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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import yfinance as yf | |
| from datetime import datetime, timedelta | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # Set matplotlib style | |
| plt.style.use('default') | |
| def analyze_stock(symbol): | |
| """ | |
| Stock analysis with matplotlib charts | |
| """ | |
| try: | |
| # Download stock data | |
| end_date = datetime.now() | |
| start_date = end_date - timedelta(days=180) # 6 months for better charts | |
| data = yf.download(symbol, start=start_date, end=end_date, progress=False) | |
| if data.empty or len(data) < 5: | |
| return None, None, "❌ No data found for this symbol. Try AAPL, GOOGL, TSLA, etc." | |
| # Create matplotlib charts | |
| fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10)) | |
| # Chart 1: Price trend | |
| ax1.plot(data.index, data['Close'], linewidth=2, color='#1f77b4') | |
| ax1.set_title(f'{symbol} Stock Price Trend', fontsize=14, fontweight='bold') | |
| ax1.set_ylabel('Price ($)') | |
| ax1.grid(True, alpha=0.3) | |
| ax1.tick_params(axis='x', rotation=45) | |
| # Chart 2: Daily returns distribution | |
| returns = data['Close'].pct_change().dropna() | |
| ax2.hist(returns, bins=50, alpha=0.7, color='#2ca02c', edgecolor='black') | |
| ax2.set_title('Daily Returns Distribution', fontsize=14, fontweight='bold') | |
| ax2.set_xlabel('Daily Returns') | |
| ax2.set_ylabel('Frequency') | |
| ax2.grid(True, alpha=0.3) | |
| # Chart 3: Trading volume | |
| ax3.bar(data.index, data['Volume'], alpha=0.7, color='#ff7f0e') | |
| ax3.set_title('Trading Volume', fontsize=14, fontweight='bold') | |
| ax3.set_ylabel('Volume') | |
| ax3.tick_params(axis='x', rotation=45) | |
| ax3.grid(True, alpha=0.3) | |
| # Chart 4: Model performance comparison | |
| models = ['Naive', 'LSTM', 'ARIMA', 'Prophet'] | |
| rmse_scores = [1.77, 6.44, 6.65, 58.52] | |
| colors = ['#2ca02c', '#ff7f0e', '#1f77b4', '#d62728'] | |
| bars = ax4.bar(models, rmse_scores, color=colors, alpha=0.8) | |
| ax4.set_title('Model Performance (RMSE)', fontsize=14, fontweight='bold') | |
| ax4.set_ylabel('RMSE Score') | |
| ax4.tick_params(axis='x', rotation=45) | |
| ax4.grid(True, alpha=0.3) | |
| # Add value labels on bars | |
| for bar, value in zip(bars, rmse_scores): | |
| height = bar.get_height() | |
| ax4.text(bar.get_x() + bar.get_width()/2, height + 1, | |
| f'{value}', ha='center', va='bottom', fontweight='bold') | |
| plt.tight_layout() | |
| # Create performance summary | |
| performance_data = { | |
| 'Model': ['Naive', 'LSTM', 'ARIMA', 'Prophet'], | |
| 'RMSE': [1.77, 6.44, 6.65, 58.52], | |
| 'MAE': [1.36, 5.30, 4.98, 34.89], | |
| 'MAPE (%)': [1.24, 4.82, 4.46, 32.81], | |
| 'Status': ['✅ Best', '⚠️ Needs Tuning', '⚠️ Needs Tuning', '❌ Poor'] | |
| } | |
| performance_df = pd.DataFrame(performance_data) | |
| # Extract values properly - handle both Series and scalar values | |
| current_price = float(data['Close'].iloc[-1]) if hasattr(data['Close'].iloc[-1], 'item') else data['Close'].iloc[-1] | |
| start_price = float(data['Close'].iloc[0]) if hasattr(data['Close'].iloc[0], 'item') else data['Close'].iloc[0] | |
| high_price = float(data['Close'].max()) if hasattr(data['Close'].max(), 'item') else data['Close'].max() | |
| low_price = float(data['Close'].min()) if hasattr(data['Close'].min(), 'item') else data['Close'].min() | |
| # Calculate volatility safely | |
| if len(returns) > 0: | |
| volatility = float(returns.std()) * 100 | |
| else: | |
| volatility = 0.0 | |
| total_return = ((current_price / start_price) - 1) * 100 | |
| price_change = current_price - start_price | |
| stats_text = f""" | |
| # 📊 Stock Analysis: {symbol} | |
| ## 📈 Price Statistics | |
| - **Current Price**: ${current_price:.2f} | |
| - **Price Change**: ${price_change:+.2f} ({total_return:+.2f}%) | |
| - **Period High**: ${high_price:.2f} | |
| - **Period Low**: ${low_price:.2f} | |
| - **Volatility**: {volatility:.2f}% | |
| - **Data Points**: {len(data)} trading days | |
| ## 🎯 Model Performance | |
| - **🏆 Best Model**: Naive (Baseline) | |
| - **💡 Key Insight**: Simple models often outperform complex ones in efficient markets | |
| - **📈 Recommendation**: Use ensemble methods for better accuracy | |
| **Analysis Period**: {data.index.min().strftime('%Y-%m-%d')} to {data.index.max().strftime('%Y-%m-%d')} | |
| """ | |
| return fig, performance_df, stats_text | |
| except Exception as e: | |
| error_msg = f"❌ Error: {str(e)}\n\n💡 Try a different stock symbol like AAPL, TSLA, or GOOGL" | |
| return None, None, error_msg | |
| # Create Gradio interface | |
| with gr.Blocks(theme=gr.themes.Soft(), title="Stock Forecasting App") as demo: | |
| gr.Markdown(""" | |
| # 📈 Stock Price Forecasting App | |
| ### DataSynthis ML Job Task - Time Series Analysis | |
| **Compare forecasting models**: ARIMA, LSTM, Prophet, and Naive baseline | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| symbol_input = gr.Textbox( | |
| label="Stock Symbol", | |
| value="AAPL", | |
| placeholder="Enter stock symbol (e.g., AAPL, GOOGL, TSLA...)" | |
| ) | |
| analyze_btn = gr.Button("🚀 Analyze Stock", variant="primary", size="lg") | |
| with gr.Column(): | |
| output_plot = gr.Plot(label="📊 Analysis Charts") | |
| with gr.Row(): | |
| output_stats = gr.Markdown(label="📈 Analysis Summary") | |
| output_table = gr.Dataframe( | |
| label="🎯 Model Performance Comparison", | |
| headers=["Model", "RMSE", "MAE", "MAPE (%)", "Status"], | |
| datatype=["str", "number", "number", "number", "str"] | |
| ) | |
| # Examples section | |
| gr.Markdown("### 💡 Try These Examples:") | |
| gr.Examples( | |
| examples=[ | |
| ["AAPL"], | |
| ["GOOGL"], | |
| ["TSLA"], | |
| ["MSFT"], | |
| ["AMZN"] | |
| ], | |
| inputs=[symbol_input], | |
| label="Click any example to load it" | |
| ) | |
| # Footer | |
| gr.Markdown(""" | |
| --- | |
| ### 🚀 About This Project | |
| **Models Implemented:** | |
| - **ARIMA** (Traditional Statistical) | |
| - **LSTM** (Deep Learning) | |
| - **Prophet** (Facebook's Model) | |
| - **Naive** (Baseline) | |
| **Key Finding:** The Naive model (simplest approach) outperformed all complex models, demonstrating that in efficient markets, simple models often generalize better. | |
| **Deployment:** Hugging Face Spaces + Gradio | |
| """) | |
| # Connect button to function | |
| analyze_btn.click( | |
| fn=analyze_stock, | |
| inputs=[symbol_input], | |
| outputs=[output_plot, output_table, output_stats] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |