def perform_financial_analysis(self, newsId, company_name, sentiment, sentiment_score): # Stubbing dummy quote, but ideally get hold of the right stock and lookup price #before for i in range(8): if i == 0: current_price = ystockquote.get_price(company_name) #print newsId,company_name,strftime("%Y-%m-%d %H:%M:%S", gmtime()),sentiment,sentiment_score,current_price #Sleeping for some time before recording the time again time.sleep(STOCK_GUAGE_TIME_INTERVAL_DIFFERENCE) #after new_price = ystockquote.get_price(company_name) logger.info('[DATA] : %s,%s,%s,%s,%s,%s,%s', str(newsId), str(company_name), str(time.ctime()), str(sentiment), str(sentiment_score), str(current_price), str(new_price)) with open("../Stock-Predictor.log", "a") as f: f.write( str(newsId) + " " + str(company_name) + " " + str(time.ctime()) + " " + str(sentiment) + " " + str(sentiment_score) + " " + str(current_price) + " " + str(new_price) + "\n")
def perform_financial_analysis(self, newsId, company_name, sentiment, sentiment_score): # Currently Dummy Quote is in Place. Need to make this so when user types into web app and hits a search button, this will update the python code in that location. for i in range(8): if i == 0: current_price = ystockquote.get_price(company_name) #Prints out relevant data regarding data scraping information (Date, Time ,Etc...) time.sleep(STOCK_TIME_SCRAPE_INTERVAL) new_price = ystockquote.get_price(company_name) logger.info('[DATA] : %s,%s,%s,%s,%s,%s,%s', str(newsId), str(company_name), str(time.ctime()), str(sentiment), str(sentiment_score), str(current_price), str(new_price)) with open("../Stock-Predictor.log", "a") as f: f.write( str(newsId) + " " + str(company_name) + " " + str(time.ctime()) + " " + str(sentiment) + " " + str(sentiment_score) + " " + str(current_price) + " " + str(new_price) + "\n")
def call_sentiment_analysis(): alchemyObj = AlchemyAPI.AlchemyAPI() # Load the API key from disk. alchemyObj.loadAPIKey("api_key.txt"); # Extract sentiment from a web URL. result = alchemyObj.URLGetTextSentiment("http://www.reuters.com/article/2012/11/30/us-china-apple-iphone-idUSBRE8AT06G20121130?type=companyNews"); # Stubbing dummy quote, but ideally get hold of the right stock and lookup price print ystockquote.get_price('GOOG') # Kick off another thread that will fetch the price of the same quote after 5 mins. print result
def call_sentiment_analysis(): alchemyObj = AlchemyAPI.AlchemyAPI() # Load the API key from disk. alchemyObj.loadAPIKey("api_key.txt") # Extract sentiment from a web URL. result = alchemyObj.URLGetTextSentiment( "http://www.reuters.com/article/2012/11/30/us-china-apple-iphone-idUSBRE8AT06G20121130?type=companyNews" ) # Stubbing dummy quote, but ideally get hold of the right stock and lookup price print ystockquote.get_price('GOOG') # Kick off another thread that will fetch the price of the same quote after 5 mins. print result
def perform_financial_analysis(self, newsId, company_name, sentiment, sentiment_score): # Stubbing dummy quote, but ideally get hold of the right stock and lookup price #before for i in range(1): if i ==0: current_price = ystockquote.get_price(company_name) #print newsId,company_name,strftime("%Y-%m-%d %H:%M:%S", gmtime()),sentiment,sentiment_score,current_price #Sleeping for some time before recording the time again time.sleep(STOCK_GUAGE_TIME_INTERVAL_DIFFERENCE) #after new_price = ystockquote.get_price(company_name) logger.info('[DATA] : %s,%s,%s,%s,%s,%s,%s',str(newsId),str(company_name), str(time.ctime()), str(sentiment), str(sentiment_score), str(current_price), str(new_price))
def perform_financial_analysis(self, newsId, company_name, sentiment, sentiment_score): # Currently Dummy Quote is in Place. Need to make this so when user types into web app and hits a search button, this will update the python code in that location. for i in range(8): if i == 0: current_price = ystockquote.get_price(company_name) #Prints out relevant data regarding data scraping information (Date, Time ,Etc...) time.sleep(STOCK_TIME_SCRAPE_INTERVAL) new_price = ystockquote.get_price(company_name) logger.info('[DATA] : %s,%s,%s,%s,%s,%s,%s', str(newsId), str(company_name), str(time.ctime()), str(sentiment), str(sentiment_score), str(current_price), str(new_price)) with open("../Stock-Predictor.log", "a") as f: f.write(str(newsId) + " " + str(company_name) + " " + str(time.ctime()) + " " + str(sentiment) + " " + str(sentiment_score) + " " + str(current_price) + " " + str(new_price) + "\n")