Update app.py

app.py

@@ -1,19 +1,19 @@
 import streamlit as st
 import requests
 import pandas as pd
-from datetime import datetime
+from datetime import datetime, timedelta
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
-def fetch_alpha_vantage_data(api_key, symbol):
-    url = f'https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={api_key}'
+def fetch_alpha_vantage_intraday(api_key, symbol):
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=1min&apikey={api_key}'
     response = requests.get(url)
     alpha_vantage_data = response.json()
     return alpha_vantage_data
 
 def main():
-    st.title("Real-Time Stock Data")
+    st.title("Latest Traded Data (Last Hour)")
 
     # User input for stock symbol
     symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
@@ -25,22 +25,28 @@ def main():
     # Use the hard-coded API key
     api_key = API_KEY
 
-    # Continuously fetch and display real-time data
-    while True:
-        # Fetch Alpha Vantage data
-        alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
+    # Set the time interval for fetching historical intraday data
+    interval = 1  # 1-minute intervals
 
-        # Extract relevant data from Alpha Vantage response
-        alpha_vantage_quote = alpha_vantage_data.get('Global Quote', {})
-        df = pd.DataFrame([alpha_vantage_quote])
-        df.index = [datetime.now()]  # Use the current timestamp as the index
-        df = df.dropna(axis=0)
+    # Fetch Alpha Vantage intraday data
+    alpha_vantage_data = fetch_alpha_vantage_intraday(api_key, symbol)
 
-        # Display the real-time data
-        st.subheader("Real-Time Data:")
-        st.write(df)
+    # Extract relevant data from Alpha Vantage response
+    alpha_vantage_time_series = alpha_vantage_data.get(f'Time Series ({interval}min)', {})
+    df = pd.DataFrame(alpha_vantage_time_series).T
+    df.index = pd.to_datetime(df.index)
 
-       
+    # Add a new column for the trading day
+    df['Trading Day'] = df.index.date
+
+    # Filter data for the last hour
+    current_time = datetime.now()
+    one_hour_ago = current_time - timedelta(hours=1)
+    filtered_df = df[df.index >= one_hour_ago]
+
+    # Display the latest traded data for the last hour including the trading day
+    st.subheader("Latest Traded Data (Last Hour):")
+    st.write(filtered_df[['Trading Day', 'open', 'high', 'low', 'close', 'volume']].tail(1))
 
 if __name__ == "__main__":
     main()