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| import yfinance as yf | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| import gradio as gr | |
| from datetime import date, timedelta | |
| from dateutil.relativedelta import relativedelta | |
| # ... (keep the existing imports and global variables) | |
| def get_company_info(ticker): | |
| stock = yf.Ticker(ticker) | |
| info = stock.info | |
| # Select relevant fundamental information | |
| fundamentals = { | |
| "Company Name": info.get("longName", "N/A"), | |
| "Sector": info.get("sector", "N/A"), | |
| "Industry": info.get("industry", "N/A"), | |
| "Market Cap": f"${info.get('marketCap', 'N/A'):,}", | |
| "P/E Ratio": round(info.get("trailingPE", 0), 2), | |
| "EPS": round(info.get("trailingEps", 0), 2), | |
| "52 Week High": f"${info.get('fiftyTwoWeekHigh', 'N/A'):,}", | |
| "52 Week Low": f"${info.get('fiftyTwoWeekLow', 'N/A'):,}", | |
| "Dividend Yield": f"{info.get('dividendYield', 0) * 100:.2f}%", | |
| "Beta": round(info.get("beta", 0), 2), | |
| } | |
| return pd.DataFrame(list(fundamentals.items()), columns=['Metric', 'Value']) | |
| def get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method): | |
| stock_name, ticker_name = stock.split(":") | |
| if ticker_dict[idx] == 'FTSE 100': | |
| ticker_name += '.L' if ticker_name[-1] != '.' else 'L' | |
| elif ticker_dict[idx] == 'CAC 40': | |
| ticker_name += '.PA' | |
| # Get stock price data | |
| series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval) | |
| series = series.reset_index() | |
| # Generate forecast | |
| predictions = forecast_series(series, model=forecast_method) | |
| # ... (keep the existing forecast date generation code) | |
| # Create graph | |
| if graph_type == 'Line Graph': | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical')) | |
| fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast')) | |
| else: # Candlestick Graph | |
| fig = go.Figure(data=[go.Candlestick(x=series['Date'], | |
| open=series['Open'], | |
| high=series['High'], | |
| low=series['Low'], | |
| close=series['Close'], | |
| name='Historical')]) | |
| fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast')) | |
| fig.update_layout(title=f"Stock Price of {stock_name}", | |
| xaxis_title="Date", | |
| yaxis_title="Price") | |
| # Get fundamental information | |
| fundamentals = get_company_info(ticker_name) | |
| return fig, fundamentals | |
| # Update the Gradio interface | |
| with demo: | |
| # ... (keep the existing input components) | |
| out_graph = gr.Plot() | |
| out_fundamentals = gr.DataFrame() | |
| inputs = [d1, d2, d3, d4, d5] | |
| outputs = [out_graph, out_fundamentals] | |
| d2.input(get_stock_graph_and_info, inputs, outputs) | |
| d3.input(get_stock_graph_and_info, inputs, outputs) | |
| d4.input(get_stock_graph_and_info, inputs, outputs) | |
| d5.input(get_stock_graph_and_info, inputs, outputs) | |
| demo.launch() |