quality round 3: adaptive name cap, REIT/FFO + GBp fixes, context-fit candidates, lens retry, percent-safe narrative, yield units

src/orchestrator.py

@@ -89,7 +89,7 @@ def _enrich_candidates(cands: list[dict], k: int = 8) -> None:
         try:
             d = fetch_holding(c["ticker"])
             c["live"] = {kk: d.get(kk) for kk in
-                         ("name", "price", "sma200", "beta", "pe", "rev_growth")}
+                         ("name", "price", "sma200", "beta", "pe", "rev_growth", "div_yield")}
             c["flags"] = M.trap_flags(d)   # same guards holdings get (value trap, inorganic EPS…)
             price, sma = d.get("price"), d.get("sma200")
             c["trend_ok"] = bool(price and (not sma or price >= sma))
@@ -108,6 +108,36 @@ def _enrich_candidates(cands: list[dict], k: int = 8) -> None:
         list(ex.map(one, cands[:k]))
 
 
+def _fit_context(cands: list[dict], context: dict | None) -> list[dict]:
+    """Stable re-order: live-verified candidates that FIT the investor's stated needs go
+    first (income → real dividend payers; conservative → low beta). Nothing is removed,
+    but the switch pairs and the CIO now see profile-appropriate names before the rest —
+    an income investor was being told to rotate REITs into zero-yield growth stocks."""
+    if not context:
+        return cands
+    want_income = "income" in str(context.get("objective") or "").lower()
+    conservative = str(context.get("risk") or "").lower() == "conservative"
+    if not (want_income or conservative):
+        return cands
+
+    def fits(c: dict) -> bool:
+        live = c.get("live") or {}
+        ok = True
+        if want_income:
+            dy = live.get("div_yield")                    # always percent (normalized at fetch)
+            ok = ok and isinstance(dy, (int, float)) and dy >= 2.5
+        if conservative:
+            b = live.get("beta")
+            ok = ok and isinstance(b, (int, float)) and b <= 1.1
+        return ok
+
+    fit = [c for c in cands if c.get("live") and fits(c)]
+    rest = [c for c in cands if c not in fit]
+    for c in fit:
+        c["signals"] = c.get("signals", []) + ["fits your stated profile"]
+    return fit + rest
+
+
 _RISK_CAPS = {"conservative": (0.10, 0.25), "balanced": (0.12, 0.30), "aggressive": (0.15, 0.40)}
 
 
@@ -188,6 +218,14 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None) -> dict:
                 h["dcf"] = dict(h["dcf"], note="fund — n/a")
             elif isinstance(eps, (int, float)) and eps == eps:
                 h["dcf"] = dict(h["dcf"], note="loss-making")
+            else:
+                h["dcf"] = dict(h["dcf"], note="no earnings data")
+            continue
+        if "REIT" in str(d.get("industry") or "").upper():
+            # REIT EPS is crushed by depreciation on appreciating property; the industry
+            # values on FFO (crucible valuation_eps) — an EPS-DCF would scream "75%
+            # overvalued" at every healthy REIT, so it is not run at all
+            h["dcf"] = dict(h["dcf"], note="REIT — valued on FFO, not EPS")
             continue
         A, src = assumptions.get(t), "ai"
         if not isinstance(A, dict):
@@ -213,11 +251,12 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None) -> dict:
                                                list(pm["sector_allocation"].items())[:6]) + ". "
                    f"Market regime: {'risk-on (S&P above 200-day)' if regime else 'risk-off (S&P below 200-day)' if regime is not None else 'unknown'}. "
                    f"Portfolio beta ~{pm.get('realized_beta') or pm.get('weighted_beta') or 'n/a'}.")
-        done_chips = []
+        done_chips: set = set()
 
         def chip_progress(k, ok):
-            done_chips.append(k)
-            emit(45 + 5 * len(done_chips), f"Examination: {len(done_chips)}/5 analysts have ruled…",
+            done_chips.add(k)          # a retried lens reports twice — count it once
+            n_done = min(len(done_chips), 5)
+            emit(45 + 5 * n_done, f"Examination: {n_done}/5 analysts have ruled…",
                  chip=k if ok else None)
 
         panel = P.run_panel(briefs, panel_ctx, progress=chip_progress)
@@ -238,7 +277,7 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None) -> dict:
                          for h in holdings],
             "metrics": {k: v for k, v in pm.items() if k != "equity_curve"},
             "risk_scorecard": cards,
-            "healthy_bands": {"per_name_cap": M.MAX_POS_W,
+            "healthy_bands": {"per_name_cap": M.effective_name_cap(len(holdings)),
                               "sector_band": [M.SECTOR_CAP_LO, M.SECTOR_CAP_HI]},
             "investor_context": context or "not provided — judge the portfolio on its own terms",
         })
@@ -248,7 +287,8 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None) -> dict:
     gaps = (strat or {}).get("gaps") or []
     candidates = _alt_candidates(holdings, gaps if isinstance(gaps, list) else [])
     emit(80, "Verifying candidate ideas against live data…")
-    _enrich_candidates(candidates)
+    _enrich_candidates(candidates, k=12 if context else 8)
+    candidates = _fit_context(candidates, context)
     if has_llm():
         emit(84, "The Chief Investment Officer drafts the verdict…", chip="cio")
         cio = P.cio_synthesis({