Quantustik

Quality 86 / 100good

8 tools, about 2,756 tokens to load them all · tool set de7df6729cab · tools last changed 2026-10-03 · checked 2026-10-20 15:57 UTC

Tool changes

• 2026-10-03get_signalremoved

  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 them: entry price, stop-loss, take-profit ladder (TP1/TP2/TP3), position-size suggestion, and risk-reward ratio generated by the Schrödinger + Feynman path-integral model. Data is refreshed on a scheduled basis (typically every few hours). A BUY verdict requires multi-signal convergence — a lone indicator never triggers one. Conviction score (0–10) reflects signal agreement depth. Call explain_signal to see the factor attribution — directional probability, expected upside, tail downside, band calibration — with the dominant driver and what argues against the call. Limits: S&P 500 universe only. Signal may be absent if the ticker was not included in the latest scan run. Timing: a BUY here is suppressed within 5 days of an earnings print — call get_earnings_calendar to see the event date and gap-risk note before entry. Args: ticker: Stock ticker symbol (e.g. "AAPL", "MSFT", "NVDA"). Case-insensitive. Returns a dict with signal, quantum forecast, generated_at, and disclaimer embedded in the payload.

• 2026-10-03fetchremoved

  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).

• 2026-10-03get_correlation_mapremoved

  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 effectively one trade. Stacking five mutually-entangled longs is one position with 5x the size, not a diversified book. • Pairs / divergence lens — strongly NEGATIVE entanglement flags names the model expects to move oppositely (hedge or pairs candidates). Honesty: every number is computed from real persisted scan output. Tickers with no usable forecast curve are listed in `missing`, never imputed. The universe is the top_n names ranked by 3mo forecast growth. Args: ticker: Optional. When set, returns only that ticker's row (correlations + most_entangled + most_divergent), not the full matrix. Case-insensitive. top_n: Universe size — top-N tickers by 3mo forecast growth (2–100, default 25). Smaller = tighter LLM payload. horizon: Forecast horizon to correlate — "1mo", "3mo", "6mo", or "1y" (default "3mo"). mode: "forecast" (model-implied, default) or "realized" (trailing-returns baseline). basis: Forecast-mode correlation basis. "mean" (default) correlates the single mean forecast curve. "ensemble" correlates across the full forecast band envelope (q05/q25/mean/q75/q95) and averages the per-band correlations — this surfaces TAIL co-movement (two names whose stress/downside paths move together) that the mean curve understates, which is exactly when diversification matters most. Ignored in realized mode. The method label becomes "quantum_forecast_ensemble" so you never conflate the two. Returns the full-matrix shape (tickers / matrix / top_pairs / missing) by default, or the single-ticker shape when `ticker` is given. Carries method, basis, generated_at, scan_date, and disclaimer in the payload.

• 2026-10-03get_signal_historyremoved

  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 meaningful if the stop_hit rate is low. Args: ticker: Stock ticker symbol (e.g. "AAPL"). Case-insensitive. limit: Number of events to return (1–100, default 10). Returns a dict with ticker, count, and events list.

• 2026-10-03get_market_indicatorsremoved

  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) - "up" — signals pushing it up - "silent" — measured, sitting in normal territory, contributing zero. Load-bearing: this is how you tell a CALM market from a panicking one, and a weak market from a frothy one. - "unavailable" — no data today. NOT the same as "silent"; do not report a missing signal as if it read "fine". `aggregate.arithmetic` gives the sum that produces the score, so the verdict can be checked rather than asserted. `aggregate.how_unusual` says what the score is measured against. This returns the SAME rows, with the SAME wording, that a human sees on the website. Quote them as they are — the labels, meanings and colours are the canonical ones and are not meant to be paraphrased into a different vocabulary. Returns a dict with title, as_of, aggregate, groups, rows, and disclaimer.

• 2026-10-03searchremoved

  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, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.

• 2026-10-03get_risk_stateremoved

  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 must never be relayed as advice about their money. "The rule holds 78% of the index" is correct. "You should hold 78%" is not, and is the failure this contract exists to prevent. THE RULE, published in full so it can be checked: 1. Hold the index while it trades 1% above its 200-day average; the state turns off on a close 1% below. 2. A close below only counts when ALL THREE stress conditions agree: corporate borrowing costs above their one-year norm, the 10-year Treasury yield below its level 40 days earlier, and consumer staples ahead of discretionary over 60 days. Returning needs the price alone. 3. Position size is set at each month end from 20-day volatility, targeting a 15% budget, capped at fully invested. `sizing.struck_on` says when. Because it is struck monthly, it will usually disagree with what today's `realised_volatility` alone implies. That is correct, not a bug. WHY THE CONDITIONS EXIST: over 32 years the plain 200-day rule left the market 48 times. Three of those absences avoided a real decline; 43 were roughly three-week dips it sat out for nothing, costing more than the three saved were worth. The conditions cut those 43 to 14 while keeping all three exits that mattered. Do not quote this paragraph: call `get_risk_state_evidence` and read `episodes`, which is where these numbers come from and where they stay current. DEGRADED READINGS ARE HONEST, NOT SILENT. A missing stress input makes an exit PERMITTED rather than blocked, and is named in `stress.degraded_inputs` with a `degraded_note`. If `status` is "unavailable", that is an outage, not a calm market, and must be relayed as such. ALWAYS RELAY THE COSTS WITH THE RETURNS. Call `get_risk_state_evidence` for the committed 32-year run before quoting any historical figure. Never quote a return from memory, and never present the simulated history as a live track record; the rule has never been run forward. Distinct from `get_market_regime`, which scores today's tape from eighteen signals and has no backtested return edge behind it. This is one rule with a committed run. Both are honest about which they are; do not merge them. Returns as_of, stress (three conditions, each with value/reference/met, plus exit_permitted and degraded_inputs), and markets.SPY (close, trend_average, distance_to_average, realised_volatility, in_market, rule_weight, sizing, state_changes_at).

• 2026-10-03get_market_overviewremoved

  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 diverge from what a human sees on the page. ONE SOURCE OF TRUTH. The payload is the composed regime snapshot, read from cache. Every field is produced by the same builder the dashboard reads; this tool adds nothing and recalculates nothing. In particular it carries: - `verdict` / `model_verdict` / `confidence_downgraded` — the single authoritative buying-conditions tier, the pre-downgrade tier, and whether a low-confidence downgrade separated them (see get_market_regime for the full reading of the two gauges). - `headline` — the one-line plain-English summary of the current state. - `score` / `score_rescale` / `buying_conditions` / `score_basis` — the 0-100 buying-conditions score and what it is measured against (it is a trailing z-score, not an absolute health percentage). - `overheating` — the froth CHECK (context within Market Conditions, not a separate metric) as TWO internal legs (0-100 each): how cheaply risk is priced, and how hard the crowd is leaning in. Report both legs; never quote the combined scalar or present these as standalone "Overheating"/"Euphoria" gauges. - `signal_coverage` — "used/total" indicators that fed today's score, so a thin-data reading is not mistaken for a confident one. - `action_plan` — the "what to do about it" overlay, conditioned on the current Market Conditions. Reach for this first when the question is about the market as a whole. Then call get_market_indicators for the full per-signal breakdown, or get_market_regime for the same snapshot with the two-gauge reading spelled out in detail. Returns the composed market snapshot dict (the fields above and the rest of the snapshot) with the educational-research disclaimer embedded — the same disclaimer that travels with every detachable forecast artifact, because this is model-state read out of page context.

• 2026-10-03scan_universeremoved

  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 (typically several times per day). Results carry scan_generated_at, and a `data_freshness` sentence only when the scheduled run did not land. Pagination: results are deterministically ordered server-side. When more results remain, next_cursor is a non-null opaque token — pass it back as cursor= to fetch the next page (scan_universe(..., cursor=next_cursor)). next_cursor is null on the last page. Args: min_conviction: Minimum conviction score (0–10). Recommended: 6.0+ for actionable signals, 7.5+ for high-conviction only. direction: Filter by signal verdict — "BUY", "WAIT", "AVOID", or "WATCH". Case-insensitive. sector: Filter by sector name (partial match, case-insensitive). Examples: "Technology", "Health Care", "Financials". limit: Page size — max results per call (default 20, max 100). cursor: Opaque pagination cursor from a previous call's next_cursor. Omit for the first page. Returns a dict with count, scan_generated_at, results list, next_cursor (null when no further pages), and disclaimer embedded in the payload.

• 2026-10-03get_track_recordremoved

  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 is grounded in stored outcome events — none are fabricated. This is the ground-truth check on signal quality: a high stated R:R only holds if the realised-R mean is positive and the stop-hit rate is low. Args: ticker: Stock ticker (e.g. "AAPL"), or "" for portfolio-wide. Returns a dict with scope, sample_size, win_rate, realised_r, and note.

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Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.