From bf6c4846c437f6b9b9fdce88cfad6dee7f4fd293 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Wed, 11 Mar 2026 18:30:55 +0000
Subject: [PATCH] refactor(dashboard): optimize signal processing with groupby

Instead of repeatedly filtering the DataFrame for each symbol in a loop,
sort the DataFrame by timestamp once and use groupby(COL_SYMBOL) to
iterate over the groups. This reduces the time complexity and significantly
speeds up the signal processing loop.

Co-authored-by: maghdam <63883156+maghdam@users.noreply.github.com>
---
 dashboard.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/dashboard.py b/dashboard.py
index 6f6b066..ba4550d 100644
--- a/dashboard.py
+++ b/dashboard.py
@@ -60,12 +60,8 @@ def display_recent_signals(df: pd.DataFrame):
     # Optional: Show a quick mini-forecast chart per symbol
     st.write("---")
     st.subheader("Mini Signal Forecasts (per symbol)")
-    for symbol in sorted(df[COL_SYMBOL].unique()):
-        mini_df = (
-            df[df[COL_SYMBOL] == symbol]
-            .sort_values(COL_TIMESTAMP)
-            .tail(N_FORWARD)
-        )
+    for symbol, group in df.sort_values(COL_TIMESTAMP).groupby(COL_SYMBOL):
+        mini_df = group.tail(N_FORWARD)
         # Only show if there is more than one unique value
         if mini_df[COL_PREDICTION].nunique() > 1:
             st.write(f"**{symbol}**")