diff --git a/backtesting/_plotting.py b/backtesting/_plotting.py
index 338454da..32cbf5cd 100644
--- a/backtesting/_plotting.py
+++ b/backtesting/_plotting.py
@@ -162,27 +162,35 @@ def try_mean_first(indicator):
     equity_data = equity_data.resample(freq, label='right').agg(_EQUITY_AGG).dropna(how='all')
     assert equity_data.index.equals(df.index)
 
-    def _weighted_returns(s, trades=trades):
-        df = trades.loc[s.index]
-        return ((df['Size'].abs() * df['ReturnPct']) / df['Size'].abs().sum()).sum()
-
-    def _group_trades(column):
-        def f(s, new_index=pd.Index(df.index.astype(np.int64)), bars=trades[column]):
-            if s.size:
-                # Via int64 because on pandas recently broken datetime
-                mean_time = int(bars.loc[s.index].astype(np.int64).mean())
-                new_bar_idx = new_index.get_indexer([mean_time], method='nearest')[0]
-                return new_bar_idx
-        return f
-
     if len(trades):  # Avoid pandas "resampling on Int64 index" error
-        trades = trades.assign(count=1).resample(freq, on='ExitTime', label='right').agg(dict(
+        abs_sizes = trades['Size'].abs()
+        trades = trades.assign(
+            count=1,
+            _abs_size=abs_sizes,
+            _weighted_ret=abs_sizes * trades['ReturnPct'],
+        )
+        trades = trades.resample(freq, on='ExitTime', label='right').agg(dict(
             TRADES_AGG,
-            ReturnPct=_weighted_returns,
             count='sum',
-            EntryBar=_group_trades('EntryTime'),
-            ExitBar=_group_trades('ExitTime'),
+            _abs_size='sum',
+            _weighted_ret='sum',
+            EntryTime='mean',
+            ExitTime='mean',
         )).dropna()
+        trades['ReturnPct'] = (
+            trades['_weighted_ret'] / trades['_abs_size']
+        )
+        trades.drop(columns=['_abs_size', '_weighted_ret'], inplace=True)
+
+        new_index = pd.Index(df.index.astype(np.int64))
+        trades['EntryBar'] = new_index.get_indexer(
+            trades['EntryTime'].astype(np.int64),
+            method='nearest',
+        )
+        trades['ExitBar'] = new_index.get_indexer(
+            trades['ExitTime'].astype(np.int64),
+            method='nearest',
+        )
 
     return df, indicators, equity_data, trades
 
diff --git a/backtesting/test/_test.py b/backtesting/test/_test.py
index 63045ce1..4883f0ae 100644
--- a/backtesting/test/_test.py
+++ b/backtesting/test/_test.py
@@ -787,6 +787,70 @@ def init(self):
             # Give browser time to open before tempfile is removed
             time.sleep(1)
 
+    def test_resample_trades_vectorized(self):
+        """Vectorized trade resampling produces correct weighted returns and bar indices."""
+        import backtesting._plotting as _plotting
+        from backtesting.lib import OHLCV_AGG, TRADES_AGG, _EQUITY_AGG
+
+        bt = Backtest(GOOG, SmaCross)
+        results = bt.run()
+        trades = results['_trades']
+        if trades.empty:
+            return
+
+        df_ohlcv = bt._data.copy()
+        equity_data = results['_equity_curve'].copy(deep=False)
+
+        freq = '1ME'
+        df_resampled = df_ohlcv.resample(
+            freq, label='right').agg(OHLCV_AGG).dropna()
+
+        # --- Reference (original callback) implementation ---
+        def _weighted_returns(s, _trades=trades):
+            d = _trades.loc[s.index]
+            return (
+                (d['Size'].abs() * d['ReturnPct'])
+                / d['Size'].abs().sum()
+            ).sum()
+
+        def _group_trades(column):
+            def f(s,
+                  new_index=pd.Index(
+                      df_resampled.index.astype(np.int64)),
+                  bars=trades[column]):
+                if s.size:
+                    mean_time = int(
+                        bars.loc[s.index].astype(np.int64).mean()
+                    )
+                    return new_index.get_indexer(
+                        [mean_time], method='nearest')[0]
+            return f
+
+        ref = trades.assign(count=1).resample(
+            freq, on='ExitTime', label='right',
+        ).agg(dict(
+            TRADES_AGG,
+            ReturnPct=_weighted_returns,
+            count='sum',
+            EntryBar=_group_trades('EntryTime'),
+            ExitBar=_group_trades('ExitTime'),
+        )).dropna()
+
+        # --- Vectorized implementation ---
+        eq = equity_data.resample(
+            freq, label='right').agg(_EQUITY_AGG).dropna(how='all')
+        _, _, _, vec_trades = _plotting._maybe_resample_data(
+            freq, df_ohlcv.copy(), [], eq, trades.copy(),
+        )
+
+        cols = ['Size', 'EntryBar', 'ExitBar', 'EntryPrice',
+                'ExitPrice', 'PnL', 'ReturnPct', 'count']
+        assert_frame_equal(
+            vec_trades[cols].reset_index(drop=True),
+            ref[cols].reset_index(drop=True),
+            check_exact=False, check_dtype=False, atol=1e-10,
+        )
+
     def test_indicator_name(self):
         test_self = self