Create app.py

app.py

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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)