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 14,000+ 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 TradingView: predictive quant research vs charting community

TradingView dominates charting and Pine Script. StoQuant adds a 149-feature ML ensemble, Benjamin Graham intrinsic value, Black-Litterman portfolio construction, and published walk-forward validation. Use them together or pick the one that matches your workflow.

Charting community vs predictive research

TradingView is unmatched for charting, Pine Script, and social idea sharing. If your edge comes from custom indicators and community scripts, TradingView is the right tool. StoQuant is built for a different job: predictive stock research. We combine 149 engineered features, a 3-model stacking ensemble, Hidden Markov Model regime detection, and Black-Litterman portfolio construction to answer 'what should I buy and how much' — not just 'what does the chart look like'.

Feature Comparison

When to choose each platform

Choose TradingView if you are a technical trader who needs the best charting, community scripts, and broker integration. Choose StoQuant if you are a quant or fundamental investor who wants predictive scoring, value screening, portfolio optimization, and a published track record. Many investors use both: TradingView for chart execution and StoQuant for idea generation and allocation.

Related on StoQuant

Try the AI Stock Picks page (stoquant.com/ai-stock-picks), the hidden-gems screener (stoquant.com/gems), or the Portfolio Optimizer (stoquant.com/portfolio-optimizer).

FAQ

Does StoQuant replace TradingView?

Not necessarily. TradingView is superior for charting and Pine Script. StoQuant is superior for predictive scoring, value screening, and portfolio construction. They are complementary for many investors.

Does StoQuant have charts?

StoQuant focuses on quant scorecards, research pages, and portfolio analytics rather than interactive charting. If advanced charting is your primary need, TradingView remains the better choice.

Which is better for AI stock analysis?

StoQuant. TradingView has no native AI/ML stock scoring. StoQuant uses a 149-feature ensemble with calibrated probabilities and publishes walk-forward validation.

Can I use StoQuant signals in TradingView?

StoQuant does not currently publish Pine Script indicators. You can export watchlists manually or use the MCP server and API to move ideas into your preferred execution platform.

Is StoQuant free?

Yes. Q-Score, hidden gems, and public research pages are free. Power tier adds the MCP server, full API, and unlimited screens.