Update app.py

app.py

@@ -1,4 +1,3 @@
-# Import necessary libraries
 import pandas as pd
 import yfinance as yf
 import numpy as np
@@ -6,46 +5,35 @@ 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
+    df = df[['Close']]
 
-    # 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
+    df['Signal'] = 0
+    df['Signal'][df['SMA_50'] > df['SMA_150']] = 1
+    df['Signal'][df['SMA_50'] < df['SMA_150']] = -1
+    df['Position'] = df['Signal'].diff()
 
-    # Initialize investment simulation
-    cash = initial_budget  # Start with user-specified cash
-    shares = 0  # No shares initially
-    portfolio_values = []  # Store portfolio values
+    cash = initial_budget
+    shares = 0
+    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
+            cash = 0
         elif row['Position'] == -1 and shares > 0:
             cash = shares * row['Close']
-            shares = 0  # All shares are sold
+            shares = 0
 
-        # 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
+    df = df.iloc[149:]
+    df['Portfolio Value'] = portfolio_values[149:]
 
-    # Plot Portfolio Value over time
     plt.figure(figsize=(14, 8))
     plt.plot(df['Portfolio Value'], label='Portfolio Value', color='purple')
     plt.xlabel('Date')
@@ -55,17 +43,14 @@ def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
     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
 
-    # Create summary text
     results = f"""
     Ticker: {ticker}
     Trading Period: {start_date} to {end_date}
@@ -75,19 +60,16 @@ def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
     Percentage Return: {percentage_return:.2f}%
     """
 
-    # Save results to a text file
     results_file = "simulation_results.txt"
     with open(results_file, "w") as f:
         f.write(results)
 
     return plot_file, results, results_file
 
-# 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)
@@ -104,7 +86,6 @@ with gr.Blocks() as app:
                 summary_text = gr.Textbox(label="Simulation Summary", lines=8)
                 download_button = gr.File(label="Download Results (.txt)")
 
-        # Tab for Instructions
         with gr.Tab("Instructions"):
             gr.Markdown("""
             ## How to Use:
@@ -113,19 +94,12 @@ with gr.Blocks() as app:
             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.
             5. Download the results as a `.txt` file using the download button.
-            
-            ### 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, download_button],
     )
 
-# Launch the app
 app.launch()
-