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

def fetch_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date, interval='60m')
    data['MA Fast'] = data['Close'].rolling(window=5).mean()
    data['MA Slow'] = data['Close'].rolling(window=10).mean()
    data['Upper Band'] = data['Close'].rolling(window=20).mean() + 2*data['Close'].rolling(20).std()
    data['Lower Band'] = data['Close'].rolling(window=20).mean() - 2*data['Close'].rolling(20).std()
    return data

def plot_data(data):
    fig = go.Figure()
    fig.add_trace(go.Candlestick(x=data.index,
                                 open=data['Open'], high=data['High'],
                                 low=data['Low'], close=data['Close'],
                                 name='Candlesticks'))
    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'))
    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'))

    # Buy and sell signals based on BBMA logic
    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.title("BBMA Scalping Strategy Visualizer")
st.markdown("""
This application visualizes the BBMA Scalping Strategy for selected stocks. 
Enter the stock ticker, choose a start and end date, and press 'Analyze' to view the strategy's buy and sell signals overlaid on the price chart.
""")

st.sidebar.header('Input Parameters')
ticker = st.sidebar.text_input('Enter ticker symbol', value='AAPL')
start_date = st.sidebar.date_input('Start Date', value=pd.to_datetime('2021-01-01'))
end_date = st.sidebar.date_input('End Date', value=pd.to_datetime('today'))

button = st.sidebar.button('Analyze')

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