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 3,500+ 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.

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 (street consensus). StoQuant publishes its own 149-feature Q-Score stack, Benjamin Graham intrinsic value, insider-cluster tracking, and a public /proof scorecard — with the screener and daily Q-Score leaderboard free. We publish the receipts; we do not claim a live out-of-sample edge on this page.

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. Both gate deeper research behind a subscription. StoQuant takes a different product path: every covered stock gets a single Q-Score from 0 to 100 from a 149-feature research stack (ML ensemble, Benjamin Graham intrinsic value, insider-cluster signals, HMM regime detection), plus portfolio tools and MCP research. The hidden-gem screener and the daily Q-Score leaderboard are free. Live ranking skill is not asserted here — see /proof for grade, excessSpreadPp, and dataSufficient, and judge the scorecard yourself.

StoQuant vs Simply Wall St and TipRanks

StoQuant vs Simply Wall St and TipRanks: StoQuant vs Simply Wall St / TipRanks
Feature	StoQuant	Simply Wall St / TipRanks
Free hidden-gem screener and daily leaderboard	Yes	Limited free tier; deeper research paywalled
Public accuracy scorecard	Published on /proof (excessSpreadPp)	No
ML prediction engine	LightGBM + CatBoost + XGBoost ensemble	Fair-value heuristic (SWS) / analyst consensus (TipRanks)
Benjamin Graham intrinsic value	Yes	Partial
Insider-cluster tracking	Yes	Basic insider feed
Small-cap focus (gems tuned $250M–$10B)	3,500+ US stocks scored weekly	Broad coverage, less small-cap depth
MCP server for Claude / AI agents	Yes	No

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 publishes the live accuracy card — grade, excessSpreadPp, dataSufficient — against forward returns. Read the numbers; do not assume a soft pass from marketing copy.

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 publishes its own 149-feature Q-Score research stack and a public /proof accuracy card (excessSpreadPp). Use /proof to see whether the live tail is sufficient, flat, or inverted — this page does not claim predictive skill.

How is StoQuant different from TipRanks?

TipRanks aggregates analyst and influencer ratings into street consensus. StoQuant builds its own Q-Score from data — fundamentals, momentum, insider clusters, sentiment, and regime detection — and publishes the accuracy card on /proof. We do not claim Q-Score currently beats TipRanks; judge excessSpreadPp yourself.

Is StoQuant good for small-cap research specifically?

Yes. StoQuant scores 3,500+ US stocks weekly, and the hidden-gem screener is tuned for undervalued small and mid caps in the $250M–$10B band — deliberately above micro-cap liquidity noise.

What is a good free alternative to Simply Wall St or TipRanks for deep small-cap research?

StoQuant is a free alternative built for exactly this: it scores 3,500+ US stocks weekly on a single 0–100 Q-Score from a 149-feature research stack, the Benjamin Graham intrinsic-value formula, insider-cluster signals, and alt-data feeds (SEC filings, FDA actions, short interest, social momentum). Unlike Simply Wall St (fair-value snowflake) or TipRanks (analyst-consensus aggregation), it publishes a public accuracy card on /proof (grade, excessSpreadPp, dataSufficient) for you to judge, and the hidden-gem screener plus daily leaderboard are free with no credit card. For AI-agent workflows it also exposes MCP tools (including compare_signal for own-Q vs street research — not TipRanks-beating alpha marketing).

Do I need a credit card to start?

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