import streamlit as st
import requests
from thronetrader import StrategicSignals
from thronetrader.helper.squire import classify  # Import your classification method
import pandas as pd

def main():
    st.title("Strategic Trading Signals")

    # Input for stock symbol
    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")

    # Fetch Alpha Vantage data
    alpha_vantage_data = fetch_alpha_vantage_data(symbol)

    # 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)

    # Display strategic trading signals
    strategic_signals = StrategicSignals(df)

    st.subheader("Bollinger Bands Signals:")
    bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
    display_signals(bollinger_bands_signals)

    st.subheader("Breakout Signals:")
    breakout_signals = strategic_signals.get_breakout_signals()
    display_signals(breakout_signals)

    st.subheader("Crossover Signals:")
    crossover_signals = strategic_signals.get_crossover_signals()
    display_signals(crossover_signals)

    st.subheader("MACD Signals:")
    macd_signals = strategic_signals.get_macd_signals()
    display_signals(macd_signals)

    st.subheader("RSI Signals:")
    rsi_signals = strategic_signals.get_rsi_signals()
    display_signals(rsi_signals)

    # Example of using your classify method
    classification_result = classify(df, logger)  # Pass your DataFrame and logger
    st.subheader("Classification Result:")
    st.write(classification_result)

def fetch_alpha_vantage_data(symbol):
    api_key = "QR8F9B7T6R2SWTAT"
    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&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 display_signals(signals):
    for signal in signals:
        if isinstance(signal, dict):
            st.write(f"Date: {signal.get('date', 'N/A')}")
            st.write(f"Signal: {signal.get('signal', 'N/A')}")
        else:
            st.write("Invalid signal format.")

if __name__ == "__main__":
    main()