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.

A free Simply Wall St and TipRanks alternative for deep small-cap research

Simply Wall St visualizes fair value; TipRanks aggregates analyst and influencer ratings. StoQuant gives you both the numbers and the receipts — a 93-feature ML ensemble, Benjamin Graham intrinsic value, insider-cluster tracking, and walk-forward out-of-sample proof — with the screener and daily Q-Score leaderboard free.

Why look past Simply Wall St and TipRanks for small caps

Simply Wall St is excellent at one thing: visualizing whether a company looks fairly valued today with its snowflake chart. TipRanks is excellent at a different thing: aggregating Wall Street analyst and financial-influencer ratings into a consensus. Neither tells you, out of sample, whether a small cap is likely to outperform from here — and both gate their deeper research behind a subscription. StoQuant takes a data-first approach: every covered stock gets a single Q-Score from 0 to 100, built from a 93-feature machine-learning ensemble (LightGBM + CatBoost + XGBoost with Venn-ABERS calibration), the Benjamin Graham intrinsic-value formula, insider-cluster signals, and Hidden Markov Model market-regime detection. The hidden-gem screener and the daily Q-Score leaderboard are free.

StoQuant vs Simply Wall St and TipRanks

How it works

Open the free screener — Start with the hidden-gem screen or the daily Q-Score leaderboard — no card required. Filter for undervalued small caps the same way you would on a paid tool.
Read the Q-Score and the why — Each stock shows a 0–100 Q-Score, the top contributing factors, intrinsic value vs price, and insider-cluster activity — the depth you would pay for elsewhere.
Check the out-of-sample receipts — The /proof page shows how the scores have actually performed against forward returns, benchmarked to Russell 2000 buy-and-hold with realistic costs.

Related on StoQuant

See the head-to-head breakdowns: StoQuant vs Simply Wall St (stoquant.com/compare/simply-wall-st). Explore the free tools: Undervalued Small Cap Stocks (stoquant.com/undervalued-small-cap-stocks) and the Best Stock Screener (stoquant.com/best-stock-screener). Learn the method: Q-Score Methodology (stoquant.com/learn/q-score-methodology).

FAQ

Is StoQuant really a free alternative to Simply Wall St and TipRanks?

Yes. The hidden-gem screener, the daily Q-Score leaderboard, and per-stock research are free. The Power tier adds the MCP server, full API, and unlimited custom screens, but you can do deep small-cap research without paying.

How is StoQuant different from Simply Wall St?

Simply Wall St visualizes current fair value with its snowflake chart. StoQuant predicts forward performance using a 93-feature ML ensemble and publishes walk-forward out-of-sample validation, so you see whether the scores have actually worked, not just how a stock looks today.

How is StoQuant different from TipRanks?

TipRanks aggregates analyst and influencer ratings into a consensus. StoQuant builds its score from data — fundamentals, momentum, insider clusters, sentiment, and regime detection — rather than from opinions, and shows the out-of-sample track record on /proof.

Is StoQuant good for small-cap research specifically?

Yes. StoQuant covers 822 stocks across the S&P 500 and Russell 2000 and deliberately excludes micro-caps below $250M to avoid liquidity noise. The hidden-gem screener is tuned for undervalued small and mid caps.

Do I need a credit card to start?

No. You can open the screener and the daily leaderboard for free without entering payment details.