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.

Market regime investing: use HMM detection to rotate between value and momentum

The same stock strategy performs differently in bull, range, and bear regimes. StoQuant detects the current regime daily and adjusts Q-Score weights and portfolio allocation automatically.

Why regime matters

A strategy that works in a bull market can fail in a bear market. Value stocks outperform in recovery phases; momentum stocks outperform in trending bull markets; quality and low-volatility names outperform in high-volatility regimes. Ignoring regime is one of the most common causes of strategy decay. StoQuant uses a Hidden Markov Model (HMM) on S&P 500 log-returns to classify the current market state daily and feed that state into both scoring and allocation.

How it works

Estimate regime probabilities — The HMM fits a state-switching model to S&P 500 log-returns and selects the number of states by Bayesian Information Criterion (BIC). Typical states include bull, range, and bear/high-volatility.
Adjust Q-Score dimension weights — Each Q-Score dimension has different predictive power in each regime. In bull markets momentum gets more weight; in bear markets quality and insider buying get more weight.
Map regime to Black-Litterman uncertainty — Regime confidence feeds into the uncertainty-of-views matrix (Omega). In ambiguous regimes, the optimizer trusts the market equilibrium more and the ML views less.
Rebalance with evidence — The result is a portfolio allocation that shifts exposure based on the current regime without requiring you to time the market manually.

From signal to allocation

Regime detection is not just a dashboard decoration at StoQuant. It changes the math. When the HMM assigns high probability to a bear/high-volatility state, the Q-Score reduces valuation weight (cheap stocks can get cheaper), increases quality and insider weight, and the Black-Litterman optimizer shrinks active positions toward the market portfolio. When the model detects a bull state, momentum and growth weights rise and the optimizer allows more concentrated active bets.

Related on StoQuant

Read the Hidden Markov Model guide (stoquant.com/learn/hidden-markov-model-stocks), explore the Portfolio Optimizer (stoquant.com/portfolio-optimizer), and check today’s market regime at /analytics/macro.

FAQ

What is market regime investing?

Market regime investing means adjusting your strategy based on the current market state — bull, bear, range, or high-volatility — rather than using the same static approach in all conditions.

How does StoQuant detect regimes?

StoQuant uses a Hidden Markov Model on S&P 500 log-returns with BIC-selected states. Both log-likelihood and state probabilities are persisted daily.

Does regime detection improve returns?

Regime-aware weighting can reduce drawdowns and improve risk-adjusted returns by avoiding value traps in bear markets and underweighting momentum in sideways chop. See /proof for out-of-sample metrics.

Can I see the current regime?

Yes. The macro analytics page at /analytics/macro shows the current HMM-classified regime and related macro indicators.

Is regime detection free?

The current regime and basic macro dashboard are available on the free tier. Advanced regime history and portfolio integration are part of Pro/Power.