cli+dashboard: --panel flag, panel dict keys, TF-dialog panel section

main.py: --panel runs the agent panel on demand (force bypasses
enabled/surface/quality toggles; daily budget + LLM cap still apply),
prints the verdict + persona takes, and report_to_dict carries the four
panel keys — which auto-propagates them into the dashboard's
snapshot.json via report_to_webapp_dict. dashboard_publisher attaches
in scan_tf gated by agent_panel.dashboard (default off); dashboard.html
renders an "AI panel (advisory)" section in the TF detail dialog.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SisdfkxnXNL9iZsXxN3T15

dashboard_publisher.py

@@ -49,6 +49,7 @@ 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
@@ -357,6 +358,13 @@ 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(),

main.py

@@ -20,6 +20,7 @@ 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
@@ -163,6 +164,13 @@ 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:
@@ -209,6 +217,10 @@ 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,
@@ -268,6 +280,9 @@ 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",
@@ -323,6 +338,11 @@ 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

static/dashboard.html

@@ -981,6 +981,22 @@ 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>

test_all.py

@@ -7282,6 +7282,25 @@ def _():
         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: