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.

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).

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.

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.

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.

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'.

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'. period selects the scoring horizon. 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.

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. For the full breakdown use stoquant_get_qscore.

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.

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.

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.

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.

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.

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.

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.

Platform-wide model scorecard: letter grade, overall hitRate, sampleSize, top- vs bottom-quintile average 90-day returns and their spreadPp (the real edge), spyAvgReturn90d (Russell 2000 benchmark preferred via IWM/^RUT, SPY fallback), trend/trendDelta, a Wilson confidence interval, and dataSufficient. Use to gauge overall reliability of StoQuant's signals right now.

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:{}}).

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.

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.

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.

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.

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 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. universe defaults to sp500; use 'full' for the broadest set. Result count is fixed by the screen definition.

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). Valid filter fields (camelCase, case-sensitive): trailingPE, forwardPE, priceToBook, marketCap, epsTrailingTwelveMonths, dividendYield, bookValue, regularMarketPrice, fiftyDayAverage, twoHundredDayAverage, fiftyDayAverageChangePercent, twoHundredDayAverageChangePercent, regularMarketChangePercent, fiftyTwoWeekHighChangePercent, regularMarketVolume, averageDailyVolume3Month, sector, industry, rsi14, twentyDayAverageChangePercent, oneHundredFiftyDayAverageChangePercent, shortPercentOfFloat, grossMargins, operatingMargins, profitMargins, returnOnEquity, returnOnAssets, debtToEquity, revenueGrowth, earningsGrowth, pegRatio, currentRatio, numberOfAnalystOpinions. Additional sort-only fields: valueScore, qScore, marginOfSafety, mlProbability. Margins/growth are decimals (0.25 = 25%); marketCap is raw USD (250M = 250000000). An unknown field is rejected with a suggestion (it would otherwise silently return zero matches). 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.

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.

Top institutional holders for a ticker: organization name, shares held, and percent ownership. Use to gauge institutional concentration and who the big holders are.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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), 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.

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.

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.