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.

Demand zone discovery: liquid commons at rising SMA support

StoQuant screens liquid common stocks whose price sits 0–3% above a rising SMA200 (else SMA50) with RSI(14) Wilder in 35–55 — the same evaluateDemandZone math behind MCP stoquant_get_demand_zone and GET /api/discovery/demand-zone (#119). Descriptive setup research only. Not a buy rating. Not a Q-Score dimension. Does not drive autopilot buys. While live /proof shows grade F with inverted excessSpreadPp (gated:no_edge), do not treat hits as ranking edge or TipRanks skill.

What a demand zone means here (and what it does not)

A demand-zone hit means price is resting just above a rising moving average with RSI in a calm mid-band — a descriptive technical setup, not a verdict. StoQuant reuses evaluateDemandZone / computeSMA / computeRSI from the channel-content library; the discovery screener does not require a Q-Score buy or strong_buy (Telegram buildDemandZone keeps that gate). Insider and institutional overlays are fail-closed objects or null. Rank prefers SMA200, then insider clusterScore, then institutional QoQ change. Limit default 25, max 50.

How it works

Screen liquid commons — Universe commons only — index ETFs and warrant tickers excluded. Price must sit 0–3% above a rising SMA200 when available, else rising SMA50.
Confirm RSI band — RSI(14) Wilder must sit in 35–55. Outside that band the name is rejected even if it hugs the MA.
Attach /proof honesty — Every MCP/REST response includes the live /proof honesty banner (grade, excessSpreadPp, dataSufficient, gatedNoEdge). While gated:no_edge, treat the list as descriptive research only — never as TipRanks skill or proven ranking edge.

Call it from Claude / MCP or REST

Pro/Power: stoquant_get_demand_zone (hosted MCP + stdio) and GET /api/discovery/demand-zone?limit=. Response shape: {asOf, items:[{ticker, zone:{ma,maValue,distancePct,rsi}, insider, institutional, qScore?}], notes, ownSignalsOnly, note, honesty}. Read honesty.banner and https://stoquant.com/proof before treating any hit as edge. Related: Proof (stoquant.com/proof), MCP hub (stoquant.com/mcp), Q-Score methodology (stoquant.com/learn/q-score-methodology).

FAQ

Is a demand-zone hit a buy rating?

No. It is a descriptive technical setup only. It is not a Q-Score dimension and does not drive autopilot buys. While live /proof excessSpreadPp is inverted (gated:no_edge), do not treat the list as ranking edge.

Does this claim TipRanks skill?

No. Own StoQuant evaluateDemandZone math only. Responses set ownSignalsOnly: true and attach the live /proof honesty banner. Never TipRanks Smart Score or TipRanks skill marketing.

How do I call the screener?

With a Pro or Power key: MCP tool stoquant_get_demand_zone (optional limit 1–50) or REST GET /api/discovery/demand-zone?limit=. Both ship from the existing #119 surface with the honesty banner attached.

What does gated:no_edge mean here?

When /proof dataSufficient is true and excessSpreadPp is negative, honesty.gatedNoEdge is true. Auto-portfolio buys stay gated; demand-zone hits remain descriptive research only until excessSpreadPp > 0.