diff --git a/frontend/visualization/indicators.py b/frontend/visualization/indicators.py
index 0f21665..93f86bf 100644
--- a/frontend/visualization/indicators.py
+++ b/frontend/visualization/indicators.py
@@ -41,3 +41,42 @@ def get_macd_traces(df, macd_fast, macd_slow, macd_signal):
                marker_color=df[f"MACDh_{macd_fast}_{macd_slow}_{macd_signal}"].apply(lambda x: '#FF6347' if x < 0 else '#32CD32'))
     ]
     return traces
+
+
+def get_supertrend_traces(df, length, multiplier):
+    tech_colors = theme.get_color_scheme()
+    df.ta.supertrend(length=length, multiplier=multiplier, append=True)
+    supertrend_d = f'SUPERTd_{length}_{multiplier}'
+    supertrend = f'SUPERT_{length}_{multiplier}'
+    df = df[df[supertrend] > 0]
+
+    # Create segments for line with different colors
+    segments = []
+    current_segment = {"x": [], "y": [], "color": None}
+
+    for i in range(len(df)):
+        if i == 0 or df[supertrend_d].iloc[i] == df[supertrend_d].iloc[i - 1]:
+            current_segment["x"].append(df.index[i])
+            current_segment["y"].append(df[supertrend].iloc[i])
+            current_segment["color"] = tech_colors['buy'] if df[supertrend_d].iloc[i] == 1 else tech_colors['sell']
+        else:
+            segments.append(current_segment)
+            current_segment = {"x": [df.index[i - 1], df.index[i]],
+                               "y": [df[supertrend].iloc[i - 1], df[supertrend].iloc[i]],
+                               "color": tech_colors['buy'] if df[supertrend_d].iloc[i] == 1 else tech_colors['sell']}
+
+    segments.append(current_segment)
+
+    # Create traces from segments
+    traces = [
+        go.Scatter(
+            x=segment["x"],
+            y=segment["y"],
+            mode='lines',
+            line=dict(color=segment["color"], width=2),
+            name='SuperTrend'
+        ) for segment in segments
+    ]
+
+    return traces
+
diff --git a/frontend/visualization/signals.py b/frontend/visualization/signals.py
index 6762c7c..1cbec29 100644
--- a/frontend/visualization/signals.py
+++ b/frontend/visualization/signals.py
@@ -3,17 +3,8 @@ import plotly.graph_objects as go
 import pandas_ta as ta  # noqa: F401
 
 
-def get_bollinger_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_short_threshold):
+def get_signal_traces(buy_signals, sell_signals):
     tech_colors = theme.get_color_scheme()
-    # Add Bollinger Bands
-    candles = df.copy()
-    candles.ta.bbands(length=bb_length, std=bb_std, append=True)
-
-    # Generate conditions
-    buy_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] < bb_long_threshold]
-    sell_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] > bb_short_threshold]
-
-    # Signals plot
     traces = [
         go.Scatter(x=buy_signals.index, y=buy_signals['close'], mode='markers',
                    marker=dict(color=tech_colors['buy_signal'], size=10, symbol='triangle-up'),
@@ -24,6 +15,17 @@ def get_bollinger_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_
     ]
     return traces
 
+def get_bollinger_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_short_threshold):
+    # Add Bollinger Bands
+    candles = df.copy()
+    candles.ta.bbands(length=bb_length, std=bb_std, append=True)
+
+    # Generate conditions
+    buy_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] < bb_long_threshold]
+    sell_signals = candles[candles[f"BBP_{bb_length}_{bb_std}"] > bb_short_threshold]
+
+    return get_signal_traces(buy_signals, sell_signals)
+
 
 def get_macdbb_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_short_threshold, macd_fast, macd_slow,
                                 macd_signal):
@@ -40,13 +42,16 @@ def get_macdbb_v1_signal_traces(df, bb_length, bb_std, bb_long_threshold, bb_sho
     buy_signals = df[(bbp < bb_long_threshold) & (macdh > 0) & (macd < 0)]
     sell_signals = df[(bbp > bb_short_threshold) & (macdh < 0) & (macd > 0)]
 
-    # Signals plot
-    traces = [
-        go.Scatter(x=buy_signals.index, y=buy_signals['close'], mode='markers',
-                   marker=dict(color=tech_colors['buy_signal'], size=10, symbol='triangle-up'),
-                   name='Buy Signal'),
-        go.Scatter(x=sell_signals.index, y=sell_signals['close'], mode='markers',
-                   marker=dict(color=tech_colors['sell_signal'], size=10, symbol='triangle-down'),
-                   name='Sell Signal')
-    ]
-    return traces
+    return get_signal_traces(buy_signals, sell_signals)
+
+
+def get_supertrend_v1_signal_traces(df, length, multiplier, percentage_threshold):
+    # Add indicators
+    df.ta.supertrend(length=length, multiplier=multiplier, append=True)
+    df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{length}_{multiplier}"]) / df["close"]
+
+    # Generate long and short conditions
+    buy_signals = df[(df[f"SUPERTd_{length}_{multiplier}"] == 1) & (df["percentage_distance"] < percentage_threshold)]
+    sell_signals = df[(df[f"SUPERTd_{length}_{multiplier}"] == -1) & (df["percentage_distance"] < percentage_threshold)]
+
+    return get_signal_traces(buy_signals, sell_signals)