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.

Portfolio optimizer with Black-Litterman and machine-learning views

Blend market equilibrium with StoQuant's ML price forecasts (the "views") using the Black-Litterman model. Adjust confidence in your views via Omega, the uncertainty matrix.

Black-Litterman: equilibrium plus your convictions

The Black-Litterman model solves a key problem in portfolio construction: how to blend market-implied returns (the equilibrium prior) with your own forecasts (the views) without creating overconfident allocations. StoQuant provides the views: a 93-feature ML ensemble predicting 30/60/90-day excess returns, combined with sentiment, analyst consensus, and macro regime signals. The Black-Litterman optimizer uses a covariance matrix (computed from rolling historical returns) and an uncertainty matrix (Omega) to blend equilibrium and views. The result is a diversified portfolio that respects your conviction level while staying grounded in market reality. Optional regime overlay adjusts the optimization strategy based on Hidden Markov Model regime detection (bull, range, bear).

How it works

Establish the market equilibrium prior — Compute market-cap-weighted expected returns (implied by the covariance matrix). This is the baseline if you had no special views.
Layer in ML forecasts as views — StoQuant's 93-feature ensemble produces probability of 30/60/90-day outperformance. Map these to expected returns (the Q vector). Specify confidence in each view via Omega, the uncertainty matrix.
Solve for posterior expected returns — Black-Litterman combines equilibrium + views using Bayesian logic. The result is a revised expected-return vector that feeds into mean-variance optimization, producing portfolio weights with optionally adjusted risk limits by market regime.

Related on StoQuant

Master the theory: Black-Litterman Explained (stoquant.com/learn/black-litterman-explained) and Hidden Markov Model for Stocks (stoquant.com/learn/hidden-markov-model-stocks). Check the current regime: Today's Market Regime (stoquant.com/today/market-regime). Compare approaches: StoQuant vs Simply Wall St (stoquant.com/compare/simply-wall-st).

FAQ

What is the Black-Litterman model?

A Bayesian portfolio optimization method that starts with market equilibrium (implied returns) and updates it with your views (forecasts). It avoids the extreme allocations that naïve mean-variance optimization often produces.

"Views" in Black-Litterman

Your forecasts of expected returns (or excess returns) for each asset. StoQuant's views come from a 93-feature ML ensemble. You specify your confidence in each view via Omega (the uncertainty matrix).

What is Omega?

The uncertainty (covariance) matrix of your views. Diagonal elements reflect confidence in each forecast; off-diagonal elements capture correlation between views. StoQuant maps ML model confidence intervals to Omega automatically.

How does regime overlay affect portfolio weights?

In bull regimes, the optimizer may increase equity exposure. In bear regimes, it tightens stop-losses or increases cash. HMM regime classification updates daily based on S&P 500 log-returns, volatility, and yield curve signals.