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 (hit rates scored against honest baselines, calibration, governance), a tamper-evident prediction ledger (get_ledger_integrity — a daily SHA-256 hash chain over every macro-regime prediction, recomputable by anyone), 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 — about 1,300 symbols plus 40+ macro instruments. 24/7 live. Research information from paper trading — not investment advice.

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 — per-cell hit rates for macro → market forecasts across 8 macro categories, served with the null models needed to read them: persistence accuracy (regimes are sticky, so raw accuracy mostly measures persistence), skill_score with an autocorrelation-corrected skill_ci_95, and the majority-class baseline (beats_majority_baseline). Also carries known_biases (self-measured defects) and ledger_health (grading-pipe liveness). Judge cells by skill and baselines, never by raw accuracy.

2. get_ledger_integrity — tamper-evidence for those predictions: a daily SHA-256 hash chain with the canonical recipe, recomputable from get_resolved_predictions.

3. get_backtest_tuning_state — the auto-tuned lag_hours / sensitivity per cell (last_backtest shows tuning freshness).

4. get_monthly_accuracy_trend — monthly accuracy series. Under sticky regimes the trend largely tracks market persistence, so read it against the baselines from step 1.

5. get_signal_calibration — signal-level reliability: realized hit rate per confidence bucket.

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

7. get_strategy_leaderboard — paper-tested RL strategies. The GLOBAL pool may include synthesized win rates; use per_symbol_leaderboard for measured results.

Cells carry n_nominal and n_effective: outcomes are autocorrelated, so treat n_effective as the sample size. Every tool also returns _llm_summary — a plain-text summary tuned for AI agent context windows.

News lead-time metrics are not part of the trust chain. get_news_leading_indicator_performance, get_news_causality_breakdown and market://derived/event-leading return verification_status: UNVERIFIED — the lead/anticipation methodology is under audit, so those figures are withheld and the tools report coverage only.

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 a signal such as "BTC BUY 0.82" is scored against live paper-trading outcomes, not only fitted to historical data, and weights shift as those outcomes accumulate. Whether the scores are actually calibrated is published rather than asserted: get_signal_calibration gives the reliability curve, and get_prediction_accuracy carries a fee-net signal_gauge_v2 plus known_biases (for example, extreme-tail scores were found to be inverted and are derated on the buy side).

Market Coverage

The pipeline runs 24/7. Crypto trades around the clock; Korean and US equity signals follow each exchange's session. Candle intervals: crypto 15m / 30m / 4h / 1d, KR and US 5m / 15m / 30m / 1d. Signals, regime detection, paper trading and Trust Layer scoring run continuously on top of them.

Related MCP server: fathom-fyi

Quick Start

Option 1: Live API — no install needed

Live data — signals refresh as new candles close (5-minute to daily). Every tool and resource is callable without a key on the free tier (1,500 calls/day).

Cursor (mcp.json) and other clients that take a remote URL in their config:

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

Claude Desktop's config file needs the mcp-remote bridge:

{
  "mcpServers": {
    "oneqaz-trading": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://api.oneqaz.com/mcp"]
    }
  }
}

Claude Code: claude mcp add --transport http oneqaz-trading https://api.oneqaz.com/mcp. Claude.ai and ChatGPT: add https://api.oneqaz.com/mcp as a custom connector. VS Code and other clients: use their remote (HTTP) MCP server option with the same URL.

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-Limit, X-RateLimit-Daily-Remaining, X-RateLimit-Minute-Remaining. When the quota is exceeded the server returns HTTP 429 with Retry-After. Pricing and key requests: https://api.oneqaz.com/pricing.

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://api.oneqaz.com/pricing",
    "key_signup_url": "https://api.oneqaz.com/keys",
    "self_correcting": true
  }
}

Response shape (dual-audience)

Tool responses carry 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 paper-trades its own signals continuously (BUY signals that pass its entry filters). These tools expose the positions and outcomes — losses included — so AI agents can check our claims. Fills are candle-based with no fees or slippage, so P&L is gross.

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

Tested against the live endpoint with fastmcp 2.14 and 4.0 (pip install fastmcp):

import asyncio, json
from fastmcp import Client

async def main():
    async with Client("https://api.oneqaz.com/mcp") as client:
        result = await client.call_tool("get_prediction_accuracy", {})
    resp = json.loads(result.content[0].text)

    print(resp["ai_summary"])
    for cat, markets in resp["full_data"]["summary"].items():
        for market, lags in markets.items():
            for lag, cell in lags.items():
                if cell.get("horizon_type") != "forecast":
                    continue  # lag 0h cells are same-cycle nowcast checks, not forecasts
                lo, hi = cell.get("skill_ci_95") or (float("nan"), float("nan"))
                print(f"{cat:12} -> {market:10} {lag:>4}  hit {cell['accuracy']:.1%}"
                      f"  persistence {cell.get('persistence_accuracy', float('nan')):.1%}"
                      f"  skill CI [{lo:+.2f}, {hi:+.2f}]"
                      f"  beats majority baseline: {cell.get('beats_majority_baseline')}")
    print("edge cells:", len(resp["full_data"]["edge_cells"]))
    print(resp["full_data"]["meta"].get("persistence_null_caveat", ""))
    print(resp["disclaimer"])

asyncio.run(main())

It prints one line per forecast cell: raw hit rate, the persistence null, the skill confidence interval, and whether the cell beats a constant "most common outcome" guess. A conservative reading treats a cell as evidence only when its skill CI lower bound is above 0 and it beats the majority baseline — and read meta.persistence_null_caveat before trusting edge_cells. ai_summary / _llm_summary state the same comparison in one line, and every response carries the disclaimer envelope.

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.

Privacy

• Hosted service (api.oneqaz.com): the authoritative policy is https://api.oneqaz.com/privacy. In short — no accounts; per-request metadata (IP, user-agent, tool name, outcome, whitelisted arguments, client/protocol metadata, API-key fingerprint) is logged; IP addresses and IP-derived session keys are irreversibly replaced after 12 months; data is never sold.

• Self-hosting: the package sends no telemetry to OneQAZ, and request logs go to your PostgreSQL. Replace the bundled /privacy page with your own notice if you expose your deployment.

Details: PRIVACY.md.

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.

• All performance figures come from paper trading (simulated, candle-based fills with no fees, taxes or slippage). Real-money results would be lower.

• 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