fix: isolate auxiliary price source failures

dashboard/server.py

@@ -206,7 +206,11 @@ def run_scrape(force_full=False):
             price_current = metrics["price"]
 
         log.info("Fetching BTC ATH...")
+        try:
             ath_data = price.fetch_ath()
+        except Exception as e:
+            cycle_errors.append(f"ATH: {e}")
+            ath_data = {}
         ath_val = ath_data.get("ath") or existing_cache.get("drawdown", {}).get("ath")
         if price_current.get("price") and ath_val:
             drawdown = price.calculate_drawdown(price_current["price"], ath_val)
@@ -218,7 +222,11 @@ def run_scrape(force_full=False):
             metrics["drawdown"] = {"value": None}
 
         log.info("Fetching historical prices for 200D SMA / Mayer...")
+        try:
             hist = price.fetch_historical()
+        except Exception as e:
+            cycle_errors.append(f"Historical price: {e}")
+            hist = []
         if hist:
             sma_200d = price.calculate_200d_sma(hist)
             mayer = price.calculate_mayer_multiple(price_current.get("price"), sma_200d)

tests/test_server_reliability.py

@@ -89,8 +89,8 @@ def test_partial_fast_scrape_preserves_last_known_good_metric(server, monkeypatc
     monkeypatch.setattr(server, "HISTORY_PATH", str(history_path))
     monkeypatch.setattr(server.fear_greed, "fetch", lambda: {"value": 25})
     monkeypatch.setattr(server.price, "fetch_current", lambda: (_ for _ in ()).throw(RuntimeError("price timeout")))
-    monkeypatch.setattr(server.price, "fetch_ath", lambda: {"ath": 70000})
+    monkeypatch.setattr(server.price, "fetch_ath", lambda: (_ for _ in ()).throw(RuntimeError("ATH timeout")))
-    monkeypatch.setattr(server.price, "fetch_historical", lambda: [])
+    monkeypatch.setattr(server.price, "fetch_historical", lambda: (_ for _ in ()).throw(RuntimeError("history timeout")))
     monkeypatch.setattr(server.engine, "score_all", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
     monkeypatch.setattr(server.engine, "score_all_ml", lambda metrics: {"composite_score": 50, "scored_count": 1, "metrics": []})
     fake_updater = types.ModuleType("scrapers.history_updater")