(feat) fix inputs

frontend/visualization/dca_builder.py

@@ -17,11 +17,12 @@ def create_dca_graph(dca_inputs, dca_amount):
     tech_colors = theme.get_color_scheme()
     dca_order_amounts = [amount_dist * dca_amount for amount_dist in dca_inputs["dca_amounts"]]
     n_levels = len(dca_inputs["dca_spreads"])
+    dca_spreads = [spread * 100 for spread in dca_inputs["dca_spreads"]]
 
     break_even_values = []
     take_profit_values = []
     for level in range(n_levels):
-        dca_spreads_normalized = [spread + 0.01 for spread in dca_inputs["dca_spreads"][:level + 1]]
+        dca_spreads_normalized = [spread + 0.01 for spread in dca_spreads[:level + 1]]
         amounts = dca_order_amounts[:level + 1]
         break_even = (sum([spread * amount for spread, amount in zip(dca_spreads_normalized, amounts)]) / sum(
             amounts)) - 0.01
@@ -31,7 +32,7 @@ def create_dca_graph(dca_inputs, dca_amount):
     accumulated_amount = [sum(dca_order_amounts[:i + 1]) for i in range(len(dca_order_amounts))]
 
     # Calculate unrealized PNL
-    cum_unrealized_pnl = calculate_unrealized_pnl(dca_inputs["dca_spreads"], break_even_values, accumulated_amount)
+    cum_unrealized_pnl = calculate_unrealized_pnl(dca_spreads, break_even_values, accumulated_amount)
 
     # Create Plotly figure with secondary y-axis and a dark theme
     fig = make_subplots(specs=[[{"secondary_y": True}]])
@@ -39,7 +40,7 @@ def create_dca_graph(dca_inputs, dca_amount):
 
     # Update the Scatter Plots and Horizontal Lines
     fig.add_trace(
-        go.Scatter(x=list(range(len(dca_inputs["dca_spreads"]))), y=dca_inputs["dca_spreads"], name='Spread (%)',
+        go.Scatter(x=list(range(len(dca_spreads))), y=dca_spreads, name='Spread (%)',
                    mode='lines+markers',
                    line=dict(width=3, color=tech_colors['spread'])), secondary_y=False)
     fig.add_trace(
@@ -99,7 +100,7 @@ def create_dca_graph(dca_inputs, dca_amount):
 
     # Update Annotations for Spread and Break Even
     for i, (spread, be_value, tp_value) in enumerate(
-            zip(dca_inputs["dca_spreads"], break_even_values, take_profit_values)):
+            zip(dca_spreads, break_even_values, take_profit_values)):
         fig.add_annotation(x=i, y=spread, text=f"{spread:.2f}%", showarrow=True, arrowhead=1, yshift=10, xshift=-2,
                            font=dict(color=tech_colors['spread']))
         fig.add_annotation(x=i, y=be_value, text=f"{be_value:.2f}%", showarrow=True, arrowhead=1, yshift=5, xshift=-2,

frontend/visualization/executors.py

@@ -15,7 +15,7 @@ def add_executors_trace(fig, executors, row, col):
 
         if executor.filled_amount_quote == 0:
             fig.add_trace(go.Scatter(x=[entry_time, exit_time], y=[entry_price, entry_price], mode='lines',
-                                     line=dict(color='blue', width=2, dash="dash"), name=name), row=row, col=col)
+                                     line=dict(color='grey', width=2, dash="dash"), name=name), 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',

frontend/visualization/executors_distribution.py

@@ -1,3 +1,4 @@
+import numpy as np
 import plotly.graph_objects as go
 import frontend.visualization.theme as theme
 
@@ -12,6 +13,10 @@ def create_executors_distribution_traces(buy_spreads, sell_spreads, buy_amounts_
     buy_order_levels = len(buy_spread_distributions)
     sell_order_levels = len(sell_spread_distributions)
 
+    # Calculate total volumes
+    total_buy_volume = sum(buy_order_amounts_quote)
+    total_sell_volume = sum(sell_order_amounts_quote)
+
     # Create the figure with a dark theme and secondary y-axis
     fig = go.Figure()
 
@@ -37,7 +42,7 @@ def create_executors_distribution_traces(buy_spreads, sell_spreads, buy_amounts_
     for i, value in enumerate(buy_order_amounts_quote):
         fig.add_annotation(
             x=-buy_spread_distributions[i],
-            y=value + 0.01 * max(buy_order_amounts_quote),  # Offset the text slightly above the bar
+            y=value + 0.03 * max(buy_order_amounts_quote),  # Offset the text slightly above the bar
             text=str(round(value, 2)),
             showarrow=False,
             font=dict(color=colors['buy'], size=10)
@@ -47,12 +52,55 @@ def create_executors_distribution_traces(buy_spreads, sell_spreads, buy_amounts_
     for i, value in enumerate(sell_order_amounts_quote):
         fig.add_annotation(
             x=sell_spread_distributions[i],
-            y=value + 0.01 * max(sell_order_amounts_quote),  # Offset the text slightly above the bar
+            y=value + 0.03 * max(sell_order_amounts_quote),  # Offset the text slightly above the bar
             text=str(round(value, 2)),
             showarrow=False,
             font=dict(color=colors['sell'], size=10)
         )
 
+    max_y = max(max(buy_order_amounts_quote), max(sell_order_amounts_quote))
+    # Add annotations for total volumes
+    fig.add_annotation(
+        x=-np.mean(buy_spread_distributions),
+        y=max_y,
+        text=f'Total Buy\n{round(total_buy_volume, 2)}',
+        showarrow=False,
+        font=dict(color=colors['buy'], size=12, family="Arial Black"),
+        align='center'
+    )
+
+    fig.add_annotation(
+        x=np.mean(sell_spread_distributions),
+        y=max_y,
+        text=f'Total Sell\n{round(total_sell_volume, 2)}',
+        showarrow=False,
+        font=dict(color=colors['sell'], size=12, family="Arial Black"),
+        align='center'
+    )
+
+    # Draw circles around the total volume annotations
+    fig.add_shape(
+        type='circle',
+        xref='x',
+        yref='y',
+        x0=-np.mean(buy_spread_distributions) - 0.4,
+        y0=max_y - 0.05 * max_y,
+        x1=-np.mean(buy_spread_distributions) + 0.4,
+        y1=max_y + 0.05 * max_y,
+        line=dict(color=colors['buy'])
+    )
+
+    fig.add_shape(
+        type='circle',
+        xref='x',
+        yref='y',
+        x0=np.mean(sell_spread_distributions) - 0.4,
+        y0=max_y - 0.05 * max_y,
+        x1=np.mean(sell_spread_distributions) + 0.4,
+        y1=max_y + 0.05 * max_y,
+        line=dict(color=colors['sell'])
+    )
+
     # Apply the theme layout
     layout_settings = theme.get_default_layout("Market Maker Order Distribution")
     fig.update_layout(**layout_settings)