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| import gradio as gr | |
| import pandas as pd | |
| import yfinance as yf | |
| import plotly.graph_objects as go | |
| from plotly.colors import n_colors | |
| from datetime import datetime, timedelta | |
| # Load stock data | |
| def load_stocks(): | |
| df = pd.read_csv('TASE_stock_list_2023.csv') | |
| df['Symbol'] = df['Symbol'] + '.TA' | |
| return df | |
| # Get stock data and calculate percentage change | |
| def get_stock_data(start_date, end_date, stocks): | |
| data = {} | |
| for _, row in stocks.iterrows(): | |
| symbol = row['Symbol'] | |
| name = row['Name'] | |
| stock = yf.Ticker(symbol) | |
| hist = stock.history(start=start_date, end=end_date) | |
| if not hist.empty: | |
| initial_price = hist['Close'].iloc[0] | |
| final_price = hist['Close'].iloc[-1] | |
| pct_change = ((final_price - initial_price) / initial_price) * 100 | |
| if pct_change < 100: # Only include stocks with pct change below 100 | |
| data[symbol] = { | |
| 'name': name, | |
| 'initial_price': initial_price, | |
| 'final_price': final_price, | |
| 'pct_change': pct_change | |
| } | |
| return data | |
| # Create bar chart | |
| def create_chart(data): | |
| sorted_data = dict(sorted(data.items(), key=lambda x: x[1]['pct_change'], reverse=True)[:100]) # Top 100 stocks | |
| fig = go.Figure() | |
| # Generate a color scale from light green to dark green | |
| colors = n_colors('rgb(144,238,144)', 'rgb(0,100,0)', len(sorted_data), colortype='rgb') | |
| # Reverse the color order so that higher values get darker colors | |
| colors = colors[::-1] | |
| fig.add_trace(go.Bar( | |
| x=list(sorted_data.keys()), | |
| y=[v['pct_change'] for v in sorted_data.values()], | |
| text=[f"{v['pct_change']:.2f}%" for v in sorted_data.values()], | |
| textposition='inside', | |
| textfont=dict(color='white'), # Set text color to white for all bars | |
| hovertext=[ | |
| f"{v['name']}<br>Symbol: {k}<br>Initial: {v['initial_price']:.2f}<br>Final: {v['final_price']:.2f}<br>Change: {v['pct_change']:.2f}%" | |
| for k, v in sorted_data.items()], | |
| hoverinfo='text', | |
| marker_color=colors | |
| )) | |
| title = 'Stocks Performance' | |
| fig.update_layout( | |
| title=title, | |
| xaxis_title='Stocks', | |
| yaxis_title='Percentage Change', | |
| hoverlabel=dict( | |
| bgcolor="black", | |
| font_size=12, | |
| font_family="Rockwell", | |
| font_color="white" # This ensures all hover text is white | |
| ) | |
| ) | |
| return fig | |
| # Main function | |
| def analyze_stocks(year, month, day, num_days): | |
| stocks = load_stocks() | |
| start_date = datetime(year, month, day) | |
| end_date = start_date + timedelta(days=num_days) | |
| data = get_stock_data(start_date, end_date, stocks) | |
| chart = create_chart(data) | |
| return chart | |
| # Set up Gradio interface | |
| current_date = datetime.now() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# TASE Stock Performance Analyzer") | |
| gr.Markdown("Analyze stock performances based on date range.") | |
| with gr.Row(): | |
| year = gr.Dropdown(choices=list(range(2000, 2025)), label="Year", value=current_date.year) | |
| month = gr.Dropdown(choices=list(range(1, 13)), label="Month", value=current_date.month) | |
| day = gr.Dropdown(choices=list(range(1, 32)), label="Day", value=1) | |
| num_days = gr.Slider(minimum=1, maximum=365, step=1, label="Number of Days", value=14) | |
| submit_btn = gr.Button("Analyze") | |
| output = gr.Plot() | |
| submit_btn.click( | |
| fn=analyze_stocks, | |
| inputs=[year, month, day, num_days], | |
| outputs=output | |
| ) | |
| demo.launch() |