app.py

import streamlit as st
import requests
from Pandas_Market_Predictor import Pandas_Market_Predictor
import pandas as pd

# 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_ichimoku_cloud(data):
    short_window = 9
    long_window = 26
    span_b_window = 52
    displacement = 26

    data['tenkan_sen'] = (data['high'].rolling(window=short_window).max() + data['low'].rolling(window=short_window).min()) / 2
    data['kijun_sen'] = (data['high'].rolling(window=long_window).max() + data['low'].rolling(window=long_window).min()) / 2
    data['senkou_span_a'] = ((data['tenkan_sen'] + data['kijun_sen']) / 2).shift(displacement)
    data['senkou_span_b'] = ((data['high'].rolling(window=span_b_window).max() + data['low'].rolling(window=span_b_window).min()) / 2).shift(displacement)

    return data  # Make sure to return the updated DataFrame

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))

    data['MA5'] = data['Close'].rolling(window=5).mean()
    data['MA20'] = data['Close'].rolling(window=20).mean()

    data['26EMA'] = data['Close'].ewm(span=26).mean()
    data['12EMA'] = data['Close'].ewm(span=12).mean()
    data['MACD'] = data['12EMA'] - data['26EMA']

    # Calculate Ichimoku Cloud
    data = calculate_ichimoku_cloud(data)

    return data

def main():
    st.title("Stock Trend Predictor")

    api_key = API_KEY

    alpha_vantage_data = fetch_alpha_vantage_data(api_key)

    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)

    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})

    df = calculate_indicators(df)

    my_market_predictor = Pandas_Market_Predictor(df)

    indicators = ["Doji", "Inside", "MA5", "MA20", "MACD", "tenkan_sen", "kijun_sen", "senkou_span_a", "senkou_span_b"]
    trend = my_market_predictor.Trend_Detection(indicators, 10)

    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}%")

    del df

if __name__ == "__main__":
    main()