below $250M to maintain data quality and liquidity.