Spaces:
Sleeping
Sleeping
File size: 3,237 Bytes
97f6d3d 3c87c5c 640f727 8c28401 97f6d3d b9eb3a2 8c28401 97f6d3d a11b754 97f6d3d ae3466a 3c87c5c 97f6d3d a11b754 3c87c5c 97f6d3d a11b754 97f6d3d 8c28401 a11b754 97f6d3d 8c28401 97f6d3d ae3466a 97f6d3d 8c28401 97f6d3d 8c28401 97f6d3d c51bf55 8c28401 97f6d3d 8c28401 97f6d3d 8c28401 97f6d3d 3c87c5c 8c28401 97f6d3d 8c28401 97f6d3d 71746cb ae3466a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
import pandas as pd
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
import io
import gradio as gr
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()
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()
cash = initial_budget
shares = 0
portfolio_values = []
for index, row in df.iterrows():
if pd.isna(row['Close']):
continue
if row['Position'] == 1 and cash > 0:
shares = cash / row['Close']
cash = 0
elif row['Position'] == -1 and shares > 0:
cash = shares * row['Close']
shares = 0
portfolio_value = cash + (shares * row['Close'])
portfolio_values.append(portfolio_value)
df = df.iloc[149:] # Skip rows without SMA values
df['Portfolio Value'] = portfolio_values[149:]
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()
plot_file = io.BytesIO()
plt.savefig(plot_file, format='png')
plot_file.seek(0)
plt.close()
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: ${profit_loss:.2f}
Percentage Return: {percentage_return:.2f}%
"""
return plot_file, results
# Define Gradio App
with gr.Blocks() as app:
gr.Markdown("# SMA Crossover Trading Strategy Simulator")
with gr.Row():
initial_budget = gr.Number(label="Initial Investment ($)", 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=[initial_budget, start_date, end_date, ticker],
outputs=[portfolio_graph, summary_text],
)
# Ensure only one app.launch() is called
if __name__ == "__main__":
app.launch()
|