Add web dev dashboard with real-time HK weather + model comparison

- Flask web server with interactive charts (Chart.js)
- Temperature, rain probability, wind speed charts with HKO vs Open-Meteo comparison
- Current conditions card with typhoon signal indicator
- Kelly criterion sizing simulator panel
- Trading signals panel with model-implied probabilities
- Raw forecast data JSON viewer
- Github-dark theme with responsive card layout
- Fixed Open-Meteo SDK compatibility and numpy bool serialization

Run: python web_dashboard.py  # http://localhost:5000

weather/openmeteo_client.py

@@ -149,9 +149,11 @@ class OpenMeteoClient:
         daily_df.index = daily_df.index.tz_convert("Asia/Hong_Kong")
 
         daily_df.attrs["model"] = self.model
-        daily_df.attrs["hourly"] = hourly_df
         daily_df.attrs["fetch_time"] = datetime.now()
 
+        # Store hourly separately to avoid pandas attrs bug with DataFrames
+        self._last_hourly = hourly_df
+
         return daily_df
 
     def get_current_conditions(self) -> dict:
@@ -196,12 +198,13 @@ class OpenMeteoClient:
     def get_precipitation_probability(self, hours_ahead: int = 24) -> float:
         """Get precipitation probability for next N hours."""
         df = self.get_forecast(lead_days=2)
-        if df is not None and hasattr(df, 'attrs') and 'hourly' in df.attrs:
-            hourly = df.attrs['hourly']
-            now = pd.Timestamp.now(tz="Asia/Hong_Kong")
-            future = hourly[hourly.index <= now + pd.Timedelta(hours=hours_ahead)]
-            if 'precipitation_probability' in future.columns:
-                return float(future['precipitation_probability'].max())
+        if df is not None:
+            hourly = getattr(self, '_last_hourly', pd.DataFrame())
+            if not hourly.empty:
+                now = pd.Timestamp.now(tz="Asia/Hong_Kong")
+                future = hourly[hourly.index <= now + pd.Timedelta(hours=hours_ahead)]
+                if 'precipitation_probability' in future.columns:
+                    return float(future['precipitation_probability'].max())
         return 0.0
 
     def get_scoring_window_summary(self, target_date: str) -> dict:
@@ -220,7 +223,7 @@ class OpenMeteoClient:
             return {}
 
         row = df.loc[target]
-        hourly = df.attrs.get("hourly", pd.DataFrame())
+        hourly = getattr(self, '_last_hourly', pd.DataFrame())
 
         if not hourly.empty:
             day_hourly = hourly[