Update app.py

app.py

@@ -3,43 +3,53 @@ import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 
-# Sidebar inputs for stock symbol and period
+# Sidebar inputs for stock symbol
 sidebar = st.sidebar
 symbol = sidebar.text_input("Enter stock symbol:", "AAPL")
 data = yf.download(symbol, start="2020-01-01", end="2021-01-01")
 
-# New sidebar inputs for defining the Fibonacci period
-fib_start_date = sidebar.date_input("Select Fibonacci start date", value=pd.to_datetime("2020-01-01"), min_value=pd.to_datetime(data.index.min()), max_value=pd.to_datetime(data.index.max()))
-fib_end_date = sidebar.date_input("Select Fibonacci end date", value=pd.to_datetime("2021-01-01"), min_value=pd.to_datetime(data.index.min()), max_value=pd.to_datetime(data.index.max()))
-
-# Ensure start date is before end date
-if fib_start_date >= fib_end_date:
-    st.error('Error: End date must be after start date.')
+# Check if data is empty
+if data.empty:
+    st.error("No data available for the selected symbol. Please try another symbol.")
 else:
-    # Filter data for the selected Fibonacci period
-    fib_data = data.loc[fib_start_date:fib_end_date]
-
-    # Find high and low prices within the Fibonacci period
-    high_price = fib_data['High'].max()
-    low_price = fib_data['Low'].min()
-
-    # Calculate Fibonacci Levels
-    price_diff = high_price - low_price
-    fib_levels = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1]
-    fib_values = [high_price - price_diff * level for level in fib_levels]
-
-    # Plotting
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='black')))
-
-    # Plot Fibonacci levels
-    for i, val in enumerate(fib_values):
-        fig.add_hline(y=val, line_dash="dot", annotation_text=f'Fib {fib_levels[i]*100}%', annotation_position="right")
-
-    # Calculate and plot moving averages
-    for ma in [20, 50, 200]:
-        data[f'MA{ma}'] = data['Close'].rolling(window=ma).mean()
-        fig.add_trace(go.Scatter(x=data.index, y=data[f'MA{ma}'], name=f'{ma}-Period MA'))
-
-    # Display the chart
-    st.plotly_chart(fig)
+    # Determine the valid date range for Fibonacci analysis
+    min_date, max_date = data.index.min().date(), data.index.max().date()
+
+    # Dynamic default dates based on data range
+    default_start, default_end = min_date, max_date
+
+    # Sidebar inputs for Fibonacci time frame
+    fib_start_date = sidebar.date_input("Fibonacci Start Date", value=default_start, min_value=min_date, max_value=max_date)
+    fib_end_date = sidebar.date_input("Fibonacci End Date", value=default_end, min_value=min_date, max_value=max_date)
+
+    if fib_start_date >= fib_end_date:
+        st.error("Error: The start date must be before the end date.")
+    else:
+        # Filter data for Fibonacci analysis
+        fib_data = data.loc[fib_start_date:fib_end_date]
+
+        # Calculate high and low prices for Fibonacci levels
+        high_price = fib_data['High'].max()
+        low_price = fib_data['Low'].min()
+
+        # Fibonacci retracement levels
+        fib_levels = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1]
+        price_diff = high_price - low_price
+
+        # Initialize figure for plotting
+        fig = go.Figure()
+
+        # Plot close prices and moving averages
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='black')))
+        for ma in [20, 50, 200]:
+            ma_label = f"MA{ma}"
+            data[ma_label] = data['Close'].rolling(window=ma).mean()
+            fig.add_trace(go.Scatter(x=data.index, y=data[ma_label], name=ma_label, line=dict(width=2)))
+
+        # Plot Fibonacci levels
+        for level in fib_levels:
+            value = high_price - (price_diff * level)
+            fig.add_hline(y=value, line_dash="dot", annotation_text=f"{level*100}%", annotation_position="right")
+
+        # Display chart
+        st.plotly_chart(fig)