Update app.py

app.py

@@ -5,28 +5,27 @@ import matplotlib.pyplot as plt
 import io
 import gradio as gr
 
-def sma_crossover_strategy():
-    # Fetch the S&P 500 ETF (SPY) data using yfinance
-    df = yf.download('SPY', start='1993-01-01', end='2023-12-31', progress=False)
-    df = df[['Close']]  # Use only the 'Close' price for SMA calculations
-
-    # Calculate SMAs
+def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
+    try:
+        df = yf.download(ticker, start=start_date, end=end_date, progress=False)
+        if df.empty:
+            return None, "No data available for the specified ticker and date range."
+    except Exception as e:
+        return None, f"Error fetching data: {str(e)}"
+
+    df = df[['Close']]
     df['SMA_50'] = df['Close'].rolling(window=50).mean()
     df['SMA_150'] = df['Close'].rolling(window=150).mean()
 
-    # Determine buy and sell signals
-    df['Signal'] = 0  # Initialize a column for signals
-    df.loc[df['SMA_50'] > df['SMA_150'], 'Signal'] = 1  # Buy signal
-    df.loc[df['SMA_50'] < df['SMA_150'], 'Signal'] = -1  # Sell signal
-    df['Position'] = df['Signal'].diff()  # Capture the points where the signal changes
+    df['Signal'] = 0
+    df.loc[df['SMA_50'] > df['SMA_150'], 'Signal'] = 1
+    df.loc[df['SMA_50'] < df['SMA_150'], 'Signal'] = -1
+    df['Position'] = df['Signal'].diff()
 
-    # Initialize investment simulation
-    initial_budget = 100
     cash = initial_budget
     shares = 0
     portfolio_values = []
 
-    # Simulate the strategy
     for index, row in df.iterrows():
         if pd.isna(row['Close']):
             continue
@@ -39,53 +38,60 @@ def sma_crossover_strategy():
         portfolio_value = cash + (shares * row['Close'])
         portfolio_values.append(portfolio_value)
 
-    # Add portfolio values to the dataframe
-    df = df.iloc[149:]  # Ignore the initial rows with NaN values due to SMA calculations
+    df = df.iloc[149:]  # Skip rows without SMA values
     df['Portfolio Value'] = portfolio_values[149:]
 
-    # Create plot
     plt.figure(figsize=(14, 8))
     plt.plot(df['Portfolio Value'], label='Portfolio Value', color='purple')
     plt.xlabel('Date')
     plt.ylabel('Portfolio Value ($)')
-    plt.title('Portfolio Value Over Time with 50/150 SMA Crossover Strategy')
+    plt.title(f'Portfolio Value Over Time with 50/150 SMA Crossover Strategy ({ticker})')
     plt.legend()
     plt.grid()
     plt.tight_layout()
 
-    # Save plot to a buffer
     plot_file = io.BytesIO()
     plt.savefig(plot_file, format='png')
     plot_file.seek(0)
     plt.close()
 
-    # Final results
     final_value = portfolio_values[-1]
-    summary = f"""
+    profit_loss = final_value - initial_budget
+    percentage_return = (profit_loss / initial_budget) * 100
+
+    results = f"""
+    Ticker: {ticker}
+    Trading Period: {start_date} to {end_date}
     Initial Investment: ${initial_budget}
     Final Portfolio Value: ${final_value:.2f}
-    Total Profit/Loss: ${final_value - initial_budget:.2f}
-    Percentage Return: {((final_value - initial_budget) / initial_budget) * 100:.2f}%
+    Total Profit/Loss: ${profit_loss:.2f}
+    Percentage Return: {percentage_return:.2f}%
     """
 
-    return plot_file, summary
+    return plot_file, results
 
-# Define Gradio Interface
+# Define Gradio App
 with gr.Blocks() as app:
-    gr.Markdown("# SMA Crossover Trading Strategy")
-
-    portfolio_plot = gr.Image()
-    summary_text = gr.Textbox(lines=5)
+    gr.Markdown("# SMA Crossover Trading Strategy Simulator")
 
     with gr.Row():
-        simulate_button = gr.Button("Run Strategy")
-
-    simulate_button.click(
+        initial_budget = gr.Number(label="Initial Investment ($)", value=100)
+        start_date = gr.Text(label="Start Date (YYYY-MM-DD)", value="1993-01-01")
+        end_date = gr.Text(label="End Date (YYYY-MM-DD)", value="2023-12-31")
+        ticker = gr.Dropdown(
+            label="Stock Ticker Symbol",
+            choices=["SPY", "TSLA", "GOOGL", "AAPL", "MSFT"],
+            value="SPY",
+        )
+    run_button = gr.Button("Run Simulation")
+    portfolio_graph = gr.Image(label="Portfolio Value Over Time")
+    summary_text = gr.Textbox(label="Simulation Summary", lines=8)
+
+    run_button.click(
         sma_crossover_strategy,
-        inputs=[],
-        outputs=[portfolio_plot, summary_text]
+        inputs=[initial_budget, start_date, end_date, ticker],
+        outputs=[portfolio_graph, summary_text],
     )
 
-app.launch()
-
+app.launch(server_port=7861)  # No share=True