OneQAZ

Showing 25 of 39 from the live probe.

• 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 many trades won this week?". When to call: past trade review, single-symbol post-mortem, win-rate audits. Prerequisites: none. Next steps: analyze_trades, market://{market_id}/signals/feedback. Caveats: paper-trading data only (not real money). limit

• 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 call: pattern audits, period-over-period performance review. Prerequisites: get_trade_history recommended for raw rows first. Next steps: market://{market_id}/signals/feedback for the upstream signals. Caveats: max 30 days; empty result when no trades

• 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 call: success-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) D

• 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: failure-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaim

• 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 / losers right now?", "how's the book doing?". Paper-trading positions (NOT real money). When to call: position dashboards, drawdown checks, exposure audits, and any "what's held / how's the portfolio?" question. Prerequisites: market://{market_id}/statu

• 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 position". When to call: full context for one ticker. Prerequisites: confirm the symbol holds a position via get_positions. Next steps: get_signal_detail, get_role_analysis. Caveats: returns an error envelope when no position exists for the symbol. Args:

• 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 종목이 잘 가고 있어?". When to call: quickly surface winning tickers. Prerequisites: none. Next steps: get_position_detail for full context. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted)

• 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?". When to call: drawdown / risk review. Prerequisites: none. Next steps: get_position_detail, get_role_analysis. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results

• 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 성적 어때?", "is one strategy dominating?". When to call: diversification audit, per-strategy performance check. Prerequisites: get_positions recommended for raw rows. Next steps: market://{market_id}/derived/strategy-fitness, signals/feedback. Caveats: empty d

• 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): "what did the system decide?", "최근에 뭐 샀어? 팔았어?", "why did you buy X?", "show recent buy/sell calls", "오늘 매매 판단 뭐 했어?", "any trades triggered today?". When to call: review recent automated decisions and their outcomes. Prerequisites: market://{m

• 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 questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest

• 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 bullish or bearish?", "any buy signals right now?". Returns a research signal + score (NOT an order or advice — always surface the disclaimer). Pair with get_latest_decisions to show what the system did. When to call: drilling into a specific signal

• 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 to call: drilling into a single ticker's signal context. Prerequisites: confirm existence via get_signals first. Next steps: get_role_analysis, get_position_detail. Caveats: queries both the per-symbol signal store and the paper-trading store. Disclai

• 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 신호가 일치해?", "short-term vs long-term signal?". When to call: multi-timeframe analysis, cross-role agreement checks. Prerequisites: get_signal_detail recommended. Next steps: market://{market_id}/unified/symbol/{symbol}, get_position_detail. Caveats: based on

• 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 with autocorrelation-corrected skill_ci_95, n_effective vs n_nominal, and accuracy_v2_only (post 2026-07-08 methodology cutover). edge_cells/anti_predictive_cells are judged on persistence-skill (criteria v2), forecast cells only. Trig

• 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 (casual questions too): "does the system self-correct?", "시스템이 스스로 보정해?", "how is it calibrated?", "튜닝 상태 보여줘", "is it adapting to what actually happened?". When to call: after get_prediction_accuracy, to show the system updates itself. Prerequisites:

• 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 performance trend?", "최근에 예측 성능 떨어졌어?", "show accuracy over time". When to call: after get_prediction_accuracy and get_backtest_tuning_state — completes the trust chain. Prerequisites: get_prediction_accuracy recommended. Next steps: none (trust chain complet

• Get News Leading Indicator Performance

  Purpose: Evidence that OneQAZ detects price moves BEFORE news publication. Returns leading_score, avg_lead_time_minutes, and accuracy_pct per event type. Strongest Trust Layer A evidence (Layer A = anticipation-capability tier of OneQAZ's 5-layer trust pyramid) — proves the system is anticipatory rather than reactive. Triggers (casual questions too): "can you predict news?", "뉴스 나오기 전에 감지해?", "how early do you catch moves?", "뉴스보다 빨라?", "do prices move before headlines?". When to

• Get News Causality Breakdown

  Purpose: Three-bucket news classification proving systematic discrimination between anticipated and surprise events. ANTICIPATED = scheduled + pre-move detected, SURPRISE_WITH_PRECURSOR = cascade anomaly (macro -> ETF -> stock) caught early, SURPRISE = pure unexpected. Triggers (casual questions too): "was that news already priced in?", "그 뉴스 예견된 거였어?", "how many surprise events this week?", "돌발 뉴스 비율 어때?", "did the market see it coming?". When to call: after get_news_leading_ind

• 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 pass independent statistical tests. Triggers (casual questions too): "do you validate your own inputs?", "피처 검증은 어떻게 해?", "which signals passed testing?", "통계 검증 통과한 피처 뭐야?", "how do you avoid junk features?". When to call: meta-level trust audit

• 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. Proves systematic edge at the sector-rotation level. Triggers (casual questions too): "how good are your sector calls?", "섹터 예측 잘 맞아?", "sector rotation accuracy?", "그룹 단위 적중률 보여줘", "can you time sector moves?". When to call: when an AI wants to see Layer D evidence (Layer D = sector-structur

• 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 questions too): "sector accuracy over time?", "구조 예측 매일 검증해?", "daily hit-rate trend?", "요즘 섹터 예측 성적 어때?", "is the sector edge holding up?". When to call: after get_structure_calibration. Prerequisites: none. Next steps: get_monthly_accuracy_trend f

• 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_leaderboard is the primary measured-edge surface for trust auditing. Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?", "top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?". When to call: final

• 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-picking). Triggers (casual questions too): "what are you predicting right now?", "지금 어떤 예측 걸려 있어?", "current forecasts?", "예측을 미리 기록해 두는 거야?", "anything on the record before it resolves?". When to call: to verify ongoing prediction activity. Prerequ

• 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 — the causal reasoning is visible and measurable. Triggers (casual questions too): "how do rates affect crypto?", "금리가 코인에 어떻게 영향 줘?", "what's your causal model?", "예측 논리가 뭐야?", "which macro drives which market?". When to call: when an AI wants to