OneQAZ Trading Intelligence

Purpose: Start here. One-page card for an agent meeting OneQAZ for the first time: what it is (public beta, operated by an individual, SLA: none), coverage (markets, bar intervals, universe sizes), data freshness (ok / degraded per pipeline component), and the measured track r...

No parameters. Call it with no arguments.

Purpose: Single-call market overview — engine macro regime + score-ranked example signals + score-sign breadth + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". Triggers (call this ev...

Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest score-ranked combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Resear...

Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clie...

Use when the question is about one stock or coin — its current signal state, the OneQAZ structure group it belongs to, and which assets have tracked lead-lag links to it. Returns self-contained headline claims first, then the symbol's observed state, upstream links (its group,...

Use when the question is about a sector, theme, ETF basket or index basket — its structure state and its members. Returns headline claims, the group's regime state per interval, its market, groups that co-move with it, member symbols with their latest signal bias, symbols the...

Use when the question is about a whole market (KR, US, CRYPTO) — its structure regime and how its groups are positioned. Returns headline claims, the market's structure and global macro regime, the macro categories assumed to affect it (with tuned lags), its groups ordered by...

Use when the question is about a macro factor — rates and bonds, the dollar and FX, VIX, credit, liquidity, inflation, commodities or energy — and which markets or stocks it is linked to. Returns headline claims, the macro category's regime state per interval and its instrumen...

Use when the question is about a specific news item or a news keyword or event (FOMC, 반도체, tariff) — which markets, symbols and topics it is tagged with. Returns headline claims, the item (or keyword match counts), its title-similarity cluster, the market, symbol and topic tag...

Purpose: Query signals with dynamic filters (symbol / interval / action / score / confidence). Triggers (casual questions too): "what's the signal for BTC / AAPL / 삼성전자?", "is X bullish or bearish?", "how does X look right now?". When the user asks whether to trade X, OneQAZ g...

Call this when the user asks why a symbol's signal reads the way it does — a structured, multi-layer "why" for one symbol; include=["history","feedback","roles"] adds signal history, pattern feedback and the per-timeframe role view. Purpose: Combines (1) technical score_trace...

Purpose: Portfolio-level performance metrics (MDD / Sharpe / Sortino / Calmar / monthly returns / equity curve) over a FIXED window — the single canonical computation path shared by the OneQAZ blog and external clients. Triggers (casual questions too): "what's the max drawdown...

Purpose: Show which OneQAZ account (or API key) this connection uses, its plan and its rate limits. Requires a connected OneQAZ account — calling it without one prompts your AI app to connect (free, no API key needed; Pro users can use their key). All other tools work without...

No parameters. Call it with no arguments.

Call this first when judging how OneQAZ's macro calls have held up — per-cell hit rates with honest baselines; granularity="monthly" for the month-by-month trend. Purpose: Per-cell (category, target_market, lag_bucket) hit rates WITH the honest baselines (schema 1.1): persiste...

Purpose: Currently pending predictions (outcome IS NULL). Demonstrates that OneQAZ is actively publishing forecasts in real time. Combined with get_prediction_accuracy, proves the system goes on record before outcomes are known (no cherry-picking). Triggers (casual questions t...

Purpose: Raw, row-level prediction ledger — every macro regime prediction's full lifecycle (created_at -> resolved_at -> outcome). This is the auditable evidence behind get_prediction_accuracy's aggregates: AI agents can snapshot open predictions, wait, then verify outcomes th...

Purpose: Tamper-evidence for the prediction ledger — a daily SHA-256 hash chain over all created/resolved prediction rows, with the exact canonical recipe published so any third party can recompute and verify. Archive a chain_hash today; if history is ever silently edited, rec...

Purpose: Reliability diagram data for Level-1 signal confidence — realized hit rate per confidence bucket ([0.5,0.6) ... [0.9,1.0]) with ECE summary. Lets an agent verify whether a 0.9-confidence signal actually hits ~90%. Triggers (casual questions too): "is your confidence c...

Call this to see how well sector / ETF-basket direction calls held up against a majority-class baseline; include_history=True adds the day-by-day validation trend. Purpose: Level 2 (ETF / basket / sector granularity — Level 1 is individual symbols) prediction calibration. Retu...

Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edg...

Call this to check how the paper-trading book's closed trades actually turned out — realized P&L per trade with action / P&L / time / symbol filters, and outcome="win" | "loss" for winners or losers. Triggers (casual questions too): "what trades happened lately?", "최근 거래 내역 보여...