Create app

app

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+# Import necessary libraries
+import pandas as pd
+import yfinance as yf
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+
+def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
+    # Fetch the selected stock data using yfinance
+    df = yf.download(ticker, start=start_date, end=end_date, progress=False)
+    df = df[['Close']]  # Use only the 'Close' price for SMA calculations
+
+    # Calculate SMAs
+    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['Signal'][df['SMA_50'] > df['SMA_150']] = 1  # Buy signal
+    df['Signal'][df['SMA_50'] < df['SMA_150']] = -1  # Sell signal
+    df['Position'] = df['Signal'].diff()  # Capture points where the signal changes
+
+    # Initialize investment simulation
+    cash = initial_budget  # Start with user-specified cash
+    shares = 0  # No shares initially
+    portfolio_values = []  # Store portfolio values
+
+    # Iterate over the dataframe to simulate trading
+    for index, row in df.iterrows():
+        # Buy signal
+        if row['Position'] == 1 and cash > 0:
+            shares = cash / row['Close']
+            cash = 0  # All money is invested in shares
+
+        # Sell signal
+        elif row['Position'] == -1 and shares > 0:
+            cash = shares * row['Close']
+            shares = 0  # All shares are sold
+
+        # Calculate current portfolio value
+        portfolio_value = cash + (shares * row['Close'])
+        portfolio_values.append(portfolio_value)
+
+    # Add portfolio values to the dataframe for visualization
+    df = df.iloc[149:]  # Ignore rows with NaN values due to SMA calculations
+    df['Portfolio Value'] = portfolio_values[149:]  # Align portfolio values
+
+    # Plot Portfolio Value over time
+    plt.figure(figsize=(14, 8))
+    plt.plot(df['Portfolio Value'], label='Portfolio Value', color='purple')
+    plt.xlabel('Date')
+    plt.ylabel('Portfolio Value ($)')
+    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 file for display
+    plot_file = "portfolio_value_plot.png"
+    plt.savefig(plot_file)
+    plt.close()
+
+    # Final results
+    final_value = portfolio_values[-1]
+    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: ${profit_loss:.2f}
+    Percentage Return: {percentage_return:.2f}%
+    """
+    
+    return plot_file, results
+
+# Define Gradio interface components
+with gr.Blocks() as app:
+    gr.Markdown("# SMA Crossover Trading Strategy Simulator")
+
+    with gr.Tabs():
+        # Tab for SMA Strategy Simulation
+        with gr.Tab("SMA Strategy Simulator"):
+            with gr.Row():
+                initial_budget = gr.Number(label="Initial Investment ($)", value=100, interactive=True)
+                start_date = gr.Text(label="Start Date (YYYY-MM-DD)", value="1993-01-01", interactive=True)
+                end_date = gr.Text(label="End Date (YYYY-MM-DD)", value="2023-12-31", interactive=True)
+                ticker = gr.Dropdown(
+                    label="Stock Ticker Symbol",
+                    choices=["SPY", "TSLA", "GOOGL", "AAPL", "MSFT"],
+                    value="SPY",
+                )
+            run_button = gr.Button("Run Simulation")
+            with gr.Row():
+                portfolio_graph = gr.Image(label="Portfolio Value Over Time")
+                summary_text = gr.Textbox(label="Simulation Summary", lines=8)
+
+        # Tab for Instructions
+        with gr.Tab("Instructions"):
+            gr.Markdown("""
+            ## How to Use:
+            1. Enter your initial investment amount.
+            2. Specify the trading period (start and end dates).
+            3. Select a stock ticker symbol (e.g., SPY, TSLA, GOOGL).
+            4. Click "Run Simulation" to visualize the portfolio value over time and view a summary of results.
+            
+            ### Notes:
+            - The 50-day and 150-day SMAs are used for buy and sell signals.
+            - Ensure the trading period is valid for the selected ticker symbol.
+            """)
+
+    # Link simulation function to UI
+    run_button.click(
+        sma_crossover_strategy,
+        inputs=[initial_budget, start_date, end_date, ticker],
+        outputs=[portfolio_graph, summary_text],
+    )
+
+# Launch the app
+app.launch()