OneQAZ Trading Intelligence MCP Server

About OneQAZ Trading Intelligence

OneQAZ (One Quorum Alpha Zero) is a 24/7 market-reading system covering about 1,300 symbols — Bithumb crypto, KOSPI 200 + KOSDAQ 150 and the S&P 500 — plus 40+ macro instruments. Signal scores are recomputed from live prices every 5–15 minutes; macro regimes update daily. Predictions are written to a timestamped ledger before outcomes are known, hash-chained daily (SHA-256), anchored publicly with OpenTimestamps and scored afterwards, so agents can audit the record with get_ledger_integrity, get_resolved_predictions and get_prediction_accuracy. Start with get_system_card or get_daily_brief. Trades and P&L are paper trading. Research information only, not investment advice.

OneQAZ Trading Intelligence tools (21)

Read from the live server on Nov 13, 2026. These are the tools your AI assistant can call once connected.

get_system_card

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

get_daily_brief

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 m

search

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. Trigger

fetch

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 s

get_symbol_context

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 obser

get_group_context

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 t

get_market_context

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 (wit

get_macro_context

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

get_news_context

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 clus

get_signals

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 u

explain_decision

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

get_performance_metrics

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 que

get_account_status

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 thei

get_prediction_accuracy

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 ho

get_active_predictions

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-p

get_resolved_predictions

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 predi

get_ledger_integrity

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

get_signal_calibration

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

get_structure_calibration

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

get_strategy_leaderboard

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_le

get_trade_history

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 t

How to connect OneQAZ Trading Intelligence to Claude, Cursor or VS Code

OneQAZ Trading Intelligence runs as a hosted remote MCP server at https://api.oneqaz.com/mcp. Add that URL to your MCP client's config (Claude Code, Cursor, Windsurf or VS Code) and the tools appear immediately, no install required.

{
  "mcpServers": {
    "oneqaz-trading-intelligence": {
      "url": "https://api.oneqaz.com/mcp"
    }
  }
}

Claude Code: claude mcp add --transport http oneqaz-trading-intelligence https://api.oneqaz.com/mcp. VS Code's .vscode/mcp.json uses a servers key instead of mcpServers (see how). Servers that require OAuth or an API key will prompt on first use.