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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() |