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.

StoQuant vs Tickeron: Open methodology + walk-forward proof vs proprietary trading bots

Tickeron offers AI-powered trading bots and signals from a black-box proprietary model. StoQuant publishes its full methodology (93 features, model files, walk-forward validation). Choose predictability and transparency over AI mystique.

Transparency trumps AI mystique

Tickeron's AI trading bots are flashy, but proprietary black-box models erode trust. You never know what they're doing, how they fail, or whether they're curve-fitted to past data. StoQuant publishes every signal: the 93 ML features, the LightGBM + CatBoost + XGBoost ensemble logic, the Benjamin Graham margin-of-safety calculation, and the Hidden Markov Model regime classifier. Model files are public. Walk-forward validation metrics are on /proof. You can verify the methodology yourself or fork it entirely. For institutions and serious traders who need to understand and trust their signals, transparency is non-negotiable.

Proprietary vs Open

Why transparency matters

Tickeron's trading bots sound intelligent, but without transparency you can't validate them, improve them, or trust them in a drawdown. StoQuant's open model means you can read the code, adjust weights, understand failures, and contribute improvements. For quantitative trading where reproducibility and auditability are essential, open methodology is the only defensible choice.

Related on StoQuant

Try it yourself: AI Stock Picks (stoquant.com/ai-stock-picks) and Walk-Forward Backtest (stoquant.com/walk-forward-backtest).

FAQ

Why choose StoQuant over Tickeron?

Tickeron relies on proprietary AI bots with no disclosed methodology or validation. StoQuant publishes all 93 features, model files, and walk-forward metrics. You can verify the predictions, understand the logic, and trust the results because they're transparent and proven on out-of-sample data.

What's the advantage of open methodology?

Open methodology means you can audit the model, detect failures early, improve specific signals, and contribute ideas. Black-box models fail silently. When StoQuant's Hidden Markov regime detector or Graham margin-of-safety filter fires, you know exactly why.

Is StoQuant free?

Yes. Hidden gems, Q-Score leaderboard, and insider tracking are free. Power tier adds unlimited API access, MCP server, and custom screens.

How is StoQuant validated?

Walk-forward validation trains on past data, then tests on future out-of-sample periods the model has never seen. Metrics are published daily on /proof. Tickeron does not disclose validation metrics.

Can I integrate StoQuant with Claude or other AI agents?

Yes. StoQuant's MCP server lets Claude and other agents query picks, run portfolio analysis, and fetch insider trades directly. Tickeron does not offer MCP integration.

How many stocks does StoQuant cover?

822 stocks (S&P 500 + Russell 2000), updated daily. We exclude micro-caps below $250M to maintain liquidity and data quality.