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

# Function to calculate moving averages
def calculate_moving_average(data, window_size):
    return data['Close'].rolling(window=window_size).mean()

# Function to detect support and resistance levels
def detect_support_resistance(data):
    # This is a simple placeholder for actual support and resistance logic
    return data['Close'].rolling(window=20).min(), data['Close'].rolling(window=20).max()

# VSA signals logic placeholder
def vsa_signals(data):
    # This should include real VSA calculation logic based on volume and spread
    buy_signals = pd.Series(index=data.index, dtype='float64')
    sell_signals = pd.Series(index=data.index, dtype='float64')
    # Dummy logic for demonstration:
    buy_signals[data['Volume'] > data['Volume'].rolling(20).mean()] = data['Low']
    sell_signals[data['Volume'] < data['Volume'].rolling(20).mean()] = data['High']
    return buy_signals, sell_signals

# Streamlit sidebar options
ticker = st.sidebar.text_input('Ticker Symbol', value='AAPL')
start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2020-01-01'))
end_date = st.sidebar.date_input('End Date', pd.to_datetime('2020-12-31'))
analyze_button = st.sidebar.button('Analyze')

if analyze_button:
    data = yf.download(ticker, start=start_date, end=end_date)
    if not data.empty:
        # Calculations
        moving_average = calculate_moving_average(data, window_size=20)
        support, resistance = detect_support_resistance(data)
        buy_signals, sell_signals = vsa_signals(data)

        # Plotting
        fig = go.Figure()

        # Add candlestick chart
        fig.add_trace(go.Candlestick(x=data.index,
                                     open=data['Open'],
                                     high=data['High'],
                                     low=data['Low'],
                                     close=data['Close'], name='Market Data'))

        # Add Moving Average Line
        fig.add_trace(go.Scatter(x=data.index, y=moving_average, mode='lines', name='20-day MA'))

        # Add Support and Resistance Lines
        fig.add_trace(go.Scatter(x=data.index, y=support, mode='lines', name='Support', line=dict(color='green')))
        fig.add_trace(go.Scatter(x=data.index, y=resistance, mode='lines', name='Resistance', line=dict(color='red')))

        # Add Buy and Sell Signals
        fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals, mode='markers', marker=dict(color='blue', size=10), name='Buy Signal'))
        fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals, mode='markers', marker=dict(color='orange', size=10), name='Sell Signal'))

        # Layout settings
        fig.update_layout(title='VSA Trading Strategy Analysis', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')

        # Display the figure
        st.plotly_chart(fig)

        # App introduction and guide
        st.title('VSA Trading Strategy Visualizer')
        st.markdown('''
        This app provides an interactive way to visualize the Volume Spread Analysis (VSA) trading strategy with buy and sell signals based on the strategy. 
        To start, enter the ticker symbol, select the start and end dates, and then click the "Analyze" button.
        The chart below will display the price action with overlays for moving averages, support and resistance levels, and buy/sell signals based on VSA analysis.
        ''')
    else:
        st.error('No data found for the selected ticker and date range.')