File size: 1,445 Bytes
ab50b27 d31c699 69a18df d31c699 8a8357f 1226aa0 69a18df 0740f1b 69a18df 1226aa0 69a18df 1226aa0 69a18df 2d114dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
import streamlit as st
import requests
import pandas as pd
# Hard-coded API key for demonstration purposes
API_KEY = "QR8F9B7T6R2SWTAT"
def fetch_alpha_vantage_data(api_key, symbol):
try:
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
response = requests.get(url)
response.raise_for_status() # Raise an error for bad responses
alpha_vantage_data = response.json()
return alpha_vantage_data
except requests.RequestException as e:
st.error(f"Error fetching data: {e}")
return None
def main():
st.title("Stock Trend Predictor")
# User input for stock symbol
symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
if not symbol:
st.warning("Please enter a valid stock symbol.")
st.stop()
# Use the hard-coded API key
api_key = API_KEY
# Fetch Alpha Vantage data
alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
if alpha_vantage_data:
# Extract relevant data from Alpha Vantage response
alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
df = pd.DataFrame(alpha_vantage_time_series).T
df.index = pd.to_datetime(df.index)
df = df.dropna(axis=0)
# Display the raw data
st.subheader("Raw Data:")
st.write(df)
if __name__ == "__main__":
main() |