import streamlit as st
from urllib.request import urlopen, Request
from bs4 import BeautifulSoup
import pandas as pd
import plotly.express as px
from dateutil import parser
import nltk
nltk.downloader.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import datetime
import requests
st.set_page_config(page_title="Stock News Sentiment Analyzer", layout="wide")
def verify_link(url, timeout=10, retries=3):
for _ in range(retries):
try:
response = requests.head(url, timeout=timeout, allow_redirects=True)
if 200 <= response.status_code < 300:
return True
except requests.RequestException:
continue
return False
def get_news(ticker):
finviz_url = 'https://finviz.com/quote.ashx?t='
url = finviz_url + ticker
req = Request(url=url, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0'})
response = urlopen(req)
html = BeautifulSoup(response, 'html.parser')
news_table = html.find(id='news-table')
return news_table
def parse_news(news_table):
parsed_news = []
for x in news_table.findAll('tr'):
try:
text = x.a.get_text()
link = x.a['href']
date_scrape = x.td.text.strip().split()
if len(date_scrape) == 1:
date = datetime.datetime.today().strftime('%Y-%m-%d')
time = date_scrape[0]
else:
date = date_scrape[0]
time = date_scrape[1]
datetime_str = f"{date} {time}"
datetime_parsed = parser.parse(datetime_str)
is_valid = verify_link(link)
parsed_news.append([datetime_parsed, text, link, is_valid])
except Exception as e:
print("Error parsing news:", e)
continue
columns = ['datetime', 'headline', 'link', 'is_valid']
parsed_news_df = pd.DataFrame(parsed_news, columns=columns)
return parsed_news_df
def score_news(parsed_news_df):
vader = SentimentIntensityAnalyzer()
scores = parsed_news_df['headline'].apply(vader.polarity_scores).tolist()
scores_df = pd.DataFrame(scores)
parsed_and_scored_news = parsed_news_df.join(scores_df, rsuffix='_right')
parsed_and_scored_news = parsed_and_scored_news.set_index('datetime')
parsed_and_scored_news = parsed_and_scored_news.rename(columns={"compound": "sentiment_score"})
return parsed_and_scored_news
def plot_hourly_sentiment(parsed_and_scored_news, ticker):
numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
mean_scores = numeric_cols.resample('h').mean()
fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score',
title=f'{ticker} Hourly Sentiment Scores',
color='sentiment_score',
color_continuous_scale=['red', 'yellow', 'green'],
range_color=[-1, 1])
fig.update_layout(coloraxis_colorbar=dict(
title="Sentiment",
tickvals=[-1, 0, 1],
ticktext=["Negative", "Neutral", "Positive"],
))
return fig
def plot_daily_sentiment(parsed_and_scored_news, ticker):
numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
mean_scores = numeric_cols.resample('D').mean()
fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score',
title=f'{ticker} Daily Sentiment Scores',
color='sentiment_score',
color_continuous_scale=['red', 'yellow', 'green'],
range_color=[-1, 1])
fig.update_layout(coloraxis_colorbar=dict(
title="Sentiment",
tickvals=[-1, 0, 1],
ticktext=["Negative", "Neutral", "Positive"],
))
return fig
def get_recommendation(sentiment_scores):
avg_sentiment = sentiment_scores['sentiment_score'].mean()
if avg_sentiment >= 0.05:
return f"Positive sentiment (Score: {avg_sentiment:.2f}). The recent news suggests a favorable outlook for this stock. Consider buying or holding if you already own it."
elif avg_sentiment <= -0.05:
return f"Negative sentiment (Score: {avg_sentiment:.2f}). The recent news suggests caution. Consider selling or avoiding this stock for now."
else:
return f"Neutral sentiment (Score: {avg_sentiment:.2f}). The recent news doesn't show a strong bias. Consider holding if you own the stock, or watch for more definitive trends before making a decision."
st.header("Stock News Sentiment Analyzer")
ticker = st.text_input('Enter Stock Ticker', '').upper()
try:
st.subheader(f"Sentiment Analysis and Recommendation for {ticker} Stock")
news_table = get_news(ticker)
parsed_news_df = parse_news(news_table)
parsed_and_scored_news = score_news(parsed_news_df)
# Generate and display recommendation
recommendation = get_recommendation(parsed_and_scored_news)
st.write(recommendation)
# Display a disclaimer
st.warning("Disclaimer: This recommendation is based solely on recent news sentiment and should not be considered as financial advice. Always do your own research and consult with a qualified financial advisor before making investment decisions.")
fig_hourly = plot_hourly_sentiment(parsed_and_scored_news, ticker)
fig_daily = plot_daily_sentiment(parsed_and_scored_news, ticker)
st.plotly_chart(fig_hourly)
st.plotly_chart(fig_daily)
description = f"""
The above charts average the sentiment scores of {ticker} stock hourly and daily.
The table below gives each of the most recent headlines of the stock and the negative, neutral, positive and an aggregated sentiment score.
The news headlines are obtained from the FinViz website.
Sentiments are given by the nltk.sentiment.vader Python library.
Links have been verified for validity.
"""
st.write(description)
parsed_and_scored_news['link'] = parsed_and_scored_news.apply(
lambda row: f'<a href="{row["link"]}" target="_blank">{"Valid✅" if row["is_valid"] else "Invalid❌"} Link</a>',
axis=1
)
st.write(parsed_and_scored_news.drop(columns=['is_valid']).to_html(escape=False), unsafe_allow_html=True)
except Exception as e:
print(str(e))
st.write("Enter a correct stock ticker, e.g. 'AAPL' above and hit Enter.")
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)