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.

Ai Infrastructure stocks ranked by Q-Score

Top StoQuant Q-Score picks in Ai Infrastructure, refreshed daily.

Ai Infrastructure top picks

NVDA — NVIDIA
AMD — Advanced Micro Devices
AVGO — Broadcom

How we rank Ai Infrastructure stocks

Each Ai Infrastructure ticker is scored on nine dimensions (momentum, technicals, fundamentals, analyst, sentiment, social, quant, gem, moat) and blended into a 0-100 Q-Score. Snapshots are append-only - yesterday's ranking is never overwritten - so the Proof page can match every pick to real forward returns.

FAQ

How often does the Ai Infrastructure list update?

Daily at 06:00 UTC. The page shows the most recent snapshot; the underlying database keeps every prior day.

Why are some picks blurred?

The top three picks are free. Sign up to see the rest.

How are these stocks chosen?

By Q-Score within the Ai Infrastructure niche. Q-Score blends ML probability, valuation, momentum, sentiment, and other factors.

What is Q-Score?

A 0-100 score that blends nine alpha dimensions and a calibrated ML probability. Higher scores have historically delivered better forward returns out-of-sample.