Add formatting

Author: doruirimescu

Trading/utils/visualize.py

@@ -5,6 +5,7 @@
 
 # Visualizations for trades
 
+
 def plot_trades_gant(all_trades: List[List[Trade]]):
     fig, ax = plt.subplots(figsize=(10, 6))
     legend_labels = []
@@ -23,59 +24,87 @@ def plot_trades_gant(all_trades: List[List[Trade]]):
         trade_start_dates_num = date2num(trade_start_dates)
         trade_end_dates_num = date2num(trade_end_dates)
 
-        ax.barh(trade_labels, trade_end_dates_num - trade_start_dates_num, left=trade_start_dates_num, height=0.5)
+        ax.barh(
+            trade_labels,
+            trade_end_dates_num - trade_start_dates_num,
+            left=trade_start_dates_num,
+            height=0.5,
+        )
 
-    ax.set_xlabel('Timeline')
-    ax.set_ylabel('Trades')
-    ax.set_title('Trade Openings Overlapping Timelines')
+    ax.set_xlabel("Timeline")
+    ax.set_ylabel("Trades")
+    ax.set_title("Trade Openings Overlapping Timelines")
     ax.legend(legend_labels)
     ax.xaxis_date()
 
     plt.show()
 
+
 # plot a list of values with dates
-def plot_list_dates(values: List[float], dates: List, title: str, ylabel: str, peaks, show_cursor=True):
+def plot_list_dates(
+    values: List[float], dates: List, title: str, ylabel: str, peaks, show_cursor=True
+):
     from datetime import datetime
     import mplcursors
     import matplotlib.dates as mdates
 
     fig, ax = plt.subplots(figsize=(10, 6))
     dates = [datetime.fromisoformat(d) for d in dates]
     ax.plot(dates, values)
-    ax.set_xlabel('Date')
+    ax.set_xlabel("Date")
     ax.set_ylabel(ylabel)
     ax.set_title(title)
     ax.xaxis_date()
 
     if show_cursor:
         cursor = mplcursors.cursor(ax, hover=True)
+
         # Customize the annotation
         @cursor.connect("add")
         def on_add(sel):
             x_date = mdates.num2date(sel.target[0])  # Convert to datetime
-            sel.annotation.set(text=f"{x_date.strftime('%Y-%m-%d')}\nValue: {sel.target[1]:.2f}")
+            sel.annotation.set(
+                text=f"{x_date.strftime('%Y-%m-%d')}\nValue: {sel.target[1]:.2f}"
+            )
+
     average_value = sum(values) / len(values)
     # plot average as dotted line
-    ax.axhline(average_value, color='orange', linestyle='--', label='Average')
+    ax.axhline(average_value, color="orange", linestyle="--", label="Average")
 
     # plot standard deviation above and below average
     from Trading.utils.calculations import calculate_standard_deviation
+
     std_dev = calculate_standard_deviation(values)
     STD_SCALER = 1.5
-    ax.axhline(average_value + STD_SCALER * std_dev, color='green', linestyle='--', label=f'Above {STD_SCALER} Std Dev')
-    ax.axhline(average_value - STD_SCALER * std_dev, color='red', linestyle='--', label=f'Below {STD_SCALER} Std Dev')
+    ax.axhline(
+        average_value + STD_SCALER * std_dev,
+        color="green",
+        linestyle="--",
+        label=f"Above {STD_SCALER} Std Dev",
+    )
+    ax.axhline(
+        average_value - STD_SCALER * std_dev,
+        color="red",
+        linestyle="--",
+        label=f"Below {STD_SCALER} Std Dev",
+    )
 
     peak_values = peaks["values"]
     peak_dates = peaks["dates"]
-    ax.plot(peak_dates, peak_values, 'ro', label='Peaks')
+    ax.plot(peak_dates, peak_values, "ro", label="Peaks")
 
     peaks_above_std = []
     peaks_above_std_dates = []
     for i in range(len(peak_values)):
-        if abs(peak_values[i] - average_value) > STD_SCALER*std_dev:
+        if abs(peak_values[i] - average_value) > STD_SCALER * std_dev:
             peaks_above_std.append(peak_values[i])
             peaks_above_std_dates.append(peak_dates[i])
-    ax.plot(peaks_above_std_dates, peaks_above_std, 'go', label=f'Peaks outside {STD_SCALER} Std Dev')
+    ax.plot(
+        peaks_above_std_dates,
+        peaks_above_std,
+        "go",
+        label=f"Peaks outside {STD_SCALER} Std Dev",
+    )
 
     plt.xticks(rotation=45)
     plt.grid(True)