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import streamlit as st
import requests
from Pandas_Market_Predictor import Pandas_Market_Predictor
import pandas as pd
from sklearn.decomposition import PCA
# Hard-coded API key for demonstration purposes
API_KEY = "QR8F9B7T6R2SWTAT"
def fetch_alpha_vantage_data(api_key):
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=60min&apikey={api_key}'
response = requests.get(url)
alpha_vantage_data = response.json()
return alpha_vantage_data
def calculate_indicators(data):
# Convert all columns to numeric
data = data.apply(pd.to_numeric, errors='coerce')
# Example: Simple condition for doji and inside
data['Doji'] = abs(data['Close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
return data
def apply_pca(data, n_components=5):
pca = PCA(n_components=n_components)
reduced_data = pca.fit_transform(data)
return pd.DataFrame(reduced_data, index=data.index)
def main():
st.title("Stock Trend Predictor")
# Use the hard-coded API key
api_key = API_KEY
# Fetch Alpha Vantage data
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 (60min)', {})
df = pd.DataFrame(alpha_vantage_time_series).T
df.index = pd.to_datetime(df.index)
df = df.dropna(axis=0)
# Rename columns
df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
# Apply PCA for dimensionality reduction
df_reduced = apply_pca(df)
# Calculate indicators on reduced data
df_reduced = calculate_indicators(df_reduced)
# Create predictor
my_market_predictor = Pandas_Market_Predictor(df_reduced)
# Predict Trend
indicators = ["Doji", "Inside"]
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() |