Create app.py

app.py

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+import streamlit as st
+from urllib.request import urlopen, Request
+from bs4 import BeautifulSoup
+import pandas as pd
+import plotly
+import plotly.express as px
+import json # for graph plotting in website
+# NLTK VADER for sentiment analysis
+import nltk
+nltk.downloader.download('vader_lexicon')
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+import subprocess
+import os
+
+import datetime
+
+st.set_page_config(page_title = "Bohmian's Stock News Sentiment Analyzer", layout = "wide")
+
+
+def get_news(ticker):
+    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)    
+    # Read the contents of the file into 'html'
+    html = BeautifulSoup(response)
+    # Find 'news-table' in the Soup and load it into 'news_table'
+    news_table = html.find(id='news-table')
+    return news_table
+	
+# parse news into dataframe
+def parse_news(news_table):
+    parsed_news = []
+    today_string = datetime.datetime.today().strftime('%Y-%m-%d')
+    
+    for x in news_table.findAll('tr'):
+        try:
+            # read the text from each tr tag into text
+            # get text from a only
+            text = x.a.get_text() 
+            # splite text in the td tag into a list 
+            date_scrape = x.td.text.split()
+            # if the length of 'date_scrape' is 1, load 'time' as the only element
+
+            if len(date_scrape) == 1:
+                time = date_scrape[0]
+                
+            # else load 'date' as the 1st element and 'time' as the second    
+            else:
+                date = date_scrape[0]
+                time = date_scrape[1]
+            
+            # Append ticker, date, time and headline as a list to the 'parsed_news' list
+            parsed_news.append([date, time, text]) 
+        except:
+            pass
+        
+        # Set column names
+        columns = ['date', 'time', 'headline']
+        # Convert the parsed_news list into a DataFrame called 'parsed_and_scored_news'
+        parsed_news_df = pd.DataFrame(parsed_news, columns=columns)        
+        # Create a pandas datetime object from the strings in 'date' and 'time' column
+        parsed_news_df['date'] = parsed_news_df['date'].replace("Today", today_string)
+        parsed_news_df['datetime'] = pd.to_datetime(parsed_news_df['date'] + ' ' + parsed_news_df['time'])
+        
+    return parsed_news_df
+        
+    
+        
+def score_news(parsed_news_df):
+    # Instantiate the sentiment intensity analyzer
+    vader = SentimentIntensityAnalyzer()
+    
+    # Iterate through the headlines and get the polarity scores using vader
+    scores = parsed_news_df['headline'].apply(vader.polarity_scores).tolist()
+
+    # Convert the 'scores' list of dicts into a DataFrame
+    scores_df = pd.DataFrame(scores)
+
+    # Join the DataFrames of the news and the list of dicts
+    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.drop(['date', 'time'], 1)          
+    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):
+   
+    # Group by date and ticker columns from scored_news and calculate the mean
+    mean_scores = parsed_and_scored_news.resample('H').mean()
+
+    # Plot a bar chart with plotly 
+    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Hourly Sentiment Scores')
+    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
+
+def plot_daily_sentiment(parsed_and_scored_news, ticker):
+   
+    # Group by date and ticker columns from scored_news and calculate the mean
+    mean_scores = parsed_and_scored_news.resample('D').mean()
+
+    # Plot a bar chart with plotly
+    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Daily Sentiment Scores')
+    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
+
+# for extracting data from finviz
+finviz_url = 'https://finviz.com/quote.ashx?t='
+
+
+st.header("Bohmian's Stock News Sentiment Analyzer")
+
+ticker = st.text_input('Enter Stock Ticker', '').upper()
+
+df = pd.DataFrame({'datetime': datetime.datetime.now(), 'ticker': ticker}, index = [0])
+
+
+try:
+	st.subheader("Hourly and Daily Sentiment of {} Stock".format(ticker))
+	news_table = get_news(ticker)
+	parsed_news_df = parse_news(news_table)
+	print(parsed_news_df)
+	parsed_and_scored_news = score_news(parsed_news_df)
+	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 = """
+		The above chart averages the sentiment scores of {} 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.
+		""".format(ticker)
+		
+	st.write(description)	 
+	st.table(parsed_and_scored_news)
+	
+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)