Stocklake — AI Stock Intelligence

AI stock intelligence: prices, fundamentals, technicals, news, macro regime, and sector signals.

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    AI stock intelligence: prices, fundamentals, technicals, news, macro regime, and sector signals.

    Список инструментов сервера (19)

    Технические названия из tools/list. Нужны только разработчикам.

    get_stockPrice, fundamentals, technical indicators, and company profile for a stock. Returns all data needed to understand a stock in a single call. Key fields: - price, change_pct, prev_close, week52_high/low, volume, avg_volume - market_cap, enterprise_value, beta - pe_trailing, pe_forward, price_to_book, dividend_yield, dividend_rate - debt_to_equity, profit_margins, return_on_equity, free_cashflow - revenue_growth, earnings_growth, revenue_ttm, gross_profit_ttm - analyst_rating: "strong_buy"|"buy"|"hold"|"sell"|"strong_sell" (analyst consensus) - analyst_rating_score: 1.0–5.0 mean analyst recommendation (1=strong_buy, 5=strong_sell) - analyst_target: mean analyst price target - analyst_count: number of analyst opinions - indicators: raw RSI, MACD, Bollinger Bands, SMA20/SMA200 (the canonical 50/200-day averages -- no separate top-level ma_50/ma_200 field), EMA20/EMA200, ATR - description: company business description - website, employees, officers (top 5: name, title, total_pay) - updated_at: last data sync timestamp Available to all tiers (raw indicator numbers, no interpretation). This basic six (RSI/MACD/Bollinger/SMA/EMA/ATR) is standard, widely-available technical analysis. Pro tier also unlocks 6 more specialized indicators inside the SAME `indicators` block (williams_r, ultimate_osc, vix_fix, williams_ad, td_sequential, elliott_wave -- the Larry Williams family, DeMark TD Sequential, and Elliott Wave) -- these are omitted entirely from the free/guest response (tier-gating sweep, 2026-08-28), not merely unlabeled; free/guest calls get indicators with only the basic six populated. Pro tier adds four interpreted blocks computed from the same indicators, no extra AI cost, plus a minimum AI-narrative slice — all five below are precomputed, none cost a live AI call: - ai_verdict / ai_headline / ai_score / ai_score_band: the minimum useful AI-narrative slice, shared by every pro-tier stock-returning tool. A bare verdict alone isn't actionable (e.g. bearish while
    get_stock_newsAI-analysed news for a stock, newest first. Only returns articles processed by our AI pipeline (sentiment, signal_score, summary). - days: look-back window in days. Requesting more than your tier's cap is silently clamped down to it: 30 free/guest, 90 pro. - limit: max articles returned. The `limit=10` default is a Pro-tier-shaped value — on free/guest it's silently clamped down to that tier's cap (5), so a free caller passing no `limit` effectively gets 5, not 10. Requesting more than your tier's cap (5 free/guest, 50 pro) is likewise clamped down. - status: "ok" = articles returned | "empty" = no news in window - Per article: title, published_at, ai_sentiment, ai_summary (full text) — Pro only, see below - signal_score (Pro only, 0-100 or null) / signal_score_band (Pro only, string or null — "Weak"/"Moderate"/"Strong"/"Very Strong"): if this symbol has a live news-sourced signal (raised in the last 90 days), every article shows that SAME number — the same one get_signals()/get_stock_research() report for this symbol (all three read the same underlying signal, via the same shared resolver), staying live/synced: if the signal is later re-scored, this reflects the update on your next call, not a frozen snapshot from classification time. Always a single number (never a two-sided split) — for a genuinely contested (two opposing theses) signal, this is the STRONGER of the two sides. NOT gated on whether Stocklake's own internal trading engine still considers the signal live — a dropped/expired signal is still a real, useful fact about what the pipeline found. When there's no live signal for this symbol at all, each article instead gets its OWN per-article score (computed from that article's sentiment/confidence/flag_score) — the same fallback get_stock_research()'s news[] block uses, so a symbol with no active thesis doesn't just go null across the board; different articles for the same symbol can then legitimately show differen
    get_stock_historyDaily OHLCV price history for a stock. - days: number of trading days to return (default 90, max 365) - Returns: { symbol, days_requested, days_returned, count, history[] } — days_returned/count can be less than days_requested if less history exists - Per bar: date, open, high, low, close, volume
    get_earnings_calendarUpcoming earnings dates for stocks in the Stocklake universe. - days: look-ahead window in days (default 7, max 30) - Returns: { window_days, from_date, to_date, count, results[] } - Each result: symbol, name, sector, market_cap, price, rsi, earnings_date (ISO UTC), is_estimate, eps_trailing, eps_forward - Sorted by earnings_date ascending. - Dates sourced from market data — treat is_estimate=true dates as approximate. Available to all tiers.
    get_market_assessmentCombined AI market assessment: macro regime + market outlook in a single call. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check regime_stale/outlook_stale below (which already account for the weekend gap) before treating either as current, especially on a Monday morning. Two distinct perspectives returned together: - REGIME (RISK_OFF/CAUTIOUS/NEUTRAL/AGGRESSIVE): answers "how much equity risk to take" → use for position sizing and asset allocation decisions - OUTLOOK (POSITIVE/NEUTRAL/NEGATIVE): answers "which direction and sectors to trade" → use for sector preference and directional bias Both share the same pipeline run so they are always in sync. - history_count: include last N prior assessments for each (0-3, default 0) - regime_*: risk posture fields — regime, risk_appetite_score (0-100 re-expression of regime, higher = current conditions support more risk-taking), regime_bias, regime_bias_note (plain sentence on whether current conditions favor long or short setups, or neither), regime_confidence, regime_rationale, key_risks, watch_for, vix_at_assessment, regime_updated_at, regime_stale - macro_score / regime_strength: macro_score is a real, continuous 0-100 read on how much risk the current environment supports (0=RISK_OFF/capital preservation, 100=AGGRESSIVE/risk-on) — the same underlying number `regime` buckets into 4 discrete categories, blending arithmetic inputs (VIX level, breadth oversold/overbought skew, SKEW-vs-VIX divergence, TD-exhaustion ratio) with regime_strength, the AI's own 1-10 read of regime conviction. Distinct from risk_appetite_score (a coarse 4-value lookup on `regime` alone) — macro_score is the real underlying number. Null on a pre-2026-08-26 assessment that predates this field. Not a call on any one stock. - macro_score_trend: {change_7d, change_30d, direction} — whether macro_score itself is improving/deterior
    get_sector_intelligenceAI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector. - sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested) - history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only) - sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) — the same underlying number `signal` buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call. - sector_score_trend: {change_7d, change_30d, direction} — whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only — this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday
    get_insider_activityGet AI-synthesized insider + institutional activity for a stock. Returns combined signal (POSITIVE/NEGATIVE/NEUTRAL etc.), signal_score (0-100, higher = stronger/more notable — the exact same field, formula, scale and bands as get_signals()'s signal_score, no separate name) with signal_score_band, per-source breakdown, and a human-readable summary. Data covers insider transactions (SEC Form 4/BaFin/AFM/CNMV, with a gap-fill from Yahoo where the regulatory source has nothing) and institutional holdings — insider_buys/insider_sells, transactions (the individual rows behind those counts, newest first, up to 50, each with date/name/role/type/shares/price/value), top_holders (a union of multiple data sources, up to 15, each with name/shares/pct_held/sources/ share_counts_by_source — the last two show which source(s) contributed to a merged row and each one's own reported share count, useful for spotting a false merge: two genuinely different holders should never collide, but if they did, their per-source counts would diverge wildly), holder_count_divergence (null unless every matched holder disagrees by the same systemic multiplier across sources — e.g. an unrecorded reverse split — a signal top_holders.shares may be unreliable for this symbol; distinct from an ordinary two-holder mismatch, which is normal data lag and stays unflagged), inst_ownership (0-100%), and total_holders. Note: `summary` is a separately-generated narrative on its own refresh cadence and may not always match the live insider_buys/insider_sells/transactions counts — trust the counts/transactions over the prose if they disagree. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
    get_stocksBatch stock data for up to 25 symbols in a single call — the same fields get_stock returns for the same key/symbol, so this is a true batch version, not a thinned-down scan. Returns a dict keyed by symbol. Missing symbols are omitted from the result. Each symbol in the batch counts as one call toward the daily limit. A request over 25 symbols is rejected outright (error: batch_too_large) rather than silently served on just the first 25 — split a larger list into multiple calls. Available to all tiers (fundamentals/indicators/company profile, free). Pro tier adds, per symbol, the same precomputed blocks get_stock adds — rating {score, direction, signals}, signals (per-indicator breakdown), relative_strength, market_risk {beta_spy_1y, corr_spy_1y}, and the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score, ai_score_band). None of this costs a live AI call — it's all precomputed and just needs projecting. NOT included, even on pro — call get_stock(symbol) for stance_signals, or get_stock_research(symbol) for the full ai_summary text (summary/key_points/ risks/near_term/longer_term) plus cross-source news/insider/signal context. Response also carries `duplicates_collapsed`: how many input symbols normalized (case-folding, share-class aliasing e.g. "BRK.B"->"BRK-B") or literally repeated onto a symbol already counted elsewhere in this batch. requested - len(missing or []) - duplicates_collapsed == count always holds.
    get_market_pulseLive market health snapshot in a single call. Aggregates key market indicators without requiring multiple tool calls. No AI cost — reads live data directly from the market data feed. Returns: - vix: VIX level and change_pct (from live stocks data) - fear_greed: value (0-100) and label (e.g. "neutral", "greed", "fear") - breadth: market-wide RSI distribution — oversold_pct, overbought_pct, neutral_pct, universe_size - indices: SPY, QQQ, IWM prices + RSI + 1-week performance - bonds_commodities: TLT (long-duration bonds), GLD (gold) - updated_at: when the breadth/fear_greed snapshot was last recorded Available to all tiers.
    get_signalsAI-screened stock signals recently surfaced by the Stocklake pipeline — sourced from news analysis, sector screening, and sentiment signals. Shows what the pipeline noticed in the last 24 hours (falling back to the most recent signals regardless of age if nothing has fired in that window — see `window` in the response). This reflects what the AI pipeline found, not whether Stocklake's own internal trading engine still holds it live — a signal it later dropped or let expire is still shown here, since that's a fact about our own trading state, not about the signal's informational value. Parameters: - direction: "POSITIVE" | "NEGATIVE" | "NEUTRAL" (default: all). NEUTRAL covers both a flat/undecided read AND a genuinely two-sided idea (real opposing bull/bear theses on the same symbol) — in the latter case signal_score is the STRONGER of the two sides (see signal_score below), so a high score alongside NEUTRAL means "real conviction here, just no directional consensus," not "nothing going on." The two-sided detail is in `rationale`. - min_signal_score: minimum composite signal score 0-100 (default 60) — a blend of conviction/confidence/flag_score, source track record, and real technical factors. This is the field to filter on. Always compared against a single number, including for NEUTRAL/two-sided ideas — a result is never returned below your threshold on both sides. - min_conviction, min_flag_score: DEPRECATED, ignored for filtering — kept in the signature only so existing callers don't hard-fail; ai.stocklake.dev's internal scoring retired the raw conviction/confidence/flag_score triad in favor of signal_score. Passing a non-default value here has no effect and is logged for a planned removal. - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50). Each returned signal counts as one call toward your daily limit. Returns: - count: number of signals retur
    get_watchlistThe caller's Stocklake watchlist (starred symbols from the web dashboard), enriched with live price, technicals, and AI verdict. Returns: - count: number of symbols on the watchlist - items[]: each with symbol, name, sector, price, change_pct, rsi, market_cap, analyst_rating, atr_pct, ai_verdict, ai_headline, ai_score (0-100), ai_score_band (Weak/Moderate/Strong/Very Strong), added_at, price_at_add - empty items[] if nothing is starred yet — star symbols at stocklake.dev/dashboard Pro tier only. For informational purposes only. Not financial advice.
    get_news_feedTop AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by signal_score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_signal_score: minimum signal_score (0-100, default 60) used to SELECT articles server-side. Resolved per-article (stored/computed magnitude preferred over an unfiltered Mongo `$gte`, since a formal live signal doesn't exist for every article — see signal_score below), then filtered/sorted in Python. - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_summary (full text), signal_score (0-100), signal_score_band (Weak/Moderate/Strong/ Very Strong) signal_score/signal_score_band: this symbol's LIVE signal score if a news-sourced signal was raised for it in the last 90 days (same number get_stock_news()/get_signals() report, kept in sync as that signal is re-scored — one $in query per distinct symbol in the result, not per article, so two articles about the same stock always show the same value); otherwise a per-article magnitude computed from THIS article's own sentiment/confidence/flag_score, so every article still gets a real, rankable number. Always a single number — for a symbol whose live signal is genuinely two-sided (real opposing bull/bear theses), this is the STRONGER of the two sides, same as get_signals()/get_stock_news(). There is deliberately no separate "news_score" field — one name for "how strong is this idea," whether it's backed by a formal signal or just this article's own classification. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial adv
    get_screenerFilter and rank stocks from the Stocklake universe — fundamentals, technicals, and AI signals in one tool. Parameters: - sector: e.g. "Technology", "Healthcare", "Financial Services" - country: e.g. "United States", "Germany" - min_rsi / max_rsi: exact RSI bounds (e.g. max_rsi=30 = oversold, min_rsi=70 = overbought) - sma_trend: "above_200" (price above 200-day MA) | "below_200" - macd_signal: "positive" (MACD line above signal) | "negative" - min_perf_1d / max_perf_1d: 1-day performance % (e.g. min_perf_1d=2.0 = up 2%+ today) - min_volume: minimum daily volume (e.g. 1000000) - min_market_cap_b / max_market_cap_b: market cap in billions - max_pe_forward: maximum forward P/E (e.g. 20 = value screen) - analyst_rating: "strong_buy" | "buy" | "hold" | "sell" | "strong_sell" - min_ai_score: minimum AI score 0-100 (pro tier only — silently ignored for free). Gates on stock_ai_summary.py's own composite ai_score — same 0-100 scale/band convention as signals.signal_score, but a distinct field/pipeline (per-stock AI summary confidence, not a directional trade idea). Renamed 2026-08-24 from the retired 0-10 min_flag_score — the raw flag_score field it used to gate on is no longer part of this fleet's public vocabulary at all (see ai_score below). - preset: "oversold" | "overbought" | "momentum" | "high_conviction" (pro only) oversold = RSI≤35 + above SMA200 · overbought = RSI≥65 momentum = RSI 50-70, above SMA200, up 0.5%+ today · high_conviction = ai_score≥70 - sort_by: "market_cap" | "rsi" | "perf_1d" | "volume" | "analyst_rating" | "rating" | "ai_score" (pro). Defaults to "market_cap", except the "high_conviction" preset defaults to "ai_score" (the dimension it's filtering by) unless you explicitly pass a different sort_by. - sort_dir: "asc" | "desc" (default "desc") - limit: 1–25 (default 20). Each returned stock counts as one call toward your daily limit. Returns: { count, preset, filters, results[] } — each result includes symbol, name, sector, indus
    get_market_moversTop market movers from the Stocklake universe — gainers, losers, most active. - category: "gainers" | "losers" | "most_active" | "all" (default "all" = all 3 categories) - limit: results per category (default 10, max 20). Each returned stock counts as one call toward your daily limit — a symbol appearing in more than one category (e.g. both "gainers" and "most_active") counts once per category it appears in. - min_market_cap_b: filter to stocks above this market cap in billions (e.g. 1.0 = $1B+) Returns per stock: symbol, name, sector, price, change_pct, volume, rsi, market_cap, analyst_rating, atr_pct (atr_pct omitted when the underlying volatility reading is missing or corrupted). Available to all tiers. Pro tier adds the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score 0-100, ai_score_band) — precomputed, no extra AI cost. A big mover's price/volume/RSI alone doesn't say whether the move matters; the one-line headline does. For the full research bundle on any one mover, call get_stock_research(symbol).
    get_indicator_historyHistorical daily indicator snapshots for a stock — ideal for charting and trend detection. Returns up to `days` days of data (max 730, default 90) from the stock_indicator_snapshots collection which is populated daily by the indicator pipeline. Every snapshot row contains: - recorded_at: ISO date string - price: closing price at snapshot time - rsi: RSI(14) value (0-100) - macd_histogram: MACD histogram value (positive = bullish momentum) - bb_pct: Bollinger Band % position (0 = at lower, 100 = at upper band) - sma20 / sma200: 20-day and 200-day simple moving averages With full=true, each row also gets: - williams_r: Williams %R (0 to -100; >-20 overbought, <-80 oversold) - ultimate_osc: Ultimate Oscillator (0-100; >70 overbought, <30 oversold) - vix_fix_value: Williams VIX Fix synthetic fear gauge (higher = more fear) - williams_ad_trend: Accumulation/Distribution trend (rising/falling/flat) - td_signal: DeMark TD Sequential signal (BUY_SETUP/SELL_SETUP/BUY_COUNTDOWN/SELL_COUNTDOWN/null) - td_phase: DeMark phase (setup_active/setup_complete/countdown_active/countdown_done/null) - analyst_rating: analyst consensus (buy/outperform/hold/underperform/sell or null) - analyst_target: mean analyst price target or null Returns {} if fewer than 3 snapshots found. Pro tier only. For informational purposes only. Not financial advice.
    get_stock_financialsFull raw financial statements — balance sheet, income statement, and cash flow line items over multiple periods. This is the underlying statement data itself, not a derived summary — for the forensic-accounting scores computed FROM these statements (Altman Z / Piotroski F / Beneish M), see get_stock()'s forensic_scores block instead. Each returned statement is shaped {line_item_name: {period_end_iso: value}} — e.g. balance_sheet["Total Revenue"]["2025-12-31"] — so a caller gets every available period per line item and can compute its own trends/ deltas/CAGRs, not just read the latest value. period="annual" (default) returns up to 5 fiscal years — Yahoo's own real ceiling, some symbols return fewer. period="quarterly" returns up to roughly 7-8 of the most recent quarters. period="both" returns both blocks in one call. ~50-60 curated line items per statement (not a raw dump of every row Yahoo reports): balance sheet structure (assets/liabilities/equity/debt/ working capital), income statement (revenue through EPS), and cash flow (operating/investing/financing, free cash flow, buybacks, stock-based comp). Coverage genuinely varies by symbol and sector — a bank has no "Inventory" line, a company with no buyback program has no "Repurchase Of Capital Stock" entry. A missing line item means Yahoo doesn't report it for this company, not a fetch error. quarterly can come back null (with a quarterly_note) for a symbol whose real quarterly data isn't available — rare in practice; live coverage testing found real quarterly statements even for semi-annual-reporting Hong Kong names. annual/quarterly can both be entirely absent if this symbol hasn't yet been through the financials sync, or if it's not an equity (this tool has no data for ETFs/crypto/forex/indices). Pro tier only. For informational purposes only. Not financial advice.
    get_economic_calendarUpcoming and recently-released macro/economic events -- interest rate decisions, CPI, GDP, PMI, unemployment, payrolls, retail sales, and more -- sourced from Yahoo Finance, the one calendar source confirmed safe for external exposure (a second internal-only source, Trading Economics, carries a ToS caveat and is not exposed here). Two buckets: - released_recent: events with a real reported value, within the last `lookback_days` days. Every item here carries a real `actual` value (never blank) plus `diff` (actual minus previous -- a plain arithmetic difference, never a beat/miss or consensus judgment; Yahoo doesn't provide point-in-time consensus data). - upcoming: not-yet-released events within `days`. No item here ever carries an `actual` value. Every item in both buckets carries `key_event`: true for the handful of event types that reliably move markets on their own (rate decisions, CPI, GDP, headline Non-Farm Payrolls) -- an event-TYPE flag only, never a beat/miss or directional judgment on the number itself. Set `key_events_only=true` to filter to just these, or `major_only=true` to restrict to the 8 largest economies. Both buckets sort major-economy-first, then by recency -- truncating to `limit` should never lose a US/EU/UK/JP/CN/DE/FR/CA print to an older or thinner-economy one. Pro tier only. For informational purposes only. Not financial advice.
    get_stock_researchFull AI research bundle for a stock in one call — fundamentals, AI-generated summary, recent AI-classified news, insider/institutional signal, and recent trade signal history. Replaces 4 separate calls: get_stock + get_stock_news + get_insider_activity + get_signals (for one symbol). Returns: - stock: price, name, sector, rsi, pe_forward, market_cap, 52-week range, analyst data - ai_summary: verdict, ai_score (0-100)/ai_score_band (Weak/Moderate/Strong/ Very Strong — stock_ai_summary.py's own composite, same scale/band convention as signal_score but a distinct field/pipeline), full summary, key_points, risks, price_at_generation, generated_at, headline (one-sentence plain-language take), near_term (stance/confidence over <4 weeks — technicals/momentum-weighted), longer_term (stance/confidence over a multi-month horizon — fundamentals/analyst/institutional-flow-weighted). headline/near_term/longer_term are null on summaries generated before this schema shipped — until that symbol's next regeneration, fall back to verdict/ai_score. - news: last 3 high-relevance articles (title, published_at, ai_sentiment, ai_summary, signal_score [0-100]/signal_score_band — this symbol's LIVE news-sourced signal score if one exists in the last 90 days [same number as the `signals` list below and get_signals()/get_stock_news(), kept in sync as it's re-scored], else a per-article magnitude computed from that article's own classification. One name, no separate "news_score" field.) - sentiment: signal, signal_score (0-100)/signal_score_band — one name, no separate "insider_score" field, same as the news block above — insider_trend (buying/selling/neutral, or null with no transactions in the window), institutional_pct - signals[]: up to the 5 most recent trade signals for this symbol in the last 90 days (direction, rationale, signal_score [0-100], signal_score_band [human-readable label — "Weak"/"Moderate"/"Strong"/"Very Strong" — or null alongside a null
    get_earnings_intelligenceUpcoming earnings with AI context — AI scores, verdicts, and risk factors per stock. Combines the earnings calendar with AI pipeline data to surface which upcoming earnings events are worth monitoring. Parameters: - days_ahead: look-ahead window in days (default 14, max 30) - sector: filter to one sector (e.g. "Technology") - min_ai_score: only return stocks with AI score >= this value, 0-100 (optional). Renamed 2026-08-24 from the retired 0-10 min_flag_score — gates on the same stock_ai_summary.py ai_score field the response already returns, rather than the raw legacy flag_score field, which is no longer part of this fleet's public vocabulary at all. Applied server-side before `limit` truncates the result — a stock with a qualifying score always counts against `limit` ahead of one without, rather than being cut off first for reporting later in the earnings window. - limit: max results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit — see the docs' rate-limit section. Returns per stock (sorted by earnings_date ascending): - earnings_date: ISO UTC timestamp · is_estimate: whether date is estimated - symbol, name, sector, price, rsi, market_cap - eps_trailing, eps_forward (earnings expectations context) - ai_verdict (positive/neutral/negative, from nightly AI pipeline) - ai_score (0-100) / ai_score_band (Weak/Moderate/Strong/Very Strong) — stock_ai_summary.py's own composite score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline; null if this stock has no ai_summary doc yet. - ai_risks: top 2 AI-identified risk factors - analyst_rating, analyst_target Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
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