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")