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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()
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