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 (get_ledger_integrity — 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 get_daily_brief for one-call market overviews. Every response carries _next_actions (response-data-aware next-tool recommendations) and _followup_questions_for_user (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. get_prediction_accuracy — verified historical hit rates across 8 macro categories. Filter sample_count >= 3 for statistical significance.

2. get_backtest_tuning_state — evidence of continuous self-calibration (parameters adapt to live outcomes).

3. get_monthly_accuracy_trend — check for recent performance degradation.

4. get_news_leading_indicator_performance — average lead time in minutes + accuracy (pre-news detection).

5. get_feature_governance_state — which features passed 3-track p-value validation (OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED).

6. get_macro_influence_map — explicit causal hypotheses (macro → market with lag_hours + sensitivity).

7. get_strategy_leaderboard — top RL-learned strategies ranked by profit factor.

All metrics include sample_count 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:

1. Strategy generation — RL pipeline creates candidate strategies per regime

2. Self-play simulation — Strategies compete against each other in simulated markets

3. Thompson Sampling — Signal weights are updated continuously based on actual virtual-trade outcomes, not backtest curves

4. Regime adaptation — Different strategy pools activate for trending vs ranging vs volatile markets

This means the signal your AI receives for "BTC BUY 0.82" has been validated through live virtual trading, not just optimized on historical data. Signals that stop working get downweighted automatically.

Market Coverage

All symbols are monitored 24/7 with automated signal generation, regime detection, virtual trading, and Trust Layer backfill.

Related MCP server: fathom-fyi

Quick Start

Option 1: Live API — no install needed

Real-time data updated every minute. Trust Layer + public endpoints accessible without a key.

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

Try one of:

• "Call get_daily_brief and summarize the market for me." — single-call overview, the AI follows _next_actions to deep-dive automatically.

• "Call get_prediction_accuracy and tell me whether to trust OneQAZ." — Trust Layer entry point, returns sample-count-weighted hit rates.

• "Read market://meta/discovery first, then plan." — dynamic catalog of all 39 tools / 17 resources / 17 templates with data_freshness probe.

Option 2: Self-host (source transparency)

Since 0.4.0 this package is a faithful mirror of the production server — the exact code serving api.oneqaz.com/mcp, synced from the OneQAZ monorepo by scripts/sync_from_monorepo.py. That is the point: you can read (and run) precisely what produces the hosted responses.

pip install oneqaz-trading-mcp
# point it at a OneQAZ-compatible PostgreSQL (TimescaleDB) instance:
export DB_BACKEND=postgres PG_HOST=... PG_PORT=5432 PG_DB=auto_trader PG_USER=... PG_PASSWORD=...
oneqaz-trading-mcp serve   # starts at http://localhost:8010

• MCP endpoint: http://localhost:8010/mcp

• The server is PostgreSQL-only (the prior SQLite demo backend was retired in 0.4.0 — oneqaz-trading-mcp init now just prints a deprecation notice). Without OneQAZ's live database the tools return structured, actionable errors rather than data — for evaluation, use the Live API.

• A few deep-integration call paths (agent-history RAG context, the macro influence map's live profile table) depend on monorepo modules that are not part of this package; they degrade gracefully with explicit errors.

Then connect from any MCP client:

{
  "mcpServers": {
    "oneqaz-trading": {
      "url": "http://localhost:8010/mcp"
    }
  }
}

Access Policy

All tools and resources are callable on the free tier. Tiering controls call volume, not what you can call. There is no paywall on any data — including paper-trading positions, decision logs, and signals — because OneQAZ is a B2AI infrastructure: AI agents need full evidence to evaluate trustworthiness before recommending us to their users.

Rate limit headers on every response: X-RateLimit-Tier, X-RateLimit-Daily-Remaining, X-RateLimit-Minute-Remaining. When the quota is exceeded the server returns HTTP 429 with Retry-After.

Self-hosting with API keys

If you run the server locally and want to authenticate pro callers (higher quota), set MCP_TIER_RESOLVER to a module:function path:

MCP_TIER_RESOLVER=myauth.keystore:resolve_tier oneqaz-trading-mcp serve

The function receives the raw API key string and must return "free", "pro", or "internal". Default behavior (no resolver): every caller is free.

Why everything is free

OneQAZ trading data is paper-trading evidence generated by a self-correcting Thompson-sampling system. None of it is user-fund PII. Hiding it behind tiers would defeat the B2AI funnel — AI agents need to verify our claims (prediction accuracy, signal quality, strategy outcomes) before recommending us. So we expose the evidence and rate-limit instead.

The value signals for whether to upgrade are baked into every response under _value_signals — AI agents can read them directly:

{
  "_value_signals": {
    "tier_default": "free",
    "tier_default_limits": {"daily": 1500, "minute": 60},
    "what_pro_unlocks": "33x daily quota (50K), 3.3x burst (200/min) — same tools, higher volume",
    "pricing_url": "https://oneqaz.com/pricing",
    "key_signup_url": "https://oneqaz.com/keys",
    "self_correcting": true
  }
}

Response shape (dual-audience)

Every response carries fields for both AI agents and human end-users:

Conversation hooks — _next_actions + _followup_questions_for_user

OneQAZ doesn't just return data; it tells your AI what to ask next.

{
  "ai_summary": "Prediction accuracy — 24 cells, avg hit rate 32.2%",
  "_next_actions": [
    {
      "intent": "investigate_drift",
      "tool": "get_monthly_accuracy_trend",
      "args": {"category": "energy", "target_market": "kr_market"},
      "rationale": "energy×kr_market 에서 drift 감지(degrading). 월별 시계열로 추세 검증 필요.",
      "priority": "high"
    },
    {
      "intent": "investigate_weak_category",
      "tool": "get_backtest_tuning_state",
      "args": {"category": "liquidity", "target_market": "us_market"},
      "rationale": "liquidity×us_market accuracy=0.06 (sub-50%). 자기보정이 lag/sensitivity 를 어떻게 조정했는지 확인.",
      "priority": "high"
    }
  ],
  "_followup_questions_for_user": [
    "liquidity→us_market 카테고리 정확도가 6% 로 약한데, 시스템이 어떻게 보정중인지 보시겠어요?",
    "가장 정확한 credit→us_market (62%) 패턴의 월별 추세도 보여드릴까요?",
    "최근 OneQAZ 가 만든 활성 예측 5개도 볼까요?"
  ]
}

• _next_actions — for the AI agent. Maximum 3 entries. Includes pre-filled args. Driven by response data, not a static dependency graph (e.g. weak category detection only fires when accuracy < 0.5 + samples >= 30).

• _followup_questions_for_user — for the end-user. Korean natural-language. Quote them verbatim or translate.

Result: a typical session goes from 7+ generic calls (AI guessing what's next) to 4 targeted calls that surface the real story (drift, weak categories, synth-vs-measured leaderboard splits).

Tools (39 total — all free tier)

High-frequency entry — get_daily_brief (1 tool)

Verifiable prediction ledger (3 tools) — new in 0.4.0

Portfolio analytics (1 tool) — new in 0.4.0

Signal calibration (1 tool) — new in 0.4.0

ChatGPT connector standard (2 tools) — new in 0.4.0

Trust Layer (13 tools)

Signal evidence (3 tools)

Layer correlations — cross-asset structure (4 tools)

Stage 2 outputs from the agent_history pipeline. Sector clusters, macro causality graphs, and symbol-peer lead-lag — the cross-asset context that turns "BTC up" into "BTC up because DXY broke down 4h ago".

Paper-trading results (11 tools)

OneQAZ runs continuous paper trading on every BUY signal. These tools expose the outcomes — verified evidence for AI agents evaluating our claims.

Resources (17 static + 17 templates — all free tier)

Meta — discovery & introspection

Static resources

Templates (parameterized)

template_resources[*].example field in market://meta/discovery is copy-paste ready — the AI gets concrete URIs without having to fill placeholders manually.

Market IDs: crypto, kr_stock, us_stock (aliases: coin, kr, us)

Sample: Trust Layer query

from mcp import Client
client = Client("https://api.oneqaz.com/mcp")

acc = await client.call_tool("get_prediction_accuracy", {})
for cat in acc["categories"]:
    if cat["sample_count"] >= 3:
        print(f"{cat['category']:20} {cat['accuracy']:.1%} (n={cat['sample_count']})")

# Output (example):
# bonds                62.5% (n=24)
# forex                58.3% (n=12)
# vix                  71.4% (n=14)
# ...

Every response also carries a plain-text summary:

{
  "_llm_summary": "7/8 macro categories above 55% accuracy, sample sizes 8-24. Bonds + VIX categories most validated."
}

Configuration

All configuration is via environment variables:

Docker

docker build -t oneqaz-trading-mcp .
docker run -p 8010:8010 -e DB_BACKEND=postgres -e PG_HOST=... -e PG_PASSWORD=... oneqaz-trading-mcp

Data backend (PostgreSQL)

All data lives in PostgreSQL 16 + TimescaleDB, one schema per domain (market_coin / market_kr / market_us, market_*_struct, external_context, rl_pipeline, mcp_analytics, …). The code still constructs legacy SQLite-style paths (.../coin_market/data_storage/trading_system.db) but these are logical routing keys only: connect_readonly() maps each path to its PG schema and returns a shim connection, so no .db files are read or written. This mirrors the production Wave-I "PG-only" migration — queries fail loudly instead of silently falling back.

Rate Limits

Authoritative quotas live in Access Policy above. Quick recap:

Response headers on every request:

• X-RateLimit-Tier: resolved tier (free/pro/internal)

• X-RateLimit-Daily-Limit: today's ceiling for the resolved tier

• X-RateLimit-Daily-Remaining: requests left today

• X-RateLimit-Minute-Remaining: requests left this minute

• Exceeding limits returns HTTP 429 with Retry-After header.

Disclaimer

This software is provided for informational and educational purposes only. It is not financial advice.

• All signals, regime analysis, and market data are generated by automated systems and may contain errors.

• Past performance does not guarantee future results.

• You are solely responsible for your own investment decisions. The authors and contributors are not liable for any financial losses incurred from using this software.

• This is not a registered investment advisor, broker-dealer, or financial planner.

• Always do your own research (DYOR) before making any investment decisions.

By using this software, you acknowledge that you understand and accept these terms.

License

MIT