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import os
import asyncio
import logging
from datetime import datetime, timedelta
from newsapi.newsapi_client import NewsApiClient
from textblob import TextBlob
import yfinance as yf
import pandas as pd
import ta
import gradio as gr
# Set up logging
logging.basicConfig(level=logging.WARNING, format='%(asctime)s - %(levelname)s - %(message)s')
# Retrieve API key from environment variables
NEWSAPI_KEY = os.getenv("NEWSAPI_KEY")
# Fetch financial news
def fetch_financial_news(stock_symbol=None, page_size=5, days=2):
try:
newsapi = NewsApiClient(api_key=NEWSAPI_KEY)
query = stock_symbol if stock_symbol else "financial news"
end_date = datetime.now()
start_date = end_date - timedelta(days=days)
articles = newsapi.get_everything(
q=query,
language='en',
from_param=start_date.strftime('%Y-%m-%d'),
to=end_date.strftime('%Y-%m-%d'),
sort_by='publishedAt',
page_size=page_size
)
results = []
for article in articles.get('articles', []):
title = article.get('title', '[Title Unavailable]')
description = article.get('description', '[Description Unavailable]')
url = article.get('url', 'URL Unavailable')
results.append(f"Title: {title}\nDescription: {description}\nURL: {url}")
return "\n\n".join(results)
except Exception as e:
return f"Error fetching news: {e}"
# Perform sentiment analysis
def analyze_sentiment(text):
try:
analysis = TextBlob(text)
polarity = analysis.sentiment.polarity
if polarity > 0.1:
return "Positive"
elif polarity < -0.1:
return "Negative"
else:
return "Neutral"
except Exception as e:
return f"Error analyzing sentiment: {e}"
# Fetch technical data
def fetch_technical_data(stock_symbol):
try:
stock = yf.Ticker(stock_symbol)
data = stock.history(period="1y")
if data.empty:
return "No data found for this stock symbol."
data['RSI'] = ta.momentum.RSIIndicator(data['Close']).rsi()
macd = ta.trend.MACD(data['Close'])
data['MACD'] = macd.macd()
data['MACD_Signal'] = macd.macd_signal()
data['SMA_50'] = data['Close'].rolling(window=50).mean()
data['SMA_200'] = data['Close'].rolling(window=200).mean()
latest_technical_data = {
"RSI": data['RSI'].iloc[-1],
"MACD": data['MACD'].iloc[-1],
"MACD Signal": data['MACD_Signal'].iloc[-1],
"50 Day SMA": data['SMA_50'].iloc[-1],
"200 Day SMA": data['SMA_200'].iloc[-1],
}
return pd.Series(latest_technical_data).to_string()
except Exception as e:
return f"Error fetching technical data: {e}"
# Define Gradio interface
def analyze_stock(stock_symbol):
news = fetch_financial_news(stock_symbol)
technical_data = fetch_technical_data(stock_symbol)
return news, technical_data
with gr.Blocks() as demo:
gr.Markdown("## Financial News and Technical Analysis Tool")
stock_input = gr.Textbox(label="Enter Stock Symbol (e.g., AAPL, TSLA)")
news_output = gr.Textbox(label="Financial News", interactive=False)
tech_output = gr.Textbox(label="Technical Analysis", interactive=False)
analyze_button = gr.Button("Analyze")
analyze_button.click(analyze_stock, inputs=[stock_input], outputs=[news_output, tech_output])
if __name__ == "__main__":
demo.launch()
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