├── tests ├── desktop.ini ├── get_website_content │ ├── DontFearAQETaperandTheMarketReaction.html │ ├── MarketWrapUpforSeptRAIINTCAMGNCOVmore.html │ ├── SocialMediaETFinFocusAsTwitterPlansIPO.html │ ├── TwitterlearnsfromFacebooksIPOfollies.html │ ├── YearsAfterLehmanBankingHasaBiggerProblem.html │ ├── OnIPOTwitterislearningfromFacebooksmistakes.html │ ├── TwittersLawyersHelpedMakeFinancialTweetsLegal.html │ ├── HinrichsShutsDownHackFwdButSharesWealthofLinks.html │ ├── SocialHighlightsFacebookIncFBCreditsLinkedInCorpLNKDs.html │ ├── TECHSTOCKSTwitterIPOBuzzPutsSocialmediaStocksInFocus.html │ ├── LinkedInCorpLNKDChangesPrivacyPolicyToLetTeenagersJoin.html │ ├── IglootoHireAdditionalManufacturingWorkersatKatyTexasFacility.html │ ├── Chamberwantsleaderwhocanengageexecs.html │ └── TwitteraddsbankstounderwritinglineupaheadofIPOsources.html └── evaluate_website_content.py ├── README.md ├── stocknews.py └── LICENSE /tests/desktop.ini: -------------------------------------------------------------------------------- 1 | [.ShellClassInfo] 2 | InfoTip=Denne mappe er delt online. 3 | IconFile=C:\Program Files (x86)\Google\Drive\googledrivesync.exe 4 | IconIndex=12 5 | -------------------------------------------------------------------------------- /tests/get_website_content/DontFearAQETaperandTheMarketReaction.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/DontFearAQETaperandTheMarketReaction.html -------------------------------------------------------------------------------- /tests/get_website_content/MarketWrapUpforSeptRAIINTCAMGNCOVmore.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/MarketWrapUpforSeptRAIINTCAMGNCOVmore.html -------------------------------------------------------------------------------- /tests/get_website_content/SocialMediaETFinFocusAsTwitterPlansIPO.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/SocialMediaETFinFocusAsTwitterPlansIPO.html -------------------------------------------------------------------------------- /tests/get_website_content/TwitterlearnsfromFacebooksIPOfollies.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/TwitterlearnsfromFacebooksIPOfollies.html -------------------------------------------------------------------------------- /tests/get_website_content/YearsAfterLehmanBankingHasaBiggerProblem.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/YearsAfterLehmanBankingHasaBiggerProblem.html -------------------------------------------------------------------------------- /tests/get_website_content/OnIPOTwitterislearningfromFacebooksmistakes.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/OnIPOTwitterislearningfromFacebooksmistakes.html -------------------------------------------------------------------------------- /tests/get_website_content/TwittersLawyersHelpedMakeFinancialTweetsLegal.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/TwittersLawyersHelpedMakeFinancialTweetsLegal.html -------------------------------------------------------------------------------- /tests/get_website_content/HinrichsShutsDownHackFwdButSharesWealthofLinks.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/HinrichsShutsDownHackFwdButSharesWealthofLinks.html -------------------------------------------------------------------------------- /tests/get_website_content/SocialHighlightsFacebookIncFBCreditsLinkedInCorpLNKDs.html: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/elgehelge/stocknews/HEAD/tests/get_website_content/SocialHighlightsFacebookIncFBCreditsLinkedInCorpLNKDs.html -------------------------------------------------------------------------------- /tests/get_website_content/TECHSTOCKSTwitterIPOBuzzPutsSocialmediaStocksInFocus.html: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /tests/get_website_content/Chamberwantsleaderwhocanengageexecs.html: -------------------------------------------------------------------------------- 1 |
2 | 3 |

5 | 6 |

-------------------------------------------------------------------------------- /tests/evaluate_website_content.py: -------------------------------------------------------------------------------- 1 | def evaluate_website_content(articles): 2 | import string 3 | for article in articles: 4 | html = article['raw_html'] 5 | most_words_path = _get_website_content(html, False)['path'] 6 | # Build style out of path 7 | path = '' 8 | for elem in most_words_path[1:]: 9 | elem_name = elem[0] 10 | elem_attr_dict = eval(elem[1]) 11 | path += elem_name 12 | for attr in elem_attr_dict: 13 | if (attr == 'class' or attr == 'id'): 14 | if type(elem_attr_dict[attr]) == unicode: 15 | elem_attr_dict[attr] = [elem_attr_dict[attr]] 16 | for value in elem_attr_dict[attr]: 17 | if value != '': 18 | path += '[%s~=%s]' % (str(attr), str(value)) 19 | path += ' ' 20 | style = path + '{border: dashed 10px lightgreen;}\n' 21 | style += path + 'div {border: dashed 10px red;}' 22 | with open('tests/get_website_content/%s.html' % ''.join( 23 | [char for char in article['title'] if char in string.letters] 24 | ), 'w') as f: 25 | f.write(article['raw_html']) 26 | f.write('' % style) 27 | f.write('

%s

' % article['content']) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | stocknews 2 | ========= 3 | 4 | A Python module for downloading stock related news. 5 | 6 | At the moment Google Finance is the only source supported, but tweets tagged with '$' will be supported soon. And maybe more sources will be suported in the future. 7 | 8 | Feel free to fork the code and add the sources you need!!! Remember to send pull requests. 9 | 10 | Usage 11 | ----- 12 | 13 | Arguments: 14 | 15 | 1. `short_name` (string) the short name of the stock (e.g. 'GOOG'). 16 | 2. `start_date` (string formatted as `%Y-%m-%d`) the start date, articles published on this date will be included. 17 | 3. `end_date` (string formatted as `%Y-%m-%d`) the end date, articles published on this date will NOT be included. 18 | 4. `order_by_date` (boolean - default: False) whether to sort the articles by date or by relevance. 19 | 20 | Output: A list of articles prepresented by dictionaries containing the following keys: 21 | - `datetime` (time.struct_time object) The date and time of when the article was published. 22 | - `title` (unicode) The title of the article (given by Google). 23 | - `wordcounts` (collections.Counter object) The bag-of-words representation of the article. 24 | - `link` (unicode) The URL of the article. 25 | - `relevance` (int) The order in which Google finds the article relavant relative to the other articles. 26 | - `content` (unicode) The content found by selecting the HTML element with most words. 27 | - `raw_html` (unicode) The HTML from where the content is extracted. 28 | 29 | Example of how to get the most relevant Google Finance articles 30 | related to the LinkedIn stock (LNKD) between the 15th of september and the 15th of 31 | october 2013: 32 | 33 | ```python 34 | >>> articles = get_google_finance_articles('LNKD', '2013-09-15', '2013-10-15') 35 | >>> len(articles) 36 | 68 37 | >>> most_relevant_article = articles[0] 38 | >>> least_relevant_article = articles[-1] 39 | >>> most_relevant_article['relevance'] 40 | 1 41 | >>> least_relevant_article['relevance'] 42 | 68 43 | >>> most_relevant_article['title'] 44 | u'LinkedIn Customers Allege Company Hacked E-Mail Addresses' 45 | 46 | >>> most_relevant_article['link'] 47 | u'http://www.bloomberg.com/news/2013-09-20/linkedin-customers-say-company- 48 | hacked-their-e-mail-address-books.html' 49 | 50 | >>> most_relevant_article['content'][:200] + '...' 51 | u'\n\n\nLinkedIn Corp. (LNKD), owner of the\nworld\u2019s most popular 52 | professional-networking website, was sued\nby customers who claim the company 53 | appropriated their identities\nfor marketing purposes by hacking...' 54 | 55 | >>> most_relevant_article['wordcounts'].most_common()[:10] 56 | [(u',', 67), (u'.', 46), (u'linkedin', 31), (u'e-m', 24), (u'ail', 24), 57 | (u'said', 19), (u'address', 10), (u'account', 9), (u'compani', 9), 58 | (u'complaint', 8)] 59 | ``` 60 | 61 | Example of how to get the earliest and the latest Google Finance article 62 | related to the same stock and within the same time frame as above: 63 | 64 | ```python 65 | >>> articles = get_google_finance_articles('LNKD', '2013-09-15', '2013-10-15', 66 | ... order_by_date=True) 67 | >>> len(articles) 68 | 68 69 | >>> earliest_article = articles[0] 70 | >>> latest_article = articles[-1] 71 | >>> import time 72 | >>> time.strftime("%d %b %Y %H:%M:%S", earliest_article['datetime']) 73 | '03 Sep 2013 12:13:00' 74 | >>> time.strftime("%d %b %Y %H:%M:%S", latest_article['datetime']) 75 | '14 Sep 2013 21:08:17' 76 | ``` 77 | -------------------------------------------------------------------------------- /stocknews.py: -------------------------------------------------------------------------------- 1 | from threading import Thread 2 | from collections import Counter 3 | from operator import itemgetter 4 | import urllib 5 | import urllib2 6 | import re 7 | import random 8 | import datetime 9 | import string 10 | import feedparser 11 | import nltk 12 | import bs4 13 | import shelve 14 | 15 | class StockNews: 16 | """ 17 | Fetch news related to stocks. At the moment Google Finance is the only source supported, but also tweets will be supported soon. And maybe more sources will be supported in the future. 18 | 19 | Feel free to fork the code on `github `_ and add the sources you need. 20 | 21 | .. note:: An instance can be created using either using short_names, start_date and end_date or the db_file_name. 22 | 23 | Arguments: 24 | 25 | - short_names -- (list of strings) the short name of the stock (e.g. 'GOOG') 26 | - start_date -- (string formatted as %Y-%m-%d) the start date, articles published on this date will be included 27 | - end_date -- (string formatted as %Y-%m-%d) the end date, articles published on this date will NOT be included 28 | - db_file_name -- (string) the file name of the database file 29 | 30 | Example showing how to get the most relevant Google Finance articles related to the LinkedIn stock (LNKD) between the 15th of august and the 15th of september 2013: 31 | 32 | >>> articles = StockNews(['LNKD', 'FB'], '2013-10-15', '2013-11-15') 33 | >>> len(articles) 34 | 124 35 | >>> articles.get_short_names() 36 | ['FB', 'LNKD'] 37 | >>> for short_name in articles.get_short_names(): 38 | ... count = sum([1 for _ in articles.iterate('title', short_name)]) 39 | ... print "%s has %i articles." % (short_name, count) 40 | FB has 59 articles. 41 | LNKD has 64 articles. 42 | >>> first_facebook_article = articles.iterate(['title', 'link', 'content', 'wordcounts'], 'FB').next() 43 | >>> title, link, content, wordcounts = first_facebook_article 44 | >>> title 45 | u'Facebook Inc (FB) Buying BlackBerry Ltd (BBRY): A Rumor That Makes Sense' 46 | >>> link 47 | u'http://www.valuewalk.com/2013/10/facebook-inc-fb-buying-blackberry-ltd-bbry-rumor/' 48 | >>> content[:200] 49 | u'Facebook Inc (NASDAQ:FB) has been linked with a deal to acquire BlackBerry Ltd (NASDAQ:BBRY) (TSE:BB). At this stage there is no reason to believe the rumors. Several companies have been linked with ' 50 | >>> wordcounts.most_common(10) 51 | [('facebook', 20), ('mobil', 8), ('inc', 8), ('blackberri', 7), ('nasdaqfb', 7), ('ha', 6), ('busi', 5), 52 | ('year', 5), ('hardwar', 5), ('compani', 5)] 53 | 54 | """ 55 | 56 | __version__ = 2.0 57 | # Changes since last update: 58 | # - Changed to class 59 | # - Iterator method for easily accessing the data 60 | 61 | USER_AGENT = "StockNewsCrawler/%s" % __version__ + \ 62 | "Source code available on github.com/elgehelge/stocknews" 63 | 64 | # Magic methods 65 | 66 | def __init__(self, short_names=None, start_date=None, end_date=None, db_file_name=None): 67 | if short_names and start_date and end_date: 68 | if db_file_name: 69 | self._db_filename = db_file_name 70 | else: 71 | self._db_filename = 'data/_stocknews_db_%d-stocks_%s-%s_%s.shelve'\ 72 | % (len(short_names), start_date, end_date, self._id_generator()) 73 | print 'Data will be stored in %s' % self._db_filename 74 | self._download_and_store_articles(short_names, start_date, end_date) 75 | elif db_file_name: 76 | self._db_filename = db_file_name 77 | db = self._open_db() 78 | db.close() 79 | else: 80 | raise ValueError("Please provide either of\n1) short_names, start_date and end_date\n2) db_file_name") 81 | 82 | def __len__(self): 83 | return sum([1 for _ in self.iterate('stock_short_name')]) 84 | 85 | # Public methods 86 | 87 | def get_short_names(self): 88 | """ 89 | Return a list of all stock names. 90 | """ 91 | db = self._open_db() 92 | short_names = db.keys() 93 | db.close() 94 | return short_names 95 | 96 | def iterate(self, attributes=None, short_names=None): 97 | """ 98 | Return a iterator that iterates the data as specified by the arguments: 99 | 100 | 'attributes' is either a string or a list of strings specifying which attributes to iterate through 101 | 102 | 'short_names' is uses to limit the iteration to specific stocks. 103 | 104 | """ 105 | 106 | db = self._open_db() 107 | 108 | if len(db.keys()) == 0: 109 | raise StopIteration 110 | 111 | # If no attributes are given, then iterate through all attribute 112 | if not attributes: 113 | attributes = ['stock_short_name', 'datetime', 'title', 'wordcounts', 'link', 'relevance', 'content', 'raw_html'] 114 | 115 | # If no stock names are given, then iterate through all stocks in the database 116 | if not short_names: 117 | short_names = db.keys() 118 | 119 | # Wrap short_name if only one is provided 120 | if not hasattr(short_names, '__iter__'): 121 | short_names = [short_names] 122 | 123 | # Iterate 124 | for short_name in short_names: 125 | article_list = db[short_name] 126 | if not hasattr(attributes, '__iter__'): # Only one or multiple attributes provided 127 | for article in article_list: 128 | yield article[attributes] 129 | else: 130 | for article in article_list: 131 | yield tuple(article[attribute] for attribute in attributes) 132 | 133 | db.close() 134 | 135 | # Private methods 136 | 137 | def _open_db(self): 138 | """ 139 | Open the database file and return the database file object. 140 | 141 | This method reduces redundancy and makes it easy to switch the database without affecting the rest of the code. 142 | 143 | """ 144 | try: 145 | return shelve.open(self._db_filename) 146 | except: 147 | raise IOError('Was not able to open the database file \'%s\'' % self._db_filename) 148 | 149 | def _download_and_store_articles(self, short_names, start_date, end_date): 150 | """Downloads the specified articles and stores it in the database file associated with this class instance.""" 151 | db = self._open_db() 152 | for short_name in short_names: 153 | db[short_name] = self._download_articles_single_stock(short_name, start_date, end_date) 154 | db.close() 155 | 156 | def _download_articles_single_stock(self, short_name, start_date, end_date): 157 | """ 158 | Downloads the articles for the specified stock in the specified time interval. 159 | 160 | Return a sorted list of dictionaries representing articles. 161 | 162 | Each dictionary represents an article and has the following keys: 163 | - 'stock_short_name' 164 | - 'datetime' 165 | - 'title' 166 | - 'wordcounts' 167 | - 'link' 168 | - 'relevance' 169 | - 'content' 170 | - 'raw_html' 171 | 172 | By default the list is sorted according to relevance (defined by Google 173 | Finance), but can be sorted by date if preferred. 174 | 175 | """ 176 | # Initializing variables 177 | article_list = [] 178 | 179 | # Fetch feed and store 'datetime', 'title', 'link' and 'relevance' 180 | feed_items = self._get_google_finance_feed(short_name, start_date, end_date) 181 | for index, item in enumerate(feed_items): 182 | article = {} 183 | article['stock_short_name'] = short_name 184 | article['datetime'] = datetime.datetime(*item['published_parsed'][:6]) 185 | article['title'] = item['title'] 186 | article['link'] = item['link'] 187 | article['relevance'] = index + 1 188 | article_list.append(article) 189 | 190 | # Fetch articles in parallel 191 | number_of_articles = len(article_list) 192 | print "Fetching %i articles online in parallel..." % number_of_articles 193 | opener = urllib2.build_opener() 194 | opener.addheaders = [("User-agent", self.USER_AGENT)] 195 | def fetch_online(article): 196 | """ 197 | Fetch online content. 198 | 199 | Request the url for an article dictionary (article['link']) and store 200 | the content within this dictionary (article['content']). 201 | 202 | """ 203 | try: 204 | raw_html = opener.open(article['link'], timeout = 10).read() 205 | except: # Including URLError and timeout (but many other exceptions can be raised) 206 | article_list.remove(article) 207 | else: 208 | article['raw_html'] = raw_html 209 | article['content'] = self._get_website_content(raw_html) 210 | threads = [Thread(target=fetch_online, args = (article,)) 211 | for article in article_list] # takes tuple as args 212 | # Start all threads and wait for them to finish 213 | for thread in threads: 214 | thread.start() 215 | for thread in threads: 216 | thread.join() 217 | print "%i Articles fetched, %i failed." % (len(article_list), 218 | number_of_articles - 219 | len(article_list)) 220 | 221 | # Count words and store 'wordcounts' in dictionary 222 | for article in article_list: 223 | # Concatenate titles and content 224 | text = article['title'].encode('utf8') + article['content'].encode('utf8') 225 | # Normalize (tokenize, lowercase, stem, remove stopwords) 226 | word_list = self._word_normalization(text) 227 | # Count words 228 | article['wordcounts'] = Counter(word_list) 229 | 230 | # Order by date instead of by relevance 231 | article_list = sorted(article_list, key=itemgetter('datetime')) 232 | 233 | return article_list 234 | 235 | 236 | #def get_tweets(short_name, start_date, end_date): 237 | # # TODO: Implement this 238 | # """ 239 | # Returns a sorted list of dictionaries with keys: 240 | # - 'datetime' 241 | # - 'content' 242 | # - 'wordcounts' 243 | # - 'no_retweets' 244 | # - 'user_id' 245 | # - 'user_followers' 246 | # 247 | # """ 248 | 249 | def _get_google_finance_feed(self, short_name, start_date, end_date): 250 | """ 251 | Return a list of feedparser items sorted by relevance. 252 | """ 253 | # Setup URL parameters 254 | args = {} 255 | args['q'] = short_name 256 | args['output'] = 'rss' 257 | args['startdate'] = start_date 258 | args['enddate'] = end_date 259 | args['start'] = 0 # pagination (start from item number <'start'> ...) 260 | args['num'] = 100 # pagination (... and show the next <'num'> items.) 261 | 262 | # Initialize variables 263 | reached_end_of_feed = False 264 | all_feed_items = [] 265 | 266 | # Fetch feed items until end of feed 267 | feedparser.USER_AGENT = self.USER_AGENT 268 | while not reached_end_of_feed: 269 | feed_url = 'https://www.google.com/finance/company_news?' \ 270 | + urllib.urlencode(args) 271 | feed = feedparser.parse(feed_url) 272 | all_feed_items += feed['items'] 273 | reached_end_of_feed = len(feed['items']) != args['num'] 274 | # Prepare for requesting next page 275 | args['start'] += args['num'] 276 | 277 | return all_feed_items 278 | 279 | 280 | def _word_normalization(self, text): 281 | """ 282 | Normalize a string. 283 | 284 | Normalize a string by... 285 | - tokenizing it. 286 | - converting all letters to lowercase. 287 | - stemming words. 288 | - removing stopwords. 289 | 290 | """ 291 | # Tokenize 292 | word_list = self._tokenization(text) 293 | 294 | # Convert letters to lowercase 295 | word_list = [word.lower() for word in word_list] 296 | 297 | # Perform word stemming (using the Porter stemming algorithm) 298 | stemmer = nltk.PorterStemmer() 299 | word_list = [stemmer.stem(word) for word in word_list] 300 | 301 | # Remove stopwords 302 | stopwords = nltk.corpus.stopwords.words('english') 303 | word_list = [word for word in word_list if word not in stopwords] 304 | 305 | return word_list 306 | 307 | 308 | def _tokenization(self, text): 309 | """ 310 | Tokenization a string. Remove non-alphanumeric characters and split on space. 311 | """ 312 | alphanumeric = re.sub(r'[^\w\s]+', '', text) 313 | pattern = r"""(?ux) # Set Unicode and verbose flag 314 | \w+ 315 | """ 316 | return nltk.regexp_tokenize(alphanumeric, pattern) 317 | 318 | def _get_website_content(self, html, return_content_only=True): 319 | """ 320 | Returns the larges block of text in terms of number of words, where a word is defined by a simple regex. 321 | """ 322 | word_regex = r" [a-zA-Z]+" # Space followed by letters 323 | text_elements = ['p', 'a', 'br', 'span', 'b', 'big', 'i', 'small', 'tt', 324 | 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'h8', 325 | 'abbr', 'acronym', 'cite', 'code', 'dfn', 'em', 'kbd', 326 | 'strong', 'samp', 'q', 'bdo', 'sub', 'sup'] 327 | ignore_elements = ['script'] 328 | 329 | def _most_words(soup_xml_tag): 330 | """ 331 | Recursive method traversing a XML tree to find the tag with the largest block of text. 332 | """ 333 | # Initialize variables 334 | current_node = {} 335 | child_nodes = [] 336 | 337 | # Build current node and find child nodes 338 | current_node['text'] = "" 339 | for content in soup_xml_tag.contents: 340 | # Grap all text not within another element 341 | if type(content) is bs4.element.NavigableString: 342 | current_node['text'] += "\n" + content 343 | # Grap content from inline elements 344 | elif ( 345 | type(content) is bs4.element.Tag and 346 | content.name in text_elements 347 | ): 348 | current_node['text'] += "\n" + content.text 349 | # Ignore script tags 350 | elif ( 351 | type(content) is bs4.element.Tag and 352 | content.name in ignore_elements 353 | ): 354 | pass 355 | # Continue traversing the html tree 356 | elif type(content) is bs4.element.Tag: 357 | child_nodes.append(_most_words(content)) 358 | current_node['number_of_words'] = len(re.findall(word_regex, 359 | current_node['text'])) 360 | current_node['path'] = [] #empty path 361 | 362 | # Select the node containing most words 363 | # and add the current location to the path 364 | node_with_most_words = max(child_nodes + [current_node], 365 | key=lambda x:x['number_of_words']) 366 | 367 | node_with_most_words['path'].insert(0, (soup_xml_tag.name, str(soup_xml_tag.attrs))) 368 | 369 | return node_with_most_words 370 | 371 | soup = bs4.BeautifulSoup(html) 372 | for elem in soup('script'): # Remove script elements 373 | elem.extract() 374 | element_with_most_words = _most_words(soup) 375 | if return_content_only: 376 | return element_with_most_words['text'] 377 | else: 378 | return element_with_most_words 379 | 380 | def _id_generator(self, length=6, chars=string.ascii_uppercase + string.digits): 381 | """ Generate a unique id. Inspired by http://stackoverflow.com/questions/2257441/python-random-string-generation-with-upper-case-letters-and-digits""" 382 | return ''.join(random.choice(chars) for x in xrange(length)) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. By contrast, the GNU General Public 13 | License is intended to guarantee your freedom to share and change free 14 | software--to make sure the software is free for all its users. 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It is safest 289 | to attach them to the start of each source file to most effectively 290 | convey the exclusion of warranty; and each file should have at least 291 | the "copyright" line and a pointer to where the full notice is found. 292 | 293 | A Python module for downloading stock related news 294 | Copyright (C) 2013 elgehelge 295 | 296 | This program is free software; you can redistribute it and/or modify 297 | it under the terms of the GNU General Public License as published by 298 | the Free Software Foundation; either version 2 of the License, or 299 | (at your option) any later version. 300 | 301 | This program is distributed in the hope that it will be useful, 302 | but WITHOUT ANY WARRANTY; without even the implied warranty of 303 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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Of course, the commands you use may 322 | be called something other than `show w' and `show c'; they could even be 323 | mouse-clicks or menu items--whatever suits your program. 324 | 325 | You should also get your employer (if you work as a programmer) or your 326 | school, if any, to sign a "copyright disclaimer" for the program, if 327 | necessary. Here is a sample; alter the names: 328 | 329 | Yoyodyne, Inc., hereby disclaims all copyright interest in the program 330 | `Gnomovision' (which makes passes at compilers) written by James Hacker. 331 | 332 | {signature of Ty Coon}, 1 April 1989 333 | Ty Coon, President of Vice 334 | 335 | This General Public License does not permit incorporating your program into 336 | proprietary programs. If your program is a subroutine library, you may 337 | consider it more useful to permit linking proprietary applications with the 338 | library. If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /tests/get_website_content/TwitteraddsbankstounderwritinglineupaheadofIPOsources.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | 12 | 13 | 14 | 15 | 16 | Twitter adds banks to underwriting lineup ahead of IPO: sources 17 | | Reuters 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 72 | 73 | 74 | 75 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 103 | 112 | 113 | 114 | 115 | 116 | 119 | 120 | 427 | 428 | 429 | 430 |
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Twitter adds banks to underwriting lineup ahead of IPO: sources

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866 | Sept 13 | 867 | Fri Sep 13, 2013 5:35pm EDT 868 |

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Sept 13 (Reuters) - Twitter has added a handful of banks to 871 | its underwriting syndicate to help sell shares during its much 872 | anticipated initial public offering.

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Goldman Sachs will serve as the lead underwriter on 874 | the deal, while other banks including JPMorgan Chase & Co 875 | , Morgan Stanley and Bank of America Corp 876 | also have a role, people familiar with the matter told Reuters. 877 | It was not immediately clear what role these other banks will 878 | play.

Twitter is in the process of adding additional banks to the 879 | syndicate, the people said. The sources could not be named 880 | because the information has not officially been disclosed to the 881 | public.

Twitter and the banks all declined to comment.

The Twitter IPO is a major win for Goldman after Morgan 882 | Stanley led the Facebook Inc IPO last May.

Goldman Sachs tops the league tables for U.S. listed 883 | technology IPOs so far this year, up from its previous No. 5 884 | spot, according to Thomson Reuters data. Morgan Stanley, 885 | however, has fallen to the No. 4 position.

Goldman has worked on recent technology IPOs including 886 | Silver Spring Networks Inc and Ruckus Wireless Inc 887 | .

Besides Facebook, Morgan Stanley has also led IPOs for Zynga 888 | Inc, Groupon Inc and LinkedIn Corp.

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1426 | 1427 | 1428 | 1429 | 1430 | 1431 | 1432 | 1433 | 1434 | 1435 | 1436 | 1437 | 1438 | 1439 | 1440 | 1441 | 1442 | 1443 | 1444 | 1445 | 1446 | 1447 | 1448 | 1449 | 1450 | 1451 | 1452 | 1453 | 1454 | 1455 | 1456 | 1457 | 1458 | 1459 | By Olivia Oran 1460 | 1461 | Sept 13 | 1462 | Fri Sep 13, 2013 5:35pm EDT 1463 | 1464 | 1465 | Sept 13 (Reuters) - Twitter has added a handful of banks to 1466 | its underwriting syndicate to help sell shares during its much 1467 | anticipated initial public offering. 1468 | Goldman Sachs will serve as the lead underwriter on 1469 | the deal, while other banks including JPMorgan Chase & Co 1470 | , Morgan Stanley and Bank of America Corp 1471 | also have a role, people familiar with the matter told Reuters. 1472 | It was not immediately clear what role these other banks will 1473 | play.Twitter is in the process of adding additional banks to the 1474 | syndicate, the people said. The sources could not be named 1475 | because the information has not officially been disclosed to the 1476 | public.Twitter and the banks all declined to comment.The Twitter IPO is a major win for Goldman after Morgan 1477 | Stanley led the Facebook Inc IPO last May.Goldman Sachs tops the league tables for U.S. listed 1478 | technology IPOs so far this year, up from its previous No. 5 1479 | spot, according to Thomson Reuters data. Morgan Stanley, 1480 | however, has fallen to the No. 4 position.Goldman has worked on recent technology IPOs including 1481 | Silver Spring Networks Inc and Ruckus Wireless Inc 1482 | .Besides Facebook, Morgan Stanley has also led IPOs for Zynga 1483 | Inc, Groupon Inc and LinkedIn Corp. 1484 | 1485 | 1486 | 1487 | 1488 | 1489 | 1490 | 1491 | 1492 | 1493 | 1494 | 1495 | 1496 | 1497 | 1498 | 1499 | 1500 | 1501 | 1502 | 1503 | 1504 | 1505 | 1506 | 1507 | 1508 | 1509 | 1510 | 1511 | 1512 | 1513 | 1514 | 1515 | 1516 | 1517 | 1518 | 1519 | 1520 | 1521 | 1522 | 1523 | 1524 | 1525 | 1526 | 1527 | 1528 | 1529 | 1530 | 1531 | 1532 | 1533 | 1534 | 1535 | 1536 | 1537 | 1538 | 1539 | 1540 | 1541 | 1542 | 1543 | 1544 | 1545 | 1546 |

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