import gradio as gr
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime

# Ensure visualization styles are set for the plots
sns.set(style="whitegrid")
plt.rcParams['figure.figsize'] = [10, 5]

def backtest_ema_strategy(asset_symbol, short_ema, long_ema, timeframe='1d'):
    # Fetching financial data with dynamic timeframe and date range
    start_date = '2020-01-01'
    end_date = '2024-01-01'
    asset_data = yf.download(asset_symbol, start=start_date, end=end_date, interval=timeframe)
    
    # Check if data is empty (can happen with incorrect symbols or network issues)
    if asset_data.empty:
        return None, "Failed to fetch data. Please check the asset symbol and date range.", 0, 0

    # Calculate the short-term and long-term EMAs
    asset_data[f'EMA{short_ema}'] = asset_data['Close'].ewm(span=short_ema, adjust=False).mean()
    asset_data[f'EMA{long_ema}'] = asset_data['Close'].ewm(span=long_ema, adjust=False).mean()

    # Generate signals
    asset_data['Signal'] = np.where(asset_data[f'EMA{short_ema}'] > asset_data[f'EMA{long_ema}'], 1, 0)
    asset_data['Position'] = asset_data['Signal'].diff()

    # Plotting
    plt.figure(figsize=(14, 7))
    plt.plot(asset_data['Close'], label=f'{asset_symbol} Close', alpha=0.5)
    plt.plot(asset_data[f'EMA{short_ema}'], label=f'{short_ema}-day EMA', alpha=0.75)
    plt.plot(asset_data[f'EMA{long_ema}'], label=f'{long_ema}-day EMA', alpha=0.75)
    plt.plot(asset_data[asset_data['Position'] == 1].index, asset_data[f'EMA{short_ema}'][asset_data['Position'] == 1], '^', markersize=10, color='g', lw=0, label='Buy Signal')
    plt.plot(asset_data[asset_data['Position'] == -1].index, asset_data[f'EMA{short_ema}'][asset_data['Position'] == -1], 'v', markersize=10, color='r', lw=0, label='Sell Signal')
    plt.title(f'{asset_symbol} EMA Crossover Trading Strategy')
    plt.legend()
    plt_path = f"{asset_symbol}_EMA_Strategy.png"
    plt.savefig(plt_path)
    plt.close()

    # Calculating total and annual returns
    initial_price = asset_data['Close'].iloc[0]
    final_price = asset_data['Close'].iloc[-1]
    total_returns = ((final_price - initial_price) / initial_price) * 100
    years_diff = (datetime.strptime(end_date, "%Y-%m-%d") - datetime.strptime(start_date, "%Y-%m-%d")).days / 365.25
    annual_returns = ((final_price / initial_price) ** (1 / years_diff) - 1) * 100

    # Returning path of the saved plot, total and annual returns
    return plt_path, "Backtest completed message", total_returns, annual_returns


def create_gradio_interface(asset_symbol, short_ema, long_ema, timeframe):
    # Convert string inputs for EMAs to integers and pass all new parameters to the backtest strategy
    short_ema = int(short_ema)
    long_ema = int(long_ema)
    
    plot_path, message,total_returns, annual_returns = backtest_ema_strategy(asset_symbol, short_ema, long_ema, timeframe)
    
    # Prepare and return the results
    result_text = [
        [message], # f"Backtest completed for {asset_symbol} from {start_date} to {end_date} using {timeframe} data.",
        [f"Total Returns: {total_returns:.2f}%"],
        [f"Annual Returns: {annual_returns:.2f}%"]
    ]
    
    return plot_path, result_text


# Expanded list of major assets to include Indian indices and major stocks
major_assets = [
    # Global and Cryptocurrencies
    "AAPL",  # Apple
    "GOOGL",  # Alphabet
    "MSFT",  # Microsoft
    "AMZN",  # Amazon
    "FB",  # Facebook
    "TSLA",  # Tesla
    "BTC-USD",  # Bitcoin
    "ETH-USD",  # Ethereum
    # # Indian Indices and Major Stocks
    # "NIFTY50.NS",  # Nifty 50 Index
    # "RELIANCE.NS",  # Reliance Industries Limited
    # "TCS.NS",  # Tata Consultancy Services
    # "HDFCBANK.NS",  # HDFC Bank
    # "INFY.NS",  # Infosys
    # "HINDUNILVR.NS",  # Hindustan Unilever
    # "ITC.NS",  # ITC Limited
    # "SBIN.NS",  # State Bank of India
    # "ICICIBANK.NS",  # ICICI Bank
    # "BHARTIARTL.NS",  # Bharti Airtel
]

# Define the Gradio app with updated inputs
iface = gr.Interface(
    fn=create_gradio_interface,
    inputs=[
        gr.Dropdown(choices=major_assets, label="Asset Symbol (Select or type, e.g., NIFTY50.NS)"),
        gr.Textbox(label="Short EMA Period"),
        gr.Textbox(label="Long EMA Period"),
        gr.Dropdown(choices=['1d', '1wk', '1mo'], label="Timeframe (1d = daily, 1wk = weekly, 1mo = monthly)"),
        
    ],
    outputs=[
        gr.Image(label="Trading Strategy Chart"),
        gr.List(label="Results")
    ],
    title="EMA Crossover Trading Strategy Backtest",
    description="Enter the asset symbol, EMA periods, timeframe, and date range to backtest the EMA Crossover Trading Strategy."
)



# Launch the app
if __name__ == "__main__":
    iface.launch(share=True)