Update utils/plotting.py

utils/plotting.py

@@ -1,7 +1,7 @@
-import streamlit as st
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 import pandas as pd
+import streamlit as st
 from pandas.plotting import register_matplotlib_converters
 
 register_matplotlib_converters()
@@ -10,37 +10,38 @@ def plot_stock_data_with_signals(data):
     # First, ensure the DataFrame's index is in datetime format.
     data.index = pd.to_datetime(data.index, errors='coerce')
 
-    # Create a new figure and set the size.
-    plt.figure(figsize=(14, 7))
+    # Creating a figure and axes explicitly
+    fig, ax = plt.subplots(figsize=(14, 7))
 
     # Plotting stock 'Close' prices
-    plt.plot(data.index, data['Close'], label='Close Price', color='black', lw=2)
+    ax.plot(data.index, data['Close'], label='Close Price', color='black', lw=2)
 
     # Check and plot EMAs if they exist
     if 'EMA_20' in data.columns and 'EMA_50' in data.columns:
-        plt.plot(data.index, data['EMA_20'], label='EMA 20', color='blue', lw=1.5)
-        plt.plot(data.index, data['EMA_50'], label='EMA 50', color='red', lw=1.5)
+        ax.plot(data.index, data['EMA_20'], label='EMA 20', color='blue', lw=1.5)
+        ax.plot(data.index, data['EMA_50'], label='EMA 50', color='red', lw=1.5)
 
     # Check and plot Bollinger Bands if they exist
     if 'BB_Upper' in data.columns and 'BB_Lower' in data.columns:
-        plt.fill_between(data.index, data['BB_Lower'], data['BB_Upper'], color='grey', alpha=0.1, label='Bollinger Bands')
+        ax.fill_between(data.index, data['BB_Lower'], data['BB_Upper'], color='grey', alpha=0.1, label='Bollinger Bands')
 
     # Highlight buy/sell signals if they exist
     if 'Combined_Signal' in data.columns:
         buy_signals = data[data['Combined_Signal'] == 'buy']
         sell_signals = data[data['Combined_Signal'] == 'sell']
-        plt.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green', alpha=1)
-        plt.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red', alpha=1)
+        ax.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green', alpha=1)
+        ax.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red', alpha=1)
 
-    plt.title('Stock Price with Buy/Sell Signals')
-    plt.xlabel('Date')
-    plt.ylabel('Price')
-    plt.legend()
+    ax.set_title('Stock Price with Buy/Sell Signals')
+    ax.set_xlabel('Date')
+    ax.set_ylabel('Price')
+    ax.legend()
 
     # Improve the x-axis date format
-    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
-    plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
-    plt.gcf().autofmt_xdate()  # Rotation
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+    ax.xaxis.set_major_locator(mdates.MonthLocator())
+    fig.autofmt_xdate()  # Rotation
 
-    st.pyplot()
+    # Use the figure object in st.pyplot()
+    st.pyplot(fig)