docs: architecture diagram (PNG + matplotlib generator)

End-to-end map: external services → providers/news layer → signal core (engine + factory + aggregator) → SignalReport → four delivery surfaces, plus the backtester replaying the same core. Regenerate with venv/bin/python docs/architecture_diagram.py after architecture changes. Claude-Session: https://claude.ai/code/session_01Aq1jZ6jBsBdEC2xT3u46SA
dashboard: clickable news — detail dialogs + on-demand AI summary endpoint
2026-08-07 09:21:52 -04:00 · 2026-08-07 09:08:49 -04:00 · 2026-08-06 23:06:29 -04:00 · 2026-08-06 22:44:48 -04:00 · 2026-08-06 22:35:13 -04:00 · 2026-08-06 22:21:02 -04:00
10 changed files with 4 additions and 1047 deletions
@@ -11,9 +11,8 @@ Every numeric threshold in `config.py` and `pair_signals/xauusd.py` was backtest
 ## Commands
 ```bash
-python test_all.py                      # QA suite: 374 tests, 23 modules, no network (mocked)
+python test_all.py                      # QA suite: 359 tests, 22 modules, no network (mocked)
-python main.py --pair XAUUSD --timeframe H1 --detail   # CLI signal report (--json for full dump,
+python main.py --pair XAUUSD --timeframe H1 --detail   # CLI signal report (--json for full dump)
                                        #  --panel to run the advisory LLM agent panel on-demand)
 python telegram_bot.py                  # the bot (refuses to boot without credentials)
 python oanda_daemon.py                  # auto-execution daemon on Oanda practice account
 python dashboard_publisher.py --once    # web dashboard: one scan+publish cycle to $DASHBOARD_OUT_DIR
@@ -35,7 +34,7 @@ Python 3.12; a `venv/` exists in-repo. Dependencies in `requirements.txt` (numpy
 ## Configuration & credentials
-Precedence for every credential/deployment setting: **env var → config.json → refuse to run** (no hardcoded fallbacks). A gitignored repo-root `.env` is loaded at `app_config` import via `os.environ.setdefault`. `config.json` (gitignored; schema in `config.json.example`) holds secrets plus deployment settings: `data_providers.active` (the live-feed switch), `data_providers.streaming` (optional tick-stream provider for the level-breach monitor), `scanning.pairs`/`timeframes`, per-pair `blocked_hours_utc`, `log_level`. Key env vars: `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`, `TIINGO_API_KEY`, `OANDA_ACCOUNT_ID`/`OANDA_API_TOKEN`/`OANDA_BASE_URL`, `DEEPSEEK_API_KEY` (news LLM; the `deepseek` config.json block holds model/enabled/timeout/daily cap). The `agent_panel` config.json block (all-defaults = feature dark) controls the advisory LLM agent panel: `enabled`, per-surface `telegram`/`dashboard` toggles, `min_quality`, `max_panels_per_day`, `reuse_ttl_seconds`.
+Precedence for every credential/deployment setting: **env var → config.json → refuse to run** (no hardcoded fallbacks). A gitignored repo-root `.env` is loaded at `app_config` import via `os.environ.setdefault`. `config.json` (gitignored; schema in `config.json.example`) holds secrets plus deployment settings: `data_providers.active` (the live-feed switch), `data_providers.streaming` (optional tick-stream provider for the level-breach monitor), `scanning.pairs`/`timeframes`, per-pair `blocked_hours_utc`, `log_level`. Key env vars: `TELEGRAM_TOKEN`, `TELEGRAM_CHAT_ID`, `TELEGRAM_CHANNEL_ID`, `TWELVE_DATA_API_KEY`, `TIINGO_API_KEY`, `OANDA_ACCOUNT_ID`/`OANDA_API_TOKEN`/`OANDA_BASE_URL`, `DEEPSEEK_API_KEY` (news LLM; the `deepseek` config.json block holds model/enabled/timeout/daily cap).
 ## Architecture
@@ -60,7 +59,6 @@ Signal currency everywhere is `SignalStrength` (−2..+2). DataFrames are UTC tz
 - **`signal_aggregator.py`** — MTF agreement, diminishing-decay penalty system, exhaustion hard block, quality grading, then an ordered chain of post-grade gates (session, XAU SELL quality/vol/hour, regime blocks, M5 noise gates), then three setup generators: XAU ATR (A+/A), generic swing-based, plus the M5 pullback (`XAU_PB`) and chart-pattern (`XAU_CP`, `chart_patterns.py`) entry paths. The PB/CP generators run AFTER the gate chain and force quality to "B"; the CP path deliberately accepts grade-F composites and re-checks only session/day/big-candle (via warning prefixes) plus its own SELL/vol/RSI gates — it does NOT re-check exhaustion or the M5 min-score/MTF gates. **This is validated behavior, not a bug** (2026-08-04 audit: 226/586 trades in the shipped M5 12mo book are XAU_CP, +38.9R, 104 of them exhaustion-flagged and net-positive) — changing it is a rule-1 revalidation event. `TradeSetup.strategy` selects trailing params; trail params are stamped on the setup from the *producing* config so per-TF overrides reach formatters.
 - **`pair_signals/`** — per-pair config factories. **Always use the factory (`produce_signal` / `make_config`), never bare `PlatformConfig()`** — the factory applies the config.json overlay first, then the validated per-TF deltas (M5 hour-9 block, M15 trail arm 0.70, M15 SELL-hour block, etc.; evidence in the docstring). A test enforces factory ≡ inline-config equivalence. Confirmation map: M5→[M15,H1], M15→[M5,H1], H1→[M15,H4], H4→[H1,D1].
 - **`news_engine.py`** — Forex Factory calendar (MyFXBook RSS fallback) + RSS keyword sentiment with gold-inverse keywords. Class-level caches (fresh engine per scan). Optional DeepSeek enrichment via `llm_client.py` (requests-only, no SDK): batched headline sentiment overriding keyword scores, gold-direction on release surprises, and a market-summary blurb (`SignalReport.news_summary`) — **display/warning-only, keyword fallback on any failure, inert without a key**; prompts are static module constants (DeepSeek prefix-cache pricing) and requests send `thinking: disabled` (V4-Flash reasoning otherwise blows the timeout). By XAU-tuned default, event risk and sentiment **warn but do not gate signals** (`event_risk_caps_signals=False`, `sentiment_affects_signals=False`) — the LLM does not change that.
 - **`agent_panel.py`** — ai-hedge-fund-style advisory panel: 4 DeepSeek persona calls (macro/technical/risk/contrarian, concurrent on a panel-private pool) + 1 synthesizer producing an **ADVISORY-ONLY** consensus (`AGREE/NEUTRAL/OPPOSE` + confidence + rationale) written onto `SignalReport.panel_*` fields. Attached **post-hoc** by consumers (`telegram_bot` alert path, `main --panel`, `dashboard_publisher`) — never runs inside `analyze_from_dataframes` (backtest parity, test-locked) and no gate reads its fields. Static prompts (prefix caching); spend bounded by an exact per-bar cache, a reuse TTL keyed (symbol, TF, direction) so persisting signals don't re-spend every bar, and a `max_panels_per_day` UTC budget (default 20 ≈ ≤100 requests) on top of the shared 500/day client cap. `<2` valid persona takes skips the synthesizer; any failure degrades to no panel, never a changed signal.
 - **`telegram_bot.py`** — owner-locked bot + subscriber channel; candle-boundary auto-scan loops (M5/M15/H1/H4, boundary+10 s), alert quality threshold + fingerprint dedup (committed only after a successful send), 📰 LLM market-context line on alerts when available, WebSocket level-breach monitor fed by the streaming provider (`data_providers.streaming`, falling back to the active provider; auto-disabled when the resolved provider can't stream or has no API key — Oanda does not stream, Tiingo does). Rotating log: `logs/telegram.log`, 3-h rotation × 240 backups.
 - **`oanda_daemon.py` / `oanda_executor.py`** — practice-account auto-execution. Book-parity rules (see daemon docstring): no broker-side TP (trailing model — TP1/TP2 advisory), 5-bar per-TF cooldown (wall-clock approximation, so it expires over weekends where the backtester would still count bars), trail ratchets at bar close once per *observed* bar (a missed/failed cycle skips those bars entirely — the intra-gap high/low never enters the peak). Accepted divergence: one position total; orders send `positionFill=OPEN_ONLY` so a state/broker divergence cancels the order instead of silently netting. State/broker re-sync runs at boot and after any suspicious fill (`_reconcile_positions`).
 - **`dashboard_publisher.py` / `static/dashboard.html`** — web dashboard as a **static publisher** (live LAMP server): standalone daemon scans at candle closes via `produce_signal` and atomically writes `snapshot.json`/`news.json`/`dashboard.html` to `$DASHBOARD_OUT_DIR` (Apache serves them; page is public by owner decision). **Hard independence rule: never imports, and is never imported by, `telegram_bot`/`oanda_daemon`** — confirmation map and boundary math are deliberate local copies, so a web-side failure can only mean a stale page. Due-TF classification uses the RAW wake time (flooring wake−buffer skips every H1/H4 scan — 2026-08-02 bug). News panel reads `NewsEngine.get_fundamental_context()` plus the additive `get_week_events()` accessor (display-only; no signal path calls it); as a separate process it has its own news caches and DeepSeek daily cap. News/calendar rows are clickable (detail dialog); headlines carry `id` = sha1(headline) in news.json, and exactly one on-demand endpoint exists: `static/news_summary.php` (installed by `install_static`), which serves a DeepSeek gold-impact summary per headline — whitelist-gated against news.json ids, cache-forever in `ai/`, own fail-closed daily cap (150/day, file-based, separate from the publisher's), key at `/etc/gold-dashboard/deepseek.key`, never reads repo config. Deployment checklist: `deploy/README-dashboard.md`.
@@ -1,541 +0,0 @@
 """
 ForexBot2 — LLM agent panel (advisory consensus)
 =================================================
 ai-hedge-fund-style persona panel for XAU/USD signals: four DeepSeek
 personas (macro, technical, risk, contrarian) each review a finished
 SignalReport from their own angle, then a synthesizer chair weighs the
 takes and issues a consensus verdict (AGREE / NEUTRAL / OPPOSE with
 confidence and a short rationale).
 Two invariants, both load-bearing (CLAUDE.md rules 1 and 4):
 - ADVISORY / DISPLAY-ONLY. The verdict is written onto SignalReport
  fields that no gate and no scoring path ever reads. Any failure —
  LLM down, cap exhausted, garbage reply — degrades to "no panel",
  never to a changed or blocked signal.
 - NEVER runs inside ``analyze_from_dataframes``. The backtester replays
  that function; consumers (telegram_bot, main --panel,
  dashboard_publisher) attach the panel POST-HOC via ``attach_panel``,
  so replayed books can never see a panel field. Backtest parity holds
  by construction.
 Cost model: every full panel spends 5 requests (4 personas + 1
 synthesizer) against the shared DeepSeek daily cap (llm_client). Two
 layers bound the spend on top of that cap:
 - ``max_panels_per_day`` (config, default 20) — a UTC-day budget of
  panel *attempts*, mirroring the client's request cap.
 - A reuse TTL (default 3600 s) keyed on (symbol, timeframe, direction):
  a persisting signal re-serves its cached verdict across bars instead
  of re-spending. Without this, one sticky M5 dashboard signal would
  burn 5 calls per 5-minute bar — 1,440/day against the 500 cap.
 Prompts are STATIC module constants (DeepSeek prefix-cache pricing —
 see news_engine.py); all variable content travels in the user payload.
 """
 import datetime as _dt
 import logging
 import threading
 import time
 from collections import OrderedDict
 from concurrent.futures import ThreadPoolExecutor, wait as _fwait
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 from app_config import get_app_config
 from llm_client import DEFAULT_TIMEOUT, get_llm_client
 if TYPE_CHECKING:  # type hints only — keep the coupling one-way
    from news_engine import FundamentalContext
    from signal_aggregator import SignalReport
 logger = logging.getLogger(__name__)
 DEFAULT_MIN_QUALITY = "B"
 DEFAULT_MAX_PANELS_PER_DAY = 20
 DEFAULT_REUSE_TTL_SECONDS = 3600.0
 # Quality ranks for the min_quality gate. Deliberately a local copy —
 # telegram_bot's ranking is private and dashboard_publisher must not
 # import telegram_bot (hard independence rule).
 _QUALITY_RANK = {"A+": 4, "A": 3, "B": 2, "C": 1}
 # ── DeepSeek prompts ─────────────────────────────────────────────────
 # STATIC constants on purpose: DeepSeek prices a repeated byte-identical
 # prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
 # be interpolated or timestamped. All variable content travels in the
 # user payload. Panel output is display-only — it never gates signals.
 _GOLD_DOCTRINE = """\
 Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
 dollar are gold-bearish; easing / rate cuts / safe-haven flows /
 geopolitical stress / weak dollar are gold-bullish.
 """
 _TAKE_CONTRACT = """\
 Your review is advisory research on an already-generated signal; you are
 one voice on a panel, not the decision maker.
 Write the note in plain everyday English for someone who has never
 traded: short simple sentences, no trading jargon, no acronyms, no
 abbreviations. Say "profit versus loss", not "RR"; "the 4-hour chart",
 not "H4"; describe what an indicator MEANS ("the price has climbed
 unusually fast"), never its name or number.
 Reply with ONLY JSON:
 {"stance": "support"|"oppose"|"neutral", "confidence": <0-100>,
 "note": "<max 25 words>"}
 "stance" says whether YOUR analysis supports the proposed trade
 direction, opposes it, or is genuinely mixed; "confidence" is how sure
 you are of your stance; "note" is your single most important point.
 """
 _MACRO_SYSTEM = """\
 You are the macro analyst on a gold (XAU/USD) trade-review panel. You
 receive JSON: a proposed trade brief ("brief": direction, timeframe,
 quality, price, regime) plus the current fundamental picture — recent
 "headlines", today's high-impact "events" (released: true means the
 number is out), a news "sentiment" score for gold, and an optional
 "news_summary" blurb.
 Judge ONE thing: does the macro backdrop support the proposed direction?
 Weigh released surprises over stale headlines; an imminent unreleased
 high-impact event is a reason for lower confidence, not a direction.
 """ + _GOLD_DOCTRINE + """\
 Example: brief direction BUY, headlines dominated by hawkish Fed
 repricing and a hot CPI print -> {"stance": "oppose", "confidence": 70,
 "note": "Inflation is running hot, so interest rates should stay high — that usually pushes gold down."}
 """ + _TAKE_CONTRACT
 _TECH_SYSTEM = """\
 You are the technical analyst on a gold (XAU/USD) trade-review panel.
 You receive JSON: a proposed trade brief ("brief") plus the technical
 evidence — per-"indicators" signal/confidence, multi-timeframe
 "mtf" signals and scores, "key_levels" versus the current price, and an
 "exhaustion" count of consecutive same-direction bars flagged by the
 engine.
 Judge ONE thing: is the technical picture internally coherent for the
 proposed direction? Look for indicators fighting each other, the signal
 fighting the regime (e.g. a BUY in trending_down), higher timeframes
 disagreeing, price pressed into a key level, or an exhausted move being
 chased.
 Example: brief direction SELL in regime trending_up with H1 and H4 both
 BUY -> {"stance": "oppose", "confidence": 80,
 "note": "This bets on gold falling while the bigger picture still points up."}
 """ + _TAKE_CONTRACT
 _RISK_SYSTEM = """\
 You are the risk manager on a gold (XAU/USD) trade-review panel. You
 receive JSON: a proposed trade brief ("brief") plus the risk facts —
 the trade "setup" (entry, stop_loss, take_profit_1/2, risk_reward,
 risk_dollars), the "volatility" regime, engine "warnings", the calendar
 "event_risk" level, and the assumed "spread".
 Judge ONE thing: is this setup's risk acceptable as constructed? Check
 reward-to-risk adequacy, stop distance versus the volatility regime
 (a tight stop in extreme vol dies to noise; a huge stop in low vol is
 oversized), spread as a fraction of the stop, and event landmines about
 to hit. A missing setup means the signal graded too low for one —
 oppose unless the rest is exceptional.
 Example: RR 1.4 with volatility extreme and a high-impact event due ->
 {"stance": "oppose", "confidence": 65,
 "note": "The possible profit is small for such wild price swings, and big economic news is due soon."}
 """ + _TAKE_CONTRACT
 _CONTRARIAN_SYSTEM = """\
 You are the contrarian on a gold (XAU/USD) trade-review panel — the
 designated skeptic. You receive JSON: a proposed trade brief ("brief"),
 the engine's own "bullish_reasons" and "bearish_reasons", its
 "warnings", and an optional "news_summary".
 Your job: construct the strongest honest case AGAINST the proposed
 trade, then score how damaging that case is. Use "oppose" only when the
 counter-case is genuinely strong, "neutral" when it is real but modest,
 and "support" when even the skeptic finds nothing that bites.
 """ + _GOLD_DOCTRINE + """\
 Example: a BUY whose own engine lists three bearish reasons and a
 counter-trend warning -> {"stance": "oppose", "confidence": 60,
 "note": "The system's own list of negatives looks stronger than its reason to buy."}
 """ + _TAKE_CONTRACT
 _SYNTH_SYSTEM = """\
 You are the chair of a gold (XAU/USD) trade-review panel. You receive
 JSON: the proposed trade "direction" and the panel's "takes" — one per
 analyst ("persona", "stance", "confidence", "note").
 Weigh the takes into ONE consensus on whether to act on the signal:
 "AGREE" (the panel backs the trade), "OPPOSE" (the panel is against it),
 or "NEUTRAL" (genuinely split or low conviction). Weigh a high-
 confidence oppose from any single analyst seriously — one strong veto
 can outweigh two lukewarm supports. Do not average blindly.
 Write the rationale in plain everyday English for someone who has never
 traded: short simple sentences, no trading jargon, no acronyms, no
 abbreviations.
 Example rationale: "Three of four analysts are against this: the
 possible profit is too small and the price has already climbed
 unusually fast. Only the news backdrop supports buying."
 Reply with ONLY JSON:
 {"verdict": "AGREE"|"NEUTRAL"|"OPPOSE", "confidence": <0-100>,
 "rationale": "<max 40 words>"}
 """
 # ── Result types ─────────────────────────────────────────────────────
@dataclass
 class PersonaTake:
    persona: str        # "macro" | "technical" | "risk" | "contrarian"
    stance: str         # "support" | "oppose" | "neutral"
    confidence: float   # 0-100
    note: str
@dataclass
 class PanelVerdict:
    verdict: str        # "AGREE" | "NEUTRAL" | "OPPOSE"
    confidence: float   # 0-100
    rationale: str
    takes: List[PersonaTake] = field(default_factory=list)
    created_ts: float = 0.0
 # ── Payload builders (pure functions; llm_client json-dumps sorted) ──
 def _direction(report: "SignalReport") -> Optional[str]:
    """BUY/SELL the panel should review; None = nothing to review."""
    if report.trade_setup is not None:
        return report.trade_setup.direction
    if report.final_signal.value > 0:
        return "BUY"
    if report.final_signal.value < 0:
        return "SELL"
    return None
 def _brief(report: "SignalReport") -> Dict[str, Any]:
    analysis = report.primary_analysis
    return {
        "pair": report.display_name,
        "timeframe": analysis.timeframe if analysis else "",
        "direction": _direction(report),
        "quality": report.signal_quality,
        "score": round(report.final_score, 1),
        "confidence": round(report.final_confidence),
        "price": round(report.current_price, 2),
        "market_regime": report.market_regime,
        "volatility_regime": report.volatility_regime,
        "mtf_agreement": report.mtf_agreement,
    }
 def _macro_payload(report: "SignalReport",
                   context: Optional["FundamentalContext"]) -> Dict[str, Any]:
    headlines: List[str] = []
    events: List[Dict[str, Any]] = []
    if context is not None:
        headlines = [h.headline for h in context.news_headlines[:10]]
        events = [
            {"event": e.event, "currency": e.currency,
             "actual": e.actual, "forecast": e.forecast,
             "released": not e.is_upcoming}
            for e in context.events_today if e.is_high_impact
        ][:8]
    return {
        "brief": _brief(report),
        "headlines": headlines,
        "events": events,
        "sentiment": round(report.news_sentiment, 3),
        "news_summary": report.news_summary,
    }
 def _technical_payload(report: "SignalReport") -> Dict[str, Any]:
    indicators: Dict[str, Dict[str, Any]] = {}
    key_levels: Dict[str, float] = {}
    analysis = report.primary_analysis
    if analysis is not None:
        # Same visibility filter as main.report_to_dict: private series
        # ride as _-prefixed pseudo-indicators, weight-0 = context-only.
        indicators = {
            name: {"signal": ind.signal.name,
                   "confidence": round(ind.confidence)}
            for name, ind in analysis.indicators.items()
            if not name.startswith("_") and ind.weight != 0
        }
        key_levels = {k: round(v, 2) for k, v in analysis.key_levels.items()}
    return {
        "brief": _brief(report),
        "indicators": indicators,
        "mtf": {tf: {"signal": sig.name,
                     "score": round(report.mtf_scores.get(tf, 0.0), 1)}
                for tf, sig in report.mtf_signals.items()},
        "key_levels": key_levels,
        "exhaustion": report.exhaustion_count,
    }
 def _risk_payload(report: "SignalReport") -> Dict[str, Any]:
    payload: Dict[str, Any] = {
        "brief": _brief(report),
        "volatility": report.volatility_regime,
        "warnings": list(report.warnings),
        "event_risk": report.event_risk,
        "event_description": report.event_description,
        "spread": round(report.spread_estimate, 2),
    }
    ts = report.trade_setup
    if ts is not None:
        payload["setup"] = {
            "direction": ts.direction,
            "entry": round(ts.entry_price, 2),
            "stop_loss": round(ts.stop_loss, 2),
            "take_profit_1": round(ts.take_profit_1, 2),
            "take_profit_2": round(ts.take_profit_2, 2),
            "risk_reward": round(ts.risk_reward, 2),
            "risk_dollars": round(ts.risk_pips * ts.pip_value, 2),
        }
    return payload
 def _contrarian_payload(report: "SignalReport") -> Dict[str, Any]:
    return {
        "brief": _brief(report),
        "bullish_reasons": report.bullish_reasons[:3],
        "bearish_reasons": report.bearish_reasons[:3],
        "warnings": list(report.warnings),
        "news_summary": report.news_summary,
    }
 # ── Orchestration ────────────────────────────────────────────────────
 # Panel-private pool: the 4 persona calls run concurrently WITHOUT
 # occupying telegram_bot's shared scan executor (which has 4 workers —
 # borrowing them would stall every other scan for the panel's duration).
 _pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix="agent-panel")
 _VALID_STANCES = ("support", "oppose", "neutral")
 _VALID_VERDICTS = ("AGREE", "NEUTRAL", "OPPOSE")
 def _clamp_confidence(raw: Any) -> Optional[float]:
    try:
        return max(0.0, min(100.0, float(raw)))
    except (TypeError, ValueError):
        return None
 def _validate_take(persona: str, reply: Any) -> Optional[PersonaTake]:
    if not isinstance(reply, dict):
        return None
    stance = reply.get("stance")
    if stance not in _VALID_STANCES:
        return None
    confidence = _clamp_confidence(reply.get("confidence"))
    if confidence is None:
        return None
    note = str(reply.get("note", "")).strip()[:160]
    return PersonaTake(persona=persona, stance=stance,
                       confidence=confidence, note=note)
 def _ask_persona(persona: str, system: str,
                 payload: Dict[str, Any]) -> Optional[PersonaTake]:
    # Client resolved at call time: tests install stubs via lc._client.
    reply = get_llm_client().chat_json(system, payload)
    return _validate_take(persona, reply)
 def run_panel(report: "SignalReport",
              context: Optional["FundamentalContext"] = None,
              ) -> Optional[PanelVerdict]:
    """Run 4 personas + synthesizer; None = no verdict (fail-safe).
    Requires ≥2 valid persona takes before spending the synthesizer
    request; the synthesizer's reply is validated strictly and there is
    deliberately NO programmatic fallback verdict — a panel that could
    not really convene reports nothing rather than something invented.
    """
    direction = _direction(report)
    if direction is None:
        return None
    jobs = (
        ("macro", _MACRO_SYSTEM, _macro_payload(report, context)),
        ("technical", _TECH_SYSTEM, _technical_payload(report)),
        ("risk", _RISK_SYSTEM, _risk_payload(report)),
        ("contrarian", _CONTRARIAN_SYSTEM, _contrarian_payload(report)),
    )
    futures = [_pool.submit(_ask_persona, name, system, payload)
               for name, system, payload in jobs]
    # The HTTP timeout inside the client bounds each call; the margin
    # covers pool queueing. Stragglers past the wait are abandoned (their
    # threads finish harmlessly) and simply don't vote.
    client_timeout = getattr(get_llm_client(), "_timeout", DEFAULT_TIMEOUT)
    done, _ = _fwait(futures, timeout=client_timeout + 8.0)
    takes = [f.result() for f in done if not f.exception()]
    takes = [t for t in takes if t is not None]
    if len(takes) < 2:
        logger.debug("agent panel: %d/4 persona takes — skipping synthesizer",
                     len(takes))
        return None
    order = {"macro": 0, "technical": 1, "risk": 2, "contrarian": 3}
    takes.sort(key=lambda t: order.get(t.persona, 9))
    reply = get_llm_client().chat_json(_SYNTH_SYSTEM, {
        "direction": direction,
        "takes": [{"persona": t.persona, "stance": t.stance,
                   "confidence": t.confidence, "note": t.note}
                  for t in takes],
    })
    if not isinstance(reply, dict):
        return None
    verdict = reply.get("verdict")
    confidence = _clamp_confidence(reply.get("confidence"))
    if verdict not in _VALID_VERDICTS or confidence is None:
        return None
    rationale = str(reply.get("rationale", "")).strip()[:240]
    return PanelVerdict(verdict=verdict, confidence=confidence,
                        rationale=rationale, takes=takes,
                        created_ts=time.time())
 # ── Cache, budget, attach ────────────────────────────────────────────
 _state_lock = threading.Lock()
 # Exact result per reviewed bar: repeated attaches on the same signal
 # (re-scan, multi-consumer) are free.
 _cache: "OrderedDict[Tuple[str, str, str, str], PanelVerdict]" = OrderedDict()
 _CACHE_MAX = 32
 # Reuse layer: a persisting signal (same symbol/tf/direction, newer bar)
 # re-serves its last verdict for reuse_ttl_seconds instead of re-spending
 # 5 requests per bar (see module docstring for the M5 dashboard math).
 _last_by_dir: Dict[Tuple[str, str, str], PanelVerdict] = {}
 # UTC-day budget of panel attempts (mirrors llm_client._under_cap).
 _panel_day: Optional[_dt.date] = None
 _panel_count: int = 0
 _warned_budget = False
 def reset_panel_state() -> None:
    """Drop caches and counters (tests / config reloads)."""
    global _panel_day, _panel_count, _warned_budget
    with _state_lock:
        _cache.clear()
        _last_by_dir.clear()
        _panel_day = None
        _panel_count = 0
        _warned_budget = False
 def panel_health() -> Dict[str, Any]:
    """Read-only counters for status displays."""
    settings = get_app_config().agent_panel_settings
    cap = int(settings.get("max_panels_per_day", DEFAULT_MAX_PANELS_PER_DAY))
    today = _dt.datetime.now(_dt.timezone.utc).date()
    with _state_lock:
        used = _panel_count if _panel_day == today else 0
    return {"used_today": used, "cap": cap}
 def _under_panel_budget(cap: int) -> bool:
    """Count one panel attempt against the UTC-day budget."""
    global _panel_day, _panel_count, _warned_budget
    today = _dt.datetime.now(_dt.timezone.utc).date()
    with _state_lock:
        if _panel_day != today:
            _panel_day = today
            _panel_count = 0
            _warned_budget = False
        if _panel_count >= cap:
            if not _warned_budget:
                _warned_budget = True
                logger.warning(
                    "agent panel: daily budget (%d) exhausted — no more "
                    "panels until UTC midnight", cap)
            return False
        _panel_count += 1
        return True
 def _write_fields(report: "SignalReport", v: PanelVerdict) -> None:
    report.panel_verdict = v.verdict
    report.panel_confidence = float(v.confidence)
    report.panel_rationale = v.rationale
    report.panel_takes = [
        {"persona": t.persona, "stance": t.stance,
         "confidence": t.confidence, "note": t.note} for t in v.takes]
 def attach_panel(report: "SignalReport",
                 settings: Optional[Dict[str, Any]] = None,
                 surface: str = "telegram",
                 force: bool = False,
                 context: Optional["FundamentalContext"] = None) -> bool:
    """Best-effort panel attach; True iff panel fields were populated.
    Advisory feature contract: NEVER raises — an exploding panel must
    not cost an alert send or a dashboard publish. ``force`` (CLI
    --panel) bypasses the enabled/surface/quality toggles but never the
    daily budget or the shared LLM cap.
    """
    try:
        if settings is None:
            settings = get_app_config().agent_panel_settings
        if not force:
            if not settings.get("enabled", False):
                return False
            surface_on = {
                "telegram": settings.get("telegram", True),
                "dashboard": settings.get("dashboard", False),
            }.get(surface, True)
            if not surface_on:
                return False
            min_quality = settings.get("min_quality", DEFAULT_MIN_QUALITY)
            if (_QUALITY_RANK.get(report.signal_quality, -1)
                    < _QUALITY_RANK.get(min_quality, 2)):
                return False
        direction = _direction(report)
        if direction is None:
            return False
        analysis = report.primary_analysis
        tf = analysis.timeframe if analysis else ""
        exact_key = (report.symbol, tf, report.timestamp, direction)
        dir_key = (report.symbol, tf, direction)
        ttl = float(settings.get("reuse_ttl_seconds",
                                 DEFAULT_REUSE_TTL_SECONDS))
        with _state_lock:
            hit = _cache.get(exact_key)
            if hit is None:
                prior = _last_by_dir.get(dir_key)
                if prior is not None and time.time() - prior.created_ts < ttl:
                    hit = prior
        if hit is not None:
            _write_fields(report, hit)
            return True
        cap = int(settings.get("max_panels_per_day",
                               DEFAULT_MAX_PANELS_PER_DAY))
        if not _under_panel_budget(cap):
            return False
        if context is None:
            # Best-effort; the news engine's class-level 600 s caches make
            # this nearly free when warm in both bot and dashboard.
            try:
                from config import PlatformConfig
                from news_engine import NewsEngine
                context = NewsEngine(
                    PlatformConfig().news).get_fundamental_context()
            except Exception as e:
                logger.debug("agent panel: news context unavailable: %s", e)
                context = None
        verdict = run_panel(report, context)
        if verdict is None:
            return False
        _write_fields(report, verdict)
        with _state_lock:
            _cache[exact_key] = verdict
            while len(_cache) > _CACHE_MAX:
                _cache.popitem(last=False)
            _last_by_dir[dir_key] = verdict
        return True
    except Exception:
        logger.exception("agent panel: attach failed — signal unaffected")
        return False
@@ -86,7 +86,7 @@ _VALID_LOG_LEVELS = ("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL")
 _SCANNABLE_TFS = ("M5", "M15", "H1", "H4")
 _KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level",
-              "oanda", "deepseek", "agent_panel"}
+              "oanda", "deepseek"}
 _KNOWN_OANDA = {
    "account_id", "api_token", "base_url", "lot_size",
    "quality_gate", "max_hold_bars",
@@ -94,10 +94,6 @@ _KNOWN_OANDA = {
 _KNOWN_DEEPSEEK = {
    "api_key", "model", "enabled", "timeout_seconds", "daily_request_cap",
 }
 _KNOWN_AGENT_PANEL = {
    "enabled", "telegram", "dashboard", "min_quality",
    "max_panels_per_day", "reuse_ttl_seconds",
 }
 _KNOWN_TELEGRAM = {"token", "chat_id", "channel_id", "alert_min_quality"}
 _KNOWN_SCANNING = {"pairs", "timeframes"}
 _KNOWN_PAIR_KEYS = {"blocked_hours_utc"}
@@ -122,7 +118,6 @@ class AppConfig:
    pair_overrides: Dict[str, Dict[str, Any]] = field(default_factory=dict)
    oanda_settings: Dict[str, Any] = field(default_factory=dict)
    deepseek_settings: Dict[str, Any] = field(default_factory=dict)
    agent_panel_settings: Dict[str, Any] = field(default_factory=dict)
    log_level: str = "INFO"
@@ -281,31 +276,6 @@ def load_app_config(path: str = CONFIG_PATH) -> AppConfig:
                "config.json: deepseek.model must be a non-empty string")
    app.deepseek_settings = dict(deepseek)
    panel = raw.get("agent_panel", {})
    _check_keys(panel, _KNOWN_AGENT_PANEL, '"agent_panel"')
    for flag in ("enabled", "telegram", "dashboard"):
        if flag in panel and not isinstance(panel[flag], bool):
            raise AppConfigError(
                f"config.json: agent_panel.{flag} must be true or false")
    if "min_quality" in panel and panel["min_quality"] not in _VALID_QUALITIES:
        raise AppConfigError(
            f'config.json: agent_panel.min_quality "{panel["min_quality"]}" '
            f"not in {_VALID_QUALITIES}")
    if "max_panels_per_day" in panel:
        mpd = panel["max_panels_per_day"]
        if isinstance(mpd, bool) or not isinstance(mpd, int) or mpd < 1:
            raise AppConfigError(
                "config.json: agent_panel.max_panels_per_day must be a "
                "positive integer")
    if "reuse_ttl_seconds" in panel:
        ttl = panel["reuse_ttl_seconds"]
        if isinstance(ttl, bool) or not isinstance(ttl, (int, float)) \
                or ttl < 0:
            raise AppConfigError(
                "config.json: agent_panel.reuse_ttl_seconds must be a "
                "non-negative number")
    app.agent_panel_settings = dict(panel)
    level = raw.get("log_level", app.log_level)
    if level not in _VALID_LOG_LEVELS:
        raise AppConfigError(
@@ -40,13 +40,5 @@
    "timeout_seconds": 12,
    "daily_request_cap": 500
  },
  "agent_panel": {
    "enabled": false,
    "telegram": true,
    "dashboard": false,
    "min_quality": "B",
    "max_panels_per_day": 20,
    "reuse_ttl_seconds": 3600
  },
  "log_level": "INFO"
 }
@@ -49,7 +49,6 @@ from datetime import datetime, timezone
 from logging.handlers import TimedRotatingFileHandler
 from typing import Dict, List, Optional
 from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, XAUUSD
 from main import report_to_dict
 from news_engine import NewsEngine
@@ -358,13 +357,6 @@ class DashboardPublisher:
        try:
            report = produce_signal(XAUUSD, tf, self._provider(),
                                    CONFIRMATIONS[tf])
            # Advisory AI panel, post-hoc and display-only. Off unless
            # config.json agent_panel.dashboard is true. The reuse-TTL
            # cache + max_panels_per_day budget bound the spend against
            # this process's own DeepSeek cap (a persisting M5 signal
            # re-serves its cached verdict instead of re-running every
            # bar); a fresh panel adds ≤~25 s before this TF publishes.
            attach_panel(report, surface="dashboard")
            self.snapshots[tf.value] = {
                "status": "ok",
                "scanned_at": datetime.now(timezone.utc).isoformat(),
@@ -20,7 +20,6 @@ if hasattr(sys.stdout, "reconfigure"):
 if hasattr(sys.stderr, "reconfigure"):
    sys.stderr.reconfigure(encoding="utf-8")
 from agent_panel import attach_panel
 from config import PlatformConfig, Timeframe, SignalStrength, XAUUSD
 from pair_signals import PAIR_PRODUCERS
 from signal_aggregator import SignalAggregator, SignalReport
@@ -164,13 +163,6 @@ def print_signal_report(report: SignalReport, detail: bool = False):
        print(f"  {Colors.MAGENTA}📰{Colors.RESET} {report.news_description}")
    if report.news_summary:
        print(f"  {Colors.MAGENTA}🧠{Colors.RESET} {report.news_summary}")
    if report.panel_verdict:
        print(f"  {Colors.CYAN}🤖{Colors.RESET} AI Panel: "
              f"{Colors.BOLD}{report.panel_verdict}{Colors.RESET} "
              f"({report.panel_confidence:.0f}%) — {report.panel_rationale}")
        for t in report.panel_takes:
            print(f"     {Colors.GRAY}{t['persona']:<11} {t['stance']:<8} "
                  f"{t['note']}{Colors.RESET}")
    # ── 6. DETAIL (only with --detail) ─────────────────────────────
    if detail and report.primary_analysis:
@@ -217,10 +209,6 @@ def report_to_dict(report: SignalReport) -> dict:
        "news_sentiment": round(report.news_sentiment, 3),
        "news_description": report.news_description,
        "news_summary": report.news_summary,
        "panel_verdict": report.panel_verdict,
        "panel_confidence": round(report.panel_confidence, 1),
        "panel_rationale": report.panel_rationale,
        "panel_takes": report.panel_takes,
        "bullish_reasons": report.bullish_reasons,
        "bearish_reasons": report.bearish_reasons,
        "warnings": report.warnings,
@@ -280,9 +268,6 @@ Examples:
                        help="Primary analysis timeframe (default: H1)")
    parser.add_argument("--detail", action="store_true",
                        help="Show full indicator breakdown")
    parser.add_argument("--panel", action="store_true",
                        help="Run the LLM agent panel on the result "
                             "(advisory only; needs a DeepSeek key)")
    parser.add_argument("--json", action="store_true",
                        help="Output as JSON")
    parser.add_argument("--verbose", "-v", action="store_true",
@@ -338,11 +323,6 @@ def run_live(args, config: PlatformConfig):
    aggregator = SignalAggregator(config)
    report = aggregator.analyze_pair(symbol=config.symbol)
    # Explicit user intent: force bypasses the enabled/surface/quality
    # toggles, but the daily panel budget and LLM cap still apply.
    if getattr(args, "panel", False):
        attach_panel(report, surface="cli", force=True)
    if args.json:
        print(json.dumps(report_to_dict(report), indent=2))
        return
@@ -88,15 +88,6 @@ class SignalReport:
    news_description: str = ""
    news_summary: str = ""  # LLM market-context blurb (display-only)
    # LLM agent-panel consensus (advisory/DISPLAY-ONLY). Populated
    # post-hoc by consumers via agent_panel.attach_panel — NEVER inside
    # analyze_from_dataframes (backtest parity) and never read by any
    # gate or scoring path.
    panel_verdict: str = ""        # "AGREE" / "NEUTRAL" / "OPPOSE" / ""
    panel_confidence: float = 0.0
    panel_rationale: str = ""
    panel_takes: List[Dict] = field(default_factory=list)
    final_signal: SignalStrength = SignalStrength.NEUTRAL
    final_score: float = 0.0
    final_confidence: float = 0.0
@@ -981,22 +981,6 @@ function detailHTML(tf) {
      ${esc(r.news_summary || "")}</div>`);
  }
  /* AI agent panel (advisory) */
  if (r.panel_verdict) {
    const vcls = r.panel_verdict === "AGREE" ? "buy"
      : r.panel_verdict === "OPPOSE" ? "sell" : "neutral";
    const takes = (r.panel_takes || []).map(t =>
      `<tr><td class="ind-k">${esc(t.persona)}</td>
        <td>${esc(t.stance)}</td>
        <td class="num">${t.confidence != null ? Math.round(t.confidence) : "—"}</td>
        <td>${esc(t.note || "")}</td></tr>`).join("");
    out += sec("AI panel (advisory)", `<div class="d-note" style="margin-bottom:8px">
      <b class="sig-txt ${vcls}">${esc(r.panel_verdict)}</b> ·
      conf ${Math.round(r.panel_confidence || 0)}%<br>
      ${esc(r.panel_rationale || "")}</div>
      ${takes ? `<table class="d-table"><tbody>${takes}</tbody></table>` : ""}`);
  }
  /* provenance */
  out += sec("Scan detail", `<table class="d-table"><tbody>
    <tr><td class="ind-k">Instrument</td><td colspan="5">${esc(r.display_name || r.symbol || "—")}</td></tr>
@@ -71,7 +71,6 @@ from app_config import (
    telegram_channel_id,
 )
 from providers import BaseDataProvider, make_data_provider
 from agent_panel import attach_panel, panel_health
 from llm_client import get_llm_client
 from news_engine import FundamentalContext, NewsEngine
 from pair_signals import PAIR_PRODUCERS
@@ -408,17 +407,6 @@ def format_signal_message(report: SignalReport) -> str:
        extra.append(f"⚠️ {_escape_md(w)}")
    if report.news_summary:
        extra.append(f"📰 {_escape_md(report.news_summary)}")
    if report.panel_verdict:
        mark = {"AGREE": "✅", "OPPOSE": "⛔", "NEUTRAL": "➖"}.get(
            report.panel_verdict, "")
        extra.append(
            f"🤖 AI Panel {mark} *{_escape_md(report.panel_verdict)}* "
            f"({report.panel_confidence:.0f}%) — "
            f"{_escape_md(report.panel_rationale)}")
        if report.panel_takes:
            votes = " · ".join(
                f"{t['persona']}: {t['stance']}" for t in report.panel_takes)
            extra.append(f"   _{_escape_md(votes)}_")
    if extra:
        lines += [""] + extra
@@ -732,13 +720,6 @@ async def _scan_timeframe(
        async with _alert_lock(key):
            if not _should_alert(key, report):
                continue
            # Panel AFTER the quality gate + dedup decision so no LLM
            # spend on suppressed alerts. Runs on the scan executor to
            # keep the event loop free; this key's alert lock is held
            # for the panel's wall time (≤~25 s), which only delays a
            # same-key duplicate that _should_alert would drop anyway.
            await asyncio.get_running_loop().run_in_executor(
                _executor, attach_panel, report)
            detail = format_signal_message(report)
            delivered = False
            for chat_id in _signal_targets():
@@ -874,10 +855,6 @@ async def _handle_breach(bot, pair_symbol: str, price: float, level_name: str) -
        if not _should_alert(key, report):
            return
        # Same post-gate panel attach as the auto-scan path (see
        # _scan_timeframe for the spend/lock rationale).
        await asyncio.get_running_loop().run_in_executor(
            _executor, attach_panel, report)
        header = (
            f"⚡ *LEVEL BREAK* — {_escape_md(display)} crossed "
            f"*{_escape_md(label)}* @ `{_fmt_price(price)}`\n\n"
@@ -1216,10 +1193,6 @@ def _news_health_lines() -> List[str]:
        else:
            lines.append(f"  ✅ DeepSeek: {used}/{cap} calls today,"
                         f" {scored} headlines scored")
        if get_app_config().agent_panel_settings.get("enabled", False):
            ph = panel_health()
            lines.append(f"  🤖 AI panel: {ph['used_today']}/{ph['cap']}"
                         " panels today")
    return lines
@@ -6943,388 +6943,6 @@ def _():
        assert "must be > 0" in str(e)
 print("\n═══ Module 23: LLM agent panel ═══")
@test("app_config: agent_panel block parses; absent block → {}")
 def _():
    app = load_app_config(_write_json({"agent_panel": {
        "enabled": True, "telegram": True, "dashboard": False,
        "min_quality": "A", "max_panels_per_day": 10,
        "reuse_ttl_seconds": 600}}))
    assert app.agent_panel_settings["enabled"] is True
    assert app.agent_panel_settings["min_quality"] == "A"
    assert app.agent_panel_settings["max_panels_per_day"] == 10
    assert load_app_config(_write_json({})).agent_panel_settings == {}
    assert AppConfig().agent_panel_settings == {}
@test("app_config: agent_panel rejects unknown keys and bad types")
 def _():
    for payload in (
        {"agent_panel": {"enabld": True}},               # unknown key
        {"agent_panel": {"enabled": "yes"}},             # not a bool
        {"agent_panel": {"telegram": 1}},                # not a bool
        {"agent_panel": {"dashboard": "no"}},            # not a bool
        {"agent_panel": {"min_quality": "Z"}},           # invalid grade
        {"agent_panel": {"max_panels_per_day": 0}},      # not positive
        {"agent_panel": {"max_panels_per_day": True}},   # bool is not an int
        {"agent_panel": {"reuse_ttl_seconds": -5}},      # negative
        {"agent_panel": "oops"},                         # not an object
    ):
        try:
            load_app_config(_write_json(payload))
            assert False, f"should have raised for {payload}"
        except AppConfigError:
            pass
 import agent_panel as ap
 from signal_aggregator import SignalReport, TradeSetup
 class _PanelStub:
    """llm_client-singleton drop-in that routes replies by system prompt."""
    available = True
    def __init__(self, replies):
        self.replies = replies      # system prompt -> reply dict | None
        self.calls = []             # (system_prompt, payload) in call order
    def chat_json(self, system_prompt, user_payload):
        self.calls.append((system_prompt, user_payload))
        reply = self.replies.get(system_prompt)
        return reply(user_payload) if callable(reply) else reply
 _TAKE_OK = {"stance": "support", "confidence": 70, "note": "fine"}
 _SYNTH_OK = {"verdict": "AGREE", "confidence": 75, "rationale": "panel backs it"}
 _ALL_OK = {ap._MACRO_SYSTEM: _TAKE_OK, ap._TECH_SYSTEM: _TAKE_OK,
           ap._RISK_SYSTEM: _TAKE_OK, ap._CONTRARIAN_SYSTEM: _TAKE_OK,
           ap._SYNTH_SYSTEM: _SYNTH_OK}
 _PANEL_ON = {"enabled": True, "telegram": True, "dashboard": False,
             "min_quality": "B", "max_panels_per_day": 20,
             "reuse_ttl_seconds": 3600}
 def _panel_report(quality="A", signal=SignalStrength.BUY):
    """Real report via the pipeline, then forced directional/quality."""
    config = PlatformConfig()
    config.primary_timeframe = Timeframe.H1
    config.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: make_ohlcv(300, seed=42),
           Timeframe.M15: make_ohlcv(300, seed=43),
           Timeframe.H4: make_ohlcv(300, seed=44)}
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    report.final_signal = signal
    report.signal_quality = quality
    return report
@test("agent_panel: payload builders compact, filtered, JSON-safe")
 def _():
    report = _panel_report()
    now = _dt_m19.datetime.now(_dt_m19.timezone.utc)
    ctx = FundamentalContext(
        news_headlines=[NewsSentiment(
            headline=f"h{i}", source="rss", timestamp=now,
            currencies_mentioned=["USD"], sentiment_score=0.1, keywords=[])
            for i in range(15)],
        events_today=[EconomicEvent(
            time=now, currency="USD", impact="high", event=f"e{i}",
            forecast="1.0", actual="2.0") for i in range(12)],
    )
    macro = ap._macro_payload(report, ctx)
    assert len(macro["headlines"]) == 10, "headline cap"
    assert len(macro["events"]) == 8, "event cap"
    assert macro["events"][0]["released"] is True
    assert ap._macro_payload(report, None)["headlines"] == []
    tech = ap._technical_payload(report)
    assert tech["indicators"], "real analysis must yield indicators"
    hidden = [n for n in tech["indicators"]
              if n.startswith("_")
              or report.primary_analysis.indicators[n].weight == 0]
    assert not hidden, f"private/weight-0 leaked: {hidden}"
    assert tech["brief"]["direction"] == "BUY"
    assert tech["brief"]["timeframe"] == report.primary_analysis.timeframe
    assert "setup" not in ap._risk_payload(report), "no setup when None"
    report.trade_setup = TradeSetup(
        pair="XAU/USD", display_name="XAU/USD", direction="SELL",
        entry_price=2000.0, stop_loss=2010.0, take_profit_1=1985.0,
        take_profit_2=1970.0, risk_reward=1.5, risk_pips=100.0,
        reward_pips=150.0, pip_value=0.1)
    risk = ap._risk_payload(report)
    assert risk["setup"]["risk_reward"] == 1.5
    assert risk["brief"]["direction"] == "SELL", "setup direction wins"
    contra = ap._contrarian_payload(report)
    assert len(contra["bullish_reasons"]) <= 3
    for payload in (macro, tech, risk, contra):
        json.dumps(payload, sort_keys=True, allow_nan=False)
@test("agent_panel: persona reply validation clamps and rejects")
 def _():
    v = ap._validate_take
    assert v("macro", {"stance": "moon", "confidence": 50, "note": "x"}) is None
    assert v("macro", {"stance": "support", "confidence": "hot"}) is None
    assert v("macro", {"stance": "support"}) is None, "missing confidence"
    assert v("macro", None) is None
    assert v("macro", "support") is None
    take = v("risk", {"stance": "oppose", "confidence": 250, "note": "n" * 300})
    assert take.confidence == 100.0 and len(take.note) == 160
    assert v("risk", {"stance": "neutral", "confidence": -3, "note": ""}).confidence == 0.0
@test("agent_panel: <2 valid takes → None without spending synthesizer")
 def _():
    try:
        stub = _PanelStub({ap._MACRO_SYSTEM: _TAKE_OK})   # others → None
        lc._client = stub
        assert ap.run_panel(_panel_report()) is None
        assert ap._SYNTH_SYSTEM not in [s for s, _ in stub.calls], \
            "synthesizer request must not be spent on a failed panel"
        assert len(stub.calls) == 4
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: synthesizer failure or garbage verdict → None")
 def _():
    try:
        for synth in (None, {"verdict": "MAYBE", "confidence": 50},
                      {"verdict": "AGREE", "confidence": "hot"}):
            lc._client = _PanelStub({**_ALL_OK, ap._SYNTH_SYSTEM: synth})
            assert ap.run_panel(_panel_report()) is None, f"synth={synth}"
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: full run yields verdict + 4 ordered takes")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        stub.replies[ap._SYNTH_SYSTEM] = {
            "verdict": "OPPOSE", "confidence": 72.4,
            "rationale": "r" * 300}
        lc._client = stub
        v = ap.run_panel(_panel_report())
        assert v.verdict == "OPPOSE" and v.confidence == 72.4
        assert len(v.rationale) == 240, "rationale truncated"
        assert [t.persona for t in v.takes] == \
            ["macro", "technical", "risk", "contrarian"]
        synth_payload = dict(stub.calls)[ap._SYNTH_SYSTEM]
        assert synth_payload["direction"] == "BUY"
        assert len(synth_payload["takes"]) == 4
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: NEUTRAL signal → no panel even forced")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        lc._client = stub
        report = _panel_report(signal=SignalStrength.NEUTRAL)
        assert ap.run_panel(report) is None
        assert ap.attach_panel(report, _PANEL_ON, force=True,
                               context=FundamentalContext()) is False
        assert stub.calls == [], "NEUTRAL must spend nothing"
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: exact + reuse-TTL cache re-attach with zero spend")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        lc._client = stub
        report = _panel_report()
        assert ap.attach_panel(report, _PANEL_ON,
                               context=FundamentalContext()) is True
        assert report.panel_verdict == "AGREE"
        assert len(stub.calls) == 5
        # same bar again → exact-key hit
        report2 = _panel_report()
        assert ap.attach_panel(report2, _PANEL_ON,
                               context=FundamentalContext()) is True
        assert report2.panel_verdict == "AGREE" and len(stub.calls) == 5
        # newer bar, same symbol/tf/direction → reuse-TTL hit
        report3 = _panel_report()
        report3.timestamp = "2099-01-01 01:00:00+00:00"
        assert ap.attach_panel(report3, _PANEL_ON,
                               context=FundamentalContext()) is True
        assert len(stub.calls) == 5, "persisting signal must not re-spend"
        # TTL 0 → the reuse layer is off; a new bar re-runs the panel
        report4 = _panel_report()
        report4.timestamp = "2099-01-01 02:00:00+00:00"
        assert ap.attach_panel(report4, {**_PANEL_ON, "reuse_ttl_seconds": 0},
                               context=FundamentalContext()) is True
        assert len(stub.calls) == 10
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: daily budget exhausts and re-arms on UTC roll")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        lc._client = stub
        settings = {**_PANEL_ON, "max_panels_per_day": 1,
                    "reuse_ttl_seconds": 0}
        assert ap.attach_panel(_panel_report(), settings,
                               context=FundamentalContext()) is True
        assert len(stub.calls) == 5
        report = _panel_report()
        report.timestamp = "2099-01-01 01:00:00+00:00"
        assert ap.attach_panel(report, settings,
                               context=FundamentalContext()) is False
        assert len(stub.calls) == 5, "budget-blocked attempt must spend nothing"
        ap._panel_day = (_dt_m19.datetime.now(_dt_m19.timezone.utc).date()
                         - _dt_m19.timedelta(days=1))
        assert ap.attach_panel(report, settings,
                               context=FundamentalContext()) is True
        assert len(stub.calls) == 10
        assert ap.panel_health()["used_today"] == 1
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("agent_panel: enabled/surface/min_quality gates; force bypasses")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        lc._client = stub
        report = _panel_report()
        ctx = FundamentalContext()
        assert ap.attach_panel(report, {}, context=ctx) is False, \
            "absent config block must keep the feature dark"
        assert ap.attach_panel(report, {**_PANEL_ON, "enabled": False},
                               context=ctx) is False
        assert ap.attach_panel(report, {**_PANEL_ON, "telegram": False},
                               context=ctx) is False
        assert ap.attach_panel(report, _PANEL_ON, surface="dashboard",
                               context=ctx) is False, "dashboard defaults off"
        assert ap.attach_panel(_panel_report(quality="C"), _PANEL_ON,
                               context=ctx) is False, "C < min_quality B"
        assert ap.attach_panel(_panel_report(quality="F"), _PANEL_ON,
                               context=ctx) is False
        assert stub.calls == [], "every gate must block before any spend"
        assert ap.attach_panel(report, {**_PANEL_ON, "dashboard": True},
                               surface="dashboard", context=ctx) is True
        ap.reset_panel_state()
        assert ap.attach_panel(_panel_report(quality="C"),
                               {**_PANEL_ON, "enabled": False},
                               force=True, context=ctx) is True, \
            "force bypasses enabled/surface/quality"
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("panel verdict flows to Telegram message; absent verdict silent")
 def _():
    import telegram_bot as tb
    report = _panel_report()
    assert "AI Panel" not in tb.format_signal_message(report)
    report.panel_verdict = "OPPOSE"
    report.panel_confidence = 64.0
    report.panel_rationale = "counter-trend against aligned H4"
    report.panel_takes = [
        {"persona": "macro", "stance": "support", "confidence": 60,
         "note": "n"},
        {"persona": "technical", "stance": "oppose", "confidence": 80,
         "note": "n"},
    ]
    msg = tb.format_signal_message(report)
    assert "AI Panel" in msg and "OPPOSE" in msg and "(64%)" in msg
    assert "counter-trend against aligned H4" in msg
    assert "macro: support" in msg and "technical: oppose" in msg
@test("backtest parity: analyze_from_dataframes never populates panel")
 def _():
    try:
        stub = _PanelStub(dict(_ALL_OK))
        lc._client = stub
        config = PlatformConfig()
        config.primary_timeframe = Timeframe.H1
        config.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: make_ohlcv(300, seed=42),
               Timeframe.M15: make_ohlcv(300, seed=43),
               Timeframe.H4: make_ohlcv(300, seed=44)}
        for use_news in (True, False):
            report = agg.analyze_from_dataframes(
                "XAU/USD", dfs, context=FundamentalContext(),
                use_news=use_news)
            assert report.panel_verdict == ""
            assert report.panel_confidence == 0.0
            assert report.panel_rationale == ""
            assert report.panel_takes == []
        assert stub.calls == [], \
            "the replayable core must never invoke the panel"
    finally:
        lc._client = None
        ap.reset_panel_state()
@test("report_to_dict carries panel keys → dashboard snapshot propagation")
 def _():
    from main import report_to_dict as _rtd
    from dashboard_publisher import report_to_webapp_dict as _rtw
    report = _panel_report()
    d = _rtd(report)
    assert d["panel_verdict"] == "" and d["panel_takes"] == []
    report.panel_verdict = "AGREE"
    report.panel_confidence = 77.5
    report.panel_rationale = "solid confluence"
    report.panel_takes = [{"persona": "risk", "stance": "support",
                           "confidence": 66.0, "note": "rr fine"}]
    wd = _rtw(report)
    assert wd["panel_verdict"] == "AGREE"
    assert wd["panel_confidence"] == 77.5
    assert wd["panel_takes"][0]["persona"] == "risk"
    json.dumps(wd, allow_nan=False)
@test("agent_panel: attach never raises (LLM explosion, garbage settings)")
 def _():
    class _Bomb:
        available = True
        def chat_json(self, system_prompt, user_payload):
            raise RuntimeError("boom")
    try:
        lc._client = _Bomb()
        report = _panel_report()
        assert ap.attach_panel(report, _PANEL_ON,
                               context=FundamentalContext()) is False
        assert report.panel_verdict == "" and report.panel_takes == []
        logging.disable(logging.CRITICAL)   # silence the expected traceback
        try:
            assert ap.attach_panel(report, 42,
                                   context=FundamentalContext()) is False
        finally:
            logging.disable(logging.NOTSET)
    finally:
        lc._client = None
        ap.reset_panel_state()
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 #  SUMMARY
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