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.

	Typical financial MCP	OneQAZ
Price / OHLCV data	✅	✅
Technical indicators	✅	✅
Regime detection (trending / ranging / volatile)	❌	✅
Self-correcting signals (Thompson Sampling on paper-trade outcomes)	❌	✅
Macro regime + market-structure overlays (two independent loops, joined at each symbol's decision)	❌	✅
Trust Layer (hit rates vs persistence & majority-class baselines + calibration)	❌	✅
Self-disclosed defects (known_biases, grading-pipe ledger_health)	❌	✅
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)	❌	✅
Hosted 24/7 endpoint (no API key needed)	❌	✅

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

Market	Exchange / source	Universe	Symbols
Crypto	Bithumb	All KRW-listed pairs	~460
Korean Stocks	KOSPI / KOSDAQ	KOSPI 200 + KOSDAQ 150	~350
US Stocks	NYSE / NASDAQ	S&P 500	~500
Macro	rates, FX, futures, ETFs	bonds · credit · FX · liquidity · commodities · energy · inflation · volatility	40+
Market structure	ETFs / sector baskets	per-market groups for structure analysis	~180

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.

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.

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-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:

Field	Audience	Purpose
full_data	AI	Raw evidence for trust verification
disclaimer, is_investment_advice, is_real_money, data_classification	Both	Research-only envelope on every response (false / false / research_information_only)
timestamp, request_id, _source	AI	Response time, a request id to quote when reporting issues, and the serving origin / data source
_contract	AI	Status / external / derived / unified resources only: provenance + entity + assessment + confidence (schema-versioned). Tools do not carry it.
_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 macro-regime prediction rows, with the exact canonical recipe published so any third party can recompute and verify. Each UTC day is hashed once it closes and chained to every prior day, so a later silent edit breaks recomputation. Note the scope: forecast horizons are 0–24h and many predictions resolve within the day they were created, so the chain proves nothing changed after a day closed — not that a hash existed before each outcome. Public anchors are additionally timestamped to the Bitcoin blockchain via OpenTimestamps (ledger anchors).
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, each linked best-effort — same symbol, 2h window — to the signal prediction that preceded entry) — 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. Paper P&L is gross (candle fills, no fees or slippage).

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	Per-cell hit rates with persistence & majority-class baselines, skill score + autocorrelation-corrected CI, known_biases, ledger_health
get_backtest_tuning_state	Active tuning parameters + last recalibration timestamp
get_monthly_accuracy_trend	Monthly accuracy series per (category, target market, lag) — read against the baselines above
get_news_leading_indicator_performance	UNVERIFIED — coverage of news event groupings; lead-time / score metrics withheld while the methodology is audited
get_news_causality_breakdown	UNVERIFIED — counts per pipeline news label (ANTICIPATED / SURPRISE_WITH_PRECURSOR / SURPRISE); lead metrics withheld
get_feature_governance_state	Features by status (OBSERVATION/CONDITIONAL/ACTIVE/DEPRECATED)
get_structure_calibration	Sector / ETF-level hit rates with the majority-class baseline needed to read them — measurement, not an edge claim
get_structure_validation_history	Historical structure-validation scores
get_strategy_leaderboard	Paper-tested RL strategies; per_symbol_leaderboard is the measured surface (the GLOBAL pool may include synthesized values)
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 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.

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 event-leading groupings — UNVERIFIED (score fields withheld)
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	market://crypto/positions
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

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:

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.

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