def get_peers(ticker_symbol, page=None):
"""
Gets the list of Top Peers for a stock as listed on the "Premium Research: Industry Analysis" section
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: a list of the Top Peers as listed on a stock's "Premium Research: Industry Analysis" section
on it's respective zacks page
"""
if page is None:
page = scrape_page(BASE_URL+ ticker_symbol)
peers = page.xpath(PEERS_XPATH)
if peers:
try:
peers.remove(ticker_symbol.upper())
except:
pass
if peers:
return peers
else:
return None
else:
return None
def scrape_site(site):
session = HTMLSession()
with open(f'links/{site}_urls.txt', 'r') as f:
urls = f.readlines()
urls = {url[:-1] for url in urls}
save_dir = f'data/{site}/'
os.makedirs(save_dir, exist_ok=True)
scraped = []
if os.path.isfile(save_dir + 'scraped_urls.txt'):
with open(save_dir + 'scraped_urls.txt', 'r') as f:
scraped = f.readlines()
if scraped:
with open(save_dir + 'data.json', 'r') as f:
data = json.load(f)
else:
data = {'title': [], 'text': [], 'site': []}
scraped = {url[:-1] for url in scraped}
to_scrape = urls - scraped
if not to_scrape:
print(f'{site} scraping completed.')
return
print(f'{site} scraping initiated!')
for i, url in enumerate(tqdm(to_scrape)):
try:
title, text = scrape_page(url, selectors[site], session, site)
data['title'].append(title)
data['text'].append(text)
data['site'].append(site)
scraped.add(url)
except IndexError as e:
scraped.add(url)
except ValueError as e:
pass
except Exception as e:
print(e)
print(site)
if i % checkpoint_steps == 0 and i > 0:
with open(save_dir + 'data.json', 'w') as f:
json.dump(data, f)
with open(save_dir + 'scraped_urls.txt', 'w') as f:
f.writelines(scraped)
# print(f'{site}: {i} of {len(to_scrape)} done.')
data_df = pd.DataFrame(data)
data_df.to_csv(save_dir + 'data_all.tsv', sep='\t', index=False)
print(f'{site} scraping completed.')
def get_bullish_sentiment(ticker_symbol, page=None):
"""
Gets the bullish sentiment of the target ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: a string of the percentage of bullish sentiment as listed on a stock's StockTwit's page
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
sentiment = page.xpath(BULLISH_SENTIMENT_XPATH)
if not sentiment:
return None
else:
return sentiment[0].replace("\n", "") + " Bullish"
def get_bullish_sentiment(ticker_symbol, page=None):
"""
Gets the bullish sentiment of the target ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: a string of the percentage of bullish sentiment as listed on a stock's StockTwit's page
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
sentiment = page.xpath(BULLISH_SENTIMENT_XPATH)
if not sentiment:
return None
else:
return sentiment[0].replace("\n", "") + " Bullish"
def get_rating(ticker_symbol, page=None):
"""
Gets the Zack's Rank Rating of the target ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: String of Zack's Rank Rating as listed on a stock's Zacks page
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
rating = page.xpath(RATING_XPATH)
if not rating:
return None
else:
return rating[0]
def get_sentiment(ticker_symbol, page=None):
"""
Gets both the bullish and bearish sentiment of the target ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: a tuple of strings containing both the bullish and bearish sentiment as listed on a stock's
StockTwits page
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
bullish_sentiment = get_bullish_sentiment(ticker_symbol, page)
if bullish_sentiment:
return bullish_sentiment, get_bearish_sentiment(ticker_symbol, page)
else:
return None
def get_sentiment(ticker_symbol, page=None):
"""
Gets both the bullish and bearish sentiment of the target ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param page: html tree structure based on the html markup of the scraped website
:return: a tuple of strings containing both the bullish and bearish sentiment as listed on a stock's
StockTwits page
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
bullish_sentiment = get_bullish_sentiment(ticker_symbol, page)
if bullish_sentiment:
return bullish_sentiment, get_bearish_sentiment(ticker_symbol, page)
else:
return None
def get_all_statistics(ticker_symbol, page=None):
"""
This function will get all the associated financial statistics from the correspoding finviz page
given the ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GGOG", "MSFT")
:param page: HTML tree structure based on the html markup of the scraped page. If one is not passed in the
function will scrape the page
:return: a dictionary of all the financial statistics listed on a stock's finviz page, otherwise None
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
table = get_statistics_table(page)
if table:
return table
else:
return None
def get_statistic(ticker_symbol, stat_name, page=None):
"""
This function will get the associated financial statistic from the corresponding finviz page given the
statistic's name and the ticker symbol
:param ticker_symbol: The ticker symbol of the interested stock (e.g., "AAPL", "GOOG", "MSFT")
:param stat_name: The name of the interested financial statistic (e.g., "P/E", "Price", "Volume").
An exhaustive list of available financial statistics can be found on a stock's finviz page
:param page: HTML tree structure based on the html markup of the scraped web page. If one is not passed in the
function will scrape the page
:return: the value of the interested financial statistic if it exists, otherwise None
"""
if page is None:
page = scrape_page(BASE_URL + ticker_symbol)
table = get_statistics_table(page)
if stat_name in table.keys() and table[stat_name]:
return table[stat_name]
else:
return None
def search():
query = str(request.form['query'])
data = scrape_page(query)
return render_template('result.html', data=data)
"http://www.pollingreport.com/d.htm",
"http://www.pollingreport.com/e-f.htm",
"http://www.pollingreport.com/g.htm",
"http://www.pollingreport.com/h-j.htm",
"http://www.pollingreport.com/k.htm",
"http://www.pollingreport.com/l.htm",
"http://www.pollingreport.com/o.htm",
"http://www.pollingreport.com/p.htm",
"http://www.pollingreport.com/r.htm",
"http://www.pollingreport.com/S-Z.htm",
# Congressional Job Ratings
"http://www.pollingreport.com/CongJob1.htm",
"http://www.pollingreport.com/cong_dem.htm",
"http://www.pollingreport.com/cong_rep.htm",
# Party Approval
"http://www.pollingreport.com/dem.htm",
"http://www.pollingreport.com/rep.htm",
]
if __name__ == '__main__':
os.makedirs("parsed", exist_ok=True)
os.makedirs("raw", exist_ok=True)
for url in urls:
filename = url.split("/")[-1] + ".csv"
with open("parsed/" + filename, "w", encoding="latin-1") as f:
scrape_page(url, f)