Fix stale GUI results, scan inputs, and persistent settings

.github/workflows/ci.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
           cache: pip
           cache-dependency-path: requirements-ci.txt
 

.github/workflows/release.yml

@@ -37,10 +37,10 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python 3.12
+      - name: Set up Python 3.11
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12"
+          python-version: "3.11"
           cache: pip
           cache-dependency-path: requirements-release.txt
 
@@ -98,20 +98,9 @@ jobs:
       - name: Resolve tag name
         id: tag
         shell: bash
-        run: |
-          if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
-            TAG="${{ github.event.inputs.version }}"
-          else
-            TAG="${GITHUB_REF_NAME}"
-          fi
-          # Normalize: strip leading whitespace
-          TAG="${TAG# }"
-          if [[ -z "$TAG" ]]; then
-            echo "Empty tag" >&2
-            exit 1
-          fi
-          echo "tag=$TAG" >> "$GITHUB_OUTPUT"
-          echo "Using tag: $TAG"
+        env:
+          DISPATCH_VERSION: ${{ github.event.inputs.version }}
+        run: bash scripts/resolve_release_tag.sh
 
       - name: Download all build artifacts
         uses: actions/download-artifact@v4

build_macos.sh

@@ -205,6 +205,10 @@ PYI_ARGS=(
   --exclude-module matplotlib.backends.backend_gtk4cairo
   --exclude-module matplotlib.backends.backend_nbagg
   --exclude-module matplotlib.backends.backend_cairo
+  --collect-all pandas_market_calendars
+  --collect-all exchange_calendars
+  --collect-all tzdata
+  --add-data "$ROOT/data:data"
   --collect-submodules matplotlib
 )
 

core/expiry_time.py

@@ -0,0 +1,37 @@
+"""US equity-option remaining session time (NYSE calendar proxy).
+
+Volatility and rates use a common 252 x 6.5-hour trading-year clock, preserving
+Sentinel's trading-day convention. This is not an ACT/365 rate conversion. Early
+closes contribute only their actual session duration. AM-settled/index products
+need a product-specific calendar/settlement adapter and are not supported here.
+"""
+from datetime import datetime, timezone
+from functools import lru_cache
+
+import pandas as pd
+
+
+@lru_cache(maxsize=64)
+def _schedule(start, end, calendar_name):
+    import pandas_market_calendars as calendars
+    return calendars.get_calendar(calendar_name).schedule(start_date=start, end_date=end)
+
+
+def remaining_years(expiry, valuation_time=None, *, calendar_name="NYSE"):
+    """Remaining open-session seconds through expiry close; zero after expiry.
+
+    Explicit valuation times must be timezone-aware. A non-session expiry uses
+    the preceding session's close. All comparisons use UTC, independent of host TZ.
+    """
+    now = pd.Timestamp(valuation_time if valuation_time is not None else datetime.now(timezone.utc))
+    if now.tzinfo is None:
+        raise ValueError("valuation_time must be timezone-aware")
+    now = now.tz_convert("UTC")
+    start = now.tz_convert("America/New_York").date()
+    end = pd.Timestamp(expiry).date()
+    if end < start:
+        return 0.0
+    schedule = _schedule(start.isoformat(), end.isoformat(), calendar_name)
+    seconds = sum(max(0.0, (row.market_close - max(now, row.market_open)).total_seconds())
+                  for row in schedule.itertuples())
+    return seconds / (252.0 * 6.5 * 3600.0)

core/graph_service.py

@@ -30,25 +30,27 @@ def categorize_peers(graph: StockGraph, ticker: str) -> Dict[str, Any]:
     node = graph.get_node(sym)
     if not node:
         raise ValueError(f"Ticker {sym!r} not found in stock graph.")
-    neighbors = graph.get_neighbors(sym, depth=1)
     categorized: Dict[str, List[dict]] = {}
-    for n, edge in neighbors:
-        categorized.setdefault(edge.relation, []).append(
-            {
-                "ticker": n.ticker,
-                "name": n.name,
-                "sector": n.sector,
-                "sub_industry": n.sub_industry,
-                "description": edge.description,
-                "weight": edge.weight,
-            }
-        )
+    for edge in graph.edges:
+        if sym not in (edge.source, edge.target):
+            continue
+        peer = edge.target if edge.source == sym else edge.source
+        n = graph.get_node(peer)
+        if n is None:
+            continue
+        categorized.setdefault(edge.relation, []).append({
+            "ticker": n.ticker, "name": n.name, "sector": n.sector,
+            "sub_industry": n.sub_industry, "description": edge.description,
+            "weight": edge.weight, "source": edge.source, "target": edge.target,
+            "direction": "bidirectional" if edge.bidirectional else "outgoing" if edge.source == sym else "incoming",
+        })
     return {
         "action": "peers",
         "ticker": sym,
         "node": node.to_dict(),
         "peer_categories": categorized,
-        "peer_count": sum(len(v) for v in categorized.values()),
+        "peer_count": len({p["ticker"] for peers in categorized.values() for p in peers}),
+        "relationship_count": sum(len(v) for v in categorized.values()),
     }
 
 
@@ -62,6 +64,7 @@ def show_connections(
             "action": "summary",
             "total_nodes": len(nodes),
             "total_edges": len(graph.edges),
+            "import_status": getattr(graph, "import_status", {"status": "not requested"}),
             "sectors": sorted({n["sector"] for n in nodes}),
             "tickers": sorted(n["ticker"] for n in nodes),
         }
@@ -77,6 +80,8 @@ def show_connections(
             "name": n.name,
             "sector": n.sector,
             "relation": edge.relation,
+            "source": edge.source,
+            "target": edge.target,
             "description": edge.description,
             "weight": edge.weight,
         }

core/scan_service.py

@@ -16,6 +16,7 @@
 import pandas as pd
 
 from core.pricing import VegaChimpCore
+from core.expiry_time import remaining_years
 from core.technicals import calculate_technicals
 from core.vol_models import (
     blend_forecast_vol,
@@ -145,7 +146,7 @@ def _as_decimal_yield(value: Any, *, percent_units: bool) -> Optional[float]:
     return d
 
 
-def resolve_dividend_yield(data_provider, stock_obj) -> float:
+def resolve_dividend_yield(data_provider, stock_obj, *, status=None) -> float:
     """Dividend yield as a decimal fraction.
 
     Per-source units (verified against live yfinance quotes, 2026-09):
@@ -155,27 +156,29 @@ def resolve_dividend_yield(data_provider, stock_obj) -> float:
     old ``> 1`` heuristic misread as 98% / 34%.
     Priority: fast_info → info.dividendYield → trailing; else 0.0.
     """
+    status = {} if status is None else status
+    status.update(source="missing", errors=[])
     try:
         fast_info = data_provider.get_fast_info(stock_obj)
-        fast_div = _as_decimal_yield(
-            fast_info.get("dividend_yield")
-            if isinstance(fast_info, dict)
-            else getattr(fast_info, "get", lambda *a: None)("dividend_yield"),
-            percent_units=False,
-        )
-        if fast_div is not None:
-            return fast_div
+        value = _as_decimal_yield(fast_info.get("dividend_yield"), percent_units=False)
+        if value is not None:
+            status["source"] = "fast_info.dividend_yield"
+            return value
+    except Exception as exc:
+        status["errors"].append({"source": "fast_info", "error": str(exc)})
+    try:
         info = data_provider.get_info(stock_obj)
-        div = _as_decimal_yield(info.get("dividendYield"), percent_units=True)
-        if div is not None:
-            return div
-        div = _as_decimal_yield(
-            info.get("trailingAnnualDividendYield"), percent_units=False
-        )
-        if div is not None:
-            return div
-    except Exception:
-        pass
+    except Exception as exc:
+        status["errors"].append({"source": "info", "error": str(exc)})
+        info = {}
+    for field, percent in (("dividendYield", True), ("trailingAnnualDividendYield", False)):
+        try:
+            value = _as_decimal_yield(info.get(field), percent_units=percent)
+            if value is not None:
+                status["source"] = f"info.{field}"
+                return value
+        except Exception as exc:
+            status["errors"].append({"source": field, "error": str(exc)})
     return 0.0
 
 
@@ -264,6 +267,10 @@ class OptionScanRow:
     verdict: str
     is_earnings: bool = False
     tag: str = ""
+    bid: float = 0.0
+    ask: float = 0.0
+    category: Optional[str] = None
+    probability_model: str = "risk-neutral lognormal, market IV, long entry at ask"
 
     def tree_vals(self) -> tuple:
         """Tuple matching Options Explorer column order."""
@@ -288,7 +295,9 @@ def tree_vals(self) -> tuple:
         )
 
     def to_dict(self) -> dict:
-        return asdict(self)
+        data = asdict(self)
+        data["category"] = self.category or self.verdict.rstrip(" !")
+        return data
 
 
 @dataclass
@@ -298,6 +307,7 @@ class ScanResult:
     forecast_vol: float = 0.0
     spot: float = 0.0
     dividend_yield: float = 0.0
+    dividend_status: dict = field(default_factory=dict)
     rules_log: str = SCAN_RULES_LOG
     errors: List[dict] = field(default_factory=list)
     requested_expiries: int = 0
@@ -443,6 +453,7 @@ def scan_option_chains(
     long_rate: Optional[float] = None,
     log: Optional[LogFn] = None,
     on_ui_batch: Optional[UiBatchFn] = None,
+    valuation_time: Optional[datetime] = None,
 ) -> ScanResult:
     """Run the Options Finder scan over ``dates``.
 
@@ -455,8 +466,9 @@ def _log(msg: str) -> None:
             log(msg)
 
     spot = float(spot)
+    dividend_status = {"source": "override", "errors": []}
     if dividend_yield is None:
-        dividend_yield = resolve_dividend_yield(data_provider, stock)
+        dividend_yield = resolve_dividend_yield(data_provider, stock, status=dividend_status)
     DIV_YIELD = float(dividend_yield)
 
     if short_rate is None or long_rate is None:
@@ -472,7 +484,7 @@ def _log(msg: str) -> None:
     )
 
     opt_type = (option_type or "all").strip().lower()
-    today = datetime.now().date()
+    valuation_time = valuation_time or pd.Timestamp.now(tz="UTC")
     ui_batch: List[Tuple[tuple, str]] = []
     under_rows: List[Tuple[float, OptionScanRow]] = []
     rows_out: List[OptionScanRow] = []
@@ -495,13 +507,10 @@ def flush_ui() -> None:
                 _log(SCAN_RULES_LOG)
                 rules_logged = True
 
-            exp_date = datetime.strptime(date, "%Y-%m-%d").date()
-            trading_days = int(np.busday_count(today, exp_date))
-            if trading_days < 0:
+            T = remaining_years(date, valuation_time)
+            if T <= 0:
                 _log(f"Skipping expired contract {date}")
                 continue
-            # 0DTE (expires today): half a session remains on average.
-            T = max(trading_days / 252.0, 0.5 / 252.0)
             RFR = interpolate_rfr(_short_rate, _long_rate, T)
 
             chain = data_provider.get_option_chain(stock, date)
@@ -644,9 +653,9 @@ def flush_ui() -> None:
 
                 try:
                     if kind_str == "call":
-                        breakeven_price = strike + market_price
+                        breakeven_price = strike + a
                     else:
-                        breakeven_price = strike - market_price
+                        breakeven_price = strike - a
                     if breakeven_price <= 0:
                         pop = 0.0
                     elif kind_str == "call":
@@ -680,9 +689,10 @@ def flush_ui() -> None:
                     "vol": float(vol_v[j]),
                     "is_earnings": is_earnings,
                     "is_good": is_undervalued,
+                    "verdict": verdict,
                 })
 
-                breakeven = strike + market_price if kind_str == "call" else strike - market_price
+                breakeven = strike + a if kind_str == "call" else strike - a
                 tag = ""
                 if is_undervalued:
                     tag = "green"
@@ -699,6 +709,8 @@ def flush_ui() -> None:
                     volume=float(vol_v[j]),
                     oi=oi,
                     mid=market_price,
+                    bid=b,
+                    ask=a,
                     spread_pct=sp,
                     breakeven=breakeven,
                     iv=iv,
@@ -711,6 +723,7 @@ def flush_ui() -> None:
                     vega=float(greeks["vega"]),
                     pop=float(pop),
                     verdict=display_verdict,
+                    category=verdict,
                     is_earnings=is_earnings,
                     tag=tag,
                 )
@@ -746,6 +759,7 @@ def flush_ui() -> None:
         forecast_vol=forecast_vol,
         spot=spot,
         dividend_yield=DIV_YIELD,
+        dividend_status=dividend_status,
         rules_log=SCAN_RULES_LOG,
         errors=errors,
         requested_expiries=len(dates),

core/stock_graph.py

@@ -149,6 +149,8 @@ def get_neighbors(
         relation_types: Optional[Sequence[str]] = None,
     ) -> List[Tuple[StockNode, GraphEdge]]:
         """Find neighboring nodes up to a certain depth."""
+        if not isinstance(depth, int) or depth < 1:
+            raise ValueError("depth must be a positive integer")
         sym = ticker.upper()
         if sym not in self._nodes:
             return []
@@ -373,9 +375,8 @@ def build_default_graph(*, include_sectivia: bool = True) -> StockGraph:
         try:
             from core.sectivia_import import merge_cached_sectivia
 
-            merge_cached_sectivia(g, optional=True)
-        except Exception:
-            # Offline / missing optional deps must not break default graph.
-            pass
+            g.import_status = merge_cached_sectivia(g, optional=True)
+        except Exception as exc:
+            g.import_status = {"status": "error", "error": str(exc)}
     return g