import streamlit as st
import yfinance as yf
import plotly.graph_objs as go
from datetime import datetime

# Function to load data
def load_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date)
    data.reset_index(inplace=True)
    return data

# Function to identify buy and sell signals
def identify_signals(data):
    # Assume bearish and bullish candles that are significantly larger than usual as signals
    data['Signal'] = 'None'  # Default no signal
    avg_body_size = (abs(data['Open'] - data['Close'])).mean()  # Average body size of the candles
    
    for i in range(1, len(data)):
        body_size = abs(data['Open'][i] - data['Close'][i])
        if body_size > avg_body_size * 1.5:  # Threshold of 150% of average body size
            if data['Close'][i] < data['Open'][i]:
                data['Signal'][i] = 'Sell'  # Bearish candle
            elif data['Close'][i] > data['Open'][i]:
                data['Signal'][i] = 'Buy'  # Bullish candle

    return data

# Function to plot candlestick chart with signals
def plot_candlestick_chart(data):
    fig = go.Figure(data=[go.Candlestick(
        x=data['Date'],
        open=data['Open'], high=data['High'],
        low=data['Low'], close=data['Close'],
        increasing_line_color='green', decreasing_line_color='red',
        name='Candlestick')])

    # Add signals to the plot
    buys = data[data['Signal'] == 'Buy']
    sells = data[data['Signal'] == 'Sell']
    for i in buys.index:
        fig.add_annotation(x=buys['Date'][i], y=buys['High'][i],
                           text='Buy', showarrow=True, arrowhead=1, arrowcolor='green', arrowsize=3)
    for i in sells.index:
        fig.add_annotation(x=sells['Date'][i], y=sells['Low'][i],
                           text='Sell', showarrow=True, arrowhead=1, arrowcolor='red', arrowsize=3)

    fig.update_layout(title='Candlestick Chart with Buy and Sell Signals', xaxis_rangeslider_visible=False)
    return fig

# Streamlit user interface
st.sidebar.header('User Input Features')
ticker = st.sidebar.text_input('Ticker', 'AAPL')
start_date = st.sidebar.date_input('Start Date', datetime(2020, 1, 1))
end_date = st.sidebar.date_input('End Date', datetime.today())

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

st.title('Fight the Tiger Trading Strategy Visualization')
st.markdown("""
This app analyzes and visualizes the "Fight the Tiger" trading strategy using historical stock data fetched from Yahoo Finance.
Enter a stock ticker and select a date range to view the candlestick chart with potential buy and sell signals based on significant candlestick formations.
""")

if button:
    if start_date < end_date:
        data = load_data(ticker, start_date, end_date)
        data = identify_signals(data)  # Call to identify signals
        fig = plot_candlestick_chart(data)
        st.plotly_chart(fig, use_container_width=True)
    else:
        st.error('Error: End date must be after start date.')