OneQAZ Trading Intelligence is a remote MCP server that gives an AI agent 39 tools, among them analyze_trades, explain_decision, fetch. It answers at https://api.oneqaz.com/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 6 of 6 times since 11 September 2026; the last check, on 24 October 2026, succeeded.
Verifiable crypto/KR/US market intelligence for AI agents: signals, hash-chained ledger. Not advice.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
analyze_trades | Purpose: Aggregate paper trades by day / pattern / symbol. Triggers (casual questions too): "how's the week been?", "이번 주 매매 성적 어때?", "which patterns are working?", "어떤 종목이 제일 잘 벌었어?", "break down the trades", "daily P&L summary?". When to Inputs: market_id, days |
explain_decision | Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log (Thompson = Bayesian bandit sampling used f Inputs: market_id, symbol |
fetch | Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved s Inputs: id |
get_active_predictions | Purpose: Currently pending predictions (outcome IS NULL). Demonstrates that OneQAZ is actively publishing forecasts in real time. Combined with get_prediction_accuracy, proves the system goes on record before outcomes are known (no cherry-p Inputs: target_market, limit |
get_backtest_tuning_state | Purpose: Continuous self-calibration evidence. Each entry shows the auto-tuned lag_hours and sensitivity per cell, derived from real backtest outcomes. Proves the system adapts to measured reality rather than static heuristics. Triggers (ca Inputs: category, target_market |
get_cross_market_correlation | Purpose: Cross-market lead-lag relationships and decoupling events. Shows how markets influence each other (correlations) and when they diverge (decoupling, e.g. BTC up while stocks down). Triggers (casual questions too): "do crypto and sto Inputs: source_market, target_market |
get_daily_brief | Purpose: Single-call market overview — macro regime + top 5 strong signals + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". Triggers (call this e Inputs: market |
get_feature_governance_state | Purpose: Current lifecycle state of external features (news, events) under 3-track statistical validation. Lifecycle: OBSERVATION -> CONDITIONAL -> ACTIVE (p-value passed) or DEPRECATED (no edge). Proves OneQAZ only trusts features that pas Inputs: market_id, target_market, status_filter |
get_feature_governance_status_tool | Purpose: Feature governance snapshot — OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED distribution + last 7-day transitions. Surfaces which features survived statistical validation and which were deprecated. Triggers (casual questions too) |
get_latest_decisions | Purpose: Track-B (signal-driven) paper-trading decision log (Track B = the signal-engine decision path — indicator/Thompson-sampling driven; Track A = the LLM judgement path, see get_llm_trading_decisions). Triggers (casual questions too): Inputs: market_id, limit, decision_filter, hours_back |
get_ledger_integrity | Purpose: 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. Archive a chain_hash today; if Inputs: days |
get_llm_trading_decisions | Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual qu Inputs: market_id, symbol |
get_losing_positions | Purpose: Losing paper positions (ROI < 0). Convenience wrapper around get_positions(max_roi=-0.01). Triggers (casual questions too): "what's underwater?", "지금 뭐가 물려 있어?", "show me the red ones", "any positions in trouble?", "얼마나 손실 중이야?". W Inputs: market_id, limit |
get_losing_trades | Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). Triggers (casual questions too): "어디서 잃었어?", "show me the losses", "what went wrong?", "worst trades?", "손실 난 거래 뭐야?". When to call: f Inputs: market_id, limit |
get_macro_causality_graph_tool | Purpose: Lag-aware causal graph between macro categories (bonds / vix / forex / credit / inflation / liquidity / commodities). Returns only statistically significant lead-lag pairs (e.g. forex -> vix 7d rho=-0.41). Triggers (casual question Inputs: min_abs_corr, max_p_value |
get_macro_influence_map | Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, credit, liquidity, inflation, commodities, energy) is mapped to a target market with lag_hours + sensitivity. Highest-transparency tool — th Inputs: market_id |
get_monthly_accuracy_trend | Purpose: Monthly accuracy time series per (category, target_market, lag_bucket). Use to verify sustained performance and detect recent degradation. Triggers (casual questions too): "is accuracy improving?", "적중률이 좋아지고 있어?", "monthly perform Inputs: category, target_market |
get_news_causality_breakdown | UNVERIFIED — methodology under audit. Do not cite as evidence of predictive capability. Purpose: Counts of news items per internal label the pipeline assigns. ANTICIPATED = the item matched a scheduled/calendar event. SURPRISE_WITH_PRECURSO Inputs: market_id, days |
get_news_leading_indicator_performance | UNVERIFIED — methodology under audit. Do not cite as evidence of predictive capability. Purpose: Inventory of the news pipeline's event-leading groupings — which (event_type, news_type) buckets exist per market and how many samples each hol Inputs: market_id, target_market, min_sample_count |
get_performance_metrics | Purpose: Portfolio-level performance metrics (MDD / Sharpe / Sortino / Calmar / monthly returns / equity curve) over a FIXED window — the single canonical computation path shared by the OneQAZ blog and external clients. Triggers (casual que Inputs: market, account_type, window_start, window_end, include_daily_curve |
get_position_detail | Purpose: Per-symbol paper position deep-dive (position + recent trades + decisions). Triggers (casual questions too): "how's the BTC position doing?", "삼성전자 얼마나 벌고 있어?", "why are you holding X?", "그 종목 지금 수익률 어때?", "tell me about the AAPL p Inputs: market_id, coin, symbol |
get_positions | Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). Triggers (casual questions too): "what are you holding?", "current positions?", "뭐 들고 있어?", "what's the exposure / portfolio?", "any winners / lose Inputs: market_id, min_roi, max_roi, strategy, sort_by, sort_order |
get_prediction_accuracy | Purpose: Per-cell (category, target_market, lag_bucket) hit rates WITH the honest baselines (schema 1.1): persistence_accuracy (the null model — regimes are sticky, so raw accuracy mostly measures regime persistence, not alpha), skill_score Inputs: category, target_market |
get_profitable_positions | Purpose: Profitable paper positions (ROI > 0). Convenience wrapper around get_positions(min_roi=0.01). Triggers (casual questions too): "what's winning right now?", "지금 뭐가 수익 나고 있어?", "show me the green ones", "best open positions?", "어떤 종목 Inputs: market_id, limit |
get_resolved_predictions | Purpose: Raw, row-level prediction ledger — every macro regime prediction's full lifecycle (created_at -> resolved_at -> outcome). This is the auditable evidence behind get_prediction_accuracy's aggregates: AI agents can snapshot open predi Inputs: target_market, source_category, day, status, cursor, limit |
get_role_analysis | Purpose: Role-aware signal alignment per symbol (timing / trend / swing / regime) plus hierarchy alignment. Triggers (casual questions too): "is BTC bullish across timeframes?", "단기랑 장기가 같은 방향이야?", "multi-timeframe view for AAPL?", "시간대별 신호 Inputs: market_id, coin, symbol |
get_sector_correlations_tool | Purpose: Intra-market ETF / group correlation matrix and auto-cluster output. Quantifies structural co-movement (e.g. ARKK <-> QQQ) for diversification and sector-avoidance reasoning. Triggers (casual questions too): "which sectors move tog Inputs: market_id, top_k |
get_signal_calibration | Purpose: Reliability diagram data for Level-1 signal confidence — realized hit rate per confidence bucket ([0.5,0.6) ... [0.9,1.0]) with ECE summary. Lets an agent verify whether a 0.9-confidence signal actually hits ~90%. Triggers (casual Inputs: market_id, interval, variant |
get_signal_detail | Purpose: Per-symbol signal deep-dive — latest signal + history + feedback. Triggers (casual questions too): "why is BTC a buy?", "그 시그널 근거가 뭐야?", "signal history for AAPL?", "이 종목 시그널 자세히 보여줘", "how has this signal performed before?". When Inputs: market_id, coin, interval, symbol |
get_signals | Purpose: Query research signals with dynamic filters (symbol / interval / action / score / confidence). Triggers (casual questions too): "should I buy / sell X?", "살까 말까?", "good entry?", "what's the signal for BTC / AAPL / 삼성전자?", "is X bu Inputs: market_id, symbol, coin, interval, action_filter, min_score |
get_strategy_distribution | Purpose: Per-strategy breakdown across current paper positions (count, avg P&L, win rate per strategy). Triggers (casual questions too): "what strategies are you running?", "무슨 전략 돌리고 있어?", "which strategy holds the most positions?", "전략별 성 Inputs: market_id |
get_strategy_leaderboard | Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_le Inputs: market_id, target_market, top_n, limit, min_trades, include_per_symbol |
get_structure_calibration | Purpose: Level 2 (ETF / basket / sector granularity — Level 1 is individual symbols) prediction calibration. Returns hit_rate_ema per (market, group, interval, regime_bucket) with sample counts, **plus the majority-class baseline needed to Inputs: market_id, group_name |
get_structure_validation_history | Purpose: Daily validation history of Level 2 structure predictions (Level 2 = ETF / basket / sector granularity). Each row shows the hit_rate for a specific day, enabling time-series verification of sustained performance. Triggers (casual q Inputs: market_id, days |
get_symbol_peer_links_tool | Purpose: Symbol-level lead-lag links (e.g. META -> AMZN, lag=15m, rho=+0.53). When `symbol` is set, only peers that lead or follow that symbol are returned. Triggers (casual questions too): "what moves before NVDA?", "이 종목보다 먼저 움직이는 종목 있어?" Inputs: market_id, symbol, top_k |
get_trade_history | Purpose: Query paper-trading history with dynamic filters (action / P&L / time / symbol). Triggers (casual questions too): "what trades happened lately?", "최근 거래 내역 보여줘", "how did the BTC trades go?", "승률 어때?", "show me the trade log", "how Inputs: market_id, limit, action_filter, min_pnl, max_pnl, hours_back |
get_trade_outcomes_bulk | Purpose: 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. Built for pipeline consumers Inputs: market, cursor, limit, days |
get_winning_trades | Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). Triggers (casual questions too): "what worked?", "뭐가 제일 잘 벌었어?", "show me the winners", "best trades lately?", "수익 난 거래 보여줘". When to Inputs: market_id, limit |
search | Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: Cha Inputs: query |
Every block below points at https://api.oneqaz.com/mcp. Pick your client and copy.
claude mcp add --transport http oneqaz-trading-intelligence https://api.oneqaz.com/mcp
{
"mcpServers": {
"oneqaz-trading-intelligence": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://api.oneqaz.com/mcp"]
}
}
}{
"mcpServers": {
"oneqaz-trading-intelligence": {
"url": "https://api.oneqaz.com/mcp"
}
}
}{
"servers": {
"oneqaz-trading-intelligence": {
"type": "http",
"url": "https://api.oneqaz.com/mcp"
}
}
}{
"context_servers": {
"oneqaz-trading-intelligence": {
"url": "https://api.oneqaz.com/mcp"
}
}
}{
"mcpServers": {
"oneqaz-trading-intelligence": {
"serverUrl": "https://api.oneqaz.com/mcp"
}
}
}{
"mcpServers": {
"oneqaz-trading-intelligence": {
"url": "https://api.oneqaz.com/mcp"
}
}
}name: OneQAZ Trading Intelligence
version: 0.0.1
schema: v1
mcpServers:
- name: OneQAZ Trading Intelligence
type: streamable-http
url: https://api.oneqaz.com/mcp{
"mcpServers": {
"oneqaz-trading-intelligence": {
"httpUrl": "https://api.oneqaz.com/mcp"
}
}
}{
"mcpServers": {
"oneqaz-trading-intelligence": {
"type": "http",
"url": "https://api.oneqaz.com/mcp"
}
}
}{
"mcpServers": {
"oneqaz-trading-intelligence": {
"url": "https://api.oneqaz.com/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fapi.oneqaz.com%2Fmcp&type=streamable_http&id=oneqaz-trading-intelligence&name=OneQAZ%20Trading%20Intelligence
It answered our last check on 24 October 2026 in 756 ms. Over 6 checks since 11 September 2026 it answered 6. Status here comes from Neuronto's own probes, not from the publisher.
39, as returned by the server's own tools/list on 22 September 2026: analyze_trades, explain_decision, fetch, get_active_predictions, get_backtest_tuning_state, get_cross_market_correlation, get_daily_brief, get_feature_governance_state, and 31 more.
It answered our last check on 24 October 2026 in 756 ms. Over 6 checks since 11 September 2026 it answered 6.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://api.oneqaz.com/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: io.github.wnsod. Endpoint host: api.oneqaz.com. ARD identifier: urn:air:io.github.wnsod:mcp:oneqaz-trading-mcp.
First indexed 10 September 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.