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.

Alternative data stock signals: edge via SEC, FDA, GitHub, and insider clusters

Price and fundamentals are crowded. StoQuant monitors 14 alternative-data feeds - SEC filings, FDA actions, insider clusters, private placements, clinical trials, GitHub activity, App Store rank trends, short interest, news tone, and social momentum - to surface catalysts and red flags before price discovery.

Edge lives where analysts are not looking

Price-to-earnings and revenue growth are widely available; thousands of funds and robo-advisors scan the same metrics. Alternative data - non-traditional sources like SEC filings, FDA actions, GitHub commits, insider clusters, and news sentiment - is where edge lives. A clinical trial readout 2 hours before the news hit Wall Street wire is edge. A startup losing traction on GitHub weeks before their valuation reset is edge. A company filing an 8-K about a key executive departure that bounces around Twitter but gets missed by most terminals is edge. StoQuant ingests 14 alternative-data feeds: SEC filings (8-K, Form D, insider Form 4), FDA regulatory actions, clinical trial events, GitHub traction, App Store rank trends, job posting growth, short interest, social sentiment (Reddit, StockTwits, Twitter), news tone via FinBERT, lobbying & congressional trades, private-placement filings, product recalls (CPSC), and Hacker News mention volume. No single feed is predictive on its own, but layered into an ensemble, they often surface catalysts and red flags before the crowd.

How it works

Ingest 14 alternative feeds daily — SEC EDGAR (8-K, D, Form 4), FDA filings, clinical trial registries (clinicaltrials.gov), GitHub API, App Store rankings, Google Trends, Hacker News, social networks (Reddit, Twitter, StockTwits), short-interest reports, news archives, and job posting platforms.
Normalize and score each signal — Convert heterogeneous data (event counts, GitHub stars, social mentions, FDA approval status) into z-score signals comparable across ticker universes. Compute signal credibility: old news scores lower than breaking news; high-engagement tweets from verified accounts score higher.
Combine into catalyst and red-flag scores — Aggregate alt-data signals into two composite indicators: catalyst-watch (positive events brewing) and red-flag watch (regulatory/insider/momentum warnings). Rank by signal recency and strength.

Example: how alternative data caught real catalysts

Job posting growth (via Adzuna API) often precedes analyst EPS raises: a biotech posting 5x more roles hints at expansion pre-announcement. GitHub stars + forks on an open-source repo surge before the maintainers announce a Series B. SEC 8-K filings (material events, key departures) move stocks within hours; StoQuant surfaces them 2 hours after filing. Clinical trials showing positive interim data on a tiny biotech often hit social first, then Wall Street. These are the opportunities alternative data exposes - and traditional screeners miss.

Related on StoQuant

Dive deeper into individual signal categories: Alt Data Stock Screener (stoquant.com/alt-data-stock-screener) for detailed filters. Understand the broader Q-Score framework in Q-Score Methodology (stoquant.com/learn/q-score-methodology) - alt-data feeds weight 25% of the composite score. Track live performance on Proof (stoquant.com/proof) and compare against competitors (stoquant.com/compare/seeking-alpha).

FAQ

What is alternative data in investing?

Non-traditional data sources outside price, earnings, and balance sheets: satellite imagery, credit card transactions, shipping logs, social media sentiment, job postings, FDA approvals, insider trades, GitHub activity. Higher-frequency and less-analyst-covered than fundamentals.

Is alternative data reliable?

Some signals are noisy (Twitter sentiment), others are highly predictive (FDA approvals for biotech, GitHub engagement for SaaS). StoQuant weights each feed by its historical predictive power (via permutation importance in the ML ensemble).

How fresh are the alternative signals?

SEC filings within 2 hours of public release. GitHub activity within 1 hour. Social sentiment refreshed every 6 hours. FDA/clinical trial updates when published. Job postings updated daily. News tone refreshed every 2 hours.

Can I screen on individual alt-data feeds?

Yes. The advanced screener lets you filter on any individual signal: "GitHub stars > X", "clinical trial status = success", "8-K filing in the last 7 days", "short interest > Y%".