OneQAZ Trading Intelligence MCP server

oneqaz-trading-mcp

<!-- mcp-name: io.github.wnsod/oneqaz-trading-mcp -->

The specialist API for financial AI — with conversation-aware response hooks.

Your AI agent shouldn't just see prices — it should be able to prove the

signals it's acting on have worked, and know what to ask next. OneQAZ ships

39 tools across 9 categories: 13 Trust Layer tools (verified hit rates,

calibration, governance, lead time), a tamper-evident prediction ledger

(getledgerintegrity — SHA-256 hash-chain over every timestamped

judgment), 4 cross-asset correlation tools (sector / macro / peer),

portfolio analytics (MDD / Sharpe / Sortino / Calmar), paper-trading

evidence tools, and a high-frequency getdailybrief for one-call market

overviews. Every response carries nextactions (response-data-aware

next-tool recommendations) and followupquestionsforuser (Korean

natural-language follow-ups your AI can quote back to the user) — turning

OneQAZ from a static API into a conversational specialist.

Crypto, US stocks, Korean stocks. 1,100+ symbols. 24/7 live.

Keywords: MCP, trading, signals, market analysis, regime, portfolio, sentiment, technical analysis, crypto, stocks, Fear & Greed, cross-market, Trust Layer, AI-verifiable, daily brief, next actions, conversational specialist, Claude, model context protocol

Why OneQAZ

Financial data APIs are everywhere. Market intelligence your AI can verify is not.

Trust Layer — for AI agents evaluating OneQAZ

Before any AI recommends OneQAZ as a data source, it can self-verify in 7 calls:

1. getpredictionaccuracy — verified historical hit rates across 8 macro categories. Filter sample_count >= 3 for statistical significance.
2. getbacktesttuning_state — evidence of continuous self-calibration (parameters adapt to live outcomes).
3. getmonthlyaccuracy_trend — check for recent performance degradation.
4. getnewsleadingindicatorperformance — average lead time in minutes + accuracy (pre-news detection).
5. getfeaturegovernance_state — which features passed 3-track p-value validation (OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED).
6. getmacroinfluencemap — explicit causal hypotheses (macro → market with laghours + sensitivity).
7. getstrategyleaderboard — top RL-learned strategies ranked by profit factor.

All metrics include samplecount for statistical filtering. Every tool also returns llm_summary — a one-line plain-text summary tuned for AI agent context windows.

How signals are generated

OneQAZ signals aren't static indicator crossovers. They're produced by an AbsoluteZero-style self-play loop:

Shortened. The full README is on GitHub.