File size: 1,515 Bytes
3f96245 |
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 |
import streamlit as st
import requests
from Pandas_Market_Predictor import Pandas_Market_Predictor
import pandas as pd
def fetch_alpha_vantage_data(api_key):
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey={api_key}'
response = requests.get(url)
alpha_vantage_data = response.json()
return alpha_vantage_data
def main():
st.title("Stock Trend Predictor")
# Get Alpha Vantage API key from user input
api_key = st.text_input("Enter your Alpha Vantage API key:")
# Fetch Alpha Vantage data
if api_key:
alpha_vantage_data = fetch_alpha_vantage_data(api_key)
# 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)
# Create predictor
my_market_predictor = Pandas_Market_Predictor(df)
# Predict Trend
indicators = ["Indicator1", "Indicator2"]
trend = my_market_predictor.Trend_Detection(indicators, 10)
# Display results
st.subheader("Predicted Trend:")
st.write("Buy Trend :", trend['BUY'])
st.write("Sell Trend :", trend['SELL'])
st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
# Delete the DataFrame to release memory
del df
if __name__ == "__main__":
main() |