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.

What is StoQuant MCP?

stoquant-mcp is a Model Context Protocol server published on npm that gives Claude Desktop, Claude Code, and any other MCP-compatible client direct, read-only access to 66 StoQuant tools across prices, research, signals, discovery, screener, ownership, macro, filings, social, catalysts, options-flow, alt-data, portfolio, backtest, and strategy categories.

How do I install StoQuant MCP for Claude Desktop?

Run npx -y stoquant-mcp install. The installer prompts for your StoQuant API key and writes the right claude_desktop_config.json for macOS, Windows, or Linux. Restart Claude Desktop and the stoquant_* tools become available.

Do I need a paid plan?

No. Any StoQuant account works — free accounts get 100 requests/day, Pro gets 5,000/day, Power is unlimited. Generate an API key at https://stoquant.com/account/api-keys.

Is the MCP server safe?

It is read-only by design — no portfolio mutation, no order placement. All requests use HTTPS with the API key in the Authorization header (never logged). Server-side, keys are hashed (SHA-256) at rest, prefix-indexed, and timing-safe-compared.

Can I use the tools from the command line without Claude?

The npm CLI only supports install / serve / --help, not per-tool subcommands. Run npx -y stoquant-mcp install or npx stoquant-mcp serve. The hosted endpoint POST https://stoquant.com/api/mcp exposes 21 read-only tools.

StoQuant MCP for Claude Desktop

The stoquant-mcp Model Context Protocol server gives Claude Desktop, Claude Code, and any other MCP-compatible client direct, read-only access to StoQuant’s quantitative stock-research stack — 71 tools across 15 categories. Any StoQuant account can use it: free accounts get 100 requests/day, Pro gets 5,000/day, Power is unlimited.

One-command install

In a terminal, run npx -y stoquant-mcp install. The installer prompts for your StoQuant API key (mint one free at /account/api-keys), writes the right claude_desktop_config.json for your operating system, and backs up any existing config. Fully quit and relaunch Claude Desktop, then ask Claude to list available tools to confirm the stoquant_* tools are loaded.

Tool catalog

All 71 tools, grouped into 15 categories. Read-only -- no order placement. Tools marked (free) work on a free API key; the rest require Pro or Power.

Prices (6 tools)

get_quote (free) — params: ticker: Real-time quote for ONE ticker: last price, day change (absolute and percent), bid/ask, day high/low, and volume. Use for a single symbol; for 2+ symbols use stoquant_get_quotes_batch (one call, far fewer requests). Prices are delayed per the data vendor, not tick-by-tick.
get_quotes_batch (free) — params: tickers: Current quotes for up to 50 tickers in a single call. Preferred over calling stoquant_get_quote in a loop — same data, one request, stays well under rate limits. Returns one quote object per ticker (price, change, volume).
get_extended_quote (free) — params: ticker, timezone?: Quote plus computed technical indicators for one ticker: RSI(14), MACD, Bollinger Bands, and moving-average context. Use when you need momentum/overbought-oversold context, not just price. Heavier than stoquant_get_quote; can occasionally time out on cold cache — retry once if so.
get_sparklines (free) — params: tickers, interval?: Historical price series (~90 days) for up to 50 tickers, for charting or trend/return computation. interval controls granularity (1d daily, 1wk weekly, 1mo monthly). Returns an array of close prices per ticker, not full OHLCV.
get_benchmarks (free): Current performance of major index ETFs (SPY, QQQ, IWM, and peers): 1-day and year-to-date return. Use to frame an individual stock against the market. Early in the trading day the response may be `{error:'Benchmarks not yet computed'}` until the daily compute runs — treat that as 'not ready', not a failure.
get_sector_performers (free) — params: sector, limit?: Top and bottom performing stocks within a GICS sector, by recent return. Use to find what is leading or lagging inside a sector. sector must be a full GICS name (see stoquant_get_macro_dashboard sectorRotation for valid names), e.g. 'Technology', 'Health Care', 'Financials'.

Research (11 tools)

get_qscore — params: ticker, period?: The Q-Score is StoQuant's headline 0-100 composite for a ticker, blending fundamentals, momentum/technicals, ML, analyst, and 'hidden gem' value dimensions on SECTOR-RESIDUALIZED forward returns (sector beta removed). Returns: score (0-100), signal ('strong_buy'|'buy'|'hold'|'sell'|'strong_sell'|'unrated'), per-dimension `components` (each with rawValue, normalizedScore, weight, and an `available` flag — unavailable dims contribute 0), `confidence`/`confidenceInterval`, `dimensionContributions`, a `verdict` and `plainLanguage` summary, and `riskAlerts`. This is the best single tool for 'is this a good stock right now'. CANONICAL FIELD: `qScore.score` is THE headline score — the same number the website's watchlist, screener, and homepage rankings show. Two other numbers in the payload are NOT the score: `confidenceInterval.mid` is a hypothetical complete-data estimate (shown on the site as 'Plausible range L–H'), and `percentile` is the score's cross-sectional rank vs the universe (the research page hero's display basis). If you need a lifecycle-stage/regime-adjusted variant, use stoquant_get_signals_v2 — do not mix its adjusted value with this score. HORIZON: the score is always computed on a fixed 30-day residualized horizon; `period` is accepted for forward compatibility but currently has NO effect. Note: early in the US trading day (before ~10am ET), the score may not yet be refreshed with intraday data — a `null` or empty score should be treated as 'not yet computed' rather than an error.
score_ticker — params: ticker: Own StoQuant Q-Score for a ticker PLUS the live /proof honesty banner in one response. OWN signals only — never TipRanks Smart Score or street consensus (use stoquant_compare_signal for own-Q vs street). Returns `score` / `signal` / `percentile` / `confidence`, `ownSignalsOnly: true`, `note`, and `honesty` (`grade`, `excessSpreadPp`, `dataSufficient`, `gatedNoEdge`, `banner`). While honesty.gatedNoEdge (dataSufficient && excessSpreadPp < 0), treat the score as descriptive research — not TipRanks skill claims, not proven ranking edge. Requires Pro or Power (same as stoquant_get_qscore).
compare_two_tickers — params: tickerA, tickerB: Side-by-side own StoQuant Q-Scores for two tickers PLUS the live /proof honesty banner. OWN signals only — never TipRanks Smart Score or street consensus. Returns `a` / `b` (`score` / `signal` / `percentile` / `confidence`), `comparison` (`scoreDelta`, `higherScore`, `signalRelation`, `summary`), `ownSignalsOnly: true`, `note`, and `honesty` (`grade`, `excessSpreadPp`, `dataSufficient`, `gatedNoEdge`, `banner`). While honesty.gatedNoEdge (dataSufficient && excessSpreadPp < 0), treat both sides as descriptive research — not TipRanks skill claims, not proven ranking edge, not a buy pick. Requires Pro or Power (same as stoquant_get_qscore).
earnings_brief — params: ticker: Own-signal earnings companion foil: StoQuant Q-Score PLUS own earnings_history calendar/surprises PLUS the live /proof honesty banner. OWN signals only — TipRanks earnings-companion foil (never TipRanks Smart Score or TipRanks earnings skill). Returns `stoquant` / `nextEarnings` / `recentPrints` / `beatMiss` / `summary` / `ownSignalsOnly: true` / `note` / `honesty`. Descriptive discovery around an earnings date — not a buy rating, not proven ranking edge. While honesty.gatedNoEdge, treat as descriptive only. Requires Pro or Power (same as stoquant_get_qscore).
compare_signal — params: ticker: StoQuant Q-Score AND street analyst consensus in one response — the TipRanks MCP differentiator. TipRanks MCP is consensus-only; this returns StoQuant's own predictive score (`stoquant.score` / `stoquant.signal`) beside street `rating` / `consensusScore`, price target, upside, analystCount, and a `divergence` summary so an agent can spot disagreement without a second round-trip. Requires Pro or Power (same as stoquant_get_qscore).
get_signals_v2 — params: ticker: Returns both the raw Q-Score (`originalQScore`) and a `stage`/regime-adjusted variant (`adjusted`) for a ticker. Use this when you want the score adjusted for the stock's lifecycle stage and current market regime rather than the raw composite. The adjusted value is a DIFFERENT flavor of the score and can differ from the headline by a large margin (e.g. raw 31 vs adjusted 50 on a mature value stock) — always say which one you are quoting, and never compare `adjusted` against the raw score another surface shows. For the full breakdown use stoquant_get_qscore.
get_ml_prediction — params: ticker, horizon?: ML model's probability that the ticker will OUTPERFORM the S&P 500 over the horizon (NOT a buy/hold/sell call). Returns `data.prediction` with: `probability` (0-1, chance of outperformance), `classification` ('bull' = predicted to outperform, 'bear' = underperform), `modelVersion`, `modelAuc`, `plainLanguage` (headline/summary/topDrivers), and `isHeuristic` (true when the trained model is gated for distribution drift and a heuristic fallback is used — weight it less). Use stoquant_explain_ml_prediction for the feature-level 'why'. horizon is in trading days. Note: early in the US trading day (before ~10am ET), the prediction may not yet be refreshed with intraday data — a `null` or empty prediction should be treated as 'not yet computed' rather than an error.
explain_ml_prediction — params: ticker, horizon?, top?: Top feature contributions behind a ticker's ML outperformance prediction — which signals pushed the probability up or down, with signed magnitudes. Use after stoquant_get_ml_prediction when the user asks WHY the model is bullish/bearish. horizon must match the prediction you are explaining.
get_strategic_edges — params: ticker: Qualitative competitive intelligence for a ticker: economic moats, structural weaknesses, upcoming catalysts, and peer comparison. Best for mid/small-caps; mega-caps and thinly-covered names may return `{data:null}` (no edges computed) — treat that as 'no data', not an error.
get_quant_analytics — params: ticker: Quantitative analytics for a ticker under `data`: factor exposures, correlation/quality metrics, and risk decomposition. Use for portfolio-construction and factor-tilt questions. Complements stoquant_get_qscore (verdict) with the underlying quant detail.
get_evidence — params: ticker: The named, two-sided evidence behind a ticker's Q-Score — the 'bull case vs bear case' as concrete facts rather than scores. Returns five grids (`momentum`, `sentiment`, `earnings`, `valuation`, `risk`), each with `cells`: every cell has a human-readable `label` (e.g. 'Price above 50-day average', 'Short interest'), a formatted `value`, and a `tone` ('bullish'|'bearish'|'neutral'|'unknown'). To assemble a balanced case, collect cells with tone 'bullish' (the bull case) and 'bearish' (the bear case) across all five grids. Use this AFTER stoquant_get_qscore when the user asks WHY the score is what it is, or wants the specific positives and negatives to weigh — it is the evidence layer the research page shows above the dimension breakdown.

Signals (3 tools)

get_market_regime: Current market-regime classification from the Hidden Markov Model over S&P 500 returns: regime label (e.g. Bull/Range/Bear) and the probability vector across states. Use to set top-down context before stock-level calls. If the model reports it could not fit, probabilities fall back to roughly uniform — read that as 'regime undetermined', not a confident Range call.
get_signal_accuracy: Historical track record per signal type: an array of records with `signalType`, `hitRate` (0-1), `totalObservations`, `avgReturnPercent`, `precision`, and `windowDays`. Use to weight how much to trust a given signal before acting on it — prefer signals with high hitRate AND large totalObservations.
get_accuracy_summary: Public, unauthenticated platform scorecard from /public/accuracy/summary (no Pro gate). Key fields: letter `grade`, `hitRate` (do NOT treat hitRate as skill — a high hit-rate on a negatively-skewed book still loses money), `excessSpreadPp` (top-minus-bottom quintile 90-day *alpha*; this is the real edge), `spreadPp` (raw return spread, not alpha), `dataSufficient` (false = sample too small, ignore the grade), `independentWindows` (independent 90-day windows behind the Wilson CI), `sampleSize`, Wilson CI. When `dataSufficient` is true AND `excessSpreadPp` < 0 the live book has an inverted tail — auto-portfolio BUY recommendations are suppressed with gateReason `no_edge`. Call this before any buy-oriented auto-portfolio or optimizer read.

Discovery (9 tools)

get_top_conviction — params: limit?: StoQuant's current highest-conviction names: the stocks with the strongest Q-Scores backed by multiple corroborating signals. Use as the starting point for 'what does the platform like right now'. May be empty between scans (`{stocks:{}}`).
get_hidden_gems — params: limit?, freshness?: Undervalued small-caps with confirming signals (the 'hidden gem' screen: low valuation, healthy balance sheet, low analyst coverage, plus momentum/ML/insider confirmation). Returns `{gems:[...], totalCount, lastScanAt}`. freshness filters by how recently a gem's signals fired. Use for value-oriented idea generation distinct from momentum.
get_multibagger_candidates — params: limit?: Small/mid-caps flagged with high (>5x) upside potential: revenue acceleration, improving fundamentals, and pre-discovery setups. Higher risk/variance than hidden gems. Use for aggressive growth idea generation.
get_signal_stacks — params: minSetups?, limit?: Tickers where MULTIPLE named setups fired on the same day — StoQuant's convergence view, and the only tool that exposes the daily setup-fire history. Returns `{tickers:[{ticker, qScore, marketCap, stackScore, firedSetups:[{id, name, headline, confidence}]}]}`, sorted by stackScore (the sum of the fired setups' confidences). Use this for 'what is worth a look today' when you want corroboration rather than a single score: two independent setups agreeing on one name is a stronger read than one high Q-Score. Complements stoquant_get_top_conviction (score-first) and stoquant_get_hidden_gems (valuation-first). Empty `{tickers:[]}` is a normal answer before the day's scan completes or when nothing converged — it is not an error.
get_related_stocks — params: ticker, by?, limit?: Find peers of a ticker under `peers`, each with qScore, quote, and market cap. `by` selects the relation: 'sector' (same sector), 'tag' (shared thematic tags), 'setup' (same technical setup firing), or 'correlation' (highest 60-day return correlation). Use to build a comparison set or find alternatives to a name.
get_signal_accuracy_heatmap — params: startDate?, endDate?: Hit-rate heatmap of signals over time/ticker for a date range — which signal types worked when. Use for backtest-style 'has this signal been reliable lately' questions. Dates are YYYY-MM-DD; omit for the default recent window.
get_demand_zone — params: limit?: Descriptive demand-zone setup screener (NOT a buy rating) PLUS live /proof honesty banner: liquid commons 0–3% above rising SMA200 (else SMA50) with RSI(14) 35–55. Returns `{asOf, items:[...], notes, ownSignalsOnly: true, note, honesty}`. Own StoQuant evaluateDemandZone only — not TipRanks Smart Score / TipRanks skill. While honesty.gatedNoEdge, treat as descriptive only. Landing + share card: https://stoquant.com/demand-zone. Pro/Power.
get_morning_brief: Descriptive own-signal pre-market morning brief (NOT a buy rating; TipRanks due-diligence-agent FOIL) PLUS live /proof honesty banner: regime, VIX, SPY/QQQ/IWM moves, earnings reporting today, optional gem spotlight. Returns `{asOf, sessionDate, regime, vix, indices, reportingToday, spotlight, summary, notes, ownSignalsOnly: true, note, honesty}`. Own StoQuant signals only — never TipRanks Smart Score / TipRanks skill. While honesty.gatedNoEdge, treat as descriptive only. Landing + share card: https://stoquant.com/morning-brief. Pro/Power.
get_enablers — params: seeds?, sector?, limit?: Descriptive thematic/co-mover screener (NOT a buy rating, NOT a verified supply-chain bottleneck graph): finds tickers near a set of seeds via tag overlap and 60-day return correlation. Provide `seeds` (1-10 tickers) or `sector` (top gainers become the seeds). Returns `{asOf, seeds, items:[{ticker, marketCap, reasons:{tagOverlap:{count,shared}, correlation:{vsSeed,r}|null, relativeStrength}, seedLinks, qScore}], notes}`. Candidates capped to $300M-$20B market cap; index ETFs and warrants excluded. Not a Q-Score dimension and does not drive autopilot buys. Pro/Power.

Screener (3 tools)

list_prebuilt_screens (free) — params: universe?: List the curated prebuilt screens with their id, name, and description. Available screens: 'undervalued-gems' (low P/E, P/B value plays), 'momentum-leaders' (strong price momentum), 'oversold-bounce' (RSI reversal setup), 'ml-outperform' (ML-predicted S&P 500 outperformance), 'hidden-gems' (small-cap value with confirmation signals), 'growth-explosion' (high-growth micro/mid-caps before discovery), 'pre-explosion' (multibagger pattern: revenue acceleration + insider + fundamentals). Call this first, then run one with stoquant_run_prebuilt_screen — it is faster and better-tuned than hand-built filters.
run_prebuilt_screen (free) — params: screenId, universe?: Run one curated screen by its id (from stoquant_list_prebuilt_screens) and get the ranked matching stocks. Prefer this over stoquant_run_screener for common intents (value, momentum, oversold, ML picks, hidden gems, growth) — the filters are pre-tuned. Omit universe and each screen runs against the one it was designed for (the small-cap screens 'hidden-gems' and 'growth-explosion' use the full universe; the rest use sp500) — pass it only to override. Result count is fixed by the screen definition.
run_screener (free) — params: filters, sortField?, sortDirection?, universe?, limit?: Run a custom stock screen. Read-only — nothing is mutated. All filters are AND-combined; returns an ARRAY of matching stocks (ticker, name, price, plus the requested metrics and overlays like valueScore/qScore/mlProbability). " + FIELD_HELP + " For per-field units and typical ranges, read the stoquant://screener-fields resource. Tip: a full-universe scan can be slow and time out — prefer universe='sp500' and a handful of filters, or use stoquant_run_prebuilt_screen.

Ownership (10 tools)

get_insider_trades — params: ticker: Recent insider (Form 4) transactions for a ticker under `data`, plus a `summary` (net buy/sell counts and dollar value). Cluster buying by officers/directors — especially large dollar buys — is a notable bullish signal. Use to check whether insiders are accumulating or distributing.
get_institutional_holders — params: ticker: Top institutional holders for a ticker: organization name, shares held, and percent ownership. Use to gauge institutional concentration and who the big holders are.
get_institutional_ratings — params: ticker: Aggregated institutional sentiment for a ticker: holder additions vs reductions and a net `signal` direction. Use as a 'smart money' flow read distinct from analyst ratings.
get_analyst_estimates — params: ticker: Sell-side analyst forward estimates for a ticker under `data` (per fiscal period: estimated EPS avg/high/low, estimated revenue avg/high/low, number of analysts) with a `count`. Use for consensus expectations and dispersion. Some future periods may have null estimates until coverage fills in.
get_analyst_price_targets — params: ticker: Analyst price targets for a ticker with a `consensus` block (avgTarget, highTarget, lowTarget, numAnalysts) and the underlying `data`. Use to gauge implied upside/downside vs the current price and the dispersion of targets.
get_analyst_changes — params: ticker: Recent analyst rating changes for a ticker — upgrades, downgrades, reiterations, and initiations — with a 90-day action breakdown. Use to see the direction of sell-side revisions (momentum in sentiment), not just the static consensus.
get_short_interest — params: ticker: Short-interest history for a ticker: append-only `snapshots` plus a `summary` (latestFloat = % of float short, latestRatio = days-to-cover, and window deltas). Rising short interest with a price squeeze can signal a short squeeze; falling can signal covering. Use for crowded-short reads.
get_insider_cluster — params: ticker: Insider-buying cluster metrics for a ticker under `data`: distinctFilers14d, officerCount14d, purchaseCount90d, totalValue90d, clusterScore, and a clusterMomentum flag. Cluster buying by multiple insiders (especially officers, large dollar value) is a strong bullish signal — this scores it. Pairs with stoquant_get_insider_trades (the raw transactions).
get_smart_money_verdict — params: ticker: Who's backing a ticker and whether they've actually been right: analyst firms, institutions (incl. new entrants this quarter), insiders, and social voices, each with a MEASURED directional accuracy (Wilson lower-bound hit rate, avg 30d excess return, letter grade) and a credibility-weighted net stance (`consensus.netTilt`, -1 bearish to +1 bullish) per category. Sources with too few matured calls are marked `gated` (accruing, not yet trustworthy) rather than silently counted at full weight. Use this instead of raw insider/institutional/analyst tools when the question is 'is anyone credible actually backing this, and are they usually right' rather than just 'who holds it'.
get_big_insider_buys — params: minValue?, days?, limit?: Market-wide list of tickers with large recent insider purchases (default >= $1M) under `items`, each with the buy detail plus a current quote and Q-Score. Use for 'where are insiders putting real money right now' discovery across the whole market.

Macro (4 tools)

get_macro_indicator — params: seriesId: Latest values for one FRED macro series under `data` (recent observations) with a `count`. Common ids: DGS10 (10y Treasury yield), DGS2 (2y), T10Y2Y (10y-2y spread), UNRATE (unemployment), CPIAUCSL (CPI), VIXCLS (VIX), FEDFUNDS (fed funds). Use for a single specific indicator; for the full picture use stoquant_get_macro_dashboard.
get_macro_dashboard: Full top-down macro picture in one call: `regime` classification, key `indicators`, `macroStress` gauge, `sectorRotation` (which sectors are favored, with valid GICS sector names), forward `calendar`, and current `themes`. Best first call for 'what's the macro backdrop' before drilling into stocks.
get_macro_calendar: Forward (~14 day) economic calendar: scheduled releases with forecast, prior value, and expected market impact. Use to flag upcoming macro events (CPI, FOMC, jobs) that could move positions.
get_macro_themes: Current macro investment themes with their related sectors — the narrative layer (e.g. rate-cut beneficiaries, AI capex). Use for thematic idea generation that ties stocks to a macro thesis.

Filings & news (3 tools)

get_sec_filings — params: ticker, days?, limit?: Recent SEC filings for a ticker under `items` (with `count`/`total`), unioning the whole suite: 8-K events (with item labels like 'Item 2.02 Results of Operations'), NT-10K/Q late filings, 13D/13G activist/passive ownership, S-1/S-3/424B dilution, DEF 14A / Form 25 / Form 15 governance, and Form 144/3 insider intent. Each item has accession, formType, filedDate, category, and a human-readable label. Use to read the actual regulatory record behind a thesis.
get_news — params: ticker, days?, limit?: Recent news headlines for a ticker under `items`: each with publishedAt, headline, source/provider, category, and a sentiment label. Use for the latest narrative around a stock. For news plus filings plus earnings in one combined view, use stoquant_get_news_events.
get_news_events — params: ticker: Combined event view for a ticker under `data`: `news` (headlines), `filings` (SEC), and `earnings` (calendar). The single best call for 'what is happening with this stock' — recent coverage, regulatory activity, and upcoming/just-reported earnings together.

Social (5 tools)

get_social_sentiment — params: ticker: Current social-media buzz for a ticker: `totalMentions`, `recentPostCount24h`, `sentimentBreakdown` (bullish/bearish/neutral), `topPlatforms`, and a sample of `recentPosts`. Use to read retail attention and crowd sentiment. Sources are credibility-weighted platform aggregates, not raw noise.
get_social_trend — params: ticker: Daily social mention-volume and sentiment trend for a ticker over the last ~30 days. Use to see whether attention and bullishness are building or fading, not just the current snapshot.
get_trending_social: Most-mentioned tickers across social platforms in the last few hours, each with mentionCount, prevMentionCount, deltaPercent, velocityScore, bullishPct, and platforms. Use for market-wide 'what is retail talking about right now' discovery and early momentum.
get_social_momentum: Tickers with a sharp (2x+) surge in social mentions in the last hour vs the prior hour — the earliest social-momentum signal, with sentiment and a recent post snippet per name. Use to catch breakouts in attention before they are widely noticed.
get_source_credibility — params: limit?: Leaderboard of social sources ranked by true directional accuracy (Wilson lower-bound on their calls). Use to judge how much weight to give a platform or author's sentiment — high-credibility sources earn more trust than raw mention volume.

Catalysts (1 tools)

get_catalysts — params: ticker?: Upcoming and recent catalysts under `data`, each with ticker, type (e.g. earnings_soon, activist_stake, dilution, fda), date, label, direction (bullish/bearish/neutral), and source. Omit ticker for the market-wide feed; pass a ticker to filter to one name (may be empty if that stock has no tracked catalysts right now). Use to find date-driven, event-based opportunities.

Options flow (1 tools)

get_unusual_options — params: ticker?, type?, newPositionsOnly?, minNotional?, limit?: Options contracts trading far above their open interest, ranked by notional (volume x price x 100). Each row under `data.rows` has ticker, optType (call/put), strike, expiry, dte, volume, openInterest, volOi, newPosition (open interest was 0), notional, priceSource (mid/last), lastVsMid and ageHours. IMPORTANT: this is a delayed QUOTE snapshot, front-month expiry only — there is no trade tape, so no buyer/seller side and no sweep detection; `lastVsMid` is only where the last print sat between bid (0) and ask (1). Open interest is published next day, so volume/OI compares today against yesterday. Descriptive, not a buy or sell signal.

Alt-data (3 tools)

get_alt_signals — params: ticker: The full alternative-data dossier for a ticker in one call, under `data`. Aggregates 15 sources: sec8k (8-K event items + counts), secLate (NT-10K/Q late filings), sec13dg (13D/13G activist/passive ownership), secDilution (S-1/S-3/424B shelf + active-dilution flag), secGovernance (DEF 14A, Form 25 delisting, Form 15 deregistration flags), secInsiderIntent (Form 144/Form 3 counts), secS8 (employee stock plans), fdaEnforcement (Class I/II/III actions), cpscRecalls (consumer recalls), clinicalTrials (phase/recruiting — biotech), clinicalReadiness (biotech pivotal-trial detail: fully-enrolled Phase 3s, days accruing endpoint events since enrollment close, how many are past the measured 269-day median dwell, partner-led trials where the ticker is only a collaborator, terminations, and per-trial registry changes), github (stars/forks/issues/push activity), appStore (iOS rank deltas), hackerNews (story velocity), gdelt (news tone). Sources with no recent activity for the ticker are null (e.g. clinicalTrials for a non-biotech). This is the platform's differentiated edge — use it to surface catalysts and red flags an ordinary data feed misses. IMPORTANT on clinicalReadiness: it is DESCRIPTIVE, not predictive. It says a readout is capable of firing soon, never that it will succeed — published Phase 3→approval odds in oncology are ~35%, and roughly 25% of fully-enrolled Phase 3s exit that state within 90 days. Do not present it as a probability of success or as a recommendation.
get_alt_data — params: ticker: Generic cached alternative-data for a ticker under `data`, keyed by source: google_trends (search interest), wikipedia_pageviews (attention), options_iv (implied-vol metrics), openinsider (insider feed), edgar_8k. Each source carries its latest values and fetchedAt. Complements stoquant_get_alt_signals with retail-attention and options-positioning reads.
get_catalyst_watch: Cross-ticker hot list of the highest-impact near-term catalysts the platform is tracking (FDA decisions, activist stakes, dilution events, clinical readouts, etc.). Use for market-wide 'what's about to move' discovery, distinct from per-ticker stoquant_get_catalysts.

Portfolio (5 tools)

portfolio_risk — params: portfolioId?: Own StoQuant portfolio risk metrics (Sortino, Sharpe, VaR/CVaR, max drawdown, Calmar, downside deviation) PLUS the live /proof honesty banner. OWN risk analytics only — not TipRanks portfolio analysis. Returns `risk`, `observationCount`, `ownSignalsOnly: true`, `note`, and `honesty`. While honesty.gatedNoEdge, do not size into buys as if the live book has edge. Requires Pro or Power and ownership of portfolioId. Read-only — does not place orders.
get_auto_portfolio_status — params: portfolioId?: Read-only snapshot of an auto-portfolio: last evaluation (`evalStatus.lastEvalResult`, `lastEvalAt`, `recsGenerated`) plus diagnostics (cash, holdings tickers, cascade/drawdown breakers, risk params, recent recommendations). `lastEvalResult` is `success` or `gated:<reason>`. Important gate: `gated:no_edge` means the public accuracy scorecard has dataSufficient && excessSpreadPp < 0, so BUY recommendations were suppressed — do not treat that as a buy signal. Other gates include `static_snapshot`, `health_critical`, `cascade_breaker_paused`, `no_cash_buys_suppressed`, `no_changes`. Requires Power-tier API key and ownership of portfolioId. This does NOT run a new evaluation and does NOT place orders.
get_auto_portfolio_history — params: portfolioId?, limit?: Read-only recommendation history for an auto-portfolio (ticker, action, status, timestamps). Use to inspect what the engine last proposed — including rows that stayed pending or were gated. Does not execute or cancel anything. Power-tier; requires portfolio ownership.
get_portfolio_health: System-wide auto-portfolio health report (subsystem statuses, degraded mode, last check time). Use to see whether the engine is healthy before trusting a last-eval snapshot. Read-only — does not trigger healing or reset the drawdown breaker.
optimize_portfolio — params: tickers, method?, initialCapital?, predictions?: Compute target weights via Black-Litterman (HRP blend fallback). Returns an `optimization` object with weights — it does NOT place orders, rebalance, or move cash. If `predictions` is omitted the server fills views from ML forecasts (up to 20 tickers). Pro/Power tier. Use the weights as a recommendation; call stoquant_get_accuracy_summary first and do not size into buys while excessSpreadPp is negative and dataSufficient is true.

Backtest (4 tools)

list_backtest_templates: The pre-built strategy templates you can backtest (`id`, name, parameters). Known ids include `momentum`, `growth_chaser`, `value`, `insider_follower`, `gem_composite`, and `research_*` variants. Call this before stoquant_run_backtest so you pass a real strategyId. Power-tier. Read-only.
run_backtest — params: strategyId, startDate, endDate, universe?, initialCapital?: Run a historical backtest for a strategy template over [startDate, endDate]. Returns the full run (metrics at 0% slippage plus bias disclosure: Deflated Sharpe, iteration count). This is a simulation — it does NOT place live or paper orders. It does persist a run record you can reload with stoquant_get_backtest. Power-tier; computationally expensive (this call can take tens of seconds). Prefer universe=sp500. Dates are YYYY-MM-DD.
get_backtest — params: id: Reload a previously stored backtest run by numeric id, including metrics and bias disclosure. Use after stoquant_run_backtest or to inspect history. Power-tier. Read-only.
get_backtest_history — params: strategyId?, limit?: Metadata for past backtest runs (ids, strategy, dates) without the full equity curve. Filter by strategyId. Power-tier. Read-only.

Strategy (3 tools)

get_strategy_performance — params: portfolioId?, view?: Read-only strategy performance for a portfolio. `view` selects the slice: `kpis` (headline returns), `equity-curve` (vs SPY, normalized to 100), `monthly-returns`, `risk-metrics` (Sharpe/Sortino/max drawdown/capture), `attribution` (P/L by signal type), `comparison` (all active portfolios). Pro-tier. Does not trade.
list_strategy_presets: Themed strategy presets (social / insider / congress, etc.) with customization knobs. Read-only signal catalog — does not create a portfolio. Use stoquant_get_strategy_preset_candidates to see the current ranked basket for one preset.
get_strategy_preset_candidates — params: presetId: Current ranked candidate tickers and target-weight basket for a themed preset (`social`, `insider`, `congress`, …). Returns `{preset, appliedOptions, candidates, basket}`. This is a signal read — it does NOT create or rebalance a paper portfolio.

Security model

Read-only by design. HTTPS only. API key in the Authorization header, never logged. Client-side rate limit 200 req/min, 15-second per-request timeout. Server-side, API keys are hashed (SHA-256) at rest and timing-safe-compared on verify. Revoke any key from /account/api-keys.

CLI and hosted endpoint

The npm CLI only supports install, serve, and --help — not per-tool subcommands. The hosted endpoint is POST https://stoquant.com/api/mcp and exposes 21 read-only tools (Bearer key). Dry-run evaluate is available; live execute is not.

Requirements

A free StoQuant account (100 requests/day) or higher. Node.js installed locally (the installer uses npx). Compatible with Claude Desktop, Claude Code, and any other MCP-compatible client.