OneQAZ Trading Intelligence

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.

	Typical financial MCP	OneQAZ
Price / OHLCV data	✅	✅
Technical indicators	✅	✅
Regime detection (trending / ranging / volatile)	❌	✅
Self-correcting signals (Thompson Sampling on live outcomes)	❌	✅
Macro → ETF → Individual context chain	❌	✅
Trust Layer (AI-verifiable hit rates + calibration)	❌	✅
Feature governance (3-track p-value validation)	❌	✅
Cross-asset correlations (sector / macro / symbol-peer lead-lag)	❌	✅
Conversation hooks (_next_actions + _followup_questions_for_user)	❌	✅
Single-call market brief (get_daily_brief)	❌	✅
Dynamic discovery (introspection + data_freshness probe)	❌	✅
Live 24/7 cloud API	❌	✅

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

Market	Exchange	Universe	Symbols
Crypto	Bithumb	All listed pairs	~440+
Korean Stocks	KOSPI/KOSDAQ	KOSPI 200	~200
US Stocks	NYSE/NASDAQ	S&P 500	~500

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

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.

Tier	Auth	Daily quota	Burst	Notes
free	none / invalid key	1,500 / day	60 / min	All 39 tools + 17 resources + 17 templates
pro	valid API key	50,000 / day	200 / min	Same surface, higher volume — for bots / schedulers
internal	owner-only	unlimited	unlimited	Reserved for real user-fund tools (currently empty)

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:

Field	Audience	Purpose
full_data	AI	Raw evidence for trust verification
_contract	AI	Provenance + entity + assessment + confidence (schema-versioned)
_llm_summary	AI	Multi-line narrative
ai_summary	AI	One-line compressed summary for context windows
_value_signals	AI	Pricing / upgrade signals for B2AI conversion judgment
summary_for_user	Human	One-line jargon-free Korean — quotable verbatim by Claude.ai etc.
_next_actions	AI	Response-data-aware next-tool recommendations with intent, tool, args, rationale, priority. Drives chain calls automatically.
_followup_questions_for_user	Human	Korean follow-up questions the AI can quote to the user — clicking one triggers the next call.

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)

Tool	Returns
get_daily_brief	Single-call market overview: macro regime + top 5 strong signals + yesterday's paper-trading P&L + active prediction count + Korean narrative. The natural first call for "what's the market doing today?"

Verifiable prediction ledger (3 tools) — new in 0.4.0

Tool	Returns
get_ledger_integrity	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. The strongest trust primitive OneQAZ offers — judgments are chained before outcomes are known.
get_resolved_predictions	Raw row-level prediction ledger: every macro regime prediction's full lifecycle (created_at → resolved_at → outcome) — audit the evidence judgment by judgment.
get_trade_outcomes_bulk	Cursor-paginated bulk export of the prediction → trade → outcome chain (paper trades with realized P&L, linked to the preceding signal prediction) — compute your own hit rates instead of trusting ours.

Portfolio analytics (1 tool) — new in 0.4.0

Tool	Returns
get_performance_metrics	Portfolio-level MDD / Sharpe / Sortino / Calmar / win-rate per market and account type (paper / live), optional daily equity curve.

Signal calibration (1 tool) — new in 0.4.0

Tool	Returns
get_signal_calibration	Reliability diagram data for signal confidence: realized hit rate per confidence bucket with ECE summary — verify whether a 0.9-confidence signal actually hits ~90%.

ChatGPT connector standard (2 tools) — new in 0.4.0

Tool	Returns
search	ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong signals across all three markets. Result ids are consumable by fetch.
fetch	Connector-standard fetch of a single result by id returned from search.

Trust Layer (13 tools)

Tool	Returns
get_prediction_accuracy	Verified hit rates per macro category (with sample_count)
get_backtest_tuning_state	Active tuning parameters + last recalibration timestamp
get_monthly_accuracy_trend	Rolling 12-month accuracy per category
get_news_leading_indicator_performance	Pre-news detection lead time + accuracy
get_news_causality_breakdown	News → market causality tags with hit rates
get_feature_governance_state	Features by status (OBSERVATION/CONDITIONAL/ACTIVE/DEPRECATED)
get_structure_calibration	Structure-learning calibration snapshot
get_structure_validation_history	Historical structure-validation scores
get_strategy_leaderboard	RL-learned strategies by profit factor
get_active_predictions	Currently-open macro predictions with outcome tracking
get_macro_influence_map	Macro → market causal hypotheses (lag hours + sensitivity)
get_cross_market_correlation	Cross-market correlation matrix
get_role_analysis	Role-based strategy analysis

Signal evidence (3 tools)

Tool	Parameters
get_signals	market_id, symbol, min_score, max_score, action_filter, interval
get_signal_detail	market_id, symbol, interval
explain_decision	market_id, symbol

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".

Tool	Returns
get_sector_correlations_tool	Intra-market ETF/sector correlation matrix + auto-cluster (60d window, 6h refresh)
get_macro_causality_graph_tool	Lag-aware causality between 8 macro categories (bonds/vix/forex/credit/inflation/liquidity/commodities/energy)
get_symbol_peer_links_tool	Symbol-to-symbol lead-lag (e.g. META → AMZN 15min lag, ρ=+0.62)
get_feature_governance_status_tool	Feature lifecycle distribution + last-7-day status transitions

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.

Tool	Returns
get_positions	Open paper positions with ROI
get_position_detail	Single position deep-dive
get_profitable_positions / get_losing_positions	Filtered by P&L
get_strategy_distribution	Position counts by strategy
get_trade_history	Closed paper trades (filters: action, P&L, time)
analyze_trades	Aggregate trade analytics
get_winning_trades / get_losing_trades	Filtered by outcome
get_latest_decisions	Recent signal → decision transitions
get_llm_trading_decisions	LLM-generated decision logs

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

Meta — discovery & introspection

Resource URI	Description
market://meta/discovery	Dynamic catalog (v2.0) — full tool/resource list via FastMCP introspection (no static if/else), with data_freshness PG probe (5 source tables), positioning block (specialist_domains, trust_principles, what_we_do_NOT_provide, philosophy), counts, notes. Call this first to understand what OneQAZ provides.
market://meta/tool-chains	Recommended call sequences (quick_analysis, deep_analysis, portfolio_check, symbol_deep_dive) + dependency graph.
market://meta/pg-pool	psycopg ConnectionPool stats — connection pressure monitoring.
market://health	Server health check.
market://info	Server metadata + data source index.

Static resources

Resource URI	Description
market://global/summary	Global macro regime summary
market://global/categories	Available macro categories list
market://global/macro_events	Active macro event lifecycle
market://all/summary	Combined summary across all 3 markets
market://structure/all	All markets ETF/basket structure
market://indicators/fear-greed	Fear & Greed Index
market://indicators/regime	4-layer regime indicators (Short/Mid/Long/SuperLong)
market://indicators/context	Fear & Greed + 4-layer regime + breadth
market://unified/cross-market	Cross-market correlation snapshot (BTC ↔ stocks ↔ FX)
market://derived/event-leading	News leading-detection score
market://derived/cross-decoupling	Cross-asset decoupling index
market://derived/reaction-speed	News reaction speed distribution

Templates (parameterized)

URI Template	Example
market://global/category/{category}	market://global/category/bonds
market://{market_id}/status	market://crypto/status
market://{market_id}/positions/snapshot	market://crypto/positions/snapshot
market://{market_id}/structure	market://kr_stock/structure
market://{market_id}/structure/group/{group_id}	market://kr_stock/structure/group/SEMICONDUCTOR
market://{market_id}/signals/summary	market://crypto/signals/summary
market://{market_id}/signals/roles	market://crypto/signals/roles
market://{market_id}/signals/feedback	market://crypto/signals/feedback
market://{market_id}/external/summary	market://crypto/external/summary
market://{market_id}/external/symbol/{symbol}	market://crypto/external/symbol/BTC
market://{market_id}/external/causality	market://crypto/external/causality
market://{market_id}/unified	market://crypto/unified
market://{market_id}/unified/symbol/{symbol}	market://crypto/unified/symbol/BTC
market://{market_id}/derived/regime-transitions	market://crypto/derived/regime-transitions
market://{market_id}/derived/strategy-fitness	market://crypto/derived/strategy-fitness
market://{market_id}/derived/all	market://crypto/derived/all

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:

Variable	Default	Description
MCP_SERVER_PORT	8010	Server port
MCP_SERVER_HOST	0.0.0.0	Bind host
MCP_LOG_LEVEL	INFO	Log level
MCP_TIER_RESOLVER	unset	module:function returning tier for an API key (self-host hook)
DB_BACKEND	postgres	Must be postgres (SQLite backend retired in 0.4.0)
PG_HOST / PG_PORT	postgres / 5432	PostgreSQL host / port
PG_DB / PG_USER / PG_PASSWORD	auto_trader / …	PostgreSQL database / credentials (a read-only role is enough)
PG_POOL_MIN / PG_POOL_MAX	0 / 50	Per-schema connection pool bounds
PG_STATEMENT_TIMEOUT_MS	30000	Server-side statement timeout
MCP_COIN_DATA_DIR / MCP_KR_DATA_DIR / MCP_US_DATA_DIR	auto	Override logical data-path roots (see below)
MCP_EXTERNAL_CONTEXT_DATA_DIR	auto	Override external-context logical root

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:

Tier	Daily Quota	Burst	Auth
Free	1,500 / day	60 / min	none / invalid key
Pro (beta)	50,000 / day	200 / min	valid API key
Internal (owner)	Unlimited	Unlimited	owner-only
Local (self-hosted)	Unlimited	Unlimited	n/a

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