Every change Wellknown observed on this agent, newest first, with what it was before and what it became. Tool-surface changes carry the definition diff. Nothing here is edited after the fact.
Removed "explain_signal", "fetch", "get_correlation_map" and 15 more; changed the definition of "get_earnings_calendar", "get_fear_greed", "get_filing_language_diff" and 3 more (26 tools before, 8 now)
⟨9 unchanged words⟩ one S&P 500 ticker. Earnings are a scheduledvolatilityevent.A swing entry held into a print carries gap risk that no stop can protect against — the price can jump the stop overnight.This tool surfaces the next-upcoming andlast-reportedlast-reported earnings datesplus a derived `earnings_risk_note` so an agent can time entries around the event rather than blindly into it. Edge / risk discipline: when the next report is <= 5 days out, swing BUYs are suppressed server-side (see get_signal / get_trade_plan) — the note here says so loudlyandflags any entry as elevated gap risk. Far from a print,thenote is neutral. Thisdaysisuntil thetiming companion tonextget_trade_plan.one.Graceful degradation: earnings data is a silently-degrading signal.When nodata is available (cold cache + no live source), `available` isfalse and`next`/`last` arenull —null; treat that as "unknown", not "no ⟨15 unchanged words⟩ dict with available, next, last, days_until_next,earnings_risk_note,as_of anddisclaimer embedded in the payload.
⟨23 unchanged words⟩ Extreme Fear) to 100 (Extreme Greed).Read it alongside get_market_regime, which exposes both gauges: high greed with good buying conditions = momentum intact; high greed with a hot overheating gauge = crowded long, a contrarian warning. Fear (below 50) is the crowd NOT leaning in — on its own that argues against calling a market frothy, whatever the buying-conditions score says.Returns a dict with value (0-100), label ⟨14 unchanged words⟩
⟨220 unchanged words⟩ extract). Read-only surfacing: this is information,NOT a buy/sell recommendation, and it does NOT feed the quantum model's conviction/verdict logic — this isnot acandidate signal still pending backtest validation.recommendation. Args: ticker: Stock ticker (e.g. "AAPL ⟨35 unchanged words⟩
⟨191 unchanged words⟩ news day. Read-only surfacing: this is information,NOTnot abuy/sell recommendation, and it does NOT feed the quantum model's conviction/verdict logic.recommendation. Args: ticker: Optional. Stock ticker for the ⟨23 unchanged words⟩
⟨165 unchanged words⟩ nothing notable. Read-only surfacing: this is information,NOT a buy/sell recommendation, and it does NOT feed the quantum model's conviction/verdict logic — insider clusters arenot acandidate signal still pending backtest validation.recommendation. Args: ticker: Optional. Stock ticker for the ⟨24 unchanged words⟩
⟨286 unchanged words⟩ via `accession_url`. Read-only surfacing: this is information,NOT a buy/sell recommendation, and it does NOT feed the quantum model's conviction/verdict logic — this isnot acandidate signal still pending backtest validation.recommendation. Args: ticker: Stock ticker (e.g. "AAPL ⟨25 unchanged words⟩
Explain WHY a ticker has its current verdict — the factor attribution behind it. get_signal gives the verdict; get_trade_plan gives the execution; explain_signal gives the *reasoning*. It decomposes the quantum signal into the individual factors that drove the call — directional probability, expected upside, tail downside, confidence-band width, band calibration, market-condition gating, and risk-reward — each with a direction, a 0..1 contribution weight, the raw value, and a plain-English sentence. Pure transform over the same data get_signal returns; no new mode…
Fetch the full Quantustik signal + forecast writeup for one ticker. Paired with search — call search(query) first to find the ticker's id, then fetch(id) here for the full readable content. Also accepts a bare ticker symbol typed directly (id need not come from a prior search call). Args: id: Ticker symbol as returned by search, e.g. "NVDA". Returns a dict with id, title, text (a plain-text signal/forecast summary suitable for quoting or summarizing), url, and metadata (verdict, conviction, generated_at).
Get the cross-ticker ENTANGLEMENT map — which S&P 500 names the quantum model expects to co-move. "Entanglement" here is the Pearson correlation of the quantum model's own FORECAST return paths (mode="forecast", the default) — a forward-looking, model-implied co-movement signal. It is NOT a claim about realised market correlation, and you must not present it as one. mode="realized" instead correlates trailing daily returns (the consensus baseline) for comparison. Two ways to read it: • Diversification / risk lens — high entanglement means two names are …
Get quantum probability forecasts across multiple time horizons for a ticker. Delivers calibrated probability distributions from the Feynman path-integral model: prob_up, expected growth %, downside %, and 90% confidence interval (ci_low / ci_high). Backtest CI90 calibration is included where available. Each measured horizon (3mo / 6mo / 1y) carries a `calibration` block, and that block deliberately contains NO coverage figure for the ticker: `ci90_empirical_coverage` is always null. BE HONEST WITH USERS — do not fill that gap. Band calibration is measured across…
Get every market indicator we compute, in plain English, with its contribution. This is the full, dry state of the market: all 18 weighted signals the model composes — breadth, institutional flow, insider clusters, Fear & Greed, VIX, credit spreads, the yield curve, our own forecast, and the rest — each with a plain-English label, its current reading, what that reading means, and exactly how much it moved today's score. Rows are grouped by what they do to the verdict, under `groups`: - "down" — signals pushing the verdict down (bad for the buyer) -…
Get the whole dashboard market picture in ONE call — the composed snapshot. This is the single-call twin of the market dashboard. The dashboard's market view stitches several readings into one coherent picture; an answer engine that had to make four separate round-trips to reconstruct "what is the market doing right now" would more likely give up or answer from a partial view. This tool returns that whole picture at once, composed from the SAME cached market-regime snapshot the dashboard renders — never recomputed here — so the tool's answer can never silently div…
Get the current MARKET CONDITIONS for the S&P 500 — ONE metric, not three. This tool (named `get_market_regime` for backward compatibility) returns a single customer-facing metric called **Market Conditions**. Present it as ONE thing. It is NOT "Market Conditions" plus a separate "market regime" plus a standalone "Overheating" and "Euphoria" — those are the internal PARTS of this one metric, and quoting them as separate top-level gauges is the exact confusion this contract exists to prevent. THE HEADLINE is the buying-conditions verdict. THE FROTH CHECK is contex…
Get the S&P 500 RISK STATE — a measurement of market risk, not a forecast. THIS TOOL DOES NOT PREDICT ANYTHING. It reports what has already happened and what one published rule holds as a result. Say "measures" and "holds", never "expects" or "signals". The distinction is not pedantry: it is the only reason the fields below can be quoted plainly instead of hedged. THE HEADLINE is `markets.SPY.rule_weight` — the share of the index the rule holds in today's conditions, between 0 and 1. It is NOT a recommended allocation, is not addressed to the person asking, and m…
Get the committed 32-year run behind every published Risk State figure. SIMULATED RESULTS, NEVER A LIVE TRACK RECORD. The rule was designed with hindsight over this same history and has never been run forward. Say so whenever you quote a number from here; a reader who takes these for a live record has been misled even if every figure is accurate. QUOTE THE COSTS IN THE SAME BREATH AS THE RETURNS. Three fields exist to make that easy and must not be dropped: * `published.longest_lag_years` — how long the rule spent behind simply owning the index. It is t…
Get the latest quantum swing signal for a single S&P 500 ticker. Returns TWO distinct facets — they answer different questions and are not interchangeable: * `verdict` (BUY | WAIT | EXIT, labelled BULLISH / NEUTRAL / BEARISH) — what the model thinks of the stock. This is the same word, over the same field, that the website shows a human. EXIT means "consider closing an existing long", never "short it". * `timing` (BUY | WAIT | AVOID | WATCH) — whether to put money in RIGHT NOW. A stock the model likes can still be a poor entry today. Alongside th…
Get historical signal events for a ticker. Events include: new_buy, tp_hit (the price actually reached a profit target we named), trail_stop_hit (the trailing stop closed the trade short of the target — may be a gain or a loss), stop_hit, position_closed. Useful for evaluating how past signals played out — the primary signal-quality verification tool. A trade WON only if payload.pnl_r > 0; the event type names the exit mechanism, never the result. Reviewing tp_hit / stop_hit ratios shows realised performance vs the stated R:R — a 2:1 R:R target is only meanin…
Get signals (or trade plans) for many S&P 500 tickers in ONE call. Built for portfolio / watchlist sweeps: a research agent analysing 10–30 names should fan out once here instead of N separate get_signal / get_trade_plan round-trips (N quota decrements, N disclaimer copies, N chances for partial failure). This is a pure transform over data already exposed by get_signal / get_trade_plan — no new model work. Per-ticker failure isolation: a not-found / timeout on one symbol records an entry in ``errors`` and the batch continues — one bad ticker never aborts the …
Get everything we compute about ONE stock, in plain English, with contributions. The per-ticker twin of `get_market_indicators`: the eight weighted components that make the conviction score (forecast direction, realistic downside, how well our past forecasts held up for this specific stock, trend, momentum, overbought/ oversold, position in the 52-week range, and path momentum — whether our forecast is in step with the stock's recent moves, a continuity check and NOT a forecast-accuracy score), the forecast band, the risk and sizing numbers, and every risk check t…
Get the realised track record: win-rate + R-multiple distribution. Aggregates actual closed outcomes (tp_hit, trail_stop_hit, stop_hit, position_closed) into an honest scorecard — win_rate, wins/losses, and the realised-R distribution (mean/median/min/max/total). Pass a ticker for its per-ticker record; omit it (or pass "") for the portfolio-wide record. HONESTY: small samples are flagged `illustrative: true` with an explanatory `note`, and `win_rate` is null when no closed P&L exists. Never present an illustrative record as a reliable hit-rate. Every number …
Get an execution-ready trade plan for one S&P 500 ticker — entry, exit, size, risk. This is the decision tool: it turns the raw quantum signal into a concrete, risk-first plan you can act on. Unlike get_signal (which reports the raw verdict and levels), get_trade_plan wraps them in execution discipline: - entry_plan: the exact entry trigger, a "don't chase" rule, and the invalidation level that kills the thesis. - exit_plan: a take-profit ladder (TP1/TP2/TP3), scale-out guidance, a volatility-aware trailing-stop rule, and a 3-month time-stop. - sizing: po…
Scan the S&P 500 universe and return filtered signals. Pushes filters + pagination to the server (GET /signals): the REST API applies min_conviction/direction/sector and returns one page of size limit, so the LLM payload stays tight regardless of how broad the universe is. Use this to discover high-conviction opportunities across the market rather than analyzing tickers one by one. The scan covers all S&P 500 tickers simultaneously, surfacing cross-ticker convergence patterns that per-ticker analysis would miss. Freshness: scan runs on a scheduled cadence (t…
Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first,…
Get a small, risk-vetted shortlist of asymmetric setups passing every gate right now. This is NOT a screener. Where scan_universe returns up to 100 sortable rows, top_opportunities answers the real question — "what are the 1-3 highest-conviction setups right now, and what's the catch on each?" — and deliberately returns FEW or ZERO names. A setup is included ONLY if it passes ALL of: • current market conditions permit new longs (not Caution / Strong Avoid), • conviction >= 7.5 (high-conviction floor — multi-signal convergence), and • risk-reward >= 2:1 …
Certificate changed, valid to 2026-12-12
Certificate recorded, valid to 2026-10-14
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First tool surface recorded: 26 tools (server version 2.0.4)
Showing the latest 5 events. The API returns up to 500 and filters by kind: ?kind=tool_surface_changed