diff --git a/frontend/visualization/backtesting.py b/frontend/visualization/backtesting.py
new file mode 100644
index 0000000..6efdc1e
--- /dev/null
+++ b/frontend/visualization/backtesting.py
@@ -0,0 +1,33 @@
+from plotly.subplots import make_subplots
+from frontend.visualization.candles import get_candlestick_trace
+from frontend.visualization.executors import add_executors_trace
+from frontend.visualization.pnl import get_pnl_trace
+from frontend.visualization.theme import get_default_layout
+
+
+def create_backtesting_figure(df, executors, config):
+    # Create subplots
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
+                        vertical_spacing=0.02, subplot_titles=('Candlestick', 'PNL Quote'),
+                        row_heights=[0.7, 0.3])
+
+    # Add candlestick trace
+    fig.add_trace(get_candlestick_trace(df), row=1, col=1)
+
+    # Add executors trace
+    fig = add_executors_trace(fig, executors, row=1, col=1)
+
+    # Add PNL trace
+    fig.add_trace(get_pnl_trace(executors), row=2, col=1)
+
+    # Apply the theme layout
+    layout_settings = get_default_layout(f"Trading Pair: {config['trading_pair']}")
+    layout_settings["showlegend"] = False
+    fig.update_layout(**layout_settings)
+
+    # Update axis properties
+    fig.update_xaxes(rangeslider_visible=False, row=1, col=1)
+    fig.update_xaxes(row=2, col=1)
+    fig.update_yaxes(title_text="Price", row=1, col=1)
+    fig.update_yaxes(title_text="PNL", row=2, col=1)
+    return fig
diff --git a/frontend/visualization/candles.py b/frontend/visualization/candles.py
new file mode 100644
index 0000000..9d0b050
--- /dev/null
+++ b/frontend/visualization/candles.py
@@ -0,0 +1,12 @@
+import plotly.graph_objects as go
+import pandas as pd
+
+def get_candlestick_trace(df):
+    df.index = pd.to_datetime(df.timestamp, unit='ms')
+    return go.Candlestick(x=df.index,
+                          open=df['open'],
+                          high=df['high'],
+                          low=df['low'],
+                          close=df['close'],
+                          name="Candlesticks",
+                          increasing_line_color='#2ECC71', decreasing_line_color='#E74C3C')
diff --git a/frontend/visualization/executors.py b/frontend/visualization/executors.py
new file mode 100644
index 0000000..ef00a2d
--- /dev/null
+++ b/frontend/visualization/executors.py
@@ -0,0 +1,23 @@
+import plotly.graph_objects as go
+import pandas as pd
+from decimal import Decimal
+
+
+def add_executors_trace(fig, executors, row, col):
+    for executor in executors:
+        entry_time = pd.to_datetime(executor.timestamp, unit='ms')
+        entry_price = executor.custom_info["current_position_average_price"]
+        exit_time = pd.to_datetime(executor.close_timestamp, unit='ms')
+        exit_price = executor.custom_info["close_price"]
+
+        if executor.filled_amount_quote == 0:
+            fig.add_trace(go.Scatter(x=[entry_time, exit_time], y=[entry_price, exit_price], mode='lines',
+                                     line=dict(color='blue', width=2, dash="dash"), name='Buy Entry/Exit'), row=row, col=col)
+        else:
+            if executor.net_pnl_quote > Decimal(0):
+                fig.add_trace(go.Scatter(x=[entry_time, exit_time], y=[entry_price, exit_price], mode='lines',
+                                         line=dict(color='green', width=2), name='Buy Entry/Exit'), row=row, col=col)
+            else:
+                fig.add_trace(go.Scatter(x=[entry_time, exit_time], y=[entry_price, exit_price], mode='lines',
+                                         line=dict(color='red', width=2), name='Sell Entry/Exit'), row=row, col=col)
+    return fig
diff --git a/frontend/visualization/pnl.py b/frontend/visualization/pnl.py
new file mode 100644
index 0000000..643c610
--- /dev/null
+++ b/frontend/visualization/pnl.py
@@ -0,0 +1,15 @@
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+
+
+def get_pnl_trace(executors):
+    pnl = [e.net_pnl_quote for e in executors]
+    cum_pnl = np.cumsum(pnl)
+    return go.Scatter(
+        x=pd.to_datetime([e.close_timestamp for e in executors], unit="ms"),
+        y=cum_pnl,
+        mode='lines',
+        line=dict(color='blue', width=2, dash="dash"),
+        name='Cumulative PNL'
+    )