app.py

import streamlit as st
import requests
from Pandas_Market_Predictor import Pandas_Market_Predictor
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# 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=5min&apikey={api_key}'
    response = requests.get(url)
    alpha_vantage_data = response.json()
    return alpha_vantage_data

def calculate_indicators(data):
    data = data.apply(pd.to_numeric, errors='coerce')
    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 prepare_data(data, target_column='Close'):
    X = data.drop(target_column, axis=1)
    y = data[target_column]
    return X, y

def train_linear_regression(X_train, y_train):
    model = LinearRegression()
    model.fit(X_train, y_train)
    return model

def main():
    st.title("Stock Price 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 (5min)', {})
    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'})

    # Calculate indicators
    df = calculate_indicators(df)

    # Create predictor
    my_market_predictor = Pandas_Market_Predictor(df)

    # Prepare data for linear regression
    X, y = prepare_data(df)

    # Split data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train linear regression model
    model = train_linear_regression(X_train, y_train)

    # Make predictions on the test set
    y_pred = model.predict(X_test)

    # Display linear regression results
    st.subheader("Linear Regression Results:")
    st.write("Mean Squared Error:", mean_squared_error(y_test, y_pred))
    st.write("R-squared Score:", r2_score(y_test, y_pred))

    # Delete the DataFrame to release memory
    del df

if __name__ == "__main__":
    main()