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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() |