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| 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) | |