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.

StoQuant vs TipRanks: own Q-Score research vs street Smart Score

TipRanks ranks analysts and blends ratings into a Smart Score (street consensus aggregation). StoQuant builds its own 149-feature Q-Score, portfolio tools, and MCP research surface — and publishes the live accuracy card on /proof (excessSpreadPp via get_accuracy_summary). We do not claim a current out-of-sample edge while that card is insufficient or inverted.

Street consensus aggregation vs own scoring stack

TipRanks is best known for ranking analysts by historical accuracy and aggregating their ratings into a Smart Score. That helps investors see what Wall Street thinks. StoQuant is a different product category: we publish our own Q-Score from a multi-feature research stack (fundamentals, ML ensemble, portfolio tools), not a TipRanks-style consensus blend. We also publish every accuracy metric on /proof — including excessSpreadPp from get_accuracy_summary — so you can see whether the live tail looks sufficient, flat, or inverted. When the public card is grade F or excessSpreadPp is negative, we do not market predictive skill, calibrated outperformance, or a live ranking win versus TipRanks Smart Score. Compare the tools; judge the receipts.

Feature Comparison

Feature Comparison: StoQuant vs TipRanks
Feature	StoQuant	TipRanks
Price	Free core tier; Power plan for full API + MCP access	Free; Premium plan for full data
Free tier limits	Full Q-Score, hidden gems, and public research pages	Limited Smart Score and top analysts
Stocks covered	3,500+ US stocks scored weekly	US stocks
Core scoring / key differentiators	149-feature ML ensemble with Venn-ABERS calibration	Smart Score blending analyst, insider, technical, fundamentals
ML / walk-forward proof	Published daily on /proof	No public excessSpreadPp card like /proof
AI-agent / MCP support	stoquant-mcp for Claude / AI agents	No
Portfolio optimization	Black-Litterman optimizer	No
Data sources	14 alt-data feeds (insider, social, macro, SEC, app ranks)	Analyst ratings, insider, blogger consensus
StoQuant vs street research tool	compare_signal MCP tool (Pro) — own Q vs street consensus side-by-side	Consensus-focused analyst tools

When to choose each platform

Choose TipRanks if you want to track analyst ratings, price targets, and insider activity with a clean UI built around street consensus. Choose StoQuant if you want an independent Q-Score, value screening, portfolio optimization, MCP research tools (including compare_signal for own-Q vs street), and a published /proof track record you can inspect yourself — without assuming the live card currently shows positive edge.

Related on StoQuant

Read the live accuracy card on /proof (stoquant.com/proof) — grade, excessSpreadPp, dataSufficient. Try the free Simply Wall St / TipRanks alternative page (stoquant.com/free-simply-wall-st-tipranks-alternative) for product overlap, and /mcp for compare_signal + get_accuracy_summary.

FAQ

Is StoQuant a free alternative to TipRanks?

StoQuant offers free Q-Score ratings, hidden-gem screening, and public research pages including /proof. It is not a TipRanks clone: TipRanks centers street consensus; StoQuant centers its own scoring stack and published accuracy card.

Does StoQuant beat TipRanks?

We do not claim that. TipRanks aggregates street consensus; StoQuant publishes its own Q-Score and the live /proof card (excessSpreadPp via get_accuracy_summary). When that card is inverted or insufficient, we fail closed — no predictive-skill marketing. Judge the receipts on /proof.

Does StoQuant use analyst ratings?

Analyst context can appear as one research input among many. The product pitch is own Q-Score + portfolio/MCP tools + public /proof, not “smarter than TipRanks Smart Score.”

What is the difference between Smart Score and Q-Score?

TipRanks Smart Score is a 1–10 blend built around analyst/insider/technical consensus. StoQuant Q-Score is StoQuant’s own 0–100 research score from its feature stack. Whether that score currently has out-of-sample edge is answered only by the public /proof card — not by this comparison page.

What is compare_signal?

A Pro MCP research tool that places StoQuant’s own score beside street consensus for a ticker. Useful for disagreement research; not marketed as TipRanks-beating alpha. Pair it with get_accuracy_summary / excessSpreadPp on /proof.

Does StoQuant track insider trading?

Yes. StoQuant includes an insider cluster signal that aggregates distinct filers, purchase value, and recent Form 4 activity.

Can I use StoQuant with Claude?

Yes. Hosted MCP and the stoquant-mcp package expose read-only tools (including compare_signal and get_accuracy_summary) to Claude Desktop, Claude Code, and other MCP clients.