mm

app.py

@@ -4,45 +4,56 @@ import pandas as pd
 import plotly.graph_objs as go
 import numpy as np
 
-# Set the Streamlit app title and icon
-st.set_page_config(page_title="Stock Analysis", page_icon="📈")
+isPswdValid = False
+try:
+    pswdVal = st.experimental_get_query_params()['pwd'][0]
+    if pswdVal==st.secrets["PSWD"]:
+        isPswdValid = True
+except:
+    pass
 
-# Create a Streamlit sidebar for user input
-st.sidebar.title("Stock Analysis")
-ticker_symbol = st.sidebar.text_input("Enter Stock Ticker Symbol:", value='AAPL')
-start_date = st.sidebar.date_input("Start Date", pd.to_datetime('2020-01-01'))
-end_date = st.sidebar.date_input("End Date", pd.to_datetime('2021-01-01'))
+if not isPswdValid:
+    st.write("Invalid Password")
+else:
+    # Set the Streamlit app title and icon
+    st.set_page_config(page_title="Stock Analysis", page_icon="📈")
 
-# Fetch stock data from Yahoo Finance
-try:
-    stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
-except Exception as e:
-    st.error("Error fetching stock data. Please check the ticker symbol and date range.")
-
-# Display basic stock information
-st.header(f"Stock Analysis for {ticker_symbol}")
-st.subheader("Basic Stock Information")
-st.write(stock_data.tail())
-
-# Plot a candlestick chart
-st.subheader("Candlestick Chart")
-fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
-            open=stock_data['Open'],
-            high=stock_data['High'],
-            low=stock_data['Low'],
-            close=stock_data['Close'])])
-fig.update_layout(title=f'{ticker_symbol} Candlestick Chart', xaxis_title='Date', yaxis_title='Price')
-st.plotly_chart(fig)
-
-# Calculate basic statistics
-st.subheader("Basic Statistics")
-st.write(f"**Average Closing Price**: ${np.mean(stock_data['Close']):.2f}")
-st.write(f"**Minimum Closing Price**: ${np.min(stock_data['Close']):.2f}")
-st.write(f"**Maximum Closing Price**: ${np.max(stock_data['Close']):.2f}")
-st.write(f"**Total Volume Traded**: {np.sum(stock_data['Volume']):,} shares")
-
-# Add a text summary
-st.subheader("Stock Summary")
-st.write("This is a brief summary of the stock's performance.")
-st.write("You can add more in-depth analysis and insights here.")
-# You can use external libraries or APIs for more advanced analysis
+    # Create a Streamlit sidebar for user input
+    st.sidebar.title("Stock Analysis")
+    ticker_symbol = st.sidebar.text_input("Enter Stock Ticker Symbol:", value='AAPL')
+    start_date = st.sidebar.date_input("Start Date", pd.to_datetime('2020-01-01'))
+    end_date = st.sidebar.date_input("End Date", pd.to_datetime('2021-01-01'))
+
+    # Fetch stock data from Yahoo Finance
+    try:
+        stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
+    except Exception as e:
+        st.error("Error fetching stock data. Please check the ticker symbol and date range.")
+
+    # Display basic stock information
+    st.header(f"Stock Analysis for {ticker_symbol}")
+    st.subheader("Basic Stock Information")
+    st.write(stock_data.tail())
+
+    # Plot a candlestick chart
+    st.subheader("Candlestick Chart")
+    fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
+                open=stock_data['Open'],
+                high=stock_data['High'],
+                low=stock_data['Low'],
+                close=stock_data['Close'])])
+    fig.update_layout(title=f'{ticker_symbol} Candlestick Chart', xaxis_title='Date', yaxis_title='Price')
+    st.plotly_chart(fig)
+
+    # Calculate basic statistics
+    st.subheader("Basic Statistics")
+    st.write(f"**Average Closing Price**: ${np.mean(stock_data['Close']):.2f}")
+    st.write(f"**Minimum Closing Price**: ${np.min(stock_data['Close']):.2f}")
+    st.write(f"**Maximum Closing Price**: ${np.max(stock_data['Close']):.2f}")
+    st.write(f"**Total Volume Traded**: {np.sum(stock_data['Volume']):,} shares")
+
+    # Add a text summary
+    st.subheader("Stock Summary")
+    st.write("This is a brief summary of the stock's performance.")
+    st.write("You can add more in-depth analysis and insights here.")
+    # You can use external libraries or APIs for more advanced analysis