import streamlit as st
import yfinance as yf
import plotly.graph_objects as go
import pandas as pd

def fetch_data(ticker):
    data = yf.download(ticker, start='2020-01-01', end='2024-01-01')
    data['MA Fast'] = data['Close'].rolling(window=5).mean()
    data['MA Slow'] = data['Close'].rolling(window=10).mean()
    data['Upper Band'], data['Lower Band'] = data['Close'].rolling(20).mean() + 2*data['Close'].rolling(20).std(), data['Close'].rolling(20).mean() - 2*data['Close'].rolling(20).std()
    return data

def plot_data(data):
    fig = go.Figure()
    # Adding Candles
    fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='Candlesticks'))
    # Adding MA lines
    fig.add_trace(go.Scatter(x=data.index, y=data['MA Fast'], line=dict(color='blue', width=1.5), name='MA Fast'))
    fig.add_trace(go.Scatter(x=data.index, y=data['MA Slow'], line=dict(color='red', width=1.5), name='MA Slow'))
    # Adding Bollinger Bands
    fig.add_trace(go.Scatter(x=data.index, y=data['Upper Band'], line=dict(color='green', width=1), name='Upper Band'))
    fig.add_trace(go.Scatter(x=data.index, y=data['Lower Band'], line=dict(color='green', width=1), name='Lower Band'))

    # Identify buy and sell signals
    buys = data[(data['Close'] > data['Lower Band']) & (data['Close'] < data['MA Slow'])]
    sells = data[(data['Close'] < data['Upper Band']) & (data['Close'] > data['MA Fast'])]

    fig.add_trace(go.Scatter(x=buys.index, y=buys['Close'], mode='markers', marker=dict(color='yellow', size=10), name='Buy Signal'))
    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', marker=dict(color='purple', size=10), name='Sell Signal'))

    return fig

# Streamlit user interface
st.sidebar.header('BBMA Re-entry Strategy')
ticker = st.sidebar.text_input('Enter ticker symbol', value='AAPL')
button = st.sidebar.button('Analyze')

if button:
    data = fetch_data(ticker)
    fig = plot_data(data)
    st.plotly_chart(fig, use_container_width=True)