Update app.py

app.py

@@ -5,7 +5,7 @@ import pandas_ta as ta
 import matplotlib.pyplot as plt
 
 # Caching the stock data fetch function to improve performance
-@st.cache_data
+@st.cache
 def fetch_stock_data(ticker, period, interval):
     return yf.download(ticker, period=period, interval=interval)
 
@@ -33,13 +33,17 @@ if analyze_button:
             stock_data['MACD'] = macd['MACD_12_26_9']
             stock_data['MACDSignal'] = macd['MACDs_12_26_9']
 
-            # Identifying SMA crossover points as breakout signals
-            crossover_points = stock_data[(stock_data['SMA50'] > stock_data['SMA200']) & (stock_data['SMA50'].shift(1) < stock_data['SMA200'].shift(1))]
+            # Adjusting the section that identifies crossover points to handle NaN values.
+            if not stock_data[['SMA50', 'SMA200']].isna().all(axis=None):  # Check if not all values are NaN
+                valid_data = stock_data.dropna(subset=['SMA50', 'SMA200'])
+                crossover_points = valid_data[(valid_data['SMA50'] > valid_data['SMA200']) & (valid_data['SMA50'].shift(1) < valid_data['SMA200'].shift(1))]
+            else:
+                crossover_points = pd.DataFrame()  # Empty DataFrame if all SMA50 or SMA200 values are NaN
 
             # Plotting
             fig, ax = plt.subplots(2, 1, figsize=(10, 12), sharex=True)
 
-            # Price and SMAs
+            # Price, SMAs, and breakout points
             ax[0].plot(stock_data['Close'], label='Close Price', color='skyblue')
             ax[0].plot(stock_data['SMA50'], label='50-Day SMA', color='green')
             ax[0].plot(stock_data['SMA200'], label='200-Day SMA', color='red')